From d1343b96ed6ebf94fedf6384731bae6a03c1c9e0 Mon Sep 17 00:00:00 2001 From: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> Date: Mon, 20 Oct 2025 14:10:19 -0700 Subject: [PATCH] Release/v1.0.0 (#3618) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * feat: add `apps` & `actions` attributes to Agent (#3504) * feat: add app attributes to Agent * feat: add actions attribute to Agent * chore: resolve linter issues * refactor: merge the apps and actions parameters into a single one * fix: remove unnecessary print * feat: logging error when CrewaiPlatformTools fails * chore: export CrewaiPlatformTools directly from crewai_tools * style: resolver linter issues * test: fix broken tests * style: solve linter issues * fix: fix broken test * feat: monorepo restructure and test/ci updates - Add crewai workspace member - Fix vcr cassette paths and restore test dirs - Resolve ci failures and update linter/pytest rules * chore: update python version to 3.13 and package metadata * feat: add crewai-tools workspace and fix tests/dependencies * feat: add crewai-tools workspace structure * Squashed 'temp-crewai-tools/' content from commit 9bae5633 git-subtree-dir: temp-crewai-tools git-subtree-split: 9bae56339096cb70f03873e600192bd2cd207ac9 * feat: configure crewai-tools workspace package with dependencies * fix: apply ruff auto-formatting to crewai-tools code * chore: update lockfile * fix: don't allow tool tests yet * fix: comment out extra pytest flags for now * fix: remove conflicting conftest.py from crewai-tools tests * fix: resolve dependency conflicts and test issues - Pin vcrpy to 7.0.0 to fix pytest-recording compatibility - Comment out types-requests to resolve urllib3 conflict - Update requests requirement in crewai-tools to >=2.32.0 * chore: update CI workflows and docs for monorepo structure * chore: update CI workflows and docs for monorepo structure * fix: actions syntax * chore: ci publish and pin versions * fix: add permission to action * chore: bump version to 1.0.0a1 across all packages - Updated version to 1.0.0a1 in pyproject.toml for crewai and crewai-tools - Adjusted version in __init__.py files for consistency * WIP: v1 docs (#3626) (cherry picked from commit d46e20fa09bcd2f5916282f5553ddeb7183bd92c) * docs: parity for all translations * docs: full name of acronym AMP * docs: fix lingering unused code * docs: expand contextual options in docs.json * docs: add contextual action to request feature on GitHub (#3635) * chore: apply linting fixes to crewai-tools * feat: add required env var validation for brightdata Co-authored-by: Greyson Lalonde * fix: handle properly anyOf oneOf allOf schema's props Co-authored-by: Greyson Lalonde * feat: bump version to 1.0.0a2 * Lorenze/native inference sdks (#3619) * ruff linted * using native sdks with litellm fallback * drop exa * drop print on completion * Refactor LLM and utility functions for type consistency - Updated `max_tokens` parameter in `LLM` class to accept `float` in addition to `int`. - Modified `create_llm` function to ensure consistent type hints and return types, now returning `LLM | BaseLLM | None`. - Adjusted type hints for various parameters in `create_llm` and `_llm_via_environment_or_fallback` functions for improved clarity and type safety. - Enhanced test cases to reflect changes in type handling and ensure proper instantiation of LLM instances. * fix agent_tests * fix litellm tests and usagemetrics fix * drop print * Refactor LLM event handling and improve test coverage - Removed commented-out event emission for LLM call failures in `llm.py`. - Added `from_agent` parameter to `CrewAgentExecutor` for better context in LLM responses. - Enhanced test for LLM call failure to simulate OpenAI API failure and updated assertions for clarity. - Updated agent and task ID assertions in tests to ensure they are consistently treated as strings. * fix test_converter * fixed tests/agents/test_agent.py * Refactor LLM context length exception handling and improve provider integration - Renamed `LLMContextLengthExceededException` to `LLMContextLengthExceededExceptionError` for clarity and consistency. - Updated LLM class to pass the provider parameter correctly during initialization. - Enhanced error handling in various LLM provider implementations to raise the new exception type. - Adjusted tests to reflect the updated exception name and ensure proper error handling in context length scenarios. * Enhance LLM context window handling across providers - Introduced CONTEXT_WINDOW_USAGE_RATIO to adjust context window sizes dynamically for Anthropic, Azure, Gemini, and OpenAI LLMs. - Added validation for context window sizes in Azure and Gemini providers to ensure they fall within acceptable limits. - Updated context window size calculations to use the new ratio, improving consistency and adaptability across different models. - Removed hardcoded context window sizes in favor of ratio-based calculations for better flexibility. * fix test agent again * fix test agent * feat: add native LLM providers for Anthropic, Azure, and Gemini - Introduced new completion implementations for Anthropic, Azure, and Gemini, integrating their respective SDKs. - Added utility functions for tool validation and extraction to support function calling across LLM providers. - Enhanced context window management and token usage extraction for each provider. - Created a common utility module for shared functionality among LLM providers. * chore: update dependencies and improve context management - Removed direct dependency on `litellm` from the main dependencies and added it under extras for better modularity. - Updated the `litellm` dependency specification to allow for greater flexibility in versioning. - Refactored context length exception handling across various LLM providers to use a consistent error class. - Enhanced platform-specific dependency markers for NVIDIA packages to ensure compatibility across different systems. * refactor(tests): update LLM instantiation to include is_litellm flag in test cases - Modified multiple test cases in test_llm.py to set the is_litellm parameter to True when instantiating the LLM class. - This change ensures that the tests are aligned with the latest LLM configuration requirements and improves consistency across test scenarios. - Adjusted relevant assertions and comments to reflect the updated LLM behavior. * linter * linted * revert constants * fix(tests): correct type hint in expected model description - Updated the expected description in the test_generate_model_description_dict_field function to use 'Dict' instead of 'dict' for consistency with type hinting conventions. - This change ensures that the test accurately reflects the expected output format for model descriptions. * refactor(llm): enhance LLM instantiation and error handling - Updated the LLM class to include validation for the model parameter, ensuring it is a non-empty string. - Improved error handling by logging warnings when the native SDK fails, allowing for a fallback to LiteLLM. - Adjusted the instantiation of LLM in test cases to consistently include the is_litellm flag, aligning with recent changes in LLM configuration. - Modified relevant tests to reflect these updates, ensuring better coverage and accuracy in testing scenarios. * fixed test * refactor(llm): enhance token usage tracking and add copy methods - Updated the LLM class to track token usage and log callbacks in streaming mode, improving monitoring capabilities. - Introduced shallow and deep copy methods for the LLM instance, allowing for better management of LLM configurations and parameters. - Adjusted test cases to instantiate LLM with the is_litellm flag, ensuring alignment with recent changes in LLM configuration. * refactor(tests): reorganize imports and enhance error messages in test cases - Cleaned up import statements in test_crew.py for better organization and readability. - Enhanced error messages in test cases to use `re.escape` for improved regex matching, ensuring more robust error handling. - Adjusted comments for clarity and consistency across test scenarios. - Ensured that all necessary modules are imported correctly to avoid potential runtime issues. * feat: add base devtooling * fix: ensure dep refs are updated for devtools * fix: allow pre-release * feat: allow release after tag * feat: bump versions to 1.0.0a3 Co-authored-by: Greyson LaLonde * fix: match tag and release title, ignore devtools build for pypi * fix: allow failed pypi publish * feat: introduce trigger listing and execution commands for local development (#3643) * chore: exclude tests from ruff linting * chore: exclude tests from GitHub Actions linter * fix: replace print statements with logger in agent and memory handling * chore: add noqa for intentional print in printer utility * fix: resolve linting errors across codebase * feat: update docs with new approach to consume Platform Actions (#3675) * fix: remove duplicate line and add explicit env var * feat: bump versions to 1.0.0a4 (#3686) * Update triggers docs (#3678) * docs: introduce triggers list & triggers run command * docs: add KO triggers docs * docs: ensure CREWAI_PLATFORM_INTEGRATION_TOKEN is mentioned on docs (#3687) * Lorenze/bedrock llm (#3693) * feat: add AWS Bedrock support and update dependencies - Introduced BedrockCompletion class for AWS Bedrock integration in LLM. - Added boto3 as a new dependency in both pyproject.toml and uv.lock. - Updated LLM class to support Bedrock provider. - Created new files for Bedrock provider implementation. * using converse api * converse * linted * refactor: update BedrockCompletion class to improve parameter handling - Changed max_tokens from a fixed integer to an optional integer. - Simplified model ID assignment by removing the inference profile mapping method. - Cleaned up comments and unnecessary code related to tool specifications and model-specific parameters. * feat: improve event bus thread safety and async support Add thread-safe, async-compatible event bus with read–write locking and handler dependency ordering. Remove blinker dependency and implement direct dispatch. Improve type safety, error handling, and deterministic event synchronization. Refactor tests to auto-wait for async handlers, ensure clean teardown, and add comprehensive concurrency coverage. Replace thread-local state in AgentEvaluator with instance-based locking for correct cross-thread access. Enhance tracing reliability and event finalization. * feat: enhance OpenAICompletion class with additional client parameters (#3701) * feat: enhance OpenAICompletion class with additional client parameters - Added support for default_headers, default_query, and client_params in the OpenAICompletion class. - Refactored client initialization to use a dedicated method for client parameter retrieval. - Introduced new test cases to validate the correct usage of OpenAICompletion with various parameters. * fix: correct test case for unsupported OpenAI model - Updated the test_openai.py to ensure that the LLM instance is created before calling the method, maintaining proper error handling for unsupported models. - This change ensures that the test accurately checks for the NotFoundError when an invalid model is specified. * fix: enhance error handling in OpenAICompletion class - Added specific exception handling for NotFoundError and APIConnectionError in the OpenAICompletion class to provide clearer error messages and improve logging. - Updated the test case for unsupported models to ensure it raises a ValueError with the appropriate message when a non-existent model is specified. - This change improves the robustness of the OpenAI API integration and enhances the clarity of error reporting. * fix: improve test for unsupported OpenAI model handling - Refactored the test case in test_openai.py to create the LLM instance after mocking the OpenAI client, ensuring proper error handling for unsupported models. - This change enhances the clarity of the test by accurately checking for ValueError when a non-existent model is specified, aligning with recent improvements in error handling for the OpenAICompletion class. * feat: bump versions to 1.0.0b1 (#3706) * Lorenze/tools drop litellm (#3710) * completely drop litellm and correctly pass config for qdrant * feat: add support for additional embedding models in EmbeddingService - Expanded the list of supported embedding models to include Google Vertex, Hugging Face, Jina, Ollama, OpenAI, Roboflow, Watson X, custom embeddings, Sentence Transformers, Text2Vec, OpenClip, and Instructor. - This enhancement improves the versatility of the EmbeddingService by allowing integration with a wider range of embedding providers. * fix: update collection parameter handling in CrewAIRagAdapter - Changed the condition for setting vectors_config in the CrewAIRagAdapter to check for QdrantConfig instance instead of using hasattr. This improves type safety and ensures proper configuration handling for Qdrant integration. * moved stagehand as optional dep (#3712) * feat: bump versions to 1.0.0b2 (#3713) * feat: enhance AnthropicCompletion class with additional client parame… (#3707) * feat: enhance AnthropicCompletion class with additional client parameters and tool handling - Added support for client_params in the AnthropicCompletion class to allow for additional client configuration. - Refactored client initialization to use a dedicated method for retrieving client parameters. - Implemented a new method to handle tool use conversation flow, ensuring proper execution and response handling. - Introduced comprehensive test cases to validate the functionality of the AnthropicCompletion class, including tool use scenarios and parameter handling. * drop print statements * test: add fixture to mock ANTHROPIC_API_KEY for tests - Introduced a pytest fixture to automatically mock the ANTHROPIC_API_KEY environment variable for all tests in the test_anthropic.py module. - This change ensures that tests can run without requiring a real API key, improving test isolation and reliability. * refactor: streamline streaming message handling in AnthropicCompletion class - Removed the 'stream' parameter from the API call as it is set internally by the SDK. - Simplified the handling of tool use events and response construction by extracting token usage from the final message. - Enhanced the flow for managing tool use conversation, ensuring proper integration with the streaming API response. * fix streaming here too * fix: improve error handling in tool conversion for AnthropicCompletion class - Enhanced exception handling during tool conversion by catching KeyError and ValueError. - Added logging for conversion errors to aid in debugging and maintain robustness in tool integration. * feat: enhance GeminiCompletion class with client parameter support (#3717) * feat: enhance GeminiCompletion class with client parameter support - Added support for client_params in the GeminiCompletion class to allow for additional client configuration. - Refactored client initialization into a dedicated method for improved parameter handling. - Introduced a new method to retrieve client parameters, ensuring compatibility with the base class. - Enhanced error handling during client initialization to provide clearer messages for missing configuration. - Updated documentation to reflect the changes in client parameter usage. * add optional dependancies * refactor: update test fixture to mock GOOGLE_API_KEY - Renamed the fixture from `mock_anthropic_api_key` to `mock_google_api_key` to reflect the change in the environment variable being mocked. - This update ensures that all tests in the module can run with a mocked GOOGLE_API_KEY, improving test isolation and reliability. * fix tests * feat: enhance BedrockCompletion class with advanced features * feat: enhance BedrockCompletion class with advanced features and error handling - Added support for guardrail configuration, additional model request fields, and custom response field paths in the BedrockCompletion class. - Improved error handling for AWS exceptions and added token usage tracking with stop reason logging. - Enhanced streaming response handling with comprehensive event management, including tool use and content block processing. - Updated documentation to reflect new features and initialization parameters. - Introduced a new test suite for BedrockCompletion to validate functionality and ensure robust integration with AWS Bedrock APIs. * chore: add boto typing * fix: use typing_extensions.Required for Python 3.10 compatibility --------- Co-authored-by: Greyson Lalonde * feat: azure native tests * feat: add Azure AI Inference support and related tests - Introduced the `azure-ai-inference` package with version `1.0.0b9` and its dependencies in `uv.lock` and `pyproject.toml`. - Added new test files for Azure LLM functionality, including tests for Azure completion and tool handling. - Implemented comprehensive test cases to validate Azure-specific behavior and integration with the CrewAI framework. - Enhanced the testing framework to mock Azure credentials and ensure proper isolation during tests. * feat: enhance AzureCompletion class with Azure OpenAI support - Added support for the Azure OpenAI endpoint in the AzureCompletion class, allowing for flexible endpoint configurations. - Implemented endpoint validation and correction to ensure proper URL formats for Azure OpenAI deployments. - Enhanced error handling to provide clearer messages for common HTTP errors, including authentication and rate limit issues. - Updated tests to validate the new endpoint handling and error messaging, ensuring robust integration with Azure AI Inference. - Refactored parameter preparation to conditionally include the model parameter based on the endpoint type. * refactor: convert project module to metaclass with full typing * Lorenze/OpenAI base url backwards support (#3723) * fix: enhance OpenAICompletion class base URL handling - Updated the base URL assignment in the OpenAICompletion class to prioritize the new `api_base` attribute and fallback to the environment variable `OPENAI_BASE_URL` if both are not set. - Added `api_base` to the list of parameters in the OpenAICompletion class to ensure proper configuration and flexibility in API endpoint management. * feat: enhance OpenAICompletion class with api_base support - Added the `api_base` parameter to the OpenAICompletion class to allow for flexible API endpoint configuration. - Updated the `_get_client_params` method to prioritize `base_url` over `api_base`, ensuring correct URL handling. - Introduced comprehensive tests to validate the behavior of `api_base` and `base_url` in various scenarios, including environment variable fallback. - Enhanced test coverage for client parameter retrieval, ensuring robust integration with the OpenAI API. * fix: improve OpenAICompletion class configuration handling - Added a debug print statement to log the client configuration parameters during initialization for better traceability. - Updated the base URL assignment logic to ensure it defaults to None if no valid base URL is provided, enhancing robustness in API endpoint configuration. - Refined the retrieval of the `api_base` environment variable to streamline the configuration process. * drop print * feat: improvements on import native sdk support (#3725) * feat: add support for Anthropic provider and enhance logging - Introduced the `anthropic` package with version `0.69.0` in `pyproject.toml` and `uv.lock`, allowing for integration with the Anthropic API. - Updated logging in the LLM class to provide clearer error messages when importing native providers, enhancing debugging capabilities. - Improved error handling in the AnthropicCompletion class to guide users on installation via the updated error message format. - Refactored import error handling in other provider classes to maintain consistency in error messaging and installation instructions. * feat: enhance LLM support with Bedrock provider and update dependencies - Added support for the `bedrock` provider in the LLM class, allowing integration with AWS Bedrock APIs. - Updated `uv.lock` to replace `boto3` with `bedrock` in the dependencies, reflecting the new provider structure. - Introduced `SUPPORTED_NATIVE_PROVIDERS` to include `bedrock` and ensure proper error handling when instantiating native providers. - Enhanced error handling in the LLM class to raise informative errors when native provider instantiation fails. - Added tests to validate the behavior of the new Bedrock provider and ensure fallback mechanisms work correctly for unsupported providers. * test: update native provider fallback tests to expect ImportError * adjust the test with the expected bevaior - raising ImportError * this is exoecting the litellm format, all gemini native tests are in test_google.py --------- Co-authored-by: Greyson LaLonde * fix: remove stdout prints, improve test determinism, and update trace handling Removed `print` statements from the `LLMStreamChunkEvent` handler to prevent LLM response chunks from being written directly to stdout. The listener now only tracks chunks internally. Fixes #3715 Added explicit return statements for trace-related tests. Updated cassette for `test_failed_evaluation` to reflect new behavior where an empty trace dict is used instead of returning early. Ensured deterministic cleanup order in test fixtures by making `clear_event_bus_handlers` depend on `setup_test_environment`. This guarantees event bus shutdown and file handle cleanup occur before temporary directory deletion, resolving intermittent “Directory not empty” errors in CI. * chore: remove lib/crewai exclusion from pre-commit hooks * feat: enhance task guardrail functionality and validation * feat: enhance task guardrail functionality and validation - Introduced support for multiple guardrails in the Task class, allowing for sequential processing of guardrails. - Added a new `guardrails` field to the Task model to accept a list of callable guardrails or string descriptions. - Implemented validation to ensure guardrails are processed correctly, including handling of retries and error messages. - Enhanced the `_invoke_guardrail_function` method to manage guardrail execution and integrate with existing task output processing. - Updated tests to cover various scenarios involving multiple guardrails, including success, failure, and retry mechanisms. This update improves the flexibility and robustness of task execution by allowing for more complex validation scenarios. * refactor: enhance guardrail type handling in Task model - Updated the Task class to improve guardrail type definitions, introducing GuardrailType and GuardrailsType for better clarity and type safety. - Simplified the validation logic for guardrails, ensuring that both single and multiple guardrails are processed correctly. - Enhanced error messages for guardrail validation to provide clearer feedback when incorrect types are provided. - This refactor improves the maintainability and robustness of task execution by standardizing guardrail handling. * feat: implement per-guardrail retry tracking in Task model - Introduced a new private attribute `_guardrail_retry_counts` to the Task class for tracking retry attempts on a per-guardrail basis. - Updated the guardrail processing logic to utilize the new retry tracking, allowing for independent retry counts for each guardrail. - Enhanced error handling to provide clearer feedback when guardrails fail validation after exceeding retry limits. - Modified existing tests to validate the new retry tracking behavior, ensuring accurate assertions on guardrail retries. This update improves the robustness and flexibility of task execution by allowing for more granular control over guardrail validation and retry mechanisms. * chore: 1.0.0b3 bump (#3734) * chore: full ruff and mypy improved linting, pre-commit setup, and internal architecture. Configured Ruff to respect .gitignore, added stricter rules, and introduced a lock pre-commit hook with virtualenv activation. Fixed type shadowing in EXASearchTool using a type_ alias to avoid PEP 563 conflicts and resolved circular imports in agent executor and guardrail modules. Removed agent-ops attributes, deprecated watson alias, and dropped crewai-enterprise tools with corresponding test updates. Refactored cache and memoization for thread safety and cleaned up structured output adapters and related logic. * New MCL DSL (#3738) * Adding MCP implementation * New tests for MCP implementation * fix tests * update docs * Revert "New tests for MCP implementation" This reverts commit 0bbe6dee90173898b3cf87002fe13fe82c1a9834. * linter * linter * fix * verify mcp pacakge exists * adjust docs to be clear only remote servers are supported * reverted * ensure args schema generated properly * properly close out --------- Co-authored-by: lorenzejay Co-authored-by: Greyson Lalonde * feat: a2a experimental experimental a2a support --------- Co-authored-by: Lucas Gomide Co-authored-by: Greyson LaLonde Co-authored-by: Tony Kipkemboi Co-authored-by: Mike Plachta Co-authored-by: João Moura --- .github/workflows/codeql.yml | 4 +- .github/workflows/linter.yml | 9 +- .github/workflows/publish.yml | 83 + .github/workflows/tests.yml | 31 +- .gitignore | 1 - .pre-commit-config.yaml | 15 +- 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tests/cassettes/test_tool_usage_information_is_appended_to_agent.yaml delete mode 100644 tests/experimental/evaluation/test_agent_evaluator.py delete mode 100644 tests/test_task_guardrails.py delete mode 100644 tests/utilities/events/test_crewai_event_bus.py diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 6e0bf7c24..2fca96dcd 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -15,11 +15,11 @@ on: push: branches: [ "main" ] paths-ignore: - - "src/crewai/cli/templates/**" + - "lib/crewai/src/crewai/cli/templates/**" pull_request: branches: [ "main" ] paths-ignore: - - "src/crewai/cli/templates/**" + - "lib/crewai/src/crewai/cli/templates/**" jobs: analyze: diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml index 33a24b1c7..ae26c4209 100644 --- a/.github/workflows/linter.yml +++ b/.github/workflows/linter.yml @@ -52,10 +52,11 @@ jobs: - name: Run Ruff on Changed Files if: ${{ steps.changed-files.outputs.files != '' }} run: | - echo "${{ steps.changed-files.outputs.files }}" \ - | tr ' ' '\n' \ - | grep -v 'src/crewai/cli/templates/' \ - | xargs -I{} uv run ruff check "{}" + echo "${{ steps.changed-files.outputs.files }}" \ + | tr ' ' '\n' \ + | grep -v 'src/crewai/cli/templates/' \ + | grep -v '/tests/' \ + | xargs -I{} uv run ruff check "{}" - name: Save uv caches if: steps.cache-restore.outputs.cache-hit != 'true' diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 000000000..1ef0891a2 --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,83 @@ +name: Publish to PyPI + +on: + release: + types: [ published ] + workflow_dispatch: + +jobs: + build: + if: github.event.release.prerelease == true + name: Build packages + runs-on: ubuntu-latest + permissions: + contents: read + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install uv + uses: astral-sh/setup-uv@v4 + + - name: Build packages + run: | + uv build --prerelease="allow" --all-packages + rm dist/.gitignore + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + name: dist + path: dist/ + + publish: + if: github.event.release.prerelease == true + name: Publish to PyPI + needs: build + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/crewai + permissions: + id-token: write + contents: read + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + version: "0.8.4" + python-version: "3.12" + enable-cache: false + + - name: Download artifacts + uses: actions/download-artifact@v4 + with: + name: dist + path: dist + + - name: Publish to PyPI + env: + UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }} + run: | + failed=0 + for package in dist/*; do + if [[ "$package" == *"crewai_devtools"* ]]; then + echo "Skipping private package: $package" + continue + fi + echo "Publishing $package" + if ! uv publish "$package"; then + echo "Failed to publish $package" + failed=1 + fi + done + if [ $failed -eq 1 ]; then + echo "Some packages failed to publish" + exit 1 + fi diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index a1b864305..0189d1364 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -8,6 +8,14 @@ permissions: env: OPENAI_API_KEY: fake-api-key PYTHONUNBUFFERED: 1 + BRAVE_API_KEY: fake-brave-key + SNOWFLAKE_USER: fake-snowflake-user + SNOWFLAKE_PASSWORD: fake-snowflake-password + SNOWFLAKE_ACCOUNT: fake-snowflake-account + SNOWFLAKE_WAREHOUSE: fake-snowflake-warehouse + SNOWFLAKE_DATABASE: fake-snowflake-database + SNOWFLAKE_SCHEMA: fake-snowflake-schema + EMBEDCHAIN_DB_URI: sqlite:///test.db jobs: tests: @@ -56,13 +64,13 @@ jobs: - name: Run tests (group ${{ matrix.group }} of 8) run: | PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_') - DURATION_FILE=".test_durations_py${PYTHON_VERSION_SAFE}" - + DURATION_FILE="../../.test_durations_py${PYTHON_VERSION_SAFE}" + # Temporarily always skip cached durations to fix test splitting # When durations don't match, pytest-split runs duplicate tests instead of splitting echo "Using even test splitting (duration cache disabled until fix merged)" DURATIONS_ARG="" - + # Original logic (disabled temporarily): # if [ ! -f "$DURATION_FILE" ]; then # echo "No cached durations found, tests will be split evenly" @@ -74,8 +82,8 @@ jobs: # echo "No test changes detected, using cached test durations for optimal splitting" # DURATIONS_ARG="--durations-path=${DURATION_FILE}" # fi - - uv run pytest \ + + cd lib/crewai && uv run pytest \ --block-network \ --timeout=30 \ -vv \ @@ -86,6 +94,19 @@ jobs: -n auto \ --maxfail=3 + - name: Run tool tests (group ${{ matrix.group }} of 8) + run: | + cd lib/crewai-tools && uv run pytest \ + --block-network \ + --timeout=30 \ + -vv \ + --splits 8 \ + --group ${{ matrix.group }} \ + --durations=10 \ + -n auto \ + --maxfail=3 + + - name: Save uv caches if: steps.cache-restore.outputs.cache-hit != 'true' uses: actions/cache/save@v4 diff --git a/.gitignore b/.gitignore index 1e4e7bf6c..adebfb42c 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,6 @@ .pytest_cache __pycache__ dist/ -lib/ .env assets/* .idea diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a2931167e..f3bc4094e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,17 +3,24 @@ repos: hooks: - id: ruff name: ruff - entry: uv run ruff check + entry: bash -c 'source .venv/bin/activate && uv run ruff check --config pyproject.toml "$@"' -- language: system + pass_filenames: true types: [python] - id: ruff-format name: ruff-format - entry: uv run ruff format + entry: bash -c 'source .venv/bin/activate && uv run ruff format --config pyproject.toml "$@"' -- language: system + pass_filenames: true types: [python] - id: mypy name: mypy - entry: uv run mypy + entry: bash -c 'source .venv/bin/activate && uv run mypy --config-file pyproject.toml "$@"' -- language: system + pass_filenames: true types: [python] - exclude: ^tests/ + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.9.3 + hooks: + - id: uv-lock + diff --git a/docs/docs.json b/docs/docs.json index 5ab2cf624..bd079f383 100644 --- a/docs/docs.json +++ b/docs/docs.json @@ -134,6 +134,7 @@ "group": "MCP Integration", "pages": [ "en/mcp/overview", + "en/mcp/dsl-integration", "en/mcp/stdio", "en/mcp/sse", "en/mcp/streamable-http", @@ -361,10 +362,20 @@ "en/enterprise/integrations/github", "en/enterprise/integrations/gmail", "en/enterprise/integrations/google_calendar", + "en/enterprise/integrations/google_contacts", + "en/enterprise/integrations/google_docs", + "en/enterprise/integrations/google_drive", "en/enterprise/integrations/google_sheets", + "en/enterprise/integrations/google_slides", "en/enterprise/integrations/hubspot", "en/enterprise/integrations/jira", "en/enterprise/integrations/linear", + "en/enterprise/integrations/microsoft_excel", + "en/enterprise/integrations/microsoft_onedrive", + "en/enterprise/integrations/microsoft_outlook", + "en/enterprise/integrations/microsoft_sharepoint", + "en/enterprise/integrations/microsoft_teams", + "en/enterprise/integrations/microsoft_word", "en/enterprise/integrations/notion", "en/enterprise/integrations/salesforce", "en/enterprise/integrations/shopify", @@ -560,6 +571,7 @@ "group": "Integração MCP", "pages": [ "pt-BR/mcp/overview", + "pt-BR/mcp/dsl-integration", "pt-BR/mcp/stdio", "pt-BR/mcp/sse", "pt-BR/mcp/streamable-http", @@ -773,10 +785,20 @@ "pt-BR/enterprise/integrations/github", "pt-BR/enterprise/integrations/gmail", "pt-BR/enterprise/integrations/google_calendar", + "pt-BR/enterprise/integrations/google_contacts", + "pt-BR/enterprise/integrations/google_docs", + "pt-BR/enterprise/integrations/google_drive", "pt-BR/enterprise/integrations/google_sheets", + "pt-BR/enterprise/integrations/google_slides", "pt-BR/enterprise/integrations/hubspot", "pt-BR/enterprise/integrations/jira", "pt-BR/enterprise/integrations/linear", + "pt-BR/enterprise/integrations/microsoft_excel", + "pt-BR/enterprise/integrations/microsoft_onedrive", + "pt-BR/enterprise/integrations/microsoft_outlook", + "pt-BR/enterprise/integrations/microsoft_sharepoint", + "pt-BR/enterprise/integrations/microsoft_teams", + "pt-BR/enterprise/integrations/microsoft_word", "pt-BR/enterprise/integrations/notion", "pt-BR/enterprise/integrations/salesforce", "pt-BR/enterprise/integrations/shopify", @@ -805,6 +827,12 @@ "group": "Triggers", "pages": [ "pt-BR/enterprise/guides/automation-triggers", + "pt-BR/enterprise/guides/gmail-trigger", + "pt-BR/enterprise/guides/google-calendar-trigger", + "pt-BR/enterprise/guides/google-drive-trigger", + "pt-BR/enterprise/guides/outlook-trigger", + "pt-BR/enterprise/guides/onedrive-trigger", + "pt-BR/enterprise/guides/microsoft-teams-trigger", "pt-BR/enterprise/guides/slack-trigger", "pt-BR/enterprise/guides/hubspot-trigger", "pt-BR/enterprise/guides/salesforce-trigger", @@ -963,6 +991,7 @@ "group": "MCP 통합", "pages": [ "ko/mcp/overview", + "ko/mcp/dsl-integration", "ko/mcp/stdio", "ko/mcp/sse", "ko/mcp/streamable-http", @@ -1188,10 +1217,20 @@ "ko/enterprise/integrations/github", "ko/enterprise/integrations/gmail", "ko/enterprise/integrations/google_calendar", + "ko/enterprise/integrations/google_contacts", + "ko/enterprise/integrations/google_docs", + "ko/enterprise/integrations/google_drive", "ko/enterprise/integrations/google_sheets", + "ko/enterprise/integrations/google_slides", "ko/enterprise/integrations/hubspot", "ko/enterprise/integrations/jira", "ko/enterprise/integrations/linear", + "ko/enterprise/integrations/microsoft_excel", + "ko/enterprise/integrations/microsoft_onedrive", + "ko/enterprise/integrations/microsoft_outlook", + "ko/enterprise/integrations/microsoft_sharepoint", + "ko/enterprise/integrations/microsoft_teams", + "ko/enterprise/integrations/microsoft_word", "ko/enterprise/integrations/notion", "ko/enterprise/integrations/salesforce", "ko/enterprise/integrations/shopify", @@ -1220,6 +1259,12 @@ "group": "트리거", "pages": [ "ko/enterprise/guides/automation-triggers", + "ko/enterprise/guides/gmail-trigger", + "ko/enterprise/guides/google-calendar-trigger", + "ko/enterprise/guides/google-drive-trigger", + "ko/enterprise/guides/outlook-trigger", + "ko/enterprise/guides/onedrive-trigger", + "ko/enterprise/guides/microsoft-teams-trigger", "ko/enterprise/guides/slack-trigger", "ko/enterprise/guides/hubspot-trigger", "ko/enterprise/guides/salesforce-trigger", diff --git a/docs/en/enterprise/features/tools-and-integrations.mdx b/docs/en/enterprise/features/tools-and-integrations.mdx index 4e60021db..db2cec5ce 100644 --- a/docs/en/enterprise/features/tools-and-integrations.mdx +++ b/docs/en/enterprise/features/tools-and-integrations.mdx @@ -43,7 +43,7 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma 1. Go to Integrations 2. Click Connect on the desired service 3. Complete the OAuth flow and grant scopes - 4. Copy your Enterprise Token from the Integration tab + 4. Copy your Enterprise Token from Integration Settings ![Enterprise Token](/images/enterprise/enterprise_action_auth_token.png) @@ -57,29 +57,37 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma uv add crewai-tools ``` + ### Environment Variable Setup + + + To use integrations with `Agent(apps=[])`, you must set the `CREWAI_PLATFORM_INTEGRATION_TOKEN` environment variable with your Enterprise Token. + + + ```bash + export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token" + ``` + + Or add it to your `.env` file: + + ``` + CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token + ``` + ### Usage Example - All services you have authenticated will be available as tools. Add `CrewaiEnterpriseTools` to your agent and you’re set. + Use the new streamlined approach to integrate enterprise apps. Simply specify the app and its actions directly in the Agent configuration. ```python from crewai import Agent, Task, Crew - from crewai_tools import CrewaiEnterpriseTools - - # Get enterprise tools (Gmail tool will be included) - enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" - ) - # print the tools - print(enterprise_tools) # Create an agent with Gmail capabilities email_agent = Agent( role="Email Manager", goal="Manage and organize email communications", backstory="An AI assistant specialized in email management and communication.", - tools=enterprise_tools + apps=['gmail', 'gmail/send_email'] # Using canonical name 'gmail' ) # Task to send an email @@ -102,21 +110,14 @@ Tools & Integrations is the central hub for connecting third‑party apps and ma ### Filtering Tools ```python - from crewai_tools import CrewaiEnterpriseTools - - enterprise_tools = CrewaiEnterpriseTools( - actions_list=["gmail_find_email"] # only gmail_find_email tool will be available - ) - - - gmail_tool = enterprise_tools["gmail_find_email"] - + from crewai import Agent, Task, Crew + # Create agent with specific Gmail actions only gmail_agent = Agent( role="Gmail Manager", goal="Manage gmail communications and notifications", backstory="An AI assistant that helps coordinate gmail communications.", - tools=[gmail_tool] + apps=['gmail/fetch_emails'] # Using canonical name with specific action ) notification_task = Task( diff --git a/docs/en/enterprise/guides/automation-triggers.mdx b/docs/en/enterprise/guides/automation-triggers.mdx index 61fe4691c..7ca9d83ab 100644 --- a/docs/en/enterprise/guides/automation-triggers.mdx +++ b/docs/en/enterprise/guides/automation-triggers.mdx @@ -117,27 +117,50 @@ Before wiring a trigger into production, make sure you: - Decide whether to pass trigger context automatically using `allow_crewai_trigger_context` - Set up monitoring—webhook logs, CrewAI execution history, and optional external alerting -### Payload & Crew Examples Repository +### Testing Triggers Locally with CLI -We maintain a comprehensive repository with end-to-end trigger examples to help you build and test your automations: +The CrewAI CLI provides powerful commands to help you develop and test trigger-driven automations without deploying to production. -This repository contains: +#### List Available Triggers -- **Realistic payload samples** for every supported trigger integration -- **Ready-to-run crew implementations** that parse each payload and turn it into a business workflow -- **Multiple scenarios per integration** (e.g., new events, updates, deletions) so you can match the shape of your data +View all available triggers for your connected integrations: -| Integration | When it fires | Payload Samples | Crew Examples | -| :-- | :-- | :-- | :-- | -| Gmail | New messages, thread updates | [New alerts, thread updates](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | [`new-email-crew.py`, `gmail-alert-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | -| Google Calendar | Event created / updated / started / ended / cancelled | [Event lifecycle payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | [`calendar-event-crew.py`, `calendar-meeting-crew.py`, `calendar-working-location-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | -| Google Drive | File created / updated / deleted | [File lifecycle payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | [`drive-file-crew.py`, `drive-file-deletion-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | -| Outlook | New email, calendar event removed | [Outlook payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | [`outlook-message-crew.py`, `outlook-event-removal-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | -| OneDrive | File operations (create, update, share, delete) | [OneDrive payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | [`onedrive-file-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | -| HubSpot | Record created / updated (contacts, companies, deals) | [HubSpot payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | [`hubspot-company-crew.py`, `hubspot-contact-crew.py`, `hubspot-record-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | -| Microsoft Teams | Chat thread created | [Teams chat payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | [`teams-chat-created-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | +```bash +crewai triggers list +``` + +This command displays all triggers available based on your connected integrations, showing: +- Integration name and connection status +- Available trigger types +- Trigger names and descriptions + +#### Simulate Trigger Execution + +Test your crew with realistic trigger payloads before deployment: + +```bash +crewai triggers run +``` + +For example: + +```bash +crewai triggers run microsoft_onedrive/file_changed +``` + +This command: +- Executes your crew locally +- Passes a complete, realistic trigger payload +- Simulates exactly how your crew will be called in production + + + **Important Development Notes:** + - Use `crewai triggers run ` to simulate trigger execution during development + - Using `crewai run` will NOT simulate trigger calls and won't pass the trigger payload + - After deployment, your crew will be executed with the actual trigger payload + - If your crew expects parameters that aren't in the trigger payload, execution may fail + -Use these samples to understand payload shape, copy the matching crew, and then replace the test payload with your live trigger data. ### Triggers with Crew @@ -241,15 +264,20 @@ def delegate_to_crew(self, crewai_trigger_payload: dict = None): ## Troubleshooting **Trigger not firing:** -- Verify the trigger is enabled -- Check integration connection status +- Verify the trigger is enabled in your deployment's Triggers tab +- Check integration connection status under Tools & Integrations +- Ensure all required environment variables are properly configured **Execution failures:** - Check the execution logs for error details -- If you are developing, make sure the inputs include the `crewai_trigger_payload` parameter with the correct payload +- Use `crewai triggers run ` to test locally and see the exact payload structure +- Verify your crew can handle the `crewai_trigger_payload` parameter +- Ensure your crew doesn't expect parameters that aren't included in the trigger payload + +**Development issues:** +- Always test with `crewai triggers run ` before deploying to see the complete payload +- Remember that `crewai run` does NOT simulate trigger calls—use `crewai triggers run` instead +- Use `crewai triggers list` to verify which triggers are available for your connected integrations +- After deployment, your crew will receive the actual trigger payload, so test thoroughly locally first Automation triggers transform your CrewAI deployments into responsive, event-driven systems that can seamlessly integrate with your existing business processes and tools. - - - Check them out on GitHub! - diff --git a/docs/en/enterprise/guides/gmail-trigger.mdx b/docs/en/enterprise/guides/gmail-trigger.mdx index 2caefc045..4e6e66e15 100644 --- a/docs/en/enterprise/guides/gmail-trigger.mdx +++ b/docs/en/enterprise/guides/gmail-trigger.mdx @@ -51,16 +51,25 @@ class GmailProcessingCrew: ) ``` -The Gmail payload will be available via the standard context mechanisms. See the payload samples repository for structure and fields. +The Gmail payload will be available via the standard context mechanisms. -### Sample payloads & crews +### Testing Locally -The [CrewAI AMP Trigger Examples repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) includes: +Test your Gmail trigger integration locally using the CrewAI CLI: -- `new-email-payload-1.json` / `new-email-payload-2.json` — production-style new message alerts with matching crews in `new-email-crew.py` -- `thread-updated-sample-1.json` — follow-up messages on an existing thread, processed by `gmail-alert-crew.py` +```bash +# View all available triggers +crewai triggers list -Use these samples to validate your parsing logic locally before wiring the trigger to your live Gmail accounts. +# Simulate a Gmail trigger with realistic payload +crewai triggers run gmail/new_email +``` + +The `crewai triggers run` command will execute your crew with a complete Gmail payload, allowing you to test your parsing logic before deployment. + + + Use `crewai triggers run gmail/new_email` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload. + ## Monitoring Executions @@ -70,16 +79,10 @@ Track history and performance of triggered runs: List of executions triggered by automation -## Payload Reference - -See the sample payloads and field descriptions: - - - Gmail samples in Trigger Examples Repo - - ## Troubleshooting - Ensure Gmail is connected in Tools & Integrations - Verify the Gmail Trigger is enabled on the Triggers tab +- Test locally with `crewai triggers run gmail/new_email` to see the exact payload structure - Check the execution logs and confirm the payload is passed as `crewai_trigger_payload` +- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution diff --git a/docs/en/enterprise/guides/google-calendar-trigger.mdx b/docs/en/enterprise/guides/google-calendar-trigger.mdx index cf2d32471..4dee7a3dd 100644 --- a/docs/en/enterprise/guides/google-calendar-trigger.mdx +++ b/docs/en/enterprise/guides/google-calendar-trigger.mdx @@ -39,16 +39,23 @@ print(result.raw) Use `crewai_trigger_payload` exactly as it is delivered by the trigger so the crew can extract the proper fields. -## Sample payloads & crews +## Testing Locally -The [Google Calendar examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) show how to handle multiple event types: +Test your Google Calendar trigger integration locally using the CrewAI CLI: -- `new-event.json` → standard event creation handled by `calendar-event-crew.py` -- `event-updated.json` / `event-started.json` / `event-ended.json` → in-flight updates processed by `calendar-meeting-crew.py` -- `event-canceled.json` → cancellation workflow that alerts attendees via `calendar-meeting-crew.py` -- Working location events use `calendar-working-location-crew.py` to extract on-site schedules +```bash +# View all available triggers +crewai triggers list -Each crew transforms raw event metadata (attendees, rooms, working locations) into the summaries your teams need. +# Simulate a Google Calendar trigger with realistic payload +crewai triggers run google_calendar/event_changed +``` + +The `crewai triggers run` command will execute your crew with a complete Calendar payload, allowing you to test your parsing logic before deployment. + + + Use `crewai triggers run google_calendar/event_changed` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload. + ## Monitoring Executions @@ -61,5 +68,7 @@ The **Executions** list in the deployment dashboard tracks every triggered run a ## Troubleshooting - Ensure the correct Google account is connected and the trigger is enabled +- Test locally with `crewai triggers run google_calendar/event_changed` to see the exact payload structure - Confirm your workflow handles all-day events (payloads use `start.date` and `end.date` instead of timestamps) - Check execution logs if reminders or attendee arrays are missing—calendar permissions can limit fields in the payload +- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution diff --git a/docs/en/enterprise/guides/google-drive-trigger.mdx b/docs/en/enterprise/guides/google-drive-trigger.mdx index 19c10837b..f0fc4e938 100644 --- a/docs/en/enterprise/guides/google-drive-trigger.mdx +++ b/docs/en/enterprise/guides/google-drive-trigger.mdx @@ -36,15 +36,23 @@ crew.kickoff({ }) ``` -## Sample payloads & crews +## Testing Locally -Explore the [Google Drive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) to cover different operations: +Test your Google Drive trigger integration locally using the CrewAI CLI: -- `new-file.json` → new uploads processed by `drive-file-crew.py` -- `updated-file.json` → file edits and metadata changes handled by `drive-file-crew.py` -- `deleted-file.json` → deletion events routed through `drive-file-deletion-crew.py` +```bash +# View all available triggers +crewai triggers list -Each crew highlights the file name, operation type, owner, permissions, and security considerations so downstream systems can respond appropriately. +# Simulate a Google Drive trigger with realistic payload +crewai triggers run google_drive/file_changed +``` + +The `crewai triggers run` command will execute your crew with a complete Drive payload, allowing you to test your parsing logic before deployment. + + + Use `crewai triggers run google_drive/file_changed` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload. + ## Monitoring Executions @@ -57,5 +65,7 @@ Track history and performance of triggered runs with the **Executions** list in ## Troubleshooting - Verify Google Drive is connected and the trigger toggle is enabled +- Test locally with `crewai triggers run google_drive/file_changed` to see the exact payload structure - If a payload is missing permission data, ensure the connected account has access to the file or folder - The trigger sends file IDs only; use the Drive API if you need to fetch binary content during the crew run +- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution diff --git a/docs/en/enterprise/guides/hubspot-trigger.mdx b/docs/en/enterprise/guides/hubspot-trigger.mdx index c2a7549f3..0c95db0f6 100644 --- a/docs/en/enterprise/guides/hubspot-trigger.mdx +++ b/docs/en/enterprise/guides/hubspot-trigger.mdx @@ -49,16 +49,4 @@ This guide provides a step-by-step process to set up HubSpot triggers for CrewAI -## Additional Resources - -### Sample payloads & crews - -You can jump-start development with the [HubSpot examples in the trigger repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot): - -- `record-created-contact.json`, `record-updated-contact.json` → contact lifecycle events handled by `hubspot-contact-crew.py` -- `record-created-company.json`, `record-updated-company.json` → company enrichment flows in `hubspot-company-crew.py` -- `record-created-deals.json`, `record-updated-deals.json` → deal pipeline automation in `hubspot-record-crew.py` - -Each crew demonstrates how to parse HubSpot record fields, enrich context, and return structured insights. - For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows). diff --git a/docs/en/enterprise/guides/microsoft-teams-trigger.mdx b/docs/en/enterprise/guides/microsoft-teams-trigger.mdx index 10878af40..00434632b 100644 --- a/docs/en/enterprise/guides/microsoft-teams-trigger.mdx +++ b/docs/en/enterprise/guides/microsoft-teams-trigger.mdx @@ -37,16 +37,28 @@ print(result.raw) The crew parses thread metadata (subject, created time, roster) and generates an action plan for the receiving team. -## Sample payloads & crews +## Testing Locally -The [Microsoft Teams examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) include: +Test your Microsoft Teams trigger integration locally using the CrewAI CLI: -- `chat-created.json` → chat creation payload processed by `teams-chat-created-crew.py` +```bash +# View all available triggers +crewai triggers list -The crew demonstrates how to extract participants, initial messages, tenant information, and compliance metadata from the Microsoft Graph webhook payload. +# Simulate a Microsoft Teams trigger with realistic payload +crewai triggers run microsoft_teams/teams_message_created +``` + +The `crewai triggers run` command will execute your crew with a complete Teams payload, allowing you to test your parsing logic before deployment. + + + Use `crewai triggers run microsoft_teams/teams_message_created` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload. + ## Troubleshooting - Ensure the Teams connection is active; it must be refreshed if the tenant revokes permissions +- Test locally with `crewai triggers run microsoft_teams/teams_message_created` to see the exact payload structure - Confirm the webhook subscription in Microsoft 365 is still valid if payloads stop arriving - Review execution logs for payload shape mismatches—Graph notifications may omit fields when a chat is private or restricted +- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution diff --git a/docs/en/enterprise/guides/onedrive-trigger.mdx b/docs/en/enterprise/guides/onedrive-trigger.mdx index 51de175db..09aabd2e2 100644 --- a/docs/en/enterprise/guides/onedrive-trigger.mdx +++ b/docs/en/enterprise/guides/onedrive-trigger.mdx @@ -36,18 +36,28 @@ crew.kickoff({ The crew inspects file metadata, user activity, and permission changes to produce a compliance-friendly summary. -## Sample payloads & crews +## Testing Locally -The [OneDrive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) showcase how to: +Test your OneDrive trigger integration locally using the CrewAI CLI: -- Parse file metadata, size, and folder paths -- Track who created and last modified the file -- Highlight permission and external sharing changes +```bash +# View all available triggers +crewai triggers list -`onedrive-file-crew.py` bundles the analysis and summarization tasks so you can add remediation steps as needed. +# Simulate a OneDrive trigger with realistic payload +crewai triggers run microsoft_onedrive/file_changed +``` + +The `crewai triggers run` command will execute your crew with a complete OneDrive payload, allowing you to test your parsing logic before deployment. + + + Use `crewai triggers run microsoft_onedrive/file_changed` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload. + ## Troubleshooting - Ensure the connected account has permission to read the file metadata included in the webhook +- Test locally with `crewai triggers run microsoft_onedrive/file_changed` to see the exact payload structure - If the trigger fires but the payload is missing `permissions`, confirm the site-level sharing settings allow Graph to return this field - For large tenants, filter notifications upstream so the crew only runs on relevant directories +- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution diff --git a/docs/en/enterprise/guides/outlook-trigger.mdx b/docs/en/enterprise/guides/outlook-trigger.mdx index 21bda5407..ac7be7d21 100644 --- a/docs/en/enterprise/guides/outlook-trigger.mdx +++ b/docs/en/enterprise/guides/outlook-trigger.mdx @@ -36,17 +36,28 @@ crew.kickoff({ The crew extracts sender details, subject, body preview, and attachments before generating a structured response. -## Sample payloads & crews +## Testing Locally -Review the [Outlook examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) for two common scenarios: +Test your Outlook trigger integration locally using the CrewAI CLI: -- `new-message.json` → new mail notifications parsed by `outlook-message-crew.py` -- `event-removed.json` → calendar cleanup handled by `outlook-event-removal-crew.py` +```bash +# View all available triggers +crewai triggers list -Each crew demonstrates how to handle Microsoft Graph payloads, normalize headers, and keep humans in-the-loop with concise summaries. +# Simulate an Outlook trigger with realistic payload +crewai triggers run microsoft_outlook/email_received +``` + +The `crewai triggers run` command will execute your crew with a complete Outlook payload, allowing you to test your parsing logic before deployment. + + + Use `crewai triggers run microsoft_outlook/email_received` (not `crewai run`) to simulate trigger execution during development. After deployment, your crew will automatically receive the trigger payload. + ## Troubleshooting - Verify the Outlook connector is still authorized; the subscription must be renewed periodically +- Test locally with `crewai triggers run microsoft_outlook/email_received` to see the exact payload structure - If attachments are missing, confirm the webhook subscription includes the `includeResourceData` flag - Review execution logs when events fail to match—cancellation payloads lack attendee lists by design and the crew should account for that +- Remember: use `crewai triggers run` (not `crewai run`) to simulate trigger execution diff --git a/docs/en/enterprise/guides/tool-repository.mdx b/docs/en/enterprise/guides/tool-repository.mdx index 5161cdfc7..aee927e63 100644 --- a/docs/en/enterprise/guides/tool-repository.mdx +++ b/docs/en/enterprise/guides/tool-repository.mdx @@ -151,3 +151,5 @@ You can check the security check status of a tool at: Contact our support team for assistance with API integration or troubleshooting. + + diff --git a/docs/en/enterprise/integrations/asana.mdx b/docs/en/enterprise/integrations/asana.mdx index 5e5a2ea46..0d507cc7a 100644 --- a/docs/en/enterprise/integrations/asana.mdx +++ b/docs/en/enterprise/integrations/asana.mdx @@ -25,7 +25,7 @@ Before using the Asana integration, ensure you have: 2. Find **Asana** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for task and project management -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,7 +36,7 @@ uv add crewai-tools ## Available Actions - + **Description:** Create a comment in Asana. **Parameters:** @@ -44,7 +44,7 @@ uv add crewai-tools - `text` (string, required): Text (example: "This is a comment."). - + **Description:** Create a project in Asana. **Parameters:** @@ -54,7 +54,7 @@ uv add crewai-tools - `notes` (string, optional): Notes (example: "These are things we need to purchase."). - + **Description:** Get a list of projects in Asana. **Parameters:** @@ -62,14 +62,14 @@ uv add crewai-tools - Options: `default`, `true`, `false` - + **Description:** Get a project by ID in Asana. **Parameters:** - `projectFilterId` (string, required): Project ID. - + **Description:** Create a task in Asana. **Parameters:** @@ -83,7 +83,7 @@ uv add crewai-tools - `gid` (string, optional): External ID - An ID from your application to associate this task with. You can use this ID to sync updates to this task later. - + **Description:** Update a task in Asana. **Parameters:** @@ -98,7 +98,7 @@ uv add crewai-tools - `gid` (string, optional): External ID - An ID from your application to associate this task with. You can use this ID to sync updates to this task later. - + **Description:** Get a list of tasks in Asana. **Parameters:** @@ -108,21 +108,21 @@ uv add crewai-tools - `completedSince` (string, optional): Completed since - Only return tasks that are either incomplete or that have been completed since this time (ISO or Unix timestamp). (example: "2014-04-25T16:15:47-04:00"). - + **Description:** Get a list of tasks by ID in Asana. **Parameters:** - `taskId` (string, required): Task ID. - + **Description:** Get a task by external ID in Asana. **Parameters:** - `gid` (string, required): External ID - The ID that this task is associated or synced with, from your application. - + **Description:** Add a task to a section in Asana. **Parameters:** @@ -132,14 +132,14 @@ uv add crewai-tools - `afterTaskId` (string, optional): After Task ID - The ID of a task in this section that this task will be inserted after. Cannot be used with Before Task ID. (example: "1204619611402340"). - + **Description:** Get a list of teams in Asana. **Parameters:** - `workspace` (string, required): Workspace - Returns the teams in this workspace visible to the authorized user. - + **Description:** Get a list of workspaces in Asana. **Parameters:** None required. @@ -152,19 +152,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Asana tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Asana capabilities asana_agent = Agent( role="Project Manager", goal="Manage tasks and projects in Asana efficiently", backstory="An AI assistant specialized in project management and task coordination.", - tools=[enterprise_tools] + apps=['asana'] # All Asana actions will be available ) # Task to create a new project @@ -186,19 +180,18 @@ crew.kickoff() ### Filtering Specific Asana Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Asana tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["asana_create_task", "asana_update_task", "asana_get_tasks"] -) +from crewai import Agent, Task, Crew +# Create agent with specific Asana actions only task_manager_agent = Agent( role="Task Manager", goal="Create and manage tasks efficiently", backstory="An AI assistant that focuses on task creation and management.", - tools=enterprise_tools + apps=[ + 'asana/create_task', + 'asana/update_task', + 'asana/get_tasks' + ] # Specific Asana actions ) # Task to create and assign a task @@ -220,17 +213,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Coordinate project activities and track progress", backstory="An experienced project coordinator who ensures projects run smoothly.", - tools=[enterprise_tools] + apps=['asana'] ) # Complex task involving multiple Asana operations diff --git a/docs/en/enterprise/integrations/box.mdx b/docs/en/enterprise/integrations/box.mdx index 472788505..1aed21613 100644 --- a/docs/en/enterprise/integrations/box.mdx +++ b/docs/en/enterprise/integrations/box.mdx @@ -25,7 +25,7 @@ Before using the Box integration, ensure you have: 2. Find **Box** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for file and folder management -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,7 +36,7 @@ uv add crewai-tools ## Available Actions - + **Description:** Save a file from URL in Box. **Parameters:** @@ -52,7 +52,7 @@ uv add crewai-tools - `file` (string, required): File URL - Files must be smaller than 50MB in size. (example: "https://picsum.photos/200/300"). - + **Description:** Save a file in Box. **Parameters:** @@ -61,14 +61,14 @@ uv add crewai-tools - `folder` (string, optional): Folder - Use Connect Portal Workflow Settings to allow users to select the File's Folder destination. Defaults to the user's root folder if left blank. - + **Description:** Get a file by ID in Box. **Parameters:** - `fileId` (string, required): File ID - The unique identifier that represents a file. (example: "12345"). - + **Description:** List files in Box. **Parameters:** @@ -93,7 +93,7 @@ uv add crewai-tools ``` - + **Description:** Create a folder in Box. **Parameters:** @@ -106,7 +106,7 @@ uv add crewai-tools ``` - + **Description:** Move a folder in Box. **Parameters:** @@ -120,14 +120,14 @@ uv add crewai-tools ``` - + **Description:** Get a folder by ID in Box. **Parameters:** - `folderId` (string, required): Folder ID - The unique identifier that represents a folder. (example: "0"). - + **Description:** Search folders in Box. **Parameters:** @@ -152,7 +152,7 @@ uv add crewai-tools ``` - + **Description:** Delete a folder in Box. **Parameters:** @@ -167,19 +167,14 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Box tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +from crewai import Agent, Task, Crew # Create an agent with Box capabilities box_agent = Agent( role="Document Manager", goal="Manage files and folders in Box efficiently", backstory="An AI assistant specialized in document management and file organization.", - tools=[enterprise_tools] + apps=['box'] # All Box actions will be available ) # Task to create a folder structure @@ -201,19 +196,14 @@ crew.kickoff() ### Filtering Specific Box Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Box tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["box_create_folder", "box_save_file", "box_list_files"] -) +from crewai import Agent, Task, Crew +# Create agent with specific Box actions only file_organizer_agent = Agent( role="File Organizer", goal="Organize and manage file storage efficiently", backstory="An AI assistant that focuses on file organization and storage management.", - tools=enterprise_tools + apps=['box/create_folder', 'box/save_file', 'box/list_files'] # Specific Box actions ) # Task to organize files @@ -235,17 +225,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) file_manager = Agent( role="File Manager", goal="Maintain organized file structure and manage document lifecycle", backstory="An experienced file manager who ensures documents are properly organized and accessible.", - tools=[enterprise_tools] + apps=['box'] ) # Complex task involving multiple Box operations diff --git a/docs/en/enterprise/integrations/clickup.mdx b/docs/en/enterprise/integrations/clickup.mdx index a8549f72a..8afd5ff68 100644 --- a/docs/en/enterprise/integrations/clickup.mdx +++ b/docs/en/enterprise/integrations/clickup.mdx @@ -25,7 +25,7 @@ Before using the ClickUp integration, ensure you have: 2. Find **ClickUp** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for task and project management -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,7 +36,7 @@ uv add crewai-tools ## Available Actions - + **Description:** Search for tasks in ClickUp using advanced filters. **Parameters:** @@ -61,7 +61,7 @@ uv add crewai-tools Available fields: `space_ids%5B%5D`, `project_ids%5B%5D`, `list_ids%5B%5D`, `statuses%5B%5D`, `include_closed`, `assignees%5B%5D`, `tags%5B%5D`, `due_date_gt`, `due_date_lt`, `date_created_gt`, `date_created_lt`, `date_updated_gt`, `date_updated_lt` - + **Description:** Get tasks in a specific list in ClickUp. **Parameters:** @@ -69,7 +69,7 @@ uv add crewai-tools - `taskFilterFormula` (string, optional): Search for tasks that match specified filters. For example: name=task1. - + **Description:** Create a task in ClickUp. **Parameters:** @@ -82,7 +82,7 @@ uv add crewai-tools - `additionalFields` (string, optional): Additional Fields - Specify additional fields to include on this task as JSON. - + **Description:** Update a task in ClickUp. **Parameters:** @@ -96,49 +96,49 @@ uv add crewai-tools - `additionalFields` (string, optional): Additional Fields - Specify additional fields to include on this task as JSON. - + **Description:** Delete a task in ClickUp. **Parameters:** - `taskId` (string, required): Task ID - The ID of the task to delete. - + **Description:** Get List information in ClickUp. **Parameters:** - `spaceId` (string, required): Space ID - The ID of the space containing the lists. - + **Description:** Get Custom Fields in a List in ClickUp. **Parameters:** - `listId` (string, required): List ID - The ID of the list to get custom fields from. - + **Description:** Get All Fields in a List in ClickUp. **Parameters:** - `listId` (string, required): List ID - The ID of the list to get all fields from. - + **Description:** Get Space information in ClickUp. **Parameters:** - `spaceId` (string, optional): Space ID - The ID of the space to retrieve. - + **Description:** Get Folders in ClickUp. **Parameters:** - `spaceId` (string, required): Space ID - The ID of the space containing the folders. - + **Description:** Get Member information in ClickUp. **Parameters:** None required. @@ -151,19 +151,14 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools +from crewai import Agent, Task, Crew -# Get enterprise tools (ClickUp tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) - -# Create an agent with ClickUp capabilities +# Create an agent with Clickup capabilities clickup_agent = Agent( role="Task Manager", goal="Manage tasks and projects in ClickUp efficiently", backstory="An AI assistant specialized in task management and productivity coordination.", - tools=[enterprise_tools] + apps=['clickup'] # All Clickup actions will be available ) # Task to create a new task @@ -185,19 +180,12 @@ crew.kickoff() ### Filtering Specific ClickUp Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific ClickUp tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["clickup_create_task", "clickup_update_task", "clickup_search_tasks"] -) task_coordinator = Agent( role="Task Coordinator", goal="Create and manage tasks efficiently", backstory="An AI assistant that focuses on task creation and status management.", - tools=enterprise_tools + apps=['clickup/create_task'] ) # Task to manage task workflow @@ -219,17 +207,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_manager = Agent( role="Project Manager", goal="Coordinate project activities and track team productivity", backstory="An experienced project manager who ensures projects are delivered on time.", - tools=[enterprise_tools] + apps=['clickup'] ) # Complex task involving multiple ClickUp operations @@ -256,17 +239,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) task_analyst = Agent( role="Task Analyst", goal="Analyze task patterns and optimize team productivity", backstory="An AI assistant that analyzes task data to improve team efficiency.", - tools=[enterprise_tools] + apps=['clickup'] ) # Task to analyze and optimize task distribution diff --git a/docs/en/enterprise/integrations/github.mdx b/docs/en/enterprise/integrations/github.mdx index 2e439b96c..5666eef3d 100644 --- a/docs/en/enterprise/integrations/github.mdx +++ b/docs/en/enterprise/integrations/github.mdx @@ -25,7 +25,7 @@ Before using the GitHub integration, ensure you have: 2. Find **GitHub** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for repository and issue management -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,7 +36,7 @@ uv add crewai-tools ## Available Actions - + **Description:** Create an issue in GitHub. **Parameters:** @@ -47,7 +47,7 @@ uv add crewai-tools - `assignees` (string, optional): Assignees - Specify the assignee(s)' GitHub login as an array of strings for this issue. (example: `["octocat"]`). - + **Description:** Update an issue in GitHub. **Parameters:** @@ -61,7 +61,7 @@ uv add crewai-tools - Options: `open`, `closed` - + **Description:** Get an issue by number in GitHub. **Parameters:** @@ -70,7 +70,7 @@ uv add crewai-tools - `issue_number` (string, required): Issue Number - Specify the number of the issue to fetch. - + **Description:** Lock an issue in GitHub. **Parameters:** @@ -81,7 +81,7 @@ uv add crewai-tools - Options: `off-topic`, `too heated`, `resolved`, `spam` - + **Description:** Search for issues in GitHub. **Parameters:** @@ -108,7 +108,7 @@ uv add crewai-tools Available fields: `assignee`, `creator`, `mentioned`, `labels` - + **Description:** Create a release in GitHub. **Parameters:** @@ -126,7 +126,7 @@ uv add crewai-tools - Options: `true`, `false` - + **Description:** Update a release in GitHub. **Parameters:** @@ -145,7 +145,7 @@ uv add crewai-tools - Options: `true`, `false` - + **Description:** Get a release by ID in GitHub. **Parameters:** @@ -154,7 +154,7 @@ uv add crewai-tools - `id` (string, required): Release ID - Specify the release ID of the release to fetch. - + **Description:** Get a release by tag name in GitHub. **Parameters:** @@ -163,7 +163,7 @@ uv add crewai-tools - `tag_name` (string, required): Name - Specify the tag of the release to fetch. (example: "v1.0.0"). - + **Description:** Delete a release in GitHub. **Parameters:** @@ -179,19 +179,14 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools +from crewai import Agent, Task, Crew -# Get enterprise tools (GitHub tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) - -# Create an agent with GitHub capabilities +# Create an agent with Github capabilities github_agent = Agent( role="Repository Manager", goal="Manage GitHub repositories, issues, and releases efficiently", backstory="An AI assistant specialized in repository management and issue tracking.", - tools=[enterprise_tools] + apps=['github'] # All Github actions will be available ) # Task to create a new issue @@ -213,19 +208,12 @@ crew.kickoff() ### Filtering Specific GitHub Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific GitHub tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["github_create_issue", "github_update_issue", "github_search_issue"] -) issue_manager = Agent( role="Issue Manager", goal="Create and manage GitHub issues efficiently", backstory="An AI assistant that focuses on issue tracking and management.", - tools=enterprise_tools + apps=['github/create_issue'] ) # Task to manage issue workflow @@ -247,17 +235,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) release_manager = Agent( role="Release Manager", goal="Manage software releases and versioning", backstory="An experienced release manager who handles version control and release processes.", - tools=[enterprise_tools] + apps=['github'] ) # Task to create a new release @@ -284,17 +267,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Track and coordinate project issues and development progress", backstory="An AI assistant that helps coordinate development work and track project progress.", - tools=[enterprise_tools] + apps=['github'] ) # Complex task involving multiple GitHub operations diff --git a/docs/en/enterprise/integrations/gmail.mdx b/docs/en/enterprise/integrations/gmail.mdx index 594ece22f..2c197467b 100644 --- a/docs/en/enterprise/integrations/gmail.mdx +++ b/docs/en/enterprise/integrations/gmail.mdx @@ -25,7 +25,7 @@ Before using the Gmail integration, ensure you have: 2. Find **Gmail** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for email and contact management -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,138 +36,103 @@ uv add crewai-tools ## Available Actions - - **Description:** Send an email in Gmail. + + **Description:** Retrieve a list of messages. **Parameters:** - - `toRecipients` (array, required): To - Specify the recipients as either a single string or a JSON array. - ```json - [ - "recipient1@domain.com", - "recipient2@domain.com" - ] - ``` - - `from` (string, required): From - Specify the email of the sender. - - `subject` (string, required): Subject - Specify the subject of the message. - - `messageContent` (string, required): Message Content - Specify the content of the email message as plain text or HTML. - - `attachments` (string, optional): Attachments - Accepts either a single file object or a JSON array of file objects. - - `additionalHeaders` (object, optional): Additional Headers - Specify any additional header fields here. - ```json - { - "reply-to": "Sender Name " - } - ``` + - `userId` (string, required): The user's email address or 'me' for the authenticated user. (default: "me") + - `q` (string, optional): Search query to filter messages (e.g., 'from:someone@example.com is:unread'). + - `maxResults` (integer, optional): Maximum number of messages to return (1-500). (default: 100) + - `pageToken` (string, optional): Page token to retrieve a specific page of results. + - `labelIds` (array, optional): Only return messages with labels that match all of the specified label IDs. + - `includeSpamTrash` (boolean, optional): Include messages from SPAM and TRASH in the results. (default: false) - - **Description:** Get an email by ID in Gmail. + + **Description:** Send an email. **Parameters:** - - `userId` (string, required): User ID - Specify the user's email address. (example: "user@domain.com"). - - `messageId` (string, required): Message ID - Specify the ID of the message to retrieve. + - `to` (string, required): Recipient email address. + - `subject` (string, required): Email subject line. + - `body` (string, required): Email message content. + - `userId` (string, optional): The user's email address or 'me' for the authenticated user. (default: "me") + - `cc` (string, optional): CC email addresses (comma-separated). + - `bcc` (string, optional): BCC email addresses (comma-separated). + - `from` (string, optional): Sender email address (if different from authenticated user). + - `replyTo` (string, optional): Reply-to email address. + - `threadId` (string, optional): Thread ID if replying to an existing conversation. - - **Description:** Search for emails in Gmail using advanced filters. + + **Description:** Delete an email by ID. **Parameters:** - - `emailFilterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions. - ```json - { - "operator": "OR", - "conditions": [ - { - "operator": "AND", - "conditions": [ - { - "field": "from", - "operator": "$stringContains", - "value": "example@domain.com" - } - ] - } - ] - } - ``` - Available fields: `from`, `to`, `date`, `label`, `subject`, `cc`, `bcc`, `category`, `deliveredto:`, `size`, `filename`, `older_than`, `newer_than`, `list`, `is:important`, `is:unread`, `is:snoozed`, `is:starred`, `is:read`, `has:drive`, `has:document`, `has:spreadsheet`, `has:presentation`, `has:attachment`, `has:youtube`, `has:userlabels` - - `paginationParameters` (object, optional): Pagination Parameters. - ```json - { - "pageCursor": "page_cursor_string" - } - ``` + - `userId` (string, required): The user's email address or 'me' for the authenticated user. + - `id` (string, required): The ID of the message to delete. - - **Description:** Delete an email in Gmail. + + **Description:** Create a new draft email. **Parameters:** - - `userId` (string, required): User ID - Specify the user's email address. (example: "user@domain.com"). - - `messageId` (string, required): Message ID - Specify the ID of the message to trash. + - `userId` (string, required): The user's email address or 'me' for the authenticated user. + - `message` (object, required): Message object containing the draft content. + - `raw` (string, required): Base64url encoded email message. - - **Description:** Create a contact in Gmail. + + **Description:** Retrieve a specific message by ID. **Parameters:** - - `givenName` (string, required): Given Name - Specify the Given Name of the Contact to create. (example: "John"). - - `familyName` (string, required): Family Name - Specify the Family Name of the Contact to create. (example: "Doe"). - - `email` (string, required): Email - Specify the Email Address of the Contact to create. - - `additionalFields` (object, optional): Additional Fields - Additional contact information. - ```json - { - "addresses": [ - { - "streetAddress": "1000 North St.", - "city": "Los Angeles" - } - ] - } - ``` + - `userId` (string, required): The user's email address or 'me' for the authenticated user. (default: "me") + - `id` (string, required): The ID of the message to retrieve. + - `format` (string, optional): The format to return the message in. Options: "full", "metadata", "minimal", "raw". (default: "full") + - `metadataHeaders` (array, optional): When given and format is METADATA, only include headers specified. - - **Description:** Get a contact by resource name in Gmail. + + **Description:** Retrieve a message attachment. **Parameters:** - - `resourceName` (string, required): Resource Name - Specify the resource name of the contact to fetch. + - `userId` (string, required): The user's email address or 'me' for the authenticated user. (default: "me") + - `messageId` (string, required): The ID of the message containing the attachment. + - `id` (string, required): The ID of the attachment to retrieve. - - **Description:** Search for a contact in Gmail. + + **Description:** Retrieve a specific email thread by ID. **Parameters:** - - `searchTerm` (string, required): Term - Specify a search term to search for near or exact matches on the names, nickNames, emailAddresses, phoneNumbers, or organizations Contact properties. + - `userId` (string, required): The user's email address or 'me' for the authenticated user. (default: "me") + - `id` (string, required): The ID of the thread to retrieve. + - `format` (string, optional): The format to return the messages in. Options: "full", "metadata", "minimal". (default: "full") + - `metadataHeaders` (array, optional): When given and format is METADATA, only include headers specified. - - **Description:** Delete a contact in Gmail. + + **Description:** Modify the labels applied to a thread. **Parameters:** - - `resourceName` (string, required): Resource Name - Specify the resource name of the contact to delete. + - `userId` (string, required): The user's email address or 'me' for the authenticated user. (default: "me") + - `id` (string, required): The ID of the thread to modify. + - `addLabelIds` (array, optional): A list of IDs of labels to add to this thread. + - `removeLabelIds` (array, optional): A list of IDs of labels to remove from this thread. - - **Description:** Create a draft in Gmail. + + **Description:** Move a thread to the trash. **Parameters:** - - `toRecipients` (array, optional): To - Specify the recipients as either a single string or a JSON array. - ```json - [ - "recipient1@domain.com", - "recipient2@domain.com" - ] - ``` - - `from` (string, optional): From - Specify the email of the sender. - - `subject` (string, optional): Subject - Specify the subject of the message. - - `messageContent` (string, optional): Message Content - Specify the content of the email message as plain text or HTML. - - `attachments` (string, optional): Attachments - Accepts either a single file object or a JSON array of file objects. - - `additionalHeaders` (object, optional): Additional Headers - Specify any additional header fields here. - ```json - { - "reply-to": "Sender Name " - } - ``` + - `userId` (string, required): The user's email address or 'me' for the authenticated user. (default: "me") + - `id` (string, required): The ID of the thread to trash. + + + + **Description:** Remove a thread from the trash. + + **Parameters:** + - `userId` (string, required): The user's email address or 'me' for the authenticated user. (default: "me") + - `id` (string, required): The ID of the thread to untrash. @@ -177,19 +142,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Gmail tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Gmail capabilities gmail_agent = Agent( role="Email Manager", - goal="Manage email communications and contacts efficiently", + goal="Manage email communications and messages efficiently", backstory="An AI assistant specialized in email management and communication.", - tools=[enterprise_tools] + apps=['gmail'] # All Gmail actions will be available ) # Task to send a follow-up email @@ -211,19 +170,18 @@ crew.kickoff() ### Filtering Specific Gmail Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Gmail tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["gmail_send_email", "gmail_search_for_email", "gmail_create_draft"] -) +from crewai import Agent, Task, Crew +# Create agent with specific Gmail actions only email_coordinator = Agent( role="Email Coordinator", goal="Coordinate email communications and manage drafts", backstory="An AI assistant that focuses on email coordination and draft management.", - tools=enterprise_tools + apps=[ + 'gmail/send_email', + 'gmail/fetch_emails', + 'gmail/create_draft' + ] ) # Task to prepare and send emails @@ -241,57 +199,17 @@ crew = Crew( crew.kickoff() ``` -### Contact Management - -```python -from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) - -contact_manager = Agent( - role="Contact Manager", - goal="Manage and organize email contacts efficiently", - backstory="An experienced contact manager who maintains organized contact databases.", - tools=[enterprise_tools] -) - -# Task to manage contacts -contact_task = Task( - description=""" - 1. Search for contacts from the 'example.com' domain - 2. Create new contacts for recent email senders not in the contact list - 3. Update contact information with recent interaction data - """, - agent=contact_manager, - expected_output="Contact database updated with new contacts and recent interactions" -) - -crew = Crew( - agents=[contact_manager], - tasks=[contact_task] -) - -crew.kickoff() -``` - ### Email Search and Analysis ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +# Create agent with Gmail search and analysis capabilities email_analyst = Agent( role="Email Analyst", goal="Analyze email patterns and provide insights", backstory="An AI assistant that analyzes email data to provide actionable insights.", - tools=[enterprise_tools] + apps=['gmail/fetch_emails', 'gmail/get_message'] # Specific actions for email analysis ) # Task to analyze email patterns @@ -313,38 +231,37 @@ crew = Crew( crew.kickoff() ``` -### Automated Email Workflows +### Thread Management ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" +# Create agent with Gmail thread management capabilities +thread_manager = Agent( + role="Thread Manager", + goal="Organize and manage email threads efficiently", + backstory="An AI assistant that specializes in email thread organization and management.", + apps=[ + 'gmail/fetch_thread', + 'gmail/modify_thread', + 'gmail/trash_thread' + ] ) -workflow_manager = Agent( - role="Email Workflow Manager", - goal="Automate email workflows and responses", - backstory="An AI assistant that manages automated email workflows and responses.", - tools=[enterprise_tools] -) - -# Complex task involving multiple Gmail operations -workflow_task = Task( +# Task to organize email threads +thread_task = Task( description=""" - 1. Search for emails with 'urgent' in the subject from the last 24 hours - 2. Create draft responses for each urgent email - 3. Send automated acknowledgment emails to senders - 4. Create a summary report of urgent items requiring attention + 1. Fetch all threads from the last month + 2. Apply appropriate labels to organize threads by project + 3. Archive or trash threads that are no longer relevant """, - agent=workflow_manager, - expected_output="Urgent emails processed with automated responses and summary report" + agent=thread_manager, + expected_output="Email threads organized with appropriate labels and cleanup completed" ) crew = Crew( - agents=[workflow_manager], - tasks=[workflow_task] + agents=[thread_manager], + tasks=[thread_task] ) crew.kickoff() diff --git a/docs/en/enterprise/integrations/google_calendar.mdx b/docs/en/enterprise/integrations/google_calendar.mdx index 01eb8a031..38b35d307 100644 --- a/docs/en/enterprise/integrations/google_calendar.mdx +++ b/docs/en/enterprise/integrations/google_calendar.mdx @@ -24,8 +24,8 @@ Before using the Google Calendar integration, ensure you have: 1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Google Calendar** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow -4. Grant the necessary permissions for calendar and contact access -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +4. Grant the necessary permissions for calendar access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,141 +36,121 @@ uv add crewai-tools ## Available Actions - - **Description:** Create an event in Google Calendar. + + **Description:** Get calendar availability (free/busy information). **Parameters:** - - `eventName` (string, required): Event name. - - `startTime` (string, required): Start time - Accepts Unix timestamp or ISO8601 date formats. - - `endTime` (string, optional): End time - Defaults to one hour after the start time if left blank. - - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank. - - `attendees` (string, optional): Attendees - Accepts an array of email addresses or email addresses separated by commas. - - `eventLocation` (string, optional): Event location. - - `eventDescription` (string, optional): Event description. - - `eventId` (string, optional): Event ID - An ID from your application to associate this event with. You can use this ID to sync updates to this event later. - - `includeMeetLink` (boolean, optional): Include Google Meet link? - Automatically creates Google Meet conference link for this event. - - - - **Description:** Update an existing event in Google Calendar. - - **Parameters:** - - `eventId` (string, required): Event ID - The ID of the event to update. - - `eventName` (string, optional): Event name. - - `startTime` (string, optional): Start time - Accepts Unix timestamp or ISO8601 date formats. - - `endTime` (string, optional): End time - Defaults to one hour after the start time if left blank. - - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank. - - `attendees` (string, optional): Attendees - Accepts an array of email addresses or email addresses separated by commas. - - `eventLocation` (string, optional): Event location. - - `eventDescription` (string, optional): Event description. - - - - **Description:** List events from Google Calendar. - - **Parameters:** - - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank. - - `after` (string, optional): After - Filters events that start after the provided date (Unix in milliseconds or ISO timestamp). (example: "2025-04-12T10:00:00Z or 1712908800000"). - - `before` (string, optional): Before - Filters events that end before the provided date (Unix in milliseconds or ISO timestamp). (example: "2025-04-12T10:00:00Z or 1712908800000"). - - - - **Description:** Get a specific event by ID from Google Calendar. - - **Parameters:** - - `eventId` (string, required): Event ID. - - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank. - - - - **Description:** Delete an event from Google Calendar. - - **Parameters:** - - `eventId` (string, required): Event ID - The ID of the calendar event to be deleted. - - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank. - - - - **Description:** Get contacts from Google Calendar. - - **Parameters:** - - `paginationParameters` (object, optional): Pagination Parameters. - ```json - { - "pageCursor": "page_cursor_string" - } - ``` - - - - **Description:** Search for contacts in Google Calendar. - - **Parameters:** - - `query` (string, optional): Search query to search contacts. - - - - **Description:** List directory people. - - **Parameters:** - - `paginationParameters` (object, optional): Pagination Parameters. - ```json - { - "pageCursor": "page_cursor_string" - } - ``` - - - - **Description:** Search directory people. - - **Parameters:** - - `query` (string, required): Search query to search contacts. - - `paginationParameters` (object, optional): Pagination Parameters. - ```json - { - "pageCursor": "page_cursor_string" - } - ``` - - - - **Description:** List other contacts. - - **Parameters:** - - `paginationParameters` (object, optional): Pagination Parameters. - ```json - { - "pageCursor": "page_cursor_string" - } - ``` - - - - **Description:** Search other contacts. - - **Parameters:** - - `query` (string, optional): Search query to search contacts. - - - - **Description:** Get availability information for calendars. - - **Parameters:** - - `timeMin` (string, required): The start of the interval. In ISO format. - - `timeMax` (string, required): The end of the interval. In ISO format. - - `timeZone` (string, optional): Time zone used in the response. Optional. The default is UTC. - - `items` (array, optional): List of calendars and/or groups to query. Defaults to the user default calendar. + - `timeMin` (string, required): Start time (RFC3339 format) + - `timeMax` (string, required): End time (RFC3339 format) + - `items` (array, required): Calendar IDs to check ```json [ { - "id": "calendar_id_1" - }, - { - "id": "calendar_id_2" + "id": "calendar_id" } ] ``` + - `timeZone` (string, optional): Time zone used in the response. The default is UTC. + - `groupExpansionMax` (integer, optional): Maximal number of calendar identifiers to be provided for a single group. Maximum: 100 + - `calendarExpansionMax` (integer, optional): Maximal number of calendars for which FreeBusy information is to be provided. Maximum: 50 + + + + **Description:** Create a new event in the specified calendar. + + **Parameters:** + - `calendarId` (string, required): Calendar ID (use 'primary' for main calendar) + - `summary` (string, required): Event title/summary + - `start_dateTime` (string, required): Start time in RFC3339 format (e.g., 2024-01-20T10:00:00-07:00) + - `end_dateTime` (string, required): End time in RFC3339 format + - `description` (string, optional): Event description + - `timeZone` (string, optional): Time zone (e.g., America/Los_Angeles) + - `location` (string, optional): Geographic location of the event as free-form text. + - `attendees` (array, optional): List of attendees for the event. + ```json + [ + { + "email": "attendee@example.com", + "displayName": "Attendee Name", + "optional": false + } + ] + ``` + - `reminders` (object, optional): Information about the event's reminders. + ```json + { + "useDefault": true, + "overrides": [ + { + "method": "email", + "minutes": 15 + } + ] + } + ``` + - `conferenceData` (object, optional): The conference-related information, such as details of a Google Meet conference. + ```json + { + "createRequest": { + "requestId": "unique-request-id", + "conferenceSolutionKey": { + "type": "hangoutsMeet" + } + } + } + ``` + - `visibility` (string, optional): Visibility of the event. Options: default, public, private, confidential. Default: default + - `transparency` (string, optional): Whether the event blocks time on the calendar. Options: opaque, transparent. Default: opaque + + + + **Description:** Retrieve events for the specified calendar. + + **Parameters:** + - `calendarId` (string, required): Calendar ID (use 'primary' for main calendar) + - `timeMin` (string, optional): Lower bound for events (RFC3339) + - `timeMax` (string, optional): Upper bound for events (RFC3339) + - `maxResults` (integer, optional): Maximum number of events (default 10). Minimum: 1, Maximum: 2500 + - `orderBy` (string, optional): The order of the events returned in the result. Options: startTime, updated. Default: startTime + - `singleEvents` (boolean, optional): Whether to expand recurring events into instances and only return single one-off events and instances of recurring events. Default: true + - `showDeleted` (boolean, optional): Whether to include deleted events (with status equals cancelled) in the result. Default: false + - `showHiddenInvitations` (boolean, optional): Whether to include hidden invitations in the result. Default: false + - `q` (string, optional): Free text search terms to find events that match these terms in any field. + - `pageToken` (string, optional): Token specifying which result page to return. + - `timeZone` (string, optional): Time zone used in the response. + - `updatedMin` (string, optional): Lower bound for an event's last modification time (RFC3339) to filter by. + - `iCalUID` (string, optional): Specifies an event ID in the iCalendar format to be provided in the response. + + + + **Description:** Update an existing event. + + **Parameters:** + - `calendarId` (string, required): Calendar ID + - `eventId` (string, required): Event ID to update + - `summary` (string, optional): Updated event title + - `description` (string, optional): Updated event description + - `start_dateTime` (string, optional): Updated start time + - `end_dateTime` (string, optional): Updated end time + + + + **Description:** Delete a specified event. + + **Parameters:** + - `calendarId` (string, required): Calendar ID + - `eventId` (string, required): Event ID to delete + + + + **Description:** Retrieve user's calendar list. + + **Parameters:** + - `maxResults` (integer, optional): Maximum number of entries returned on one result page. Minimum: 1 + - `pageToken` (string, optional): Token specifying which result page to return. + - `showDeleted` (boolean, optional): Whether to include deleted calendar list entries in the result. Default: false + - `showHidden` (boolean, optional): Whether to show hidden entries. Default: false + - `minAccessRole` (string, optional): The minimum access role for the user in the returned entries. Options: freeBusyReader, owner, reader, writer @@ -180,19 +160,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Google Calendar tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Google Calendar capabilities calendar_agent = Agent( role="Schedule Manager", goal="Manage calendar events and scheduling efficiently", backstory="An AI assistant specialized in calendar management and scheduling coordination.", - tools=[enterprise_tools] + apps=['google_calendar'] # All Google Calendar actions will be available ) # Task to create a meeting @@ -214,19 +188,11 @@ crew.kickoff() ### Filtering Specific Calendar Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Google Calendar tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["google_calendar_create_event", "google_calendar_list_events", "google_calendar_get_availability"] -) - meeting_coordinator = Agent( role="Meeting Coordinator", goal="Coordinate meetings and check availability", backstory="An AI assistant that focuses on meeting scheduling and availability management.", - tools=enterprise_tools + apps=['google_calendar/create_event', 'google_calendar/get_availability'] ) # Task to schedule a meeting with availability check @@ -248,17 +214,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) event_manager = Agent( role="Event Manager", goal="Manage and update calendar events efficiently", backstory="An experienced event manager who handles event logistics and updates.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Task to manage event updates @@ -266,10 +227,10 @@ event_management = Task( description=""" 1. List all events for this week 2. Update any events that need location changes to include video conference links - 3. Send calendar invitations to new team members for recurring meetings + 3. Check availability for upcoming meetings """, agent=event_manager, - expected_output="Weekly events updated with proper locations and new attendees added" + expected_output="Weekly events updated with proper locations and availability checked" ) crew = Crew( @@ -280,33 +241,28 @@ crew = Crew( crew.kickoff() ``` -### Contact and Availability Management +### Availability and Calendar Management ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) availability_coordinator = Agent( role="Availability Coordinator", - goal="Coordinate availability and manage contacts for scheduling", - backstory="An AI assistant that specializes in availability management and contact coordination.", - tools=[enterprise_tools] + goal="Coordinate availability and manage calendars for scheduling", + backstory="An AI assistant that specializes in availability management and calendar coordination.", + apps=['google_calendar'] ) # Task to coordinate availability availability_task = Task( description=""" - 1. Search for contacts in the engineering department - 2. Check availability for all engineers next Friday afternoon + 1. Get the list of available calendars + 2. Check availability for all calendars next Friday afternoon 3. Create a team meeting for the first available 2-hour slot 4. Include Google Meet link and send invitations """, agent=availability_coordinator, - expected_output="Team meeting scheduled based on availability with all engineers invited" + expected_output="Team meeting scheduled based on availability with all team members invited" ) crew = Crew( @@ -321,17 +277,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) scheduling_automator = Agent( role="Scheduling Automator", goal="Automate scheduling workflows and calendar management", backstory="An AI assistant that automates complex scheduling scenarios and calendar workflows.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Complex scheduling automation task @@ -365,21 +316,16 @@ crew.kickoff() - Check if calendar sharing settings allow the required access level **Event Creation Issues** -- Verify that time formats are correct (ISO8601 or Unix timestamps) +- Verify that time formats are correct (RFC3339 format) - Ensure attendee email addresses are properly formatted - Check that the target calendar exists and is accessible - Verify time zones are correctly specified **Availability and Time Conflicts** -- Use proper ISO format for time ranges when checking availability +- Use proper RFC3339 format for time ranges when checking availability - Ensure time zones are consistent across all operations - Verify that calendar IDs are correct when checking multiple calendars -**Contact and People Search** -- Ensure search queries are properly formatted -- Check that directory access permissions are granted -- Verify that contact information is up to date and accessible - **Event Updates and Deletions** - Verify that event IDs are correct and events exist - Ensure you have edit permissions for the events diff --git a/docs/en/enterprise/integrations/google_contacts.mdx b/docs/en/enterprise/integrations/google_contacts.mdx new file mode 100644 index 000000000..6892c9e3d --- /dev/null +++ b/docs/en/enterprise/integrations/google_contacts.mdx @@ -0,0 +1,402 @@ +--- +title: Google Contacts Integration +description: "Contact and directory management with Google Contacts integration for CrewAI." +icon: "address-book" +mode: "wide" +--- + +## Overview + +Enable your agents to manage contacts and directory information through Google Contacts. Access personal contacts, search directory people, create and update contact information, and manage contact groups with AI-powered automation. + +## Prerequisites + +Before using the Google Contacts integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Google account with Google Contacts access +- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Google Contacts Integration + +### 1. Connect Your Google Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Google Contacts** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for contacts and directory access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Retrieve user's contacts from Google Contacts. + + **Parameters:** + - `pageSize` (integer, optional): Number of contacts to return (max 1000). Minimum: 1, Maximum: 1000 + - `pageToken` (string, optional): The token of the page to retrieve. + - `personFields` (string, optional): Fields to include (e.g., 'names,emailAddresses,phoneNumbers'). Default: names,emailAddresses,phoneNumbers + - `requestSyncToken` (boolean, optional): Whether the response should include a sync token. Default: false + - `sortOrder` (string, optional): The order in which the connections should be sorted. Options: LAST_MODIFIED_ASCENDING, LAST_MODIFIED_DESCENDING, FIRST_NAME_ASCENDING, LAST_NAME_ASCENDING + + + + **Description:** Search for contacts using a query string. + + **Parameters:** + - `query` (string, required): Search query string + - `readMask` (string, required): Fields to read (e.g., 'names,emailAddresses,phoneNumbers') + - `pageSize` (integer, optional): Number of results to return. Minimum: 1, Maximum: 30 + - `pageToken` (string, optional): Token specifying which result page to return. + - `sources` (array, optional): The sources to search in. Options: READ_SOURCE_TYPE_CONTACT, READ_SOURCE_TYPE_PROFILE. Default: READ_SOURCE_TYPE_CONTACT + + + + **Description:** List people in the authenticated user's directory. + + **Parameters:** + - `sources` (array, required): Directory sources to search within. Options: DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE, DIRECTORY_SOURCE_TYPE_DOMAIN_CONTACT. Default: DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE + - `pageSize` (integer, optional): Number of people to return. Minimum: 1, Maximum: 1000 + - `pageToken` (string, optional): Token specifying which result page to return. + - `readMask` (string, optional): Fields to read (e.g., 'names,emailAddresses') + - `requestSyncToken` (boolean, optional): Whether the response should include a sync token. Default: false + - `mergeSources` (array, optional): Additional data to merge into the directory people responses. Options: CONTACT + + + + **Description:** Search for people in the directory. + + **Parameters:** + - `query` (string, required): Search query + - `sources` (string, required): Directory sources (use 'DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE') + - `pageSize` (integer, optional): Number of results to return + - `readMask` (string, optional): Fields to read + + + + **Description:** List other contacts (not in user's personal contacts). + + **Parameters:** + - `pageSize` (integer, optional): Number of contacts to return. Minimum: 1, Maximum: 1000 + - `pageToken` (string, optional): Token specifying which result page to return. + - `readMask` (string, optional): Fields to read + - `requestSyncToken` (boolean, optional): Whether the response should include a sync token. Default: false + + + + **Description:** Search other contacts. + + **Parameters:** + - `query` (string, required): Search query + - `readMask` (string, required): Fields to read (e.g., 'names,emailAddresses') + - `pageSize` (integer, optional): Number of results + + + + **Description:** Get a single person's contact information by resource name. + + **Parameters:** + - `resourceName` (string, required): The resource name of the person to get (e.g., 'people/c123456789') + - `personFields` (string, optional): Fields to include (e.g., 'names,emailAddresses,phoneNumbers'). Default: names,emailAddresses,phoneNumbers + + + + **Description:** Create a new contact in the user's address book. + + **Parameters:** + - `names` (array, optional): Person's names + ```json + [ + { + "givenName": "John", + "familyName": "Doe", + "displayName": "John Doe" + } + ] + ``` + - `emailAddresses` (array, optional): Email addresses + ```json + [ + { + "value": "john.doe@example.com", + "type": "work" + } + ] + ``` + - `phoneNumbers` (array, optional): Phone numbers + ```json + [ + { + "value": "+1234567890", + "type": "mobile" + } + ] + ``` + - `addresses` (array, optional): Postal addresses + ```json + [ + { + "formattedValue": "123 Main St, City, State 12345", + "type": "home" + } + ] + ``` + - `organizations` (array, optional): Organizations/companies + ```json + [ + { + "name": "Company Name", + "title": "Job Title", + "type": "work" + } + ] + ``` + + + + **Description:** Update an existing contact's information. + + **Parameters:** + - `resourceName` (string, required): The resource name of the person to update (e.g., 'people/c123456789') + - `updatePersonFields` (string, required): Fields to update (e.g., 'names,emailAddresses,phoneNumbers') + - `names` (array, optional): Person's names + - `emailAddresses` (array, optional): Email addresses + - `phoneNumbers` (array, optional): Phone numbers + + + + **Description:** Delete a contact from the user's address book. + + **Parameters:** + - `resourceName` (string, required): The resource name of the person to delete (e.g., 'people/c123456789') + + + + **Description:** Get information about multiple people in a single request. + + **Parameters:** + - `resourceNames` (array, required): Resource names of people to get. Maximum: 200 items + - `personFields` (string, optional): Fields to include (e.g., 'names,emailAddresses,phoneNumbers'). Default: names,emailAddresses,phoneNumbers + + + + **Description:** List the user's contact groups (labels). + + **Parameters:** + - `pageSize` (integer, optional): Number of contact groups to return. Minimum: 1, Maximum: 1000 + - `pageToken` (string, optional): Token specifying which result page to return. + - `groupFields` (string, optional): Fields to include (e.g., 'name,memberCount,clientData'). Default: name,memberCount + + + +## Usage Examples + +### Basic Google Contacts Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Google Contacts capabilities +contacts_agent = Agent( + role="Contact Manager", + goal="Manage contacts and directory information efficiently", + backstory="An AI assistant specialized in contact management and directory operations.", + apps=['google_contacts'] # All Google Contacts actions will be available +) + +# Task to retrieve and organize contacts +contact_management_task = Task( + description="Retrieve all contacts and organize them by company affiliation", + agent=contacts_agent, + expected_output="Contacts retrieved and organized by company with summary report" +) + +# Run the task +crew = Crew( + agents=[contacts_agent], + tasks=[contact_management_task] +) + +crew.kickoff() +``` + +### Directory Search and Management + +```python +from crewai import Agent, Task, Crew + +directory_manager = Agent( + role="Directory Manager", + goal="Search and manage directory people and contacts", + backstory="An AI assistant that specializes in directory management and people search.", + apps=[ + 'google_contacts/search_directory_people', + 'google_contacts/list_directory_people', + 'google_contacts/search_contacts' + ] +) + +# Task to search and manage directory +directory_task = Task( + description="Search for team members in the company directory and create a team contact list", + agent=directory_manager, + expected_output="Team directory compiled with contact information" +) + +crew = Crew( + agents=[directory_manager], + tasks=[directory_task] +) + +crew.kickoff() +``` + +### Contact Creation and Updates + +```python +from crewai import Agent, Task, Crew + +contact_curator = Agent( + role="Contact Curator", + goal="Create and update contact information systematically", + backstory="An AI assistant that maintains accurate and up-to-date contact information.", + apps=['google_contacts'] +) + +# Task to create and update contacts +curation_task = Task( + description=""" + 1. Search for existing contacts related to new business partners + 2. Create new contacts for partners not in the system + 3. Update existing contact information with latest details + 4. Organize contacts into appropriate groups + """, + agent=contact_curator, + expected_output="Contact database updated with new partners and organized groups" +) + +crew = Crew( + agents=[contact_curator], + tasks=[curation_task] +) + +crew.kickoff() +``` + +### Contact Group Management + +```python +from crewai import Agent, Task, Crew + +group_organizer = Agent( + role="Contact Group Organizer", + goal="Organize contacts into meaningful groups and categories", + backstory="An AI assistant that specializes in contact organization and group management.", + apps=['google_contacts'] +) + +# Task to organize contact groups +organization_task = Task( + description=""" + 1. List all existing contact groups + 2. Analyze contact distribution across groups + 3. Create new groups for better organization + 4. Move contacts to appropriate groups based on their information + """, + agent=group_organizer, + expected_output="Contacts organized into logical groups with improved structure" +) + +crew = Crew( + agents=[group_organizer], + tasks=[organization_task] +) + +crew.kickoff() +``` + +### Comprehensive Contact Management + +```python +from crewai import Agent, Task, Crew + +contact_specialist = Agent( + role="Contact Management Specialist", + goal="Provide comprehensive contact management across all sources", + backstory="An AI assistant that handles all aspects of contact management including personal, directory, and other contacts.", + apps=['google_contacts'] +) + +# Complex contact management task +comprehensive_task = Task( + description=""" + 1. Retrieve contacts from all sources (personal, directory, other) + 2. Search for duplicate contacts and merge information + 3. Update outdated contact information + 4. Create missing contacts for important stakeholders + 5. Organize contacts into meaningful groups + 6. Generate a comprehensive contact report + """, + agent=contact_specialist, + expected_output="Complete contact management performed with unified contact database and detailed report" +) + +crew = Crew( + agents=[contact_specialist], + tasks=[comprehensive_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Permission Errors** +- Ensure your Google account has appropriate permissions for contacts access +- Verify that the OAuth connection includes required scopes for Google Contacts API +- Check that directory access permissions are granted for organization contacts + +**Resource Name Format Issues** +- Ensure resource names follow the correct format (e.g., 'people/c123456789' for contacts) +- Verify that contact group resource names use the format 'contactGroups/groupId' +- Check that resource names exist and are accessible + +**Search and Query Issues** +- Ensure search queries are properly formatted and not empty +- Use appropriate readMask fields for the data you need +- Verify that search sources are correctly specified (contacts vs profiles) + +**Contact Creation and Updates** +- Ensure required fields are provided when creating contacts +- Verify that email addresses and phone numbers are properly formatted +- Check that updatePersonFields parameter includes all fields being updated + +**Directory Access Issues** +- Ensure you have appropriate permissions to access organization directory +- Verify that directory sources are correctly specified +- Check that your organization allows API access to directory information + +**Pagination and Limits** +- Be mindful of page size limits (varies by endpoint) +- Use pageToken for pagination through large result sets +- Respect API rate limits and implement appropriate delays + +**Contact Groups and Organization** +- Ensure contact group names are unique when creating new groups +- Verify that contacts exist before adding them to groups +- Check that you have permissions to modify contact groups + +### Getting Help + + + Contact our support team for assistance with Google Contacts integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/google_docs.mdx b/docs/en/enterprise/integrations/google_docs.mdx new file mode 100644 index 000000000..6b553f5bb --- /dev/null +++ b/docs/en/enterprise/integrations/google_docs.mdx @@ -0,0 +1,228 @@ +--- +title: Google Docs Integration +description: "Document creation and editing with Google Docs integration for CrewAI." +icon: "file-lines" +mode: "wide" +--- + +## Overview + +Enable your agents to create, edit, and manage Google Docs documents with text manipulation and formatting. Automate document creation, insert and replace text, manage content ranges, and streamline your document workflows with AI-powered automation. + +## Prerequisites + +Before using the Google Docs integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Google account with Google Docs access +- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Google Docs Integration + +### 1. Connect Your Google Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Google Docs** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for document access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Create a new Google Document. + + **Parameters:** + - `title` (string, optional): The title for the new document. + + + + **Description:** Get the contents and metadata of a Google Document. + + **Parameters:** + - `documentId` (string, required): The ID of the document to retrieve. + - `includeTabsContent` (boolean, optional): Whether to include tab content. Default is `false`. + - `suggestionsViewMode` (string, optional): The suggestions view mode to apply to the document. Enum: `DEFAULT_FOR_CURRENT_ACCESS`, `PREVIEW_SUGGESTIONS_ACCEPTED`, `PREVIEW_WITHOUT_SUGGESTIONS`. Default is `DEFAULT_FOR_CURRENT_ACCESS`. + + + + **Description:** Apply one or more updates to a Google Document. + + **Parameters:** + - `documentId` (string, required): The ID of the document to update. + - `requests` (array, required): A list of updates to apply to the document. Each item is an object representing a request. + - `writeControl` (object, optional): Provides control over how write requests are executed. Contains `requiredRevisionId` (string) and `targetRevisionId` (string). + + + + **Description:** Insert text into a Google Document at a specific location. + + **Parameters:** + - `documentId` (string, required): The ID of the document to update. + - `text` (string, required): The text to insert. + - `index` (integer, optional): The zero-based index where to insert the text. Default is `1`. + + + + **Description:** Replace all instances of text in a Google Document. + + **Parameters:** + - `documentId` (string, required): The ID of the document to update. + - `containsText` (string, required): The text to find and replace. + - `replaceText` (string, required): The text to replace it with. + - `matchCase` (boolean, optional): Whether the search should respect case. Default is `false`. + + + + **Description:** Delete content from a specific range in a Google Document. + + **Parameters:** + - `documentId` (string, required): The ID of the document to update. + - `startIndex` (integer, required): The start index of the range to delete. + - `endIndex` (integer, required): The end index of the range to delete. + + + + **Description:** Insert a page break at a specific location in a Google Document. + + **Parameters:** + - `documentId` (string, required): The ID of the document to update. + - `index` (integer, optional): The zero-based index where to insert the page break. Default is `1`. + + + + **Description:** Create a named range in a Google Document. + + **Parameters:** + - `documentId` (string, required): The ID of the document to update. + - `name` (string, required): The name for the named range. + - `startIndex` (integer, required): The start index of the range. + - `endIndex` (integer, required): The end index of the range. + + + +## Usage Examples + +### Basic Google Docs Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Google Docs capabilities +docs_agent = Agent( + role="Document Creator", + goal="Create and manage Google Docs documents efficiently", + backstory="An AI assistant specialized in Google Docs document creation and editing.", + apps=['google_docs'] # All Google Docs actions will be available +) + +# Task to create a new document +create_doc_task = Task( + description="Create a new Google Document titled 'Project Status Report'", + agent=docs_agent, + expected_output="New Google Document 'Project Status Report' created successfully" +) + +# Run the task +crew = Crew( + agents=[docs_agent], + tasks=[create_doc_task] +) + +crew.kickoff() +``` + +### Text Editing and Content Management + +```python +from crewai import Agent, Task, Crew + +# Create an agent focused on text editing +text_editor = Agent( + role="Document Editor", + goal="Edit and update content in Google Docs documents", + backstory="An AI assistant skilled in precise text editing and content management.", + apps=['google_docs/insert_text', 'google_docs/replace_text', 'google_docs/delete_content_range'] +) + +# Task to edit document content +edit_content_task = Task( + description="In document 'your_document_id', insert the text 'Executive Summary: ' at the beginning, then replace all instances of 'TODO' with 'COMPLETED'.", + agent=text_editor, + expected_output="Document updated with new text inserted and TODO items replaced." +) + +crew = Crew( + agents=[text_editor], + tasks=[edit_content_task] +) + +crew.kickoff() +``` + +### Advanced Document Operations + +```python +from crewai import Agent, Task, Crew + +# Create an agent for advanced document operations +document_formatter = Agent( + role="Document Formatter", + goal="Apply advanced formatting and structure to Google Docs", + backstory="An AI assistant that handles complex document formatting and organization.", + apps=['google_docs/batch_update', 'google_docs/insert_page_break', 'google_docs/create_named_range'] +) + +# Task to format document +format_doc_task = Task( + description="In document 'your_document_id', insert a page break at position 100, create a named range called 'Introduction' for characters 1-50, and apply batch formatting updates.", + agent=document_formatter, + expected_output="Document formatted with page break, named range, and styling applied." +) + +crew = Crew( + agents=[document_formatter], + tasks=[format_doc_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Authentication Errors** +- Ensure your Google account has the necessary permissions for Google Docs access. +- Verify that the OAuth connection includes all required scopes (`https://www.googleapis.com/auth/documents`). + +**Document ID Issues** +- Double-check document IDs for correctness. +- Ensure the document exists and is accessible to your account. +- Document IDs can be found in the Google Docs URL. + +**Text Insertion and Range Operations** +- When using `insert_text` or `delete_content_range`, ensure index positions are valid. +- Remember that Google Docs uses zero-based indexing. +- The document must have content at the specified index positions. + +**Batch Update Request Formatting** +- When using `batch_update`, ensure the `requests` array is correctly formatted according to the Google Docs API documentation. +- Complex updates require specific JSON structures for each request type. + +**Replace Text Operations** +- For `replace_text`, ensure the `containsText` parameter exactly matches the text you want to replace. +- Use `matchCase` parameter to control case sensitivity. + +### Getting Help + + + Contact our support team for assistance with Google Docs integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/google_drive.mdx b/docs/en/enterprise/integrations/google_drive.mdx new file mode 100644 index 000000000..11f88a02a --- /dev/null +++ b/docs/en/enterprise/integrations/google_drive.mdx @@ -0,0 +1,213 @@ +--- +title: Google Drive Integration +description: "File storage and management with Google Drive integration for CrewAI." +icon: "google" +mode: "wide" +--- + +## Overview + +Enable your agents to manage files and folders through Google Drive. Upload, download, organize, and share files, create folders, and streamline your document management workflows with AI-powered automation. + +## Prerequisites + +Before using the Google Drive integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Google account with Google Drive access +- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Google Drive Integration + +### 1. Connect Your Google Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Google Drive** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for file and folder management +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Get a file by ID from Google Drive. + + **Parameters:** + - `file_id` (string, required): The ID of the file to retrieve. + + + + **Description:** List files in Google Drive. + + **Parameters:** + - `q` (string, optional): Query string to filter files (example: "name contains 'report'"). + - `page_size` (integer, optional): Maximum number of files to return (default: 100, max: 1000). + - `page_token` (string, optional): Token for retrieving the next page of results. + - `order_by` (string, optional): Sort order (example: "name", "createdTime desc", "modifiedTime"). + - `spaces` (string, optional): Comma-separated list of spaces to query (drive, appDataFolder, photos). + + + + **Description:** Upload a file to Google Drive. + + **Parameters:** + - `name` (string, required): Name of the file to create. + - `content` (string, required): Content of the file to upload. + - `mime_type` (string, optional): MIME type of the file (example: "text/plain", "application/pdf"). + - `parent_folder_id` (string, optional): ID of the parent folder where the file should be created. + - `description` (string, optional): Description of the file. + + + + **Description:** Download a file from Google Drive. + + **Parameters:** + - `file_id` (string, required): The ID of the file to download. + - `mime_type` (string, optional): MIME type for export (required for Google Workspace documents). + + + + **Description:** Create a new folder in Google Drive. + + **Parameters:** + - `name` (string, required): Name of the folder to create. + - `parent_folder_id` (string, optional): ID of the parent folder where the new folder should be created. + - `description` (string, optional): Description of the folder. + + + + **Description:** Delete a file from Google Drive. + + **Parameters:** + - `file_id` (string, required): The ID of the file to delete. + + + + **Description:** Share a file in Google Drive with specific users or make it public. + + **Parameters:** + - `file_id` (string, required): The ID of the file to share. + - `role` (string, required): The role granted by this permission (reader, writer, commenter, owner). + - `type` (string, required): The type of the grantee (user, group, domain, anyone). + - `email_address` (string, optional): The email address of the user or group to share with (required for user/group types). + - `domain` (string, optional): The domain to share with (required for domain type). + - `send_notification_email` (boolean, optional): Whether to send a notification email (default: true). + - `email_message` (string, optional): A plain text custom message to include in the notification email. + + + + **Description:** Update an existing file in Google Drive. + + **Parameters:** + - `file_id` (string, required): The ID of the file to update. + - `name` (string, optional): New name for the file. + - `content` (string, optional): New content for the file. + - `mime_type` (string, optional): New MIME type for the file. + - `description` (string, optional): New description for the file. + - `add_parents` (string, optional): Comma-separated list of parent folder IDs to add. + - `remove_parents` (string, optional): Comma-separated list of parent folder IDs to remove. + + + +## Usage Examples + +### Basic Google Drive Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Google Drive capabilities +drive_agent = Agent( + role="File Manager", + goal="Manage files and folders in Google Drive efficiently", + backstory="An AI assistant specialized in document and file management.", + apps=['google_drive'] # All Google Drive actions will be available +) + +# Task to organize files +organize_files_task = Task( + description="List all files in the root directory and organize them into appropriate folders", + agent=drive_agent, + expected_output="Summary of files organized with folder structure" +) + +# Run the task +crew = Crew( + agents=[drive_agent], + tasks=[organize_files_task] +) + +crew.kickoff() +``` + +### Filtering Specific Google Drive Tools + +```python +from crewai import Agent, Task, Crew + +# Create agent with specific Google Drive actions only +file_manager_agent = Agent( + role="Document Manager", + goal="Upload and manage documents efficiently", + backstory="An AI assistant that focuses on document upload and organization.", + apps=[ + 'google_drive/upload_file', + 'google_drive/create_folder', + 'google_drive/share_file' + ] # Specific Google Drive actions +) + +# Task to upload and share documents +document_task = Task( + description="Upload the quarterly report and share it with the finance team", + agent=file_manager_agent, + expected_output="Document uploaded and sharing permissions configured" +) + +crew = Crew( + agents=[file_manager_agent], + tasks=[document_task] +) + +crew.kickoff() +``` + +### Advanced File Management + +```python +from crewai import Agent, Task, Crew + +file_organizer = Agent( + role="File Organizer", + goal="Maintain organized file structure and manage permissions", + backstory="An experienced file manager who ensures proper organization and access control.", + apps=['google_drive'] +) + +# Complex task involving multiple Google Drive operations +organization_task = Task( + description=""" + 1. List all files in the shared folder + 2. Create folders for different document types (Reports, Presentations, Spreadsheets) + 3. Move files to appropriate folders based on their type + 4. Set appropriate sharing permissions for each folder + 5. Create a summary document of the organization changes + """, + agent=file_organizer, + expected_output="Files organized into categorized folders with proper permissions and summary report" +) + +crew = Crew( + agents=[file_organizer], + tasks=[organization_task] +) + +crew.kickoff() +``` diff --git a/docs/en/enterprise/integrations/google_sheets.mdx b/docs/en/enterprise/integrations/google_sheets.mdx index 9ccad0d33..61183edc9 100644 --- a/docs/en/enterprise/integrations/google_sheets.mdx +++ b/docs/en/enterprise/integrations/google_sheets.mdx @@ -26,7 +26,7 @@ Before using the Google Sheets integration, ensure you have: 2. Find **Google Sheets** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for spreadsheet access -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -37,64 +37,74 @@ uv add crewai-tools ## Available Actions - - **Description:** Get rows from a Google Sheets spreadsheet. + + **Description:** Retrieve properties and data of a spreadsheet. **Parameters:** - - `spreadsheetId` (string, required): Spreadsheet - Use Connect Portal Workflow Settings to allow users to select a spreadsheet. Defaults to using the first worksheet in the selected spreadsheet. - - `limit` (string, optional): Limit rows - Limit the maximum number of rows to return. + - `spreadsheetId` (string, required): The ID of the spreadsheet to retrieve. + - `ranges` (array, optional): The ranges to retrieve from the spreadsheet. + - `includeGridData` (boolean, optional): True if grid data should be returned. Default: false + - `fields` (string, optional): The fields to include in the response. Use this to improve performance by only returning needed data. - - **Description:** Create a new row in a Google Sheets spreadsheet. + + **Description:** Returns a range of values from a spreadsheet. **Parameters:** - - `spreadsheetId` (string, required): Spreadsheet - Use Connect Portal Workflow Settings to allow users to select a spreadsheet. Defaults to using the first worksheet in the selected spreadsheet.. - - `worksheet` (string, required): Worksheet - Your worksheet must have column headers. - - `additionalFields` (object, required): Fields - Include fields to create this row with, as an object with keys of Column Names. Use Connect Portal Workflow Settings to allow users to select a Column Mapping. + - `spreadsheetId` (string, required): The ID of the spreadsheet to retrieve data from. + - `range` (string, required): The A1 notation or R1C1 notation of the range to retrieve values from. + - `valueRenderOption` (string, optional): How values should be represented in the output. Options: FORMATTED_VALUE, UNFORMATTED_VALUE, FORMULA. Default: FORMATTED_VALUE + - `dateTimeRenderOption` (string, optional): How dates, times, and durations should be represented in the output. Options: SERIAL_NUMBER, FORMATTED_STRING. Default: SERIAL_NUMBER + - `majorDimension` (string, optional): The major dimension that results should use. Options: ROWS, COLUMNS. Default: ROWS + + + + **Description:** Sets values in a range of a spreadsheet. + + **Parameters:** + - `spreadsheetId` (string, required): The ID of the spreadsheet to update. + - `range` (string, required): The A1 notation of the range to update. + - `values` (array, required): The data to be written. Each array represents a row. ```json - { - "columnName1": "columnValue1", - "columnName2": "columnValue2", - "columnName3": "columnValue3", - "columnName4": "columnValue4" - } + [ + ["Value1", "Value2", "Value3"], + ["Value4", "Value5", "Value6"] + ] ``` + - `valueInputOption` (string, optional): How the input data should be interpreted. Options: RAW, USER_ENTERED. Default: USER_ENTERED - - **Description:** Update existing rows in a Google Sheets spreadsheet. + + **Description:** Appends values to a spreadsheet. **Parameters:** - - `spreadsheetId` (string, required): Spreadsheet - Use Connect Portal Workflow Settings to allow users to select a spreadsheet. Defaults to using the first worksheet in the selected spreadsheet. - - `worksheet` (string, required): Worksheet - Your worksheet must have column headers. - - `filterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions to identify which rows to update. + - `spreadsheetId` (string, required): The ID of the spreadsheet to update. + - `range` (string, required): The A1 notation of a range to search for a logical table of data. + - `values` (array, required): The data to append. Each array represents a row. ```json - { - "operator": "OR", - "conditions": [ - { - "operator": "AND", - "conditions": [ - { - "field": "status", - "operator": "$stringExactlyMatches", - "value": "pending" - } - ] + [ + ["Value1", "Value2", "Value3"], + ["Value4", "Value5", "Value6"] + ] + ``` + - `valueInputOption` (string, optional): How the input data should be interpreted. Options: RAW, USER_ENTERED. Default: USER_ENTERED + - `insertDataOption` (string, optional): How the input data should be inserted. Options: OVERWRITE, INSERT_ROWS. Default: INSERT_ROWS + + + + **Description:** Creates a new spreadsheet. + + **Parameters:** + - `title` (string, required): The title of the new spreadsheet. + - `sheets` (array, optional): The sheets that are part of the spreadsheet. + ```json + [ + { + "properties": { + "title": "Sheet1" } - ] - } - ``` - Available operators: `$stringContains`, `$stringDoesNotContain`, `$stringExactlyMatches`, `$stringDoesNotExactlyMatch`, `$stringStartsWith`, `$stringDoesNotStartWith`, `$stringEndsWith`, `$stringDoesNotEndWith`, `$numberGreaterThan`, `$numberLessThan`, `$numberEquals`, `$numberDoesNotEqual`, `$dateTimeAfter`, `$dateTimeBefore`, `$dateTimeEquals`, `$booleanTrue`, `$booleanFalse`, `$exists`, `$doesNotExist` - - `additionalFields` (object, required): Fields - Include fields to update, as an object with keys of Column Names. Use Connect Portal Workflow Settings to allow users to select a Column Mapping. - ```json - { - "columnName1": "newValue1", - "columnName2": "newValue2", - "columnName3": "newValue3", - "columnName4": "newValue4" - } + } + ] ``` @@ -105,19 +115,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Google Sheets tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Google Sheets capabilities sheets_agent = Agent( role="Data Manager", goal="Manage spreadsheet data and track information efficiently", backstory="An AI assistant specialized in data management and spreadsheet operations.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Task to add new data to a spreadsheet @@ -139,19 +143,17 @@ crew.kickoff() ### Filtering Specific Google Sheets Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Google Sheets tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["google_sheets_get_row", "google_sheets_create_row"] -) +from crewai import Agent, Task, Crew +# Create agent with specific Google Sheets actions only data_collector = Agent( role="Data Collector", goal="Collect and organize data in spreadsheets", backstory="An AI assistant that focuses on data collection and organization.", - tools=enterprise_tools + apps=[ + 'google_sheets/get_values', + 'google_sheets/update_values' + ] ) # Task to collect and organize data @@ -173,17 +175,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_analyst = Agent( role="Data Analyst", goal="Analyze spreadsheet data and generate insights", backstory="An experienced data analyst who extracts insights from spreadsheet data.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Task to analyze data and create reports @@ -205,33 +202,59 @@ crew = Crew( crew.kickoff() ``` +### Spreadsheet Creation and Management + +```python +from crewai import Agent, Task, Crew + +spreadsheet_manager = Agent( + role="Spreadsheet Manager", + goal="Create and manage spreadsheets efficiently", + backstory="An AI assistant that specializes in creating and organizing spreadsheets.", + apps=['google_sheets'] +) + +# Task to create and set up new spreadsheets +setup_task = Task( + description=""" + 1. Create a new spreadsheet for quarterly reports + 2. Set up proper headers and structure + 3. Add initial data and formatting + """, + agent=spreadsheet_manager, + expected_output="New quarterly report spreadsheet created and properly structured" +) + +crew = Crew( + agents=[spreadsheet_manager], + tasks=[setup_task] +) + +crew.kickoff() +``` + ### Automated Data Updates ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_updater = Agent( role="Data Updater", goal="Automatically update and maintain spreadsheet data", backstory="An AI assistant that maintains data accuracy and updates records automatically.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Task to update data based on conditions update_task = Task( description=""" - 1. Find all pending orders in the orders spreadsheet - 2. Update their status to 'processing' - 3. Add a timestamp for when the status was updated - 4. Log the changes in a separate tracking sheet + 1. Get spreadsheet properties and structure + 2. Read current data from specific ranges + 3. Update values in target ranges with new data + 4. Append new records to the bottom of the sheet """, agent=data_updater, - expected_output="All pending orders updated to processing status with timestamps logged" + expected_output="Spreadsheet data updated successfully with new values and records" ) crew = Crew( @@ -246,30 +269,25 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) workflow_manager = Agent( role="Data Workflow Manager", goal="Manage complex data workflows across multiple spreadsheets", backstory="An AI assistant that orchestrates complex data operations across multiple spreadsheets.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Complex workflow task workflow_task = Task( description=""" 1. Get all customer data from the main customer spreadsheet - 2. Create monthly summary entries for active customers - 3. Update customer status based on activity in the last 30 days - 4. Generate a monthly report with customer metrics - 5. Archive inactive customer records to a separate sheet + 2. Create a new monthly summary spreadsheet + 3. Append summary data to the new spreadsheet + 4. Update customer status based on activity metrics + 5. Generate reports with proper formatting """, agent=workflow_manager, - expected_output="Monthly customer workflow completed with updated statuses and generated reports" + expected_output="Monthly customer workflow completed with new spreadsheet and updated data" ) crew = Crew( @@ -291,29 +309,28 @@ crew.kickoff() **Spreadsheet Structure Issues** - Ensure worksheets have proper column headers before creating or updating rows -- Verify that column names in `additionalFields` match the actual column headers -- Check that the specified worksheet exists in the spreadsheet +- Verify that range notation (A1 format) is correct for the target cells +- Check that the specified spreadsheet ID exists and is accessible **Data Type and Format Issues** - Ensure data values match the expected format for each column - Use proper date formats for date columns (ISO format recommended) - Verify that numeric values are properly formatted for number columns -**Filter Formula Issues** -- Ensure filter formulas follow the correct JSON structure for disjunctive normal form -- Use valid field names that match actual column headers -- Test simple filters before building complex multi-condition queries -- Verify that operator types match the data types in the columns +**Range and Cell Reference Issues** +- Use proper A1 notation for ranges (e.g., "A1:C10", "Sheet1!A1:B5") +- Ensure range references don't exceed the actual spreadsheet dimensions +- Verify that sheet names in range references match actual sheet names -**Row Limits and Performance** -- Be mindful of row limits when using `GOOGLE_SHEETS_GET_ROW` -- Consider pagination for large datasets -- Use specific filters to reduce the amount of data processed +**Value Input and Rendering Options** +- Choose appropriate `valueInputOption` (RAW vs USER_ENTERED) for your data +- Select proper `valueRenderOption` based on how you want data formatted +- Consider `dateTimeRenderOption` for consistent date/time handling -**Update Operations** -- Ensure filter conditions properly identify the intended rows for updates -- Test filter conditions with small datasets before large updates -- Verify that all required fields are included in update operations +**Spreadsheet Creation Issues** +- Ensure spreadsheet titles are unique and follow naming conventions +- Verify that sheet properties are properly structured when creating sheets +- Check that you have permissions to create new spreadsheets in your account ### Getting Help diff --git a/docs/en/enterprise/integrations/google_slides.mdx b/docs/en/enterprise/integrations/google_slides.mdx new file mode 100644 index 000000000..fc0b28ea0 --- /dev/null +++ b/docs/en/enterprise/integrations/google_slides.mdx @@ -0,0 +1,371 @@ +--- +title: Google Slides Integration +description: "Presentation creation and management with Google Slides integration for CrewAI." +icon: "chart-bar" +mode: "wide" +--- + +## Overview + +Enable your agents to create, edit, and manage Google Slides presentations. Create presentations, update content, import data from Google Sheets, manage pages and thumbnails, and streamline your presentation workflows with AI-powered automation. + +## Prerequisites + +Before using the Google Slides integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Google account with Google Slides access +- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Google Slides Integration + +### 1. Connect Your Google Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Google Slides** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for presentations, spreadsheets, and drive access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Creates a blank presentation with no content. + + **Parameters:** + - `title` (string, required): The title of the presentation. + + + + **Description:** Retrieves a presentation by ID. + + **Parameters:** + - `presentationId` (string, required): The ID of the presentation to retrieve. + - `fields` (string, optional): The fields to include in the response. Use this to improve performance by only returning needed data. + + + + **Description:** Applies updates, add content, or remove content from a presentation. + + **Parameters:** + - `presentationId` (string, required): The ID of the presentation to update. + - `requests` (array, required): A list of updates to apply to the presentation. + ```json + [ + { + "insertText": { + "objectId": "slide_id", + "text": "Your text content here" + } + } + ] + ``` + - `writeControl` (object, optional): Provides control over how write requests are executed. + ```json + { + "requiredRevisionId": "revision_id_string" + } + ``` + + + + **Description:** Retrieves a specific page by its ID. + + **Parameters:** + - `presentationId` (string, required): The ID of the presentation. + - `pageObjectId` (string, required): The ID of the page to retrieve. + + + + **Description:** Generates a page thumbnail. + + **Parameters:** + - `presentationId` (string, required): The ID of the presentation. + - `pageObjectId` (string, required): The ID of the page for thumbnail generation. + + + + **Description:** Imports data from a Google Sheet into a presentation. + + **Parameters:** + - `presentationId` (string, required): The ID of the presentation. + - `sheetId` (string, required): The ID of the Google Sheet to import from. + - `dataRange` (string, required): The range of data to import from the sheet. + + + + **Description:** Uploads a file to Google Drive associated with the presentation. + + **Parameters:** + - `file` (string, required): The file data to upload. + - `presentationId` (string, required): The ID of the presentation to link the uploaded file. + + + + **Description:** Links a file in Google Drive to a presentation. + + **Parameters:** + - `presentationId` (string, required): The ID of the presentation. + - `fileId` (string, required): The ID of the file to link. + + + + **Description:** Lists all presentations accessible to the user. + + **Parameters:** + - `pageSize` (integer, optional): The number of presentations to return per page. + - `pageToken` (string, optional): A token for pagination. + + + + **Description:** Deletes a presentation by ID. + + **Parameters:** + - `presentationId` (string, required): The ID of the presentation to delete. + + + +## Usage Examples + +### Basic Google Slides Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Google Slides capabilities +slides_agent = Agent( + role="Presentation Manager", + goal="Create and manage presentations efficiently", + backstory="An AI assistant specialized in presentation creation and content management.", + apps=['google_slides'] # All Google Slides actions will be available +) + +# Task to create a presentation +create_presentation_task = Task( + description="Create a new presentation for the quarterly business review with key slides", + agent=slides_agent, + expected_output="Quarterly business review presentation created with structured content" +) + +# Run the task +crew = Crew( + agents=[slides_agent], + tasks=[create_presentation_task] +) + +crew.kickoff() +``` + +### Presentation Content Management + +```python +from crewai import Agent, Task, Crew + +content_manager = Agent( + role="Content Manager", + goal="Manage presentation content and updates", + backstory="An AI assistant that focuses on content creation and presentation updates.", + apps=[ + 'google_slides/create_blank_presentation', + 'google_slides/batch_update_presentation', + 'google_slides/get_presentation' + ] +) + +# Task to create and update presentations +content_task = Task( + description="Create a new presentation and add content slides with charts and text", + agent=content_manager, + expected_output="Presentation created with updated content and visual elements" +) + +crew = Crew( + agents=[content_manager], + tasks=[content_task] +) + +crew.kickoff() +``` + +### Data Integration and Visualization + +```python +from crewai import Agent, Task, Crew + +data_visualizer = Agent( + role="Data Visualizer", + goal="Create presentations with data imported from spreadsheets", + backstory="An AI assistant that specializes in data visualization and presentation integration.", + apps=['google_slides'] +) + +# Task to create data-driven presentations +visualization_task = Task( + description=""" + 1. Create a new presentation for monthly sales report + 2. Import data from the sales spreadsheet + 3. Create charts and visualizations from the imported data + 4. Generate thumbnails for slide previews + """, + agent=data_visualizer, + expected_output="Data-driven presentation created with imported spreadsheet data and visualizations" +) + +crew = Crew( + agents=[data_visualizer], + tasks=[visualization_task] +) + +crew.kickoff() +``` + +### Presentation Library Management + +```python +from crewai import Agent, Task, Crew + +library_manager = Agent( + role="Presentation Library Manager", + goal="Manage and organize presentation libraries", + backstory="An AI assistant that manages presentation collections and file organization.", + apps=['google_slides'] +) + +# Task to manage presentation library +library_task = Task( + description=""" + 1. List all existing presentations + 2. Generate thumbnails for presentation previews + 3. Upload supporting files to Drive and link to presentations + 4. Organize presentations by topic and date + """, + agent=library_manager, + expected_output="Presentation library organized with thumbnails and linked supporting files" +) + +crew = Crew( + agents=[library_manager], + tasks=[library_task] +) + +crew.kickoff() +``` + +### Automated Presentation Workflows + +```python +from crewai import Agent, Task, Crew + +presentation_automator = Agent( + role="Presentation Automator", + goal="Automate presentation creation and management workflows", + backstory="An AI assistant that automates complex presentation workflows and content generation.", + apps=['google_slides'] +) + +# Complex presentation automation task +automation_task = Task( + description=""" + 1. Create multiple presentations for different departments + 2. Import relevant data from various spreadsheets + 3. Update existing presentations with new content + 4. Generate thumbnails for all presentations + 5. Link supporting documents from Drive + 6. Create a master index presentation with links to all others + """, + agent=presentation_automator, + expected_output="Automated presentation workflow completed with multiple presentations and organized structure" +) + +crew = Crew( + agents=[presentation_automator], + tasks=[automation_task] +) + +crew.kickoff() +``` + +### Template and Content Creation + +```python +from crewai import Agent, Task, Crew + +template_creator = Agent( + role="Template Creator", + goal="Create presentation templates and standardized content", + backstory="An AI assistant that creates consistent presentation templates and content standards.", + apps=['google_slides'] +) + +# Task to create templates +template_task = Task( + description=""" + 1. Create blank presentation templates for different use cases + 2. Add standard layouts and content placeholders + 3. Create sample presentations with best practices + 4. Generate thumbnails for template previews + 5. Upload template assets to Drive and link appropriately + """, + agent=template_creator, + expected_output="Presentation templates created with standardized layouts and linked assets" +) + +crew = Crew( + agents=[template_creator], + tasks=[template_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Permission Errors** +- Ensure your Google account has appropriate permissions for Google Slides +- Verify that the OAuth connection includes required scopes for presentations, spreadsheets, and drive access +- Check that presentations are shared with the authenticated account + +**Presentation ID Issues** +- Verify that presentation IDs are correct and presentations exist +- Ensure you have access permissions to the presentations you're trying to modify +- Check that presentation IDs are properly formatted + +**Content Update Issues** +- Ensure batch update requests are properly formatted according to Google Slides API specifications +- Verify that object IDs for slides and elements exist in the presentation +- Check that write control revision IDs are current if using optimistic concurrency + +**Data Import Issues** +- Verify that Google Sheet IDs are correct and accessible +- Ensure data ranges are properly specified using A1 notation +- Check that you have read permissions for the source spreadsheets + +**File Upload and Linking Issues** +- Ensure file data is properly encoded for upload +- Verify that Drive file IDs are correct when linking files +- Check that you have appropriate Drive permissions for file operations + +**Page and Thumbnail Operations** +- Verify that page object IDs exist in the specified presentation +- Ensure presentations have content before attempting to generate thumbnails +- Check that page structure is valid for thumbnail generation + +**Pagination and Listing Issues** +- Use appropriate page sizes for listing presentations +- Implement proper pagination using page tokens for large result sets +- Handle empty result sets gracefully + +### Getting Help + + + Contact our support team for assistance with Google Slides integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/hubspot.mdx b/docs/en/enterprise/integrations/hubspot.mdx index e3aeda8ab..f51fe3194 100644 --- a/docs/en/enterprise/integrations/hubspot.mdx +++ b/docs/en/enterprise/integrations/hubspot.mdx @@ -25,7 +25,7 @@ Before using the HubSpot integration, ensure you have: 2. Find **HubSpot** in the Authentication Integrations section. 3. Click **Connect** and complete the OAuth flow. 4. Grant the necessary permissions for company and contact management. -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account). +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,7 +36,7 @@ uv add crewai-tools ## Available Actions - + **Description:** Create a new company record in HubSpot. **Parameters:** @@ -101,7 +101,7 @@ uv add crewai-tools - `founded_year` (string, optional): Year Founded. - + **Description:** Create a new contact record in HubSpot. **Parameters:** @@ -200,7 +200,7 @@ uv add crewai-tools - `hs_googleplusid` (string, optional): googleplus ID. - + **Description:** Create a new deal record in HubSpot. **Parameters:** @@ -215,7 +215,7 @@ uv add crewai-tools - `hs_priority` (string, optional): The priority of the deal. Available values: `low`, `medium`, `high`. - + **Description:** Create a new engagement (e.g., note, email, call, meeting, task) in HubSpot. **Parameters:** @@ -232,7 +232,7 @@ uv add crewai-tools - `hs_meeting_end_time` (string, optional): The end time of the meeting. (Used for `MEETING`) - + **Description:** Update an existing company record in HubSpot. **Parameters:** @@ -249,7 +249,7 @@ uv add crewai-tools - `description` (string, optional): Description. - + **Description:** Create a record for a specified object type in HubSpot. **Parameters:** @@ -257,7 +257,7 @@ uv add crewai-tools - Additional parameters depend on the custom object's schema. - + **Description:** Update an existing contact record in HubSpot. **Parameters:** @@ -271,7 +271,7 @@ uv add crewai-tools - `lifecyclestage` (string, optional): Lifecycle Stage. - + **Description:** Update an existing deal record in HubSpot. **Parameters:** @@ -284,7 +284,7 @@ uv add crewai-tools - `dealtype` (string, optional): The type of deal. - + **Description:** Update an existing engagement in HubSpot. **Parameters:** @@ -295,7 +295,7 @@ uv add crewai-tools - `hs_task_status` (string, optional): The status of the task. - + **Description:** Update a record for a specified object type in HubSpot. **Parameters:** @@ -304,28 +304,28 @@ uv add crewai-tools - Additional parameters depend on the custom object's schema. - + **Description:** Get a list of company records from HubSpot. **Parameters:** - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Get a list of contact records from HubSpot. **Parameters:** - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Get a list of deal records from HubSpot. **Parameters:** - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Get a list of engagement records from HubSpot. **Parameters:** @@ -333,7 +333,7 @@ uv add crewai-tools - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Get a list of records for any specified object type in HubSpot. **Parameters:** @@ -341,35 +341,35 @@ uv add crewai-tools - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Get a single company record by its ID. **Parameters:** - `recordId` (string, required): The ID of the company to retrieve. - + **Description:** Get a single contact record by its ID. **Parameters:** - `recordId` (string, required): The ID of the contact to retrieve. - + **Description:** Get a single deal record by its ID. **Parameters:** - `recordId` (string, required): The ID of the deal to retrieve. - + **Description:** Get a single engagement record by its ID. **Parameters:** - `recordId` (string, required): The ID of the engagement to retrieve. - + **Description:** Get a single record of any specified object type by its ID. **Parameters:** @@ -377,7 +377,7 @@ uv add crewai-tools - `recordId` (string, required): The ID of the record to retrieve. - + **Description:** Search for company records in HubSpot using a filter formula. **Parameters:** @@ -385,7 +385,7 @@ uv add crewai-tools - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Search for contact records in HubSpot using a filter formula. **Parameters:** @@ -393,7 +393,7 @@ uv add crewai-tools - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Search for deal records in HubSpot using a filter formula. **Parameters:** @@ -401,7 +401,7 @@ uv add crewai-tools - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Search for engagement records in HubSpot using a filter formula. **Parameters:** @@ -409,7 +409,7 @@ uv add crewai-tools - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Search for records of any specified object type in HubSpot. **Parameters:** @@ -418,35 +418,35 @@ uv add crewai-tools - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages. - + **Description:** Delete a company record by its ID. **Parameters:** - `recordId` (string, required): The ID of the company to delete. - + **Description:** Delete a contact record by its ID. **Parameters:** - `recordId` (string, required): The ID of the contact to delete. - + **Description:** Delete a deal record by its ID. **Parameters:** - `recordId` (string, required): The ID of the deal to delete. - + **Description:** Delete an engagement record by its ID. **Parameters:** - `recordId` (string, required): The ID of the engagement to delete. - + **Description:** Delete a record of any specified object type by its ID. **Parameters:** @@ -454,7 +454,7 @@ uv add crewai-tools - `recordId` (string, required): The ID of the record to delete. - + **Description:** Get contacts from a specific list by its ID. **Parameters:** @@ -462,7 +462,7 @@ uv add crewai-tools - `paginationParameters` (object, optional): Use `pageCursor` for subsequent pages. - + **Description:** Get the expected schema for a given object type and operation. **Parameters:** @@ -477,19 +477,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (HubSpot tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with HubSpot capabilities hubspot_agent = Agent( role="CRM Manager", goal="Manage company and contact records in HubSpot", backstory="An AI assistant specialized in CRM management.", - tools=[enterprise_tools] + apps=['hubspot'] # All HubSpot actions will be available ) # Task to create a new company @@ -511,19 +505,14 @@ crew.kickoff() ### Filtering Specific HubSpot Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only the tool to create contacts -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["hubspot_create_record_contacts"] -) +from crewai import Agent, Task, Crew +# Create agent with specific HubSpot actions only contact_creator = Agent( role="Contact Creator", goal="Create new contacts in HubSpot", backstory="An AI assistant that focuses on creating new contact entries in the CRM.", - tools=[enterprise_tools] + apps=['hubspot/create_contact'] # Only contact creation action ) # Task to create a contact @@ -545,17 +534,13 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +# Create agent with HubSpot contact management capabilities crm_manager = Agent( role="CRM Manager", goal="Manage and organize HubSpot contacts efficiently.", backstory="An experienced CRM manager who maintains an organized contact database.", - tools=[enterprise_tools] + apps=['hubspot'] # All HubSpot actions including contact management ) # Task to manage contacts diff --git a/docs/en/enterprise/integrations/jira.mdx b/docs/en/enterprise/integrations/jira.mdx index 1eedb8fb9..783a7cfb6 100644 --- a/docs/en/enterprise/integrations/jira.mdx +++ b/docs/en/enterprise/integrations/jira.mdx @@ -25,7 +25,7 @@ Before using the Jira integration, ensure you have: 2. Find **Jira** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for issue and project management -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,7 +36,7 @@ uv add crewai-tools ## Available Actions - + **Description:** Create an issue in Jira. **Parameters:** @@ -56,7 +56,7 @@ uv add crewai-tools ``` - + **Description:** Update an issue in Jira. **Parameters:** @@ -71,14 +71,14 @@ uv add crewai-tools - `additionalFields` (string, optional): Additional Fields - Specify any other fields that should be included in JSON format. - + **Description:** Get an issue by key in Jira. **Parameters:** - `issueKey` (string, required): Issue Key (example: "TEST-1234"). - + **Description:** Search issues in Jira using filters. **Parameters:** @@ -104,7 +104,7 @@ uv add crewai-tools - `limit` (string, optional): Limit results - Limit the maximum number of issues to return. Defaults to 10 if left blank. - + **Description:** Search issues by JQL in Jira. **Parameters:** @@ -117,13 +117,13 @@ uv add crewai-tools ``` - + **Description:** Update any issue in Jira. Use DESCRIBE_ACTION_SCHEMA to get properties schema for this function. **Parameters:** No specific parameters - use JIRA_DESCRIBE_ACTION_SCHEMA first to get the expected schema. - + **Description:** Get the expected schema for an issue type. Use this function first if no other function matches the issue type you want to operate on. **Parameters:** @@ -132,7 +132,7 @@ uv add crewai-tools - `operation` (string, required): Operation Type value, for example CREATE_ISSUE or UPDATE_ISSUE. - + **Description:** Get Projects in Jira. **Parameters:** @@ -144,27 +144,27 @@ uv add crewai-tools ``` - + **Description:** Get Issue Types by project in Jira. **Parameters:** - `project` (string, required): Project key. - + **Description:** Get all Issue Types in Jira. **Parameters:** None required. - + **Description:** Get issue statuses for a given project. **Parameters:** - `project` (string, required): Project key. - + **Description:** Get assignees for a given project. **Parameters:** @@ -178,19 +178,14 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Jira tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +from crewai import Agent, Task, Crew # Create an agent with Jira capabilities jira_agent = Agent( role="Issue Manager", goal="Manage Jira issues and track project progress efficiently", backstory="An AI assistant specialized in issue tracking and project management.", - tools=[enterprise_tools] + apps=['jira'] # All Jira actions will be available ) # Task to create a bug report @@ -212,19 +207,12 @@ crew.kickoff() ### Filtering Specific Jira Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Jira tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["jira_create_issue", "jira_update_issue", "jira_search_by_jql"] -) issue_coordinator = Agent( role="Issue Coordinator", goal="Create and manage Jira issues efficiently", backstory="An AI assistant that focuses on issue creation and management.", - tools=enterprise_tools + apps=['jira'] ) # Task to manage issue workflow @@ -246,17 +234,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_analyst = Agent( role="Project Analyst", goal="Analyze project data and generate insights from Jira", backstory="An experienced project analyst who extracts insights from project management data.", - tools=[enterprise_tools] + apps=['jira'] ) # Task to analyze project status @@ -283,17 +266,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) automation_manager = Agent( role="Automation Manager", goal="Automate issue management and workflow processes", backstory="An AI assistant that automates repetitive issue management tasks.", - tools=[enterprise_tools] + apps=['jira'] ) # Task to automate issue management @@ -321,17 +299,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) schema_specialist = Agent( role="Schema Specialist", goal="Handle complex Jira operations using dynamic schemas", backstory="An AI assistant that can work with dynamic Jira schemas and custom issue types.", - tools=[enterprise_tools] + apps=['jira'] ) # Task using schema-based operations diff --git a/docs/en/enterprise/integrations/linear.mdx b/docs/en/enterprise/integrations/linear.mdx index 875c64808..35cb4e174 100644 --- a/docs/en/enterprise/integrations/linear.mdx +++ b/docs/en/enterprise/integrations/linear.mdx @@ -25,7 +25,7 @@ Before using the Linear integration, ensure you have: 2. Find **Linear** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for issue and project management -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,7 +36,7 @@ uv add crewai-tools ## Available Actions - + **Description:** Create a new issue in Linear. **Parameters:** @@ -56,7 +56,7 @@ uv add crewai-tools ``` - + **Description:** Update an issue in Linear. **Parameters:** @@ -76,21 +76,21 @@ uv add crewai-tools ``` - + **Description:** Get an issue by ID in Linear. **Parameters:** - `issueId` (string, required): Issue ID - Specify the record ID of the issue to fetch. (example: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **Description:** Get an issue by issue identifier in Linear. **Parameters:** - `externalId` (string, required): External ID - Specify the human-readable Issue identifier of the issue to fetch. (example: "ABC-1"). - + **Description:** Search issues in Linear. **Parameters:** @@ -117,21 +117,21 @@ uv add crewai-tools Available operators: `$stringExactlyMatches`, `$stringDoesNotExactlyMatch`, `$stringIsIn`, `$stringIsNotIn`, `$stringStartsWith`, `$stringDoesNotStartWith`, `$stringEndsWith`, `$stringDoesNotEndWith`, `$stringContains`, `$stringDoesNotContain`, `$stringGreaterThan`, `$stringLessThan`, `$numberGreaterThanOrEqualTo`, `$numberLessThanOrEqualTo`, `$numberGreaterThan`, `$numberLessThan`, `$dateTimeAfter`, `$dateTimeBefore` - + **Description:** Delete an issue in Linear. **Parameters:** - `issueId` (string, required): Issue ID - Specify the record ID of the issue to delete. (example: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **Description:** Archive an issue in Linear. **Parameters:** - `issueId` (string, required): Issue ID - Specify the record ID of the issue to archive. (example: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **Description:** Create a sub-issue in Linear. **Parameters:** @@ -147,7 +147,7 @@ uv add crewai-tools ``` - + **Description:** Create a new project in Linear. **Parameters:** @@ -169,7 +169,7 @@ uv add crewai-tools ``` - + **Description:** Update a project in Linear. **Parameters:** @@ -185,21 +185,21 @@ uv add crewai-tools ``` - + **Description:** Get a project by ID in Linear. **Parameters:** - `projectId` (string, required): Project ID - Specify the Project ID of the project to fetch. (example: "a6634484-6061-4ac7-9739-7dc5e52c796b"). - + **Description:** Delete a project in Linear. **Parameters:** - `projectId` (string, required): Project ID - Specify the Project ID of the project to delete. (example: "a6634484-6061-4ac7-9739-7dc5e52c796b"). - + **Description:** Search teams in Linear. **Parameters:** @@ -231,19 +231,14 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Linear tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +from crewai import Agent, Task, Crew # Create an agent with Linear capabilities linear_agent = Agent( role="Development Manager", goal="Manage Linear issues and track development progress efficiently", backstory="An AI assistant specialized in software development project management.", - tools=[enterprise_tools] + apps=['linear'] # All Linear actions will be available ) # Task to create a bug report @@ -265,19 +260,12 @@ crew.kickoff() ### Filtering Specific Linear Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Linear tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["linear_create_issue", "linear_update_issue", "linear_search_issue"] -) issue_manager = Agent( role="Issue Manager", goal="Create and manage Linear issues efficiently", backstory="An AI assistant that focuses on issue creation and lifecycle management.", - tools=enterprise_tools + apps=['linear/create_issue'] ) # Task to manage issue workflow @@ -299,17 +287,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Coordinate projects and teams in Linear efficiently", backstory="An experienced project coordinator who manages development cycles and team workflows.", - tools=[enterprise_tools] + apps=['linear'] ) # Task to coordinate project setup @@ -336,17 +319,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) task_organizer = Agent( role="Task Organizer", goal="Organize complex issues into manageable sub-tasks", backstory="An AI assistant that breaks down complex development work into organized sub-tasks.", - tools=[enterprise_tools] + apps=['linear'] ) # Task to create issue hierarchy @@ -373,17 +351,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) workflow_automator = Agent( role="Workflow Automator", goal="Automate development workflow processes in Linear", backstory="An AI assistant that automates repetitive development workflow tasks.", - tools=[enterprise_tools] + apps=['linear'] ) # Complex workflow automation task diff --git a/docs/en/enterprise/integrations/microsoft_excel.mdx b/docs/en/enterprise/integrations/microsoft_excel.mdx new file mode 100644 index 000000000..8d462f423 --- /dev/null +++ b/docs/en/enterprise/integrations/microsoft_excel.mdx @@ -0,0 +1,446 @@ +--- +title: Microsoft Excel Integration +description: "Workbook and data management with Microsoft Excel integration for CrewAI." +icon: "table" +mode: "wide" +--- + +## Overview + +Enable your agents to create and manage Excel workbooks, worksheets, tables, and charts in OneDrive or SharePoint. Manipulate data ranges, create visualizations, manage tables, and streamline your spreadsheet workflows with AI-powered automation. + +## Prerequisites + +Before using the Microsoft Excel integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Microsoft 365 account with Excel and OneDrive/SharePoint access +- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Microsoft Excel Integration + +### 1. Connect Your Microsoft Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Microsoft Excel** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for files and Excel workbook access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Create a new Excel workbook in OneDrive or SharePoint. + + **Parameters:** + - `file_path` (string, required): Path where to create the workbook (e.g., 'MyWorkbook.xlsx') + - `worksheets` (array, optional): Initial worksheets to create + ```json + [ + { + "name": "Sheet1" + }, + { + "name": "Data" + } + ] + ``` + + + + **Description:** Get all Excel workbooks from OneDrive or SharePoint. + + **Parameters:** + - `select` (string, optional): Select specific properties to return + - `filter` (string, optional): Filter results using OData syntax + - `expand` (string, optional): Expand related resources inline + - `top` (integer, optional): Number of items to return. Minimum: 1, Maximum: 999 + - `orderby` (string, optional): Order results by specified properties + + + + **Description:** Get all worksheets in an Excel workbook. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `select` (string, optional): Select specific properties to return (e.g., 'id,name,position') + - `filter` (string, optional): Filter results using OData syntax + - `expand` (string, optional): Expand related resources inline + - `top` (integer, optional): Number of items to return. Minimum: 1, Maximum: 999 + - `orderby` (string, optional): Order results by specified properties + + + + **Description:** Create a new worksheet in an Excel workbook. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `name` (string, required): Name of the new worksheet + + + + **Description:** Get data from a specific range in an Excel worksheet. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + - `range` (string, required): Range address (e.g., 'A1:C10') + + + + **Description:** Update data in a specific range in an Excel worksheet. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + - `range` (string, required): Range address (e.g., 'A1:C10') + - `values` (array, required): 2D array of values to set in the range + ```json + [ + ["Name", "Age", "City"], + ["John", 30, "New York"], + ["Jane", 25, "Los Angeles"] + ] + ``` + + + + **Description:** Create a table in an Excel worksheet. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + - `range` (string, required): Range for the table (e.g., 'A1:D10') + - `has_headers` (boolean, optional): Whether the first row contains headers. Default: true + + + + **Description:** Get all tables in an Excel worksheet. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + + + + **Description:** Add a new row to an Excel table. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + - `table_name` (string, required): Name of the table + - `values` (array, required): Array of values for the new row + ```json + ["John Doe", 35, "Manager", "Sales"] + ``` + + + + **Description:** Create a chart in an Excel worksheet. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + - `chart_type` (string, required): Type of chart (e.g., 'ColumnClustered', 'Line', 'Pie') + - `source_data` (string, required): Range of data for the chart (e.g., 'A1:B10') + - `series_by` (string, optional): How to interpret the data ('Auto', 'Columns', or 'Rows'). Default: Auto + + + + **Description:** Get the value of a single cell in an Excel worksheet. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + - `row` (integer, required): Row number (0-based) + - `column` (integer, required): Column number (0-based) + + + + **Description:** Get the used range of an Excel worksheet (contains all data). + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + + + + **Description:** Get all charts in an Excel worksheet. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + + + + **Description:** Delete a worksheet from an Excel workbook. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet to delete + + + + **Description:** Delete a table from an Excel worksheet. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + - `worksheet_name` (string, required): Name of the worksheet + - `table_name` (string, required): Name of the table to delete + + + + **Description:** Get all named ranges in an Excel workbook. + + **Parameters:** + - `file_id` (string, required): The ID of the Excel file + + + +## Usage Examples + +### Basic Excel Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Excel capabilities +excel_agent = Agent( + role="Excel Data Manager", + goal="Manage Excel workbooks and data efficiently", + backstory="An AI assistant specialized in Excel data management and analysis.", + apps=['microsoft_excel'] # All Excel actions will be available +) + +# Task to create and populate a workbook +data_management_task = Task( + description="Create a new sales report workbook with data analysis and charts", + agent=excel_agent, + expected_output="Excel workbook created with sales data, analysis, and visualizations" +) + +# Run the task +crew = Crew( + agents=[excel_agent], + tasks=[data_management_task] +) + +crew.kickoff() +``` + +### Data Analysis and Reporting + +```python +from crewai import Agent, Task, Crew + +data_analyst = Agent( + role="Data Analyst", + goal="Analyze data in Excel and create comprehensive reports", + backstory="An AI assistant that specializes in data analysis and Excel reporting.", + apps=[ + 'microsoft_excel/get_workbooks', + 'microsoft_excel/get_range_data', + 'microsoft_excel/create_chart', + 'microsoft_excel/add_table' + ] +) + +# Task to analyze existing data +analysis_task = Task( + description="Analyze sales data in existing workbooks and create summary charts and tables", + agent=data_analyst, + expected_output="Data analyzed with summary charts and tables created" +) + +crew = Crew( + agents=[data_analyst], + tasks=[analysis_task] +) + +crew.kickoff() +``` + +### Workbook Creation and Structure + +```python +from crewai import Agent, Task, Crew + +workbook_creator = Agent( + role="Workbook Creator", + goal="Create structured Excel workbooks with multiple worksheets and data organization", + backstory="An AI assistant that creates well-organized Excel workbooks for various business needs.", + apps=['microsoft_excel'] +) + +# Task to create structured workbooks +creation_task = Task( + description=""" + 1. Create a new quarterly report workbook + 2. Add multiple worksheets for different departments + 3. Create tables with headers for data organization + 4. Set up charts for key metrics visualization + """, + agent=workbook_creator, + expected_output="Structured workbook created with multiple worksheets, tables, and charts" +) + +crew = Crew( + agents=[workbook_creator], + tasks=[creation_task] +) + +crew.kickoff() +``` + +### Data Manipulation and Updates + +```python +from crewai import Agent, Task, Crew + +data_manipulator = Agent( + role="Data Manipulator", + goal="Update and manipulate data in Excel worksheets efficiently", + backstory="An AI assistant that handles data updates, table management, and range operations.", + apps=['microsoft_excel'] +) + +# Task to manipulate data +manipulation_task = Task( + description=""" + 1. Get data from existing worksheets + 2. Update specific ranges with new information + 3. Add new rows to existing tables + 4. Create additional charts based on updated data + 5. Organize data across multiple worksheets + """, + agent=data_manipulator, + expected_output="Data updated across worksheets with new charts and organized structure" +) + +crew = Crew( + agents=[data_manipulator], + tasks=[manipulation_task] +) + +crew.kickoff() +``` + +### Advanced Excel Automation + +```python +from crewai import Agent, Task, Crew + +excel_automator = Agent( + role="Excel Automator", + goal="Automate complex Excel workflows and data processing", + backstory="An AI assistant that automates sophisticated Excel operations and data workflows.", + apps=['microsoft_excel'] +) + +# Complex automation task +automation_task = Task( + description=""" + 1. Scan all Excel workbooks for specific data patterns + 2. Create consolidated reports from multiple workbooks + 3. Generate charts and tables for trend analysis + 4. Set up named ranges for easy data reference + 5. Create dashboard worksheets with key metrics + 6. Clean up unused worksheets and tables + """, + agent=excel_automator, + expected_output="Automated Excel workflow completed with consolidated reports and dashboards" +) + +crew = Crew( + agents=[excel_automator], + tasks=[automation_task] +) + +crew.kickoff() +``` + +### Financial Modeling and Analysis + +```python +from crewai import Agent, Task, Crew + +financial_modeler = Agent( + role="Financial Modeler", + goal="Create financial models and analysis in Excel", + backstory="An AI assistant specialized in financial modeling and analysis using Excel.", + apps=['microsoft_excel'] +) + +# Task for financial modeling +modeling_task = Task( + description=""" + 1. Create financial model workbooks with multiple scenarios + 2. Set up input tables for assumptions and variables + 3. Create calculation worksheets with formulas and logic + 4. Generate charts for financial projections and trends + 5. Add summary tables for key financial metrics + 6. Create sensitivity analysis tables + """, + agent=financial_modeler, + expected_output="Financial model created with scenarios, calculations, and analysis charts" +) + +crew = Crew( + agents=[financial_modeler], + tasks=[modeling_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Permission Errors** +- Ensure your Microsoft account has appropriate permissions for Excel and OneDrive/SharePoint +- Verify that the OAuth connection includes required scopes (Files.Read.All, Files.ReadWrite.All) +- Check that you have access to the specific workbooks you're trying to modify + +**File ID and Path Issues** +- Verify that file IDs are correct and files exist in your OneDrive or SharePoint +- Ensure file paths are properly formatted when creating new workbooks +- Check that workbook files have the correct .xlsx extension + +**Worksheet and Range Issues** +- Verify that worksheet names exist in the specified workbook +- Ensure range addresses are properly formatted (e.g., 'A1:C10') +- Check that ranges don't exceed worksheet boundaries + +**Data Format Issues** +- Ensure data values are properly formatted for Excel (strings, numbers, integers) +- Verify that 2D arrays for ranges have consistent row and column counts +- Check that table data includes proper headers when has_headers is true + +**Chart Creation Issues** +- Verify that chart types are supported (ColumnClustered, Line, Pie, etc.) +- Ensure source data ranges contain appropriate data for the chart type +- Check that the source data range exists and contains data + +**Table Management Issues** +- Ensure table names are unique within worksheets +- Verify that table ranges don't overlap with existing tables +- Check that new row data matches the table's column structure + +**Cell and Range Operations** +- Verify that row and column indices are 0-based for cell operations +- Ensure ranges contain data when using get_used_range +- Check that named ranges exist before referencing them + +### Getting Help + + + Contact our support team for assistance with Microsoft Excel integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/microsoft_onedrive.mdx b/docs/en/enterprise/integrations/microsoft_onedrive.mdx new file mode 100644 index 000000000..c0ef2f93f --- /dev/null +++ b/docs/en/enterprise/integrations/microsoft_onedrive.mdx @@ -0,0 +1,250 @@ +--- +title: Microsoft OneDrive Integration +description: "File and folder management with Microsoft OneDrive integration for CrewAI." +icon: "cloud" +mode: "wide" +--- + +## Overview + +Enable your agents to upload, download, and manage files and folders in Microsoft OneDrive. Automate file operations, organize content, create sharing links, and streamline your cloud storage workflows with AI-powered automation. + +## Prerequisites + +Before using the Microsoft OneDrive integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Microsoft account with OneDrive access +- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Microsoft OneDrive Integration + +### 1. Connect Your Microsoft Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Microsoft OneDrive** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for file access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** List files and folders in OneDrive. + + **Parameters:** + - `top` (integer, optional): Number of items to retrieve (max 1000). Default is `50`. + - `orderby` (string, optional): Order by field (e.g., "name asc", "lastModifiedDateTime desc"). Default is "name asc". + - `filter` (string, optional): OData filter expression. + + + + **Description:** Get information about a specific file or folder. + + **Parameters:** + - `item_id` (string, required): The ID of the file or folder. + + + + **Description:** Download a file from OneDrive. + + **Parameters:** + - `item_id` (string, required): The ID of the file to download. + + + + **Description:** Upload a file to OneDrive. + + **Parameters:** + - `file_name` (string, required): Name of the file to upload. + - `content` (string, required): Base64 encoded file content. + + + + **Description:** Create a new folder in OneDrive. + + **Parameters:** + - `folder_name` (string, required): Name of the folder to create. + + + + **Description:** Delete a file or folder from OneDrive. + + **Parameters:** + - `item_id` (string, required): The ID of the file or folder to delete. + + + + **Description:** Copy a file or folder in OneDrive. + + **Parameters:** + - `item_id` (string, required): The ID of the file or folder to copy. + - `parent_id` (string, optional): The ID of the destination folder (optional, defaults to root). + - `new_name` (string, optional): New name for the copied item (optional). + + + + **Description:** Move a file or folder in OneDrive. + + **Parameters:** + - `item_id` (string, required): The ID of the file or folder to move. + - `parent_id` (string, required): The ID of the destination folder. + - `new_name` (string, optional): New name for the item (optional). + + + + **Description:** Search for files and folders in OneDrive. + + **Parameters:** + - `query` (string, required): Search query string. + - `top` (integer, optional): Number of results to return (max 1000). Default is `50`. + + + + **Description:** Create a sharing link for a file or folder. + + **Parameters:** + - `item_id` (string, required): The ID of the file or folder to share. + - `type` (string, optional): Type of sharing link. Enum: `view`, `edit`, `embed`. Default is `view`. + - `scope` (string, optional): Scope of the sharing link. Enum: `anonymous`, `organization`. Default is `anonymous`. + + + + **Description:** Get thumbnails for a file. + + **Parameters:** + - `item_id` (string, required): The ID of the file. + + + +## Usage Examples + +### Basic Microsoft OneDrive Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Microsoft OneDrive capabilities +onedrive_agent = Agent( + role="File Manager", + goal="Manage files and folders in OneDrive efficiently", + backstory="An AI assistant specialized in Microsoft OneDrive file operations and organization.", + apps=['microsoft_onedrive'] # All OneDrive actions will be available +) + +# Task to list files and create a folder +organize_files_task = Task( + description="List all files in my OneDrive root directory and create a new folder called 'Project Documents'.", + agent=onedrive_agent, + expected_output="List of files displayed and new folder 'Project Documents' created." +) + +# Run the task +crew = Crew( + agents=[onedrive_agent], + tasks=[organize_files_task] +) + +crew.kickoff() +``` + +### File Upload and Management + +```python +from crewai import Agent, Task, Crew + +# Create an agent focused on file operations +file_operator = Agent( + role="File Operator", + goal="Upload, download, and manage files with precision", + backstory="An AI assistant skilled in file handling and content management.", + apps=['microsoft_onedrive/upload_file', 'microsoft_onedrive/download_file', 'microsoft_onedrive/get_file_info'] +) + +# Task to upload and manage a file +file_management_task = Task( + description="Upload a text file named 'report.txt' with content 'This is a sample report for the project.' Then get information about the uploaded file.", + agent=file_operator, + expected_output="File uploaded successfully and file information retrieved." +) + +crew = Crew( + agents=[file_operator], + tasks=[file_management_task] +) + +crew.kickoff() +``` + +### File Organization and Sharing + +```python +from crewai import Agent, Task, Crew + +# Create an agent for file organization and sharing +file_organizer = Agent( + role="File Organizer", + goal="Organize files and create sharing links for collaboration", + backstory="An AI assistant that excels at organizing files and managing sharing permissions.", + apps=['microsoft_onedrive/search_files', 'microsoft_onedrive/move_item', 'microsoft_onedrive/share_item', 'microsoft_onedrive/create_folder'] +) + +# Task to organize and share files +organize_share_task = Task( + description="Search for files containing 'presentation' in the name, create a folder called 'Presentations', move the found files to this folder, and create a view-only sharing link for the folder.", + agent=file_organizer, + expected_output="Files organized into 'Presentations' folder and sharing link created." +) + +crew = Crew( + agents=[file_organizer], + tasks=[organize_share_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Authentication Errors** +- Ensure your Microsoft account has the necessary permissions for file access (e.g., `Files.Read`, `Files.ReadWrite`). +- Verify that the OAuth connection includes all required scopes. + +**File Upload Issues** +- Ensure `file_name` and `content` are provided for file uploads. +- Content must be Base64 encoded for binary files. +- Check that you have write permissions to OneDrive. + +**File/Folder ID Issues** +- Double-check item IDs for correctness when accessing specific files or folders. +- Item IDs are returned by other operations like `list_files` or `search_files`. +- Ensure the referenced items exist and are accessible. + +**Search and Filter Operations** +- Use appropriate search terms for `search_files` operations. +- For `filter` parameters, use proper OData syntax. + +**File Operations (Copy/Move)** +- For `move_item`, ensure both `item_id` and `parent_id` are provided. +- For `copy_item`, only `item_id` is required; `parent_id` defaults to root if not specified. +- Verify that destination folders exist and are accessible. + +**Sharing Link Creation** +- Ensure the item exists before creating sharing links. +- Choose appropriate `type` and `scope` based on your sharing requirements. +- `anonymous` scope allows access without sign-in; `organization` requires organizational account. + +### Getting Help + + + Contact our support team for assistance with Microsoft OneDrive integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/microsoft_outlook.mdx b/docs/en/enterprise/integrations/microsoft_outlook.mdx new file mode 100644 index 000000000..de5ceb0c2 --- /dev/null +++ b/docs/en/enterprise/integrations/microsoft_outlook.mdx @@ -0,0 +1,232 @@ +--- +title: Microsoft Outlook Integration +description: "Email, calendar, and contact management with Microsoft Outlook integration for CrewAI." +icon: "envelope" +mode: "wide" +--- + +## Overview + +Enable your agents to access and manage Outlook emails, calendar events, and contacts. Send emails, retrieve messages, manage calendar events, and organize contacts with AI-powered automation. + +## Prerequisites + +Before using the Microsoft Outlook integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Microsoft account with Outlook access +- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Microsoft Outlook Integration + +### 1. Connect Your Microsoft Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Microsoft Outlook** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for mail, calendar, and contact access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Get email messages from the user's mailbox. + + **Parameters:** + - `top` (integer, optional): Number of messages to retrieve (max 1000). Default is `10`. + - `filter` (string, optional): OData filter expression (e.g., "isRead eq false"). + - `search` (string, optional): Search query string. + - `orderby` (string, optional): Order by field (e.g., "receivedDateTime desc"). Default is "receivedDateTime desc". + - `select` (string, optional): Select specific properties to return. + - `expand` (string, optional): Expand related resources inline. + + + + **Description:** Send an email message. + + **Parameters:** + - `to_recipients` (array, required): Array of recipient email addresses. + - `cc_recipients` (array, optional): Array of CC recipient email addresses. + - `bcc_recipients` (array, optional): Array of BCC recipient email addresses. + - `subject` (string, required): Email subject. + - `body` (string, required): Email body content. + - `body_type` (string, optional): Body content type. Enum: `Text`, `HTML`. Default is `HTML`. + - `importance` (string, optional): Message importance level. Enum: `low`, `normal`, `high`. Default is `normal`. + - `reply_to` (array, optional): Array of reply-to email addresses. + - `save_to_sent_items` (boolean, optional): Whether to save the message to Sent Items folder. Default is `true`. + + + + **Description:** Get calendar events from the user's calendar. + + **Parameters:** + - `top` (integer, optional): Number of events to retrieve (max 1000). Default is `10`. + - `skip` (integer, optional): Number of events to skip. Default is `0`. + - `filter` (string, optional): OData filter expression (e.g., "start/dateTime ge '2024-01-01T00:00:00Z'"). + - `orderby` (string, optional): Order by field (e.g., "start/dateTime asc"). Default is "start/dateTime asc". + + + + **Description:** Create a new calendar event. + + **Parameters:** + - `subject` (string, required): Event subject/title. + - `body` (string, optional): Event body/description. + - `start_datetime` (string, required): Start date and time in ISO 8601 format (e.g., '2024-01-20T10:00:00'). + - `end_datetime` (string, required): End date and time in ISO 8601 format. + - `timezone` (string, optional): Time zone (e.g., 'Pacific Standard Time'). Default is `UTC`. + - `location` (string, optional): Event location. + - `attendees` (array, optional): Array of attendee email addresses. + + + + **Description:** Get contacts from the user's address book. + + **Parameters:** + - `top` (integer, optional): Number of contacts to retrieve (max 1000). Default is `10`. + - `skip` (integer, optional): Number of contacts to skip. Default is `0`. + - `filter` (string, optional): OData filter expression. + - `orderby` (string, optional): Order by field (e.g., "displayName asc"). Default is "displayName asc". + + + + **Description:** Create a new contact in the user's address book. + + **Parameters:** + - `displayName` (string, required): Contact's display name. + - `givenName` (string, optional): Contact's first name. + - `surname` (string, optional): Contact's last name. + - `emailAddresses` (array, optional): Array of email addresses. Each item is an object with `address` (string) and `name` (string). + - `businessPhones` (array, optional): Array of business phone numbers. + - `homePhones` (array, optional): Array of home phone numbers. + - `jobTitle` (string, optional): Contact's job title. + - `companyName` (string, optional): Contact's company name. + + + +## Usage Examples + +### Basic Microsoft Outlook Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Microsoft Outlook capabilities +outlook_agent = Agent( + role="Email Assistant", + goal="Manage emails, calendar events, and contacts efficiently", + backstory="An AI assistant specialized in Microsoft Outlook operations and communication management.", + apps=['microsoft_outlook'] # All Outlook actions will be available +) + +# Task to send an email +send_email_task = Task( + description="Send an email to 'colleague@example.com' with subject 'Project Update' and body 'Hi, here is the latest project update. Best regards.'", + agent=outlook_agent, + expected_output="Email sent successfully to colleague@example.com" +) + +# Run the task +crew = Crew( + agents=[outlook_agent], + tasks=[send_email_task] +) + +crew.kickoff() +``` + +### Email Management and Search + +```python +from crewai import Agent, Task, Crew + +# Create an agent focused on email management +email_manager = Agent( + role="Email Manager", + goal="Retrieve, search, and organize email messages", + backstory="An AI assistant skilled in email organization and management.", + apps=['microsoft_outlook/get_messages'] +) + +# Task to search and retrieve emails +search_emails_task = Task( + description="Get the latest 20 unread emails and provide a summary of the most important ones.", + agent=email_manager, + expected_output="Summary of the most important unread emails with key details." +) + +crew = Crew( + agents=[email_manager], + tasks=[search_emails_task] +) + +crew.kickoff() +``` + +### Calendar and Contact Management + +```python +from crewai import Agent, Task, Crew + +# Create an agent for calendar and contact management +scheduler = Agent( + role="Calendar and Contact Manager", + goal="Manage calendar events and maintain contact information", + backstory="An AI assistant that handles scheduling and contact organization.", + apps=['microsoft_outlook/create_calendar_event', 'microsoft_outlook/get_calendar_events', 'microsoft_outlook/create_contact'] +) + +# Task to create a meeting and add a contact +schedule_task = Task( + description="Create a calendar event for tomorrow at 2 PM titled 'Team Meeting' with location 'Conference Room A', and create a new contact for 'John Smith' with email 'john.smith@example.com' and job title 'Project Manager'.", + agent=scheduler, + expected_output="Calendar event created and new contact added successfully." +) + +crew = Crew( + agents=[scheduler], + tasks=[schedule_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Authentication Errors** +- Ensure your Microsoft account has the necessary permissions for mail, calendar, and contact access. +- Required scopes include: `Mail.Read`, `Mail.Send`, `Calendars.Read`, `Calendars.ReadWrite`, `Contacts.Read`, `Contacts.ReadWrite`. +- Verify that the OAuth connection includes all required scopes. + +**Email Sending Issues** +- Ensure `to_recipients`, `subject`, and `body` are provided for `send_email`. +- Check that email addresses are properly formatted. +- Verify that the account has `Mail.Send` permissions. + +**Calendar Event Creation** +- Ensure `subject`, `start_datetime`, and `end_datetime` are provided. +- Use proper ISO 8601 format for datetime fields (e.g., '2024-01-20T10:00:00'). +- Verify timezone settings if events appear at incorrect times. + +**Contact Management** +- For `create_contact`, ensure `displayName` is provided as it's required. +- When providing `emailAddresses`, use the proper object format with `address` and `name` properties. + +**Search and Filter Issues** +- Use proper OData syntax for `filter` parameters. +- For date filters, use ISO 8601 format (e.g., "receivedDateTime ge '2024-01-01T00:00:00Z'"). + +### Getting Help + + + Contact our support team for assistance with Microsoft Outlook integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/microsoft_sharepoint.mdx b/docs/en/enterprise/integrations/microsoft_sharepoint.mdx new file mode 100644 index 000000000..8c4e3021a --- /dev/null +++ b/docs/en/enterprise/integrations/microsoft_sharepoint.mdx @@ -0,0 +1,388 @@ +--- +title: Microsoft SharePoint Integration +description: "Site, list, and document management with Microsoft SharePoint integration for CrewAI." +icon: "folder-tree" +mode: "wide" +--- + +## Overview + +Enable your agents to access and manage SharePoint sites, lists, and document libraries. Retrieve site information, manage list items, upload and organize files, and streamline your SharePoint workflows with AI-powered automation. + +## Prerequisites + +Before using the Microsoft SharePoint integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Microsoft 365 account with SharePoint access +- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Microsoft SharePoint Integration + +### 1. Connect Your Microsoft Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Microsoft SharePoint** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for SharePoint sites and content access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Get all SharePoint sites the user has access to. + + **Parameters:** + - `search` (string, optional): Search query to filter sites + - `select` (string, optional): Select specific properties to return (e.g., 'displayName,id,webUrl') + - `filter` (string, optional): Filter results using OData syntax + - `expand` (string, optional): Expand related resources inline + - `top` (integer, optional): Number of items to return. Minimum: 1, Maximum: 999 + - `skip` (integer, optional): Number of items to skip. Minimum: 0 + - `orderby` (string, optional): Order results by specified properties (e.g., 'displayName desc') + + + + **Description:** Get information about a specific SharePoint site. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + - `select` (string, optional): Select specific properties to return (e.g., 'displayName,id,webUrl,drives') + - `expand` (string, optional): Expand related resources inline (e.g., 'drives,lists') + + + + **Description:** Get all lists in a SharePoint site. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + + + + **Description:** Get information about a specific list. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + - `list_id` (string, required): The ID of the list + + + + **Description:** Get items from a SharePoint list. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + - `list_id` (string, required): The ID of the list + - `expand` (string, optional): Expand related data (e.g., 'fields') + + + + **Description:** Create a new item in a SharePoint list. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + - `list_id` (string, required): The ID of the list + - `fields` (object, required): The field values for the new item + ```json + { + "Title": "New Item Title", + "Description": "Item description", + "Status": "Active" + } + ``` + + + + **Description:** Update an item in a SharePoint list. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + - `list_id` (string, required): The ID of the list + - `item_id` (string, required): The ID of the item to update + - `fields` (object, required): The field values to update + ```json + { + "Title": "Updated Title", + "Status": "Completed" + } + ``` + + + + **Description:** Delete an item from a SharePoint list. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + - `list_id` (string, required): The ID of the list + - `item_id` (string, required): The ID of the item to delete + + + + **Description:** Upload a file to a SharePoint document library. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + - `file_path` (string, required): The path where to upload the file (e.g., 'folder/filename.txt') + - `content` (string, required): The file content to upload + + + + **Description:** Get files and folders from a SharePoint document library. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + + + + **Description:** Delete a file or folder from SharePoint document library. + + **Parameters:** + - `site_id` (string, required): The ID of the SharePoint site + - `item_id` (string, required): The ID of the file or folder to delete + + + +## Usage Examples + +### Basic SharePoint Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with SharePoint capabilities +sharepoint_agent = Agent( + role="SharePoint Manager", + goal="Manage SharePoint sites, lists, and documents efficiently", + backstory="An AI assistant specialized in SharePoint content management and collaboration.", + apps=['microsoft_sharepoint'] # All SharePoint actions will be available +) + +# Task to organize SharePoint content +content_organization_task = Task( + description="List all accessible SharePoint sites and organize content by department", + agent=sharepoint_agent, + expected_output="SharePoint sites listed and content organized by department" +) + +# Run the task +crew = Crew( + agents=[sharepoint_agent], + tasks=[content_organization_task] +) + +crew.kickoff() +``` + +### List Management and Data Operations + +```python +from crewai import Agent, Task, Crew + +list_manager = Agent( + role="List Manager", + goal="Manage SharePoint lists and data efficiently", + backstory="An AI assistant that focuses on SharePoint list management and data operations.", + apps=[ + 'microsoft_sharepoint/get_site_lists', + 'microsoft_sharepoint/get_list_items', + 'microsoft_sharepoint/create_list_item', + 'microsoft_sharepoint/update_list_item' + ] +) + +# Task to manage list data +list_management_task = Task( + description="Get all lists from the project site, review items, and update status for completed tasks", + agent=list_manager, + expected_output="SharePoint lists reviewed and task statuses updated" +) + +crew = Crew( + agents=[list_manager], + tasks=[list_management_task] +) + +crew.kickoff() +``` + +### Document Library Management + +```python +from crewai import Agent, Task, Crew + +document_manager = Agent( + role="Document Manager", + goal="Manage SharePoint document libraries and files", + backstory="An AI assistant that specializes in document organization and file management.", + apps=['microsoft_sharepoint'] +) + +# Task to manage documents +document_task = Task( + description=""" + 1. Get all files from the main document library + 2. Upload new policy documents to the appropriate folders + 3. Organize files by department and date + 4. Remove outdated documents + """, + agent=document_manager, + expected_output="Document library organized with new files uploaded and outdated files removed" +) + +crew = Crew( + agents=[document_manager], + tasks=[document_task] +) + +crew.kickoff() +``` + +### Site Administration and Analysis + +```python +from crewai import Agent, Task, Crew + +site_administrator = Agent( + role="Site Administrator", + goal="Administer and analyze SharePoint sites", + backstory="An AI assistant that handles site administration and provides insights on site usage.", + apps=['microsoft_sharepoint'] +) + +# Task for site administration +admin_task = Task( + description=""" + 1. Get information about all accessible SharePoint sites + 2. Analyze site structure and content organization + 3. Identify sites with low activity or outdated content + 4. Generate recommendations for site optimization + """, + agent=site_administrator, + expected_output="Site analysis completed with optimization recommendations" +) + +crew = Crew( + agents=[site_administrator], + tasks=[admin_task] +) + +crew.kickoff() +``` + +### Automated Content Workflows + +```python +from crewai import Agent, Task, Crew + +workflow_automator = Agent( + role="Workflow Automator", + goal="Automate SharePoint content workflows and processes", + backstory="An AI assistant that automates complex SharePoint workflows and content management processes.", + apps=['microsoft_sharepoint'] +) + +# Complex workflow automation task +automation_task = Task( + description=""" + 1. Monitor project lists across multiple sites + 2. Create status reports based on list data + 3. Upload reports to designated document libraries + 4. Update project tracking lists with completion status + 5. Archive completed project documents + 6. Send notifications for overdue items + """, + agent=workflow_automator, + expected_output="Automated workflow completed with status reports generated and project tracking updated" +) + +crew = Crew( + agents=[workflow_automator], + tasks=[automation_task] +) + +crew.kickoff() +``` + +### Data Integration and Reporting + +```python +from crewai import Agent, Task, Crew + +data_integrator = Agent( + role="Data Integrator", + goal="Integrate and analyze data across SharePoint sites and lists", + backstory="An AI assistant that specializes in data integration and cross-site analysis.", + apps=['microsoft_sharepoint'] +) + +# Task for data integration +integration_task = Task( + description=""" + 1. Get data from multiple SharePoint lists across different sites + 2. Consolidate information into comprehensive reports + 3. Create new list items with aggregated data + 4. Upload analytical reports to executive document library + 5. Update dashboard lists with key metrics + """, + agent=data_integrator, + expected_output="Data integrated across sites with comprehensive reports and updated dashboards" +) + +crew = Crew( + agents=[data_integrator], + tasks=[integration_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Permission Errors** +- Ensure your Microsoft account has appropriate permissions for SharePoint sites +- Verify that the OAuth connection includes required scopes (Sites.Read.All, Sites.ReadWrite.All) +- Check that you have access to the specific sites and lists you're trying to access + +**Site and List ID Issues** +- Verify that site IDs and list IDs are correct and properly formatted +- Ensure that sites and lists exist and are accessible to your account +- Use the get_sites and get_site_lists actions to discover valid IDs + +**Field and Schema Issues** +- Ensure field names match exactly with the SharePoint list schema +- Verify that required fields are included when creating or updating list items +- Check that field types and values are compatible with the list column definitions + +**File Upload Issues** +- Ensure file paths are properly formatted and don't contain invalid characters +- Verify that you have write permissions to the target document library +- Check that file content is properly encoded for upload + +**OData Query Issues** +- Use proper OData syntax for filter, select, expand, and orderby parameters +- Verify that property names used in queries exist in the target resources +- Test simple queries before building complex filter expressions + +**Pagination and Performance** +- Use top and skip parameters appropriately for large result sets +- Implement proper pagination for lists with many items +- Consider using select parameters to return only needed properties + +**Document Library Operations** +- Ensure you have proper permissions for document library operations +- Verify that drive item IDs are correct when deleting files or folders +- Check that file paths don't conflict with existing content + +### Getting Help + + + Contact our support team for assistance with Microsoft SharePoint integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/microsoft_teams.mdx b/docs/en/enterprise/integrations/microsoft_teams.mdx new file mode 100644 index 000000000..6b9115704 --- /dev/null +++ b/docs/en/enterprise/integrations/microsoft_teams.mdx @@ -0,0 +1,212 @@ +--- +title: Microsoft Teams Integration +description: "Team collaboration and communication with Microsoft Teams integration for CrewAI." +icon: "users" +mode: "wide" +--- + +## Overview + +Enable your agents to access Teams data, send messages, create meetings, and manage channels. Automate team communication, schedule meetings, retrieve messages, and streamline your collaboration workflows with AI-powered automation. + +## Prerequisites + +Before using the Microsoft Teams integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Microsoft account with Teams access +- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Microsoft Teams Integration + +### 1. Connect Your Microsoft Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Microsoft Teams** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for Teams access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Get all teams the user is a member of. + + **Parameters:** + - No parameters required. + + + + **Description:** Get channels in a specific team. + + **Parameters:** + - `team_id` (string, required): The ID of the team. + + + + **Description:** Send a message to a Teams channel. + + **Parameters:** + - `team_id` (string, required): The ID of the team. + - `channel_id` (string, required): The ID of the channel. + - `message` (string, required): The message content. + - `content_type` (string, optional): Content type (html or text). Enum: `html`, `text`. Default is `text`. + + + + **Description:** Get messages from a Teams channel. + + **Parameters:** + - `team_id` (string, required): The ID of the team. + - `channel_id` (string, required): The ID of the channel. + - `top` (integer, optional): Number of messages to retrieve (max 50). Default is `20`. + + + + **Description:** Create a Teams meeting. + + **Parameters:** + - `subject` (string, required): Meeting subject/title. + - `startDateTime` (string, required): Meeting start time (ISO 8601 format with timezone). + - `endDateTime` (string, required): Meeting end time (ISO 8601 format with timezone). + + + + **Description:** Search online meetings by Join Web URL. + + **Parameters:** + - `join_web_url` (string, required): The join web URL of the meeting to search for. + + + +## Usage Examples + +### Basic Microsoft Teams Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Microsoft Teams capabilities +teams_agent = Agent( + role="Teams Coordinator", + goal="Manage Teams communication and meetings efficiently", + backstory="An AI assistant specialized in Microsoft Teams operations and team collaboration.", + apps=['microsoft_teams'] # All Teams actions will be available +) + +# Task to list teams and channels +explore_teams_task = Task( + description="List all teams I'm a member of and then get the channels for the first team.", + agent=teams_agent, + expected_output="List of teams and channels displayed." +) + +# Run the task +crew = Crew( + agents=[teams_agent], + tasks=[explore_teams_task] +) + +crew.kickoff() +``` + +### Messaging and Communication + +```python +from crewai import Agent, Task, Crew + +# Create an agent focused on messaging +messenger = Agent( + role="Teams Messenger", + goal="Send and retrieve messages in Teams channels", + backstory="An AI assistant skilled in team communication and message management.", + apps=['microsoft_teams/send_message', 'microsoft_teams/get_messages'] +) + +# Task to send a message and retrieve recent messages +messaging_task = Task( + description="Send a message 'Hello team! This is an automated update from our AI assistant.' to the General channel of team 'your_team_id', then retrieve the last 10 messages from that channel.", + agent=messenger, + expected_output="Message sent successfully and recent messages retrieved." +) + +crew = Crew( + agents=[messenger], + tasks=[messaging_task] +) + +crew.kickoff() +``` + +### Meeting Management + +```python +from crewai import Agent, Task, Crew + +# Create an agent for meeting management +meeting_scheduler = Agent( + role="Meeting Scheduler", + goal="Create and manage Teams meetings", + backstory="An AI assistant that handles meeting scheduling and organization.", + apps=['microsoft_teams/create_meeting', 'microsoft_teams/search_online_meetings_by_join_url'] +) + +# Task to create a meeting +schedule_meeting_task = Task( + description="Create a Teams meeting titled 'Weekly Team Sync' scheduled for tomorrow at 10:00 AM lasting for 1 hour (use proper ISO 8601 format with timezone).", + agent=meeting_scheduler, + expected_output="Teams meeting created successfully with meeting details." +) + +crew = Crew( + agents=[meeting_scheduler], + tasks=[schedule_meeting_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Authentication Errors** +- Ensure your Microsoft account has the necessary permissions for Teams access. +- Required scopes include: `Team.ReadBasic.All`, `Channel.ReadBasic.All`, `ChannelMessage.Send`, `ChannelMessage.Read.All`, `OnlineMeetings.ReadWrite`, `OnlineMeetings.Read`. +- Verify that the OAuth connection includes all required scopes. + +**Team and Channel Access** +- Ensure you are a member of the teams you're trying to access. +- Double-check team IDs and channel IDs for correctness. +- Team and channel IDs can be obtained using the `get_teams` and `get_channels` actions. + +**Message Sending Issues** +- Ensure `team_id`, `channel_id`, and `message` are provided for `send_message`. +- Verify that you have permissions to send messages to the specified channel. +- Choose appropriate `content_type` (text or html) based on your message format. + +**Meeting Creation** +- Ensure `subject`, `startDateTime`, and `endDateTime` are provided. +- Use proper ISO 8601 format with timezone for datetime fields (e.g., '2024-01-20T10:00:00-08:00'). +- Verify that the meeting times are in the future. + +**Message Retrieval Limitations** +- The `get_messages` action can retrieve a maximum of 50 messages per request. +- Messages are returned in reverse chronological order (newest first). + +**Meeting Search** +- For `search_online_meetings_by_join_url`, ensure the join URL is exact and properly formatted. +- The URL should be the complete Teams meeting join URL. + +### Getting Help + + + Contact our support team for assistance with Microsoft Teams integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/microsoft_word.mdx b/docs/en/enterprise/integrations/microsoft_word.mdx new file mode 100644 index 000000000..ff1e70e52 --- /dev/null +++ b/docs/en/enterprise/integrations/microsoft_word.mdx @@ -0,0 +1,192 @@ +--- +title: Microsoft Word Integration +description: "Document creation and management with Microsoft Word integration for CrewAI." +icon: "file-word" +mode: "wide" +--- + +## Overview + +Enable your agents to create, read, and manage Word documents and text files in OneDrive or SharePoint. Automate document creation, retrieve content, manage document properties, and streamline your document workflows with AI-powered automation. + +## Prerequisites + +Before using the Microsoft Word integration, ensure you have: + +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription +- A Microsoft account with Word and OneDrive/SharePoint access +- Connected your Microsoft account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) + +## Setting Up Microsoft Word Integration + +### 1. Connect Your Microsoft Account + +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) +2. Find **Microsoft Word** in the Authentication Integrations section +3. Click **Connect** and complete the OAuth flow +4. Grant the necessary permissions for file access +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Install Required Package + +```bash +uv add crewai-tools +``` + +## Available Actions + + + + **Description:** Get all Word documents from OneDrive or SharePoint. + + **Parameters:** + - `select` (string, optional): Select specific properties to return. + - `filter` (string, optional): Filter results using OData syntax. + - `expand` (string, optional): Expand related resources inline. + - `top` (integer, optional): Number of items to return (min 1, max 999). + - `orderby` (string, optional): Order results by specified properties. + + + + **Description:** Create a text document (.txt) with content. RECOMMENDED for programmatic content creation that needs to be readable and editable. + + **Parameters:** + - `file_name` (string, required): Name of the text document (should end with .txt). + - `content` (string, optional): Text content for the document. Default is "This is a new text document created via API." + + + + **Description:** Get the content of a document (works best with text files). + + **Parameters:** + - `file_id` (string, required): The ID of the document. + + + + **Description:** Get properties and metadata of a document. + + **Parameters:** + - `file_id` (string, required): The ID of the document. + + + + **Description:** Delete a document. + + **Parameters:** + - `file_id` (string, required): The ID of the document to delete. + + + +## Usage Examples + +### Basic Microsoft Word Agent Setup + +```python +from crewai import Agent, Task, Crew + +# Create an agent with Microsoft Word capabilities +word_agent = Agent( + role="Document Manager", + goal="Manage Word documents and text files efficiently", + backstory="An AI assistant specialized in Microsoft Word document operations and content management.", + apps=['microsoft_word'] # All Word actions will be available +) + +# Task to create a new text document +create_doc_task = Task( + description="Create a new text document named 'meeting_notes.txt' with content 'Meeting Notes from January 2024: Key discussion points and action items.'", + agent=word_agent, + expected_output="New text document 'meeting_notes.txt' created successfully." +) + +# Run the task +crew = Crew( + agents=[word_agent], + tasks=[create_doc_task] +) + +crew.kickoff() +``` + +### Reading and Managing Documents + +```python +from crewai import Agent, Task, Crew + +# Create an agent focused on document operations +document_reader = Agent( + role="Document Reader", + goal="Retrieve and analyze document content and properties", + backstory="An AI assistant skilled in reading and analyzing document content.", + apps=['microsoft_word/get_documents', 'microsoft_word/get_document_content', 'microsoft_word/get_document_properties'] +) + +# Task to list and read documents +read_docs_task = Task( + description="List all Word documents in my OneDrive, then get the content and properties of the first document found.", + agent=document_reader, + expected_output="List of documents with content and properties of the first document." +) + +crew = Crew( + agents=[document_reader], + tasks=[read_docs_task] +) + +crew.kickoff() +``` + +### Document Cleanup and Organization + +```python +from crewai import Agent, Task, Crew + +# Create an agent for document management +document_organizer = Agent( + role="Document Organizer", + goal="Organize and clean up document collections", + backstory="An AI assistant that helps maintain organized document libraries.", + apps=['microsoft_word/get_documents', 'microsoft_word/get_document_properties', 'microsoft_word/delete_document'] +) + +# Task to organize documents +organize_task = Task( + description="List all documents, check their properties, and identify any documents that might be duplicates or outdated for potential cleanup.", + agent=document_organizer, + expected_output="Analysis of document library with recommendations for organization." +) + +crew = Crew( + agents=[document_organizer], + tasks=[organize_task] +) + +crew.kickoff() +``` + +## Troubleshooting + +### Common Issues + +**Authentication Errors** +- Ensure your Microsoft account has the necessary permissions for file access (e.g., `Files.Read.All`, `Files.ReadWrite.All`). +- Verify that the OAuth connection includes all required scopes. + +**File Creation Issues** +- When creating text documents, ensure the `file_name` ends with `.txt` extension. +- Verify that you have write permissions to the target location (OneDrive/SharePoint). + +**Document Access Issues** +- Double-check document IDs for correctness when accessing specific documents. +- Ensure the referenced documents exist and are accessible. +- Note that this integration works best with text files (.txt) for content operations. + +**Content Retrieval Limitations** +- The `get_document_content` action works best with text files (.txt). +- For complex Word documents (.docx), consider using the document properties action to get metadata. + +### Getting Help + + + Contact our support team for assistance with Microsoft Word integration setup or troubleshooting. + diff --git a/docs/en/enterprise/integrations/notion.mdx b/docs/en/enterprise/integrations/notion.mdx index 5fb1afaf0..0665bb128 100644 --- a/docs/en/enterprise/integrations/notion.mdx +++ b/docs/en/enterprise/integrations/notion.mdx @@ -1,13 +1,13 @@ --- title: Notion Integration -description: "Page and database management with Notion integration for CrewAI." +description: "User management and commenting with Notion integration for CrewAI." icon: "book" mode: "wide" --- ## Overview -Enable your agents to manage pages, databases, and content through Notion. Create and update pages, manage content blocks, organize knowledge bases, and streamline your documentation workflows with AI-powered automation. +Enable your agents to manage users and create comments through Notion. Access workspace user information and create comments on pages and discussions, streamlining your collaboration workflows with AI-powered automation. ## Prerequisites @@ -24,8 +24,8 @@ Before using the Notion integration, ensure you have: 1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Notion** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow -4. Grant the necessary permissions for page and database management -5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account) +4. Grant the necessary permissions for user access and comment creation +5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Install Required Package @@ -36,242 +36,50 @@ uv add crewai-tools ## Available Actions - - **Description:** Create a page in Notion. + + **Description:** List all users in the workspace. **Parameters:** - - `parent` (object, required): Parent - The parent page or database where the new page is inserted, represented as a JSON object with a page_id or database_id key. + - `page_size` (integer, optional): Number of items returned in the response. Minimum: 1, Maximum: 100, Default: 100 + - `start_cursor` (string, optional): Cursor for pagination. Return results after this cursor. + + + + **Description:** Retrieve a specific user by ID. + + **Parameters:** + - `user_id` (string, required): The ID of the user to retrieve. + + + + **Description:** Create a comment on a page or discussion. + + **Parameters:** + - `parent` (object, required): The parent page or discussion to comment on. ```json { - "database_id": "DATABASE_ID" + "type": "page_id", + "page_id": "PAGE_ID_HERE" } ``` - - `properties` (object, required): Properties - The values of the page's properties. If the parent is a database, then the schema must match the parent database's properties. + or ```json { - "title": [ - { - "text": { - "content": "My Page" - } - } - ] + "type": "discussion_id", + "discussion_id": "DISCUSSION_ID_HERE" } ``` - - `icon` (object, required): Icon - The page icon. - ```json - { - "emoji": "🥬" - } - ``` - - `children` (object, optional): Children - Content blocks to add to the page. + - `rich_text` (array, required): The rich text content of the comment. ```json [ { - "object": "block", - "type": "heading_2", - "heading_2": { - "rich_text": [ - { - "type": "text", - "text": { - "content": "Lacinato kale" - } - } - ] + "type": "text", + "text": { + "content": "This is my comment text" } } ] ``` - - `cover` (object, optional): Cover - The page cover image. - ```json - { - "external": { - "url": "https://upload.wikimedia.org/wikipedia/commons/6/62/Tuscankale.jpg" - } - } - ``` - - - - **Description:** Update a page in Notion. - - **Parameters:** - - `pageId` (string, required): Page ID - Specify the ID of the Page to Update. (example: "59833787-2cf9-4fdf-8782-e53db20768a5"). - - `icon` (object, required): Icon - The page icon. - ```json - { - "emoji": "🥬" - } - ``` - - `archived` (boolean, optional): Archived - Whether the page is archived (deleted). Set to true to archive a page. Set to false to un-archive (restore) a page. - - `properties` (object, optional): Properties - The property values to update for the page. - ```json - { - "title": [ - { - "text": { - "content": "My Updated Page" - } - } - ] - } - ``` - - `cover` (object, optional): Cover - The page cover image. - ```json - { - "external": { - "url": "https://upload.wikimedia.org/wikipedia/commons/6/62/Tuscankale.jpg" - } - } - ``` - - - - **Description:** Get a page by ID in Notion. - - **Parameters:** - - `pageId` (string, required): Page ID - Specify the ID of the Page to Get. (example: "59833787-2cf9-4fdf-8782-e53db20768a5"). - - - - **Description:** Archive a page in Notion. - - **Parameters:** - - `pageId` (string, required): Page ID - Specify the ID of the Page to Archive. (example: "59833787-2cf9-4fdf-8782-e53db20768a5"). - - - - **Description:** Search pages in Notion using filters. - - **Parameters:** - - `searchByTitleFilterSearch` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions. - ```json - { - "operator": "OR", - "conditions": [ - { - "operator": "AND", - "conditions": [ - { - "field": "query", - "operator": "$stringExactlyMatches", - "value": "meeting notes" - } - ] - } - ] - } - ``` - Available fields: `query`, `filter.value`, `direction`, `page_size` - - - - **Description:** Get page content (blocks) in Notion. - - **Parameters:** - - `blockId` (string, required): Page ID - Specify a Block or Page ID to receive all of its block's children in order. (example: "59833787-2cf9-4fdf-8782-e53db20768a5"). - - - - **Description:** Update a block in Notion. - - **Parameters:** - - `blockId` (string, required): Block ID - Specify the ID of the Block to Update. (example: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6"). - - `archived` (boolean, optional): Archived - Set to true to archive (delete) a block. Set to false to un-archive (restore) a block. - - `paragraph` (object, optional): Paragraph content. - ```json - { - "rich_text": [ - { - "type": "text", - "text": { - "content": "Lacinato kale", - "link": null - } - } - ], - "color": "default" - } - ``` - - `image` (object, optional): Image block. - ```json - { - "type": "external", - "external": { - "url": "https://website.domain/images/image.png" - } - } - ``` - - `bookmark` (object, optional): Bookmark block. - ```json - { - "caption": [], - "url": "https://companywebsite.com" - } - ``` - - `code` (object, optional): Code block. - ```json - { - "rich_text": [ - { - "type": "text", - "text": { - "content": "const a = 3" - } - } - ], - "language": "javascript" - } - ``` - - `pdf` (object, optional): PDF block. - ```json - { - "type": "external", - "external": { - "url": "https://website.domain/files/doc.pdf" - } - } - ``` - - `table` (object, optional): Table block. - ```json - { - "table_width": 2, - "has_column_header": false, - "has_row_header": false - } - ``` - - `tableOfContent` (object, optional): Table of Contents block. - ```json - { - "color": "default" - } - ``` - - `additionalFields` (object, optional): Additional block types. - ```json - { - "child_page": { - "title": "Lacinato kale" - }, - "child_database": { - "title": "My database" - } - } - ``` - - - - **Description:** Get a block by ID in Notion. - - **Parameters:** - - `blockId` (string, required): Block ID - Specify the ID of the Block to Get. (example: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6"). - - - - **Description:** Delete a block in Notion. - - **Parameters:** - - `blockId` (string, required): Block ID - Specify the ID of the Block to Delete. (example: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6"). @@ -281,32 +89,26 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Notion tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Notion capabilities notion_agent = Agent( - role="Documentation Manager", - goal="Manage documentation and knowledge base in Notion efficiently", - backstory="An AI assistant specialized in content management and documentation.", - tools=[enterprise_tools] + role="Workspace Manager", + goal="Manage workspace users and facilitate collaboration through comments", + backstory="An AI assistant specialized in user management and team collaboration.", + apps=['notion'] # All Notion actions will be available ) -# Task to create a meeting notes page -create_notes_task = Task( - description="Create a new meeting notes page in the team database with today's date and agenda items", +# Task to list workspace users +user_management_task = Task( + description="List all users in the workspace and provide a summary of team members", agent=notion_agent, - expected_output="Meeting notes page created successfully with structured content" + expected_output="Complete list of workspace users with their details" ) # Run the task crew = Crew( agents=[notion_agent], - tasks=[create_notes_task] + tasks=[user_management_task] ) crew.kickoff() @@ -315,144 +117,116 @@ crew.kickoff() ### Filtering Specific Notion Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Notion tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["notion_create_page", "notion_update_block", "notion_search_pages"] +comment_manager = Agent( + role="Comment Manager", + goal="Create and manage comments on Notion pages", + backstory="An AI assistant that focuses on facilitating discussions through comments.", + apps=['notion/create_comment'] ) -content_manager = Agent( - role="Content Manager", - goal="Create and manage content pages efficiently", - backstory="An AI assistant that focuses on content creation and management.", - tools=enterprise_tools -) - -# Task to manage content workflow -content_workflow = Task( - description="Create a new project documentation page and add structured content blocks for requirements and specifications", - agent=content_manager, - expected_output="Project documentation created with organized content sections" +# Task to create comments on pages +comment_task = Task( + description="Create a summary comment on the project status page with key updates", + agent=comment_manager, + expected_output="Comment created successfully with project status updates" ) crew = Crew( - agents=[content_manager], - tasks=[content_workflow] + agents=[comment_manager], + tasks=[comment_task] ) crew.kickoff() ``` -### Knowledge Base Management +### User Information and Team Management ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" +team_coordinator = Agent( + role="Team Coordinator", + goal="Coordinate team activities and manage user information", + backstory="An AI assistant that helps coordinate team activities and manages user information.", + apps=['notion'] ) -knowledge_curator = Agent( - role="Knowledge Curator", - goal="Curate and organize knowledge base content in Notion", - backstory="An experienced knowledge manager who organizes and maintains comprehensive documentation.", - tools=[enterprise_tools] -) - -# Task to curate knowledge base -curation_task = Task( +# Task to coordinate team activities +coordination_task = Task( description=""" - 1. Search for existing documentation pages related to our new product feature - 2. Create a comprehensive feature documentation page with proper structure - 3. Add code examples, images, and links to related resources - 4. Update existing pages with cross-references to the new documentation + 1. List all users in the workspace + 2. Get detailed information for specific team members + 3. Create comments on relevant pages to notify team members about updates """, - agent=knowledge_curator, - expected_output="Feature documentation created and integrated with existing knowledge base" + agent=team_coordinator, + expected_output="Team coordination completed with user information gathered and notifications sent" ) crew = Crew( - agents=[knowledge_curator], - tasks=[curation_task] + agents=[team_coordinator], + tasks=[coordination_task] ) crew.kickoff() ``` -### Content Structure and Organization +### Collaboration and Communication ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" +collaboration_facilitator = Agent( + role="Collaboration Facilitator", + goal="Facilitate team collaboration through comments and user management", + backstory="An AI assistant that specializes in team collaboration and communication.", + apps=['notion'] ) -content_organizer = Agent( - role="Content Organizer", - goal="Organize and structure content blocks for optimal readability", - backstory="An AI assistant that specializes in content structure and user experience.", - tools=[enterprise_tools] -) - -# Task to organize content structure -organization_task = Task( +# Task to facilitate collaboration +collaboration_task = Task( description=""" - 1. Get content from existing project pages - 2. Analyze the structure and identify improvement opportunities - 3. Update content blocks to use proper headings, tables, and formatting - 4. Add table of contents and improve navigation between related pages - 5. Create templates for future documentation consistency + 1. Identify active users in the workspace + 2. Create contextual comments on project pages to facilitate discussions + 3. Provide status updates and feedback through comments """, - agent=content_organizer, - expected_output="Content reorganized with improved structure and navigation" + agent=collaboration_facilitator, + expected_output="Collaboration facilitated with comments created and team members notified" ) crew = Crew( - agents=[content_organizer], - tasks=[organization_task] + agents=[collaboration_facilitator], + tasks=[collaboration_task] ) crew.kickoff() ``` -### Automated Documentation Workflows +### Automated Team Communication ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" +communication_automator = Agent( + role="Communication Automator", + goal="Automate team communication and user management workflows", + backstory="An AI assistant that automates communication workflows and manages user interactions.", + apps=['notion'] ) -doc_automator = Agent( - role="Documentation Automator", - goal="Automate documentation workflows and maintenance", - backstory="An AI assistant that automates repetitive documentation tasks.", - tools=[enterprise_tools] -) - -# Complex documentation automation task +# Complex communication automation task automation_task = Task( description=""" - 1. Search for pages that haven't been updated in the last 30 days - 2. Review and update outdated content blocks - 3. Create weekly team update pages with consistent formatting - 4. Add status indicators and progress tracking to project pages - 5. Generate monthly documentation health reports - 6. Archive completed project pages and organize them in archive sections + 1. List all workspace users and identify team roles + 2. Get specific user information for project stakeholders + 3. Create automated status update comments on key project pages + 4. Facilitate team communication through targeted comments """, - agent=doc_automator, - expected_output="Documentation automated with updated content, weekly reports, and organized archives" + agent=communication_automator, + expected_output="Automated communication workflow completed with user management and comments" ) crew = Crew( - agents=[doc_automator], + agents=[communication_automator], tasks=[automation_task] ) @@ -464,44 +238,29 @@ crew.kickoff() ### Common Issues **Permission Errors** -- Ensure your Notion account has edit access to the target workspace -- Verify that the OAuth connection includes required scopes for Notion API -- Check that pages and databases are shared with the authenticated integration +- Ensure your Notion account has appropriate permissions to read user information +- Verify that the OAuth connection includes required scopes for user access and comment creation +- Check that you have permissions to comment on the target pages or discussions -**Invalid Page and Block IDs** -- Double-check page IDs and block IDs for correct UUID format -- Ensure referenced pages and blocks exist and are accessible -- Verify that parent page or database IDs are valid when creating new pages +**User Access Issues** +- Ensure you have workspace admin permissions to list all users +- Verify that user IDs are correct and users exist in the workspace +- Check that the workspace allows API access to user information -**Property Schema Issues** -- Ensure page properties match the database schema when creating pages in databases -- Verify that property names and types are correct for the target database -- Check that required properties are included when creating or updating pages +**Comment Creation Issues** +- Verify that page IDs or discussion IDs are correct and accessible +- Ensure that rich text content follows Notion's API format specifications +- Check that you have comment permissions on the target pages or discussions -**Content Block Structure** -- Ensure block content follows Notion's rich text format specifications -- Verify that nested block structures are properly formatted -- Check that media URLs are accessible and properly formatted +**API Rate Limits** +- Be mindful of Notion's API rate limits when making multiple requests +- Implement appropriate delays between requests if needed +- Consider pagination for large user lists -**Search and Filter Issues** -- Ensure search queries are properly formatted and not empty -- Use valid field names in filter formulas: `query`, `filter.value`, `direction`, `page_size` -- Test simple searches before building complex filter conditions - -**Parent-Child Relationships** -- Verify that parent page or database exists before creating child pages -- Ensure proper permissions exist for the parent container -- Check that database schemas allow the properties you're trying to set - -**Rich Text and Media Content** -- Ensure URLs for external images, PDFs, and bookmarks are accessible -- Verify that rich text formatting follows Notion's API specifications -- Check that code block language types are supported by Notion - -**Archive and Deletion Operations** -- Understand the difference between archiving (reversible) and deleting (permanent) -- Verify that you have permissions to archive or delete the target content -- Be cautious with bulk operations that might affect multiple pages or blocks +**Parent Object Specification** +- Ensure parent object type is correctly specified (page_id or discussion_id) +- Verify that the parent page or discussion exists and is accessible +- Check that the parent object ID format is correct ### Getting Help diff --git a/docs/en/enterprise/integrations/salesforce.mdx b/docs/en/enterprise/integrations/salesforce.mdx index 38fec82d2..48ede3d38 100644 --- a/docs/en/enterprise/integrations/salesforce.mdx +++ b/docs/en/enterprise/integrations/salesforce.mdx @@ -22,7 +22,7 @@ Before using the Salesforce integration, ensure you have: ### **Record Management** - + **Description:** Create a new Contact record in Salesforce. **Parameters:** @@ -35,7 +35,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Contact fields - + **Description:** Create a new Lead record in Salesforce. **Parameters:** @@ -51,7 +51,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Lead fields - + **Description:** Create a new Opportunity record in Salesforce. **Parameters:** @@ -66,7 +66,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Opportunity fields - + **Description:** Create a new Task record in Salesforce. **Parameters:** @@ -84,7 +84,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Task fields - + **Description:** Create a new Account record in Salesforce. **Parameters:** @@ -96,7 +96,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Account fields - + **Description:** Create a record of any object type in Salesforce. **Note:** This is a flexible tool for creating records of custom or unknown object types. @@ -106,7 +106,7 @@ Before using the Salesforce integration, ensure you have: ### **Record Updates** - + **Description:** Update an existing Contact record in Salesforce. **Parameters:** @@ -120,7 +120,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Contact fields - + **Description:** Update an existing Lead record in Salesforce. **Parameters:** @@ -137,7 +137,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Lead fields - + **Description:** Update an existing Opportunity record in Salesforce. **Parameters:** @@ -153,7 +153,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Opportunity fields - + **Description:** Update an existing Task record in Salesforce. **Parameters:** @@ -171,7 +171,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Task fields - + **Description:** Update an existing Account record in Salesforce. **Parameters:** @@ -184,7 +184,7 @@ Before using the Salesforce integration, ensure you have: - `additionalFields` (object, optional): Additional fields in JSON format for custom Account fields - + **Description:** Update a record of any object type in Salesforce. **Note:** This is a flexible tool for updating records of custom or unknown object types. @@ -194,42 +194,42 @@ Before using the Salesforce integration, ensure you have: ### **Record Retrieval** - + **Description:** Get a Contact record by its ID. **Parameters:** - `recordId` (string, required): Record ID of the Contact - + **Description:** Get a Lead record by its ID. **Parameters:** - `recordId` (string, required): Record ID of the Lead - + **Description:** Get an Opportunity record by its ID. **Parameters:** - `recordId` (string, required): Record ID of the Opportunity - + **Description:** Get a Task record by its ID. **Parameters:** - `recordId` (string, required): Record ID of the Task - + **Description:** Get an Account record by its ID. **Parameters:** - `recordId` (string, required): Record ID of the Account - + **Description:** Get a record of any object type by its ID. **Parameters:** @@ -241,7 +241,7 @@ Before using the Salesforce integration, ensure you have: ### **Record Search** - + **Description:** Search for Contact records with advanced filtering. **Parameters:** @@ -252,7 +252,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Search for Lead records with advanced filtering. **Parameters:** @@ -263,7 +263,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Search for Opportunity records with advanced filtering. **Parameters:** @@ -274,7 +274,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Search for Task records with advanced filtering. **Parameters:** @@ -285,7 +285,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Search for Account records with advanced filtering. **Parameters:** @@ -296,7 +296,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Search for records of any object type. **Parameters:** @@ -310,7 +310,7 @@ Before using the Salesforce integration, ensure you have: ### **List View Retrieval** - + **Description:** Get Contact records from a specific List View. **Parameters:** @@ -318,7 +318,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Get Lead records from a specific List View. **Parameters:** @@ -326,7 +326,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Get Opportunity records from a specific List View. **Parameters:** @@ -334,7 +334,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Get Task records from a specific List View. **Parameters:** @@ -342,7 +342,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Get Account records from a specific List View. **Parameters:** @@ -350,7 +350,7 @@ Before using the Salesforce integration, ensure you have: - `paginationParameters` (object, optional): Pagination settings with pageCursor - + **Description:** Get records of any object type from a specific List View. **Parameters:** @@ -363,7 +363,7 @@ Before using the Salesforce integration, ensure you have: ### **Custom Fields** - + **Description:** Deploy custom fields for Contact objects. **Parameters:** @@ -379,7 +379,7 @@ Before using the Salesforce integration, ensure you have: - `defaultFieldValue` (string, optional): Default field value - + **Description:** Deploy custom fields for Lead objects. **Parameters:** @@ -395,7 +395,7 @@ Before using the Salesforce integration, ensure you have: - `defaultFieldValue` (string, optional): Default field value - + **Description:** Deploy custom fields for Opportunity objects. **Parameters:** @@ -411,7 +411,7 @@ Before using the Salesforce integration, ensure you have: - `defaultFieldValue` (string, optional): Default field value - + **Description:** Deploy custom fields for Task objects. **Parameters:** @@ -427,7 +427,7 @@ Before using the Salesforce integration, ensure you have: - `defaultFieldValue` (string, optional): Default field value - + **Description:** Deploy custom fields for Account objects. **Parameters:** @@ -443,7 +443,7 @@ Before using the Salesforce integration, ensure you have: - `defaultFieldValue` (string, optional): Default field value - + **Description:** Deploy custom fields for any object type. **Note:** This is a flexible tool for creating custom fields on custom or unknown object types. @@ -453,14 +453,14 @@ Before using the Salesforce integration, ensure you have: ### **Advanced Operations** - + **Description:** Execute custom SOQL queries against your Salesforce data. **Parameters:** - `query` (string, required): SOQL Query (e.g., "SELECT Id, Name FROM Account WHERE Name = 'Example'") - + **Description:** Deploy a new custom object in Salesforce. **Parameters:** @@ -470,7 +470,7 @@ Before using the Salesforce integration, ensure you have: - `recordName` (string, required): Record Name that appears in layouts and searches (e.g., "Account Name") - + **Description:** Get the expected schema for operations on specific object types. **Parameters:** @@ -487,19 +487,14 @@ Before using the Salesforce integration, ensure you have: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Salesforce tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +from crewai import Agent, Task, Crew # Create an agent with Salesforce capabilities salesforce_agent = Agent( role="CRM Manager", goal="Manage customer relationships and sales processes efficiently", backstory="An AI assistant specialized in CRM operations and sales automation.", - tools=[enterprise_tools] + apps=['salesforce'] # All Salesforce actions will be available ) # Task to create a new lead @@ -521,19 +516,12 @@ crew.kickoff() ### Filtering Specific Salesforce Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Salesforce tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["salesforce_create_record_lead", "salesforce_update_record_opportunity", "salesforce_search_records_contact"] -) sales_manager = Agent( role="Sales Manager", goal="Manage leads and opportunities in the sales pipeline", backstory="An experienced sales manager who handles lead qualification and opportunity management.", - tools=enterprise_tools + apps=['salesforce/create_record_lead'] ) # Task to manage sales pipeline @@ -555,17 +543,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) account_manager = Agent( role="Account Manager", goal="Manage customer accounts and maintain strong relationships", backstory="An AI assistant that specializes in account management and customer relationship building.", - tools=[enterprise_tools] + apps=['salesforce'] ) # Task to manage customer accounts @@ -591,17 +574,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_analyst = Agent( role="Sales Data Analyst", goal="Generate insights from Salesforce data using SOQL queries", backstory="An analytical AI that excels at extracting meaningful insights from CRM data.", - tools=[enterprise_tools] + apps=['salesforce'] ) # Complex task involving SOQL queries and data analysis diff --git a/docs/en/enterprise/integrations/shopify.mdx b/docs/en/enterprise/integrations/shopify.mdx index 671570bfe..29684ae55 100644 --- a/docs/en/enterprise/integrations/shopify.mdx +++ b/docs/en/enterprise/integrations/shopify.mdx @@ -22,7 +22,7 @@ Before using the Shopify integration, ensure you have: ### **Customer Management** - + **Description:** Retrieve a list of customers from your Shopify store. **Parameters:** @@ -34,7 +34,7 @@ Before using the Shopify integration, ensure you have: - `limit` (string, optional): Maximum number of customers to return (defaults to 250) - + **Description:** Search for customers using advanced filtering criteria. **Parameters:** @@ -42,7 +42,7 @@ Before using the Shopify integration, ensure you have: - `limit` (string, optional): Maximum number of customers to return (defaults to 250) - + **Description:** Create a new customer in your Shopify store. **Parameters:** @@ -63,7 +63,7 @@ Before using the Shopify integration, ensure you have: - `metafields` (object, optional): Additional metafields in JSON format - + **Description:** Update an existing customer in your Shopify store. **Parameters:** @@ -89,7 +89,7 @@ Before using the Shopify integration, ensure you have: ### **Order Management** - + **Description:** Retrieve a list of orders from your Shopify store. **Parameters:** @@ -101,7 +101,7 @@ Before using the Shopify integration, ensure you have: - `limit` (string, optional): Maximum number of orders to return (defaults to 250) - + **Description:** Create a new order in your Shopify store. **Parameters:** @@ -114,7 +114,7 @@ Before using the Shopify integration, ensure you have: - `note` (string, optional): Order note - + **Description:** Update an existing order in your Shopify store. **Parameters:** @@ -128,7 +128,7 @@ Before using the Shopify integration, ensure you have: - `note` (string, optional): Order note - + **Description:** Retrieve abandoned carts from your Shopify store. **Parameters:** @@ -144,7 +144,7 @@ Before using the Shopify integration, ensure you have: ### **Product Management (REST API)** - + **Description:** Retrieve a list of products from your Shopify store using REST API. **Parameters:** @@ -160,7 +160,7 @@ Before using the Shopify integration, ensure you have: - `limit` (string, optional): Maximum number of products to return (defaults to 250) - + **Description:** Create a new product in your Shopify store using REST API. **Parameters:** @@ -176,7 +176,7 @@ Before using the Shopify integration, ensure you have: - `publishToPointToSale` (boolean, optional): Whether to publish to point of sale - + **Description:** Update an existing product in your Shopify store using REST API. **Parameters:** @@ -197,14 +197,14 @@ Before using the Shopify integration, ensure you have: ### **Product Management (GraphQL)** - + **Description:** Retrieve products using advanced GraphQL filtering capabilities. **Parameters:** - `productFilterFormula` (object, optional): Advanced filter in disjunctive normal form with support for fields like id, title, vendor, status, handle, tag, created_at, updated_at, published_at - + **Description:** Create a new product using GraphQL API with enhanced media support. **Parameters:** @@ -217,7 +217,7 @@ Before using the Shopify integration, ensure you have: - `additionalFields` (object, optional): Additional product fields like status, requiresSellingPlan, giftCard - + **Description:** Update an existing product using GraphQL API with enhanced media support. **Parameters:** @@ -238,19 +238,14 @@ Before using the Shopify integration, ensure you have: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Shopify tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +from crewai import Agent, Task, Crew # Create an agent with Shopify capabilities shopify_agent = Agent( role="E-commerce Manager", goal="Manage online store operations and customer relationships efficiently", backstory="An AI assistant specialized in e-commerce operations and online store management.", - tools=[enterprise_tools] + apps=['shopify'] # All Shopify actions will be available ) # Task to create a new customer @@ -272,19 +267,12 @@ crew.kickoff() ### Filtering Specific Shopify Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Shopify tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["shopify_create_customer", "shopify_create_order", "shopify_get_products"] -) store_manager = Agent( role="Store Manager", goal="Manage customer orders and product catalog", backstory="An experienced store manager who handles customer relationships and inventory management.", - tools=enterprise_tools + apps=['shopify/create_customer'] ) # Task to manage store operations @@ -306,17 +294,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) product_manager = Agent( role="Product Manager", goal="Manage product catalog and inventory with advanced GraphQL capabilities", backstory="An AI assistant that specializes in product management and catalog optimization.", - tools=[enterprise_tools] + apps=['shopify'] ) # Task to manage product catalog @@ -343,17 +326,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) analytics_agent = Agent( role="E-commerce Analyst", goal="Analyze customer behavior and order patterns to optimize store performance", backstory="An analytical AI that excels at extracting insights from e-commerce data.", - tools=[enterprise_tools] + apps=['shopify'] ) # Complex task involving multiple operations diff --git a/docs/en/enterprise/integrations/slack.mdx b/docs/en/enterprise/integrations/slack.mdx index ee1a17fc2..66b396795 100644 --- a/docs/en/enterprise/integrations/slack.mdx +++ b/docs/en/enterprise/integrations/slack.mdx @@ -22,21 +22,21 @@ Before using the Slack integration, ensure you have: ### **User Management** - + **Description:** List all members in a Slack channel. **Parameters:** - No parameters required - retrieves all channel members - + **Description:** Find a user in your Slack workspace by their email address. **Parameters:** - `email` (string, required): The email address of a user in the workspace - + **Description:** Search for users by their name or display name. **Parameters:** @@ -50,7 +50,7 @@ Before using the Slack integration, ensure you have: ### **Channel Management** - + **Description:** List all channels in your Slack workspace. **Parameters:** @@ -61,7 +61,7 @@ Before using the Slack integration, ensure you have: ### **Messaging** - + **Description:** Send a message to a Slack channel. **Parameters:** @@ -73,7 +73,7 @@ Before using the Slack integration, ensure you have: - `authenticatedUser` (boolean, optional): If true, message appears to come from your authenticated Slack user instead of the application (defaults to false) - + **Description:** Send a direct message to a specific user in Slack. **Parameters:** @@ -89,7 +89,7 @@ Before using the Slack integration, ensure you have: ### **Search & Discovery** - + **Description:** Search for messages across your Slack workspace. **Parameters:** @@ -150,19 +150,13 @@ Slack's Block Kit allows you to create rich, interactive messages. Here are some ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Slack tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Slack capabilities slack_agent = Agent( role="Team Communication Manager", goal="Facilitate team communication and coordinate collaboration efficiently", backstory="An AI assistant specialized in team communication and workspace coordination.", - tools=[enterprise_tools] + apps=['slack'] # All Slack actions will be available ) # Task to send project updates @@ -184,19 +178,18 @@ crew.kickoff() ### Filtering Specific Slack Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Slack tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["slack_send_message", "slack_send_direct_message", "slack_search_messages"] -) +from crewai import Agent, Task, Crew +# Create agent with specific Slack actions only communication_manager = Agent( role="Communication Coordinator", goal="Manage team communications and ensure important messages reach the right people", backstory="An experienced communication coordinator who handles team messaging and notifications.", - tools=enterprise_tools + apps=[ + 'slack/send_message', + 'slack/send_direct_message', + 'slack/search_messages' + ] # Using canonical action names from canonical_integrations.yml ) # Task to coordinate team communication @@ -218,17 +211,13 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +# Create agent with Slack messaging capabilities notification_agent = Agent( role="Notification Manager", goal="Create rich, interactive notifications and manage workspace communication", backstory="An AI assistant that specializes in creating engaging team notifications and updates.", - tools=[enterprise_tools] + apps=['slack/send_message'] # Specific action for sending messages ) # Task to send rich notifications @@ -254,17 +243,17 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +# Create agent with Slack search and user management capabilities analytics_agent = Agent( role="Communication Analyst", goal="Analyze team communication patterns and extract insights from conversations", backstory="An analytical AI that excels at understanding team dynamics through communication data.", - tools=[enterprise_tools] + apps=[ + 'slack/search_messages', + 'slack/get_user_by_email', + 'slack/list_members' + ] # Using canonical action names from canonical_integrations.yml ) # Complex task involving search and analysis diff --git a/docs/en/enterprise/integrations/stripe.mdx b/docs/en/enterprise/integrations/stripe.mdx index cd25f1c5c..8a3b594a2 100644 --- a/docs/en/enterprise/integrations/stripe.mdx +++ b/docs/en/enterprise/integrations/stripe.mdx @@ -22,7 +22,7 @@ Before using the Stripe integration, ensure you have: ### **Customer Management** - + **Description:** Create a new customer in your Stripe account. **Parameters:** @@ -32,14 +32,14 @@ Before using the Stripe integration, ensure you have: - `metadataCreateCustomer` (object, optional): Additional metadata as key-value pairs (e.g., `{"field1": 1, "field2": 2}`) - + **Description:** Retrieve a specific customer by their Stripe customer ID. **Parameters:** - `idGetCustomer` (string, required): The Stripe customer ID to retrieve - + **Description:** Retrieve a list of customers with optional filtering. **Parameters:** @@ -49,7 +49,7 @@ Before using the Stripe integration, ensure you have: - `limitGetCustomers` (string, optional): Maximum number of customers to return (defaults to 10) - + **Description:** Update an existing customer's information. **Parameters:** @@ -64,7 +64,7 @@ Before using the Stripe integration, ensure you have: ### **Subscription Management** - + **Description:** Create a new subscription for a customer. **Parameters:** @@ -73,7 +73,7 @@ Before using the Stripe integration, ensure you have: - `metadataCreateSubscription` (object, optional): Additional metadata for the subscription - + **Description:** Retrieve subscriptions with optional filtering. **Parameters:** @@ -86,7 +86,7 @@ Before using the Stripe integration, ensure you have: ### **Product Management** - + **Description:** Create a new product in your Stripe catalog. **Parameters:** @@ -95,14 +95,14 @@ Before using the Stripe integration, ensure you have: - `metadataProduct` (object, optional): Additional product metadata as key-value pairs - + **Description:** Retrieve a specific product by its Stripe product ID. **Parameters:** - `productId` (string, required): The Stripe product ID to retrieve - + **Description:** Retrieve a list of products with optional filtering. **Parameters:** @@ -115,7 +115,7 @@ Before using the Stripe integration, ensure you have: ### **Financial Operations** - + **Description:** Retrieve balance transactions from your Stripe account. **Parameters:** @@ -124,7 +124,7 @@ Before using the Stripe integration, ensure you have: - `pageCursor` (string, optional): Page cursor for pagination - + **Description:** Retrieve subscription plans from your Stripe account. **Parameters:** @@ -140,19 +140,14 @@ Before using the Stripe integration, ensure you have: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Stripe tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +from crewai import Agent, Task, Crew # Create an agent with Stripe capabilities stripe_agent = Agent( role="Payment Manager", goal="Manage customer payments, subscriptions, and billing operations efficiently", backstory="An AI assistant specialized in payment processing and subscription management.", - tools=[enterprise_tools] + apps=['stripe'] # All Stripe actions will be available ) # Task to create a new customer @@ -174,19 +169,12 @@ crew.kickoff() ### Filtering Specific Stripe Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Stripe tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["stripe_create_customer", "stripe_create_subscription", "stripe_get_balance_transactions"] -) billing_manager = Agent( role="Billing Manager", goal="Handle customer billing, subscriptions, and payment processing", backstory="An experienced billing manager who handles subscription lifecycle and payment operations.", - tools=enterprise_tools + apps=['stripe'] ) # Task to manage billing operations @@ -208,17 +196,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) subscription_manager = Agent( role="Subscription Manager", goal="Manage customer subscriptions and optimize recurring revenue", backstory="An AI assistant that specializes in subscription lifecycle management and customer retention.", - tools=[enterprise_tools] + apps=['stripe'] ) # Task to manage subscription operations @@ -245,17 +228,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) financial_analyst = Agent( role="Financial Analyst", goal="Analyze payment data and generate financial insights", backstory="An analytical AI that excels at extracting insights from payment and subscription data.", - tools=[enterprise_tools] + apps=['stripe'] ) # Complex task involving financial analysis diff --git a/docs/en/enterprise/integrations/zendesk.mdx b/docs/en/enterprise/integrations/zendesk.mdx index b7b025679..ba75c4aa5 100644 --- a/docs/en/enterprise/integrations/zendesk.mdx +++ b/docs/en/enterprise/integrations/zendesk.mdx @@ -22,7 +22,7 @@ Before using the Zendesk integration, ensure you have: ### **Ticket Management** - + **Description:** Create a new support ticket in Zendesk. **Parameters:** @@ -40,7 +40,7 @@ Before using the Zendesk integration, ensure you have: - `ticketCustomFields` (object, optional): Custom field values in JSON format - + **Description:** Update an existing support ticket in Zendesk. **Parameters:** @@ -58,14 +58,14 @@ Before using the Zendesk integration, ensure you have: - `ticketCustomFields` (object, optional): Updated custom field values - + **Description:** Retrieve a specific ticket by its ID. **Parameters:** - `ticketId` (string, required): The ticket ID to retrieve (e.g., "35436") - + **Description:** Add a comment or internal note to an existing ticket. **Parameters:** @@ -75,7 +75,7 @@ Before using the Zendesk integration, ensure you have: - `isPublic` (boolean, optional): True for public comments, false for internal notes - + **Description:** Search for tickets using various filters and criteria. **Parameters:** @@ -100,7 +100,7 @@ Before using the Zendesk integration, ensure you have: ### **User Management** - + **Description:** Create a new user in Zendesk. **Parameters:** @@ -113,7 +113,7 @@ Before using the Zendesk integration, ensure you have: - `notes` (string, optional): Internal notes about the user - + **Description:** Update an existing user's information. **Parameters:** @@ -127,14 +127,14 @@ Before using the Zendesk integration, ensure you have: - `notes` (string, optional): Updated internal notes - + **Description:** Retrieve a specific user by their ID. **Parameters:** - `userId` (string, required): The user ID to retrieve - + **Description:** Search for users using various criteria. **Parameters:** @@ -150,7 +150,7 @@ Before using the Zendesk integration, ensure you have: ### **Administrative Tools** - + **Description:** Retrieve all standard and custom fields available for tickets. **Parameters:** @@ -158,7 +158,7 @@ Before using the Zendesk integration, ensure you have: - `pageCursor` (string, optional): Page cursor for pagination - + **Description:** Get audit records (read-only history) for tickets. **Parameters:** @@ -205,19 +205,14 @@ Standard ticket status progression: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Zendesk tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) +from crewai import Agent, Task, Crew # Create an agent with Zendesk capabilities zendesk_agent = Agent( role="Support Manager", goal="Manage customer support tickets and provide excellent customer service", backstory="An AI assistant specialized in customer support operations and ticket management.", - tools=[enterprise_tools] + apps=['zendesk'] # All Zendesk actions will be available ) # Task to create a new support ticket @@ -239,19 +234,14 @@ crew.kickoff() ### Filtering Specific Zendesk Tools ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Zendesk tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["zendesk_create_ticket", "zendesk_update_ticket", "zendesk_add_comment_to_ticket"] -) +from crewai import Agent, Task, Crew +# Create agent with specific Zendesk actions only support_agent = Agent( role="Customer Support Agent", goal="Handle customer inquiries and resolve support issues efficiently", backstory="An experienced support agent who specializes in ticket resolution and customer communication.", - tools=enterprise_tools + apps=['zendesk/create_ticket'] # Specific Zendesk actions ) # Task to manage support workflow @@ -273,17 +263,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) ticket_manager = Agent( role="Ticket Manager", goal="Manage support ticket workflows and ensure timely resolution", backstory="An AI assistant that specializes in support ticket triage and workflow optimization.", - tools=[enterprise_tools] + apps=['zendesk'] ) # Task to manage ticket lifecycle @@ -310,17 +295,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) support_analyst = Agent( role="Support Analyst", goal="Analyze support metrics and generate insights for team performance", backstory="An analytical AI that excels at extracting insights from support data and ticket patterns.", - tools=[enterprise_tools] + apps=['zendesk'] ) # Complex task involving analytics and reporting diff --git a/docs/en/mcp/dsl-integration.mdx b/docs/en/mcp/dsl-integration.mdx new file mode 100644 index 000000000..78f1e884d --- /dev/null +++ b/docs/en/mcp/dsl-integration.mdx @@ -0,0 +1,344 @@ +--- +title: MCP DSL Integration +description: Learn how to use CrewAI's simple DSL syntax to integrate MCP servers directly with your agents using the mcps field. +icon: code +mode: "wide" +--- + +## Overview + +CrewAI's MCP DSL (Domain Specific Language) integration provides the **simplest way** to connect your agents to MCP (Model Context Protocol) servers. Just add an `mcps` field to your agent and CrewAI handles all the complexity automatically. + + +This is the **recommended approach** for most MCP use cases. For advanced scenarios requiring manual connection management, see [MCPServerAdapter](/en/mcp/overview#advanced-mcpserveradapter). + + +## Basic Usage + +Add MCP servers to your agent using the `mcps` field: + +```python +from crewai import Agent + +agent = Agent( + role="Research Assistant", + goal="Help with research and analysis tasks", + backstory="Expert assistant with access to advanced research tools", + mcps=[ + "https://mcp.exa.ai/mcp?api_key=your_key&profile=research" + ] +) + +# MCP tools are now automatically available! +# No need for manual connection management or tool configuration +``` + +## Supported Reference Formats + +### External MCP Remote Servers + +```python +# Basic HTTPS server +"https://api.example.com/mcp" + +# Server with authentication +"https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile" + +# Server with custom path +"https://services.company.com/api/v1/mcp" +``` + +### Specific Tool Selection + +Use the `#` syntax to select specific tools from a server: + +```python +# Get only the forecast tool from weather server +"https://weather.api.com/mcp#get_forecast" + +# Get only the search tool from Exa +"https://mcp.exa.ai/mcp?api_key=your_key#web_search_exa" +``` + +### CrewAI AMP Marketplace + +Access tools from the CrewAI AMP marketplace: + +```python +# Full service with all tools +"crewai-amp:financial-data" + +# Specific tool from AMP service +"crewai-amp:research-tools#pubmed_search" + +# Multiple AMP services +mcps=[ + "crewai-amp:weather-insights", + "crewai-amp:market-analysis", + "crewai-amp:social-media-monitoring" +] +``` + +## Complete Example + +Here's a complete example using multiple MCP servers: + +```python +from crewai import Agent, Task, Crew, Process + +# Create agent with multiple MCP sources +multi_source_agent = Agent( + role="Multi-Source Research Analyst", + goal="Conduct comprehensive research using multiple data sources", + backstory="""Expert researcher with access to web search, weather data, + financial information, and academic research tools""", + mcps=[ + # External MCP servers + "https://mcp.exa.ai/mcp?api_key=your_exa_key&profile=research", + "https://weather.api.com/mcp#get_current_conditions", + + # CrewAI AMP marketplace + "crewai-amp:financial-insights", + "crewai-amp:academic-research#pubmed_search", + "crewai-amp:market-intelligence#competitor_analysis" + ] +) + +# Create comprehensive research task +research_task = Task( + description="""Research the impact of AI agents on business productivity. + Include current weather impacts on remote work, financial market trends, + and recent academic publications on AI agent frameworks.""", + expected_output="""Comprehensive report covering: + 1. AI agent business impact analysis + 2. Weather considerations for remote work + 3. Financial market trends related to AI + 4. Academic research citations and insights + 5. Competitive landscape analysis""", + agent=multi_source_agent +) + +# Create and execute crew +research_crew = Crew( + agents=[multi_source_agent], + tasks=[research_task], + process=Process.sequential, + verbose=True +) + +result = research_crew.kickoff() +print(f"Research completed with {len(multi_source_agent.mcps)} MCP data sources") +``` + +## Tool Naming and Organization + +CrewAI automatically handles tool naming to prevent conflicts: + +```python +# Original MCP server has tools: "search", "analyze" +# CrewAI creates tools: "mcp_exa_ai_search", "mcp_exa_ai_analyze" + +agent = Agent( + role="Tool Organization Demo", + goal="Show how tool naming works", + backstory="Demonstrates automatic tool organization", + mcps=[ + "https://mcp.exa.ai/mcp?api_key=key", # Tools: mcp_exa_ai_* + "https://weather.service.com/mcp", # Tools: weather_service_com_* + "crewai-amp:financial-data" # Tools: financial_data_* + ] +) + +# Each server's tools get unique prefixes based on the server name +# This prevents naming conflicts between different MCP servers +``` + +## Error Handling and Resilience + +The MCP DSL is designed to be robust and user-friendly: + +### Graceful Server Failures + +```python +agent = Agent( + role="Resilient Researcher", + goal="Research despite server issues", + backstory="Experienced researcher who adapts to available tools", + mcps=[ + "https://primary-server.com/mcp", # Primary data source + "https://backup-server.com/mcp", # Backup if primary fails + "https://unreachable-server.com/mcp", # Will be skipped with warning + "crewai-amp:reliable-service" # Reliable AMP service + ] +) + +# Agent will: +# 1. Successfully connect to working servers +# 2. Log warnings for failing servers +# 3. Continue with available tools +# 4. Not crash or hang on server failures +``` + +### Timeout Protection + +All MCP operations have built-in timeouts: + +- **Connection timeout**: 10 seconds +- **Tool execution timeout**: 30 seconds +- **Discovery timeout**: 15 seconds + +```python +# These servers will timeout gracefully if unresponsive +mcps=[ + "https://slow-server.com/mcp", # Will timeout after 10s if unresponsive + "https://overloaded-api.com/mcp" # Will timeout if discovery takes > 15s +] +``` + +## Performance Features + +### Automatic Caching + +Tool schemas are cached for 5 minutes to improve performance: + +```python +# First agent creation - discovers tools from server +agent1 = Agent(role="First", goal="Test", backstory="Test", + mcps=["https://api.example.com/mcp"]) + +# Second agent creation (within 5 minutes) - uses cached tool schemas +agent2 = Agent(role="Second", goal="Test", backstory="Test", + mcps=["https://api.example.com/mcp"]) # Much faster! +``` + +### On-Demand Connections + +Tool connections are established only when tools are actually used: + +```python +# Agent creation is fast - no MCP connections made yet +agent = Agent( + role="On-Demand Agent", + goal="Use tools efficiently", + backstory="Efficient agent that connects only when needed", + mcps=["https://api.example.com/mcp"] +) + +# MCP connection is made only when a tool is actually executed +# This minimizes connection overhead and improves startup performance +``` + +## Integration with Existing Features + +MCP tools work seamlessly with other CrewAI features: + +```python +from crewai.tools import BaseTool + +class CustomTool(BaseTool): + name: str = "custom_analysis" + description: str = "Custom analysis tool" + + def _run(self, **kwargs): + return "Custom analysis result" + +agent = Agent( + role="Full-Featured Agent", + goal="Use all available tool types", + backstory="Agent with comprehensive tool access", + + # All tool types work together + tools=[CustomTool()], # Custom tools + apps=["gmail", "slack"], # Platform integrations + mcps=[ # MCP servers + "https://mcp.exa.ai/mcp?api_key=key", + "crewai-amp:research-tools" + ], + + verbose=True, + max_iter=15 +) +``` + +## Best Practices + +### 1. Use Specific Tools When Possible + +```python +# Good - only get the tools you need +mcps=["https://weather.api.com/mcp#get_forecast"] + +# Less efficient - gets all tools from server +mcps=["https://weather.api.com/mcp"] +``` + +### 2. Handle Authentication Securely + +```python +import os + +# Store API keys in environment variables +exa_key = os.getenv("EXA_API_KEY") +exa_profile = os.getenv("EXA_PROFILE") + +agent = Agent( + role="Secure Agent", + goal="Use MCP tools securely", + backstory="Security-conscious agent", + mcps=[f"https://mcp.exa.ai/mcp?api_key={exa_key}&profile={exa_profile}"] +) +``` + +### 3. Plan for Server Failures + +```python +# Always include backup options +mcps=[ + "https://primary-api.com/mcp", # Primary choice + "https://backup-api.com/mcp", # Backup option + "crewai-amp:reliable-service" # AMP fallback +] +``` + +### 4. Use Descriptive Agent Roles + +```python +agent = Agent( + role="Weather-Enhanced Market Analyst", + goal="Analyze markets considering weather impacts", + backstory="Financial analyst with access to weather data for agricultural market insights", + mcps=[ + "https://weather.service.com/mcp#get_forecast", + "crewai-amp:financial-data#stock_analysis" + ] +) +``` + +## Troubleshooting + +### Common Issues + +**No tools discovered:** +```python +# Check your MCP server URL and authentication +# Verify the server is running and accessible +mcps=["https://mcp.example.com/mcp?api_key=valid_key"] +``` + +**Connection timeouts:** +```python +# Server may be slow or overloaded +# CrewAI will log warnings and continue with other servers +# Check server status or try backup servers +``` + +**Authentication failures:** +```python +# Verify API keys and credentials +# Check server documentation for required parameters +# Ensure query parameters are properly URL encoded +``` + +## Advanced: MCPServerAdapter + +For complex scenarios requiring manual connection management, use the `MCPServerAdapter` class from `crewai-tools`. Using a Python context manager (`with` statement) is the recommended approach as it automatically handles starting and stopping the connection to the MCP server. diff --git a/docs/en/mcp/overview.mdx b/docs/en/mcp/overview.mdx index 14d7ac48c..63bdab5d6 100644 --- a/docs/en/mcp/overview.mdx +++ b/docs/en/mcp/overview.mdx @@ -8,14 +8,39 @@ mode: "wide" ## Overview The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) provides a standardized way for AI agents to provide context to LLMs by communicating with external services, known as MCP Servers. -The `crewai-tools` library extends CrewAI's capabilities by allowing you to seamlessly integrate tools from these MCP servers into your agents. -This gives your crews access to a vast ecosystem of functionalities. + +CrewAI offers **two approaches** for MCP integration: + +### Simple DSL Integration** (Recommended) + +Use the `mcps` field directly on agents for seamless MCP tool integration: + +```python +from crewai import Agent + +agent = Agent( + role="Research Analyst", + goal="Research and analyze information", + backstory="Expert researcher with access to external tools", + mcps=[ + "https://mcp.exa.ai/mcp?api_key=your_key", # External MCP server + "https://api.weather.com/mcp#get_forecast", # Specific tool from server + "crewai-amp:financial-data", # CrewAI AMP marketplace + "crewai-amp:research-tools#pubmed_search" # Specific AMP tool + ] +) +# MCP tools are now automatically available to your agent! +``` + +### 🔧 **Advanced: MCPServerAdapter** (For Complex Scenarios) + +For advanced use cases requiring manual connection management, the `crewai-tools` library provides the `MCPServerAdapter` class. We currently support the following transport mechanisms: - **Stdio**: for local servers (communication via standard input/output between processes on the same machine) - **Server-Sent Events (SSE)**: for remote servers (unidirectional, real-time data streaming from server to client over HTTP) -- **Streamable HTTP**: for remote servers (flexible, potentially bi-directional communication over HTTP, often utilizing SSE for server-to-client streams) +- **Streamable HTTPS**: for remote servers (flexible, potentially bi-directional communication over HTTPS, often utilizing SSE for server-to-client streams) ## Video Tutorial Watch this video tutorial for a comprehensive guide on MCP integration with CrewAI: @@ -31,17 +56,125 @@ Watch this video tutorial for a comprehensive guide on MCP integration with Crew ## Installation -Before you start using MCP with `crewai-tools`, you need to install the `mcp` extra `crewai-tools` dependency with the following command: +CrewAI MCP integration requires the `mcp` library: ```shell +# For Simple DSL Integration (Recommended) +uv add mcp + +# For Advanced MCPServerAdapter usage uv pip install 'crewai-tools[mcp]' ``` -## Key Concepts & Getting Started +## Quick Start: Simple DSL Integration -The `MCPServerAdapter` class from `crewai-tools` is the primary way to connect to an MCP server and make its tools available to your CrewAI agents. It supports different transport mechanisms and simplifies connection management. +The easiest way to integrate MCP servers is using the `mcps` field on your agents: -Using a Python context manager (`with` statement) is the **recommended approach** for `MCPServerAdapter`. It automatically handles starting and stopping the connection to the MCP server. +```python +from crewai import Agent, Task, Crew + +# Create agent with MCP tools +research_agent = Agent( + role="Research Analyst", + goal="Find and analyze information using advanced search tools", + backstory="Expert researcher with access to multiple data sources", + mcps=[ + "https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile", + "crewai-amp:weather-service#current_conditions" + ] +) + +# Create task +research_task = Task( + description="Research the latest developments in AI agent frameworks", + expected_output="Comprehensive research report with citations", + agent=research_agent +) + +# Create and run crew +crew = Crew(agents=[research_agent], tasks=[research_task]) +result = crew.kickoff() +``` + +That's it! The MCP tools are automatically discovered and available to your agent. + +## MCP Reference Formats + +The `mcps` field supports various reference formats for maximum flexibility: + +### External MCP Servers + +```python +mcps=[ + # Full server - get all available tools + "https://mcp.example.com/api", + + # Specific tool from server using # syntax + "https://api.weather.com/mcp#get_current_weather", + + # Server with authentication parameters + "https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile" +] +``` + +### CrewAI AMP Marketplace + +```python +mcps=[ + # Full AMP MCP service - get all available tools + "crewai-amp:financial-data", + + # Specific tool from AMP service using # syntax + "crewai-amp:research-tools#pubmed_search", + + # Multiple AMP services + "crewai-amp:weather-service", + "crewai-amp:market-analysis" +] +``` + +### Mixed References + +```python +mcps=[ + "https://external-api.com/mcp", # External server + "https://weather.service.com/mcp#forecast", # Specific external tool + "crewai-amp:financial-insights", # AMP service + "crewai-amp:data-analysis#sentiment_tool" # Specific AMP tool +] +``` + +## Key Features + +- 🔄 **Automatic Tool Discovery**: Tools are automatically discovered and integrated +- 🏷️ **Name Collision Prevention**: Server names are prefixed to tool names +- ⚡ **Performance Optimized**: On-demand connections with schema caching +- 🛡️ **Error Resilience**: Graceful handling of unavailable servers +- ⏱️ **Timeout Protection**: Built-in timeouts prevent hanging connections +- 📊 **Transparent Integration**: Works seamlessly with existing CrewAI features + +## Error Handling + +The MCP DSL integration is designed to be resilient: + +```python +agent = Agent( + role="Resilient Agent", + goal="Continue working despite server issues", + backstory="Agent that handles failures gracefully", + mcps=[ + "https://reliable-server.com/mcp", # Will work + "https://unreachable-server.com/mcp", # Will be skipped gracefully + "https://slow-server.com/mcp", # Will timeout gracefully + "crewai-amp:working-service" # Will work + ] +) +# Agent will use tools from working servers and log warnings for failing ones +``` + +## Advanced: MCPServerAdapter + +For complex scenarios requiring manual connection management, use the `MCPServerAdapter` class from `crewai-tools`. Using a Python context manager (`with` statement) is the recommended approach as it automatically handles starting and stopping the connection to the MCP server. ## Connection Configuration @@ -241,11 +374,19 @@ class CrewWithCustomTimeout: ## Explore MCP Integrations + + **Recommended**: Use the simple `mcps=[]` field syntax for effortless MCP integration. + Connect to local MCP servers via standard input/output. Ideal for scripts and local executables. @@ -253,7 +394,7 @@ class CrewWithCustomTimeout: title="SSE Transport" icon="wifi" href="/en/mcp/sse" - color="#10B981" + color="#F59E0B" > Integrate with remote MCP servers using Server-Sent Events for real-time data streaming. @@ -261,7 +402,7 @@ class CrewWithCustomTimeout: title="Streamable HTTP Transport" icon="globe" href="/en/mcp/streamable-http" - color="#F59E0B" + color="#8B5CF6" > Utilize flexible Streamable HTTP for robust communication with remote MCP servers. @@ -269,7 +410,7 @@ class CrewWithCustomTimeout: title="Connecting to Multiple Servers" icon="layer-group" href="/en/mcp/multiple-servers" - color="#8B5CF6" + color="#EF4444" > Aggregate tools from several MCP servers simultaneously using a single adapter. @@ -277,7 +418,7 @@ class CrewWithCustomTimeout: title="Security Considerations" icon="lock" href="/en/mcp/security" - color="#EF4444" + color="#DC2626" > Review important security best practices for MCP integration to keep your agents safe. diff --git a/docs/ko/enterprise/features/tools-and-integrations.mdx b/docs/ko/enterprise/features/tools-and-integrations.mdx index 84a5760c0..23085ec31 100644 --- a/docs/ko/enterprise/features/tools-and-integrations.mdx +++ b/docs/ko/enterprise/features/tools-and-integrations.mdx @@ -43,7 +43,7 @@ mode: "wide" 1. Integrations로 이동 2. 원하는 서비스에서 Connect 클릭 3. OAuth 플로우 완료 및 스코프 승인 - 4. Integration 탭에서 Enterprise Token 복사 + 4. 통합 설정에서 Enterprise Token 복사 ![Enterprise Token](/images/enterprise/enterprise_action_auth_token.png) @@ -57,26 +57,37 @@ mode: "wide" uv add crewai-tools ``` + ### 환경 변수 설정 + + + `Agent(apps=[])`와 함께 통합을 사용하려면 Enterprise Token으로 `CREWAI_PLATFORM_INTEGRATION_TOKEN` 환경 변수를 설정해야 합니다. + + + ```bash + export CREWAI_PLATFORM_INTEGRATION_TOKEN="your_enterprise_token" + ``` + + 또는 `.env` 파일에 추가하세요: + + ``` + CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token + ``` + ### 사용 예시 - 인증된 모든 서비스는 도구로 제공됩니다. 에이전트에 `CrewaiEnterpriseTools`를 추가하세요. + 새로운 간소화된 접근 방식을 사용하여 엔터프라이즈 앱을 통합하세요. Agent 구성에서 앱과 해당 액션을 직접 지정하기만 하면 됩니다. ```python from crewai import Agent, Task, Crew - from crewai_tools import CrewaiEnterpriseTools - - enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" - ) - print(enterprise_tools) + # Gmail 기능을 가진 에이전트 생성 email_agent = Agent( role="이메일 매니저", goal="이메일 커뮤니케이션 관리", backstory="이메일 관리에 특화된 AI 어시스턴트", - tools=enterprise_tools + apps=['gmail', 'gmail/send_email'] # 정식 이름 'gmail' 사용 ) email_task = Task( @@ -92,19 +103,14 @@ mode: "wide" ### 도구 필터링 ```python - from crewai_tools import CrewaiEnterpriseTools - - enterprise_tools = CrewaiEnterpriseTools( - actions_list=["gmail_find_email"] - ) - - gmail_tool = enterprise_tools["gmail_find_email"] + from crewai import Agent, Task, Crew + # 특정 Gmail 액션만 사용하는 에이전트 생성 gmail_agent = Agent( role="Gmail 매니저", goal="Gmail 커뮤니케이션 및 알림 관리", backstory="Gmail 커뮤니케이션 조율 AI 어시스턴트", - tools=[gmail_tool] + apps=['gmail/fetch_emails'] # 정식 이름과 특정 액션 사용 ) notification_task = Task( diff --git a/docs/ko/enterprise/guides/automation-triggers.mdx b/docs/ko/enterprise/guides/automation-triggers.mdx index 84b31b6bf..963fb7591 100644 --- a/docs/ko/enterprise/guides/automation-triggers.mdx +++ b/docs/ko/enterprise/guides/automation-triggers.mdx @@ -110,19 +110,49 @@ CrewAI AMP 트리거는 팀이 이미 사용하고 있는 도구의 실시간 - `allow_crewai_trigger_context` 옵션으로 컨텍스트 자동 주입 여부를 결정했나요? - 웹훅 로그, CrewAI 실행 기록, 외부 알림 등 모니터링을 준비했나요? -### Payload & Crew 예제 저장소 +### CLI로 로컬에서 트리거 테스트 -| 통합 | 동작 시점 | Payload 예제 | Crew 예제 | -| :-- | :-- | :-- | :-- | -| Gmail | 신규 메일, 스레드 업데이트 | [Gmail payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | [`new-email-crew.py`, `gmail-alert-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | -| Google Calendar | 이벤트 생성/수정/시작/종료/취소 | [Calendar payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | [`calendar-event-crew.py`, `calendar-meeting-crew.py`, `calendar-working-location-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | -| Google Drive | 파일 생성/수정/삭제 | [Drive payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | [`drive-file-crew.py`, `drive-file-deletion-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | -| Outlook | 새 이메일, 이벤트 제거 | [Outlook payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | [`outlook-message-crew.py`, `outlook-event-removal-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | -| OneDrive | 파일 작업(생성, 수정, 공유, 삭제) | [OneDrive payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | [`onedrive-file-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | -| HubSpot | 레코드 생성/업데이트(연락처, 회사, 딜) | [HubSpot payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | [`hubspot-company-crew.py`, `hubspot-contact-crew.py`, `hubspot-record-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | -| Microsoft Teams | 채팅 생성 | [Teams payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | [`teams-chat-created-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | +CrewAI CLI는 프로덕션에 배포하기 전에 트리거 기반 자동화를 개발하고 테스트할 수 있는 강력한 명령을 제공합니다. -예제 payload를 참고해 파싱 로직을 검증하고, 제공되는 crew를 복사해 실제 데이터로 교체하세요. +#### 사용 가능한 트리거 목록 보기 + +연결된 통합에 사용 가능한 모든 트리거를 확인하세요: + +```bash +crewai triggers list +``` + +이 명령은 연결된 통합을 기반으로 사용 가능한 모든 트리거를 표시합니다: +- 통합 이름 및 연결 상태 +- 사용 가능한 트리거 유형 +- 트리거 이름 및 설명 + +#### 트리거 실행 시뮬레이션 + +배포 전에 실제 트리거 payload로 크루를 테스트하세요: + +```bash +crewai triggers run <트리거_이름> +``` + +예시: + +```bash +crewai triggers run microsoft_onedrive/file_changed +``` + +이 명령은: +- 로컬에서 크루를 실행합니다 +- 완전하고 실제적인 트리거 payload를 전달합니다 +- 프로덕션에서 크루가 호출되는 방식을 정확히 시뮬레이션합니다 + + + **중요한 개발 노트:** + - 개발 중 트리거 실행을 시뮬레이션하려면 `crewai triggers run `를 사용하세요 + - `crewai run`을 사용하면 트리거 호출을 시뮬레이션하지 않으며 트리거 payload를 전달하지 않습니다 + - 배포 후에는 실제 트리거 payload로 크루가 실행됩니다 + - 크루가 트리거 payload에 없는 매개변수를 기대하면 실행이 실패할 수 있습니다 + ### 트리거와 Crew 연동 @@ -191,17 +221,20 @@ def delegate_to_crew(self, crewai_trigger_payload: dict = None): ## 문제 해결 **트리거가 실행되지 않나요?** -- 트리거가 활성 상태인지 확인하세요. -- 통합 연결 상태를 확인하세요. +- 배포의 Triggers 탭에서 트리거가 활성화되어 있는지 확인하세요 +- Tools & Integrations에서 통합 연결 상태를 확인하세요 +- 필요한 모든 환경 변수가 올바르게 구성되어 있는지 확인하세요 **실행 중 오류가 발생하나요?** -- 실행 로그에서 오류 메시지를 확인하세요. -- 개발 중이라면 `crewai_trigger_payload`가 올바른 데이터로 전달되고 있는지 확인하세요. +- 실행 로그에서 오류 세부 정보를 확인하세요 +- `crewai triggers run <트리거_이름>`을 사용하여 로컬에서 테스트하고 정확한 payload 구조를 확인하세요 +- 크루가 `crewai_trigger_payload` 매개변수를 처리할 수 있는지 확인하세요 +- 크루가 트리거 payload에 포함되지 않은 매개변수를 기대하지 않는지 확인하세요 + +**개발 문제:** +- 배포하기 전에 항상 `crewai triggers run `로 테스트하여 전체 payload를 확인하세요 +- `crewai run`은 트리거 호출을 시뮬레이션하지 않으므로 `crewai triggers run`을 대신 사용하세요 +- `crewai triggers list`를 사용하여 연결된 통합에 사용 가능한 트리거를 확인하세요 +- 배포 후 크루는 실제 트리거 payload를 받으므로 먼저 로컬에서 철저히 테스트하세요 트리거를 활용하면 CrewAI 자동화를 이벤트 기반 시스템으로 전환하여 기존 비즈니스 프로세스와 도구에 자연스럽게 녹여낼 수 있습니다. - - - - CrewAI AMP Trigger Examples - - diff --git a/docs/ko/enterprise/guides/gmail-trigger.mdx b/docs/ko/enterprise/guides/gmail-trigger.mdx index 2caefc045..ddca4f63b 100644 --- a/docs/ko/enterprise/guides/gmail-trigger.mdx +++ b/docs/ko/enterprise/guides/gmail-trigger.mdx @@ -51,16 +51,25 @@ class GmailProcessingCrew: ) ``` -The Gmail payload will be available via the standard context mechanisms. See the payload samples repository for structure and fields. +The Gmail payload will be available via the standard context mechanisms. -### Sample payloads & crews +### 로컬에서 테스트 -The [CrewAI AMP Trigger Examples repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) includes: +CrewAI CLI를 사용하여 Gmail 트리거 통합을 로컬에서 테스트하세요: -- `new-email-payload-1.json` / `new-email-payload-2.json` — production-style new message alerts with matching crews in `new-email-crew.py` -- `thread-updated-sample-1.json` — follow-up messages on an existing thread, processed by `gmail-alert-crew.py` +```bash +# 사용 가능한 모든 트리거 보기 +crewai triggers list -Use these samples to validate your parsing logic locally before wiring the trigger to your live Gmail accounts. +# 실제 payload로 Gmail 트리거 시뮬레이션 +crewai triggers run gmail/new_email +``` + +`crewai triggers run` 명령은 완전한 Gmail payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다. + + + 개발 중에는 `crewai triggers run gmail/new_email`을 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다. + ## Monitoring Executions @@ -70,16 +79,10 @@ Track history and performance of triggered runs: List of executions triggered by automation -## Payload Reference - -See the sample payloads and field descriptions: - - - Gmail samples in Trigger Examples Repo - - ## Troubleshooting - Ensure Gmail is connected in Tools & Integrations - Verify the Gmail Trigger is enabled on the Triggers tab +- `crewai triggers run gmail/new_email`로 로컬 테스트하여 정확한 payload 구조를 확인하세요 - Check the execution logs and confirm the payload is passed as `crewai_trigger_payload` +- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님) diff --git a/docs/ko/enterprise/guides/google-calendar-trigger.mdx b/docs/ko/enterprise/guides/google-calendar-trigger.mdx index cf2d32471..6f279602e 100644 --- a/docs/ko/enterprise/guides/google-calendar-trigger.mdx +++ b/docs/ko/enterprise/guides/google-calendar-trigger.mdx @@ -39,16 +39,23 @@ print(result.raw) Use `crewai_trigger_payload` exactly as it is delivered by the trigger so the crew can extract the proper fields. -## Sample payloads & crews +## 로컬에서 테스트 -The [Google Calendar examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) show how to handle multiple event types: +CrewAI CLI를 사용하여 Google Calendar 트리거 통합을 로컬에서 테스트하세요: -- `new-event.json` → standard event creation handled by `calendar-event-crew.py` -- `event-updated.json` / `event-started.json` / `event-ended.json` → in-flight updates processed by `calendar-meeting-crew.py` -- `event-canceled.json` → cancellation workflow that alerts attendees via `calendar-meeting-crew.py` -- Working location events use `calendar-working-location-crew.py` to extract on-site schedules +```bash +# 사용 가능한 모든 트리거 보기 +crewai triggers list -Each crew transforms raw event metadata (attendees, rooms, working locations) into the summaries your teams need. +# 실제 payload로 Google Calendar 트리거 시뮬레이션 +crewai triggers run google_calendar/event_changed +``` + +`crewai triggers run` 명령은 완전한 Calendar payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다. + + + 개발 중에는 `crewai triggers run google_calendar/event_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다. + ## Monitoring Executions @@ -61,5 +68,7 @@ The **Executions** list in the deployment dashboard tracks every triggered run a ## Troubleshooting - Ensure the correct Google account is connected and the trigger is enabled +- `crewai triggers run google_calendar/event_changed`로 로컬 테스트하여 정확한 payload 구조를 확인하세요 - Confirm your workflow handles all-day events (payloads use `start.date` and `end.date` instead of timestamps) - Check execution logs if reminders or attendee arrays are missing—calendar permissions can limit fields in the payload +- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님) diff --git a/docs/ko/enterprise/guides/google-drive-trigger.mdx b/docs/ko/enterprise/guides/google-drive-trigger.mdx index 19c10837b..3fd27bcd6 100644 --- a/docs/ko/enterprise/guides/google-drive-trigger.mdx +++ b/docs/ko/enterprise/guides/google-drive-trigger.mdx @@ -36,15 +36,23 @@ crew.kickoff({ }) ``` -## Sample payloads & crews +## 로컬에서 테스트 -Explore the [Google Drive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) to cover different operations: +CrewAI CLI를 사용하여 Google Drive 트리거 통합을 로컬에서 테스트하세요: -- `new-file.json` → new uploads processed by `drive-file-crew.py` -- `updated-file.json` → file edits and metadata changes handled by `drive-file-crew.py` -- `deleted-file.json` → deletion events routed through `drive-file-deletion-crew.py` +```bash +# 사용 가능한 모든 트리거 보기 +crewai triggers list -Each crew highlights the file name, operation type, owner, permissions, and security considerations so downstream systems can respond appropriately. +# 실제 payload로 Google Drive 트리거 시뮬레이션 +crewai triggers run google_drive/file_changed +``` + +`crewai triggers run` 명령은 완전한 Drive payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다. + + + 개발 중에는 `crewai triggers run google_drive/file_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다. + ## Monitoring Executions @@ -57,5 +65,7 @@ Track history and performance of triggered runs with the **Executions** list in ## Troubleshooting - Verify Google Drive is connected and the trigger toggle is enabled +- `crewai triggers run google_drive/file_changed`로 로컬 테스트하여 정확한 payload 구조를 확인하세요 - If a payload is missing permission data, ensure the connected account has access to the file or folder - The trigger sends file IDs only; use the Drive API if you need to fetch binary content during the crew run +- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님) diff --git a/docs/ko/enterprise/guides/hubspot-trigger.mdx b/docs/ko/enterprise/guides/hubspot-trigger.mdx index 462778478..1818e48b1 100644 --- a/docs/ko/enterprise/guides/hubspot-trigger.mdx +++ b/docs/ko/enterprise/guides/hubspot-trigger.mdx @@ -49,6 +49,4 @@ mode: "wide" -## 추가 자료 - 사용 가능한 작업과 사용자 지정 옵션에 대한 자세한 정보는 [HubSpot 워크플로우 문서](https://knowledge.hubspot.com/workflows/create-workflows)를 참고하세요. diff --git a/docs/ko/enterprise/guides/microsoft-teams-trigger.mdx b/docs/ko/enterprise/guides/microsoft-teams-trigger.mdx index 10878af40..621561690 100644 --- a/docs/ko/enterprise/guides/microsoft-teams-trigger.mdx +++ b/docs/ko/enterprise/guides/microsoft-teams-trigger.mdx @@ -37,16 +37,28 @@ print(result.raw) The crew parses thread metadata (subject, created time, roster) and generates an action plan for the receiving team. -## Sample payloads & crews +## 로컬에서 테스트 -The [Microsoft Teams examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) include: +CrewAI CLI를 사용하여 Microsoft Teams 트리거 통합을 로컬에서 테스트하세요: -- `chat-created.json` → chat creation payload processed by `teams-chat-created-crew.py` +```bash +# 사용 가능한 모든 트리거 보기 +crewai triggers list -The crew demonstrates how to extract participants, initial messages, tenant information, and compliance metadata from the Microsoft Graph webhook payload. +# 실제 payload로 Microsoft Teams 트리거 시뮬레이션 +crewai triggers run microsoft_teams/teams_message_created +``` + +`crewai triggers run` 명령은 완전한 Teams payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다. + + + 개발 중에는 `crewai triggers run microsoft_teams/teams_message_created`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다. + ## Troubleshooting - Ensure the Teams connection is active; it must be refreshed if the tenant revokes permissions +- `crewai triggers run microsoft_teams/teams_message_created`로 로컬 테스트하여 정확한 payload 구조를 확인하세요 - Confirm the webhook subscription in Microsoft 365 is still valid if payloads stop arriving - Review execution logs for payload shape mismatches—Graph notifications may omit fields when a chat is private or restricted +- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님) diff --git a/docs/ko/enterprise/guides/onedrive-trigger.mdx b/docs/ko/enterprise/guides/onedrive-trigger.mdx index 51de175db..ce0a0e7cd 100644 --- a/docs/ko/enterprise/guides/onedrive-trigger.mdx +++ b/docs/ko/enterprise/guides/onedrive-trigger.mdx @@ -36,18 +36,28 @@ crew.kickoff({ The crew inspects file metadata, user activity, and permission changes to produce a compliance-friendly summary. -## Sample payloads & crews +## 로컬에서 테스트 -The [OneDrive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) showcase how to: +CrewAI CLI를 사용하여 OneDrive 트리거 통합을 로컬에서 테스트하세요: -- Parse file metadata, size, and folder paths -- Track who created and last modified the file -- Highlight permission and external sharing changes +```bash +# 사용 가능한 모든 트리거 보기 +crewai triggers list -`onedrive-file-crew.py` bundles the analysis and summarization tasks so you can add remediation steps as needed. +# 실제 payload로 OneDrive 트리거 시뮬레이션 +crewai triggers run microsoft_onedrive/file_changed +``` + +`crewai triggers run` 명령은 완전한 OneDrive payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다. + + + 개발 중에는 `crewai triggers run microsoft_onedrive/file_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다. + ## Troubleshooting - Ensure the connected account has permission to read the file metadata included in the webhook +- `crewai triggers run microsoft_onedrive/file_changed`로 로컬 테스트하여 정확한 payload 구조를 확인하세요 - If the trigger fires but the payload is missing `permissions`, confirm the site-level sharing settings allow Graph to return this field - For large tenants, filter notifications upstream so the crew only runs on relevant directories +- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님) diff --git a/docs/ko/enterprise/guides/outlook-trigger.mdx b/docs/ko/enterprise/guides/outlook-trigger.mdx index 21bda5407..908d312e0 100644 --- a/docs/ko/enterprise/guides/outlook-trigger.mdx +++ b/docs/ko/enterprise/guides/outlook-trigger.mdx @@ -36,17 +36,28 @@ crew.kickoff({ The crew extracts sender details, subject, body preview, and attachments before generating a structured response. -## Sample payloads & crews +## 로컬에서 테스트 -Review the [Outlook examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) for two common scenarios: +CrewAI CLI를 사용하여 Outlook 트리거 통합을 로컬에서 테스트하세요: -- `new-message.json` → new mail notifications parsed by `outlook-message-crew.py` -- `event-removed.json` → calendar cleanup handled by `outlook-event-removal-crew.py` +```bash +# 사용 가능한 모든 트리거 보기 +crewai triggers list -Each crew demonstrates how to handle Microsoft Graph payloads, normalize headers, and keep humans in-the-loop with concise summaries. +# 실제 payload로 Outlook 트리거 시뮬레이션 +crewai triggers run microsoft_outlook/email_received +``` + +`crewai triggers run` 명령은 완전한 Outlook payload로 크루를 실행하여 배포 전에 파싱 로직을 테스트할 수 있게 해줍니다. + + + 개발 중에는 `crewai triggers run microsoft_outlook/email_received`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다. + ## Troubleshooting - Verify the Outlook connector is still authorized; the subscription must be renewed periodically +- `crewai triggers run microsoft_outlook/email_received`로 로컬 테스트하여 정확한 payload 구조를 확인하세요 - If attachments are missing, confirm the webhook subscription includes the `includeResourceData` flag - Review execution logs when events fail to match—cancellation payloads lack attendee lists by design and the crew should account for that +- 주의: 트리거 실행을 시뮬레이션하려면 `crewai triggers run`을 사용하세요 (`crewai run`이 아님) diff --git a/docs/ko/enterprise/integrations/asana.mdx b/docs/ko/enterprise/integrations/asana.mdx index 898265311..c23905184 100644 --- a/docs/ko/enterprise/integrations/asana.mdx +++ b/docs/ko/enterprise/integrations/asana.mdx @@ -25,7 +25,7 @@ Asana 연동을 사용하기 전에 다음을 확인하세요: 2. 인증 통합 섹션에서 **Asana**를 찾습니다. 3. **Connect**를 클릭하고 OAuth 플로우를 완료합니다. 4. 작업 및 프로젝트 관리를 위한 필요한 권한을 부여합니다. -5. [계정 설정](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 작업 - + **설명:** Asana에 댓글을 생성합니다. **매개변수:** @@ -44,7 +44,7 @@ uv add crewai-tools - `text` (string, 필수): 텍스트 (예: "This is a comment."). - + **설명:** Asana에 프로젝트를 생성합니다. **매개변수:** @@ -54,7 +54,7 @@ uv add crewai-tools - `notes` (string, 선택): 노트 (예: "These are things we need to purchase."). - + **설명:** Asana의 프로젝트 목록을 가져옵니다. **매개변수:** @@ -62,14 +62,14 @@ uv add crewai-tools - 옵션: `default`, `true`, `false` - + **설명:** Asana에서 ID로 프로젝트를 가져옵니다. **매개변수:** - `projectFilterId` (string, 필수): 프로젝트 ID. - + **설명:** Asana에 작업을 생성합니다. **매개변수:** @@ -83,7 +83,7 @@ uv add crewai-tools - `gid` (string, 선택): 외부 ID - 이 작업과 연결할 애플리케이션의 ID입니다. 이 ID를 사용하여 이후 작업 업데이트를 동기화할 수 있습니다. - + **설명:** Asana의 작업을 업데이트합니다. **매개변수:** @@ -98,7 +98,7 @@ uv add crewai-tools - `gid` (string, 선택): 외부 ID - 이 작업과 연결할 애플리케이션의 ID입니다. 이 ID를 사용하여 이후 작업 업데이트를 동기화할 수 있습니다. - + **설명:** Asana의 작업 목록을 가져옵니다. **매개변수:** @@ -108,21 +108,21 @@ uv add crewai-tools - `completedSince` (string, 선택): 이후 완료됨 - 미완료이거나 해당 시간(ISO 또는 Unix 타임스탬프) 이후에 완료된 작업만 반환합니다. (예: "2014-04-25T16:15:47-04:00"). - + **설명:** Asana에서 ID로 작업 목록을 가져옵니다. **매개변수:** - `taskId` (string, 필수): 작업 ID. - + **설명:** Asana에서 외부 ID로 작업을 가져옵니다. **매개변수:** - `gid` (string, 필수): 외부 ID - 이 작업이 애플리케이션과 연동(또는 동기화)된 ID입니다. - + **설명:** Asana에서 섹션에 작업을 추가합니다. **매개변수:** @@ -132,14 +132,14 @@ uv add crewai-tools - `afterTaskId` (string, 선택): 이후 작업 ID - 이 작업이 삽입될 섹션 내의 작업 ID입니다. 이전 작업 ID와 함께 사용할 수 없습니다. (예: "1204619611402340"). - + **설명:** Asana에서 팀 목록을 가져옵니다. **매개변수:** - `workspace` (string, 필수): 워크스페이스 - 인증된 사용자가 볼 수 있는 이 워크스페이스 내의 팀을 반환합니다. - + **설명:** Asana에서 워크스페이스 목록을 가져옵니다. **매개변수:** 필요 없음. @@ -152,19 +152,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Asana tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Asana capabilities asana_agent = Agent( role="Project Manager", goal="Manage tasks and projects in Asana efficiently", backstory="An AI assistant specialized in project management and task coordination.", - tools=[enterprise_tools] + apps=['asana'] ) # Task to create a new project @@ -186,19 +180,12 @@ crew.kickoff() ### 특정 Asana 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Asana tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["asana_create_task", "asana_update_task", "asana_get_tasks"] -) task_manager_agent = Agent( role="Task Manager", goal="Create and manage tasks efficiently", backstory="An AI assistant that focuses on task creation and management.", - tools=enterprise_tools + apps=['asana'] ) # Task to create and assign a task @@ -220,17 +207,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Coordinate project activities and track progress", backstory="An experienced project coordinator who ensures projects run smoothly.", - tools=[enterprise_tools] + apps=['asana'] ) # Complex task involving multiple Asana operations diff --git a/docs/ko/enterprise/integrations/box.mdx b/docs/ko/enterprise/integrations/box.mdx index 15de12f6b..79632ec0f 100644 --- a/docs/ko/enterprise/integrations/box.mdx +++ b/docs/ko/enterprise/integrations/box.mdx @@ -25,7 +25,7 @@ Box 통합을 사용하기 전에 다음을 확인하세요: 2. 인증 통합 섹션에서 **Box**를 찾습니다. 3. **Connect**를 클릭하고 OAuth 흐름을 완료합니다. 4. 파일 및 폴더 관리를 위한 필요한 권한을 부여합니다. -5. [Account Settings](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 액션 - + **설명:** Box에서 URL로부터 파일을 저장합니다. **파라미터:** @@ -52,7 +52,7 @@ uv add crewai-tools - `file` (string, 필수): 파일 URL - 파일 크기는 50MB 미만이어야 합니다. (예시: "https://picsum.photos/200/300"). - + **설명:** Box에 파일을 저장합니다. **파라미터:** @@ -61,14 +61,14 @@ uv add crewai-tools - `folder` (string, 선택): 폴더 - Connect Portal Workflow Settings를 사용하여 사용자가 파일의 폴더 목적지를 선택할 수 있도록 합니다. 비워두면 기본적으로 사용자의 루트 폴더에 저장됩니다. - + **설명:** Box에서 ID로 파일을 가져옵니다. **파라미터:** - `fileId` (string, 필수): 파일 ID - 파일을 나타내는 고유 식별자. (예시: "12345"). - + **설명:** Box에서 파일 목록을 조회합니다. **파라미터:** @@ -93,7 +93,7 @@ uv add crewai-tools ``` - + **설명:** Box에 폴더를 생성합니다. **파라미터:** @@ -106,7 +106,7 @@ uv add crewai-tools ``` - + **설명:** Box에서 폴더를 이동합니다. **파라미터:** @@ -120,14 +120,14 @@ uv add crewai-tools ``` - + **설명:** Box에서 ID로 폴더를 가져옵니다. **파라미터:** - `folderId` (string, 필수): 폴더 ID - 폴더를 나타내는 고유 식별자. (예시: "0"). - + **설명:** Box에서 폴더를 검색합니다. **파라미터:** @@ -152,7 +152,7 @@ uv add crewai-tools ``` - + **설명:** Box에서 폴더를 삭제합니다. **파라미터:** @@ -167,19 +167,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Box tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Box capabilities box_agent = Agent( role="Document Manager", goal="Manage files and folders in Box efficiently", backstory="An AI assistant specialized in document management and file organization.", - tools=[enterprise_tools] + apps=['box'] ) # Task to create a folder structure @@ -201,19 +195,12 @@ crew.kickoff() ### 특정 Box 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Box tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["box_create_folder", "box_save_file", "box_list_files"] -) file_organizer_agent = Agent( role="File Organizer", goal="Organize and manage file storage efficiently", backstory="An AI assistant that focuses on file organization and storage management.", - tools=enterprise_tools + apps=['box'] ) # Task to organize files @@ -235,17 +222,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) file_manager = Agent( role="File Manager", goal="Maintain organized file structure and manage document lifecycle", backstory="An experienced file manager who ensures documents are properly organized and accessible.", - tools=[enterprise_tools] + apps=['box'] ) # Complex task involving multiple Box operations diff --git a/docs/ko/enterprise/integrations/clickup.mdx b/docs/ko/enterprise/integrations/clickup.mdx index f72cd53d5..81cf54122 100644 --- a/docs/ko/enterprise/integrations/clickup.mdx +++ b/docs/ko/enterprise/integrations/clickup.mdx @@ -25,7 +25,7 @@ ClickUp 통합을 사용하기 전에 다음을 준비해야 합니다: 2. 인증 통합 섹션에서 **ClickUp**을 찾습니다. 3. **Connect**를 클릭하고 OAuth 과정을 완료합니다. 4. 작업 및 프로젝트 관리에 필요한 권한을 부여합니다. -5. [계정 설정](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 동작 - + **설명:** 고급 필터를 사용하여 ClickUp에서 작업을 검색합니다. **파라미터:** @@ -61,7 +61,7 @@ uv add crewai-tools 사용 가능한 필드: `space_ids%5B%5D`, `project_ids%5B%5D`, `list_ids%5B%5D`, `statuses%5B%5D`, `include_closed`, `assignees%5B%5D`, `tags%5B%5D`, `due_date_gt`, `due_date_lt`, `date_created_gt`, `date_created_lt`, `date_updated_gt`, `date_updated_lt` - + **설명:** ClickUp의 특정 목록에서 작업을 가져옵니다. **파라미터:** @@ -69,7 +69,7 @@ uv add crewai-tools - `taskFilterFormula` (string, 선택): 지정된 필터와 일치하는 작업을 검색합니다. 예: name=task1. - + **설명:** ClickUp에 작업을 생성합니다. **파라미터:** @@ -82,7 +82,7 @@ uv add crewai-tools - `additionalFields` (string, 선택): 추가 필드 - 이 작업에 포함할 추가 필드를 JSON으로 지정합니다. - + **설명:** ClickUp의 작업을 업데이트합니다. **파라미터:** @@ -96,49 +96,49 @@ uv add crewai-tools - `additionalFields` (string, 선택): 추가 필드 - 이 작업에 포함할 추가 필드를 JSON으로 지정합니다. - + **설명:** ClickUp에서 작업을 삭제합니다. **파라미터:** - `taskId` (string, 필수): 작업 ID - 삭제할 작업의 ID입니다. - + **설명:** ClickUp에서 목록 정보를 가져옵니다. **파라미터:** - `spaceId` (string, 필수): 스페이스 ID - 목록이 포함된 스페이스의 ID입니다. - + **설명:** ClickUp에서 목록의 사용자 정의 필드를 가져옵니다. **파라미터:** - `listId` (string, 필수): 목록 ID - 사용자 정의 필드를 가져올 목록의 ID입니다. - + **설명:** ClickUp에서 목록의 모든 필드를 가져옵니다. **파라미터:** - `listId` (string, 필수): 목록 ID - 모든 필드를 가져올 목록의 ID입니다. - + **설명:** ClickUp에서 스페이스 정보를 가져옵니다. **파라미터:** - `spaceId` (string, 선택): 스페이스 ID - 조회할 스페이스의 ID입니다. - + **설명:** ClickUp에서 폴더를 가져옵니다. **파라미터:** - `spaceId` (string, 필수): 스페이스 ID - 폴더가 포함된 스페이스의 ID입니다. - + **설명:** ClickUp에서 멤버 정보를 가져옵니다. **파라미터:** 필요 없음. @@ -151,19 +151,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (ClickUp tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with ClickUp capabilities clickup_agent = Agent( role="Task Manager", goal="Manage tasks and projects in ClickUp efficiently", backstory="An AI assistant specialized in task management and productivity coordination.", - tools=[enterprise_tools] + apps=['clickup'] ) # Task to create a new task @@ -185,19 +179,12 @@ crew.kickoff() ### 특정 ClickUp 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific ClickUp tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["clickup_create_task", "clickup_update_task", "clickup_search_tasks"] -) task_coordinator = Agent( role="Task Coordinator", goal="Create and manage tasks efficiently", backstory="An AI assistant that focuses on task creation and status management.", - tools=enterprise_tools + apps=['clickup'] ) # Task to manage task workflow @@ -219,17 +206,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_manager = Agent( role="Project Manager", goal="Coordinate project activities and track team productivity", backstory="An experienced project manager who ensures projects are delivered on time.", - tools=[enterprise_tools] + apps=['clickup'] ) # Complex task involving multiple ClickUp operations @@ -256,17 +238,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) task_analyst = Agent( role="Task Analyst", goal="Analyze task patterns and optimize team productivity", backstory="An AI assistant that analyzes task data to improve team efficiency.", - tools=[enterprise_tools] + apps=['clickup'] ) # Task to analyze and optimize task distribution diff --git a/docs/ko/enterprise/integrations/github.mdx b/docs/ko/enterprise/integrations/github.mdx index e0b2dbe32..c5066bb52 100644 --- a/docs/ko/enterprise/integrations/github.mdx +++ b/docs/ko/enterprise/integrations/github.mdx @@ -25,7 +25,7 @@ GitHub 통합을 사용하기 전에 다음을 확인하세요: 2. 인증 통합 섹션에서 **GitHub**을 찾습니다. 3. **Connect**를 클릭하고 OAuth 흐름을 완료합니다. 4. 리포지토리 및 이슈 관리를 위한 필수 권한을 부여합니다. -5. [계정 설정](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 작업 - + **설명:** GitHub에 이슈를 생성합니다. **파라미터:** @@ -47,7 +47,7 @@ uv add crewai-tools - `assignees` (string, 선택): 담당자 - 이 이슈의 담당자 GitHub 로그인을 문자열 배열로 지정합니다. (예시: `["octocat"]`). - + **설명:** GitHub에서 이슈를 업데이트합니다. **파라미터:** @@ -61,7 +61,7 @@ uv add crewai-tools - 옵션: `open`, `closed` - + **설명:** GitHub에서 번호로 이슈를 조회합니다. **파라미터:** @@ -70,7 +70,7 @@ uv add crewai-tools - `issue_number` (string, 필수): 이슈 번호 - 가져올 이슈의 번호를 지정합니다. - + **설명:** GitHub에서 이슈를 잠급니다. **파라미터:** @@ -81,7 +81,7 @@ uv add crewai-tools - 옵션: `off-topic`, `too heated`, `resolved`, `spam` - + **설명:** GitHub에서 이슈를 검색합니다. **파라미터:** @@ -108,7 +108,7 @@ uv add crewai-tools 사용 가능한 필드: `assignee`, `creator`, `mentioned`, `labels` - + **설명:** GitHub에 릴리스를 생성합니다. **파라미터:** @@ -126,7 +126,7 @@ uv add crewai-tools - 옵션: `true`, `false` - + **설명:** GitHub에서 릴리스를 업데이트합니다. **파라미터:** @@ -145,7 +145,7 @@ uv add crewai-tools - 옵션: `true`, `false` - + **설명:** GitHub에서 ID로 릴리스를 조회합니다. **파라미터:** @@ -154,7 +154,7 @@ uv add crewai-tools - `id` (string, 필수): 릴리스 ID - 조회할 릴리스의 ID를 지정합니다. - + **설명:** GitHub에서 태그 이름으로 릴리스를 조회합니다. **파라미터:** @@ -163,7 +163,7 @@ uv add crewai-tools - `tag_name` (string, 필수): 이름 - 가져올 릴리스의 태그를 지정합니다. (예시: "v1.0.0"). - + **설명:** GitHub에서 릴리스를 삭제합니다. **파라미터:** @@ -179,19 +179,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (GitHub tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with GitHub capabilities github_agent = Agent( role="Repository Manager", goal="Manage GitHub repositories, issues, and releases efficiently", backstory="An AI assistant specialized in repository management and issue tracking.", - tools=[enterprise_tools] + apps=['github'] ) # Task to create a new issue @@ -213,19 +207,12 @@ crew.kickoff() ### 특정 GitHub 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific GitHub tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["github_create_issue", "github_update_issue", "github_search_issue"] -) issue_manager = Agent( role="Issue Manager", goal="Create and manage GitHub issues efficiently", backstory="An AI assistant that focuses on issue tracking and management.", - tools=enterprise_tools + apps=['github'] ) # Task to manage issue workflow @@ -247,17 +234,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) release_manager = Agent( role="Release Manager", goal="Manage software releases and versioning", backstory="An experienced release manager who handles version control and release processes.", - tools=[enterprise_tools] + apps=['github'] ) # Task to create a new release @@ -284,17 +266,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Track and coordinate project issues and development progress", backstory="An AI assistant that helps coordinate development work and track project progress.", - tools=[enterprise_tools] + apps=['github'] ) # Complex task involving multiple GitHub operations diff --git a/docs/ko/enterprise/integrations/gmail.mdx b/docs/ko/enterprise/integrations/gmail.mdx index dcd1c1973..19290e418 100644 --- a/docs/ko/enterprise/integrations/gmail.mdx +++ b/docs/ko/enterprise/integrations/gmail.mdx @@ -25,7 +25,7 @@ Gmail 통합을 사용하기 전에 다음을 확인하세요: 2. 인증 통합 섹션에서 **Gmail**을 찾습니다. 3. **Connect**를 클릭하고 OAuth 흐름을 완료합니다. 4. 이메일 및 연락처 관리를 위한 필요한 권한을 부여합니다. -5. [Account Settings](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 작업 - + **설명:** Gmail에서 이메일을 보냅니다. **파라미터:** @@ -59,7 +59,7 @@ uv add crewai-tools ``` - + **설명:** Gmail에서 ID로 이메일을 조회합니다. **파라미터:** @@ -67,7 +67,7 @@ uv add crewai-tools - `messageId` (string, 필수): 메시지 ID - 조회할 메시지의 ID를 지정합니다. - + **설명:** 고급 필터를 사용하여 Gmail에서 이메일을 검색합니다. **파라미터:** @@ -98,7 +98,7 @@ uv add crewai-tools ``` - + **설명:** Gmail에서 이메일을 삭제합니다. **파라미터:** @@ -106,7 +106,7 @@ uv add crewai-tools - `messageId` (string, 필수): 메시지 ID - 휴지통으로 보낼 메시지의 ID를 지정합니다. - + **설명:** Gmail에서 연락처를 생성합니다. **파라미터:** @@ -126,28 +126,28 @@ uv add crewai-tools ``` - + **설명:** Gmail에서 리소스 이름으로 연락처를 조회합니다. **파라미터:** - `resourceName` (string, 필수): 리소스 이름 - 조회할 연락처의 리소스 이름을 지정합니다. - + **설명:** Gmail에서 연락처를 검색합니다. **파라미터:** - `searchTerm` (string, 필수): 검색어 - 이름, 닉네임, 이메일 주소, 전화번호 또는 조직 연락처 속성에서 유사하거나 정확히 일치하는 항목을 검색할 검색어를 지정합니다. - + **설명:** Gmail에서 연락처를 삭제합니다. **파라미터:** - `resourceName` (string, 필수): 리소스 이름 - 삭제할 연락처의 리소스 이름을 지정합니다. - + **설명:** Gmail에서 임시 저장 메일을 만듭니다. **파라미터:** @@ -177,19 +177,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Gmail tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Gmail capabilities gmail_agent = Agent( role="Email Manager", goal="Manage email communications and contacts efficiently", backstory="An AI assistant specialized in email management and communication.", - tools=[enterprise_tools] + apps=['gmail'] ) # Task to send a follow-up email @@ -211,19 +205,12 @@ crew.kickoff() ### 특정 Gmail 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Gmail tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["gmail_send_email", "gmail_search_for_email", "gmail_create_draft"] -) email_coordinator = Agent( role="Email Coordinator", goal="Coordinate email communications and manage drafts", backstory="An AI assistant that focuses on email coordination and draft management.", - tools=enterprise_tools + apps=['gmail'] ) # Task to prepare and send emails @@ -245,17 +232,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) contact_manager = Agent( role="Contact Manager", goal="Manage and organize email contacts efficiently", backstory="An experienced contact manager who maintains organized contact databases.", - tools=[enterprise_tools] + apps=['gmail'] ) # Task to manage contacts @@ -281,17 +263,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) email_analyst = Agent( role="Email Analyst", goal="Analyze email patterns and provide insights", backstory="An AI assistant that analyzes email data to provide actionable insights.", - tools=[enterprise_tools] + apps=['gmail'] ) # Task to analyze email patterns @@ -317,17 +294,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) workflow_manager = Agent( role="Email Workflow Manager", goal="Automate email workflows and responses", backstory="An AI assistant that manages automated email workflows and responses.", - tools=[enterprise_tools] + apps=['gmail'] ) # Complex task involving multiple Gmail operations diff --git a/docs/ko/enterprise/integrations/google_calendar.mdx b/docs/ko/enterprise/integrations/google_calendar.mdx index a850e0d11..7cd93e7f3 100644 --- a/docs/ko/enterprise/integrations/google_calendar.mdx +++ b/docs/ko/enterprise/integrations/google_calendar.mdx @@ -25,7 +25,7 @@ Google Calendar 통합을 사용하기 전에 다음을 준비해야 합니다: 2. 인증 통합 섹션에서 **Google Calendar**를 찾습니다. 3. **Connect**를 클릭하고 OAuth 과정을 완료합니다. 4. 캘린더 및 연락처 접근 권한을 허용합니다. -5. [Account Settings](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 작업 - + **설명:** Google 캘린더에 이벤트를 생성합니다. **파라미터:** @@ -51,7 +51,7 @@ uv add crewai-tools - `includeMeetLink` (boolean, 선택): Google Meet 링크 포함 여부? - 이 이벤트에 대해 Google Meet 컨퍼런스 링크를 자동으로 생성합니다. - + **설명:** Google 캘린더에서 기존 이벤트를 업데이트합니다. **파라미터:** @@ -65,7 +65,7 @@ uv add crewai-tools - `eventDescription` (string, 선택): 이벤트 설명. - + **설명:** Google 캘린더에서 이벤트 목록을 가져옵니다. **파라미터:** @@ -74,7 +74,7 @@ uv add crewai-tools - `before` (string, 선택): 이전 - 제공된 날짜 이전에 종료되는 이벤트를 필터링합니다 (밀리초 단위의 Unix 또는 ISO 타임스탬프). (예시: "2025-04-12T10:00:00Z 또는 1712908800000"). - + **설명:** Google 캘린더에서 ID로 특정 이벤트를 가져옵니다. **파라미터:** @@ -82,7 +82,7 @@ uv add crewai-tools - `calendar` (string, 선택): 캘린더 - Connect Portal Workflow Settings를 사용하여 사용자가 이벤트를 추가할 캘린더를 선택할 수 있도록 합니다. 비워두면 사용자의 기본 캘린더로 기본 설정됩니다. - + **설명:** Google 캘린더에서 이벤트를 삭제합니다. **파라미터:** @@ -90,7 +90,7 @@ uv add crewai-tools - `calendar` (string, 선택): 캘린더 - Connect Portal Workflow Settings를 사용하여 사용자가 이벤트를 추가할 캘린더를 선택할 수 있도록 합니다. 비워두면 사용자의 기본 캘린더로 기본 설정됩니다. - + **설명:** Google 캘린더에서 연락처를 가져옵니다. **파라미터:** @@ -102,14 +102,14 @@ uv add crewai-tools ``` - + **설명:** Google 캘린더에서 연락처를 검색합니다. **파라미터:** - `query` (string, 선택): 연락처를 검색할 검색 쿼리. - + **설명:** 디렉토리 구성원 목록을 가져옵니다. **파라미터:** @@ -121,7 +121,7 @@ uv add crewai-tools ``` - + **설명:** 디렉토리 구성원을 검색합니다. **파라미터:** @@ -134,7 +134,7 @@ uv add crewai-tools ``` - + **설명:** 기타 연락처 목록을 가져옵니다. **파라미터:** @@ -146,14 +146,14 @@ uv add crewai-tools ``` - + **설명:** 기타 연락처를 검색합니다. **파라미터:** - `query` (string, 선택): 연락처를 검색할 검색 쿼리. - + **설명:** 캘린더의 가용성 정보를 가져옵니다. **파라미터:** @@ -180,19 +180,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Google Calendar tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Google Calendar capabilities calendar_agent = Agent( role="Schedule Manager", goal="Manage calendar events and scheduling efficiently", backstory="An AI assistant specialized in calendar management and scheduling coordination.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Task to create a meeting @@ -214,19 +208,12 @@ crew.kickoff() ### 특정 캘린더 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Google Calendar tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["google_calendar_create_event", "google_calendar_list_events", "google_calendar_get_availability"] -) meeting_coordinator = Agent( role="Meeting Coordinator", goal="Coordinate meetings and check availability", backstory="An AI assistant that focuses on meeting scheduling and availability management.", - tools=enterprise_tools + apps=['google_calendar'] ) # Task to schedule a meeting with availability check @@ -248,17 +235,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) event_manager = Agent( role="Event Manager", goal="Manage and update calendar events efficiently", backstory="An experienced event manager who handles event logistics and updates.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Task to manage event updates @@ -284,17 +266,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) availability_coordinator = Agent( role="Availability Coordinator", goal="Coordinate availability and manage contacts for scheduling", backstory="An AI assistant that specializes in availability management and contact coordination.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Task to coordinate availability @@ -321,17 +298,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) scheduling_automator = Agent( role="Scheduling Automator", goal="Automate scheduling workflows and calendar management", backstory="An AI assistant that automates complex scheduling scenarios and calendar workflows.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Complex scheduling automation task diff --git a/docs/ko/enterprise/integrations/google_contacts.mdx b/docs/ko/enterprise/integrations/google_contacts.mdx new file mode 100644 index 000000000..0c11a3699 --- /dev/null +++ b/docs/ko/enterprise/integrations/google_contacts.mdx @@ -0,0 +1,221 @@ +--- +title: Google Contacts 통합 +description: "CrewAI를 위한 Google Contacts 통합으로 연락처 및 디렉토리 관리." +icon: "address-book" +mode: "wide" +--- + +## 개요 + +에이전트가 Google Contacts를 통해 연락처와 디렉토리 정보를 관리할 수 있도록 합니다. 개인 연락처에 액세스하고, 디렉토리 사람들을 검색하고, 연락처 정보를 생성 및 업데이트하고, AI 기반 자동화로 연락처 그룹을 관리합니다. + +## 전제 조건 + +Google Contacts 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- Google Contacts 액세스 권한이 있는 Google 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Google 계정 연결 + +## Google Contacts 통합 설정 + +### 1. Google 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Google Contacts** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. 연락처 및 디렉토리 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** Google Contacts에서 사용자의 연락처를 검색합니다. + + **매개변수:** + - `pageSize` (integer, 선택사항): 반환할 연락처 수 (최대 1000). 최소: 1, 최대: 1000 + - `pageToken` (string, 선택사항): 검색할 페이지의 토큰. + - `personFields` (string, 선택사항): 포함할 필드 (예: 'names,emailAddresses,phoneNumbers'). 기본값: names,emailAddresses,phoneNumbers + - `requestSyncToken` (boolean, 선택사항): 응답에 동기화 토큰을 포함할지 여부. 기본값: false + - `sortOrder` (string, 선택사항): 연결을 정렬할 순서. 옵션: LAST_MODIFIED_ASCENDING, LAST_MODIFIED_DESCENDING, FIRST_NAME_ASCENDING, LAST_NAME_ASCENDING + + + + **설명:** 쿼리 문자열을 사용하여 연락처를 검색합니다. + + **매개변수:** + - `query` (string, 필수): 검색 쿼리 문자열 + - `readMask` (string, 필수): 읽을 필드 (예: 'names,emailAddresses,phoneNumbers') + - `pageSize` (integer, 선택사항): 반환할 결과 수. 최소: 1, 최대: 30 + - `pageToken` (string, 선택사항): 반환할 결과 페이지를 지정하는 토큰. + - `sources` (array, 선택사항): 검색할 소스. 옵션: READ_SOURCE_TYPE_CONTACT, READ_SOURCE_TYPE_PROFILE. 기본값: READ_SOURCE_TYPE_CONTACT + + + + **설명:** 인증된 사용자의 디렉토리에 있는 사람들을 나열합니다. + + **매개변수:** + - `sources` (array, 필수): 검색할 디렉토리 소스. 옵션: DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE, DIRECTORY_SOURCE_TYPE_DOMAIN_CONTACT. 기본값: DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE + - `pageSize` (integer, 선택사항): 반환할 사람 수. 최소: 1, 최대: 1000 + - `pageToken` (string, 선택사항): 반환할 결과 페이지를 지정하는 토큰. + - `readMask` (string, 선택사항): 읽을 필드 (예: 'names,emailAddresses') + - `requestSyncToken` (boolean, 선택사항): 응답에 동기화 토큰을 포함할지 여부. 기본값: false + - `mergeSources` (array, 선택사항): 디렉토리 사람 응답에 병합할 추가 데이터. 옵션: CONTACT + + + + **설명:** 디렉토리에서 사람을 검색합니다. + + **매개변수:** + - `query` (string, 필수): 검색 쿼리 + - `sources` (string, 필수): 디렉토리 소스 ('DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE' 사용) + - `pageSize` (integer, 선택사항): 반환할 결과 수 + - `readMask` (string, 선택사항): 읽을 필드 + + + + **설명:** 기타 연락처를 나열합니다 (사용자의 개인 연락처에 없는). + + **매개변수:** + - `pageSize` (integer, 선택사항): 반환할 연락처 수. 최소: 1, 최대: 1000 + - `pageToken` (string, 선택사항): 반환할 결과 페이지를 지정하는 토큰. + - `readMask` (string, 선택사항): 읽을 필드 + - `requestSyncToken` (boolean, 선택사항): 응답에 동기화 토큰을 포함할지 여부. 기본값: false + + + + **설명:** 기타 연락처를 검색합니다. + + **매개변수:** + - `query` (string, 필수): 검색 쿼리 + - `readMask` (string, 필수): 읽을 필드 (예: 'names,emailAddresses') + - `pageSize` (integer, 선택사항): 결과 수 + + + + **설명:** 리소스 이름으로 한 사람의 연락처 정보를 가져옵니다. + + **매개변수:** + - `resourceName` (string, 필수): 가져올 사람의 리소스 이름 (예: 'people/c123456789') + - `personFields` (string, 선택사항): 포함할 필드 (예: 'names,emailAddresses,phoneNumbers'). 기본값: names,emailAddresses,phoneNumbers + + + + **설명:** 사용자의 주소록에 새 연락처를 만듭니다. + + **매개변수:** + - `names` (array, 선택사항): 사람의 이름들. 각 항목은 `givenName` (string), `familyName` (string), `displayName` (string)이 있는 객체. + - `emailAddresses` (array, 선택사항): 이메일 주소들. 각 항목은 `value` (string, 이메일 주소)와 `type` (string, 'home', 'work', 'other', 기본값 'other')이 있는 객체. + - `phoneNumbers` (array, 선택사항): 전화번호들. 각 항목은 `value` (string, 전화번호)와 `type` (string, 'home', 'work', 'mobile', 'other', 기본값 'other')이 있는 객체. + - `addresses` (array, 선택사항): 우편 주소들. 각 항목은 `formattedValue` (string, 형식화된 주소)와 `type` (string, 'home', 'work', 'other', 기본값 'other')이 있는 객체. + - `organizations` (array, 선택사항): 조직/회사들. 각 항목은 `name` (string, 조직 이름), `title` (string, 직책), `type` (string, 'work', 'other', 기본값 'work')이 있는 객체. + + + + **설명:** 기존 연락처의 정보를 업데이트합니다. + + **매개변수:** + - `resourceName` (string, 필수): 업데이트할 사람의 리소스 이름 (예: 'people/c123456789'). + - `updatePersonFields` (string, 필수): 업데이트할 필드 (예: 'names,emailAddresses,phoneNumbers'). + - `names` (array, 선택사항): 사람의 이름들. 각 항목은 `givenName` (string), `familyName` (string), `displayName` (string)이 있는 객체. + - `emailAddresses` (array, 선택사항): 이메일 주소들. 각 항목은 `value` (string, 이메일 주소)와 `type` (string, 'home', 'work', 'other')이 있는 객체. + - `phoneNumbers` (array, 선택사항): 전화번호들. 각 항목은 `value` (string, 전화번호)와 `type` (string, 'home', 'work', 'mobile', 'other')이 있는 객체. + + + + **설명:** 사용자의 주소록에서 연락처를 삭제합니다. + + **매개변수:** + - `resourceName` (string, 필수): 삭제할 사람의 리소스 이름 (예: 'people/c123456789'). + + + + **설명:** 한 번의 요청으로 여러 사람에 대한 정보를 가져옵니다. + + **매개변수:** + - `resourceNames` (array, 필수): 가져올 사람들의 리소스 이름 (최대 200개 항목). + - `personFields` (string, 선택사항): 포함할 필드 (예: 'names,emailAddresses,phoneNumbers'). 기본값: names,emailAddresses,phoneNumbers + + + + **설명:** 사용자의 연락처 그룹(라벨)을 나열합니다. + + **매개변수:** + - `pageSize` (integer, 선택사항): 반환할 연락처 그룹 수. 최소: 1, 최대: 1000 + - `pageToken` (string, 선택사항): 반환할 결과 페이지를 지정하는 토큰. + - `groupFields` (string, 선택사항): 포함할 필드 (예: 'name,memberCount,clientData'). 기본값: name,memberCount + + + + **설명:** 리소스 이름으로 특정 연락처 그룹을 가져옵니다. + + **매개변수:** + - `resourceName` (string, 필수): 연락처 그룹의 리소스 이름 (예: 'contactGroups/myContactGroup'). + - `maxMembers` (integer, 선택사항): 포함할 최대 멤버 수. 최소: 0, 최대: 20000 + - `groupFields` (string, 선택사항): 포함할 필드 (예: 'name,memberCount,clientData'). 기본값: name,memberCount + + + + **설명:** 새 연락처 그룹(라벨)을 만듭니다. + + **매개변수:** + - `name` (string, 필수): 연락처 그룹의 이름. + - `clientData` (array, 선택사항): 클라이언트별 데이터. 각 항목은 `key` (string)와 `value` (string)가 있는 객체. + + + +## 사용 예제 + +### 기본 Google Contacts 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Google Contacts 기능을 가진 에이전트 생성 +contacts_agent = Agent( + role="연락처 관리자", + goal="Google Contacts를 효율적으로 관리", + backstory="연락처 관리 및 조직 전문 AI 어시스턴트.", + apps=['google_contacts'] # 모든 Google Contacts 작업을 사용할 수 있습니다 +) + +# 새 연락처 생성 작업 +create_contact_task = Task( + description="'김철수'라는 이름으로 이메일 'kim.chulsoo@example.com'과 전화번호 '010-1234-5678'로 새 연락처를 만드세요", + agent=contacts_agent, + expected_output="새 연락처가 성공적으로 생성됨" +) + +# 작업 실행 +crew = Crew( + agents=[contacts_agent], + tasks=[create_contact_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Google 계정이 연락처 및 디렉토리 액세스에 필요한 권한을 가지고 있는지 확인하세요. +- OAuth 연결이 Google People API에 필요한 모든 범위를 포함하는지 확인하세요. + +**연락처 생성/업데이트 문제** +- 연락처 생성 시 `email`과 같은 필수 필드가 제공되는지 확인하세요. +- 연락처를 업데이트하거나 삭제할 때 `resourceName`이 올바른지 확인하세요. + +### 도움 받기 + + + Google Contacts 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/google_docs.mdx b/docs/ko/enterprise/integrations/google_docs.mdx new file mode 100644 index 000000000..5816f0012 --- /dev/null +++ b/docs/ko/enterprise/integrations/google_docs.mdx @@ -0,0 +1,158 @@ +--- +title: Google Docs 통합 +description: "CrewAI를 위한 Google Docs 통합으로 문서 생성 및 편집." +icon: "file-lines" +mode: "wide" +--- + +## 개요 + +에이전트가 텍스트 조작 및 서식을 사용하여 Google Docs 문서를 생성, 편집 및 관리할 수 있도록 합니다. AI 기반 자동화로 문서 생성을 자동화하고, 텍스트를 삽입 및 교체하고, 콘텐츠 범위를 관리하며, 문서 워크플로를 간소화합니다. + +## 전제 조건 + +Google Docs 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- Google Docs 액세스 권한이 있는 Google 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Google 계정 연결 + +## Google Docs 통합 설정 + +### 1. Google 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Google Docs** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. 문서 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** 새 Google 문서를 만듭니다. + + **매개변수:** + - `title` (string, 선택사항): 새 문서의 제목. + + + + **설명:** Google 문서의 내용과 메타데이터를 가져옵니다. + + **매개변수:** + - `documentId` (string, 필수): 검색할 문서의 ID. + - `includeTabsContent` (boolean, 선택사항): 탭 내용을 포함할지 여부. 기본값: false + - `suggestionsViewMode` (string, 선택사항): 문서에 적용할 제안 보기 모드. 옵션: DEFAULT_FOR_CURRENT_ACCESS, PREVIEW_SUGGESTIONS_ACCEPTED, PREVIEW_WITHOUT_SUGGESTIONS. 기본값: DEFAULT_FOR_CURRENT_ACCESS + + + + **설명:** Google 문서에 하나 이상의 업데이트를 적용합니다. + + **매개변수:** + - `documentId` (string, 필수): 업데이트할 문서의 ID. + - `requests` (array, 필수): 문서에 적용할 업데이트 목록. 각 항목은 요청을 나타내는 객체. + - `writeControl` (object, 선택사항): 쓰기 요청이 실행되는 방식을 제어합니다. `requiredRevisionId` (string)와 `targetRevisionId` (string)를 포함. + + + + **설명:** Google 문서의 특정 위치에 텍스트를 삽입합니다. + + **매개변수:** + - `documentId` (string, 필수): 업데이트할 문서의 ID. + - `text` (string, 필수): 삽입할 텍스트. + - `index` (integer, 선택사항): 텍스트를 삽입할 0 기반 인덱스. 기본값: 1 + + + + **설명:** Google 문서에서 텍스트의 모든 인스턴스를 교체합니다. + + **매개변수:** + - `documentId` (string, 필수): 업데이트할 문서의 ID. + - `containsText` (string, 필수): 찾아서 교체할 텍스트. + - `replaceText` (string, 필수): 교체할 텍스트. + - `matchCase` (boolean, 선택사항): 검색이 대소문자를 구분할지 여부. 기본값: false + + + + **설명:** Google 문서의 특정 범위에서 내용을 삭제합니다. + + **매개변수:** + - `documentId` (string, 필수): 업데이트할 문서의 ID. + - `startIndex` (integer, 필수): 삭제할 범위의 시작 인덱스. + - `endIndex` (integer, 필수): 삭제할 범위의 끝 인덱스. + + + + **설명:** Google 문서의 특정 위치에 페이지 나누기를 삽입합니다. + + **매개변수:** + - `documentId` (string, 필수): 업데이트할 문서의 ID. + - `index` (integer, 선택사항): 페이지 나누기를 삽입할 0 기반 인덱스. 기본값: 1 + + + + **설명:** Google 문서에 명명된 범위를 만듭니다. + + **매개변수:** + - `documentId` (string, 필수): 업데이트할 문서의 ID. + - `name` (string, 필수): 명명된 범위의 이름. + - `startIndex` (integer, 필수): 범위의 시작 인덱스. + - `endIndex` (integer, 필수): 범위의 끝 인덱스. + + + +## 사용 예제 + +### 기본 Google Docs 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Google Docs 기능을 가진 에이전트 생성 +docs_agent = Agent( + role="문서 작성자", + goal="Google Docs 문서를 효율적으로 생성하고 관리", + backstory="Google Docs 문서 생성 및 편집 전문 AI 어시스턴트.", + apps=['google_docs'] # 모든 Google Docs 작업을 사용할 수 있습니다 +) + +# 새 문서 생성 작업 +create_doc_task = Task( + description="'프로젝트 상태 보고서'라는 제목으로 새 Google 문서를 만드세요", + agent=docs_agent, + expected_output="새 Google 문서 '프로젝트 상태 보고서'가 성공적으로 생성됨" +) + +# 작업 실행 +crew = Crew( + agents=[docs_agent], + tasks=[create_doc_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Google 계정이 Google Docs 액세스에 필요한 권한을 가지고 있는지 확인하세요. +- OAuth 연결이 필요한 모든 범위(`https://www.googleapis.com/auth/documents`)를 포함하는지 확인하세요. + +**문서 ID 문제** +- 문서 ID가 올바른지 다시 확인하세요. +- 문서가 존재하고 계정에서 액세스할 수 있는지 확인하세요. + +### 도움 받기 + + + Google Docs 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/google_drive.mdx b/docs/ko/enterprise/integrations/google_drive.mdx new file mode 100644 index 000000000..4391a6033 --- /dev/null +++ b/docs/ko/enterprise/integrations/google_drive.mdx @@ -0,0 +1,30 @@ +--- +title: Google Drive 통합 +description: "CrewAI를 위한 Google Drive 통합으로 파일 및 폴더 관리." +icon: "google" +mode: "wide" +--- + +## 개요 + +에이전트가 Google Drive의 파일과 폴더에 액세스하고 관리할 수 있도록 합니다. AI 기반 자동화로 파일을 업로드, 다운로드, 콘텐츠 구성, 공유 링크 생성 및 클라우드 스토리지 워크플로를 간소화합니다. + +## 전제 조건 + +Google Drive 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- Google Drive 액세스 권한이 있는 Google 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Google 계정 연결 + +## 사용 가능한 작업 + +자세한 매개변수 및 사용법은 [영어 문서](../../../en/enterprise/integrations/google_drive)를 참조하세요. + +## 문제 해결 + +### 도움 받기 + + + Google Drive 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/google_sheets.mdx b/docs/ko/enterprise/integrations/google_sheets.mdx index 28a158fd1..7defb3797 100644 --- a/docs/ko/enterprise/integrations/google_sheets.mdx +++ b/docs/ko/enterprise/integrations/google_sheets.mdx @@ -26,7 +26,7 @@ Google Sheets 통합을 사용하기 전에 다음을 확인하세요: 2. 인증 통합 섹션에서 **Google Sheets**를 찾습니다. 3. **Connect**를 클릭하고 OAuth 흐름을 완료합니다. 4. 스프레드시트 접근에 필요한 권한을 허용합니다. -5. [Account Settings](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -37,7 +37,7 @@ uv add crewai-tools ## 사용 가능한 작업 - + **설명:** Google Sheets 스프레드시트에서 행을 가져옵니다. **매개변수:** @@ -45,7 +45,7 @@ uv add crewai-tools - `limit` (string, 선택): 행 제한 - 반환할 최대 행 수를 제한합니다. - + **설명:** Google Sheets 스프레드시트에 새로운 행을 만듭니다. **매개변수:** @@ -62,7 +62,7 @@ uv add crewai-tools ``` - + **설명:** Google Sheets 스프레드시트의 기존 행을 업데이트합니다. **매개변수:** @@ -105,19 +105,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Google Sheets tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Google Sheets capabilities sheets_agent = Agent( role="Data Manager", goal="Manage spreadsheet data and track information efficiently", backstory="An AI assistant specialized in data management and spreadsheet operations.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Task to add new data to a spreadsheet @@ -139,19 +133,12 @@ crew.kickoff() ### 특정 Google Sheets 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Google Sheets tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["google_sheets_get_row", "google_sheets_create_row"] -) data_collector = Agent( role="Data Collector", goal="Collect and organize data in spreadsheets", backstory="An AI assistant that focuses on data collection and organization.", - tools=enterprise_tools + apps=['google_sheets'] ) # Task to collect and organize data @@ -173,17 +160,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_analyst = Agent( role="Data Analyst", goal="Analyze spreadsheet data and generate insights", backstory="An experienced data analyst who extracts insights from spreadsheet data.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Task to analyze data and create reports @@ -209,17 +191,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_updater = Agent( role="Data Updater", goal="Automatically update and maintain spreadsheet data", backstory="An AI assistant that maintains data accuracy and updates records automatically.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Task to update data based on conditions @@ -246,17 +223,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) workflow_manager = Agent( role="Data Workflow Manager", goal="Manage complex data workflows across multiple spreadsheets", backstory="An AI assistant that orchestrates complex data operations across multiple spreadsheets.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Complex workflow task diff --git a/docs/ko/enterprise/integrations/google_slides.mdx b/docs/ko/enterprise/integrations/google_slides.mdx new file mode 100644 index 000000000..4d5b43c0d --- /dev/null +++ b/docs/ko/enterprise/integrations/google_slides.mdx @@ -0,0 +1,167 @@ +--- +title: Google Slides 통합 +description: "CrewAI를 위한 Google Slides 통합으로 프레젠테이션 생성 및 관리." +icon: "chart-bar" +mode: "wide" +--- + +## 개요 + +에이전트가 Google Slides 프레젠테이션을 생성, 편집 및 관리할 수 있도록 합니다. AI 기반 자동화로 프레젠테이션 생성을 자동화하고, 콘텐츠를 업데이트하고, Google Sheets에서 데이터를 가져오며, 프레젠테이션 워크플로를 간소화합니다. + +## 전제 조건 + +Google Slides 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- Google Slides 액세스 권한이 있는 Google 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Google 계정 연결 + +## Google Slides 통합 설정 + +### 1. Google 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Google Slides** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. 프레젠테이션, 스프레드시트 및 드라이브 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** 내용이 없는 빈 프레젠테이션을 만듭니다. + + **매개변수:** + - `title` (string, 필수): 프레젠테이션의 제목. + + + + **설명:** ID로 프레젠테이션을 검색합니다. + + **매개변수:** + - `presentationId` (string, 필수): 검색할 프레젠테이션의 ID. + - `fields` (string, 선택사항): 응답에 포함할 필드. 성능 향상을 위해 필요한 데이터만 반환하는 데 사용. + + + + **설명:** 프레젠테이션에 업데이트를 적용하거나 콘텐츠를 추가하거나 제거합니다. + + **매개변수:** + - `presentationId` (string, 필수): 업데이트할 프레젠테이션의 ID. + - `requests` (array, 필수): 프레젠테이션에 적용할 업데이트 목록. 각 항목은 요청을 나타내는 객체. + - `writeControl` (object, 선택사항): 쓰기 요청이 실행되는 방식을 제어합니다. `requiredRevisionId` (string)를 포함. + + + + **설명:** ID로 특정 페이지를 검색합니다. + + **매개변수:** + - `presentationId` (string, 필수): 프레젠테이션의 ID. + - `pageObjectId` (string, 필수): 검색할 페이지의 ID. + + + + **설명:** 페이지 썸네일을 생성합니다. + + **매개변수:** + - `presentationId` (string, 필수): 프레젠테이션의 ID. + - `pageObjectId` (string, 필수): 썸네일 생성을 위한 페이지의 ID. + + + + **설명:** Google 시트에서 프레젠테이션으로 데이터를 가져옵니다. + + **매개변수:** + - `presentationId` (string, 필수): 프레젠테이션의 ID. + - `sheetId` (string, 필수): 가져올 Google 시트의 ID. + - `dataRange` (string, 필수): 시트에서 가져올 데이터 범위. + + + + **설명:** 프레젠테이션과 연결된 Google 드라이브에 파일을 업로드합니다. + + **매개변수:** + - `file` (string, 필수): 업로드할 파일 데이터. + - `presentationId` (string, 필수): 업로드된 파일을 연결할 프레젠테이션의 ID. + + + + **설명:** Google 드라이브의 파일을 프레젠테이션에 연결합니다. + + **매개변수:** + - `presentationId` (string, 필수): 프레젠테이션의 ID. + - `fileId` (string, 필수): 연결할 파일의 ID. + + + + **설명:** 사용자가 액세스할 수 있는 모든 프레젠테이션을 나열합니다. + + **매개변수:** + - `pageSize` (integer, 선택사항): 페이지당 반환할 프레젠테이션 수. + - `pageToken` (string, 선택사항): 페이지네이션을 위한 토큰. + + + + **설명:** ID로 프레젠테이션을 삭제합니다. + + **매개변수:** + - `presentationId` (string, 필수): 삭제할 프레젠테이션의 ID. + + + +## 사용 예제 + +### 기본 Google Slides 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Google Slides 기능을 가진 에이전트 생성 +slides_agent = Agent( + role="프레젠테이션 작성자", + goal="Google Slides 프레젠테이션을 효율적으로 생성하고 관리", + backstory="프레젠테이션 디자인 및 콘텐츠 관리 전문 AI 어시스턴트.", + apps=['google_slides'] # 모든 Google Slides 작업을 사용할 수 있습니다 +) + +# 새 프레젠테이션 생성 작업 +create_presentation_task = Task( + description="'분기별 매출 보고서'라는 제목으로 새 빈 프레젠테이션을 만드세요", + agent=slides_agent, + expected_output="새 프레젠테이션 '분기별 매출 보고서'가 성공적으로 생성됨" +) + +# 작업 실행 +crew = Crew( + agents=[slides_agent], + tasks=[create_presentation_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Google 계정이 Google Slides 및 Google Drive 액세스에 필요한 권한을 가지고 있는지 확인하세요. +- OAuth 연결이 필요한 모든 범위를 포함하는지 확인하세요. + +**프레젠테이션/페이지 ID 문제** +- 프레젠테이션 ID와 페이지 객체 ID가 올바른지 다시 확인하세요. +- 프레젠테이션이나 페이지가 존재하고 액세스할 수 있는지 확인하세요. + +### 도움 받기 + + + Google Slides 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/hubspot.mdx b/docs/ko/enterprise/integrations/hubspot.mdx index ba1b02310..a9fc79829 100644 --- a/docs/ko/enterprise/integrations/hubspot.mdx +++ b/docs/ko/enterprise/integrations/hubspot.mdx @@ -25,7 +25,7 @@ HubSpot 통합을 사용하기 전에 다음을 확인하세요. 2. 인증 통합 섹션에서 **HubSpot**을 찾습니다. 3. **Connect**를 클릭하고 OAuth 플로우를 완료합니다. 4. 회사 및 연락처 관리를 위한 필요한 권한을 부여합니다. -5. [계정 설정](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 액션 - + **설명:** HubSpot에서 새로운 회사 레코드를 생성합니다. **파라미터:** @@ -101,7 +101,7 @@ uv add crewai-tools - `founded_year` (string, 선택): 설립 연도. - + **설명:** HubSpot에서 새로운 연락처 레코드를 생성합니다. **파라미터:** @@ -200,7 +200,7 @@ uv add crewai-tools - `hs_googleplusid` (string, 선택): googleplus ID. - + **설명:** HubSpot에서 새로운 거래(deal) 레코드를 생성합니다. **파라미터:** @@ -215,7 +215,7 @@ uv add crewai-tools - `hs_priority` (string, 선택): 거래 우선순위. 사용 가능한 값: `low`, `medium`, `high`. - + **설명:** HubSpot에서 새로운 참여(예: 노트, 이메일, 통화, 미팅, 작업)를 생성합니다. **파라미터:** @@ -232,7 +232,7 @@ uv add crewai-tools - `hs_meeting_end_time` (string, 선택): 미팅 종료 시간. (`MEETING`에서 사용) - + **설명:** HubSpot에서 기존 회사 레코드를 업데이트합니다. **파라미터:** @@ -249,7 +249,7 @@ uv add crewai-tools - `description` (string, 선택): 설명. - + **설명:** HubSpot에서 지정된 오브젝트 타입의 레코드를 생성합니다. **파라미터:** @@ -257,7 +257,7 @@ uv add crewai-tools - 추가 파라미터는 커스텀 오브젝트의 스키마에 따라 다릅니다. - + **설명:** HubSpot에서 기존 연락처 레코드를 업데이트합니다. **파라미터:** @@ -271,7 +271,7 @@ uv add crewai-tools - `lifecyclestage` (string, 선택): 라이프사이클 단계. - + **설명:** HubSpot에서 기존 거래 레코드를 업데이트합니다. **파라미터:** @@ -284,7 +284,7 @@ uv add crewai-tools - `dealtype` (string, 선택): 거래 유형. - + **설명:** HubSpot에서 기존 참여(engagement)를 업데이트합니다. **파라미터:** @@ -295,7 +295,7 @@ uv add crewai-tools - `hs_task_status` (string, 선택): 작업 상태. - + **설명:** HubSpot에서 지정된 오브젝트 타입의 레코드를 업데이트합니다. **파라미터:** @@ -304,28 +304,28 @@ uv add crewai-tools - 추가 파라미터는 커스텀 오브젝트의 스키마에 따라 다릅니다. - + **설명:** HubSpot에서 회사 레코드 목록을 가져옵니다. **파라미터:** - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** HubSpot에서 연락처 레코드 목록을 가져옵니다. **파라미터:** - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** HubSpot에서 거래 레코드 목록을 가져옵니다. **파라미터:** - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** HubSpot에서 참여(engagement) 레코드 목록을 가져옵니다. **파라미터:** @@ -333,7 +333,7 @@ uv add crewai-tools - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** HubSpot에서 지정된 오브젝트 타입의 레코드 목록을 가져옵니다. **파라미터:** @@ -341,35 +341,35 @@ uv add crewai-tools - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** ID로 단일 회사 레코드를 가져옵니다. **파라미터:** - `recordId` (string, 필수): 가져올 회사의 ID. - + **설명:** ID로 단일 연락처 레코드를 가져옵니다. **파라미터:** - `recordId` (string, 필수): 가져올 연락처의 ID. - + **설명:** ID로 단일 거래 레코드를 가져옵니다. **파라미터:** - `recordId` (string, 필수): 가져올 거래의 ID. - + **설명:** ID로 단일 참여(engagement) 레코드를 가져옵니다. **파라미터:** - `recordId` (string, 필수): 가져올 참여의 ID. - + **설명:** 지정된 오브젝트 타입의 단일 레코드를 ID로 가져옵니다. **파라미터:** @@ -377,7 +377,7 @@ uv add crewai-tools - `recordId` (string, 필수): 가져올 레코드의 ID. - + **설명:** 필터 수식을 사용해 HubSpot에서 회사 레코드를 검색합니다. **파라미터:** @@ -385,7 +385,7 @@ uv add crewai-tools - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** 필터 수식을 사용해 HubSpot에서 연락처 레코드를 검색합니다. **파라미터:** @@ -393,7 +393,7 @@ uv add crewai-tools - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** 필터 수식을 사용해 HubSpot에서 거래 레코드를 검색합니다. **파라미터:** @@ -401,7 +401,7 @@ uv add crewai-tools - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** 필터 수식을 사용해 HubSpot에서 참여(engagement) 레코드를 검색합니다. **파라미터:** @@ -409,7 +409,7 @@ uv add crewai-tools - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** HubSpot에서 지정된 오브젝트 타입의 레코드를 검색합니다. **파라미터:** @@ -418,35 +418,35 @@ uv add crewai-tools - `paginationParameters` (object, 선택): 다음 페이지를 가져오려면 `pageCursor`를 사용하세요. - + **설명:** ID로 회사 레코드를 삭제합니다. **파라미터:** - `recordId` (string, 필수): 삭제할 회사의 ID. - + **설명:** ID로 연락처 레코드를 삭제합니다. **파라미터:** - `recordId` (string, 필수): 삭제할 연락처의 ID. - + **설명:** ID로 거래 레코드를 삭제합니다. **파라미터:** - `recordId` (string, 필수): 삭제할 거래의 ID. - + **설명:** ID로 참여(engagement) 레코드를 삭제합니다. **파라미터:** - `recordId` (string, 필수): 삭제할 참여의 ID. - + **설명:** 지정된 오브젝트 타입의 레코드를 ID로 삭제합니다. **파라미터:** @@ -454,7 +454,7 @@ uv add crewai-tools - `recordId` (string, 필수): 삭제할 레코드의 ID. - + **설명:** 지정된 리스트 ID로부터 연락처 목록을 가져옵니다. **파라미터:** @@ -462,7 +462,7 @@ uv add crewai-tools - `paginationParameters` (object, 선택): 이후 페이지를 위해 `pageCursor` 사용. - + **설명:** 특정 오브젝트 타입 및 작업에 대한 예상 스키마를 가져옵니다. **파라미터:** @@ -477,19 +477,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (HubSpot tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with HubSpot capabilities hubspot_agent = Agent( role="CRM Manager", goal="Manage company and contact records in HubSpot", backstory="An AI assistant specialized in CRM management.", - tools=[enterprise_tools] + apps=['hubspot'] ) # Task to create a new company @@ -511,19 +505,16 @@ crew.kickoff() ### 특정 HubSpot 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools # Get only the tool to create contacts -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["hubspot_create_record_contacts"] + actions_list=["hubspot/create_contact"] ) contact_creator = Agent( role="Contact Creator", goal="Create new contacts in HubSpot", backstory="An AI assistant that focuses on creating new contact entries in the CRM.", - tools=[enterprise_tools] + apps=['hubspot'] ) # Task to create a contact @@ -545,17 +536,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) crm_manager = Agent( role="CRM Manager", goal="Manage and organize HubSpot contacts efficiently.", backstory="An experienced CRM manager who maintains an organized contact database.", - tools=[enterprise_tools] + apps=['hubspot'] ) # Task to manage contacts diff --git a/docs/ko/enterprise/integrations/jira.mdx b/docs/ko/enterprise/integrations/jira.mdx index f98f20456..06b472d99 100644 --- a/docs/ko/enterprise/integrations/jira.mdx +++ b/docs/ko/enterprise/integrations/jira.mdx @@ -25,7 +25,7 @@ Jira 통합을 사용하기 전에 다음을 준비하세요: 2. **Jira**를 인증 통합 섹션에서 찾습니다. 3. **Connect**를 클릭하고 OAuth 절차를 완료합니다. 4. 이슈 및 프로젝트 관리를 위한 필요한 권한을 부여합니다. -5. [Account Settings](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 작업 - + **설명:** Jira에서 이슈를 생성합니다. **파라미터:** @@ -56,7 +56,7 @@ uv add crewai-tools ``` - + **설명:** Jira에서 이슈를 업데이트합니다. **파라미터:** @@ -71,14 +71,14 @@ uv add crewai-tools - `additionalFields` (string, 선택): 추가 필드 - 포함해야 하는 다른 필드를 JSON 형식으로 지정하세요. - + **설명:** Jira에서 키로 이슈를 조회합니다. **파라미터:** - `issueKey` (string, 필수): 이슈 키 (예시: "TEST-1234"). - + **설명:** 필터를 사용하여 Jira에서 이슈를 검색합니다. **파라미터:** @@ -104,7 +104,7 @@ uv add crewai-tools - `limit` (string, 선택): 결과 제한 - 반환되는 최대 이슈 수를 제한합니다. 입력하지 않으면 기본값은 10입니다. - + **설명:** Jira에서 JQL로 이슈를 검색합니다. **파라미터:** @@ -117,13 +117,13 @@ uv add crewai-tools ``` - + **설명:** Jira에서 임의의 이슈를 업데이트합니다. 이 기능의 속성 스키마를 얻으려면 DESCRIBE_ACTION_SCHEMA를 사용하세요. **파라미터:** 특정 파라미터 없음 - 예상 스키마를 먼저 확인하려면 JIRA_DESCRIBE_ACTION_SCHEMA를 사용하세요. - + **설명:** 이슈 유형에 대한 예상 스키마를 가져옵니다. 사용하려는 이슈 유형과 일치하는 다른 기능이 없을 경우 먼저 이 기능을 사용하세요. **파라미터:** @@ -132,7 +132,7 @@ uv add crewai-tools - `operation` (string, 필수): 작업 유형 값(예: CREATE_ISSUE 또는 UPDATE_ISSUE). - + **설명:** Jira에서 프로젝트를 가져옵니다. **파라미터:** @@ -144,27 +144,27 @@ uv add crewai-tools ``` - + **설명:** Jira에서 프로젝트별 이슈 유형을 조회합니다. **파라미터:** - `project` (string, 필수): 프로젝트 키. - + **설명:** Jira에서 모든 이슈 유형을 조회합니다. **파라미터:** 필요 없음. - + **설명:** 주어진 프로젝트의 이슈 상태를 조회합니다. **파라미터:** - `project` (string, 필수): 프로젝트 키. - + **설명:** 주어진 프로젝트의 담당자 목록을 조회합니다. **파라미터:** @@ -178,19 +178,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Jira tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Jira capabilities jira_agent = Agent( role="Issue Manager", goal="Manage Jira issues and track project progress efficiently", backstory="An AI assistant specialized in issue tracking and project management.", - tools=[enterprise_tools] + apps=['jira'] ) # Task to create a bug report @@ -212,19 +206,12 @@ crew.kickoff() ### 특정 Jira 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Jira tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["jira_create_issue", "jira_update_issue", "jira_search_by_jql"] -) issue_coordinator = Agent( role="Issue Coordinator", goal="Create and manage Jira issues efficiently", backstory="An AI assistant that focuses on issue creation and management.", - tools=enterprise_tools + apps=['jira'] ) # Task to manage issue workflow @@ -246,17 +233,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_analyst = Agent( role="Project Analyst", goal="Analyze project data and generate insights from Jira", backstory="An experienced project analyst who extracts insights from project management data.", - tools=[enterprise_tools] + apps=['jira'] ) # Task to analyze project status @@ -283,17 +265,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) automation_manager = Agent( role="Automation Manager", goal="Automate issue management and workflow processes", backstory="An AI assistant that automates repetitive issue management tasks.", - tools=[enterprise_tools] + apps=['jira'] ) # Task to automate issue management @@ -321,17 +298,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) schema_specialist = Agent( role="Schema Specialist", goal="Handle complex Jira operations using dynamic schemas", backstory="An AI assistant that can work with dynamic Jira schemas and custom issue types.", - tools=[enterprise_tools] + apps=['jira'] ) # Task using schema-based operations diff --git a/docs/ko/enterprise/integrations/linear.mdx b/docs/ko/enterprise/integrations/linear.mdx index 94aabe578..88b51180b 100644 --- a/docs/ko/enterprise/integrations/linear.mdx +++ b/docs/ko/enterprise/integrations/linear.mdx @@ -25,7 +25,7 @@ Linear 통합을 사용하기 전에 다음을 확인하세요: 2. 인증 통합( Authentication Integrations ) 섹션에서 **Linear**를 찾습니다. 3. **Connect**를 클릭하고 OAuth 절차를 완료합니다. 4. 이슈 및 프로젝트 관리를 위한 필수 권한을 부여합니다. -5. [계정 설정](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 작업 - + **설명:** Linear에서 새로운 이슈를 생성합니다. **파라미터:** @@ -56,7 +56,7 @@ uv add crewai-tools ``` - + **설명:** Linear에서 이슈를 업데이트합니다. **파라미터:** @@ -76,21 +76,21 @@ uv add crewai-tools ``` - + **설명:** Linear에서 ID로 이슈를 가져옵니다. **파라미터:** - `issueId` (string, 필수): 이슈 ID - 가져올 이슈의 레코드 ID를 지정합니다. (예: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **설명:** Linear에서 이슈 식별자로 이슈를 가져옵니다. **파라미터:** - `externalId` (string, 필수): 외부 ID - 가져올 이슈의 사람이 읽을 수 있는 이슈 식별자를 지정합니다. (예: "ABC-1"). - + **설명:** Linear에서 이슈를 검색합니다. **파라미터:** @@ -117,21 +117,21 @@ uv add crewai-tools 사용 가능한 연산자: `$stringExactlyMatches`, `$stringDoesNotExactlyMatch`, `$stringIsIn`, `$stringIsNotIn`, `$stringStartsWith`, `$stringDoesNotStartWith`, `$stringEndsWith`, `$stringDoesNotEndWith`, `$stringContains`, `$stringDoesNotContain`, `$stringGreaterThan`, `$stringLessThan`, `$numberGreaterThanOrEqualTo`, `$numberLessThanOrEqualTo`, `$numberGreaterThan`, `$numberLessThan`, `$dateTimeAfter`, `$dateTimeBefore` - + **설명:** Linear에서 이슈를 삭제합니다. **파라미터:** - `issueId` (string, 필수): 이슈 ID - 삭제할 이슈의 레코드 ID를 지정합니다. (예: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **설명:** Linear에서 이슈를 아카이브합니다. **파라미터:** - `issueId` (string, 필수): 이슈 ID - 아카이브할 이슈의 레코드 ID를 지정합니다. (예: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **설명:** Linear에서 하위 이슈를 생성합니다. **파라미터:** @@ -147,7 +147,7 @@ uv add crewai-tools ``` - + **설명:** Linear에서 새로운 프로젝트를 생성합니다. **파라미터:** @@ -169,7 +169,7 @@ uv add crewai-tools ``` - + **설명:** Linear에서 프로젝트를 업데이트합니다. **파라미터:** @@ -185,21 +185,21 @@ uv add crewai-tools ``` - + **설명:** Linear에서 ID로 프로젝트를 가져옵니다. **파라미터:** - `projectId` (string, 필수): 프로젝트 ID - 가져올 프로젝트의 프로젝트 ID를 지정합니다. (예: "a6634484-6061-4ac7-9739-7dc5e52c796b"). - + **설명:** Linear에서 프로젝트를 삭제합니다. **파라미터:** - `projectId` (string, 필수): 프로젝트 ID - 삭제할 프로젝트의 프로젝트 ID를 지정합니다. (예: "a6634484-6061-4ac7-9739-7dc5e52c796b"). - + **설명:** Linear에서 팀을 검색합니다. **파라미터:** @@ -231,19 +231,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Linear tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Linear capabilities linear_agent = Agent( role="Development Manager", goal="Manage Linear issues and track development progress efficiently", backstory="An AI assistant specialized in software development project management.", - tools=[enterprise_tools] + apps=['linear'] ) # Task to create a bug report @@ -265,19 +259,12 @@ crew.kickoff() ### 특정 Linear 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Linear tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["linear_create_issue", "linear_update_issue", "linear_search_issue"] -) issue_manager = Agent( role="Issue Manager", goal="Create and manage Linear issues efficiently", backstory="An AI assistant that focuses on issue creation and lifecycle management.", - tools=enterprise_tools + apps=['linear'] ) # Task to manage issue workflow @@ -299,17 +286,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Coordinate projects and teams in Linear efficiently", backstory="An experienced project coordinator who manages development cycles and team workflows.", - tools=[enterprise_tools] + apps=['linear'] ) # Task to coordinate project setup @@ -336,17 +318,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) task_organizer = Agent( role="Task Organizer", goal="Organize complex issues into manageable sub-tasks", backstory="An AI assistant that breaks down complex development work into organized sub-tasks.", - tools=[enterprise_tools] + apps=['linear'] ) # Task to create issue hierarchy @@ -373,17 +350,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) workflow_automator = Agent( role="Workflow Automator", goal="Automate development workflow processes in Linear", backstory="An AI assistant that automates repetitive development workflow tasks.", - tools=[enterprise_tools] + apps=['linear'] ) # Complex workflow automation task diff --git a/docs/ko/enterprise/integrations/microsoft_excel.mdx b/docs/ko/enterprise/integrations/microsoft_excel.mdx new file mode 100644 index 000000000..fcd27265a --- /dev/null +++ b/docs/ko/enterprise/integrations/microsoft_excel.mdx @@ -0,0 +1,234 @@ +--- +title: Microsoft Excel 통합 +description: "CrewAI를 위한 Microsoft Excel 통합으로 통합 문서 및 데이터 관리." +icon: "table" +mode: "wide" +--- + +## 개요 + +에이전트가 OneDrive 또는 SharePoint에서 Excel 통합 문서, 워크시트, 테이블 및 차트를 생성하고 관리할 수 있도록 합니다. AI 기반 자동화로 데이터 범위를 조작하고, 시각화를 생성하고, 테이블을 관리하며, 스프레드시트 워크플로를 간소화합니다. + +## 전제 조건 + +Microsoft Excel 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- Excel 및 OneDrive/SharePoint 액세스 권한이 있는 Microsoft 365 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Microsoft 계정 연결 + +## Microsoft Excel 통합 설정 + +### 1. Microsoft 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Microsoft Excel** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. 파일 및 Excel 통합 문서 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** OneDrive 또는 SharePoint에 새 Excel 통합 문서를 만듭니다. + + **매개변수:** + - `file_path` (string, 필수): 통합 문서를 만들 경로 (예: 'MyWorkbook.xlsx') + - `worksheets` (array, 선택사항): 만들 초기 워크시트들. 각 항목은 `name` (string, 워크시트 이름)이 있는 객체. + + + + **설명:** OneDrive 또는 SharePoint에서 모든 Excel 통합 문서를 가져옵니다. + + **매개변수:** + - `select` (string, 선택사항): 반환할 특정 속성 선택. + - `filter` (string, 선택사항): OData 구문을 사용하여 결과 필터링. + - `expand` (string, 선택사항): 관련 리소스를 인라인으로 확장. + - `top` (integer, 선택사항): 반환할 항목 수 (최소 1, 최대 999). + - `orderby` (string, 선택사항): 지정된 속성으로 결과 정렬. + + + + **설명:** Excel 통합 문서의 모든 워크시트를 가져옵니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `select` (string, 선택사항): 반환할 특정 속성 선택 (예: 'id,name,position'). + - `filter` (string, 선택사항): OData 구문을 사용하여 결과 필터링. + - `expand` (string, 선택사항): 관련 리소스를 인라인으로 확장. + - `top` (integer, 선택사항): 반환할 항목 수 (최소 1, 최대 999). + - `orderby` (string, 선택사항): 지정된 속성으로 결과 정렬. + + + + **설명:** Excel 통합 문서에 새 워크시트를 만듭니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `name` (string, 필수): 새 워크시트의 이름. + + + + **설명:** Excel 워크시트의 특정 범위에서 데이터를 가져옵니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + - `range` (string, 필수): 범위 주소 (예: 'A1:C10'). + + + + **설명:** Excel 워크시트의 특정 범위에서 데이터를 업데이트합니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + - `range` (string, 필수): 범위 주소 (예: 'A1:C10'). + - `values` (array, 필수): 범위에 설정할 값들의 2D 배열. 각 내부 배열은 행을 나타내며, 요소는 string, number 또는 integer일 수 있음. + + + + **설명:** Excel 워크시트에 테이블을 만듭니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + - `range` (string, 필수): 테이블의 범위 (예: 'A1:D10'). + - `has_headers` (boolean, 선택사항): 첫 번째 행이 헤더를 포함하는지 여부. 기본값: true. + + + + **설명:** Excel 워크시트의 모든 테이블을 가져옵니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + + + + **설명:** Excel 테이블에 새 행을 추가합니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + - `table_name` (string, 필수): 테이블의 이름. + - `values` (array, 필수): 새 행의 값들 배열. 요소는 string, number 또는 integer일 수 있음. + + + + **설명:** Excel 워크시트에 차트를 만듭니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + - `chart_type` (string, 필수): 차트 유형 (예: 'ColumnClustered', 'Line', 'Pie'). + - `source_data` (string, 필수): 차트의 데이터 범위 (예: 'A1:B10'). + - `series_by` (string, 선택사항): 데이터 해석 방법 ('Auto', 'Columns' 또는 'Rows'). 기본값: 'Auto'. + + + + **설명:** Excel 워크시트의 단일 셀 값을 가져옵니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + - `row` (integer, 필수): 행 번호 (0 기반). + - `column` (integer, 필수): 열 번호 (0 기반). + + + + **설명:** Excel 워크시트의 사용된 범위를 가져옵니다 (모든 데이터를 포함). + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + + + + **설명:** Excel 워크시트의 모든 차트를 가져옵니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + + + + **설명:** Excel 통합 문서에서 워크시트를 삭제합니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 삭제할 워크시트의 이름. + + + + **설명:** Excel 워크시트에서 테이블을 삭제합니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + - `worksheet_name` (string, 필수): 워크시트의 이름. + - `table_name` (string, 필수): 삭제할 테이블의 이름. + + + + **설명:** Excel 통합 문서의 모든 명명된 범위를 가져옵니다. + + **매개변수:** + - `file_id` (string, 필수): Excel 파일의 ID. + + + +## 사용 예제 + +### 기본 Microsoft Excel 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Microsoft Excel 기능을 가진 에이전트 생성 +excel_agent = Agent( + role="Excel 데이터 관리자", + goal="Excel 통합 문서와 데이터를 효율적으로 관리", + backstory="Microsoft Excel 작업 및 데이터 조작 전문 AI 어시스턴트.", + apps=['microsoft_excel'] # 모든 Excel 작업을 사용할 수 있습니다 +) + +# 새 통합 문서 생성 작업 +create_workbook_task = Task( + description="'월간보고서.xlsx'라는 이름으로 새 Excel 통합 문서를 만들고 '매출데이터'라는 초기 워크시트를 포함하세요.", + agent=excel_agent, + expected_output="새 통합 문서 '월간보고서.xlsx'가 '매출데이터' 워크시트와 함께 생성됨." +) + +# 작업 실행 +crew = Crew( + agents=[excel_agent], + tasks=[create_workbook_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Microsoft 계정이 파일 액세스에 필요한 권한을 가지고 있는지 확인하세요 (예: `Files.Read.All`, `Files.ReadWrite.All`). +- OAuth 연결이 필요한 모든 범위를 포함하는지 확인하세요. + +**파일 생성 문제** +- 통합 문서를 만들 때 `file_path`가 `.xlsx` 확장자로 끝나는지 확인하세요. +- 대상 위치(OneDrive/SharePoint)에 쓰기 권한이 있는지 확인하세요. + +### 도움 받기 + + + Microsoft Excel 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/microsoft_onedrive.mdx b/docs/ko/enterprise/integrations/microsoft_onedrive.mdx new file mode 100644 index 000000000..3bf634544 --- /dev/null +++ b/docs/ko/enterprise/integrations/microsoft_onedrive.mdx @@ -0,0 +1,174 @@ +--- +title: Microsoft OneDrive 통합 +description: "CrewAI를 위한 Microsoft OneDrive 통합으로 파일 및 폴더 관리." +icon: "cloud" +mode: "wide" +--- + +## 개요 + +에이전트가 Microsoft OneDrive에서 파일과 폴더를 업로드, 다운로드 및 관리할 수 있도록 합니다. AI 기반 자동화로 파일 작업을 자동화하고, 콘텐츠를 구성하고, 공유 링크를 생성하며, 클라우드 스토리지 워크플로를 간소화합니다. + +## 전제 조건 + +Microsoft OneDrive 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- OneDrive 액세스 권한이 있는 Microsoft 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Microsoft 계정 연결 + +## Microsoft OneDrive 통합 설정 + +### 1. Microsoft 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Microsoft OneDrive** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. 파일 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** OneDrive의 파일과 폴더를 나열합니다. + + **매개변수:** + - `top` (integer, 선택사항): 검색할 항목 수 (최대 1000). 기본값: 50. + - `orderby` (string, 선택사항): 필드별 정렬 (예: "name asc", "lastModifiedDateTime desc"). 기본값: "name asc". + - `filter` (string, 선택사항): OData 필터 표현식. + + + + **설명:** 특정 파일 또는 폴더에 대한 정보를 가져옵니다. + + **매개변수:** + - `item_id` (string, 필수): 파일 또는 폴더의 ID. + + + + **설명:** OneDrive에서 파일을 다운로드합니다. + + **매개변수:** + - `item_id` (string, 필수): 다운로드할 파일의 ID. + + + + **설명:** OneDrive에 파일을 업로드합니다. + + **매개변수:** + - `file_name` (string, 필수): 업로드할 파일의 이름. + - `content` (string, 필수): Base64로 인코딩된 파일 내용. + + + + **설명:** OneDrive에 새 폴더를 만듭니다. + + **매개변수:** + - `folder_name` (string, 필수): 만들 폴더의 이름. + + + + **설명:** OneDrive에서 파일 또는 폴더를 삭제합니다. + + **매개변수:** + - `item_id` (string, 필수): 삭제할 파일 또는 폴더의 ID. + + + + **설명:** OneDrive에서 파일 또는 폴더를 복사합니다. + + **매개변수:** + - `item_id` (string, 필수): 복사할 파일 또는 폴더의 ID. + - `parent_id` (string, 선택사항): 대상 폴더의 ID (선택사항, 기본값은 루트). + - `new_name` (string, 선택사항): 복사된 항목의 새 이름 (선택사항). + + + + **설명:** OneDrive에서 파일 또는 폴더를 이동합니다. + + **매개변수:** + - `item_id` (string, 필수): 이동할 파일 또는 폴더의 ID. + - `parent_id` (string, 필수): 대상 폴더의 ID. + - `new_name` (string, 선택사항): 항목의 새 이름 (선택사항). + + + + **설명:** OneDrive에서 파일과 폴더를 검색합니다. + + **매개변수:** + - `query` (string, 필수): 검색 쿼리 문자열. + - `top` (integer, 선택사항): 반환할 결과 수 (최대 1000). 기본값: 50. + + + + **설명:** 파일 또는 폴더의 공유 링크를 만듭니다. + + **매개변수:** + - `item_id` (string, 필수): 공유할 파일 또는 폴더의 ID. + - `type` (string, 선택사항): 공유 링크 유형. 옵션: view, edit, embed. 기본값: view. + - `scope` (string, 선택사항): 공유 링크 범위. 옵션: anonymous, organization. 기본값: anonymous. + + + + **설명:** 파일의 썸네일을 가져옵니다. + + **매개변수:** + - `item_id` (string, 필수): 파일의 ID. + + + +## 사용 예제 + +### 기본 Microsoft OneDrive 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Microsoft OneDrive 기능을 가진 에이전트 생성 +onedrive_agent = Agent( + role="파일 관리자", + goal="OneDrive에서 파일과 폴더를 효율적으로 관리", + backstory="Microsoft OneDrive 파일 작업 및 구성 전문 AI 어시스턴트.", + apps=['microsoft_onedrive'] # 모든 OneDrive 작업을 사용할 수 있습니다 +) + +# 파일 나열 및 폴더 생성 작업 +organize_files_task = Task( + description="OneDrive 루트 디렉토리의 모든 파일을 나열하고 '프로젝트 문서'라는 새 폴더를 만드세요.", + agent=onedrive_agent, + expected_output="파일 목록이 표시되고 새 폴더 '프로젝트 문서'가 생성됨." +) + +# 작업 실행 +crew = Crew( + agents=[onedrive_agent], + tasks=[organize_files_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Microsoft 계정이 파일 액세스에 필요한 권한을 가지고 있는지 확인하세요 (예: `Files.Read`, `Files.ReadWrite`). +- OAuth 연결이 필요한 모든 범위를 포함하는지 확인하세요. + +**파일 업로드 문제** +- 파일 업로드 시 `file_name`과 `content`가 제공되는지 확인하세요. +- 바이너리 파일의 경우 내용이 Base64로 인코딩되어야 합니다. + +### 도움 받기 + + + Microsoft OneDrive 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/microsoft_outlook.mdx b/docs/ko/enterprise/integrations/microsoft_outlook.mdx new file mode 100644 index 000000000..1fc2d8964 --- /dev/null +++ b/docs/ko/enterprise/integrations/microsoft_outlook.mdx @@ -0,0 +1,161 @@ +--- +title: Microsoft Outlook 통합 +description: "CrewAI를 위한 Microsoft Outlook 통합으로 이메일, 캘린더 및 연락처 관리." +icon: "envelope" +mode: "wide" +--- + +## 개요 + +에이전트가 Outlook 이메일, 캘린더 이벤트 및 연락처에 액세스하고 관리할 수 있도록 합니다. AI 기반 자동화로 이메일을 보내고, 메시지를 검색하고, 캘린더 이벤트를 관리하며, 연락처를 구성합니다. + +## 전제 조건 + +Microsoft Outlook 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- Outlook 액세스 권한이 있는 Microsoft 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Microsoft 계정 연결 + +## Microsoft Outlook 통합 설정 + +### 1. Microsoft 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Microsoft Outlook** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. 이메일, 캘린더 및 연락처 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** 사용자의 사서함에서 이메일 메시지를 가져옵니다. + + **매개변수:** + - `top` (integer, 선택사항): 검색할 메시지 수 (최대 1000). 기본값: 10. + - `filter` (string, 선택사항): OData 필터 표현식 (예: "isRead eq false"). + - `search` (string, 선택사항): 검색 쿼리 문자열. + - `orderby` (string, 선택사항): 필드별 정렬 (예: "receivedDateTime desc"). 기본값: "receivedDateTime desc". + - `select` (string, 선택사항): 반환할 특정 속성 선택. + - `expand` (string, 선택사항): 관련 리소스를 인라인으로 확장. + + + + **설명:** 이메일 메시지를 보냅니다. + + **매개변수:** + - `to_recipients` (array, 필수): 받는 사람의 이메일 주소 배열. + - `cc_recipients` (array, 선택사항): 참조 받는 사람의 이메일 주소 배열. + - `bcc_recipients` (array, 선택사항): 숨은 참조 받는 사람의 이메일 주소 배열. + - `subject` (string, 필수): 이메일 제목. + - `body` (string, 필수): 이메일 본문 내용. + - `body_type` (string, 선택사항): 본문 내용 유형. 옵션: Text, HTML. 기본값: HTML. + - `importance` (string, 선택사항): 메시지 중요도 수준. 옵션: low, normal, high. 기본값: normal. + - `reply_to` (array, 선택사항): 회신용 이메일 주소 배열. + - `save_to_sent_items` (boolean, 선택사항): 보낸 편지함 폴더에 메시지를 저장할지 여부. 기본값: true. + + + + **설명:** 사용자의 캘린더에서 캘린더 이벤트를 가져옵니다. + + **매개변수:** + - `top` (integer, 선택사항): 검색할 이벤트 수 (최대 1000). 기본값: 10. + - `skip` (integer, 선택사항): 건너뛸 이벤트 수. 기본값: 0. + - `filter` (string, 선택사항): OData 필터 표현식 (예: "start/dateTime ge '2024-01-01T00:00:00Z'"). + - `orderby` (string, 선택사항): 필드별 정렬 (예: "start/dateTime asc"). 기본값: "start/dateTime asc". + + + + **설명:** 새 캘린더 이벤트를 만듭니다. + + **매개변수:** + - `subject` (string, 필수): 이벤트 제목/제목. + - `body` (string, 선택사항): 이벤트 본문/설명. + - `start_datetime` (string, 필수): ISO 8601 형식의 시작 날짜 및 시간 (예: '2024-01-20T10:00:00'). + - `end_datetime` (string, 필수): ISO 8601 형식의 종료 날짜 및 시간. + - `timezone` (string, 선택사항): 시간대 (예: 'Pacific Standard Time'). 기본값: UTC. + - `location` (string, 선택사항): 이벤트 위치. + - `attendees` (array, 선택사항): 참석자의 이메일 주소 배열. + + + + **설명:** 사용자의 주소록에서 연락처를 가져옵니다. + + **매개변수:** + - `top` (integer, 선택사항): 검색할 연락처 수 (최대 1000). 기본값: 10. + - `skip` (integer, 선택사항): 건너뛸 연락처 수. 기본값: 0. + - `filter` (string, 선택사항): OData 필터 표현식. + - `orderby` (string, 선택사항): 필드별 정렬 (예: "displayName asc"). 기본값: "displayName asc". + + + + **설명:** 사용자의 주소록에 새 연락처를 만듭니다. + + **매개변수:** + - `displayName` (string, 필수): 연락처의 표시 이름. + - `givenName` (string, 선택사항): 연락처의 이름. + - `surname` (string, 선택사항): 연락처의 성. + - `emailAddresses` (array, 선택사항): 이메일 주소 배열. 각 항목은 `address` (string)와 `name` (string)이 있는 객체. + - `businessPhones` (array, 선택사항): 사업용 전화번호 배열. + - `homePhones` (array, 선택사항): 집 전화번호 배열. + - `jobTitle` (string, 선택사항): 연락처의 직책. + - `companyName` (string, 선택사항): 연락처의 회사 이름. + + + +## 사용 예제 + +### 기본 Microsoft Outlook 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Microsoft Outlook 기능을 가진 에이전트 생성 +outlook_agent = Agent( + role="이메일 어시스턴트", + goal="이메일, 캘린더 이벤트 및 연락처를 효율적으로 관리", + backstory="Microsoft Outlook 작업 및 커뮤니케이션 관리 전문 AI 어시스턴트.", + apps=['microsoft_outlook'] # 모든 Outlook 작업을 사용할 수 있습니다 +) + +# 이메일 보내기 작업 +send_email_task = Task( + description="'colleague@example.com'에게 제목 '프로젝트 업데이트'와 본문 '안녕하세요, 프로젝트의 최신 업데이트입니다. 감사합니다.'로 이메일을 보내세요", + agent=outlook_agent, + expected_output="colleague@example.com에게 이메일이 성공적으로 전송됨" +) + +# 작업 실행 +crew = Crew( + agents=[outlook_agent], + tasks=[send_email_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Microsoft 계정이 이메일, 캘린더 및 연락처 액세스에 필요한 권한을 가지고 있는지 확인하세요. +- 필요한 범위: `Mail.Read`, `Mail.Send`, `Calendars.Read`, `Calendars.ReadWrite`, `Contacts.Read`, `Contacts.ReadWrite`. + +**이메일 보내기 문제** +- `send_email`에 `to_recipients`, `subject`, `body`가 제공되는지 확인하세요. +- 이메일 주소가 올바르게 형식화되어 있는지 확인하세요. + +### 도움 받기 + + + Microsoft Outlook 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/microsoft_sharepoint.mdx b/docs/ko/enterprise/integrations/microsoft_sharepoint.mdx new file mode 100644 index 000000000..d397e68c0 --- /dev/null +++ b/docs/ko/enterprise/integrations/microsoft_sharepoint.mdx @@ -0,0 +1,185 @@ +--- +title: Microsoft SharePoint 통합 +description: "CrewAI를 위한 Microsoft SharePoint 통합으로 사이트, 목록 및 문서 관리." +icon: "folder-tree" +mode: "wide" +--- + +## 개요 + +에이전트가 SharePoint 사이트, 목록 및 문서 라이브러리에 액세스하고 관리할 수 있도록 합니다. AI 기반 자동화로 사이트 정보를 검색하고, 목록 항목을 관리하고, 파일을 업로드 및 구성하며, SharePoint 워크플로를 간소화합니다. + +## 전제 조건 + +Microsoft SharePoint 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- SharePoint 액세스 권한이 있는 Microsoft 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Microsoft 계정 연결 + +## Microsoft SharePoint 통합 설정 + +### 1. Microsoft 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Microsoft SharePoint** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. SharePoint 사이트 및 파일 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** 사용자가 액세스할 수 있는 모든 SharePoint 사이트를 가져옵니다. + + **매개변수:** + - `search` (string, 선택사항): 사이트를 필터링하기 위한 검색 쿼리. + - `select` (string, 선택사항): 반환할 특정 속성 선택 (예: 'displayName,id,webUrl'). + - `filter` (string, 선택사항): OData 구문을 사용하여 결과 필터링. + - `expand` (string, 선택사항): 관련 리소스를 인라인으로 확장. + - `top` (integer, 선택사항): 반환할 항목 수 (최소 1, 최대 999). + - `skip` (integer, 선택사항): 건너뛸 항목 수 (최소 0). + - `orderby` (string, 선택사항): 지정된 속성으로 결과 정렬 (예: 'displayName desc'). + + + + **설명:** 특정 SharePoint 사이트에 대한 정보를 가져옵니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + - `select` (string, 선택사항): 반환할 특정 속성 선택 (예: 'displayName,id,webUrl,drives'). + - `expand` (string, 선택사항): 관련 리소스를 인라인으로 확장 (예: 'drives,lists'). + + + + **설명:** SharePoint 사이트의 모든 목록을 가져옵니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + + + + **설명:** 특정 목록에 대한 정보를 가져옵니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + - `list_id` (string, 필수): 목록의 ID. + + + + **설명:** SharePoint 목록에서 항목을 가져옵니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + - `list_id` (string, 필수): 목록의 ID. + - `expand` (string, 선택사항): 관련 데이터 확장 (예: 'fields'). + + + + **설명:** SharePoint 목록에 새 항목을 만듭니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + - `list_id` (string, 필수): 목록의 ID. + - `fields` (object, 필수): 새 항목의 필드 값. + + + + **설명:** SharePoint 목록의 항목을 업데이트합니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + - `list_id` (string, 필수): 목록의 ID. + - `item_id` (string, 필수): 업데이트할 항목의 ID. + - `fields` (object, 필수): 업데이트할 필드 값. + + + + **설명:** SharePoint 목록에서 항목을 삭제합니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + - `list_id` (string, 필수): 목록의 ID. + - `item_id` (string, 필수): 삭제할 항목의 ID. + + + + **설명:** SharePoint 문서 라이브러리에 파일을 업로드합니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + - `file_path` (string, 필수): 파일을 업로드할 경로 (예: 'folder/fileName.txt'). + - `content` (string, 필수): 업로드할 파일의 내용. + + + + **설명:** SharePoint 문서 라이브러리에서 파일과 폴더를 가져옵니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + + + + **설명:** SharePoint 문서 라이브러리에서 파일 또는 폴더를 삭제합니다. + + **매개변수:** + - `site_id` (string, 필수): SharePoint 사이트의 ID. + - `item_id` (string, 필수): 삭제할 파일 또는 폴더의 ID. + + + +## 사용 예제 + +### 기본 Microsoft SharePoint 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Microsoft SharePoint 기능을 가진 에이전트 생성 +sharepoint_agent = Agent( + role="SharePoint 관리자", + goal="SharePoint 사이트, 목록 및 문서를 효율적으로 관리", + backstory="Microsoft SharePoint 관리 및 콘텐츠 관리 전문 AI 어시스턴트.", + apps=['microsoft_sharepoint'] # 모든 SharePoint 작업을 사용할 수 있습니다 +) + +# 모든 사이트 가져오기 작업 +get_sites_task = Task( + description="액세스할 수 있는 모든 SharePoint 사이트를 나열하세요.", + agent=sharepoint_agent, + expected_output="표시 이름과 URL이 포함된 SharePoint 사이트 목록." +) + +# 작업 실행 +crew = Crew( + agents=[sharepoint_agent], + tasks=[get_sites_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Microsoft 계정이 SharePoint 액세스에 필요한 권한을 가지고 있는지 확인하세요 (예: `Sites.Read.All`, `Sites.ReadWrite.All`). +- OAuth 연결이 필요한 모든 범위를 포함하는지 확인하세요. + +**사이트/목록/항목 ID 문제** +- 사이트, 목록, 항목 ID가 올바른지 다시 확인하세요. +- 참조된 리소스가 존재하고 액세스할 수 있는지 확인하세요. + +### 도움 받기 + + + Microsoft SharePoint 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/microsoft_teams.mdx b/docs/ko/enterprise/integrations/microsoft_teams.mdx new file mode 100644 index 000000000..7f242b367 --- /dev/null +++ b/docs/ko/enterprise/integrations/microsoft_teams.mdx @@ -0,0 +1,136 @@ +--- +title: Microsoft Teams 통합 +description: "CrewAI를 위한 Microsoft Teams 통합으로 팀 협업 및 커뮤니케이션." +icon: "users" +mode: "wide" +--- + +## 개요 + +에이전트가 Teams 데이터에 액세스하고, 메시지를 보내고, 회의를 만들고, 채널을 관리할 수 있도록 합니다. AI 기반 자동화로 팀 커뮤니케이션을 자동화하고, 회의를 예약하고, 메시지를 검색하며, 협업 워크플로를 간소화합니다. + +## 전제 조건 + +Microsoft Teams 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- Teams 액세스 권한이 있는 Microsoft 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Microsoft 계정 연결 + +## Microsoft Teams 통합 설정 + +### 1. Microsoft 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Microsoft Teams** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. Teams 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** 사용자가 멤버인 모든 팀을 가져옵니다. + + **매개변수:** + - 매개변수가 필요하지 않습니다. + + + + **설명:** 특정 팀의 채널을 가져옵니다. + + **매개변수:** + - `team_id` (string, 필수): 팀의 ID. + + + + **설명:** Teams 채널에 메시지를 보냅니다. + + **매개변수:** + - `team_id` (string, 필수): 팀의 ID. + - `channel_id` (string, 필수): 채널의 ID. + - `message` (string, 필수): 메시지 내용. + - `content_type` (string, 선택사항): 콘텐츠 유형 (html 또는 text). 옵션: html, text. 기본값: text. + + + + **설명:** Teams 채널에서 메시지를 가져옵니다. + + **매개변수:** + - `team_id` (string, 필수): 팀의 ID. + - `channel_id` (string, 필수): 채널의 ID. + - `top` (integer, 선택사항): 검색할 메시지 수 (최대 50). 기본값: 20. + + + + **설명:** Teams 회의를 만듭니다. + + **매개변수:** + - `subject` (string, 필수): 회의 제목/제목. + - `startDateTime` (string, 필수): 회의 시작 시간 (시간대가 포함된 ISO 8601 형식). + - `endDateTime` (string, 필수): 회의 종료 시간 (시간대가 포함된 ISO 8601 형식). + + + + **설명:** 웹 참가 URL로 온라인 회의를 검색합니다. + + **매개변수:** + - `join_web_url` (string, 필수): 검색할 회의의 웹 참가 URL. + + + +## 사용 예제 + +### 기본 Microsoft Teams 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Microsoft Teams 기능을 가진 에이전트 생성 +teams_agent = Agent( + role="Teams 코디네이터", + goal="Teams 커뮤니케이션 및 회의를 효율적으로 관리", + backstory="Microsoft Teams 작업 및 팀 협업 전문 AI 어시스턴트.", + apps=['microsoft_teams'] # 모든 Teams 작업을 사용할 수 있습니다 +) + +# 팀 및 채널 탐색 작업 +explore_teams_task = Task( + description="내가 멤버인 모든 팀을 나열한 다음 첫 번째 팀의 채널을 가져오세요.", + agent=teams_agent, + expected_output="팀 및 채널 목록이 표시됨." +) + +# 작업 실행 +crew = Crew( + agents=[teams_agent], + tasks=[explore_teams_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Microsoft 계정이 Teams 액세스에 필요한 권한을 가지고 있는지 확인하세요. +- 필요한 범위: `Team.ReadBasic.All`, `Channel.ReadBasic.All`, `ChannelMessage.Send`, `ChannelMessage.Read.All`, `OnlineMeetings.ReadWrite`, `OnlineMeetings.Read`. + +**팀 및 채널 액세스** +- 액세스하려는 팀의 멤버인지 확인하세요. +- 팀 및 채널 ID가 올바른지 다시 확인하세요. + +### 도움 받기 + + + Microsoft Teams 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/microsoft_word.mdx b/docs/ko/enterprise/integrations/microsoft_word.mdx new file mode 100644 index 000000000..a43732b6b --- /dev/null +++ b/docs/ko/enterprise/integrations/microsoft_word.mdx @@ -0,0 +1,127 @@ +--- +title: Microsoft Word 통합 +description: "CrewAI를 위한 Microsoft Word 통합으로 문서 생성 및 관리." +icon: "file-word" +mode: "wide" +--- + +## 개요 + +에이전트가 OneDrive 또는 SharePoint에서 Word 문서와 텍스트 파일을 생성, 읽기 및 관리할 수 있도록 합니다. AI 기반 자동화로 문서 생성을 자동화하고, 콘텐츠를 검색하고, 문서 속성을 관리하며, 문서 워크플로를 간소화합니다. + +## 전제 조건 + +Microsoft Word 통합을 사용하기 전에 다음 사항을 확인하세요: + +- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정 +- Word 및 OneDrive/SharePoint 액세스 권한이 있는 Microsoft 계정 +- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Microsoft 계정 연결 + +## Microsoft Word 통합 설정 + +### 1. Microsoft 계정 연결 + +1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동 +2. 인증 통합 섹션에서 **Microsoft Word** 찾기 +3. **연결**을 클릭하고 OAuth 플로우 완료 +4. 파일 액세스에 필요한 권한 부여 +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token 복사 + +### 2. 필요한 패키지 설치 + +```bash +uv add crewai-tools +``` + +## 사용 가능한 작업 + + + + **설명:** OneDrive 또는 SharePoint에서 모든 Word 문서를 가져옵니다. + + **매개변수:** + - `select` (string, 선택사항): 반환할 특정 속성 선택. + - `filter` (string, 선택사항): OData 구문을 사용하여 결과 필터링. + - `expand` (string, 선택사항): 관련 리소스를 인라인으로 확장. + - `top` (integer, 선택사항): 반환할 항목 수 (최소 1, 최대 999). + - `orderby` (string, 선택사항): 지정된 속성으로 결과 정렬. + + + + **설명:** 내용이 있는 텍스트 문서(.txt)를 만듭니다. 읽기 가능하고 편집 가능해야 하는 프로그래밍 방식 콘텐츠 생성에 권장됩니다. + + **매개변수:** + - `file_name` (string, 필수): 텍스트 문서의 이름 (.txt로 끝나야 함). + - `content` (string, 선택사항): 문서의 텍스트 내용. 기본값: "API를 통해 생성된 새 텍스트 문서입니다." + + + + **설명:** 문서의 내용을 가져옵니다 (텍스트 파일에서 가장 잘 작동). + + **매개변수:** + - `file_id` (string, 필수): 문서의 ID. + + + + **설명:** 문서의 속성과 메타데이터를 가져옵니다. + + **매개변수:** + - `file_id` (string, 필수): 문서의 ID. + + + + **설명:** 문서를 삭제합니다. + + **매개변수:** + - `file_id` (string, 필수): 삭제할 문서의 ID. + + + +## 사용 예제 + +### 기본 Microsoft Word 에이전트 설정 + +```python +from crewai import Agent, Task, Crew + +# Microsoft Word 기능을 가진 에이전트 생성 +word_agent = Agent( + role="문서 관리자", + goal="Word 문서와 텍스트 파일을 효율적으로 관리", + backstory="Microsoft Word 문서 작업 및 콘텐츠 관리 전문 AI 어시스턴트.", + apps=['microsoft_word'] # 모든 Word 작업을 사용할 수 있습니다 +) + +# 새 텍스트 문서 생성 작업 +create_doc_task = Task( + description="'회의노트.txt'라는 새 텍스트 문서를 만들고 내용은 '2024년 1월 회의 노트: 주요 토론 사항 및 실행 항목.'으로 하세요", + agent=word_agent, + expected_output="새 텍스트 문서 '회의노트.txt'가 성공적으로 생성됨." +) + +# 작업 실행 +crew = Crew( + agents=[word_agent], + tasks=[create_doc_task] +) + +crew.kickoff() +``` + +## 문제 해결 + +### 일반적인 문제 + +**인증 오류** +- Microsoft 계정이 파일 액세스에 필요한 권한을 가지고 있는지 확인하세요 (예: `Files.Read.All`, `Files.ReadWrite.All`). +- OAuth 연결이 필요한 모든 범위를 포함하는지 확인하세요. + +**파일 생성 문제** +- 텍스트 문서를 만들 때 `file_name`이 `.txt` 확장자로 끝나는지 확인하세요. +- 대상 위치(OneDrive/SharePoint)에 쓰기 권한이 있는지 확인하세요. + +### 도움 받기 + + + Microsoft Word 통합 설정 또는 문제 해결에 대한 지원이 필요하시면 지원팀에 문의하세요. + diff --git a/docs/ko/enterprise/integrations/notion.mdx b/docs/ko/enterprise/integrations/notion.mdx index 00b324ed1..807f4265b 100644 --- a/docs/ko/enterprise/integrations/notion.mdx +++ b/docs/ko/enterprise/integrations/notion.mdx @@ -25,7 +25,7 @@ Notion 통합을 사용하기 전에 다음을 확인하세요: 2. 인증 통합(Auhtentication Integrations) 섹션에서 **Notion**을(를) 찾습니다. 3. **Connect**를 클릭하고 OAuth 플로우를 완료합니다. 4. 페이지 및 데이터베이스 관리를 위한 필요한 권한을 부여합니다. -5. [Account Settings](https://app.crewai.com/crewai_plus/settings/account)에서 Enterprise Token을 복사합니다. +5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다. ### 2. 필수 패키지 설치 @@ -36,7 +36,7 @@ uv add crewai-tools ## 사용 가능한 액션 - + **설명:** Notion에서 페이지를 생성합니다. **파라미터:** @@ -93,7 +93,7 @@ uv add crewai-tools ``` - + **설명:** Notion에서 페이지를 업데이트합니다. **파라미터:** @@ -127,21 +127,21 @@ uv add crewai-tools ``` - + **설명:** Notion에서 ID로 페이지를 가져옵니다. **파라미터:** - `pageId` (string, 필수): 페이지 ID - 가져올 페이지의 ID를 지정합니다. (예: "59833787-2cf9-4fdf-8782-e53db20768a5"). - + **설명:** Notion에서 페이지를 보관합니다. **파라미터:** - `pageId` (string, 필수): 페이지 ID - 보관할 페이지의 ID를 지정합니다. (예: "59833787-2cf9-4fdf-8782-e53db20768a5"). - + **설명:** 필터를 사용하여 Notion에서 페이지를 검색합니다. **파라미터:** @@ -166,14 +166,14 @@ uv add crewai-tools 사용 가능한 필드: `query`, `filter.value`, `direction`, `page_size` - + **설명:** Notion에서 페이지 콘텐츠(블록)를 가져옵니다. **파라미터:** - `blockId` (string, 필수): 페이지 ID - 해당 블록이나 페이지의 모든 자식 블록을 순서대로 가져오기 위해 Block 또는 Page ID를 지정합니다. (예: "59833787-2cf9-4fdf-8782-e53db20768a5"). - + **설명:** Notion에서 블록을 업데이트합니다. **파라미터:** @@ -260,14 +260,14 @@ uv add crewai-tools ``` - + **설명:** Notion에서 ID로 블록을 가져옵니다. **파라미터:** - `blockId` (string, 필수): 블록 ID - 가져올 블록의 ID를 지정합니다. (예: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6"). - + **설명:** Notion에서 블록을 삭제합니다. **파라미터:** @@ -281,19 +281,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Notion tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Notion capabilities notion_agent = Agent( role="Documentation Manager", goal="Manage documentation and knowledge base in Notion efficiently", backstory="An AI assistant specialized in content management and documentation.", - tools=[enterprise_tools] + apps=['notion'] ) # Task to create a meeting notes page @@ -315,19 +309,12 @@ crew.kickoff() ### 특정 Notion 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Notion tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["notion_create_page", "notion_update_block", "notion_search_pages"] -) content_manager = Agent( role="Content Manager", goal="Create and manage content pages efficiently", backstory="An AI assistant that focuses on content creation and management.", - tools=enterprise_tools + apps=['notion'] ) # Task to manage content workflow @@ -349,17 +336,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) knowledge_curator = Agent( role="Knowledge Curator", goal="Curate and organize knowledge base content in Notion", backstory="An experienced knowledge manager who organizes and maintains comprehensive documentation.", - tools=[enterprise_tools] + apps=['notion'] ) # Task to curate knowledge base @@ -386,17 +368,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) content_organizer = Agent( role="Content Organizer", goal="Organize and structure content blocks for optimal readability", backstory="An AI assistant that specializes in content structure and user experience.", - tools=[enterprise_tools] + apps=['notion'] ) # Task to organize content structure @@ -424,17 +401,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) doc_automator = Agent( role="Documentation Automator", goal="Automate documentation workflows and maintenance", backstory="An AI assistant that automates repetitive documentation tasks.", - tools=[enterprise_tools] + apps=['notion'] ) # Complex documentation automation task diff --git a/docs/ko/enterprise/integrations/salesforce.mdx b/docs/ko/enterprise/integrations/salesforce.mdx index 1ffac80a9..94f68d497 100644 --- a/docs/ko/enterprise/integrations/salesforce.mdx +++ b/docs/ko/enterprise/integrations/salesforce.mdx @@ -22,7 +22,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: ### **레코드 관리** - + **설명:** Salesforce에서 새로운 Contact 레코드를 생성합니다. **파라미터:** @@ -35,7 +35,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 사용자 정의 Contact 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 새로운 Lead 레코드를 생성합니다. **파라미터:** @@ -51,7 +51,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 사용자 정의 Lead 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 새로운 Opportunity 레코드를 생성합니다. **파라미터:** @@ -66,7 +66,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 사용자 정의 Opportunity 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 새로운 Task 레코드를 생성합니다. **파라미터:** @@ -84,7 +84,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 사용자 정의 Task 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 새로운 Account 레코드를 생성합니다. **파라미터:** @@ -96,7 +96,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 사용자 정의 Account 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 모든 오브젝트 유형의 레코드를 생성합니다. **참고:** 이 기능은 사용자 정의 또는 알려지지 않은 오브젝트 유형의 레코드를 생성할 때 유연하게 사용할 수 있습니다. @@ -106,7 +106,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: ### **레코드 업데이트** - + **설명:** Salesforce에서 기존 연락처(Contact) 레코드를 업데이트합니다. **파라미터:** @@ -120,7 +120,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 커스텀 연락처 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 기존 리드(Lead) 레코드를 업데이트합니다. **파라미터:** @@ -137,7 +137,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 커스텀 리드 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 기존 기회(Opportunity) 레코드를 업데이트합니다. **파라미터:** @@ -153,7 +153,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 커스텀 기회 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 기존 작업(Task) 레코드를 업데이트합니다. **파라미터:** @@ -171,7 +171,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 커스텀 작업 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 기존 계정(Account) 레코드를 업데이트합니다. **파라미터:** @@ -184,7 +184,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): 커스텀 계정 필드를 위한 JSON 형식의 추가 필드 - + **설명:** Salesforce에서 어떤 객체 유형이든 레코드를 업데이트합니다. **참고:** 이는 커스텀 또는 미확인 객체 유형의 레코드 업데이트를 위한 유연한 도구입니다. @@ -194,42 +194,42 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: ### **레코드 조회** - + **설명:** ID로 Contact 레코드를 조회합니다. **파라미터:** - `recordId` (string, 필수): Contact의 레코드 ID - + **설명:** ID로 Lead 레코드를 조회합니다. **파라미터:** - `recordId` (string, 필수): Lead의 레코드 ID - + **설명:** ID로 Opportunity 레코드를 조회합니다. **파라미터:** - `recordId` (string, 필수): Opportunity의 레코드 ID - + **설명:** ID로 Task 레코드를 조회합니다. **파라미터:** - `recordId` (string, 필수): Task의 레코드 ID - + **설명:** ID로 Account 레코드를 조회합니다. **파라미터:** - `recordId` (string, 필수): Account의 레코드 ID - + **설명:** ID로 임의 객체 유형의 레코드를 조회합니다. **파라미터:** @@ -241,7 +241,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: ### **레코드 검색** - + **설명:** 고급 필터링으로 연락처(Contact) 레코드를 검색합니다. **파라미터:** @@ -252,7 +252,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor를 포함한 페이지네이션 설정 - + **설명:** 고급 필터링으로 리드(Lead) 레코드를 검색합니다. **파라미터:** @@ -263,7 +263,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor를 포함한 페이지네이션 설정 - + **설명:** 고급 필터링으로 기회(Opportunity) 레코드를 검색합니다. **파라미터:** @@ -274,7 +274,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor를 포함한 페이지네이션 설정 - + **설명:** 고급 필터링으로 작업(Task) 레코드를 검색합니다. **파라미터:** @@ -285,7 +285,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor를 포함한 페이지네이션 설정 - + **설명:** 고급 필터링으로 계정(Account) 레코드를 검색합니다. **파라미터:** @@ -296,7 +296,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor를 포함한 페이지네이션 설정 - + **설명:** 모든 오브젝트 유형의 레코드를 검색합니다. **파라미터:** @@ -310,7 +310,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: ### **리스트 뷰 조회** - + **설명:** 특정 리스트 뷰에서 Contact 레코드를 가져옵니다. **파라미터:** @@ -318,7 +318,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor와 함께 사용하는 페이지네이션 설정 - + **설명:** 특정 리스트 뷰에서 Lead 레코드를 가져옵니다. **파라미터:** @@ -326,7 +326,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor와 함께 사용하는 페이지네이션 설정 - + **설명:** 특정 리스트 뷰에서 Opportunity 레코드를 가져옵니다. **파라미터:** @@ -334,7 +334,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor와 함께 사용하는 페이지네이션 설정 - + **설명:** 특정 리스트 뷰에서 Task 레코드를 가져옵니다. **파라미터:** @@ -342,7 +342,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor와 함께 사용하는 페이지네이션 설정 - + **설명:** 특정 리스트 뷰에서 Account 레코드를 가져옵니다. **파라미터:** @@ -350,7 +350,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `paginationParameters` (object, 선택): pageCursor와 함께 사용하는 페이지네이션 설정 - + **설명:** 특정 리스트 뷰에서 임의의 객체 유형의 레코드를 가져옵니다. **파라미터:** @@ -363,7 +363,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: ### **커스텀 필드** - + **설명:** Contact 오브젝트에 대한 커스텀 필드를 배포합니다. **파라미터:** @@ -379,7 +379,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `defaultFieldValue` (string, 선택): 필드의 기본값 - + **설명:** Lead 오브젝트에 대한 커스텀 필드를 배포합니다. **파라미터:** @@ -395,7 +395,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `defaultFieldValue` (string, 선택): 필드의 기본값 - + **설명:** Opportunity 오브젝트에 대한 커스텀 필드를 배포합니다. **파라미터:** @@ -411,7 +411,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `defaultFieldValue` (string, 선택): 필드의 기본값 - + **설명:** Task 오브젝트에 대한 커스텀 필드를 배포합니다. **파라미터:** @@ -427,7 +427,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `defaultFieldValue` (string, 선택): 필드의 기본값 - + **설명:** Account 오브젝트에 대한 커스텀 필드를 배포합니다. **파라미터:** @@ -443,7 +443,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `defaultFieldValue` (string, 선택): 필드의 기본값 - + **설명:** 모든 오브젝트 타입에 대한 커스텀 필드를 배포합니다. **참고:** 커스텀 또는 미지의 오브젝트 타입에 커스텀 필드를 생성할 수 있는 유연한 도구입니다. @@ -453,14 +453,14 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: ### **고급 작업** - + **설명:** Salesforce 데이터에 대해 커스텀 SOQL 쿼리를 실행합니다. **파라미터:** - `query` (string, 필수): SOQL 쿼리 (예: "SELECT Id, Name FROM Account WHERE Name = 'Example'") - + **설명:** Salesforce에 새로운 커스텀 오브젝트를 배포합니다. **파라미터:** @@ -470,7 +470,7 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: - `recordName` (string, 필수): 레이아웃과 검색에 표시되는 레코드 이름 (예: "Account Name") - + **설명:** 특정 오브젝트 타입에 대한 작업의 예상 스키마를 가져옵니다. **파라미터:** @@ -487,19 +487,13 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Salesforce tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Salesforce capabilities salesforce_agent = Agent( role="CRM Manager", goal="Manage customer relationships and sales processes efficiently", backstory="An AI assistant specialized in CRM operations and sales automation.", - tools=[enterprise_tools] + apps=['salesforce'] ) # Task to create a new lead @@ -521,19 +515,12 @@ crew.kickoff() ### 특정 Salesforce 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Salesforce tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["salesforce_create_record_lead", "salesforce_update_record_opportunity", "salesforce_search_records_contact"] -) sales_manager = Agent( role="Sales Manager", goal="Manage leads and opportunities in the sales pipeline", backstory="An experienced sales manager who handles lead qualification and opportunity management.", - tools=enterprise_tools + apps=['salesforce'] ) # Task to manage sales pipeline @@ -555,17 +542,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) account_manager = Agent( role="Account Manager", goal="Manage customer accounts and maintain strong relationships", backstory="An AI assistant that specializes in account management and customer relationship building.", - tools=[enterprise_tools] + apps=['salesforce'] ) # Task to manage customer accounts @@ -591,17 +573,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_analyst = Agent( role="Sales Data Analyst", goal="Generate insights from Salesforce data using SOQL queries", backstory="An analytical AI that excels at extracting meaningful insights from CRM data.", - tools=[enterprise_tools] + apps=['salesforce'] ) # Complex task involving SOQL queries and data analysis diff --git a/docs/ko/enterprise/integrations/shopify.mdx b/docs/ko/enterprise/integrations/shopify.mdx index be1d7bde9..9119dc572 100644 --- a/docs/ko/enterprise/integrations/shopify.mdx +++ b/docs/ko/enterprise/integrations/shopify.mdx @@ -22,7 +22,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: ### **고객 관리** - + **설명:** Shopify 스토어에서 고객 목록을 조회합니다. **파라미터:** @@ -34,7 +34,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `limit` (string, 선택): 반환할 최대 고객 수 (기본값 250) - + **설명:** 고급 필터링 기준을 사용하여 고객을 검색합니다. **파라미터:** @@ -42,7 +42,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `limit` (string, 선택): 반환할 최대 고객 수 (기본값 250) - + **설명:** Shopify 스토어에 새로운 고객을 생성합니다. **파라미터:** @@ -63,7 +63,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `metafields` (object, 선택): 추가 메타필드(JSON 형식) - + **설명:** Shopify 스토어에 기존 고객을 업데이트합니다. **파라미터:** @@ -89,7 +89,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: ### **주문 관리** - + **설명:** Shopify 스토어에서 주문 목록을 조회합니다. **파라미터:** @@ -101,7 +101,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `limit` (string, optional): 반환할 주문의 최대 개수 (기본값: 250) - + **설명:** Shopify 스토어에 새 주문을 생성합니다. **파라미터:** @@ -114,7 +114,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `note` (string, optional): 주문 메모 - + **설명:** Shopify 스토어에서 기존 주문을 업데이트합니다. **파라미터:** @@ -128,7 +128,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `note` (string, optional): 주문 메모 - + **설명:** Shopify 스토어에서 방치된 장바구니를 조회합니다. **파라미터:** @@ -144,7 +144,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: ### **제품 관리 (REST API)** - + **설명:** REST API를 사용하여 Shopify 스토어에서 제품 목록을 조회합니다. **파라미터:** @@ -160,7 +160,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `limit` (string, optional): 반환할 최대 제품 수 (기본값: 250) - + **설명:** REST API를 사용하여 Shopify 스토어에 새로운 제품을 생성합니다. **파라미터:** @@ -176,7 +176,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `publishToPointToSale` (boolean, optional): 포인트 오브 세일(Point of Sale)에 공개 여부 - + **설명:** REST API를 사용하여 Shopify 스토어의 기존 제품을 업데이트합니다. **파라미터:** @@ -197,14 +197,14 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: ### **제품 관리 (GraphQL)** - + **설명:** 고급 GraphQL 필터링 기능을 사용하여 제품을 조회합니다. **파라미터:** - `productFilterFormula` (object, 선택): id, title, vendor, status, handle, tag, created_at, updated_at, published_at와 같은 필드를 지원하는 불리언 정규합형(DNF) 기반의 고급 필터 - + **설명:** 미디어 지원이 강화된 GraphQL API를 사용하여 새 제품을 생성합니다. **파라미터:** @@ -217,7 +217,7 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: - `additionalFields` (object, 선택): status, requiresSellingPlan, giftCard와 같은 추가 제품 필드 - + **설명:** 미디어 지원이 강화된 GraphQL API를 사용하여 기존 제품을 업데이트합니다. **파라미터:** @@ -238,19 +238,13 @@ Shopify 연동을 사용하기 전에 다음을 확인하세요: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Shopify tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Shopify capabilities shopify_agent = Agent( role="E-commerce Manager", goal="Manage online store operations and customer relationships efficiently", backstory="An AI assistant specialized in e-commerce operations and online store management.", - tools=[enterprise_tools] + apps=['shopify'] ) # Task to create a new customer @@ -272,19 +266,12 @@ crew.kickoff() ### 특정 Shopify 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Shopify tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["shopify_create_customer", "shopify_create_order", "shopify_get_products"] -) store_manager = Agent( role="Store Manager", goal="Manage customer orders and product catalog", backstory="An experienced store manager who handles customer relationships and inventory management.", - tools=enterprise_tools + apps=['shopify'] ) # Task to manage store operations @@ -306,17 +293,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) product_manager = Agent( role="Product Manager", goal="Manage product catalog and inventory with advanced GraphQL capabilities", backstory="An AI assistant that specializes in product management and catalog optimization.", - tools=[enterprise_tools] + apps=['shopify'] ) # Task to manage product catalog @@ -343,17 +325,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) analytics_agent = Agent( role="E-commerce Analyst", goal="Analyze customer behavior and order patterns to optimize store performance", backstory="An analytical AI that excels at extracting insights from e-commerce data.", - tools=[enterprise_tools] + apps=['shopify'] ) # Complex task involving multiple operations diff --git a/docs/ko/enterprise/integrations/slack.mdx b/docs/ko/enterprise/integrations/slack.mdx index 8097415f5..8ca09ad90 100644 --- a/docs/ko/enterprise/integrations/slack.mdx +++ b/docs/ko/enterprise/integrations/slack.mdx @@ -22,21 +22,21 @@ Slack 통합을 사용하기 전에 다음을 확인하십시오: ### **사용자 관리** - + **설명:** Slack 채널의 모든 멤버를 나열합니다. **파라미터:** - 파라미터 없음 - 모든 채널 멤버를 조회합니다 - + **설명:** 이메일 주소로 Slack 워크스페이스에서 사용자를 찾습니다. **파라미터:** - `email` (string, 필수): 워크스페이스 내 사용자의 이메일 주소 - + **설명:** 이름 또는 표시 이름으로 사용자를 검색합니다. **파라미터:** @@ -50,7 +50,7 @@ Slack 통합을 사용하기 전에 다음을 확인하십시오: ### **채널 관리** - + **설명:** Slack 워크스페이스의 모든 채널을 나열합니다. **파라미터:** @@ -61,7 +61,7 @@ Slack 통합을 사용하기 전에 다음을 확인하십시오: ### **메시징** - + **설명:** Slack 채널에 메시지를 전송합니다. **파라미터:** @@ -73,7 +73,7 @@ Slack 통합을 사용하기 전에 다음을 확인하십시오: - `authenticatedUser` (boolean, 선택): true이면 메시지가 애플리케이션이 아니라 인증된 Slack 사용자로부터 보낸 것처럼 표시됩니다(기본값은 false) - + **설명:** Slack에서 특정 사용자에게 다이렉트 메시지를 전송합니다. **파라미터:** @@ -89,7 +89,7 @@ Slack 통합을 사용하기 전에 다음을 확인하십시오: ### **검색 및 탐색** - + **설명:** Slack 워크스페이스 전체에서 메시지를 검색합니다. **매개변수:** @@ -150,19 +150,13 @@ Slack의 Block Kit을 사용하면 풍부하고 상호작용이 가능한 메시 ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Slack tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Slack capabilities slack_agent = Agent( role="Team Communication Manager", goal="Facilitate team communication and coordinate collaboration efficiently", backstory="An AI assistant specialized in team communication and workspace coordination.", - tools=[enterprise_tools] + apps=['slack'] ) # Task to send project updates @@ -184,19 +178,12 @@ crew.kickoff() ### 특정 Slack 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Slack tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["slack_send_message", "slack_send_direct_message", "slack_search_messages"] -) communication_manager = Agent( role="Communication Coordinator", goal="Manage team communications and ensure important messages reach the right people", backstory="An experienced communication coordinator who handles team messaging and notifications.", - tools=enterprise_tools + apps=['slack'] ) # Task to coordinate team communication @@ -218,17 +205,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) notification_agent = Agent( role="Notification Manager", goal="Create rich, interactive notifications and manage workspace communication", backstory="An AI assistant that specializes in creating engaging team notifications and updates.", - tools=[enterprise_tools] + apps=['slack'] ) # Task to send rich notifications @@ -254,17 +236,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) analytics_agent = Agent( role="Communication Analyst", goal="Analyze team communication patterns and extract insights from conversations", backstory="An analytical AI that excels at understanding team dynamics through communication data.", - tools=[enterprise_tools] + apps=['slack'] ) # Complex task involving search and analysis diff --git a/docs/ko/enterprise/integrations/stripe.mdx b/docs/ko/enterprise/integrations/stripe.mdx index 59c3e5e6b..1c0e3c1b9 100644 --- a/docs/ko/enterprise/integrations/stripe.mdx +++ b/docs/ko/enterprise/integrations/stripe.mdx @@ -22,7 +22,7 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: ### **고객 관리** - + **설명:** Stripe 계정에 새로운 고객을 생성합니다. **파라미터:** @@ -32,14 +32,14 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: - `metadataCreateCustomer` (object, 선택): 추가 메타데이터를 key-value 쌍으로 입력 (예: `{"field1": 1, "field2": 2}`) - + **설명:** Stripe 고객 ID로 특정 고객을 조회합니다. **파라미터:** - `idGetCustomer` (string, 필수): 조회할 Stripe 고객 ID - + **설명:** 필터링 옵션과 함께 고객 리스트를 조회합니다. **파라미터:** @@ -49,7 +49,7 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: - `limitGetCustomers` (string, 선택): 반환할 최대 고객 수 (기본값 10) - + **설명:** 기존 고객의 정보를 업데이트합니다. **파라미터:** @@ -64,7 +64,7 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: ### **구독 관리** - + **설명:** 고객을 위한 새로운 구독을 생성합니다. **파라미터:** @@ -73,7 +73,7 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: - `metadataCreateSubscription` (object, 선택): 구독에 대한 추가 메타데이터 - + **설명:** 선택적 필터링으로 구독을 조회합니다. **파라미터:** @@ -86,7 +86,7 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: ### **제품 관리** - + **설명:** Stripe 카탈로그에 새 제품을 생성합니다. **파라미터:** @@ -95,14 +95,14 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: - `metadataProduct` (object, 선택): 키-값 쌍으로 구성된 추가 제품 메타데이터 - + **설명:** Stripe 제품 ID로 특정 제품을 조회합니다. **파라미터:** - `productId` (string, 필수): 조회할 Stripe 제품 ID - + **설명:** 선택적 필터링을 통해 제품 목록을 조회합니다. **파라미터:** @@ -115,7 +115,7 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: ### **금융 운영** - + **설명:** Stripe 계정에서 잔액 거래를 조회합니다. **매개변수:** @@ -124,7 +124,7 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: - `pageCursor` (string, 선택 사항): 페이지네이션을 위한 페이지 커서 - + **설명:** Stripe 계정에서 구독 플랜을 조회합니다. **매개변수:** @@ -140,19 +140,13 @@ Stripe 통합을 사용하기 전에 다음 사항을 확인하세요: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Stripe tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Stripe capabilities stripe_agent = Agent( role="Payment Manager", goal="Manage customer payments, subscriptions, and billing operations efficiently", backstory="An AI assistant specialized in payment processing and subscription management.", - tools=[enterprise_tools] + apps=['stripe'] ) # Task to create a new customer @@ -174,19 +168,12 @@ crew.kickoff() ### 특정 Stripe 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Stripe tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["stripe_create_customer", "stripe_create_subscription", "stripe_get_balance_transactions"] -) billing_manager = Agent( role="Billing Manager", goal="Handle customer billing, subscriptions, and payment processing", backstory="An experienced billing manager who handles subscription lifecycle and payment operations.", - tools=enterprise_tools + apps=['stripe'] ) # Task to manage billing operations @@ -208,17 +195,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) subscription_manager = Agent( role="Subscription Manager", goal="Manage customer subscriptions and optimize recurring revenue", backstory="An AI assistant that specializes in subscription lifecycle management and customer retention.", - tools=[enterprise_tools] + apps=['stripe'] ) # Task to manage subscription operations @@ -245,17 +227,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) financial_analyst = Agent( role="Financial Analyst", goal="Analyze payment data and generate financial insights", backstory="An analytical AI that excels at extracting insights from payment and subscription data.", - tools=[enterprise_tools] + apps=['stripe'] ) # Complex task involving financial analysis diff --git a/docs/ko/enterprise/integrations/zendesk.mdx b/docs/ko/enterprise/integrations/zendesk.mdx index f009e0bf8..62b2a72c0 100644 --- a/docs/ko/enterprise/integrations/zendesk.mdx +++ b/docs/ko/enterprise/integrations/zendesk.mdx @@ -22,7 +22,7 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. ### **티켓 관리** - + **설명:** Zendesk에 새로운 지원 티켓을 생성합니다. **매개변수:** @@ -40,7 +40,7 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. - `ticketCustomFields` (object, 선택): JSON 형식의 사용자 정의 필드 값 - + **설명:** Zendesk의 기존 지원 티켓을 업데이트합니다. **매개변수:** @@ -58,14 +58,14 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. - `ticketCustomFields` (object, 선택): 업데이트된 사용자 정의 필드 값 - + **설명:** ID로 특정 티켓을 조회합니다. **매개변수:** - `ticketId` (string, 필수): 조회할 티켓의 ID (예: "35436") - + **설명:** 기존 티켓에 댓글이나 내부 노트를 추가합니다. **매개변수:** @@ -75,7 +75,7 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. - `isPublic` (boolean, 선택): 공개 댓글이면 true, 내부 노트이면 false - + **설명:** 다양한 필터 및 조건을 사용하여 티켓을 검색합니다. **매개변수:** @@ -100,7 +100,7 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. ### **사용자 관리** - + **설명:** Zendesk에서 새로운 사용자를 생성합니다. **매개변수:** @@ -113,7 +113,7 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. - `notes` (string, 선택): 사용자에 대한 내부 메모 - + **설명:** 기존 사용자의 정보를 업데이트합니다. **매개변수:** @@ -127,14 +127,14 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. - `notes` (string, 선택): 업데이트된 내부 메모 - + **설명:** ID로 특정 사용자를 조회합니다. **매개변수:** - `userId` (string, 필수): 조회할 사용자 ID - + **설명:** 다양한 기준으로 사용자를 검색합니다. **매개변수:** @@ -150,7 +150,7 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. ### **관리 도구** - + **설명:** 티켓에 사용할 수 있는 모든 표준 및 맞춤 필드를 검색합니다. **파라미터:** @@ -158,7 +158,7 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. - `pageCursor` (string, 선택 사항): 페이지네이션을 위한 페이지 커서 - + **설명:** 티켓의 감사 기록(읽기 전용 이력)을 가져옵니다. **파라미터:** @@ -205,19 +205,13 @@ Zendesk 통합을 사용하기 전에 다음을 확인하세요. ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Zendesk tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Zendesk capabilities zendesk_agent = Agent( role="Support Manager", goal="Manage customer support tickets and provide excellent customer service", backstory="An AI assistant specialized in customer support operations and ticket management.", - tools=[enterprise_tools] + apps=['zendesk'] ) # Task to create a new support ticket @@ -239,19 +233,12 @@ crew.kickoff() ### 특정 Zendesk 도구 필터링 ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Zendesk tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["zendesk_create_ticket", "zendesk_update_ticket", "zendesk_add_comment_to_ticket"] -) support_agent = Agent( role="Customer Support Agent", goal="Handle customer inquiries and resolve support issues efficiently", backstory="An experienced support agent who specializes in ticket resolution and customer communication.", - tools=enterprise_tools + apps=['zendesk'] ) # Task to manage support workflow @@ -273,17 +260,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) ticket_manager = Agent( role="Ticket Manager", goal="Manage support ticket workflows and ensure timely resolution", backstory="An AI assistant that specializes in support ticket triage and workflow optimization.", - tools=[enterprise_tools] + apps=['zendesk'] ) # Task to manage ticket lifecycle @@ -310,17 +292,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) support_analyst = Agent( role="Support Analyst", goal="Analyze support metrics and generate insights for team performance", backstory="An analytical AI that excels at extracting insights from support data and ticket patterns.", - tools=[enterprise_tools] + apps=['zendesk'] ) # Complex task involving analytics and reporting diff --git a/docs/ko/mcp/dsl-integration.mdx b/docs/ko/mcp/dsl-integration.mdx new file mode 100644 index 000000000..2916d235c --- /dev/null +++ b/docs/ko/mcp/dsl-integration.mdx @@ -0,0 +1,232 @@ +--- +title: MCP DSL 통합 +description: CrewAI의 간단한 DSL 구문을 사용하여 mcps 필드로 MCP 서버를 에이전트와 직접 통합하는 방법을 알아보세요. +icon: code +mode: "wide" +--- + +## 개요 + +CrewAI의 MCP DSL(Domain Specific Language) 통합은 에이전트를 MCP(Model Context Protocol) 서버에 연결하는 **가장 간단한 방법**을 제공합니다. 에이전트에 `mcps` 필드만 추가하면 CrewAI가 모든 복잡성을 자동으로 처리합니다. + + +이는 대부분의 MCP 사용 사례에 **권장되는 접근 방식**입니다. 수동 연결 관리가 필요한 고급 시나리오의 경우 [MCPServerAdapter](/ko/mcp/overview#advanced-mcpserveradapter)를 참조하세요. + + +## 기본 사용법 + +`mcps` 필드를 사용하여 에이전트에 MCP 서버를 추가하세요: + +```python +from crewai import Agent + +agent = Agent( + role="연구 보조원", + goal="연구 및 분석 업무 지원", + backstory="고급 연구 도구에 접근할 수 있는 전문가 보조원", + mcps=[ + "https://mcp.exa.ai/mcp?api_key=your_key&profile=research" + ] +) + +# MCP 도구들이 이제 자동으로 사용 가능합니다! +# 수동 연결 관리나 도구 구성이 필요 없습니다 +``` + +## 지원되는 참조 형식 + +### 외부 MCP 원격 서버 + +```python +# 기본 HTTPS 서버 +"https://api.example.com/mcp" + +# 인증이 포함된 서버 +"https://mcp.exa.ai/mcp?api_key=your_key&profile=your_profile" + +# 사용자 정의 경로가 있는 서버 +"https://services.company.com/api/v1/mcp" +``` + +### 특정 도구 선택 + +`#` 구문을 사용하여 서버에서 특정 도구를 선택하세요: + +```python +# 날씨 서버에서 예보 도구만 가져오기 +"https://weather.api.com/mcp#get_forecast" + +# Exa에서 검색 도구만 가져오기 +"https://mcp.exa.ai/mcp?api_key=your_key#web_search_exa" +``` + +### CrewAI AMP 마켓플레이스 + +CrewAI AMP 마켓플레이스의 도구에 액세스하세요: + +```python +# 모든 도구가 포함된 전체 서비스 +"crewai-amp:financial-data" + +# AMP 서비스의 특정 도구 +"crewai-amp:research-tools#pubmed_search" + +# 다중 AMP 서비스 +mcps=[ + "crewai-amp:weather-insights", + "crewai-amp:market-analysis", + "crewai-amp:social-media-monitoring" +] +``` + +## 완전한 예제 + +다음은 여러 MCP 서버를 사용하는 완전한 예제입니다: + +```python +from crewai import Agent, Task, Crew, Process + +# 다중 MCP 소스를 가진 에이전트 생성 +multi_source_agent = Agent( + role="다중 소스 연구 분석가", + goal="다중 데이터 소스를 사용한 종합적인 연구 수행", + backstory="""웹 검색, 날씨 데이터, 금융 정보, + 학술 연구 도구에 접근할 수 있는 전문가 연구원""", + mcps=[ + # 외부 MCP 서버 + "https://mcp.exa.ai/mcp?api_key=your_exa_key&profile=research", + "https://weather.api.com/mcp#get_current_conditions", + + # CrewAI AMP 마켓플레이스 + "crewai-amp:financial-insights", + "crewai-amp:academic-research#pubmed_search", + "crewai-amp:market-intelligence#competitor_analysis" + ] +) + +# 종합적인 연구 작업 생성 +research_task = Task( + description="""AI 에이전트가 비즈니스 생산성에 미치는 영향을 연구하세요. + 원격 근무에 대한 현재 날씨 영향, 금융 시장 트렌드, + AI 에이전트 프레임워크에 대한 최근 학술 발표를 포함하세요.""", + expected_output="""다음을 다루는 종합 보고서: + 1. AI 에이전트 비즈니스 영향 분석 + 2. 원격 근무를 위한 날씨 고려사항 + 3. AI 관련 금융 시장 트렌드 + 4. 학술 연구 인용 및 통찰 + 5. 경쟁 환경 분석""", + agent=multi_source_agent +) + +# crew 생성 및 실행 +research_crew = Crew( + agents=[multi_source_agent], + tasks=[research_task], + process=Process.sequential, + verbose=True +) + +result = research_crew.kickoff() +print(f"{len(multi_source_agent.mcps)}개의 MCP 데이터 소스로 연구 완료") +``` + +## 주요 기능 + +- 🔄 **자동 도구 발견**: 도구들이 자동으로 발견되고 통합됩니다 +- 🏷️ **이름 충돌 방지**: 서버 이름이 도구 이름에 접두사로 붙습니다 +- ⚡ **성능 최적화**: 스키마 캐싱과 온디맨드 연결 +- 🛡️ **오류 복원력**: 사용할 수 없는 서버의 우아한 처리 +- ⏱️ **타임아웃 보호**: 내장 타임아웃으로 연결 중단 방지 +- 📊 **투명한 통합**: 기존 CrewAI 기능과 완벽한 연동 + +## 오류 처리 + +MCP DSL 통합은 복원력 있게 설계되었습니다: + +```python +agent = Agent( + role="복원력 있는 에이전트", + goal="서버 문제에도 불구하고 작업 계속", + backstory="장애를 우아하게 처리하는 에이전트", + mcps=[ + "https://reliable-server.com/mcp", # 작동할 것 + "https://unreachable-server.com/mcp", # 우아하게 건너뛸 것 + "https://slow-server.com/mcp", # 우아하게 타임아웃될 것 + "crewai-amp:working-service" # 작동할 것 + ] +) +# 에이전트는 작동하는 서버의 도구를 사용하고 실패한 서버에 대한 경고를 로그에 남깁니다 +``` + +## 성능 기능 + +### 자동 캐싱 + +도구 스키마는 성능 향상을 위해 5분간 캐시됩니다: + +```python +# 첫 번째 에이전트 생성 - 서버에서 도구 발견 +agent1 = Agent(role="첫 번째", goal="테스트", backstory="테스트", + mcps=["https://api.example.com/mcp"]) + +# 두 번째 에이전트 생성 (5분 이내) - 캐시된 도구 스키마 사용 +agent2 = Agent(role="두 번째", goal="테스트", backstory="테스트", + mcps=["https://api.example.com/mcp"]) # 훨씬 빠릅니다! +``` + +### 온디맨드 연결 + +도구 연결은 실제로 사용될 때만 설정됩니다: + +```python +# 에이전트 생성은 빠름 - 아직 MCP 연결을 만들지 않음 +agent = Agent( + role="온디맨드 에이전트", + goal="도구를 효율적으로 사용", + backstory="필요할 때만 연결하는 효율적인 에이전트", + mcps=["https://api.example.com/mcp"] +) + +# MCP 연결은 도구가 실제로 실행될 때만 만들어집니다 +# 이는 연결 오버헤드를 최소화하고 시작 성능을 개선합니다 +``` + +## 모범 사례 + +### 1. 가능하면 특정 도구 사용 + +```python +# 좋음 - 필요한 도구만 가져오기 +mcps=["https://weather.api.com/mcp#get_forecast"] + +# 덜 효율적 - 서버의 모든 도구 가져오기 +mcps=["https://weather.api.com/mcp"] +``` + +### 2. 인증을 안전하게 처리 + +```python +import os + +# 환경 변수에 API 키 저장 +exa_key = os.getenv("EXA_API_KEY") +exa_profile = os.getenv("EXA_PROFILE") + +agent = Agent( + role="안전한 에이전트", + goal="MCP 도구를 안전하게 사용", + backstory="보안을 고려하는 에이전트", + mcps=[f"https://mcp.exa.ai/mcp?api_key={exa_key}&profile={exa_profile}"] +) +``` + +### 3. 서버 장애 계획 + +```python +# 항상 백업 옵션 포함 +mcps=[ + "https://primary-api.com/mcp", # 주요 선택 + "https://backup-api.com/mcp", # 백업 옵션 + "crewai-amp:reliable-service" # AMP 폴백 +] +``` diff --git a/docs/ko/mcp/overview.mdx b/docs/ko/mcp/overview.mdx index 3a8b1ccb3..7cfb362b2 100644 --- a/docs/ko/mcp/overview.mdx +++ b/docs/ko/mcp/overview.mdx @@ -8,12 +8,37 @@ mode: "wide" ## 개요 [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP)는 AI 에이전트가 MCP 서버로 알려진 외부 서비스와 통신함으로써 LLM에 컨텍스트를 제공할 수 있도록 표준화된 방식을 제공합니다. -`crewai-tools` 라이브러리는 CrewAI의 기능을 확장하여, 이러한 MCP 서버에서 제공하는 툴을 에이전트에 원활하게 통합할 수 있도록 해줍니다. -이를 통해 여러분의 crew는 방대한 기능 에코시스템에 접근할 수 있습니다. + +CrewAI는 MCP 통합을 위한 **두 가지 접근 방식**을 제공합니다: + +### 🚀 **새로운 기능: 간단한 DSL 통합** (권장) + +에이전트에 `mcps` 필드를 직접 사용하여 완벽한 MCP 도구 통합을 구현하세요: + +```python +from crewai import Agent + +agent = Agent( + role="연구 분석가", + goal="정보를 연구하고 분석", + backstory="외부 도구에 접근할 수 있는 전문가 연구원", + mcps=[ + "https://mcp.exa.ai/mcp?api_key=your_key", # 외부 MCP 서버 + "https://api.weather.com/mcp#get_forecast", # 서버의 특정 도구 + "crewai-amp:financial-data", # CrewAI AMP 마켓플레이스 + "crewai-amp:research-tools#pubmed_search" # 특정 AMP 도구 + ] +) +# MCP 도구들이 이제 자동으로 에이전트에서 사용 가능합니다! +``` + +### 🔧 **고급: MCPServerAdapter** (복잡한 시나리오용) + +수동 연결 관리가 필요한 고급 사용 사례의 경우 `crewai-tools` 라이브러리는 `MCPServerAdapter` 클래스를 제공합니다. 현재 다음과 같은 전송 메커니즘을 지원합니다: -- **Stdio**: 로컬 서버용 (동일 머신 내 프로세스 간 표준 입력/출력을 통한 통신) +- **HTTPS**: 원격 서버용 (HTTPS를 통한 보안 통신) - **Server-Sent Events (SSE)**: 원격 서버용 (서버에서 클라이언트로의 일방향, 실시간 데이터 스트리밍, HTTP 기반) - **Streamable HTTP**: 원격 서버용 (유연하며 잠재적으로 양방향 통신이 가능, 주로 SSE를 활용한 서버-클라이언트 스트림 제공, HTTP 기반) diff --git a/docs/pt-BR/enterprise/features/tools-and-integrations.mdx b/docs/pt-BR/enterprise/features/tools-and-integrations.mdx index 8fb2bb10f..5d1d00de8 100644 --- a/docs/pt-BR/enterprise/features/tools-and-integrations.mdx +++ b/docs/pt-BR/enterprise/features/tools-and-integrations.mdx @@ -43,7 +43,7 @@ Ferramentas & Integrações é o hub central para conectar aplicações de terce 1. Acesse Integrações 2. Clique em Conectar no serviço desejado 3. Conclua o fluxo OAuth e conceda os escopos - 4. Copie seu Token Enterprise na aba Integração + 4. Copie seu Token Enterprise em Configurações de Integração ![Token Enterprise](/images/enterprise/enterprise_action_auth_token.png) @@ -57,26 +57,37 @@ Ferramentas & Integrações é o hub central para conectar aplicações de terce uv add crewai-tools ``` + ### Configuração de variável de ambiente + + + Para usar integrações com `Agent(apps=[])`, você deve definir a variável de ambiente `CREWAI_PLATFORM_INTEGRATION_TOKEN` com seu Enterprise Token. + + + ```bash + export CREWAI_PLATFORM_INTEGRATION_TOKEN="seu_enterprise_token" + ``` + + Ou adicione ao seu arquivo `.env`: + + ``` + CREWAI_PLATFORM_INTEGRATION_TOKEN=seu_enterprise_token + ``` + ### Exemplo de uso - Todos os serviços autenticados ficam disponíveis como ferramentas. Adicione `CrewaiEnterpriseTools` ao agente e pronto. + Use a nova abordagem simplificada para integrar aplicativos empresariais. Simplesmente especifique o aplicativo e suas ações diretamente na configuração do Agent. ```python from crewai import Agent, Task, Crew - from crewai_tools import CrewaiEnterpriseTools - - enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="seu_enterprise_token" - ) - print(enterprise_tools) + # Crie um agente com capacidades do Gmail email_agent = Agent( role="Gerente de Email", goal="Gerenciar e organizar comunicações por email", backstory="Assistente de IA especializado em gestão de emails", - tools=enterprise_tools + apps=['gmail', 'gmail/send_email'] # Usando nome canônico 'gmail' ) email_task = Task( @@ -92,19 +103,14 @@ Ferramentas & Integrações é o hub central para conectar aplicações de terce ### Filtrando ferramentas ```python - from crewai_tools import CrewaiEnterpriseTools - - enterprise_tools = CrewaiEnterpriseTools( - actions_list=["gmail_find_email"] - ) - - gmail_tool = enterprise_tools["gmail_find_email"] + from crewai import Agent, Task, Crew + # Crie agente com ações específicas do Gmail apenas gmail_agent = Agent( role="Gerente de Gmail", goal="Gerenciar comunicações e notificações no Gmail", backstory="Assistente de IA para coordenação de emails", - tools=[gmail_tool] + apps=['gmail/fetch_emails'] # Usando nome canônico com ação específica ) notification_task = Task( diff --git a/docs/pt-BR/enterprise/guides/automation-triggers.mdx b/docs/pt-BR/enterprise/guides/automation-triggers.mdx index 07f5a757a..abfe9224c 100644 --- a/docs/pt-BR/enterprise/guides/automation-triggers.mdx +++ b/docs/pt-BR/enterprise/guides/automation-triggers.mdx @@ -116,19 +116,49 @@ Antes de ativar em produção, confirme que você: - Decidiu se usará `allow_crewai_trigger_context` para injetar contexto automaticamente - Configurou monitoramento (webhooks, históricos da CrewAI, alertas externos) -### Repositório de Payloads e Crews de Exemplo +### Testando Triggers Localmente com CLI -| Integração | Quando dispara | Amostras de payload | Crews de exemplo | -| :-- | :-- | :-- | :-- | -| Gmail | Novas mensagens, atualização de threads | [Payloads de alertas e threads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | [`new-email-crew.py`, `gmail-alert-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | -| Google Calendar | Evento criado/atualizado/iniciado/encerrado/cancelado | [Payloads de eventos](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | [`calendar-event-crew.py`, `calendar-meeting-crew.py`, `calendar-working-location-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | -| Google Drive | Arquivo criado/atualizado/excluído | [Payloads de arquivos](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | [`drive-file-crew.py`, `drive-file-deletion-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | -| Outlook | Novo e‑mail, evento removido | [Payloads do Outlook](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | [`outlook-message-crew.py`, `outlook-event-removal-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | -| OneDrive | Operações de arquivo (criar, atualizar, compartilhar, excluir) | [Payloads do OneDrive](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | [`onedrive-file-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | -| HubSpot | Registros criados/atualizados (contatos, empresas, negócios) | [Payloads do HubSpot](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | [`hubspot-company-crew.py`, `hubspot-contact-crew.py`, `hubspot-record-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | -| Microsoft Teams | Chat criado | [Payload do Teams](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | [`teams-chat-created-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | +A CLI da CrewAI fornece comandos poderosos para ajudá-lo a desenvolver e testar automações orientadas por triggers sem fazer deploy para produção. -Use essas amostras para ajustar o parsing, copiar a crew correspondente e substituir o payload de teste pelo dado real. +#### Listar Triggers Disponíveis + +Visualize todos os triggers disponíveis para suas integrações conectadas: + +```bash +crewai triggers list +``` + +Este comando exibe todos os triggers disponíveis baseados nas suas integrações conectadas, mostrando: +- Nome da integração e status de conexão +- Tipos de triggers disponíveis +- Nomes e descrições dos triggers + +#### Simular Execução de Trigger + +Teste sua crew com payloads realistas de triggers antes do deployment: + +```bash +crewai triggers run +``` + +Por exemplo: + +```bash +crewai triggers run microsoft_onedrive/file_changed +``` + +Este comando: +- Executa sua crew localmente +- Passa um payload de trigger completo e realista +- Simula exatamente como sua crew será chamada em produção + + + **Notas Importantes de Desenvolvimento:** + - Use `crewai triggers run ` para simular execução de trigger durante o desenvolvimento + - Usar `crewai run` NÃO simulará chamadas de trigger e não passará o payload do trigger + - Após o deployment, sua crew será executada com o payload real do trigger + - Se sua crew espera parâmetros que não estão no payload do trigger, a execução pode falhar + ### Triggers com Crews @@ -203,17 +233,20 @@ def delegar_para_crew(self, crewai_trigger_payload: dict = None): ## Solução de Problemas **Trigger não dispara:** -- Verifique se está habilitado -- Confira o status da conexão +- Verifique se o trigger está habilitado na aba Triggers do seu deployment +- Confira o status da conexão em Tools & Integrations +- Garanta que todas as variáveis de ambiente necessárias estão configuradas **Falhas de execução:** -- Consulte os logs para entender o erro -- Durante o desenvolvimento, garanta que `crewai_trigger_payload` está presente com o payload correto +- Consulte os logs de execução para detalhes do erro +- Use `crewai triggers run ` para testar localmente e ver a estrutura exata do payload +- Verifique se sua crew pode processar o parâmetro `crewai_trigger_payload` +- Garanta que sua crew não espera parâmetros que não estão incluídos no payload do trigger + +**Problemas de desenvolvimento:** +- Sempre teste com `crewai triggers run ` antes de fazer deploy para ver o payload completo +- Lembre-se que `crewai run` NÃO simula chamadas de trigger—use `crewai triggers run` em vez disso +- Use `crewai triggers list` para verificar quais triggers estão disponíveis para suas integrações conectadas +- Após o deployment, sua crew receberá o payload real do trigger, então teste minuciosamente localmente primeiro Os triggers transformam suas implantações CrewAI em sistemas orientados por eventos, integrando-se perfeitamente aos processos e ferramentas já usados pelo seu time. - - - - Repositório CrewAI AMP Trigger Examples - - diff --git a/docs/pt-BR/enterprise/guides/gmail-trigger.mdx b/docs/pt-BR/enterprise/guides/gmail-trigger.mdx index 2caefc045..0f0663fb6 100644 --- a/docs/pt-BR/enterprise/guides/gmail-trigger.mdx +++ b/docs/pt-BR/enterprise/guides/gmail-trigger.mdx @@ -51,16 +51,25 @@ class GmailProcessingCrew: ) ``` -The Gmail payload will be available via the standard context mechanisms. See the payload samples repository for structure and fields. +The Gmail payload will be available via the standard context mechanisms. -### Sample payloads & crews +### Testando Localmente -The [CrewAI AMP Trigger Examples repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) includes: +Teste sua integração de trigger do Gmail localmente usando a CLI da CrewAI: -- `new-email-payload-1.json` / `new-email-payload-2.json` — production-style new message alerts with matching crews in `new-email-crew.py` -- `thread-updated-sample-1.json` — follow-up messages on an existing thread, processed by `gmail-alert-crew.py` +```bash +# Visualize todos os triggers disponíveis +crewai triggers list -Use these samples to validate your parsing logic locally before wiring the trigger to your live Gmail accounts. +# Simule um trigger do Gmail com payload realista +crewai triggers run gmail/new_email +``` + +O comando `crewai triggers run` executará sua crew com um payload completo do Gmail, permitindo que você teste sua lógica de parsing antes do deployment. + + + Use `crewai triggers run gmail/new_email` (não `crewai run`) para simular execução de trigger durante o desenvolvimento. Após o deployment, sua crew receberá automaticamente o payload do trigger. + ## Monitoring Executions @@ -70,16 +79,10 @@ Track history and performance of triggered runs: List of executions triggered by automation -## Payload Reference - -See the sample payloads and field descriptions: - - - Gmail samples in Trigger Examples Repo - - ## Troubleshooting - Ensure Gmail is connected in Tools & Integrations - Verify the Gmail Trigger is enabled on the Triggers tab +- Teste localmente com `crewai triggers run gmail/new_email` para ver a estrutura exata do payload - Check the execution logs and confirm the payload is passed as `crewai_trigger_payload` +- Lembre-se: use `crewai triggers run` (não `crewai run`) para simular execução de trigger diff --git a/docs/pt-BR/enterprise/guides/google-calendar-trigger.mdx b/docs/pt-BR/enterprise/guides/google-calendar-trigger.mdx index cf2d32471..0d9a7dbc8 100644 --- a/docs/pt-BR/enterprise/guides/google-calendar-trigger.mdx +++ b/docs/pt-BR/enterprise/guides/google-calendar-trigger.mdx @@ -39,16 +39,23 @@ print(result.raw) Use `crewai_trigger_payload` exactly as it is delivered by the trigger so the crew can extract the proper fields. -## Sample payloads & crews +## Testando Localmente -The [Google Calendar examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) show how to handle multiple event types: +Teste sua integração de trigger do Google Calendar localmente usando a CLI da CrewAI: -- `new-event.json` → standard event creation handled by `calendar-event-crew.py` -- `event-updated.json` / `event-started.json` / `event-ended.json` → in-flight updates processed by `calendar-meeting-crew.py` -- `event-canceled.json` → cancellation workflow that alerts attendees via `calendar-meeting-crew.py` -- Working location events use `calendar-working-location-crew.py` to extract on-site schedules +```bash +# Visualize todos os triggers disponíveis +crewai triggers list -Each crew transforms raw event metadata (attendees, rooms, working locations) into the summaries your teams need. +# Simule um trigger do Google Calendar com payload realista +crewai triggers run google_calendar/event_changed +``` + +O comando `crewai triggers run` executará sua crew com um payload completo do Calendar, permitindo que você teste sua lógica de parsing antes do deployment. + + + Use `crewai triggers run google_calendar/event_changed` (não `crewai run`) para simular execução de trigger durante o desenvolvimento. Após o deployment, sua crew receberá automaticamente o payload do trigger. + ## Monitoring Executions @@ -61,5 +68,7 @@ The **Executions** list in the deployment dashboard tracks every triggered run a ## Troubleshooting - Ensure the correct Google account is connected and the trigger is enabled +- Teste localmente com `crewai triggers run google_calendar/event_changed` para ver a estrutura exata do payload - Confirm your workflow handles all-day events (payloads use `start.date` and `end.date` instead of timestamps) - Check execution logs if reminders or attendee arrays are missing—calendar permissions can limit fields in the payload +- Lembre-se: use `crewai triggers run` (não `crewai run`) para simular execução de trigger diff --git a/docs/pt-BR/enterprise/guides/google-drive-trigger.mdx b/docs/pt-BR/enterprise/guides/google-drive-trigger.mdx index 19c10837b..d4f2f2ed8 100644 --- a/docs/pt-BR/enterprise/guides/google-drive-trigger.mdx +++ b/docs/pt-BR/enterprise/guides/google-drive-trigger.mdx @@ -36,15 +36,23 @@ crew.kickoff({ }) ``` -## Sample payloads & crews +## Testando Localmente -Explore the [Google Drive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) to cover different operations: +Teste sua integração de trigger do Google Drive localmente usando a CLI da CrewAI: -- `new-file.json` → new uploads processed by `drive-file-crew.py` -- `updated-file.json` → file edits and metadata changes handled by `drive-file-crew.py` -- `deleted-file.json` → deletion events routed through `drive-file-deletion-crew.py` +```bash +# Visualize todos os triggers disponíveis +crewai triggers list -Each crew highlights the file name, operation type, owner, permissions, and security considerations so downstream systems can respond appropriately. +# Simule um trigger do Google Drive com payload realista +crewai triggers run google_drive/file_changed +``` + +O comando `crewai triggers run` executará sua crew com um payload completo do Drive, permitindo que você teste sua lógica de parsing antes do deployment. + + + Use `crewai triggers run google_drive/file_changed` (não `crewai run`) para simular execução de trigger durante o desenvolvimento. Após o deployment, sua crew receberá automaticamente o payload do trigger. + ## Monitoring Executions @@ -57,5 +65,7 @@ Track history and performance of triggered runs with the **Executions** list in ## Troubleshooting - Verify Google Drive is connected and the trigger toggle is enabled +- Teste localmente com `crewai triggers run google_drive/file_changed` para ver a estrutura exata do payload - If a payload is missing permission data, ensure the connected account has access to the file or folder - The trigger sends file IDs only; use the Drive API if you need to fetch binary content during the crew run +- Lembre-se: use `crewai triggers run` (não `crewai run`) para simular execução de trigger diff --git a/docs/pt-BR/enterprise/guides/microsoft-teams-trigger.mdx b/docs/pt-BR/enterprise/guides/microsoft-teams-trigger.mdx index 10878af40..1dd5a1bb1 100644 --- a/docs/pt-BR/enterprise/guides/microsoft-teams-trigger.mdx +++ b/docs/pt-BR/enterprise/guides/microsoft-teams-trigger.mdx @@ -37,16 +37,28 @@ print(result.raw) The crew parses thread metadata (subject, created time, roster) and generates an action plan for the receiving team. -## Sample payloads & crews +## Testando Localmente -The [Microsoft Teams examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) include: +Teste sua integração de trigger do Microsoft Teams localmente usando a CLI da CrewAI: -- `chat-created.json` → chat creation payload processed by `teams-chat-created-crew.py` +```bash +# Visualize todos os triggers disponíveis +crewai triggers list -The crew demonstrates how to extract participants, initial messages, tenant information, and compliance metadata from the Microsoft Graph webhook payload. +# Simule um trigger do Microsoft Teams com payload realista +crewai triggers run microsoft_teams/teams_message_created +``` + +O comando `crewai triggers run` executará sua crew com um payload completo do Teams, permitindo que você teste sua lógica de parsing antes do deployment. + + + Use `crewai triggers run microsoft_teams/teams_message_created` (não `crewai run`) para simular execução de trigger durante o desenvolvimento. Após o deployment, sua crew receberá automaticamente o payload do trigger. + ## Troubleshooting - Ensure the Teams connection is active; it must be refreshed if the tenant revokes permissions +- Teste localmente com `crewai triggers run microsoft_teams/teams_message_created` para ver a estrutura exata do payload - Confirm the webhook subscription in Microsoft 365 is still valid if payloads stop arriving - Review execution logs for payload shape mismatches—Graph notifications may omit fields when a chat is private or restricted +- Lembre-se: use `crewai triggers run` (não `crewai run`) para simular execução de trigger diff --git a/docs/pt-BR/enterprise/guides/onedrive-trigger.mdx b/docs/pt-BR/enterprise/guides/onedrive-trigger.mdx index 51de175db..f3659c5b4 100644 --- a/docs/pt-BR/enterprise/guides/onedrive-trigger.mdx +++ b/docs/pt-BR/enterprise/guides/onedrive-trigger.mdx @@ -36,18 +36,28 @@ crew.kickoff({ The crew inspects file metadata, user activity, and permission changes to produce a compliance-friendly summary. -## Sample payloads & crews +## Testando Localmente -The [OneDrive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) showcase how to: +Teste sua integração de trigger do OneDrive localmente usando a CLI da CrewAI: -- Parse file metadata, size, and folder paths -- Track who created and last modified the file -- Highlight permission and external sharing changes +```bash +# Visualize todos os triggers disponíveis +crewai triggers list -`onedrive-file-crew.py` bundles the analysis and summarization tasks so you can add remediation steps as needed. +# Simule um trigger do OneDrive com payload realista +crewai triggers run microsoft_onedrive/file_changed +``` + +O comando `crewai triggers run` executará sua crew com um payload completo do OneDrive, permitindo que você teste sua lógica de parsing antes do deployment. + + + Use `crewai triggers run microsoft_onedrive/file_changed` (não `crewai run`) para simular execução de trigger durante o desenvolvimento. Após o deployment, sua crew receberá automaticamente o payload do trigger. + ## Troubleshooting - Ensure the connected account has permission to read the file metadata included in the webhook +- Teste localmente com `crewai triggers run microsoft_onedrive/file_changed` para ver a estrutura exata do payload - If the trigger fires but the payload is missing `permissions`, confirm the site-level sharing settings allow Graph to return this field - For large tenants, filter notifications upstream so the crew only runs on relevant directories +- Lembre-se: use `crewai triggers run` (não `crewai run`) para simular execução de trigger diff --git a/docs/pt-BR/enterprise/guides/outlook-trigger.mdx b/docs/pt-BR/enterprise/guides/outlook-trigger.mdx index 21bda5407..c63d3c704 100644 --- a/docs/pt-BR/enterprise/guides/outlook-trigger.mdx +++ b/docs/pt-BR/enterprise/guides/outlook-trigger.mdx @@ -36,17 +36,28 @@ crew.kickoff({ The crew extracts sender details, subject, body preview, and attachments before generating a structured response. -## Sample payloads & crews +## Testando Localmente -Review the [Outlook examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) for two common scenarios: +Teste sua integração de trigger do Outlook localmente usando a CLI da CrewAI: -- `new-message.json` → new mail notifications parsed by `outlook-message-crew.py` -- `event-removed.json` → calendar cleanup handled by `outlook-event-removal-crew.py` +```bash +# Visualize todos os triggers disponíveis +crewai triggers list -Each crew demonstrates how to handle Microsoft Graph payloads, normalize headers, and keep humans in-the-loop with concise summaries. +# Simule um trigger do Outlook com payload realista +crewai triggers run microsoft_outlook/email_received +``` + +O comando `crewai triggers run` executará sua crew com um payload completo do Outlook, permitindo que você teste sua lógica de parsing antes do deployment. + + + Use `crewai triggers run microsoft_outlook/email_received` (não `crewai run`) para simular execução de trigger durante o desenvolvimento. Após o deployment, sua crew receberá automaticamente o payload do trigger. + ## Troubleshooting - Verify the Outlook connector is still authorized; the subscription must be renewed periodically +- Teste localmente com `crewai triggers run microsoft_outlook/email_received` para ver a estrutura exata do payload - If attachments are missing, confirm the webhook subscription includes the `includeResourceData` flag - Review execution logs when events fail to match—cancellation payloads lack attendee lists by design and the crew should account for that +- Lembre-se: use `crewai triggers run` (não `crewai run`) para simular execução de trigger diff --git a/docs/pt-BR/enterprise/integrations/asana.mdx b/docs/pt-BR/enterprise/integrations/asana.mdx index d2902c882..e30f06dec 100644 --- a/docs/pt-BR/enterprise/integrations/asana.mdx +++ b/docs/pt-BR/enterprise/integrations/asana.mdx @@ -25,7 +25,7 @@ Antes de usar a integração com o Asana, assegure-se de ter: 2. Encontre **Asana** na seção Integrações de Autenticação 3. Clique em **Conectar** e complete o fluxo OAuth 4. Conceda as permissões necessárias para gerenciamento de tarefas e projetos -5. Copie seu Token Enterprise em [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o Pacote Necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Cria um comentário no Asana. **Parâmetros:** @@ -44,7 +44,7 @@ uv add crewai-tools - `text` (string, obrigatório): Texto (exemplo: "Este é um comentário."). - + **Descrição:** Cria um projeto no Asana. **Parâmetros:** @@ -54,7 +54,7 @@ uv add crewai-tools - `notes` (string, opcional): Notas (exemplo: "Esses são itens que precisamos comprar."). - + **Descrição:** Obtém uma lista de projetos do Asana. **Parâmetros:** @@ -62,14 +62,14 @@ uv add crewai-tools - Opções: `default`, `true`, `false` - + **Descrição:** Obtém um projeto pelo ID no Asana. **Parâmetros:** - `projectFilterId` (string, obrigatório): ID do Projeto. - + **Descrição:** Cria uma tarefa no Asana. **Parâmetros:** @@ -83,7 +83,7 @@ uv add crewai-tools - `gid` (string, opcional): ID Externo - Um ID da sua aplicação para associar esta tarefa. Você pode usar este ID para sincronizar atualizações com esta tarefa posteriormente. - + **Descrição:** Atualiza uma tarefa no Asana. **Parâmetros:** @@ -98,7 +98,7 @@ uv add crewai-tools - `gid` (string, opcional): ID Externo - Um ID da sua aplicação para associar a tarefa. Você pode usar este ID para sincronizar atualizações posteriormente. - + **Descrição:** Obtém uma lista de tarefas no Asana. **Parâmetros:** @@ -108,21 +108,21 @@ uv add crewai-tools - `completedSince` (string, opcional): Concluída desde - Retorna apenas tarefas que estejam incompletas ou que tenham sido concluídas desde este horário (timestamp ISO ou Unix). (exemplo: "2014-04-25T16:15:47-04:00"). - + **Descrição:** Obtém uma lista de tarefas pelo ID no Asana. **Parâmetros:** - `taskId` (string, obrigatório): ID da Tarefa. - + **Descrição:** Obtém uma tarefa pelo ID externo no Asana. **Parâmetros:** - `gid` (string, obrigatório): ID Externo - O ID que esta tarefa está associada ou sincronizada, de sua aplicação. - + **Descrição:** Adiciona uma tarefa a uma seção no Asana. **Parâmetros:** @@ -132,14 +132,14 @@ uv add crewai-tools - `afterTaskId` (string, opcional): Após a Tarefa - O ID de uma tarefa nesta seção após a qual esta tarefa será inserida. Não pode ser usada junto com Before Task ID. (exemplo: "1204619611402340"). - + **Descrição:** Obtém uma lista de equipes no Asana. **Parâmetros:** - `workspace` (string, obrigatório): Área de trabalho - Retorna as equipes nesta área de trabalho visíveis para o usuário autorizado. - + **Descrição:** Obtém uma lista de áreas de trabalho do Asana. **Parâmetros:** Nenhum obrigatório. @@ -152,19 +152,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Asana tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Asana capabilities asana_agent = Agent( role="Project Manager", goal="Manage tasks and projects in Asana efficiently", backstory="An AI assistant specialized in project management and task coordination.", - tools=[enterprise_tools] + apps=['asana'] ) # Task to create a new project @@ -186,19 +180,12 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do Asana ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Asana tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["asana_create_task", "asana_update_task", "asana_get_tasks"] -) task_manager_agent = Agent( role="Task Manager", goal="Create and manage tasks efficiently", backstory="An AI assistant that focuses on task creation and management.", - tools=enterprise_tools + apps=['asana'] ) # Task to create and assign a task @@ -220,17 +207,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Coordinate project activities and track progress", backstory="An experienced project coordinator who ensures projects run smoothly.", - tools=[enterprise_tools] + apps=['asana'] ) # Complex task involving multiple Asana operations diff --git a/docs/pt-BR/enterprise/integrations/box.mdx b/docs/pt-BR/enterprise/integrations/box.mdx index 2fef40ed6..906b1adab 100644 --- a/docs/pt-BR/enterprise/integrations/box.mdx +++ b/docs/pt-BR/enterprise/integrations/box.mdx @@ -25,7 +25,7 @@ Antes de utilizar a integração com o Box, assegure-se de que você possui: 2. Encontre **Box** na seção de Integrações de Autenticação 3. Clique em **Conectar** e conclua o fluxo de OAuth 4. Conceda as permissões necessárias para gerenciamento de arquivos e pastas -5. Copie seu Token Enterprise em [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o pacote necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Salva um arquivo a partir de uma URL no Box. **Parâmetros:** @@ -52,7 +52,7 @@ uv add crewai-tools - `file` (string, obrigatório): URL do arquivo - Os arquivos devem ter menos de 50MB. (exemplo: "https://picsum.photos/200/300"). - + **Descrição:** Salva um arquivo no Box. **Parâmetros:** @@ -61,14 +61,14 @@ uv add crewai-tools - `folder` (string, opcional): Pasta - Use as configurações de workflow do Connect Portal para permitir que usuários escolham o destino da pasta. Caso em branco, o padrão é a pasta raiz do usuário. - + **Descrição:** Obtém um arquivo pelo ID no Box. **Parâmetros:** - `fileId` (string, obrigatório): ID do arquivo - Identificador único que representa um arquivo. (exemplo: "12345"). - + **Descrição:** Lista arquivos no Box. **Parâmetros:** @@ -93,7 +93,7 @@ uv add crewai-tools ``` - + **Descrição:** Cria uma pasta no Box. **Parâmetros:** @@ -106,7 +106,7 @@ uv add crewai-tools ``` - + **Descrição:** Move uma pasta no Box. **Parâmetros:** @@ -120,14 +120,14 @@ uv add crewai-tools ``` - + **Descrição:** Obtém uma pasta pelo ID no Box. **Parâmetros:** - `folderId` (string, obrigatório): ID da pasta - Identificador único que representa uma pasta. (exemplo: "0"). - + **Descrição:** Pesquisa pastas no Box. **Parâmetros:** @@ -152,7 +152,7 @@ uv add crewai-tools ``` - + **Descrição:** Exclui uma pasta no Box. **Parâmetros:** @@ -167,19 +167,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Box tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Box capabilities box_agent = Agent( role="Document Manager", goal="Manage files and folders in Box efficiently", backstory="An AI assistant specialized in document management and file organization.", - tools=[enterprise_tools] + apps=['box'] ) # Task to create a folder structure @@ -201,19 +195,12 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do Box ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Box tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["box_create_folder", "box_save_file", "box_list_files"] -) file_organizer_agent = Agent( role="File Organizer", goal="Organize and manage file storage efficiently", backstory="An AI assistant that focuses on file organization and storage management.", - tools=enterprise_tools + apps=['box'] ) # Task to organize files @@ -235,17 +222,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) file_manager = Agent( role="File Manager", goal="Maintain organized file structure and manage document lifecycle", backstory="An experienced file manager who ensures documents are properly organized and accessible.", - tools=[enterprise_tools] + apps=['box'] ) # Complex task involving multiple Box operations diff --git a/docs/pt-BR/enterprise/integrations/clickup.mdx b/docs/pt-BR/enterprise/integrations/clickup.mdx index 9839ad032..3017befc8 100644 --- a/docs/pt-BR/enterprise/integrations/clickup.mdx +++ b/docs/pt-BR/enterprise/integrations/clickup.mdx @@ -25,7 +25,7 @@ Antes de utilizar a integração com o ClickUp, certifique-se de que você possu 2. Encontre **ClickUp** na seção Integrações de Autenticação 3. Clique em **Conectar** e complete o fluxo OAuth 4. Conceda as permissões necessárias para gerenciamento de tarefas e projetos -5. Copie seu Token Enterprise das [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o Pacote Necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Busque tarefas no ClickUp utilizando filtros avançados. **Parâmetros:** @@ -61,7 +61,7 @@ uv add crewai-tools Campos disponíveis: `space_ids%5B%5D`, `project_ids%5B%5D`, `list_ids%5B%5D`, `statuses%5B%5D`, `include_closed`, `assignees%5B%5D`, `tags%5B%5D`, `due_date_gt`, `due_date_lt`, `date_created_gt`, `date_created_lt`, `date_updated_gt`, `date_updated_lt` - + **Descrição:** Obtenha tarefas em uma lista específica do ClickUp. **Parâmetros:** @@ -69,7 +69,7 @@ uv add crewai-tools - `taskFilterFormula` (string, opcional): Busque tarefas que correspondam aos filtros especificados. Por exemplo: name=task1. - + **Descrição:** Crie uma tarefa no ClickUp. **Parâmetros:** @@ -82,7 +82,7 @@ uv add crewai-tools - `additionalFields` (string, opcional): Campos Adicionais - Especifique campos adicionais para incluir nesta tarefa em formato JSON. - + **Descrição:** Atualize uma tarefa no ClickUp. **Parâmetros:** @@ -96,49 +96,49 @@ uv add crewai-tools - `additionalFields` (string, opcional): Campos Adicionais - Especifique campos adicionais para incluir nesta tarefa em formato JSON. - + **Descrição:** Exclua uma tarefa no ClickUp. **Parâmetros:** - `taskId` (string, obrigatório): ID da tarefa - O ID da tarefa a ser excluída. - + **Descrição:** Obtenha informações da Lista no ClickUp. **Parâmetros:** - `spaceId` (string, obrigatório): ID do Espaço - O ID do espaço que contém as listas. - + **Descrição:** Obtenha Campos Personalizados em uma Lista no ClickUp. **Parâmetros:** - `listId` (string, obrigatório): ID da Lista - O ID da lista da qual obter os campos personalizados. - + **Descrição:** Obtenha Todos os Campos em uma Lista no ClickUp. **Parâmetros:** - `listId` (string, obrigatório): ID da Lista - O ID da lista da qual obter todos os campos. - + **Descrição:** Obtenha informações do Espaço no ClickUp. **Parâmetros:** - `spaceId` (string, opcional): ID do Espaço - O ID do espaço a ser recuperado. - + **Descrição:** Obtenha Pastas no ClickUp. **Parâmetros:** - `spaceId` (string, obrigatório): ID do Espaço - O ID do espaço que contém as pastas. - + **Descrição:** Obtenha informações de Membro no ClickUp. **Parâmetros:** Nenhum obrigatório. @@ -151,19 +151,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (ClickUp tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with ClickUp capabilities clickup_agent = Agent( role="Task Manager", goal="Manage tasks and projects in ClickUp efficiently", backstory="An AI assistant specialized in task management and productivity coordination.", - tools=[enterprise_tools] + apps=['clickup'] ) # Task to create a new task @@ -185,19 +179,12 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do ClickUp ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific ClickUp tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["clickup_create_task", "clickup_update_task", "clickup_search_tasks"] -) task_coordinator = Agent( role="Task Coordinator", goal="Create and manage tasks efficiently", backstory="An AI assistant that focuses on task creation and status management.", - tools=enterprise_tools + apps=['clickup'] ) # Task to manage task workflow @@ -219,17 +206,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_manager = Agent( role="Project Manager", goal="Coordinate project activities and track team productivity", backstory="An experienced project manager who ensures projects are delivered on time.", - tools=[enterprise_tools] + apps=['clickup'] ) # Complex task involving multiple ClickUp operations @@ -256,17 +238,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) task_analyst = Agent( role="Task Analyst", goal="Analyze task patterns and optimize team productivity", backstory="An AI assistant that analyzes task data to improve team efficiency.", - tools=[enterprise_tools] + apps=['clickup'] ) # Task to analyze and optimize task distribution diff --git a/docs/pt-BR/enterprise/integrations/github.mdx b/docs/pt-BR/enterprise/integrations/github.mdx index 3ed227f5b..493869239 100644 --- a/docs/pt-BR/enterprise/integrations/github.mdx +++ b/docs/pt-BR/enterprise/integrations/github.mdx @@ -25,7 +25,7 @@ Antes de usar a integração do GitHub, assegure-se de ter: 2. Encontre **GitHub** na seção de Integrações de Autenticação 3. Clique em **Conectar** e complete o fluxo OAuth 4. Conceda as permissões necessárias para gerenciamento de repositório e issues -5. Copie seu Token Enterprise nas [Configurações de Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o pacote necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Cria uma issue no GitHub. **Parâmetros:** @@ -47,7 +47,7 @@ uv add crewai-tools - `assignees` (string, opcional): Responsáveis - Especifique o login dos responsáveis no GitHub como um array de strings para esta issue. (exemplo: `["octocat"]`). - + **Descrição:** Atualiza uma issue no GitHub. **Parâmetros:** @@ -61,7 +61,7 @@ uv add crewai-tools - Opções: `open`, `closed` - + **Descrição:** Obtém uma issue pelo número no GitHub. **Parâmetros:** @@ -70,7 +70,7 @@ uv add crewai-tools - `issue_number` (string, obrigatório): Número da Issue - Especifique o número da issue a ser buscada. - + **Descrição:** Bloqueia uma issue no GitHub. **Parâmetros:** @@ -81,7 +81,7 @@ uv add crewai-tools - Opções: `off-topic`, `too heated`, `resolved`, `spam` - + **Descrição:** Busca por issues no GitHub. **Parâmetros:** @@ -108,7 +108,7 @@ uv add crewai-tools Campos disponíveis: `assignee`, `creator`, `mentioned`, `labels` - + **Descrição:** Cria um release no GitHub. **Parâmetros:** @@ -126,7 +126,7 @@ uv add crewai-tools - Opções: `true`, `false` - + **Descrição:** Atualiza um release no GitHub. **Parâmetros:** @@ -145,7 +145,7 @@ uv add crewai-tools - Opções: `true`, `false` - + **Descrição:** Obtém um release por ID no GitHub. **Parâmetros:** @@ -154,7 +154,7 @@ uv add crewai-tools - `id` (string, obrigatório): ID do Release - Especifique o ID do release a ser recuperado. - + **Descrição:** Obtém um release pelo nome da tag no GitHub. **Parâmetros:** @@ -163,7 +163,7 @@ uv add crewai-tools - `tag_name` (string, obrigatório): Nome - Especifique o nome da tag do release a ser recuperado. (exemplo: "v1.0.0"). - + **Descrição:** Exclui um release no GitHub. **Parâmetros:** @@ -179,19 +179,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (GitHub tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with GitHub capabilities github_agent = Agent( role="Repository Manager", goal="Manage GitHub repositories, issues, and releases efficiently", backstory="An AI assistant specialized in repository management and issue tracking.", - tools=[enterprise_tools] + apps=['github'] ) # Task to create a new issue @@ -213,19 +207,12 @@ crew.kickoff() ### Filtrando Ferramentas GitHub Específicas ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific GitHub tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["github_create_issue", "github_update_issue", "github_search_issue"] -) issue_manager = Agent( role="Issue Manager", goal="Create and manage GitHub issues efficiently", backstory="An AI assistant that focuses on issue tracking and management.", - tools=enterprise_tools + apps=['github'] ) # Task to manage issue workflow @@ -247,17 +234,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) release_manager = Agent( role="Release Manager", goal="Manage software releases and versioning", backstory="An experienced release manager who handles version control and release processes.", - tools=[enterprise_tools] + apps=['github'] ) # Task to create a new release @@ -284,17 +266,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Track and coordinate project issues and development progress", backstory="An AI assistant that helps coordinate development work and track project progress.", - tools=[enterprise_tools] + apps=['github'] ) # Complex task involving multiple GitHub operations diff --git a/docs/pt-BR/enterprise/integrations/gmail.mdx b/docs/pt-BR/enterprise/integrations/gmail.mdx index 21f135086..eea74e4ba 100644 --- a/docs/pt-BR/enterprise/integrations/gmail.mdx +++ b/docs/pt-BR/enterprise/integrations/gmail.mdx @@ -25,7 +25,7 @@ Antes de usar a integração com o Gmail, certifique-se de que você possui: 2. Encontre **Gmail** na seção de Integrações de Autenticação 3. Clique em **Conectar** e conclua o fluxo OAuth 4. Conceda as permissões necessárias para o gerenciamento de e-mail e contato -5. Copie seu Token Empresarial em [Configurações de Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Empresarial em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o Pacote Necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Envia um e-mail pelo Gmail. **Parâmetros:** @@ -59,7 +59,7 @@ uv add crewai-tools ``` - + **Descrição:** Obtém um e-mail pelo ID no Gmail. **Parâmetros:** @@ -67,7 +67,7 @@ uv add crewai-tools - `messageId` (string, obrigatório): ID da Mensagem - Especifique o ID da mensagem a ser recuperada. - + **Descrição:** Pesquisa e-mails no Gmail usando filtros avançados. **Parâmetros:** @@ -98,7 +98,7 @@ uv add crewai-tools ``` - + **Descrição:** Exclui um e-mail no Gmail. **Parâmetros:** @@ -106,7 +106,7 @@ uv add crewai-tools - `messageId` (string, obrigatório): ID da Mensagem - Especifique o ID da mensagem para enviar para a lixeira. - + **Descrição:** Cria um contato no Gmail. **Parâmetros:** @@ -126,28 +126,28 @@ uv add crewai-tools ``` - + **Descrição:** Obtém um contato pelo nome do recurso no Gmail. **Parâmetros:** - `resourceName` (string, obrigatório): Nome do Recurso - Especifique o nome do recurso do contato a ser buscado. - + **Descrição:** Pesquisa um contato no Gmail. **Parâmetros:** - `searchTerm` (string, obrigatório): Termo - Especifique um termo para buscar correspondências aproximadas ou exatas nos campos nome, apelido, endereços de e-mail, números de telefone ou organizações do contato. - + **Descrição:** Exclui um contato no Gmail. **Parâmetros:** - `resourceName` (string, obrigatório): Nome do Recurso - Especifique o nome do recurso do contato a ser excluído. - + **Descrição:** Cria um rascunho no Gmail. **Parâmetros:** @@ -177,19 +177,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Gmail tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Gmail capabilities gmail_agent = Agent( role="Email Manager", goal="Manage email communications and contacts efficiently", backstory="An AI assistant specialized in email management and communication.", - tools=[enterprise_tools] + apps=['gmail'] ) # Task to send a follow-up email @@ -211,19 +205,12 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do Gmail ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Gmail tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["gmail_send_email", "gmail_search_for_email", "gmail_create_draft"] -) email_coordinator = Agent( role="Email Coordinator", goal="Coordinate email communications and manage drafts", backstory="An AI assistant that focuses on email coordination and draft management.", - tools=enterprise_tools + apps=['gmail'] ) # Task to prepare and send emails @@ -245,17 +232,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) contact_manager = Agent( role="Contact Manager", goal="Manage and organize email contacts efficiently", backstory="An experienced contact manager who maintains organized contact databases.", - tools=[enterprise_tools] + apps=['gmail'] ) # Task to manage contacts @@ -281,17 +263,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) email_analyst = Agent( role="Email Analyst", goal="Analyze email patterns and provide insights", backstory="An AI assistant that analyzes email data to provide actionable insights.", - tools=[enterprise_tools] + apps=['gmail'] ) # Task to analyze email patterns @@ -317,17 +294,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) workflow_manager = Agent( role="Email Workflow Manager", goal="Automate email workflows and responses", backstory="An AI assistant that manages automated email workflows and responses.", - tools=[enterprise_tools] + apps=['gmail'] ) # Complex task involving multiple Gmail operations diff --git a/docs/pt-BR/enterprise/integrations/google_calendar.mdx b/docs/pt-BR/enterprise/integrations/google_calendar.mdx index 271ed87ba..163ee688e 100644 --- a/docs/pt-BR/enterprise/integrations/google_calendar.mdx +++ b/docs/pt-BR/enterprise/integrations/google_calendar.mdx @@ -25,7 +25,7 @@ Antes de usar a integração com o Google Calendar, certifique-se de ter: 2. Encontre **Google Calendar** na seção de Integrações de Autenticação 3. Clique em **Conectar** e complete o fluxo OAuth 4. Conceda as permissões necessárias para acesso ao calendário e contatos -5. Copie seu Token Enterprise nas [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o Pacote Necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Cria um evento no Google Calendar. **Parâmetros:** @@ -51,7 +51,7 @@ uv add crewai-tools - `includeMeetLink` (boolean, opcional): Incluir link do Google Meet? – Cria automaticamente um link para conferência Google Meet para este evento. - + **Descrição:** Atualiza um evento existente no Google Calendar. **Parâmetros:** @@ -65,7 +65,7 @@ uv add crewai-tools - `eventDescription` (string, opcional): Descrição do evento. - + **Descrição:** Lista eventos do Google Calendar. **Parâmetros:** @@ -74,7 +74,7 @@ uv add crewai-tools - `before` (string, opcional): Antes – Filtra eventos que terminam antes da data fornecida (Unix em milissegundos ou timestamp ISO). (exemplo: "2025-04-12T10:00:00Z ou 1712908800000"). - + **Descrição:** Obtém um evento específico pelo ID no Google Calendar. **Parâmetros:** @@ -82,7 +82,7 @@ uv add crewai-tools - `calendar` (string, opcional): Calendário – Use as Configurações de Workflow do Connect Portal para permitir que o usuário selecione em qual calendário o evento será adicionado. Padrão para o calendário principal do usuário se deixado em branco. - + **Descrição:** Exclui um evento do Google Calendar. **Parâmetros:** @@ -90,7 +90,7 @@ uv add crewai-tools - `calendar` (string, opcional): Calendário – Use as Configurações de Workflow do Connect Portal para permitir que o usuário selecione em qual calendário o evento será adicionado. Padrão para o calendário principal do usuário se deixado em branco. - + **Descrição:** Obtém contatos do Google Calendar. **Parâmetros:** @@ -102,14 +102,14 @@ uv add crewai-tools ``` - + **Descrição:** Pesquisa contatos no Google Calendar. **Parâmetros:** - `query` (string, opcional): Termo de pesquisa para buscar contatos. - + **Descrição:** Lista pessoas do diretório. **Parâmetros:** @@ -121,7 +121,7 @@ uv add crewai-tools ``` - + **Descrição:** Pesquisa pessoas no diretório. **Parâmetros:** @@ -134,7 +134,7 @@ uv add crewai-tools ``` - + **Descrição:** Lista outros contatos. **Parâmetros:** @@ -146,14 +146,14 @@ uv add crewai-tools ``` - + **Descrição:** Pesquisa outros contatos. **Parâmetros:** - `query` (string, opcional): Termo de pesquisa para buscar contatos. - + **Descrição:** Obtém informações de disponibilidade para calendários. **Parâmetros:** @@ -180,19 +180,15 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools # Obter ferramentas empresariais (as ferramentas do Google Calendar serão incluídas) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Criar um agente com capacidades do Google Calendar calendar_agent = Agent( role="Schedule Manager", goal="Gerenciar eventos de calendário e agendamento de maneira eficiente", backstory="Um assistente de IA especializado em gerenciamento de agendas e coordenação de horários.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Tarefa de criação de reunião @@ -214,19 +210,16 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do Calendário ```python -from crewai_tools import CrewaiEnterpriseTools # Obter apenas ferramentas específicas do Google Calendar -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["google_calendar_create_event", "google_calendar_list_events", "google_calendar_get_availability"] + actions_list=["google_calendar/create_event", "google_calendar/view_events", "google_calendar/get_availability"] ) meeting_coordinator = Agent( role="Meeting Coordinator", goal="Coordenar reuniões e verificar disponibilidade", backstory="Um assistente de IA que foca em agendamento de reuniões e gerenciamento de disponibilidade.", - tools=enterprise_tools + apps=['google_calendar'] ) # Tarefa para agendar reunião com verificação de disponibilidade @@ -248,17 +241,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) event_manager = Agent( role="Event Manager", goal="Gerenciar e atualizar eventos de calendário de forma eficiente", backstory="Um experiente gestor de eventos responsável pela logística e atualizações dos eventos.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Tarefa para gerenciar atualizações de eventos @@ -284,17 +272,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) availability_coordinator = Agent( role="Availability Coordinator", goal="Coordenar disponibilidade e gerenciar contatos para agendamento", backstory="Um assistente de IA que se especializa em gerenciamento de disponibilidade e coordenação de contatos.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Tarefa de coordenação de disponibilidade @@ -321,17 +304,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) scheduling_automator = Agent( role="Scheduling Automator", goal="Automatizar workflows de agendamento e gerenciamento de calendários", backstory="Um assistente de IA que automatiza cenários complexos de agendamento e workflows de agenda.", - tools=[enterprise_tools] + apps=['google_calendar'] ) # Tarefa de automação de agendamento complexo diff --git a/docs/pt-BR/enterprise/integrations/google_contacts.mdx b/docs/pt-BR/enterprise/integrations/google_contacts.mdx new file mode 100644 index 000000000..7b11bef2d --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/google_contacts.mdx @@ -0,0 +1,286 @@ +--- +title: Integração Google Contacts +description: "Gerenciamento de contatos e diretório com integração Google Contacts para CrewAI." +icon: "address-book" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes gerenciem informações de contatos e diretório através do Google Contacts. Acesse contatos pessoais, pesquise pessoas no diretório, crie e atualize informações de contato, e gerencie grupos de contatos com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Google Contacts, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Google com acesso ao Google Contacts +- Conectado sua conta Google através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Google Contacts + +### 1. Conecte sua Conta Google + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Google Contacts** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a contatos e diretório +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Recuperar contatos do usuário do Google Contacts. + + **Parâmetros:** + - `pageSize` (integer, opcional): Número de contatos a retornar (máx 1000). Mínimo: 1, Máximo: 1000 + - `pageToken` (string, opcional): O token da página a recuperar. + - `personFields` (string, opcional): Campos a incluir (ex: 'names,emailAddresses,phoneNumbers'). Padrão: names,emailAddresses,phoneNumbers + - `requestSyncToken` (boolean, opcional): Se a resposta deve incluir um token de sincronização. Padrão: false + - `sortOrder` (string, opcional): A ordem na qual as conexões devem ser classificadas. Opções: LAST_MODIFIED_ASCENDING, LAST_MODIFIED_DESCENDING, FIRST_NAME_ASCENDING, LAST_NAME_ASCENDING + + + + **Descrição:** Pesquisar por contatos usando uma string de consulta. + + **Parâmetros:** + - `query` (string, obrigatório): String de consulta de pesquisa + - `readMask` (string, obrigatório): Campos a ler (ex: 'names,emailAddresses,phoneNumbers') + - `pageSize` (integer, opcional): Número de resultados a retornar. Mínimo: 1, Máximo: 30 + - `pageToken` (string, opcional): Token especificando qual página de resultado retornar. + - `sources` (array, opcional): As fontes para pesquisar. Opções: READ_SOURCE_TYPE_CONTACT, READ_SOURCE_TYPE_PROFILE. Padrão: READ_SOURCE_TYPE_CONTACT + + + + **Descrição:** Listar pessoas no diretório do usuário autenticado. + + **Parâmetros:** + - `sources` (array, obrigatório): Fontes de diretório para pesquisar. Opções: DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE, DIRECTORY_SOURCE_TYPE_DOMAIN_CONTACT. Padrão: DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE + - `pageSize` (integer, opcional): Número de pessoas a retornar. Mínimo: 1, Máximo: 1000 + - `pageToken` (string, opcional): Token especificando qual página de resultado retornar. + - `readMask` (string, opcional): Campos a ler (ex: 'names,emailAddresses') + - `requestSyncToken` (boolean, opcional): Se a resposta deve incluir um token de sincronização. Padrão: false + - `mergeSources` (array, opcional): Dados adicionais para mesclar nas respostas de pessoas do diretório. Opções: CONTACT + + + + **Descrição:** Pesquisar por pessoas no diretório. + + **Parâmetros:** + - `query` (string, obrigatório): Consulta de pesquisa + - `sources` (string, obrigatório): Fontes de diretório (use 'DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE') + - `pageSize` (integer, opcional): Número de resultados a retornar + - `readMask` (string, opcional): Campos a ler + + + + **Descrição:** Listar outros contatos (não nos contatos pessoais do usuário). + + **Parâmetros:** + - `pageSize` (integer, opcional): Número de contatos a retornar. Mínimo: 1, Máximo: 1000 + - `pageToken` (string, opcional): Token especificando qual página de resultado retornar. + - `readMask` (string, opcional): Campos a ler + - `requestSyncToken` (boolean, opcional): Se a resposta deve incluir um token de sincronização. Padrão: false + + + + **Descrição:** Pesquisar outros contatos. + + **Parâmetros:** + - `query` (string, obrigatório): Consulta de pesquisa + - `readMask` (string, obrigatório): Campos a ler (ex: 'names,emailAddresses') + - `pageSize` (integer, opcional): Número de resultados + + + + **Descrição:** Obter informações de contato de uma única pessoa por nome do recurso. + + **Parâmetros:** + - `resourceName` (string, obrigatório): O nome do recurso da pessoa a obter (ex: 'people/c123456789') + - `personFields` (string, opcional): Campos a incluir (ex: 'names,emailAddresses,phoneNumbers'). Padrão: names,emailAddresses,phoneNumbers + + + + **Descrição:** Criar um novo contato no catálogo de endereços do usuário. + + **Parâmetros:** + - `names` (array, opcional): Nomes da pessoa. Cada item é um objeto com `givenName` (string), `familyName` (string), `displayName` (string). + - `emailAddresses` (array, opcional): Endereços de email. Cada item é um objeto com `value` (string, endereço de email) e `type` (string, 'home', 'work', 'other', padrão 'other'). + - `phoneNumbers` (array, opcional): Números de telefone. Cada item é um objeto com `value` (string, número de telefone) e `type` (string, 'home', 'work', 'mobile', 'other', padrão 'other'). + - `addresses` (array, opcional): Endereços postais. Cada item é um objeto com `formattedValue` (string, endereço formatado) e `type` (string, 'home', 'work', 'other', padrão 'other'). + - `organizations` (array, opcional): Organizações/empresas. Cada item é um objeto com `name` (string, nome da organização), `title` (string, cargo) e `type` (string, 'work', 'other', padrão 'work'). + + + + **Descrição:** Atualizar informações de um contato existente. + + **Parâmetros:** + - `resourceName` (string, obrigatório): O nome do recurso da pessoa a atualizar (ex: 'people/c123456789'). + - `updatePersonFields` (string, obrigatório): Campos a atualizar (ex: 'names,emailAddresses,phoneNumbers'). + - `names` (array, opcional): Nomes da pessoa. Cada item é um objeto com `givenName` (string), `familyName` (string), `displayName` (string). + - `emailAddresses` (array, opcional): Endereços de email. Cada item é um objeto com `value` (string, endereço de email) e `type` (string, 'home', 'work', 'other'). + - `phoneNumbers` (array, opcional): Números de telefone. Cada item é um objeto com `value` (string, número de telefone) e `type` (string, 'home', 'work', 'mobile', 'other'). + + + + **Descrição:** Excluir um contato do catálogo de endereços do usuário. + + **Parâmetros:** + - `resourceName` (string, obrigatório): O nome do recurso da pessoa a excluir (ex: 'people/c123456789'). + + + + **Descrição:** Obter informações sobre várias pessoas em uma única solicitação. + + **Parâmetros:** + - `resourceNames` (array, obrigatório): Nomes de recursos das pessoas a obter (máx 200 itens). + - `personFields` (string, opcional): Campos a incluir (ex: 'names,emailAddresses,phoneNumbers'). Padrão: names,emailAddresses,phoneNumbers + + + + **Descrição:** Listar os grupos de contatos (rótulos) do usuário. + + **Parâmetros:** + - `pageSize` (integer, opcional): Número de grupos de contatos a retornar. Mínimo: 1, Máximo: 1000 + - `pageToken` (string, opcional): Token especificando qual página de resultado retornar. + - `groupFields` (string, opcional): Campos a incluir (ex: 'name,memberCount,clientData'). Padrão: name,memberCount + + + + **Descrição:** Obter um grupo de contatos específico por nome do recurso. + + **Parâmetros:** + - `resourceName` (string, obrigatório): O nome do recurso do grupo de contatos (ex: 'contactGroups/myContactGroup'). + - `maxMembers` (integer, opcional): Número máximo de membros a incluir. Mínimo: 0, Máximo: 20000 + - `groupFields` (string, opcional): Campos a incluir (ex: 'name,memberCount,clientData'). Padrão: name,memberCount + + + + **Descrição:** Criar um novo grupo de contatos (rótulo). + + **Parâmetros:** + - `name` (string, obrigatório): O nome do grupo de contatos. + - `clientData` (array, opcional): Dados específicos do cliente. Cada item é um objeto com `key` (string) e `value` (string). + + + +## Exemplos de Uso + +### Configuração Básica do Agente Google Contacts + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Google Contacts +contacts_agent = Agent( + role="Gerenciador de Contatos", + goal="Gerenciar Google Contacts de forma eficiente", + backstory="Um assistente IA especializado em gerenciamento e organização de contatos.", + apps=['google_contacts'] # Todas as ações do Google Contacts estarão disponíveis +) + +# Tarefa para criar um novo contato +create_contact_task = Task( + description="Criar um novo contato chamado 'João Silva' com email 'joao.silva@exemplo.com' e telefone '11-98765-4321'", + agent=contacts_agent, + expected_output="Novo contato criado com sucesso" +) + +# Execute a tarefa +crew = Crew( + agents=[contacts_agent], + tasks=[create_contact_task] +) + +crew.kickoff() +``` + +### Pesquisando e Listando Contatos + +```python +from crewai import Agent, Task, Crew + +# Crie um agente focado em pesquisar contatos +search_agent = Agent( + role="Pesquisador de Contatos", + goal="Encontrar e recuperar informações de contato", + backstory="Um assistente IA habilidoso em pesquisar e listar contatos.", + apps=['google_contacts/search_contacts', 'google_contacts/get_contacts'] +) + +# Tarefa para pesquisar contatos +search_task = Task( + description="Pesquisar por contatos chamados 'Maria' e listar seus endereços de email e números de telefone.", + agent=search_agent, + expected_output="Lista de contatos correspondentes a 'Maria' com seus detalhes de email e telefone." +) + +crew = Crew( + agents=[search_agent], + tasks=[search_task] +) + +crew.kickoff() +``` + +### Gerenciando Grupos de Contatos + +```python +from crewai import Agent, Task, Crew + +# Crie um agente para gerenciar grupos de contatos +group_manager = Agent( + role="Organizador de Grupos de Contatos", + goal="Organizar contatos em grupos e gerenciar membros dos grupos", + backstory="Um assistente IA especializado em criar e gerenciar grupos do Google Contacts.", + apps=['google_contacts/create_contact_group', 'google_contacts/list_contact_groups'] +) + +# Tarefa para criar um novo grupo de contatos +create_group_task = Task( + description="Criar um novo grupo de contatos chamado 'Equipe de Marketing' e listar todos os grupos existentes.", + agent=group_manager, + expected_output="Novo grupo de contatos 'Equipe de Marketing' criado e lista de todos os grupos retornada." +) + +crew = Crew( + agents=[group_manager], + tasks=[create_group_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Google tenha as permissões necessárias para acesso a contatos e diretório. +- Verifique se a conexão OAuth inclui todos os escopos necessários para a API Google People. + +**Problemas de Criação/Atualização de Contatos** +- Certifique-se de que campos obrigatórios como `email` sejam fornecidos para criação de contatos. +- Verifique se o `resourceName` está correto ao atualizar ou excluir contatos. +- Confirme se o formato dos dados para `names`, `emailAddresses`, `phoneNumbers`, etc., corresponde às especificações da API. + +**Problemas de Pesquisa e Filtro** +- Certifique-se de que os parâmetros de `query` e `readMask` estejam especificados corretamente para `search_contacts` e `search_other_contacts`. +- Para pesquisas de diretório, certifique-se de que `sources` esteja definido corretamente (ex: 'DIRECTORY_SOURCE_TYPE_DOMAIN_PROFILE'). + +**Gerenciamento de Grupos de Contatos** +- Ao criar um grupo de contatos, certifique-se de que o `name` seja fornecido. +- Para `get_contact_group`, certifique-se de que o `resourceName` esteja correto. + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Google Contacts. + diff --git a/docs/pt-BR/enterprise/integrations/google_docs.mdx b/docs/pt-BR/enterprise/integrations/google_docs.mdx new file mode 100644 index 000000000..aaa42b00b --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/google_docs.mdx @@ -0,0 +1,228 @@ +--- +title: Integração Google Docs +description: "Criação e edição de documentos com integração Google Docs para CrewAI." +icon: "file-lines" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes criem, editem e gerenciem documentos do Google Docs com manipulação de texto e formatação. Automatize a criação de documentos, insira e substitua texto, gerencie intervalos de conteúdo e simplifique seus fluxos de trabalho de documentos com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Google Docs, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Google com acesso ao Google Docs +- Conectado sua conta Google através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Google Docs + +### 1. Conecte sua Conta Google + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Google Docs** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a documentos +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Criar um novo documento do Google. + + **Parâmetros:** + - `title` (string, opcional): O título para o novo documento. + + + + **Descrição:** Obter o conteúdo e metadados de um documento do Google. + + **Parâmetros:** + - `documentId` (string, obrigatório): O ID do documento a recuperar. + - `includeTabsContent` (boolean, opcional): Se deve incluir conteúdo de abas. Padrão: false + - `suggestionsViewMode` (string, opcional): O modo de visualização de sugestões a aplicar ao documento. Opções: DEFAULT_FOR_CURRENT_ACCESS, PREVIEW_SUGGESTIONS_ACCEPTED, PREVIEW_WITHOUT_SUGGESTIONS. Padrão: DEFAULT_FOR_CURRENT_ACCESS + + + + **Descrição:** Aplicar uma ou mais atualizações a um documento do Google. + + **Parâmetros:** + - `documentId` (string, obrigatório): O ID do documento a atualizar. + - `requests` (array, obrigatório): Uma lista de atualizações a aplicar ao documento. Cada item é um objeto representando uma solicitação. + - `writeControl` (object, opcional): Fornece controle sobre como as solicitações de escrita são executadas. Contém `requiredRevisionId` (string) e `targetRevisionId` (string). + + + + **Descrição:** Inserir texto em um documento do Google em um local específico. + + **Parâmetros:** + - `documentId` (string, obrigatório): O ID do documento a atualizar. + - `text` (string, obrigatório): O texto a inserir. + - `index` (integer, opcional): O índice baseado em zero onde inserir o texto. Padrão: 1 + + + + **Descrição:** Substituir todas as instâncias de texto em um documento do Google. + + **Parâmetros:** + - `documentId` (string, obrigatório): O ID do documento a atualizar. + - `containsText` (string, obrigatório): O texto a encontrar e substituir. + - `replaceText` (string, obrigatório): O texto para substituir. + - `matchCase` (boolean, opcional): Se a pesquisa deve respeitar maiúsculas e minúsculas. Padrão: false + + + + **Descrição:** Excluir conteúdo de um intervalo específico em um documento do Google. + + **Parâmetros:** + - `documentId` (string, obrigatório): O ID do documento a atualizar. + - `startIndex` (integer, obrigatório): O índice inicial do intervalo a excluir. + - `endIndex` (integer, obrigatório): O índice final do intervalo a excluir. + + + + **Descrição:** Inserir uma quebra de página em um local específico em um documento do Google. + + **Parâmetros:** + - `documentId` (string, obrigatório): O ID do documento a atualizar. + - `index` (integer, opcional): O índice baseado em zero onde inserir a quebra de página. Padrão: 1 + + + + **Descrição:** Criar um intervalo nomeado em um documento do Google. + + **Parâmetros:** + - `documentId` (string, obrigatório): O ID do documento a atualizar. + - `name` (string, obrigatório): O nome para o intervalo nomeado. + - `startIndex` (integer, obrigatório): O índice inicial do intervalo. + - `endIndex` (integer, obrigatório): O índice final do intervalo. + + + +## Exemplos de Uso + +### Configuração Básica do Agente Google Docs + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Google Docs +docs_agent = Agent( + role="Criador de Documentos", + goal="Criar e gerenciar documentos do Google Docs de forma eficiente", + backstory="Um assistente IA especializado em criação e edição de documentos do Google Docs.", + apps=['google_docs'] # Todas as ações do Google Docs estarão disponíveis +) + +# Tarefa para criar um novo documento +create_doc_task = Task( + description="Criar um novo documento do Google intitulado 'Relatório de Status do Projeto'", + agent=docs_agent, + expected_output="Novo documento do Google 'Relatório de Status do Projeto' criado com sucesso" +) + +# Execute a tarefa +crew = Crew( + agents=[docs_agent], + tasks=[create_doc_task] +) + +crew.kickoff() +``` + +### Edição de Texto e Gerenciamento de Conteúdo + +```python +from crewai import Agent, Task, Crew + +# Crie um agente focado em edição de texto +text_editor = Agent( + role="Editor de Documentos", + goal="Editar e atualizar conteúdo em documentos do Google Docs", + backstory="Um assistente IA habilidoso em edição precisa de texto e gerenciamento de conteúdo.", + apps=['google_docs/insert_text', 'google_docs/replace_text', 'google_docs/delete_content_range'] +) + +# Tarefa para editar conteúdo do documento +edit_content_task = Task( + description="No documento 'your_document_id', inserir o texto 'Resumo Executivo: ' no início, depois substituir todas as instâncias de 'TODO' por 'CONCLUÍDO'.", + agent=text_editor, + expected_output="Documento atualizado com novo texto inserido e itens TODO substituídos." +) + +crew = Crew( + agents=[text_editor], + tasks=[edit_content_task] +) + +crew.kickoff() +``` + +### Operações Avançadas de Documentos + +```python +from crewai import Agent, Task, Crew + +# Crie um agente para operações avançadas de documentos +document_formatter = Agent( + role="Formatador de Documentos", + goal="Aplicar formatação avançada e estrutura a documentos do Google", + backstory="Um assistente IA que lida com formatação complexa de documentos e organização.", + apps=['google_docs/batch_update', 'google_docs/insert_page_break', 'google_docs/create_named_range'] +) + +# Tarefa para formatar documento +format_doc_task = Task( + description="No documento 'your_document_id', inserir uma quebra de página na posição 100, criar um intervalo nomeado chamado 'Introdução' para caracteres 1-50, e aplicar atualizações de formatação em lote.", + agent=document_formatter, + expected_output="Documento formatado com quebra de página, intervalo nomeado e estilo aplicado." +) + +crew = Crew( + agents=[document_formatter], + tasks=[format_doc_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Google tenha as permissões necessárias para acesso ao Google Docs. +- Verifique se a conexão OAuth inclui todos os escopos necessários (`https://www.googleapis.com/auth/documents`). + +**Problemas de ID do Documento** +- Verifique novamente os IDs dos documentos para correção. +- Certifique-se de que o documento existe e está acessível à sua conta. +- IDs de documentos podem ser encontrados na URL do Google Docs. + +**Inserção de Texto e Operações de Intervalo** +- Ao usar `insert_text` ou `delete_content_range`, certifique-se de que as posições de índice sejam válidas. +- Lembre-se de que o Google Docs usa indexação baseada em zero. +- O documento deve ter conteúdo nas posições de índice especificadas. + +**Formatação de Solicitação de Atualização em Lote** +- Ao usar `batch_update`, certifique-se de que o array `requests` esteja formatado corretamente de acordo com a documentação da API do Google Docs. +- Atualizações complexas requerem estruturas JSON específicas para cada tipo de solicitação. + +**Operações de Substituição de Texto** +- Para `replace_text`, certifique-se de que o parâmetro `containsText` corresponda exatamente ao texto que você deseja substituir. +- Use o parâmetro `matchCase` para controlar a sensibilidade a maiúsculas e minúsculas. + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Google Docs. + diff --git a/docs/pt-BR/enterprise/integrations/google_drive.mdx b/docs/pt-BR/enterprise/integrations/google_drive.mdx new file mode 100644 index 000000000..3a4a59806 --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/google_drive.mdx @@ -0,0 +1,51 @@ +--- +title: Integração Google Drive +description: "Gerenciamento de arquivos e pastas com integração Google Drive para CrewAI." +icon: "google" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes acessem e gerenciem arquivos e pastas no Google Drive. Faça upload, download, organize conteúdo, crie links de compartilhamento e simplifique seus fluxos de trabalho de armazenamento em nuvem com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Google Drive, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Google com acesso ao Google Drive +- Conectado sua conta Google através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Google Drive + +### 1. Conecte sua Conta Google + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Google Drive** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a arquivos +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + +Para informações detalhadas sobre parâmetros e uso, consulte a [documentação em inglês](../../../en/enterprise/integrations/google_drive). + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Google tenha as permissões necessárias para acesso ao Google Drive. + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Google Drive. + diff --git a/docs/pt-BR/enterprise/integrations/google_sheets.mdx b/docs/pt-BR/enterprise/integrations/google_sheets.mdx index acc083e5c..81b4f563e 100644 --- a/docs/pt-BR/enterprise/integrations/google_sheets.mdx +++ b/docs/pt-BR/enterprise/integrations/google_sheets.mdx @@ -26,7 +26,7 @@ Antes de utilizar a integração com o Google Sheets, certifique-se de que você 2. Localize **Google Sheets** na seção Integrações de Autenticação 3. Clique em **Conectar** e conclua o fluxo OAuth 4. Conceda as permissões necessárias para acesso à planilha -5. Copie seu Token Enterprise em [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o Pacote Necessário @@ -37,7 +37,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Obtém linhas de uma planilha Google Sheets. **Parâmetros:** @@ -45,7 +45,7 @@ uv add crewai-tools - `limit` (string, opcional): Limite de linhas - Limita o número máximo de linhas retornadas. - + **Descrição:** Cria uma nova linha em uma planilha Google Sheets. **Parâmetros:** @@ -62,7 +62,7 @@ uv add crewai-tools ``` - + **Descrição:** Atualiza linhas existentes em uma planilha Google Sheets. **Parâmetros:** @@ -105,19 +105,15 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools # Obtenha as ferramentas enterprise (ferramentas Google Sheets incluídas) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Crie um agente com capacidades para Google Sheets sheets_agent = Agent( role="Data Manager", goal="Gerenciar dados de planilha e rastrear informações de maneira eficiente", backstory="Um assistente de IA especializado em gestão de dados e operações em planilhas.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Tarefa para adicionar novos dados a uma planilha @@ -139,19 +135,16 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do Google Sheets ```python -from crewai_tools import CrewaiEnterpriseTools # Obtenha apenas ferramentas específicas do Google Sheets -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["google_sheets_get_row", "google_sheets_create_row"] + actions_list=["google_sheets/get_values", "google_sheets/update_values"] ) data_collector = Agent( role="Data Collector", goal="Coletar e organizar dados em planilhas", backstory="Um assistente de IA dedicado à coleta e organização de dados.", - tools=enterprise_tools + apps=['google_sheets'] ) # Tarefa para coletar e organizar dados @@ -173,17 +166,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_analyst = Agent( role="Data Analyst", goal="Analisar dados de planilhas e gerar insights", backstory="Um analista de dados experiente que extrai insights dos dados de planilhas.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Tarefa para analisar dados e criar relatórios @@ -209,17 +197,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_updater = Agent( role="Data Updater", goal="Atualizar e manter dados de planilhas automaticamente", backstory="Um assistente de IA que mantém a precisão dos dados e atualiza registros automaticamente.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Tarefa para atualizar dados com base em condições @@ -246,17 +229,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) workflow_manager = Agent( role="Data Workflow Manager", goal="Gerenciar fluxos de dados complexos entre várias planilhas", backstory="Um assistente de IA que orquestra operações complexas de dados entre várias planilhas.", - tools=[enterprise_tools] + apps=['google_sheets'] ) # Tarefa de workflow complexa diff --git a/docs/pt-BR/enterprise/integrations/google_slides.mdx b/docs/pt-BR/enterprise/integrations/google_slides.mdx new file mode 100644 index 000000000..3113adce7 --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/google_slides.mdx @@ -0,0 +1,232 @@ +--- +title: Integração Google Slides +description: "Criação e gerenciamento de apresentações com integração Google Slides para CrewAI." +icon: "chart-bar" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes criem, editem e gerenciem apresentações do Google Slides. Crie apresentações, atualize conteúdo, importe dados do Google Sheets, gerencie páginas e miniaturas, e simplifique seus fluxos de trabalho de apresentações com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Google Slides, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Google com acesso ao Google Slides +- Conectado sua conta Google através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Google Slides + +### 1. Conecte sua Conta Google + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Google Slides** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a apresentações, planilhas e drive +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Cria uma apresentação em branco sem conteúdo. + + **Parâmetros:** + - `title` (string, obrigatório): O título da apresentação. + + + + **Descrição:** Recupera uma apresentação por ID. + + **Parâmetros:** + - `presentationId` (string, obrigatório): O ID da apresentação a ser recuperada. + - `fields` (string, opcional): Os campos a incluir na resposta. Use isso para melhorar o desempenho retornando apenas os dados necessários. + + + + **Descrição:** Aplica atualizações, adiciona conteúdo ou remove conteúdo de uma apresentação. + + **Parâmetros:** + - `presentationId` (string, obrigatório): O ID da apresentação a ser atualizada. + - `requests` (array, obrigatório): Uma lista de atualizações a aplicar à apresentação. Cada item é um objeto representando uma solicitação. + - `writeControl` (object, opcional): Fornece controle sobre como as solicitações de escrita são executadas. Contém `requiredRevisionId` (string). + + + + **Descrição:** Recupera uma página específica por seu ID. + + **Parâmetros:** + - `presentationId` (string, obrigatório): O ID da apresentação. + - `pageObjectId` (string, obrigatório): O ID da página a ser recuperada. + + + + **Descrição:** Gera uma miniatura da página. + + **Parâmetros:** + - `presentationId` (string, obrigatório): O ID da apresentação. + - `pageObjectId` (string, obrigatório): O ID da página para geração de miniatura. + + + + **Descrição:** Importa dados de uma planilha do Google para uma apresentação. + + **Parâmetros:** + - `presentationId` (string, obrigatório): O ID da apresentação. + - `sheetId` (string, obrigatório): O ID da planilha do Google para importar. + - `dataRange` (string, obrigatório): O intervalo de dados a importar da planilha. + + + + **Descrição:** Faz upload de um arquivo para o Google Drive associado à apresentação. + + **Parâmetros:** + - `file` (string, obrigatório): Os dados do arquivo a fazer upload. + - `presentationId` (string, obrigatório): O ID da apresentação para vincular o arquivo carregado. + + + + **Descrição:** Vincula um arquivo no Google Drive a uma apresentação. + + **Parâmetros:** + - `presentationId` (string, obrigatório): O ID da apresentação. + - `fileId` (string, obrigatório): O ID do arquivo a vincular. + + + + **Descrição:** Lista todas as apresentações acessíveis ao usuário. + + **Parâmetros:** + - `pageSize` (integer, opcional): O número de apresentações a retornar por página. + - `pageToken` (string, opcional): Um token para paginação. + + + + **Descrição:** Exclui uma apresentação por ID. + + **Parâmetros:** + - `presentationId` (string, obrigatório): O ID da apresentação a ser excluída. + + + +## Exemplos de Uso + +### Configuração Básica do Agente Google Slides + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Google Slides +slides_agent = Agent( + role="Criador de Apresentações", + goal="Criar e gerenciar apresentações do Google Slides de forma eficiente", + backstory="Um assistente IA especializado em design de apresentações e gerenciamento de conteúdo.", + apps=['google_slides'] # Todas as ações do Google Slides estarão disponíveis +) + +# Tarefa para criar uma nova apresentação +create_presentation_task = Task( + description="Criar uma nova apresentação em branco intitulada 'Relatório de Vendas Trimestral'", + agent=slides_agent, + expected_output="Nova apresentação 'Relatório de Vendas Trimestral' criada com sucesso" +) + +# Execute a tarefa +crew = Crew( + agents=[slides_agent], + tasks=[create_presentation_task] +) + +crew.kickoff() +``` + +### Atualizando Conteúdo da Apresentação + +```python +from crewai import Agent, Task, Crew + +# Crie um agente focado em atualizar apresentações +updater_agent = Agent( + role="Atualizador de Apresentações", + goal="Atualizar e modificar apresentações existentes do Google Slides", + backstory="Um assistente IA habilidoso em fazer atualizações precisas no conteúdo de apresentações.", + apps=['google_slides/batch_update_presentation'] +) + +# Tarefa para atualizar uma apresentação +update_presentation_task = Task( + description="Atualizar a apresentação com ID 'your_presentation_id' para adicionar uma nova caixa de texto no primeiro slide com o conteúdo 'Destaques Principais'.", + agent=updater_agent, + expected_output="Apresentação atualizada com novo conteúdo." +) + +crew = Crew( + agents=[updater_agent], + tasks=[update_presentation_task] +) + +crew.kickoff() +``` + +### Importando Dados e Gerenciando Arquivos + +```python +from crewai import Agent, Task, Crew + +# Crie um agente para importação de dados e gerenciamento de arquivos +data_presenter = Agent( + role="Apresentador de Dados", + goal="Importar dados para apresentações e gerenciar arquivos vinculados", + backstory="Um assistente IA que integra dados de várias fontes em apresentações.", + apps=['google_slides/import_data_from_sheet', 'google_slides/upload_file_to_drive'] +) + +# Tarefa para importar dados de uma planilha +import_data_task = Task( + description="Importar dados da planilha do Google 'your_sheet_id' intervalo 'A1:C10' para a apresentação 'your_presentation_id'.", + agent=data_presenter, + expected_output="Dados importados da planilha do Google para a apresentação." +) + +crew = Crew( + agents=[data_presenter], + tasks=[import_data_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Google tenha as permissões necessárias para acesso ao Google Slides e Google Drive. +- Verifique se a conexão OAuth inclui todos os escopos necessários. + +**Problemas de ID de Apresentação/Página** +- Verifique novamente os IDs de apresentação e IDs de objeto de página para correção. +- Certifique-se de que a apresentação ou página existe e está acessível. + +**Formatação de Solicitação de Atualização em Lote** +- Ao usar `batch_update_presentation`, certifique-se de que o array `requests` esteja formatado corretamente de acordo com a documentação da API do Google Slides. +- Atualizações complexas frequentemente requerem estruturas JSON específicas para cada tipo de solicitação (ex: `createText`, `insertShape`). + +**Problemas de Upload/Vinculação de Arquivos** +- Certifique-se de que o conteúdo do `file` esteja fornecido corretamente para `upload_file_to_drive`. +- Verifique se o `fileId` está correto ao vincular arquivos a uma apresentação. +- Verifique as permissões do Google Drive para acesso a arquivos. + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Google Slides. + diff --git a/docs/pt-BR/enterprise/integrations/hubspot.mdx b/docs/pt-BR/enterprise/integrations/hubspot.mdx index d12c78440..1b49064da 100644 --- a/docs/pt-BR/enterprise/integrations/hubspot.mdx +++ b/docs/pt-BR/enterprise/integrations/hubspot.mdx @@ -25,7 +25,7 @@ Antes de utilizar a integração com o HubSpot, certifique-se de que você possu 2. Encontre **HubSpot** na seção de Integrações de Autenticação. 3. Clique em **Conectar** e complete o fluxo OAuth. 4. Conceda as permissões necessárias para gerenciamento de empresas e contatos. -5. Copie o seu Token Enterprise nas [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account). +5. Copie o seu Token Enterprise nas [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o Pacote Necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Crie um novo registro de empresa no HubSpot. **Parâmetros:** @@ -101,7 +101,7 @@ uv add crewai-tools - `founded_year` (string, opcional): Ano de fundação. - + **Descrição:** Crie um novo registro de contato no HubSpot. **Parâmetros:** @@ -200,7 +200,7 @@ uv add crewai-tools - `hs_googleplusid` (string, opcional): googleplus ID. - + **Descrição:** Crie um novo registro de negócio (deal) no HubSpot. **Parâmetros:** @@ -215,7 +215,7 @@ uv add crewai-tools - `hs_priority` (string, opcional): Prioridade do negócio. Valores disponíveis: `low`, `medium`, `high`. - + **Descrição:** Crie um novo engajamento (ex: nota, e-mail, ligação, reunião, tarefa) no HubSpot. **Parâmetros:** @@ -232,7 +232,7 @@ uv add crewai-tools - `hs_meeting_end_time` (string, opcional): Horário de término da reunião. (Utilizado para `MEETING`) - + **Descrição:** Atualize um registro de empresa existente no HubSpot. **Parâmetros:** @@ -249,7 +249,7 @@ uv add crewai-tools - `description` (string, opcional): Descrição. - + **Descrição:** Crie um registro para um tipo de objeto especificado no HubSpot. **Parâmetros:** @@ -257,7 +257,7 @@ uv add crewai-tools - Parâmetros adicionais dependem do esquema do objeto personalizado. - + **Descrição:** Atualize um registro de contato existente no HubSpot. **Parâmetros:** @@ -271,7 +271,7 @@ uv add crewai-tools - `lifecyclestage` (string, opcional): Estágio no ciclo de vida. - + **Descrição:** Atualize um registro de negócio existente no HubSpot. **Parâmetros:** @@ -284,7 +284,7 @@ uv add crewai-tools - `dealtype` (string, opcional): Tipo de negócio. - + **Descrição:** Atualize um engajamento existente no HubSpot. **Parâmetros:** @@ -295,7 +295,7 @@ uv add crewai-tools - `hs_task_status` (string, opcional): Status da tarefa. - + **Descrição:** Atualize um registro para um tipo de objeto especificado no HubSpot. **Parâmetros:** @@ -304,28 +304,28 @@ uv add crewai-tools - Parâmetros adicionais dependem do esquema do objeto personalizado. - + **Descrição:** Obtenha uma lista de registros de empresas do HubSpot. **Parâmetros:** - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Obtenha uma lista de registros de contatos do HubSpot. **Parâmetros:** - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Obtenha uma lista de registros de negócios do HubSpot. **Parâmetros:** - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Obtenha uma lista de registros de engajamentos do HubSpot. **Parâmetros:** @@ -333,7 +333,7 @@ uv add crewai-tools - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Obtenha uma lista de registros de qualquer tipo de objeto no HubSpot. **Parâmetros:** @@ -341,35 +341,35 @@ uv add crewai-tools - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Obtenha um registro de empresa pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID da empresa a ser consultada. - + **Descrição:** Obtenha um registro de contato pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do contato a ser consultado. - + **Descrição:** Obtenha um registro de negócio pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do negócio a ser consultado. - + **Descrição:** Obtenha um registro de engajamento pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do engajamento a ser consultado. - + **Descrição:** Obtenha um registro de qualquer tipo de objeto especificado pelo seu ID. **Parâmetros:** @@ -377,7 +377,7 @@ uv add crewai-tools - `recordId` (string, obrigatório): ID do registro a ser consultado. - + **Descrição:** Pesquise registros de empresas no HubSpot utilizando uma fórmula de filtro. **Parâmetros:** @@ -385,7 +385,7 @@ uv add crewai-tools - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Pesquise registros de contatos no HubSpot utilizando uma fórmula de filtro. **Parâmetros:** @@ -393,7 +393,7 @@ uv add crewai-tools - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Pesquise registros de negócios no HubSpot utilizando uma fórmula de filtro. **Parâmetros:** @@ -401,7 +401,7 @@ uv add crewai-tools - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Pesquise registros de engajamento no HubSpot utilizando uma fórmula de filtro. **Parâmetros:** @@ -409,7 +409,7 @@ uv add crewai-tools - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Pesquise registros de qualquer tipo de objeto no HubSpot. **Parâmetros:** @@ -418,35 +418,35 @@ uv add crewai-tools - `paginationParameters` (object, opcional): Use `pageCursor` para buscar páginas subsequentes. - + **Descrição:** Exclua um registro de empresa pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID da empresa a ser excluída. - + **Descrição:** Exclua um registro de contato pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do contato a ser excluído. - + **Descrição:** Exclua um registro de negócio pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do negócio a ser excluído. - + **Descrição:** Exclua um registro de engajamento pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do engajamento a ser excluído. - + **Descrição:** Exclua um registro de qualquer tipo de objeto especificado pelo seu ID. **Parâmetros:** @@ -454,7 +454,7 @@ uv add crewai-tools - `recordId` (string, obrigatório): ID do registro a ser excluído. - + **Descrição:** Obtenha contatos de uma lista específica pelo seu ID. **Parâmetros:** @@ -462,7 +462,7 @@ uv add crewai-tools - `paginationParameters` (object, opcional): Use `pageCursor` para páginas subsequentes. - + **Descrição:** Obtenha o esquema esperado para um dado tipo de objeto e operação. **Parâmetros:** @@ -477,19 +477,15 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools # Obtenha as ferramentas enterprise (ferramentas HubSpot incluídas) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Crie um agente com capacidades HubSpot hubspot_agent = Agent( role="CRM Manager", goal="Manage company and contact records in HubSpot", backstory="An AI assistant specialized in CRM management.", - tools=[enterprise_tools] + apps=['hubspot'] ) # Task para criar nova empresa @@ -511,19 +507,16 @@ crew.kickoff() ### Filtrando Ferramentas HubSpot Específicas ```python -from crewai_tools import CrewaiEnterpriseTools # Obtenha somente a ferramenta para criar contatos -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["hubspot_create_record_contacts"] + actions_list=["hubspot/create_contact"] ) contact_creator = Agent( role="Contact Creator", goal="Create new contacts in HubSpot", backstory="An AI assistant that focuses on creating new contact entries in the CRM.", - tools=[enterprise_tools] + apps=['hubspot'] ) # Task para criar contato @@ -545,17 +538,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) crm_manager = Agent( role="CRM Manager", goal="Manage and organize HubSpot contacts efficiently.", backstory="An experienced CRM manager who maintains an organized contact database.", - tools=[enterprise_tools] + apps=['hubspot'] ) # Task para gerenciar contatos diff --git a/docs/pt-BR/enterprise/integrations/jira.mdx b/docs/pt-BR/enterprise/integrations/jira.mdx index a645a8d27..d87d2d5c1 100644 --- a/docs/pt-BR/enterprise/integrations/jira.mdx +++ b/docs/pt-BR/enterprise/integrations/jira.mdx @@ -25,7 +25,7 @@ Antes de usar a integração com o Jira, certifique-se de ter: 2. Encontre **Jira** na seção de Integrações de Autenticação 3. Clique em **Conectar** e complete o fluxo do OAuth 4. Conceda as permissões necessárias para gestão de issues e projetos -5. Copie seu Token Enterprise em [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instalar o Pacote Necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Cria uma issue no Jira. **Parâmetros:** @@ -56,7 +56,7 @@ uv add crewai-tools ``` - + **Descrição:** Atualiza uma issue no Jira. **Parâmetros:** @@ -71,14 +71,14 @@ uv add crewai-tools - `additionalFields` (string, opcional): Campos Adicionais - Especifique outros campos em formato JSON. - + **Descrição:** Obtém uma issue pelo identificador no Jira. **Parâmetros:** - `issueKey` (string, obrigatório): Chave da Issue (exemplo: "TEST-1234"). - + **Descrição:** Busca issues no Jira usando filtros. **Parâmetros:** @@ -104,7 +104,7 @@ uv add crewai-tools - `limit` (string, opcional): Limitar resultados - Limite máximo de issues retornados. Padrão para 10 se estiver em branco. - + **Descrição:** Busca issues no Jira utilizando JQL. **Parâmetros:** @@ -117,13 +117,13 @@ uv add crewai-tools ``` - + **Descrição:** Atualiza qualquer issue no Jira. Use DESCRIBE_ACTION_SCHEMA para obter o schema de propriedades dessa função. **Parâmetros:** Nenhum parâmetro específico - use JIRA_DESCRIBE_ACTION_SCHEMA primeiro para obter o schema esperado. - + **Descrição:** Obtém o schema esperado para um tipo de issue. Use esta função caso nenhuma outra função atenda ao tipo de issue que deseja operar. **Parâmetros:** @@ -132,7 +132,7 @@ uv add crewai-tools - `operation` (string, obrigatório): Tipo de Operação, por exemplo CREATE_ISSUE ou UPDATE_ISSUE. - + **Descrição:** Obtém os projetos no Jira. **Parâmetros:** @@ -144,27 +144,27 @@ uv add crewai-tools ``` - + **Descrição:** Obtém os tipos de issues por projeto no Jira. **Parâmetros:** - `project` (string, obrigatório): Chave do projeto. - + **Descrição:** Obtém todos os tipos de issues no Jira. **Parâmetros:** Nenhum obrigatório. - + **Descrição:** Obtém os status das issues de um projeto específico. **Parâmetros:** - `project` (string, obrigatório): Chave do projeto. - + **Descrição:** Obtém os responsáveis por um projeto específico. **Parâmetros:** @@ -178,19 +178,15 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools # Obtenha as ferramentas enterprise (incluirá ferramentas do Jira) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Criação de um agente com capacidades Jira jira_agent = Agent( role="Issue Manager", goal="Gerenciar issues do Jira e acompanhar o progresso do projeto de forma eficiente", backstory="Um assistente de IA especializado em rastreamento de issues e gestão de projetos.", - tools=[enterprise_tools] + apps=['jira'] ) # Tarefa para criar um relatório de bug @@ -212,19 +208,16 @@ crew.kickoff() ### Filtrando Ferramentas Jira Específicas ```python -from crewai_tools import CrewaiEnterpriseTools # Obtenha apenas ferramentas Jira específicas -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["jira_create_issue", "jira_update_issue", "jira_search_by_jql"] + actions_list=["jira/create_issue", "jira/update_issue", "jira/search_by_jql"] ) issue_coordinator = Agent( role="Issue Coordinator", goal="Criar e gerenciar issues Jira de forma eficiente", backstory="Um assistente de IA focado na criação e gestão de issues.", - tools=enterprise_tools + apps=['jira'] ) # Tarefa para gerenciar workflow de issues @@ -246,17 +239,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_analyst = Agent( role="Project Analyst", goal="Analisar dados de projetos e gerar insights a partir do Jira", backstory="Um analista de projetos experiente que extrai insights de dados de gestão de projetos.", - tools=[enterprise_tools] + apps=['jira'] ) # Tarefa para analisar status do projeto @@ -283,17 +271,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) automation_manager = Agent( role="Automation Manager", goal="Automatizar gestão de issues e processos de workflow", backstory="Um assistente de IA que automatiza tarefas repetitivas de gestão de issues.", - tools=[enterprise_tools] + apps=['jira'] ) # Tarefa para automatizar gestão de issues @@ -321,17 +304,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) schema_specialist = Agent( role="Schema Specialist", goal="Executar operações complexas no Jira usando schemas dinâmicos", backstory="Um assistente de IA que manipula schemas dinâmicos e tipos de issues customizadas do Jira.", - tools=[enterprise_tools] + apps=['jira'] ) # Tarefa usando operações baseadas em schema diff --git a/docs/pt-BR/enterprise/integrations/linear.mdx b/docs/pt-BR/enterprise/integrations/linear.mdx index 2cd287ab8..0cefde14d 100644 --- a/docs/pt-BR/enterprise/integrations/linear.mdx +++ b/docs/pt-BR/enterprise/integrations/linear.mdx @@ -25,7 +25,7 @@ Antes de utilizar a integração com o Linear, certifique-se de que você possui 2. Encontre **Linear** na seção Integrações de Autenticação 3. Clique em **Conectar** e complete o fluxo OAuth 4. Conceda as permissões necessárias para gerenciamento de issues e projetos -5. Copie seu Token Empresarial em [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Empresarial em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o Pacote Necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Crie uma nova issue no Linear. **Parâmetros:** @@ -56,7 +56,7 @@ uv add crewai-tools ``` - + **Descrição:** Atualize uma issue no Linear. **Parâmetros:** @@ -76,21 +76,21 @@ uv add crewai-tools ``` - + **Descrição:** Obtenha uma issue pelo ID no Linear. **Parâmetros:** - `issueId` (string, obrigatório): ID da Issue - Especifique o ID do registro da issue a ser buscada. (exemplo: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **Descrição:** Obtenha uma issue através do identificador da issue no Linear. **Parâmetros:** - `externalId` (string, obrigatório): ID Externo - Especifique o identificador legível da issue a ser buscada. (exemplo: "ABC-1"). - + **Descrição:** Pesquise issues no Linear. **Parâmetros:** @@ -117,21 +117,21 @@ uv add crewai-tools Operadores disponíveis: `$stringExactlyMatches`, `$stringDoesNotExactlyMatch`, `$stringIsIn`, `$stringIsNotIn`, `$stringStartsWith`, `$stringDoesNotStartWith`, `$stringEndsWith`, `$stringDoesNotEndWith`, `$stringContains`, `$stringDoesNotContain`, `$stringGreaterThan`, `$stringLessThan`, `$numberGreaterThanOrEqualTo`, `$numberLessThanOrEqualTo`, `$numberGreaterThan`, `$numberLessThan`, `$dateTimeAfter`, `$dateTimeBefore` - + **Descrição:** Exclua uma issue no Linear. **Parâmetros:** - `issueId` (string, obrigatório): ID da Issue - Especifique o ID do registro da issue a ser excluída. (exemplo: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **Descrição:** Arquive uma issue no Linear. **Parâmetros:** - `issueId` (string, obrigatório): ID da Issue - Especifique o ID do registro da issue a ser arquivada. (exemplo: "90fbc706-18cd-42c9-ae66-6bd344cc8977"). - + **Descrição:** Crie uma sub-issue no Linear. **Parâmetros:** @@ -147,7 +147,7 @@ uv add crewai-tools ``` - + **Descrição:** Crie um novo projeto no Linear. **Parâmetros:** @@ -169,7 +169,7 @@ uv add crewai-tools ``` - + **Descrição:** Atualize um projeto no Linear. **Parâmetros:** @@ -185,21 +185,21 @@ uv add crewai-tools ``` - + **Descrição:** Obtenha um projeto pelo ID no Linear. **Parâmetros:** - `projectId` (string, obrigatório): ID do Projeto - Especifique o ID do projeto a ser buscado. (exemplo: "a6634484-6061-4ac7-9739-7dc5e52c796b"). - + **Descrição:** Exclua um projeto no Linear. **Parâmetros:** - `projectId` (string, obrigatório): ID do Projeto - Especifique o ID do projeto a ser excluído. (exemplo: "a6634484-6061-4ac7-9739-7dc5e52c796b"). - + **Descrição:** Pesquise equipes no Linear. **Parâmetros:** @@ -231,19 +231,15 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools # Obtenha ferramentas empresariais (ferramentas do Linear serão incluídas) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Crie um agente com funcionalidades do Linear linear_agent = Agent( role="Development Manager", goal="Gerenciar issues do Linear e acompanhar o progresso do desenvolvimento de forma eficiente", backstory="Um assistente de IA especializado em gerenciamento de projetos de desenvolvimento de software.", - tools=[enterprise_tools] + apps=['linear'] ) # Tarefa para criar um relatório de bug @@ -265,19 +261,16 @@ crew.kickoff() ### Filtrando Ferramentas Lineares Específicas ```python -from crewai_tools import CrewaiEnterpriseTools # Obtenha apenas ferramentas lineares específicas -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["linear_create_issue", "linear_update_issue", "linear_search_issue"] + actions_list=["linear/create_issue", "linear/update_issue", "linear/search_issue"] ) issue_manager = Agent( role="Issue Manager", goal="Criar e gerenciar issues no Linear de forma eficiente", backstory="Um assistente de IA focado na criação e no gerenciamento do ciclo de vida de issues.", - tools=enterprise_tools + apps=['linear'] ) # Tarefa para gerenciar fluxo de issues @@ -299,17 +292,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) project_coordinator = Agent( role="Project Coordinator", goal="Coordenar projetos e equipes no Linear de forma eficiente", backstory="Um coordenador de projetos experiente que gerencia ciclos de desenvolvimento e fluxos de trabalho de equipe.", - tools=[enterprise_tools] + apps=['linear'] ) # Tarefa para coordenar a configuração de projeto @@ -336,17 +324,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) task_organizer = Agent( role="Task Organizer", goal="Organizar issues complexas em sub-tarefas gerenciáveis", backstory="Um assistente de IA que divide trabalhos de desenvolvimento complexos em sub-tarefas organizadas.", - tools=[enterprise_tools] + apps=['linear'] ) # Tarefa para criar hierarquia de issues @@ -373,17 +356,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) workflow_automator = Agent( role="Workflow Automator", goal="Automatizar processos de fluxo de trabalho de desenvolvimento no Linear", backstory="Um assistente de IA que automatiza tarefas repetitivas de fluxo de trabalho de desenvolvimento.", - tools=[enterprise_tools] + apps=['linear'] ) # Tarefa de automação de workflow complexa diff --git a/docs/pt-BR/enterprise/integrations/microsoft_excel.mdx b/docs/pt-BR/enterprise/integrations/microsoft_excel.mdx new file mode 100644 index 000000000..a4e251bb1 --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/microsoft_excel.mdx @@ -0,0 +1,234 @@ +--- +title: Integração Microsoft Excel +description: "Gerenciamento de pastas de trabalho e dados com integração Microsoft Excel para CrewAI." +icon: "table" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes criem e gerenciem pastas de trabalho, planilhas, tabelas e gráficos do Excel no OneDrive ou SharePoint. Manipule intervalos de dados, crie visualizações, gerencie tabelas e simplifique seus fluxos de trabalho de planilhas com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Microsoft Excel, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Microsoft 365 com acesso ao Excel e OneDrive/SharePoint +- Conectado sua conta Microsoft através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Microsoft Excel + +### 1. Conecte sua Conta Microsoft + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Microsoft Excel** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a arquivos e pastas de trabalho do Excel +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Criar uma nova pasta de trabalho do Excel no OneDrive ou SharePoint. + + **Parâmetros:** + - `file_path` (string, obrigatório): Caminho onde criar a pasta de trabalho (ex: 'MinhaPastaDeTrabalho.xlsx') + - `worksheets` (array, opcional): Planilhas iniciais para criar. Cada item é um objeto com `name` (string, nome da planilha). + + + + **Descrição:** Obter todas as pastas de trabalho do Excel do OneDrive ou SharePoint. + + **Parâmetros:** + - `select` (string, opcional): Selecionar propriedades específicas para retornar. + - `filter` (string, opcional): Filtrar resultados usando sintaxe OData. + - `expand` (string, opcional): Expandir recursos relacionados inline. + - `top` (integer, opcional): Número de itens a retornar (mín 1, máx 999). + - `orderby` (string, opcional): Ordenar resultados por propriedades especificadas. + + + + **Descrição:** Obter todas as planilhas em uma pasta de trabalho do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `select` (string, opcional): Selecionar propriedades específicas para retornar (ex: 'id,name,position'). + - `filter` (string, opcional): Filtrar resultados usando sintaxe OData. + - `expand` (string, opcional): Expandir recursos relacionados inline. + - `top` (integer, opcional): Número de itens a retornar (mín 1, máx 999). + - `orderby` (string, opcional): Ordenar resultados por propriedades especificadas. + + + + **Descrição:** Criar uma nova planilha em uma pasta de trabalho do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `name` (string, obrigatório): Nome da nova planilha. + + + + **Descrição:** Obter dados de um intervalo específico em uma planilha do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + - `range` (string, obrigatório): Endereço do intervalo (ex: 'A1:C10'). + + + + **Descrição:** Atualizar dados em um intervalo específico em uma planilha do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + - `range` (string, obrigatório): Endereço do intervalo (ex: 'A1:C10'). + - `values` (array, obrigatório): Array 2D de valores para definir no intervalo. Cada array interno representa uma linha, e elementos podem ser string, number ou integer. + + + + **Descrição:** Criar uma tabela em uma planilha do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + - `range` (string, obrigatório): Intervalo para a tabela (ex: 'A1:D10'). + - `has_headers` (boolean, opcional): Se a primeira linha contém cabeçalhos. Padrão: true. + + + + **Descrição:** Obter todas as tabelas em uma planilha do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + + + + **Descrição:** Adicionar uma nova linha a uma tabela do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + - `table_name` (string, obrigatório): Nome da tabela. + - `values` (array, obrigatório): Array de valores para a nova linha. Elementos podem ser string, number ou integer. + + + + **Descrição:** Criar um gráfico em uma planilha do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + - `chart_type` (string, obrigatório): Tipo de gráfico (ex: 'ColumnClustered', 'Line', 'Pie'). + - `source_data` (string, obrigatório): Intervalo de dados para o gráfico (ex: 'A1:B10'). + - `series_by` (string, opcional): Como interpretar os dados ('Auto', 'Columns' ou 'Rows'). Padrão: 'Auto'. + + + + **Descrição:** Obter o valor de uma única célula em uma planilha do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + - `row` (integer, obrigatório): Número da linha (baseado em 0). + - `column` (integer, obrigatório): Número da coluna (baseado em 0). + + + + **Descrição:** Obter o intervalo usado de uma planilha do Excel (contém todos os dados). + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + + + + **Descrição:** Obter todos os gráficos em uma planilha do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + + + + **Descrição:** Excluir uma planilha de uma pasta de trabalho do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha a excluir. + + + + **Descrição:** Excluir uma tabela de uma planilha do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + - `worksheet_name` (string, obrigatório): Nome da planilha. + - `table_name` (string, obrigatório): Nome da tabela a excluir. + + + + **Descrição:** Obter todos os intervalos nomeados em uma pasta de trabalho do Excel. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do arquivo Excel. + + + +## Exemplos de Uso + +### Configuração Básica do Agente Microsoft Excel + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Microsoft Excel +excel_agent = Agent( + role="Gerenciador de Dados Excel", + goal="Gerenciar pastas de trabalho e dados do Excel de forma eficiente", + backstory="Um assistente IA especializado em operações do Microsoft Excel e manipulação de dados.", + apps=['microsoft_excel'] # Todas as ações do Excel estarão disponíveis +) + +# Tarefa para criar uma nova pasta de trabalho +create_workbook_task = Task( + description="Criar uma nova pasta de trabalho do Excel chamada 'RelatorioMensal.xlsx' com uma planilha inicial chamada 'DadosVendas'.", + agent=excel_agent, + expected_output="Nova pasta de trabalho 'RelatorioMensal.xlsx' criada com planilha 'DadosVendas'." +) + +# Execute a tarefa +crew = Crew( + agents=[excel_agent], + tasks=[create_workbook_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Microsoft tenha as permissões necessárias para acesso a arquivos (ex: `Files.Read.All`, `Files.ReadWrite.All`). +- Verifique se a conexão OAuth inclui todos os escopos necessários. + +**Problemas de Criação de Arquivos** +- Ao criar pastas de trabalho, certifique-se de que o `file_path` termine com extensão `.xlsx`. +- Verifique se você tem permissões de escrita no local de destino (OneDrive/SharePoint). + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Microsoft Excel. + diff --git a/docs/pt-BR/enterprise/integrations/microsoft_onedrive.mdx b/docs/pt-BR/enterprise/integrations/microsoft_onedrive.mdx new file mode 100644 index 000000000..ace1d7f4f --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/microsoft_onedrive.mdx @@ -0,0 +1,175 @@ +--- +title: Integração Microsoft OneDrive +description: "Gerenciamento de arquivos e pastas com integração Microsoft OneDrive para CrewAI." +icon: "cloud" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes façam upload, download e gerenciem arquivos e pastas no Microsoft OneDrive. Automatize operações de arquivos, organize conteúdo, crie links de compartilhamento e simplifique seus fluxos de trabalho de armazenamento em nuvem com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Microsoft OneDrive, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Microsoft com acesso ao OneDrive +- Conectado sua conta Microsoft através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Microsoft OneDrive + +### 1. Conecte sua Conta Microsoft + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Microsoft OneDrive** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a arquivos +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Listar arquivos e pastas no OneDrive. + + **Parâmetros:** + - `top` (integer, opcional): Número de itens a recuperar (máx 1000). Padrão: 50. + - `orderby` (string, opcional): Ordenar por campo (ex: "name asc", "lastModifiedDateTime desc"). Padrão: "name asc". + - `filter` (string, opcional): Expressão de filtro OData. + + + + **Descrição:** Obter informações sobre um arquivo ou pasta específica. + + **Parâmetros:** + - `item_id` (string, obrigatório): O ID do arquivo ou pasta. + + + + **Descrição:** Baixar um arquivo do OneDrive. + + **Parâmetros:** + - `item_id` (string, obrigatório): O ID do arquivo a baixar. + + + + **Descrição:** Fazer upload de um arquivo para o OneDrive. + + **Parâmetros:** + - `file_name` (string, obrigatório): Nome do arquivo a fazer upload. + - `content` (string, obrigatório): Conteúdo do arquivo codificado em Base64. + + + + **Descrição:** Criar uma nova pasta no OneDrive. + + **Parâmetros:** + - `folder_name` (string, obrigatório): Nome da pasta a criar. + + + + **Descrição:** Excluir um arquivo ou pasta do OneDrive. + + **Parâmetros:** + - `item_id` (string, obrigatório): O ID do arquivo ou pasta a excluir. + + + + **Descrição:** Copiar um arquivo ou pasta no OneDrive. + + **Parâmetros:** + - `item_id` (string, obrigatório): O ID do arquivo ou pasta a copiar. + - `parent_id` (string, opcional): O ID da pasta de destino (opcional, padrão para raiz). + - `new_name` (string, opcional): Novo nome para o item copiado (opcional). + + + + **Descrição:** Mover um arquivo ou pasta no OneDrive. + + **Parâmetros:** + - `item_id` (string, obrigatório): O ID do arquivo ou pasta a mover. + - `parent_id` (string, obrigatório): O ID da pasta de destino. + - `new_name` (string, opcional): Novo nome para o item (opcional). + + + + **Descrição:** Pesquisar arquivos e pastas no OneDrive. + + **Parâmetros:** + - `query` (string, obrigatório): String de consulta de pesquisa. + - `top` (integer, opcional): Número de resultados a retornar (máx 1000). Padrão: 50. + + + + **Descrição:** Criar um link de compartilhamento para um arquivo ou pasta. + + **Parâmetros:** + - `item_id` (string, obrigatório): O ID do arquivo ou pasta a compartilhar. + - `type` (string, opcional): Tipo de link de compartilhamento. Opções: view, edit, embed. Padrão: view. + - `scope` (string, opcional): Escopo do link de compartilhamento. Opções: anonymous, organization. Padrão: anonymous. + + + + **Descrição:** Obter miniaturas para um arquivo. + + **Parâmetros:** + - `item_id` (string, obrigatório): O ID do arquivo. + + + +## Exemplos de Uso + +### Configuração Básica do Agente Microsoft OneDrive + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Microsoft OneDrive +onedrive_agent = Agent( + role="Gerenciador de Arquivos", + goal="Gerenciar arquivos e pastas no OneDrive de forma eficiente", + backstory="Um assistente IA especializado em operações de arquivos do Microsoft OneDrive e organização.", + apps=['microsoft_onedrive'] # Todas as ações do OneDrive estarão disponíveis +) + +# Tarefa para listar arquivos e criar pasta +organize_files_task = Task( + description="Listar todos os arquivos no diretório raiz do meu OneDrive e criar uma nova pasta chamada 'Documentos do Projeto'.", + agent=onedrive_agent, + expected_output="Lista de arquivos exibida e nova pasta 'Documentos do Projeto' criada." +) + +# Execute a tarefa +crew = Crew( + agents=[onedrive_agent], + tasks=[organize_files_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Microsoft tenha as permissões necessárias para acesso a arquivos (ex: `Files.Read`, `Files.ReadWrite`). +- Verifique se a conexão OAuth inclui todos os escopos necessários. + +**Problemas de Upload de Arquivos** +- Certifique-se de que `file_name` e `content` sejam fornecidos para uploads de arquivos. +- O conteúdo deve ser codificado em Base64 para arquivos binários. +- Verifique se você tem permissões de escrita no OneDrive. + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Microsoft OneDrive. + diff --git a/docs/pt-BR/enterprise/integrations/microsoft_outlook.mdx b/docs/pt-BR/enterprise/integrations/microsoft_outlook.mdx new file mode 100644 index 000000000..0f7c55a40 --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/microsoft_outlook.mdx @@ -0,0 +1,161 @@ +--- +title: Integração Microsoft Outlook +description: "Gerenciamento de email, calendário e contatos com integração Microsoft Outlook para CrewAI." +icon: "envelope" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes acessem e gerenciem emails, eventos de calendário e contatos do Outlook. Envie emails, recupere mensagens, gerencie eventos de calendário e organize contatos com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Microsoft Outlook, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Microsoft com acesso ao Outlook +- Conectado sua conta Microsoft através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Microsoft Outlook + +### 1. Conecte sua Conta Microsoft + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Microsoft Outlook** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a email, calendário e contatos +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Obter mensagens de email da caixa de correio do usuário. + + **Parâmetros:** + - `top` (integer, opcional): Número de mensagens a recuperar (máx 1000). Padrão: 10. + - `filter` (string, opcional): Expressão de filtro OData (ex: "isRead eq false"). + - `search` (string, opcional): String de consulta de pesquisa. + - `orderby` (string, opcional): Ordenar por campo (ex: "receivedDateTime desc"). Padrão: "receivedDateTime desc". + - `select` (string, opcional): Selecionar propriedades específicas para retornar. + - `expand` (string, opcional): Expandir recursos relacionados inline. + + + + **Descrição:** Enviar uma mensagem de email. + + **Parâmetros:** + - `to_recipients` (array, obrigatório): Array de endereços de email dos destinatários. + - `cc_recipients` (array, opcional): Array de endereços de email dos destinatários em cópia. + - `bcc_recipients` (array, opcional): Array de endereços de email dos destinatários em cópia oculta. + - `subject` (string, obrigatório): Assunto do email. + - `body` (string, obrigatório): Conteúdo do corpo do email. + - `body_type` (string, opcional): Tipo de conteúdo do corpo. Opções: Text, HTML. Padrão: HTML. + - `importance` (string, opcional): Nível de importância da mensagem. Opções: low, normal, high. Padrão: normal. + - `reply_to` (array, opcional): Array de endereços de email para resposta. + - `save_to_sent_items` (boolean, opcional): Se deve salvar a mensagem na pasta Itens Enviados. Padrão: true. + + + + **Descrição:** Obter eventos de calendário do calendário do usuário. + + **Parâmetros:** + - `top` (integer, opcional): Número de eventos a recuperar (máx 1000). Padrão: 10. + - `skip` (integer, opcional): Número de eventos a pular. Padrão: 0. + - `filter` (string, opcional): Expressão de filtro OData (ex: "start/dateTime ge '2024-01-01T00:00:00Z'"). + - `orderby` (string, opcional): Ordenar por campo (ex: "start/dateTime asc"). Padrão: "start/dateTime asc". + + + + **Descrição:** Criar um novo evento de calendário. + + **Parâmetros:** + - `subject` (string, obrigatório): Assunto/título do evento. + - `body` (string, opcional): Corpo/descrição do evento. + - `start_datetime` (string, obrigatório): Data e hora de início no formato ISO 8601 (ex: '2024-01-20T10:00:00'). + - `end_datetime` (string, obrigatório): Data e hora de término no formato ISO 8601. + - `timezone` (string, opcional): Fuso horário (ex: 'Pacific Standard Time'). Padrão: UTC. + - `location` (string, opcional): Local do evento. + - `attendees` (array, opcional): Array de endereços de email dos participantes. + + + + **Descrição:** Obter contatos do catálogo de endereços do usuário. + + **Parâmetros:** + - `top` (integer, opcional): Número de contatos a recuperar (máx 1000). Padrão: 10. + - `skip` (integer, opcional): Número de contatos a pular. Padrão: 0. + - `filter` (string, opcional): Expressão de filtro OData. + - `orderby` (string, opcional): Ordenar por campo (ex: "displayName asc"). Padrão: "displayName asc". + + + + **Descrição:** Criar um novo contato no catálogo de endereços do usuário. + + **Parâmetros:** + - `displayName` (string, obrigatório): Nome de exibição do contato. + - `givenName` (string, opcional): Primeiro nome do contato. + - `surname` (string, opcional): Sobrenome do contato. + - `emailAddresses` (array, opcional): Array de endereços de email. Cada item é um objeto com `address` (string) e `name` (string). + - `businessPhones` (array, opcional): Array de números de telefone comerciais. + - `homePhones` (array, opcional): Array de números de telefone residenciais. + - `jobTitle` (string, opcional): Cargo do contato. + - `companyName` (string, opcional): Nome da empresa do contato. + + + +## Exemplos de Uso + +### Configuração Básica do Agente Microsoft Outlook + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Microsoft Outlook +outlook_agent = Agent( + role="Assistente de Email", + goal="Gerenciar emails, eventos de calendário e contatos de forma eficiente", + backstory="Um assistente IA especializado em operações do Microsoft Outlook e gerenciamento de comunicação.", + apps=['microsoft_outlook'] # Todas as ações do Outlook estarão disponíveis +) + +# Tarefa para enviar um email +send_email_task = Task( + description="Enviar um email para 'colega@exemplo.com' com assunto 'Atualização do Projeto' e corpo 'Olá, aqui está a última atualização do projeto. Atenciosamente.'", + agent=outlook_agent, + expected_output="Email enviado com sucesso para colega@exemplo.com" +) + +# Execute a tarefa +crew = Crew( + agents=[outlook_agent], + tasks=[send_email_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Microsoft tenha as permissões necessárias para acesso a email, calendário e contatos. +- Escopos necessários incluem: `Mail.Read`, `Mail.Send`, `Calendars.Read`, `Calendars.ReadWrite`, `Contacts.Read`, `Contacts.ReadWrite`. + +**Problemas de Envio de Email** +- Certifique-se de que `to_recipients`, `subject` e `body` sejam fornecidos para `send_email`. +- Verifique se os endereços de email estão formatados corretamente. + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Microsoft Outlook. + diff --git a/docs/pt-BR/enterprise/integrations/microsoft_sharepoint.mdx b/docs/pt-BR/enterprise/integrations/microsoft_sharepoint.mdx new file mode 100644 index 000000000..005f60ece --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/microsoft_sharepoint.mdx @@ -0,0 +1,185 @@ +--- +title: Integração Microsoft SharePoint +description: "Gerenciamento de sites, listas e documentos com integração Microsoft SharePoint para CrewAI." +icon: "folder-tree" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes acessem e gerenciem sites, listas e bibliotecas de documentos do SharePoint. Recupere informações do site, gerencie itens de lista, faça upload e organize arquivos, e simplifique seus fluxos de trabalho do SharePoint com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Microsoft SharePoint, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Microsoft com acesso ao SharePoint +- Conectado sua conta Microsoft através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Microsoft SharePoint + +### 1. Conecte sua Conta Microsoft + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Microsoft SharePoint** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a sites e arquivos do SharePoint +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Obter todos os sites do SharePoint aos quais o usuário tem acesso. + + **Parâmetros:** + - `search` (string, opcional): Consulta de pesquisa para filtrar sites. + - `select` (string, opcional): Selecionar propriedades específicas para retornar (ex: 'displayName,id,webUrl'). + - `filter` (string, opcional): Filtrar resultados usando sintaxe OData. + - `expand` (string, opcional): Expandir recursos relacionados inline. + - `top` (integer, opcional): Número de itens a retornar (mín 1, máx 999). + - `skip` (integer, opcional): Número de itens a pular (mín 0). + - `orderby` (string, opcional): Ordenar resultados por propriedades especificadas (ex: 'displayName desc'). + + + + **Descrição:** Obter informações sobre um site específico do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + - `select` (string, opcional): Selecionar propriedades específicas para retornar (ex: 'displayName,id,webUrl,drives'). + - `expand` (string, opcional): Expandir recursos relacionados inline (ex: 'drives,lists'). + + + + **Descrição:** Obter todas as listas em um site do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + + + + **Descrição:** Obter informações sobre uma lista específica. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + - `list_id` (string, obrigatório): O ID da lista. + + + + **Descrição:** Obter itens de uma lista do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + - `list_id` (string, obrigatório): O ID da lista. + - `expand` (string, opcional): Expandir dados relacionados (ex: 'fields'). + + + + **Descrição:** Criar um novo item em uma lista do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + - `list_id` (string, obrigatório): O ID da lista. + - `fields` (object, obrigatório): Os valores de campo para o novo item. + + + + **Descrição:** Atualizar um item em uma lista do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + - `list_id` (string, obrigatório): O ID da lista. + - `item_id` (string, obrigatório): O ID do item a atualizar. + - `fields` (object, obrigatório): Os valores de campo a atualizar. + + + + **Descrição:** Excluir um item de uma lista do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + - `list_id` (string, obrigatório): O ID da lista. + - `item_id` (string, obrigatório): O ID do item a excluir. + + + + **Descrição:** Fazer upload de um arquivo para uma biblioteca de documentos do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + - `file_path` (string, obrigatório): O caminho onde fazer upload do arquivo (ex: 'pasta/nomeDoArquivo.txt'). + - `content` (string, obrigatório): O conteúdo do arquivo a fazer upload. + + + + **Descrição:** Obter arquivos e pastas de uma biblioteca de documentos do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + + + + **Descrição:** Excluir um arquivo ou pasta da biblioteca de documentos do SharePoint. + + **Parâmetros:** + - `site_id` (string, obrigatório): O ID do site do SharePoint. + - `item_id` (string, obrigatório): O ID do arquivo ou pasta a excluir. + + + +## Exemplos de Uso + +### Configuração Básica do Agente Microsoft SharePoint + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Microsoft SharePoint +sharepoint_agent = Agent( + role="Gerenciador SharePoint", + goal="Gerenciar sites, listas e documentos do SharePoint de forma eficiente", + backstory="Um assistente IA especializado em administração do Microsoft SharePoint e gerenciamento de conteúdo.", + apps=['microsoft_sharepoint'] # Todas as ações do SharePoint estarão disponíveis +) + +# Tarefa para obter todos os sites +get_sites_task = Task( + description="Listar todos os sites do SharePoint aos quais tenho acesso.", + agent=sharepoint_agent, + expected_output="Uma lista de sites do SharePoint com seus nomes de exibição e URLs." +) + +# Execute a tarefa +crew = Crew( + agents=[sharepoint_agent], + tasks=[get_sites_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Microsoft tenha as permissões necessárias para acesso ao SharePoint (ex: `Sites.Read.All`, `Sites.ReadWrite.All`). +- Verifique se a conexão OAuth inclui todos os escopos necessários. + +**Problemas de ID de Site/Lista/Item** +- Verifique novamente os IDs de site, lista e item para correção. +- Certifique-se de que os recursos referenciados existem e estão acessíveis. + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Microsoft SharePoint. + diff --git a/docs/pt-BR/enterprise/integrations/microsoft_teams.mdx b/docs/pt-BR/enterprise/integrations/microsoft_teams.mdx new file mode 100644 index 000000000..2bf8698a3 --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/microsoft_teams.mdx @@ -0,0 +1,136 @@ +--- +title: Integração Microsoft Teams +description: "Colaboração em equipe e comunicação com integração Microsoft Teams para CrewAI." +icon: "users" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes acessem dados do Teams, enviem mensagens, criem reuniões e gerenciem canais. Automatize a comunicação da equipe, agende reuniões, recupere mensagens e simplifique seus fluxos de trabalho de colaboração com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Microsoft Teams, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Microsoft com acesso ao Teams +- Conectado sua conta Microsoft através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Microsoft Teams + +### 1. Conecte sua Conta Microsoft + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Microsoft Teams** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso ao Teams +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Obter todas as equipes das quais o usuário é membro. + + **Parâmetros:** + - Nenhum parâmetro necessário. + + + + **Descrição:** Obter canais em uma equipe específica. + + **Parâmetros:** + - `team_id` (string, obrigatório): O ID da equipe. + + + + **Descrição:** Enviar uma mensagem para um canal do Teams. + + **Parâmetros:** + - `team_id` (string, obrigatório): O ID da equipe. + - `channel_id` (string, obrigatório): O ID do canal. + - `message` (string, obrigatório): O conteúdo da mensagem. + - `content_type` (string, opcional): Tipo de conteúdo (html ou text). Opções: html, text. Padrão: text. + + + + **Descrição:** Obter mensagens de um canal do Teams. + + **Parâmetros:** + - `team_id` (string, obrigatório): O ID da equipe. + - `channel_id` (string, obrigatório): O ID do canal. + - `top` (integer, opcional): Número de mensagens a recuperar (máx 50). Padrão: 20. + + + + **Descrição:** Criar uma reunião do Teams. + + **Parâmetros:** + - `subject` (string, obrigatório): Assunto/título da reunião. + - `startDateTime` (string, obrigatório): Hora de início da reunião (formato ISO 8601 com fuso horário). + - `endDateTime` (string, obrigatório): Hora de término da reunião (formato ISO 8601 com fuso horário). + + + + **Descrição:** Pesquisar reuniões online por URL de participação na web. + + **Parâmetros:** + - `join_web_url` (string, obrigatório): A URL de participação na web da reunião a pesquisar. + + + +## Exemplos de Uso + +### Configuração Básica do Agente Microsoft Teams + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Microsoft Teams +teams_agent = Agent( + role="Coordenador do Teams", + goal="Gerenciar comunicação e reuniões do Teams de forma eficiente", + backstory="Um assistente IA especializado em operações do Microsoft Teams e colaboração em equipe.", + apps=['microsoft_teams'] # Todas as ações do Teams estarão disponíveis +) + +# Tarefa para listar equipes e canais +explore_teams_task = Task( + description="Listar todas as equipes das quais sou membro e depois obter os canais da primeira equipe.", + agent=teams_agent, + expected_output="Lista de equipes e canais exibida." +) + +# Execute a tarefa +crew = Crew( + agents=[teams_agent], + tasks=[explore_teams_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Microsoft tenha as permissões necessárias para acesso ao Teams. +- Escopos necessários incluem: `Team.ReadBasic.All`, `Channel.ReadBasic.All`, `ChannelMessage.Send`, `ChannelMessage.Read.All`, `OnlineMeetings.ReadWrite`, `OnlineMeetings.Read`. + +**Acesso a Equipes e Canais** +- Certifique-se de que você é membro das equipes que está tentando acessar. +- Verifique novamente os IDs de equipe e canal para correção. + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Microsoft Teams. + diff --git a/docs/pt-BR/enterprise/integrations/microsoft_word.mdx b/docs/pt-BR/enterprise/integrations/microsoft_word.mdx new file mode 100644 index 000000000..14b23e44a --- /dev/null +++ b/docs/pt-BR/enterprise/integrations/microsoft_word.mdx @@ -0,0 +1,127 @@ +--- +title: Integração Microsoft Word +description: "Criação e gerenciamento de documentos com integração Microsoft Word para CrewAI." +icon: "file-word" +mode: "wide" +--- + +## Visão Geral + +Permita que seus agentes criem, leiam e gerenciem documentos do Word e arquivos de texto no OneDrive ou SharePoint. Automatize a criação de documentos, recupere conteúdo, gerencie propriedades de documentos e simplifique seus fluxos de trabalho de documentos com automação alimentada por IA. + +## Pré-requisitos + +Antes de usar a integração Microsoft Word, certifique-se de ter: + +- Uma conta [CrewAI AMP](https://app.crewai.com) com assinatura ativa +- Uma conta Microsoft com acesso ao Word e OneDrive/SharePoint +- Conectado sua conta Microsoft através da [página de Integrações](https://app.crewai.com/crewai_plus/connectors) + +## Configurando a Integração Microsoft Word + +### 1. Conecte sua Conta Microsoft + +1. Navegue para [Integrações CrewAI AMP](https://app.crewai.com/crewai_plus/connectors) +2. Encontre **Microsoft Word** na seção de Integrações de Autenticação +3. Clique em **Conectar** e complete o fluxo OAuth +4. Conceda as permissões necessárias para acesso a arquivos +5. Copie seu Token Enterprise das [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) + +### 2. Instale o Pacote Necessário + +```bash +uv add crewai-tools +``` + +## Ações Disponíveis + + + + **Descrição:** Obter todos os documentos do Word do OneDrive ou SharePoint. + + **Parâmetros:** + - `select` (string, opcional): Selecionar propriedades específicas para retornar. + - `filter` (string, opcional): Filtrar resultados usando sintaxe OData. + - `expand` (string, opcional): Expandir recursos relacionados inline. + - `top` (integer, opcional): Número de itens a retornar (mín 1, máx 999). + - `orderby` (string, opcional): Ordenar resultados por propriedades especificadas. + + + + **Descrição:** Criar um documento de texto (.txt) com conteúdo. RECOMENDADO para criação de conteúdo programático que precisa ser legível e editável. + + **Parâmetros:** + - `file_name` (string, obrigatório): Nome do documento de texto (deve terminar com .txt). + - `content` (string, opcional): Conteúdo de texto para o documento. Padrão: "Este é um novo documento de texto criado via API." + + + + **Descrição:** Obter o conteúdo de um documento (funciona melhor com arquivos de texto). + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do documento. + + + + **Descrição:** Obter propriedades e metadados de um documento. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do documento. + + + + **Descrição:** Excluir um documento. + + **Parâmetros:** + - `file_id` (string, obrigatório): O ID do documento a excluir. + + + +## Exemplos de Uso + +### Configuração Básica do Agente Microsoft Word + +```python +from crewai import Agent, Task, Crew + +# Crie um agente com capacidades do Microsoft Word +word_agent = Agent( + role="Gerenciador de Documentos", + goal="Gerenciar documentos do Word e arquivos de texto de forma eficiente", + backstory="Um assistente IA especializado em operações de documentos do Microsoft Word e gerenciamento de conteúdo.", + apps=['microsoft_word'] # Todas as ações do Word estarão disponíveis +) + +# Tarefa para criar um novo documento de texto +create_doc_task = Task( + description="Criar um novo documento de texto chamado 'notas_reuniao.txt' com conteúdo 'Notas da Reunião de Janeiro de 2024: Pontos-chave de discussão e itens de ação.'", + agent=word_agent, + expected_output="Novo documento de texto 'notas_reuniao.txt' criado com sucesso." +) + +# Execute a tarefa +crew = Crew( + agents=[word_agent], + tasks=[create_doc_task] +) + +crew.kickoff() +``` + +## Solução de Problemas + +### Problemas Comuns + +**Erros de Autenticação** +- Certifique-se de que sua conta Microsoft tenha as permissões necessárias para acesso a arquivos (ex: `Files.Read.All`, `Files.ReadWrite.All`). +- Verifique se a conexão OAuth inclui todos os escopos necessários. + +**Problemas de Criação de Arquivos** +- Ao criar documentos de texto, certifique-se de que o `file_name` termine com extensão `.txt`. +- Verifique se você tem permissões de escrita no local de destino (OneDrive/SharePoint). + +### Obtendo Ajuda + + + Entre em contato com nossa equipe de suporte para assistência com configuração ou solução de problemas da integração Microsoft Word. + diff --git a/docs/pt-BR/enterprise/integrations/notion.mdx b/docs/pt-BR/enterprise/integrations/notion.mdx index e81c1ea27..8fc91bc1c 100644 --- a/docs/pt-BR/enterprise/integrations/notion.mdx +++ b/docs/pt-BR/enterprise/integrations/notion.mdx @@ -25,7 +25,7 @@ Antes de usar a integração com o Notion, certifique-se de que você tem: 2. Procure por **Notion** na seção de Integrações de Autenticação 3. Clique em **Conectar** e complete o fluxo de OAuth 4. Conceda as permissões necessárias para gerenciamento de páginas e bancos de dados -5. Copie seu Token Enterprise em [Configurações da Conta](https://app.crewai.com/crewai_plus/settings/account) +5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations) ### 2. Instale o Pacote Necessário @@ -36,7 +36,7 @@ uv add crewai-tools ## Ações Disponíveis - + **Descrição:** Cria uma página no Notion. **Parâmetros:** @@ -93,7 +93,7 @@ uv add crewai-tools ``` - + **Descrição:** Atualiza uma página no Notion. **Parâmetros:** @@ -127,21 +127,21 @@ uv add crewai-tools ``` - + **Descrição:** Busca uma página pelo ID no Notion. **Parâmetros:** - `pageId` (string, obrigatório): Page ID - Especifique o ID da Página a ser buscada. (exemplo: "59833787-2cf9-4fdf-8782-e53db20768a5"). - + **Descrição:** Arquiva uma página no Notion. **Parâmetros:** - `pageId` (string, obrigatório): Page ID - Especifique o ID da Página a ser arquivada. (exemplo: "59833787-2cf9-4fdf-8782-e53db20768a5"). - + **Descrição:** Pesquisa páginas no Notion utilizando filtros. **Parâmetros:** @@ -166,14 +166,14 @@ uv add crewai-tools Campos disponíveis: `query`, `filter.value`, `direction`, `page_size` - + **Descrição:** Obtém o conteúdo (blocos) de uma página no Notion. **Parâmetros:** - `blockId` (string, obrigatório): Page ID - Especifique o ID de um Bloco ou Página para receber todos os seus blocos filhos na ordem correta. (exemplo: "59833787-2cf9-4fdf-8782-e53db20768a5"). - + **Descrição:** Atualiza um bloco no Notion. **Parâmetros:** @@ -260,14 +260,14 @@ uv add crewai-tools ``` - + **Descrição:** Busca um bloco pelo ID no Notion. **Parâmetros:** - `blockId` (string, obrigatório): Block ID - Especifique o ID do Bloco a ser buscado. (exemplo: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6"). - + **Descrição:** Exclui um bloco no Notion. **Parâmetros:** @@ -281,19 +281,13 @@ uv add crewai-tools ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Notion tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Notion capabilities notion_agent = Agent( role="Documentation Manager", goal="Manage documentation and knowledge base in Notion efficiently", backstory="An AI assistant specialized in content management and documentation.", - tools=[enterprise_tools] + apps=['notion'] ) # Task to create a meeting notes page @@ -315,19 +309,12 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do Notion ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Notion tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["notion_create_page", "notion_update_block", "notion_search_pages"] -) content_manager = Agent( role="Content Manager", goal="Create and manage content pages efficiently", backstory="An AI assistant that focuses on content creation and management.", - tools=enterprise_tools + apps=['notion'] ) # Task to manage content workflow @@ -349,17 +336,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) knowledge_curator = Agent( role="Knowledge Curator", goal="Curate and organize knowledge base content in Notion", backstory="An experienced knowledge manager who organizes and maintains comprehensive documentation.", - tools=[enterprise_tools] + apps=['notion'] ) # Task to curate knowledge base @@ -386,17 +368,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) content_organizer = Agent( role="Content Organizer", goal="Organize and structure content blocks for optimal readability", backstory="An AI assistant that specializes in content structure and user experience.", - tools=[enterprise_tools] + apps=['notion'] ) # Task to organize content structure @@ -424,17 +401,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) doc_automator = Agent( role="Documentation Automator", goal="Automate documentation workflows and maintenance", backstory="An AI assistant that automates repetitive documentation tasks.", - tools=[enterprise_tools] + apps=['notion'] ) # Complex documentation automation task diff --git a/docs/pt-BR/enterprise/integrations/salesforce.mdx b/docs/pt-BR/enterprise/integrations/salesforce.mdx index b33853245..8157a7c03 100644 --- a/docs/pt-BR/enterprise/integrations/salesforce.mdx +++ b/docs/pt-BR/enterprise/integrations/salesforce.mdx @@ -22,7 +22,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: ### **Gerenciamento de Registros** - + **Descrição:** Crie um novo registro de Contato no Salesforce. **Parâmetros:** @@ -35,7 +35,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Contato - + **Descrição:** Crie um novo registro de Lead no Salesforce. **Parâmetros:** @@ -51,7 +51,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Lead - + **Descrição:** Crie um novo registro de Oportunidade no Salesforce. **Parâmetros:** @@ -66,7 +66,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Oportunidade - + **Descrição:** Crie um novo registro de Tarefa no Salesforce. **Parâmetros:** @@ -84,7 +84,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Tarefa - + **Descrição:** Crie um novo registro de Conta no Salesforce. **Parâmetros:** @@ -96,7 +96,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Conta - + **Descrição:** Crie um registro de qualquer tipo de objeto no Salesforce. **Nota:** Esta é uma ferramenta flexível para criar registros de tipos de objetos personalizados ou desconhecidos. @@ -106,7 +106,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: ### **Atualização de Registros** - + **Descrição:** Atualize um registro de Contato existente no Salesforce. **Parâmetros:** @@ -120,7 +120,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Contato - + **Descrição:** Atualize um registro de Lead existente no Salesforce. **Parâmetros:** @@ -137,7 +137,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Lead - + **Descrição:** Atualize um registro de Oportunidade existente no Salesforce. **Parâmetros:** @@ -153,7 +153,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Oportunidade - + **Descrição:** Atualize um registro de Tarefa existente no Salesforce. **Parâmetros:** @@ -171,7 +171,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Tarefa - + **Descrição:** Atualize um registro de Conta existente no Salesforce. **Parâmetros:** @@ -184,7 +184,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `additionalFields` (object, opcional): Campos adicionais no formato JSON para campos personalizados de Conta - + **Descrição:** Atualize um registro de qualquer tipo de objeto no Salesforce. **Nota:** Esta é uma ferramenta flexível para atualizar registros de tipos de objetos personalizados ou desconhecidos. @@ -194,42 +194,42 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: ### **Recuperação de Registros** - + **Descrição:** Obtenha um registro de Contato pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do registro do Contato - + **Descrição:** Obtenha um registro de Lead pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do registro do Lead - + **Descrição:** Obtenha um registro de Oportunidade pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do registro da Oportunidade - + **Descrição:** Obtenha um registro de Tarefa pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do registro da Tarefa - + **Descrição:** Obtenha um registro de Conta pelo seu ID. **Parâmetros:** - `recordId` (string, obrigatório): ID do registro da Conta - + **Descrição:** Obtenha um registro de qualquer tipo de objeto pelo seu ID. **Parâmetros:** @@ -241,7 +241,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: ### **Busca de Registros** - + **Descrição:** Pesquise registros de Contato com filtragem avançada. **Parâmetros:** @@ -252,7 +252,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Pesquise registros de Lead com filtragem avançada. **Parâmetros:** @@ -263,7 +263,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Pesquise registros de Oportunidade com filtragem avançada. **Parâmetros:** @@ -274,7 +274,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Pesquise registros de Tarefa com filtragem avançada. **Parâmetros:** @@ -285,7 +285,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Pesquise registros de Conta com filtragem avançada. **Parâmetros:** @@ -296,7 +296,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Pesquise registros de qualquer tipo de objeto. **Parâmetros:** @@ -310,7 +310,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: ### **Recuperação por List View** - + **Descrição:** Obtenha registros de Contato de um List View específico. **Parâmetros:** @@ -318,7 +318,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Obtenha registros de Lead de um List View específico. **Parâmetros:** @@ -326,7 +326,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Obtenha registros de Oportunidade de um List View específico. **Parâmetros:** @@ -334,7 +334,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Obtenha registros de Tarefa de um List View específico. **Parâmetros:** @@ -342,7 +342,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Obtenha registros de Conta de um List View específico. **Parâmetros:** @@ -350,7 +350,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `paginationParameters` (object, opcional): Configurações de paginação com pageCursor - + **Descrição:** Obtenha registros de qualquer tipo de objeto a partir de um List View específico. **Parâmetros:** @@ -363,7 +363,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: ### **Campos Personalizados** - + **Descrição:** Crie campos personalizados para objetos de Contato. **Parâmetros:** @@ -379,7 +379,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `defaultFieldValue` (string, opcional): Valor padrão do campo - + **Descrição:** Crie campos personalizados para objetos de Lead. **Parâmetros:** @@ -395,7 +395,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `defaultFieldValue` (string, opcional): Valor padrão do campo - + **Descrição:** Crie campos personalizados para objetos de Oportunidade. **Parâmetros:** @@ -411,7 +411,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `defaultFieldValue` (string, opcional): Valor padrão do campo - + **Descrição:** Crie campos personalizados para objetos de Tarefa. **Parâmetros:** @@ -427,7 +427,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `defaultFieldValue` (string, opcional): Valor padrão do campo - + **Descrição:** Crie campos personalizados para objetos de Conta. **Parâmetros:** @@ -443,7 +443,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `defaultFieldValue` (string, opcional): Valor padrão do campo - + **Descrição:** Crie campos personalizados para qualquer tipo de objeto. **Nota:** Esta é uma ferramenta flexível para criar campos personalizados para tipos de objetos personalizados ou desconhecidos. @@ -453,14 +453,14 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: ### **Operações Avançadas** - + **Descrição:** Execute consultas SOQL personalizadas em seus dados do Salesforce. **Parâmetros:** - `query` (string, obrigatório): Consulta SOQL (ex.: "SELECT Id, Name FROM Account WHERE Name = 'Exemplo'") - + **Descrição:** Crie um novo objeto personalizado no Salesforce. **Parâmetros:** @@ -470,7 +470,7 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: - `recordName` (string, obrigatório): Nome do registro exibido em layouts e buscas (ex.: "Nome da Conta") - + **Descrição:** Obtenha o schema esperado para operações em tipos de objetos específicos. **Parâmetros:** @@ -487,19 +487,15 @@ Antes de usar a integração Salesforce, certifique-se de que você possui: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools # Obtenha ferramentas enterprise (ferramentas Salesforce serão incluídas) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Crie um agente com capacidades Salesforce salesforce_agent = Agent( role="CRM Manager", goal="Manage customer relationships and sales processes efficiently", backstory="An AI assistant specialized in CRM operations and sales automation.", - tools=[enterprise_tools] + apps=['salesforce'] ) # Task to create a new lead @@ -521,19 +517,16 @@ crew.kickoff() ### Filtrando Ferramentas Salesforce Específicas ```python -from crewai_tools import CrewaiEnterpriseTools # Obtenha apenas ferramentas Salesforce específicas -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["salesforce_create_record_lead", "salesforce_update_record_opportunity", "salesforce_search_records_contact"] + actions_list=["salesforce/create_record_lead", "salesforce/update_record_opportunity", "salesforce/search_records_contact"] ) sales_manager = Agent( role="Sales Manager", goal="Manage leads and opportunities in the sales pipeline", backstory="An experienced sales manager who handles lead qualification and opportunity management.", - tools=enterprise_tools + apps=['salesforce'] ) # Task to manage sales pipeline @@ -555,17 +548,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) account_manager = Agent( role="Account Manager", goal="Manage customer accounts and maintain strong relationships", backstory="An AI assistant that specializes in account management and customer relationship building.", - tools=[enterprise_tools] + apps=['salesforce'] ) # Task to manage customer accounts @@ -591,17 +579,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) data_analyst = Agent( role="Sales Data Analyst", goal="Generate insights from Salesforce data using SOQL queries", backstory="An analytical AI that excels at extracting meaningful insights from CRM data.", - tools=[enterprise_tools] + apps=['salesforce'] ) # Complex task involving SOQL queries and data analysis diff --git a/docs/pt-BR/enterprise/integrations/shopify.mdx b/docs/pt-BR/enterprise/integrations/shopify.mdx index 01d8995c8..67843a98c 100644 --- a/docs/pt-BR/enterprise/integrations/shopify.mdx +++ b/docs/pt-BR/enterprise/integrations/shopify.mdx @@ -22,7 +22,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu ### **Gerenciamento de Clientes** - + **Descrição:** Recupera uma lista de clientes da sua loja Shopify. **Parâmetros:** @@ -34,7 +34,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `limit` (string, opcional): Número máximo de clientes a retornar (padrão 250) - + **Descrição:** Pesquise por clientes usando critérios de filtragem avançados. **Parâmetros:** @@ -42,7 +42,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `limit` (string, opcional): Número máximo de clientes a retornar (padrão 250) - + **Descrição:** Crie um novo cliente em sua loja Shopify. **Parâmetros:** @@ -63,7 +63,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `metafields` (object, opcional): Metacampos adicionais em formato JSON - + **Descrição:** Atualize um cliente existente em sua loja Shopify. **Parâmetros:** @@ -89,7 +89,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu ### **Gestão de Pedidos** - + **Descrição:** Recupera uma lista de pedidos da sua loja Shopify. **Parâmetros:** @@ -101,7 +101,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `limit` (string, opcional): Número máximo de pedidos a retornar (padrão 250) - + **Descrição:** Crie um novo pedido em sua loja Shopify. **Parâmetros:** @@ -114,7 +114,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `note` (string, opcional): Observação do pedido - + **Descrição:** Atualize um pedido existente em sua loja Shopify. **Parâmetros:** @@ -128,7 +128,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `note` (string, opcional): Observação do pedido - + **Descrição:** Recupera carrinhos abandonados da sua loja Shopify. **Parâmetros:** @@ -144,7 +144,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu ### **Gestão de Produtos (REST API)** - + **Descrição:** Recupera uma lista de produtos da sua loja Shopify utilizando a REST API. **Parâmetros:** @@ -160,7 +160,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `limit` (string, opcional): Número máximo de produtos a retornar (padrão 250) - + **Descrição:** Crie um novo produto em sua loja Shopify utilizando a REST API. **Parâmetros:** @@ -176,7 +176,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `publishToPointToSale` (boolean, opcional): Se deve publicar no ponto de venda - + **Descrição:** Atualize um produto existente em sua loja Shopify utilizando a REST API. **Parâmetros:** @@ -197,14 +197,14 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu ### **Gestão de Produtos (GraphQL)** - + **Descrição:** Recupere produtos utilizando filtros avançados do GraphQL. **Parâmetros:** - `productFilterFormula` (object, opcional): Filtro avançado em forma normal disjuntiva com suporte a campos como id, title, vendor, status, handle, tag, created_at, updated_at, published_at - + **Descrição:** Crie um novo produto utilizando a API GraphQL com suporte aprimorado a mídias. **Parâmetros:** @@ -217,7 +217,7 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu - `additionalFields` (object, opcional): Campos adicionais do produto como status, requiresSellingPlan, giftCard - + **Descrição:** Atualize um produto existente utilizando a API GraphQL com suporte aprimorado a mídias. **Parâmetros:** @@ -238,19 +238,13 @@ Antes de utilizar a integração com o Shopify, certifique-se de que você possu ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Shopify tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Shopify capabilities shopify_agent = Agent( role="E-commerce Manager", goal="Manage online store operations and customer relationships efficiently", backstory="An AI assistant specialized in e-commerce operations and online store management.", - tools=[enterprise_tools] + apps=['shopify'] ) # Task to create a new customer @@ -272,19 +266,12 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do Shopify ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Shopify tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["shopify_create_customer", "shopify_create_order", "shopify_get_products"] -) store_manager = Agent( role="Store Manager", goal="Manage customer orders and product catalog", backstory="An experienced store manager who handles customer relationships and inventory management.", - tools=enterprise_tools + apps=['shopify'] ) # Task to manage store operations @@ -306,17 +293,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) product_manager = Agent( role="Product Manager", goal="Manage product catalog and inventory with advanced GraphQL capabilities", backstory="An AI assistant that specializes in product management and catalog optimization.", - tools=[enterprise_tools] + apps=['shopify'] ) # Task to manage product catalog @@ -343,17 +325,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) analytics_agent = Agent( role="E-commerce Analyst", goal="Analyze customer behavior and order patterns to optimize store performance", backstory="An analytical AI that excels at extracting insights from e-commerce data.", - tools=[enterprise_tools] + apps=['shopify'] ) # Complex task involving multiple operations diff --git a/docs/pt-BR/enterprise/integrations/slack.mdx b/docs/pt-BR/enterprise/integrations/slack.mdx index c1798194b..888abd1a0 100644 --- a/docs/pt-BR/enterprise/integrations/slack.mdx +++ b/docs/pt-BR/enterprise/integrations/slack.mdx @@ -22,21 +22,21 @@ Antes de usar a integração com o Slack, certifique-se de que você tenha: ### **Gerenciamento de Usuários** - + **Descrição:** Lista todos os membros de um canal do Slack. **Parâmetros:** - Nenhum parâmetro necessário – recupera todos os membros do canal - + **Descrição:** Encontre um usuário no seu workspace do Slack pelo endereço de e-mail. **Parâmetros:** - `email` (string, obrigatório): O endereço de e-mail de um usuário do workspace - + **Descrição:** Pesquise usuários pelo nome ou nome de exibição. **Parâmetros:** @@ -50,7 +50,7 @@ Antes de usar a integração com o Slack, certifique-se de que você tenha: ### **Gerenciamento de Canais** - + **Descrição:** Lista todos os canais do seu workspace no Slack. **Parâmetros:** @@ -61,7 +61,7 @@ Antes de usar a integração com o Slack, certifique-se de que você tenha: ### **Mensagens** - + **Descrição:** Envie uma mensagem para um canal do Slack. **Parâmetros:** @@ -73,7 +73,7 @@ Antes de usar a integração com o Slack, certifique-se de que você tenha: - `authenticatedUser` (boolean, opcional): Se verdadeiro, a mensagem aparecerá como enviada pelo seu usuário autenticado do Slack ao invés do aplicativo (por padrão é falso) - + **Descrição:** Envie uma mensagem direta para um usuário específico no Slack. **Parâmetros:** @@ -89,7 +89,7 @@ Antes de usar a integração com o Slack, certifique-se de que você tenha: ### **Pesquisa & Descoberta** - + **Descrição:** Procure por mensagens em todo o seu workspace do Slack. **Parâmetros:** @@ -150,19 +150,13 @@ O Block Kit do Slack permite criar mensagens ricas e interativas. Veja alguns ex ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Slack tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Slack capabilities slack_agent = Agent( role="Team Communication Manager", goal="Facilitate team communication and coordinate collaboration efficiently", backstory="An AI assistant specialized in team communication and workspace coordination.", - tools=[enterprise_tools] + apps=['slack'] ) # Task to send project updates @@ -184,19 +178,12 @@ crew.kickoff() ### Filtrando Ferramentas Específicas do Slack ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Slack tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["slack_send_message", "slack_send_direct_message", "slack_search_messages"] -) communication_manager = Agent( role="Communication Coordinator", goal="Manage team communications and ensure important messages reach the right people", backstory="An experienced communication coordinator who handles team messaging and notifications.", - tools=enterprise_tools + apps=['slack'] ) # Task to coordinate team communication @@ -218,17 +205,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) notification_agent = Agent( role="Notification Manager", goal="Create rich, interactive notifications and manage workspace communication", backstory="An AI assistant that specializes in creating engaging team notifications and updates.", - tools=[enterprise_tools] + apps=['slack'] ) # Task to send rich notifications @@ -254,17 +236,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) analytics_agent = Agent( role="Communication Analyst", goal="Analyze team communication patterns and extract insights from conversations", backstory="An analytical AI that excels at understanding team dynamics through communication data.", - tools=[enterprise_tools] + apps=['slack'] ) # Complex task involving search and analysis diff --git a/docs/pt-BR/enterprise/integrations/stripe.mdx b/docs/pt-BR/enterprise/integrations/stripe.mdx index 294936ff7..31ba313b4 100644 --- a/docs/pt-BR/enterprise/integrations/stripe.mdx +++ b/docs/pt-BR/enterprise/integrations/stripe.mdx @@ -22,7 +22,7 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: ### **Gerenciamento de Clientes** - + **Descrição:** Crie um novo cliente em sua conta Stripe. **Parâmetros:** @@ -32,14 +32,14 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: - `metadataCreateCustomer` (objeto, opcional): Metadados adicionais como pares chave-valor (exemplo: `{"field1": 1, "field2": 2}`) - + **Descrição:** Recupera um cliente específico pelo ID do cliente Stripe. **Parâmetros:** - `idGetCustomer` (string, obrigatório): O ID do cliente Stripe a ser recuperado - + **Descrição:** Recupera uma lista de clientes com filtragem opcional. **Parâmetros:** @@ -49,7 +49,7 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: - `limitGetCustomers` (string, opcional): Número máximo de clientes a retornar (padrão: 10) - + **Descrição:** Atualiza as informações de um cliente existente. **Parâmetros:** @@ -64,7 +64,7 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: ### **Gerenciamento de Assinaturas** - + **Descrição:** Cria uma nova assinatura para um cliente. **Parâmetros:** @@ -73,7 +73,7 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: - `metadataCreateSubscription` (objeto, opcional): Metadados adicionais para a assinatura - + **Descrição:** Recupera assinaturas com filtragem opcional. **Parâmetros:** @@ -86,7 +86,7 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: ### **Gerenciamento de Produtos** - + **Descrição:** Cria um novo produto no seu catálogo Stripe. **Parâmetros:** @@ -95,14 +95,14 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: - `metadataProduct` (objeto, opcional): Metadados adicionais do produto como pares chave-valor - + **Descrição:** Recupera um produto específico pelo ID do produto Stripe. **Parâmetros:** - `productId` (string, obrigatório): O ID do produto Stripe a ser recuperado - + **Descrição:** Recupera uma lista de produtos com filtragem opcional. **Parâmetros:** @@ -115,7 +115,7 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: ### **Operações Financeiras** - + **Descrição:** Recupera transações de saldo da sua conta Stripe. **Parâmetros:** @@ -124,7 +124,7 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: - `pageCursor` (string, opcional): Cursor da página para paginação - + **Descrição:** Recupera planos de assinatura da sua conta Stripe. **Parâmetros:** @@ -140,19 +140,13 @@ Antes de usar a integração com o Stripe, certifique-se de que você tem: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Stripe tools will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Create an agent with Stripe capabilities stripe_agent = Agent( role="Payment Manager", goal="Manage customer payments, subscriptions, and billing operations efficiently", backstory="An AI assistant specialized in payment processing and subscription management.", - tools=[enterprise_tools] + apps=['stripe'] ) # Task to create a new customer @@ -174,19 +168,12 @@ crew.kickoff() ### Filtrando Ferramentas Stripe Específicas ```python -from crewai_tools import CrewaiEnterpriseTools - -# Get only specific Stripe tools -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["stripe_create_customer", "stripe_create_subscription", "stripe_get_balance_transactions"] -) billing_manager = Agent( role="Billing Manager", goal="Handle customer billing, subscriptions, and payment processing", backstory="An experienced billing manager who handles subscription lifecycle and payment operations.", - tools=enterprise_tools + apps=['stripe'] ) # Task to manage billing operations @@ -208,17 +195,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) subscription_manager = Agent( role="Subscription Manager", goal="Manage customer subscriptions and optimize recurring revenue", backstory="An AI assistant that specializes in subscription lifecycle management and customer retention.", - tools=[enterprise_tools] + apps=['stripe'] ) # Task to manage subscription operations @@ -245,17 +227,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) financial_analyst = Agent( role="Financial Analyst", goal="Analyze payment data and generate financial insights", backstory="An analytical AI that excels at extracting insights from payment and subscription data.", - tools=[enterprise_tools] + apps=['stripe'] ) # Complex task involving financial analysis diff --git a/docs/pt-BR/enterprise/integrations/zendesk.mdx b/docs/pt-BR/enterprise/integrations/zendesk.mdx index a904bd135..65baa0544 100644 --- a/docs/pt-BR/enterprise/integrations/zendesk.mdx +++ b/docs/pt-BR/enterprise/integrations/zendesk.mdx @@ -22,7 +22,7 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: ### **Gerenciamento de Tickets** - + **Descrição:** Crie um novo ticket de suporte no Zendesk. **Parâmetros:** @@ -40,7 +40,7 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: - `ticketCustomFields` (object, opcional): Valores de campos personalizados em formato JSON - + **Descrição:** Atualize um ticket de suporte existente no Zendesk. **Parâmetros:** @@ -58,14 +58,14 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: - `ticketCustomFields` (object, opcional): Valores atualizados dos campos personalizados - + **Descrição:** Recupere um ticket específico pelo ID. **Parâmetros:** - `ticketId` (string, obrigatório): ID do ticket a ser recuperado (ex.: "35436") - + **Descrição:** Adicione um comentário ou nota interna a um ticket existente. **Parâmetros:** @@ -75,7 +75,7 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: - `isPublic` (boolean, opcional): Verdadeiro para comentários públicos, falso para notas internas - + **Descrição:** Busque tickets usando diversos filtros e critérios. **Parâmetros:** @@ -100,7 +100,7 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: ### **Gerenciamento de Usuários** - + **Descrição:** Crie um novo usuário no Zendesk. **Parâmetros:** @@ -113,7 +113,7 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: - `notes` (string, opcional): Notas internas sobre o usuário - + **Descrição:** Atualize informações de um usuário existente. **Parâmetros:** @@ -127,14 +127,14 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: - `notes` (string, opcional): Novas notas internas - + **Descrição:** Recupere um usuário específico pelo ID. **Parâmetros:** - `userId` (string, obrigatório): ID do usuário a ser recuperado - + **Descrição:** Busque usuários utilizando vários critérios. **Parâmetros:** @@ -150,7 +150,7 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: ### **Ferramentas Administrativas** - + **Descrição:** Recupere todos os campos padrão e personalizados disponíveis para tickets. **Parâmetros:** @@ -158,7 +158,7 @@ Antes de usar a integração com o Zendesk, certifique-se de que você possui: - `pageCursor` (string, opcional): Cursor de página para paginação - + **Descrição:** Obtenha registros de auditoria (histórico somente leitura) dos tickets. **Parâmetros:** @@ -205,19 +205,15 @@ Progresso padrão de status dos tickets: ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools # Obtenha as ferramentas enterprise (as ferramentas Zendesk serão incluídas) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) # Crie um agente com capacidades Zendesk zendesk_agent = Agent( role="Gerente de Suporte", goal="Gerenciar tickets de suporte ao cliente e oferecer excelente atendimento", backstory="Um assistente de IA especializado em operações de suporte ao cliente e gerenciamento de tickets.", - tools=[enterprise_tools] + apps=['zendesk'] ) # Tarefa para criar um novo ticket de suporte @@ -239,19 +235,16 @@ crew.kickoff() ### Filtrando Ferramentas Zendesk Específicas ```python -from crewai_tools import CrewaiEnterpriseTools # Obtenha apenas ferramentas Zendesk específicas -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token", - actions_list=["zendesk_create_ticket", "zendesk_update_ticket", "zendesk_add_comment_to_ticket"] + actions_list=["zendesk/create_ticket", "zendesk/update_ticket", "zendesk/add_comment_to_ticket"] ) support_agent = Agent( role="Agente de Suporte ao Cliente", goal="Atender consultas de clientes e resolver issues de suporte de forma eficiente", backstory="Um agente de suporte experiente que se especializa em resolução de tickets e comunicação com clientes.", - tools=enterprise_tools + apps=['zendesk'] ) # Tarefa para gerenciar o fluxo de suporte @@ -273,17 +266,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) ticket_manager = Agent( role="Gerente de Tickets", goal="Gerenciar fluxos de tickets de suporte e garantir resolução tempestiva", backstory="Um assistente de IA que se especializa em triagem de tickets de suporte e otimização de fluxos de trabalho.", - tools=[enterprise_tools] + apps=['zendesk'] ) # Tarefa para gerenciar o ciclo de vida do ticket @@ -310,17 +298,12 @@ crew.kickoff() ```python from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) support_analyst = Agent( role="Analista de Suporte", goal="Analisar métricas de suporte e gerar insights para desempenho da equipe", backstory="Um IA analítico que se destaca na extração de insights a partir de dados de suporte e padrões de tickets.", - tools=[enterprise_tools] + apps=['zendesk'] ) # Tarefa complexa envolvendo análise e geração de relatórios diff --git a/docs/pt-BR/mcp/dsl-integration.mdx b/docs/pt-BR/mcp/dsl-integration.mdx new file mode 100644 index 000000000..c5e34d78a --- /dev/null +++ b/docs/pt-BR/mcp/dsl-integration.mdx @@ -0,0 +1,232 @@ +--- +title: Integração DSL MCP +description: Aprenda a usar a sintaxe DSL simples do CrewAI para integrar servidores MCP diretamente com seus agentes usando o campo mcps. +icon: code +mode: "wide" +--- + +## Visão Geral + +A integração DSL (Domain Specific Language) MCP do CrewAI oferece a **forma mais simples** de conectar seus agentes aos servidores MCP (Model Context Protocol). Basta adicionar um campo `mcps` ao seu agente e o CrewAI cuida de toda a complexidade automaticamente. + + +Esta é a **abordagem recomendada** para a maioria dos casos de uso de MCP. Para cenários avançados que requerem gerenciamento manual de conexão, veja [MCPServerAdapter](/pt-BR/mcp/overview#advanced-mcpserveradapter). + + +## Uso Básico + +Adicione servidores MCP ao seu agente usando o campo `mcps`: + +```python +from crewai import Agent + +agent = Agent( + role="Assistente de Pesquisa", + goal="Ajudar com tarefas de pesquisa e análise", + backstory="Assistente especialista com acesso a ferramentas avançadas de pesquisa", + mcps=[ + "https://mcp.exa.ai/mcp?api_key=sua_chave&profile=pesquisa" + ] +) + +# As ferramentas MCP agora estão automaticamente disponíveis! +# Não é necessário gerenciamento manual de conexão ou configuração de ferramentas +``` + +## Formatos de Referência Suportados + +### Servidores MCP Remotos Externos + +```python +# Servidor HTTPS básico +"https://api.example.com/mcp" + +# Servidor com autenticação +"https://mcp.exa.ai/mcp?api_key=sua_chave&profile=seu_perfil" + +# Servidor com caminho personalizado +"https://services.company.com/api/v1/mcp" +``` + +### Seleção de Ferramentas Específicas + +Use a sintaxe `#` para selecionar ferramentas específicas de um servidor: + +```python +# Obter apenas a ferramenta de previsão do servidor meteorológico +"https://weather.api.com/mcp#get_forecast" + +# Obter apenas a ferramenta de busca do Exa +"https://mcp.exa.ai/mcp?api_key=sua_chave#web_search_exa" +``` + +### Marketplace CrewAI AMP + +Acesse ferramentas do marketplace CrewAI AMP: + +```python +# Serviço completo com todas as ferramentas +"crewai-amp:financial-data" + +# Ferramenta específica do serviço AMP +"crewai-amp:research-tools#pubmed_search" + +# Múltiplos serviços AMP +mcps=[ + "crewai-amp:weather-insights", + "crewai-amp:market-analysis", + "crewai-amp:social-media-monitoring" +] +``` + +## Exemplo Completo + +Aqui está um exemplo completo usando múltiplos servidores MCP: + +```python +from crewai import Agent, Task, Crew, Process + +# Criar agente com múltiplas fontes MCP +agente_multi_fonte = Agent( + role="Analista de Pesquisa Multi-Fonte", + goal="Conduzir pesquisa abrangente usando múltiplas fontes de dados", + backstory="""Pesquisador especialista com acesso a busca web, dados meteorológicos, + informações financeiras e ferramentas de pesquisa acadêmica""", + mcps=[ + # Servidores MCP externos + "https://mcp.exa.ai/mcp?api_key=sua_chave_exa&profile=pesquisa", + "https://weather.api.com/mcp#get_current_conditions", + + # Marketplace CrewAI AMP + "crewai-amp:financial-insights", + "crewai-amp:academic-research#pubmed_search", + "crewai-amp:market-intelligence#competitor_analysis" + ] +) + +# Criar tarefa de pesquisa abrangente +tarefa_pesquisa = Task( + description="""Pesquisar o impacto dos agentes de IA na produtividade empresarial. + Incluir impactos climáticos atuais no trabalho remoto, tendências do mercado financeiro, + e publicações acadêmicas recentes sobre frameworks de agentes de IA.""", + expected_output="""Relatório abrangente cobrindo: + 1. Análise do impacto dos agentes de IA nos negócios + 2. Considerações climáticas para trabalho remoto + 3. Tendências do mercado financeiro relacionadas à IA + 4. Citações e insights de pesquisa acadêmica + 5. Análise do cenário competitivo""", + agent=agente_multi_fonte +) + +# Criar e executar crew +crew_pesquisa = Crew( + agents=[agente_multi_fonte], + tasks=[tarefa_pesquisa], + process=Process.sequential, + verbose=True +) + +resultado = crew_pesquisa.kickoff() +print(f"Pesquisa concluída com {len(agente_multi_fonte.mcps)} fontes de dados MCP") +``` + +## Recursos Principais + +- 🔄 **Descoberta Automática de Ferramentas**: Ferramentas são descobertas e integradas automaticamente +- 🏷️ **Prevenção de Colisão de Nomes**: Nomes de servidor são prefixados aos nomes das ferramentas +- ⚡ **Otimizado para Performance**: Conexões sob demanda com cache de esquemas +- 🛡️ **Resiliência a Erros**: Tratamento gracioso de servidores indisponíveis +- ⏱️ **Proteção por Timeout**: Timeouts integrados previnem conexões travadas +- 📊 **Integração Transparente**: Funciona perfeitamente com recursos existentes do CrewAI + +## Tratamento de Erros + +A integração DSL MCP é projetada para ser resiliente: + +```python +agente = Agent( + role="Agente Resiliente", + goal="Continuar trabalhando apesar de problemas no servidor", + backstory="Agente que lida graciosamente com falhas", + mcps=[ + "https://servidor-confiavel.com/mcp", # Vai funcionar + "https://servidor-inalcancavel.com/mcp", # Será ignorado graciosamente + "https://servidor-lento.com/mcp", # Timeout gracioso + "crewai-amp:servico-funcionando" # Vai funcionar + ] +) +# O agente usará ferramentas de servidores funcionais e registrará avisos para os que falharem +``` + +## Recursos de Performance + +### Cache Automático + +Esquemas de ferramentas são cacheados por 5 minutos para melhorar a performance: + +```python +# Primeira criação de agente - descobre ferramentas do servidor +agente1 = Agent(role="Primeiro", goal="Teste", backstory="Teste", + mcps=["https://api.example.com/mcp"]) + +# Segunda criação de agente (dentro de 5 minutos) - usa esquemas cacheados +agente2 = Agent(role="Segundo", goal="Teste", backstory="Teste", + mcps=["https://api.example.com/mcp"]) # Muito mais rápido! +``` + +### Conexões Sob Demanda + +Conexões de ferramentas são estabelecidas apenas quando as ferramentas são realmente usadas: + +```python +# Criação do agente é rápida - nenhuma conexão MCP feita ainda +agente = Agent( + role="Agente Sob Demanda", + goal="Usar ferramentas eficientemente", + backstory="Agente eficiente que conecta apenas quando necessário", + mcps=["https://api.example.com/mcp"] +) + +# Conexão MCP é feita apenas quando uma ferramenta é realmente executada +# Isso minimiza o overhead de conexão e melhora a performance de inicialização +``` + +## Melhores Práticas + +### 1. Use Ferramentas Específicas Quando Possível + +```python +# Bom - obter apenas as ferramentas necessárias +mcps=["https://weather.api.com/mcp#get_forecast"] + +# Menos eficiente - obter todas as ferramentas do servidor +mcps=["https://weather.api.com/mcp"] +``` + +### 2. Lidar com Autenticação de Forma Segura + +```python +import os + +# Armazenar chaves API em variáveis de ambiente +exa_key = os.getenv("EXA_API_KEY") +exa_profile = os.getenv("EXA_PROFILE") + +agente = Agent( + role="Agente Seguro", + goal="Usar ferramentas MCP com segurança", + backstory="Agente consciente da segurança", + mcps=[f"https://mcp.exa.ai/mcp?api_key={exa_key}&profile={exa_profile}"] +) +``` + +### 3. Planejar para Falhas de Servidor + +```python +# Sempre incluir opções de backup +mcps=[ + "https://api-principal.com/mcp", # Escolha principal + "https://api-backup.com/mcp", # Opção de backup + "crewai-amp:servico-confiavel" # Fallback AMP +] +``` diff --git a/docs/pt-BR/mcp/overview.mdx b/docs/pt-BR/mcp/overview.mdx index ed7247464..c960cbb11 100644 --- a/docs/pt-BR/mcp/overview.mdx +++ b/docs/pt-BR/mcp/overview.mdx @@ -8,12 +8,37 @@ mode: "wide" ## Visão Geral O [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) fornece uma maneira padronizada para agentes de IA fornecerem contexto para LLMs comunicando-se com serviços externos, conhecidos como Servidores MCP. -A biblioteca `crewai-tools` expande as capacidades do CrewAI permitindo que você integre facilmente ferramentas desses servidores MCP em seus agentes. -Isso oferece às suas crews acesso a um vasto ecossistema de funcionalidades. + +O CrewAI oferece **duas abordagens** para integração MCP: + +### 🚀 **Novo: Integração DSL Simples** (Recomendado) + +Use o campo `mcps` diretamente nos agentes para integração perfeita de ferramentas MCP: + +```python +from crewai import Agent + +agent = Agent( + role="Analista de Pesquisa", + goal="Pesquisar e analisar informações", + backstory="Pesquisador especialista com acesso a ferramentas externas", + mcps=[ + "https://mcp.exa.ai/mcp?api_key=sua_chave", # Servidor MCP externo + "https://api.weather.com/mcp#get_forecast", # Ferramenta específica do servidor + "crewai-amp:financial-data", # Marketplace CrewAI AMP + "crewai-amp:research-tools#pubmed_search" # Ferramenta AMP específica + ] +) +# Ferramentas MCP agora estão automaticamente disponíveis para seu agente! +``` + +### 🔧 **Avançado: MCPServerAdapter** (Para Cenários Complexos) + +Para casos de uso avançados que requerem gerenciamento manual de conexão, a biblioteca `crewai-tools` fornece a classe `MCPServerAdapter`. Atualmente, suportamos os seguintes mecanismos de transporte: -- **Stdio**: para servidores locais (comunicação via entrada/saída padrão entre processos na mesma máquina) +- **HTTPS**: para servidores remotos (comunicação segura via HTTPS) - **Server-Sent Events (SSE)**: para servidores remotos (transmissão de dados unidirecional em tempo real do servidor para o cliente via HTTP) - **Streamable HTTP**: para servidores remotos (comunicação flexível e potencialmente bidirecional via HTTP, geralmente utilizando SSE para streams do servidor para o cliente) diff --git a/lib/crewai-tools/BUILDING_TOOLS.md b/lib/crewai-tools/BUILDING_TOOLS.md new file mode 100644 index 000000000..2994b918e --- /dev/null +++ b/lib/crewai-tools/BUILDING_TOOLS.md @@ -0,0 +1,335 @@ +## Building CrewAI Tools + +This guide shows you how to build high‑quality CrewAI tools that match the patterns in this repository and are ready to be merged. It focuses on: architecture, conventions, environment variables, dependencies, testing, documentation, and a complete example. + +### Who this is for +- Contributors creating new tools under `crewai_tools/tools/*` +- Maintainers reviewing PRs for consistency and DX + +--- + +## Quick‑start checklist +1. Create a new folder under `crewai_tools/tools//` with a `README.md` and a `.py`. +2. Implement a class that ends with `Tool` and subclasses `BaseTool` (or `RagTool` when appropriate). +3. Define a Pydantic `args_schema` with explicit field descriptions and validation. +4. Declare `env_vars` and `package_dependencies` in the class when needed. +5. Lazily initialize clients in `__init__` or `_run` and handle missing credentials with clear errors. +6. Implement `_run(...) -> str | dict` and, if needed, `_arun(...)`. +7. Add tests under `tests/tools/` (unit, no real network calls; mock or record safely). +8. Add a concise tool `README.md` with usage and required env vars. +9. If you add optional dependencies, register them in `pyproject.toml` under `[project.optional-dependencies]` and reference that extra in your tool docs. +10. Run `uv run pytest` and `pre-commit run -a` locally; ensure green. + +--- + +## Tool anatomy and conventions + +### BaseTool pattern +All tools follow this structure: + +```python +from typing import Any, List, Optional, Type + +import os +from pydantic import BaseModel, Field +from crewai.tools import BaseTool, EnvVar + + +class MyToolInput(BaseModel): + """Input schema for MyTool.""" + query: str = Field(..., description="Your input description here") + limit: int = Field(5, ge=1, le=50, description="Max items to return") + + +class MyTool(BaseTool): + name: str = "My Tool" + description: str = "Explain succinctly what this tool does and when to use it." + args_schema: Type[BaseModel] = MyToolInput + + # Only include when applicable + env_vars: List[EnvVar] = [ + EnvVar(name="MY_API_KEY", description="API key for My service", required=True), + ] + package_dependencies: List[str] = ["my-sdk"] + + def __init__(self, **kwargs: Any) -> None: + super().__init__(**kwargs) + # Lazy import to keep base install light + try: + import my_sdk # noqa: F401 + except Exception as exc: + raise ImportError( + "Missing optional dependency 'my-sdk'. Install with: \n" + " uv add crewai-tools --extra my-sdk\n" + "or\n" + " pip install my-sdk\n" + ) from exc + + if "MY_API_KEY" not in os.environ: + raise ValueError("Environment variable MY_API_KEY is required for MyTool") + + def _run(self, query: str, limit: int = 5, **_: Any) -> str: + """Synchronous execution. Return a concise string or JSON string.""" + # Implement your logic here; do not print. Return the content. + # Handle errors gracefully, return clear messages. + return f"Processed {query} with limit={limit}" + + async def _arun(self, *args: Any, **kwargs: Any) -> str: + """Optional async counterpart if your client supports it.""" + # Prefer delegating to _run when the client is thread-safe + return self._run(*args, **kwargs) +``` + +Key points: +- Class name must end with `Tool` to be auto‑discovered by our tooling. +- Use `args_schema` for inputs; always include `description` and validation. +- Validate env vars early and fail with actionable errors. +- Keep outputs deterministic and compact; favor `str` (possibly JSON‑encoded) or small dicts converted to strings. +- Avoid printing; return the final string. + +### Error handling +- Wrap network and I/O with try/except and return a helpful message. See `BraveSearchTool` and others for patterns. +- Validate required inputs and environment configuration with clear messages. +- Keep exceptions user‑friendly; do not leak stack traces. + +### Rate limiting and retries +- If the upstream API enforces request pacing, implement minimal rate limiting (see `BraveSearchTool`). +- Consider idempotency and backoff for transient errors where appropriate. + +### Async support +- Implement `_arun` only if your library has a true async client or your sync calls are thread‑safe. +- Otherwise, delegate `_arun` to `_run` as in multiple existing tools. + +### Returning values +- Return a string (or JSON string) that’s ready to display in an agent transcript. +- If returning structured data, keep it small and human‑readable. Use stable keys and ordering. + +--- + +## RAG tools and adapters + +If your tool is a knowledge source, consider extending `RagTool` and/or creating an adapter. + +- `RagTool` exposes `add(...)` and a `query(question: str) -> str` contract through an `Adapter`. +- See `crewai_tools/tools/rag/rag_tool.py` and adapters like `embedchain_adapter.py` and `lancedb_adapter.py`. + +Minimal adapter example: + +```python +from typing import Any +from pydantic import BaseModel +from crewai_tools.tools.rag.rag_tool import Adapter, RagTool + + +class MemoryAdapter(Adapter): + store: list[str] = [] + + def add(self, text: str, **_: Any) -> None: + self.store.append(text) + + def query(self, question: str) -> str: + # naive demo: return all text containing any word from the question + tokens = set(question.lower().split()) + hits = [t for t in self.store if tokens & set(t.lower().split())] + return "\n".join(hits) if hits else "No relevant content found." + + +class MemoryRagTool(RagTool): + name: str = "In‑memory RAG" + description: str = "Toy RAG that stores text in memory and returns matches." + adapter: Adapter = MemoryAdapter() +``` + +When using external vector DBs (MongoDB, Qdrant, Weaviate), study the existing tools to follow indexing, embedding, and query configuration patterns closely. + +--- + +## Toolkits (multiple related tools) + +Some integrations expose a toolkit (a group of tools) rather than a single class. See Bedrock `browser_toolkit.py` and `code_interpreter_toolkit.py`. + +Guidelines: +- Provide small, focused `BaseTool` classes for each operation (e.g., `navigate`, `click`, `extract_text`). +- Offer a helper `create__toolkit(...) -> Tuple[ToolkitClass, List[BaseTool]]` to create tools and manage resources. +- If you open external resources (browsers, interpreters), support cleanup methods and optionally context manager usage. + +--- + +## Environment variables and dependencies + +### env_vars +- Declare as `env_vars: List[EnvVar]` with `name`, `description`, `required`, and optional `default`. +- Validate presence in `__init__` or on first `_run` call. + +### Dependencies +- List runtime packages in `package_dependencies` on the class. +- If they are genuinely optional, add an extra under `[project.optional-dependencies]` in `pyproject.toml` (e.g., `tavily-python`, `serpapi`, `scrapfly-sdk`). +- Use lazy imports to avoid hard deps for users who don’t need the tool. + +--- + +## Testing + +Place tests under `tests/tools/` and follow these rules: +- Do not hit real external services in CI. Use mocks, fakes, or recorded fixtures where allowed. +- Validate input validation, env var handling, error messages, and happy path output formatting. +- Keep tests fast and deterministic. + +Example skeleton (`tests/tools/my_tool_test.py`): + +```python +import os +import pytest +from crewai_tools.tools.my_tool.my_tool import MyTool + + +def test_requires_env_var(monkeypatch): + monkeypatch.delenv("MY_API_KEY", raising=False) + with pytest.raises(ValueError): + MyTool() + + +def test_happy_path(monkeypatch): + monkeypatch.setenv("MY_API_KEY", "test") + tool = MyTool() + result = tool.run(query="hello", limit=2) + assert "hello" in result +``` + +Run locally: + +```bash +uv run pytest +pre-commit run -a +``` + +--- + +## Documentation + +Each tool must include a `README.md` in its folder with: +- What it does and when to use it +- Required env vars and optional extras (with install snippet) +- Minimal usage example + +Update the root `README.md` only if the tool introduces a new category or notable capability. + +--- + +## Discovery and specs + +Our internal tooling discovers classes whose names end with `Tool`. Keep your class exported from the module path under `crewai_tools/tools/...` to be picked up by scripts like `generate_tool_specs.py`. + +--- + +## Full example: “Weather Search Tool” + +This example demonstrates: `args_schema`, `env_vars`, `package_dependencies`, lazy imports, validation, and robust error handling. + +```python +# file: crewai_tools/tools/weather_tool/weather_tool.py +from typing import Any, List, Optional, Type +import os +import requests +from pydantic import BaseModel, Field +from crewai.tools import BaseTool, EnvVar + + +class WeatherToolInput(BaseModel): + """Input schema for WeatherTool.""" + city: str = Field(..., description="City name, e.g., 'Berlin'") + country: Optional[str] = Field(None, description="ISO country code, e.g., 'DE'") + units: str = Field( + default="metric", + description="Units system: 'metric' or 'imperial'", + pattern=r"^(metric|imperial)$", + ) + + +class WeatherTool(BaseTool): + name: str = "Weather Search" + description: str = ( + "Look up current weather for a city using a public weather API." + ) + args_schema: Type[BaseModel] = WeatherToolInput + + env_vars: List[EnvVar] = [ + EnvVar( + name="WEATHER_API_KEY", + description="API key for the weather service", + required=True, + ), + ] + package_dependencies: List[str] = ["requests"] + + base_url: str = "https://api.openweathermap.org/data/2.5/weather" + + def __init__(self, **kwargs: Any) -> None: + super().__init__(**kwargs) + if "WEATHER_API_KEY" not in os.environ: + raise ValueError("WEATHER_API_KEY is required for WeatherTool") + + def _run(self, city: str, country: Optional[str] = None, units: str = "metric") -> str: + try: + q = f"{city},{country}" if country else city + params = { + "q": q, + "units": units, + "appid": os.environ["WEATHER_API_KEY"], + } + resp = requests.get(self.base_url, params=params, timeout=10) + resp.raise_for_status() + data = resp.json() + + main = data.get("weather", [{}])[0].get("main", "Unknown") + desc = data.get("weather", [{}])[0].get("description", "") + temp = data.get("main", {}).get("temp") + feels = data.get("main", {}).get("feels_like") + city_name = data.get("name", city) + + return ( + f"Weather in {city_name}: {main} ({desc}). " + f"Temperature: {temp}°, feels like {feels}°." + ) + except requests.Timeout: + return "Weather service timed out. Please try again later." + except requests.HTTPError as e: + return f"Weather service error: {e.response.status_code} {e.response.text[:120]}" + except Exception as e: + return f"Unexpected error fetching weather: {e}" +``` + +Folder layout: + +``` +crewai_tools/tools/weather_tool/ + ├─ weather_tool.py + └─ README.md +``` + +And `README.md` should document env vars and usage. + +--- + +## PR checklist +- [ ] Tool lives under `crewai_tools/tools//` +- [ ] Class ends with `Tool` and subclasses `BaseTool` (or `RagTool`) +- [ ] Precise `args_schema` with descriptions and validation +- [ ] `env_vars` declared (if any) and validated +- [ ] `package_dependencies` and optional extras added in `pyproject.toml` (if any) +- [ ] Clear error handling; no prints +- [ ] Unit tests added (`tests/tools/`), fast and deterministic +- [ ] Tool `README.md` with usage and env vars +- [ ] `pre-commit` and `pytest` pass locally + +--- + +## Tips for great DX +- Keep responses short and useful—agents quote your tool output directly. +- Validate early; fail fast with actionable guidance. +- Prefer lazy imports; minimize default install surface. +- Mirror patterns from similar tools in this repo for a consistent developer experience. + +Happy building! + + diff --git a/lib/crewai-tools/README.md b/lib/crewai-tools/README.md new file mode 100644 index 000000000..693e1a175 --- /dev/null +++ b/lib/crewai-tools/README.md @@ -0,0 +1,229 @@ +
+ +![Logo of crewAI, two people rowing on a boat](./assets/crewai_logo.png) + +
+ +# CrewAI Tools + +Empower your CrewAI agents with powerful, customizable tools to elevate their capabilities and tackle sophisticated, real-world tasks. + +CrewAI Tools provide the essential functionality to extend your agents, helping you rapidly enhance your automations with reliable, ready-to-use tools or custom-built solutions tailored precisely to your needs. + +--- + +## Quick Links + +[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Examples](https://github.com/crewAIInc/crewAI-examples) | [Community](https://community.crewai.com/) + +--- + +## Available Tools + +CrewAI provides an extensive collection of powerful tools ready to enhance your agents: + +- **File Management**: `FileReadTool`, `FileWriteTool` +- **Web Scraping**: `ScrapeWebsiteTool`, `SeleniumScrapingTool` +- **Database Integrations**: `MySQLSearchTool` +- **Vector Database Integrations**: `MongoDBVectorSearchTool`, `QdrantVectorSearchTool`, `WeaviateVectorSearchTool` +- **API Integrations**: `SerperApiTool`, `EXASearchTool` +- **AI-powered Tools**: `DallETool`, `VisionTool`, `StagehandTool` + +And many more robust tools to simplify your agent integrations. + +--- + +## Creating Custom Tools + +CrewAI offers two straightforward approaches to creating custom tools: + +### Subclassing `BaseTool` + +Define your tool by subclassing: + +```python +from crewai.tools import BaseTool + +class MyCustomTool(BaseTool): + name: str = "Tool Name" + description: str = "Detailed description here." + + def _run(self, *args, **kwargs): + # Your tool logic here +``` + +### Using the `tool` Decorator + +Quickly create lightweight tools using decorators: + +```python +from crewai import tool + +@tool("Tool Name") +def my_custom_function(input): + # Tool logic here + return output +``` + +--- + +## CrewAI Tools and MCP + +CrewAI Tools supports the Model Context Protocol (MCP). It gives you access to thousands of tools from the hundreds of MCP servers out there built by the community. + +Before you start using MCP with CrewAI tools, you need to install the `mcp` extra dependencies: + +```bash +pip install crewai-tools[mcp] +# or +uv add crewai-tools --extra mcp +``` + +To quickly get started with MCP in CrewAI you have 2 options: + +### Option 1: Fully managed connection + +In this scenario we use a contextmanager (`with` statement) to start and stop the the connection with the MCP server. +This is done in the background and you only get to interact with the CrewAI tools corresponding to the MCP server's tools. + +For an STDIO based MCP server: + +```python +from mcp import StdioServerParameters +from crewai_tools import MCPServerAdapter + +serverparams = StdioServerParameters( + command="uvx", + args=["--quiet", "pubmedmcp@0.1.3"], + env={"UV_PYTHON": "3.12", **os.environ}, +) + +with MCPServerAdapter(serverparams) as tools: + # tools is now a list of CrewAI Tools matching 1:1 with the MCP server's tools + agent = Agent(..., tools=tools) + task = Task(...) + crew = Crew(..., agents=[agent], tasks=[task]) + crew.kickoff(...) +``` +For an SSE based MCP server: + +```python +serverparams = {"url": "http://localhost:8000/sse"} +with MCPServerAdapter(serverparams) as tools: + # tools is now a list of CrewAI Tools matching 1:1 with the MCP server's tools + agent = Agent(..., tools=tools) + task = Task(...) + crew = Crew(..., agents=[agent], tasks=[task]) + crew.kickoff(...) +``` + +### Option 2: More control over the MCP connection + +If you need more control over the MCP connection, you can instanciate the MCPServerAdapter into an `mcp_server_adapter` object which can be used to manage the connection with the MCP server and access the available tools. + +**important**: in this case you need to call `mcp_server_adapter.stop()` to make sure the connection is correctly stopped. We recommend that you use a `try ... finally` block run to make sure the `.stop()` is called even in case of errors. + +Here is the same example for an STDIO MCP Server: + +```python +from mcp import StdioServerParameters +from crewai_tools import MCPServerAdapter + +serverparams = StdioServerParameters( + command="uvx", + args=["--quiet", "pubmedmcp@0.1.3"], + env={"UV_PYTHON": "3.12", **os.environ}, +) + +try: + mcp_server_adapter = MCPServerAdapter(serverparams) + tools = mcp_server_adapter.tools + # tools is now a list of CrewAI Tools matching 1:1 with the MCP server's tools + agent = Agent(..., tools=tools) + task = Task(...) + crew = Crew(..., agents=[agent], tasks=[task]) + crew.kickoff(...) + +# ** important ** don't forget to stop the connection +finally: + mcp_server_adapter.stop() +``` + +And finally the same thing but for an SSE MCP Server: + +```python +from mcp import StdioServerParameters +from crewai_tools import MCPServerAdapter + +serverparams = {"url": "http://localhost:8000/sse"} + +try: + mcp_server_adapter = MCPServerAdapter(serverparams) + tools = mcp_server_adapter.tools + # tools is now a list of CrewAI Tools matching 1:1 with the MCP server's tools + agent = Agent(..., tools=tools) + task = Task(...) + crew = Crew(..., agents=[agent], tasks=[task]) + crew.kickoff(...) + +# ** important ** don't forget to stop the connection +finally: + mcp_server_adapter.stop() +``` + +### Considerations & Limitations + +#### Staying Safe with MCP + +Always make sure that you trust the MCP Server before using it. Using an STDIO server will execute code on your machine. Using SSE is still not a silver bullet with many injection possible into your application from a malicious MCP server. + +#### Limitations + +* At this time we only support tools from MCP Server not other type of primitives like prompts, resources... +* We only return the first text output returned by the MCP Server tool using `.content[0].text` + +--- + +## Why Use CrewAI Tools? + +- **Simplicity & Flexibility**: Easy-to-use yet powerful enough for complex workflows. +- **Rapid Integration**: Seamlessly incorporate external services, APIs, and databases. +- **Enterprise Ready**: Built for stability, performance, and consistent results. + +--- + +## Contribution Guidelines + +We welcome contributions from the community! + +1. Fork and clone the repository. +2. Create a new branch (`git checkout -b feature/my-feature`). +3. Commit your changes (`git commit -m 'Add my feature'`). +4. Push your branch (`git push origin feature/my-feature`). +5. Open a pull request. + +--- + +## Developer Quickstart + +```shell +pip install crewai[tools] +``` + +### Development Setup + +- Install dependencies: `uv sync` +- Run tests: `uv run pytest` +- Run static type checking: `uv run pyright` +- Set up pre-commit hooks: `pre-commit install` + +--- + +## Support and Community + +Join our rapidly growing community and receive real-time support: + +- [Discourse](https://community.crewai.com/) +- [Open an Issue](https://github.com/crewAIInc/crewAI/issues) + +Build smarter, faster, and more powerful AI solutions—powered by CrewAI Tools. diff --git a/lib/crewai-tools/generate_tool_specs.py b/lib/crewai-tools/generate_tool_specs.py new file mode 100644 index 000000000..af97191c4 --- /dev/null +++ b/lib/crewai-tools/generate_tool_specs.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python3 + +from collections.abc import Mapping +import inspect +import json +from pathlib import Path +from typing import Any, cast + +from crewai.tools.base_tool import BaseTool, EnvVar +from crewai_tools import tools +from pydantic import BaseModel +from pydantic.json_schema import GenerateJsonSchema +from pydantic_core import PydanticOmit + + +class SchemaGenerator(GenerateJsonSchema): + def handle_invalid_for_json_schema(self, schema, error_info): + raise PydanticOmit + + +class ToolSpecExtractor: + def __init__(self) -> None: + self.tools_spec: list[dict[str, Any]] = [] + self.processed_tools: set[str] = set() + + def extract_all_tools(self) -> list[dict[str, Any]]: + for name in dir(tools): + if name.endswith("Tool") and name not in self.processed_tools: + obj = getattr(tools, name, None) + if inspect.isclass(obj) and issubclass(obj, BaseTool): + self.extract_tool_info(obj) + self.processed_tools.add(name) + return self.tools_spec + + def extract_tool_info(self, tool_class: type[BaseTool]) -> None: + try: + core_schema = tool_class.__pydantic_core_schema__ + if not core_schema: + return + + schema = self._unwrap_schema(core_schema) + fields = schema.get("schema", {}).get("fields", {}) + + tool_info = { + "name": tool_class.__name__, + "humanized_name": self._extract_field_default( + fields.get("name"), fallback=tool_class.__name__ + ), + "description": str( + self._extract_field_default(fields.get("description")) + ).strip(), + "run_params_schema": self._extract_params(fields.get("args_schema")), + "init_params_schema": self._extract_init_params(tool_class), + "env_vars": self._extract_env_vars(fields.get("env_vars")), + "package_dependencies": self._extract_field_default( + fields.get("package_dependencies"), fallback=[] + ), + } + + self.tools_spec.append(tool_info) + + except Exception: # noqa: S110 + pass + + @staticmethod + def _unwrap_schema(schema: Mapping[str, Any] | dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = dict(schema) + while ( + result.get("type") in {"function-after", "default"} and "schema" in result + ): + result = dict(result["schema"]) + return result + + @staticmethod + def _extract_field_default( + field: dict | None, fallback: str | list[Any] = "" + ) -> str | list[Any] | int: + if not field: + return fallback + + schema = field.get("schema", {}) + default = schema.get("default") + return default if isinstance(default, (list, str, int)) else fallback + + @staticmethod + def _extract_params(args_schema_field: dict | None) -> dict[str, Any]: + if not args_schema_field: + return {} + + args_schema_class = args_schema_field.get("schema", {}).get("default") + if not ( + inspect.isclass(args_schema_class) + and issubclass(args_schema_class, BaseModel) + ): + return {} + + # Cast to type[BaseModel] after runtime check + schema_class = cast(type[BaseModel], args_schema_class) + try: + return schema_class.model_json_schema(schema_generator=SchemaGenerator) + except Exception: + return {} + + @staticmethod + def _extract_env_vars(env_vars_field: dict | None) -> list[dict[str, Any]]: + if not env_vars_field: + return [] + + return [ + { + "name": env_var.name, + "description": env_var.description, + "required": env_var.required, + "default": env_var.default, + } + for env_var in env_vars_field.get("schema", {}).get("default", []) + if isinstance(env_var, EnvVar) + ] + + @staticmethod + def _extract_init_params(tool_class: type[BaseTool]) -> dict[str, Any]: + ignored_init_params = [ + "name", + "description", + "env_vars", + "args_schema", + "description_updated", + "cache_function", + "result_as_answer", + "max_usage_count", + "current_usage_count", + "package_dependencies", + ] + + json_schema = tool_class.model_json_schema( + schema_generator=SchemaGenerator, mode="serialization" + ) + + json_schema["properties"] = { + key: value + for key, value in json_schema["properties"].items() + if key not in ignored_init_params + } + return json_schema + + def save_to_json(self, output_path: str) -> None: + with open(output_path, "w", encoding="utf-8") as f: + json.dump({"tools": self.tools_spec}, f, indent=2, sort_keys=True) + + +if __name__ == "__main__": + output_file = Path(__file__).parent / "tool.specs.json" + extractor = ToolSpecExtractor() + + extractor.extract_all_tools() + extractor.save_to_json(str(output_file)) diff --git a/lib/crewai-tools/pyproject.toml b/lib/crewai-tools/pyproject.toml new file mode 100644 index 000000000..934dc309c --- /dev/null +++ b/lib/crewai-tools/pyproject.toml @@ -0,0 +1,152 @@ +[project] +name = "crewai-tools" +dynamic = ["version"] +description = "Set of tools for the crewAI framework" +readme = "README.md" +authors = [ + { name = "João Moura", email = "joaomdmoura@gmail.com" }, +] +requires-python = ">=3.10, <3.14" +dependencies = [ + "lancedb>=0.5.4", + "pytube>=15.0.0", + "requests>=2.32.5", + "docker>=7.1.0", + "crewai==1.0.0b3", + "lancedb>=0.5.4", + "tiktoken>=0.8.0", + "beautifulsoup4>=4.13.4", + "pypdf>=5.9.0", + "python-docx>=1.2.0", + "youtube-transcript-api>=1.2.2", +] + + +[project.urls] +Homepage = "https://crewai.com" +Repository = "https://github.com/crewAIInc/crewAI" +Documentation = "https://docs.crewai.com" + + +[project.optional-dependencies] +scrapfly-sdk = [ + "scrapfly-sdk>=0.8.19", +] +sqlalchemy = [ + "sqlalchemy>=2.0.35", +] +multion = [ + "multion>=1.1.0", +] +firecrawl-py = [ + "firecrawl-py>=1.8.0", +] +composio-core = [ + "composio-core>=0.6.11.post1", +] +browserbase = [ + "browserbase>=1.0.5", +] +weaviate-client = [ + "weaviate-client>=4.10.2", +] +patronus = [ + "patronus>=0.0.16", +] +serpapi = [ + "serpapi>=0.1.5", +] +beautifulsoup4 = [ + "beautifulsoup4>=4.12.3", +] +selenium = [ + "selenium>=4.27.1", +] +spider-client = [ + "spider-client>=0.1.25", +] +scrapegraph-py = [ + "scrapegraph-py>=1.9.0", +] +linkup-sdk = [ + "linkup-sdk>=0.2.2", +] +tavily-python = [ + "tavily-python>=0.5.4", +] +hyperbrowser = [ + "hyperbrowser>=0.18.0", +] +snowflake = [ + "cryptography>=43.0.3", + "snowflake-connector-python>=3.12.4", + "snowflake-sqlalchemy>=1.7.3", +] +singlestore = [ + "singlestoredb>=1.12.4", + "SQLAlchemy>=2.0.40", +] +exa-py = [ + "exa-py>=1.8.7", +] +qdrant-client = [ + "qdrant-client>=1.12.1", +] +apify = [ + "langchain-apify>=0.1.2,<1.0.0", +] + +databricks-sdk = [ + "databricks-sdk>=0.46.0", +] +couchbase = [ + "couchbase>=4.3.5", +] +mcp = [ + "mcp>=1.6.0", + "mcpadapt>=0.1.9", +] +stagehand = [ + "stagehand>=0.4.1", +] +github = [ + "gitpython==3.1.38", + "PyGithub==1.59.1", +] +rag = [ + "python-docx>=1.1.0", + "lxml>=5.3.0,<5.4.0", # Pin to avoid etree import issues in 5.4.0 +] +xml = [ + "unstructured[local-inference, all-docs]>=0.17.2" +] +oxylabs = [ + "oxylabs==2.0.0" +] +mongodb = [ + "pymongo>=4.13" +] +mysql = [ + "pymysql>=1.1.1" +] +postgresql = [ + "psycopg2-binary>=2.9.10" +] +bedrock = [ + "beautifulsoup4>=4.13.4", + "bedrock-agentcore>=0.1.0", + "playwright>=1.52.0", + "nest-asyncio>=1.6.0", +] +contextual = [ + "contextual-client>=0.1.0", + "nest-asyncio>=1.6.0", +] + + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.version] +path = "src/crewai_tools/__init__.py" diff --git a/lib/crewai-tools/src/crewai_tools/__init__.py b/lib/crewai-tools/src/crewai_tools/__init__.py new file mode 100644 index 000000000..035fed57b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/__init__.py @@ -0,0 +1,290 @@ +from crewai_tools.adapters.enterprise_adapter import EnterpriseActionTool +from crewai_tools.adapters.mcp_adapter import MCPServerAdapter +from crewai_tools.adapters.zapier_adapter import ZapierActionTool +from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool +from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import ( + BedrockKBRetrieverTool, +) +from crewai_tools.aws.s3.reader_tool import S3ReaderTool +from crewai_tools.aws.s3.writer_tool import S3WriterTool +from crewai_tools.tools.ai_mind_tool.ai_mind_tool import AIMindTool +from crewai_tools.tools.apify_actors_tool.apify_actors_tool import ApifyActorsTool +from crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool import ArxivPaperTool +from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool +from crewai_tools.tools.brightdata_tool.brightdata_dataset import ( + BrightDataDatasetTool, +) +from crewai_tools.tools.brightdata_tool.brightdata_serp import BrightDataSearchTool +from crewai_tools.tools.brightdata_tool.brightdata_unlocker import ( + BrightDataWebUnlockerTool, +) +from crewai_tools.tools.browserbase_load_tool.browserbase_load_tool import ( + BrowserbaseLoadTool, +) +from crewai_tools.tools.code_docs_search_tool.code_docs_search_tool import ( + CodeDocsSearchTool, +) +from crewai_tools.tools.code_interpreter_tool.code_interpreter_tool import ( + CodeInterpreterTool, +) +from crewai_tools.tools.composio_tool.composio_tool import ComposioTool +from crewai_tools.tools.contextualai_create_agent_tool.contextual_create_agent_tool import ( + ContextualAICreateAgentTool, +) +from crewai_tools.tools.contextualai_parse_tool.contextual_parse_tool import ( + ContextualAIParseTool, +) +from crewai_tools.tools.contextualai_query_tool.contextual_query_tool import ( + ContextualAIQueryTool, +) +from crewai_tools.tools.contextualai_rerank_tool.contextual_rerank_tool import ( + ContextualAIRerankTool, +) +from crewai_tools.tools.couchbase_tool.couchbase_tool import ( + CouchbaseFTSVectorSearchTool, +) +from crewai_tools.tools.crewai_platform_tools.crewai_platform_tools import ( + CrewaiPlatformTools, +) +from crewai_tools.tools.csv_search_tool.csv_search_tool import CSVSearchTool +from crewai_tools.tools.dalle_tool.dalle_tool import DallETool +from crewai_tools.tools.databricks_query_tool.databricks_query_tool import ( + DatabricksQueryTool, +) +from crewai_tools.tools.directory_read_tool.directory_read_tool import ( + DirectoryReadTool, +) +from crewai_tools.tools.directory_search_tool.directory_search_tool import ( + DirectorySearchTool, +) +from crewai_tools.tools.docx_search_tool.docx_search_tool import DOCXSearchTool +from crewai_tools.tools.exa_tools.exa_search_tool import EXASearchTool +from crewai_tools.tools.file_read_tool.file_read_tool import FileReadTool +from crewai_tools.tools.file_writer_tool.file_writer_tool import FileWriterTool +from crewai_tools.tools.files_compressor_tool.files_compressor_tool import ( + FileCompressorTool, +) +from crewai_tools.tools.firecrawl_crawl_website_tool.firecrawl_crawl_website_tool import ( + FirecrawlCrawlWebsiteTool, +) +from crewai_tools.tools.firecrawl_scrape_website_tool.firecrawl_scrape_website_tool import ( + FirecrawlScrapeWebsiteTool, +) +from crewai_tools.tools.firecrawl_search_tool.firecrawl_search_tool import ( + FirecrawlSearchTool, +) +from crewai_tools.tools.generate_crewai_automation_tool.generate_crewai_automation_tool import ( + GenerateCrewaiAutomationTool, +) +from crewai_tools.tools.github_search_tool.github_search_tool import GithubSearchTool +from crewai_tools.tools.hyperbrowser_load_tool.hyperbrowser_load_tool import ( + HyperbrowserLoadTool, +) +from crewai_tools.tools.invoke_crewai_automation_tool.invoke_crewai_automation_tool import ( + InvokeCrewAIAutomationTool, +) +from crewai_tools.tools.jina_scrape_website_tool.jina_scrape_website_tool import ( + JinaScrapeWebsiteTool, +) +from crewai_tools.tools.json_search_tool.json_search_tool import JSONSearchTool +from crewai_tools.tools.linkup.linkup_search_tool import LinkupSearchTool +from crewai_tools.tools.llamaindex_tool.llamaindex_tool import LlamaIndexTool +from crewai_tools.tools.mdx_search_tool.mdx_search_tool import MDXSearchTool +from crewai_tools.tools.mongodb_vector_search_tool.vector_search import ( + MongoDBVectorSearchConfig, + MongoDBVectorSearchTool, +) +from crewai_tools.tools.multion_tool.multion_tool import MultiOnTool +from crewai_tools.tools.mysql_search_tool.mysql_search_tool import MySQLSearchTool +from crewai_tools.tools.nl2sql.nl2sql_tool import NL2SQLTool +from crewai_tools.tools.ocr_tool.ocr_tool import OCRTool +from crewai_tools.tools.oxylabs_amazon_product_scraper_tool.oxylabs_amazon_product_scraper_tool import ( + OxylabsAmazonProductScraperTool, +) +from crewai_tools.tools.oxylabs_amazon_search_scraper_tool.oxylabs_amazon_search_scraper_tool import ( + OxylabsAmazonSearchScraperTool, +) +from crewai_tools.tools.oxylabs_google_search_scraper_tool.oxylabs_google_search_scraper_tool import ( + OxylabsGoogleSearchScraperTool, +) +from crewai_tools.tools.oxylabs_universal_scraper_tool.oxylabs_universal_scraper_tool import ( + OxylabsUniversalScraperTool, +) +from crewai_tools.tools.parallel_tools.parallel_search_tool import ParallelSearchTool +from crewai_tools.tools.patronus_eval_tool.patronus_eval_tool import PatronusEvalTool +from crewai_tools.tools.patronus_eval_tool.patronus_local_evaluator_tool import ( + PatronusLocalEvaluatorTool, +) +from crewai_tools.tools.patronus_eval_tool.patronus_predefined_criteria_eval_tool import ( + PatronusPredefinedCriteriaEvalTool, +) +from crewai_tools.tools.pdf_search_tool.pdf_search_tool import PDFSearchTool +from crewai_tools.tools.qdrant_vector_search_tool.qdrant_search_tool import ( + QdrantVectorSearchTool, +) +from crewai_tools.tools.rag.rag_tool import RagTool +from crewai_tools.tools.scrape_element_from_website.scrape_element_from_website import ( + ScrapeElementFromWebsiteTool, +) +from crewai_tools.tools.scrape_website_tool.scrape_website_tool import ( + ScrapeWebsiteTool, +) +from crewai_tools.tools.scrapegraph_scrape_tool.scrapegraph_scrape_tool import ( + ScrapegraphScrapeTool, + ScrapegraphScrapeToolSchema, +) +from crewai_tools.tools.scrapfly_scrape_website_tool.scrapfly_scrape_website_tool import ( + ScrapflyScrapeWebsiteTool, +) +from crewai_tools.tools.selenium_scraping_tool.selenium_scraping_tool import ( + SeleniumScrapingTool, +) +from crewai_tools.tools.serpapi_tool.serpapi_google_search_tool import ( + SerpApiGoogleSearchTool, +) +from crewai_tools.tools.serpapi_tool.serpapi_google_shopping_tool import ( + SerpApiGoogleShoppingTool, +) +from crewai_tools.tools.serper_dev_tool.serper_dev_tool import SerperDevTool +from crewai_tools.tools.serper_scrape_website_tool.serper_scrape_website_tool import ( + SerperScrapeWebsiteTool, +) +from crewai_tools.tools.serply_api_tool.serply_job_search_tool import ( + SerplyJobSearchTool, +) +from crewai_tools.tools.serply_api_tool.serply_news_search_tool import ( + SerplyNewsSearchTool, +) +from crewai_tools.tools.serply_api_tool.serply_scholar_search_tool import ( + SerplyScholarSearchTool, +) +from crewai_tools.tools.serply_api_tool.serply_web_search_tool import ( + SerplyWebSearchTool, +) +from crewai_tools.tools.serply_api_tool.serply_webpage_to_markdown_tool import ( + SerplyWebpageToMarkdownTool, +) +from crewai_tools.tools.singlestore_search_tool.singlestore_search_tool import ( + SingleStoreSearchTool, +) +from crewai_tools.tools.snowflake_search_tool.snowflake_search_tool import ( + SnowflakeConfig, + SnowflakeSearchTool, +) +from crewai_tools.tools.spider_tool.spider_tool import SpiderTool +from crewai_tools.tools.stagehand_tool.stagehand_tool import StagehandTool +from crewai_tools.tools.tavily_extractor_tool.tavily_extractor_tool import ( + TavilyExtractorTool, +) +from crewai_tools.tools.tavily_search_tool.tavily_search_tool import TavilySearchTool +from crewai_tools.tools.txt_search_tool.txt_search_tool import TXTSearchTool +from crewai_tools.tools.vision_tool.vision_tool import VisionTool +from crewai_tools.tools.weaviate_tool.vector_search import WeaviateVectorSearchTool +from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool +from crewai_tools.tools.xml_search_tool.xml_search_tool import XMLSearchTool +from crewai_tools.tools.youtube_channel_search_tool.youtube_channel_search_tool import ( + YoutubeChannelSearchTool, +) +from crewai_tools.tools.youtube_video_search_tool.youtube_video_search_tool import ( + YoutubeVideoSearchTool, +) +from crewai_tools.tools.zapier_action_tool.zapier_action_tool import ZapierActionTools + + +__all__ = [ + "AIMindTool", + "ApifyActorsTool", + "ArxivPaperTool", + "BedrockInvokeAgentTool", + "BedrockKBRetrieverTool", + "BraveSearchTool", + "BrightDataDatasetTool", + "BrightDataSearchTool", + "BrightDataWebUnlockerTool", + "BrowserbaseLoadTool", + "CSVSearchTool", + "CodeDocsSearchTool", + "CodeInterpreterTool", + "ComposioTool", + "ContextualAICreateAgentTool", + "ContextualAIParseTool", + "ContextualAIQueryTool", + "ContextualAIRerankTool", + "CouchbaseFTSVectorSearchTool", + "CrewaiPlatformTools", + "DOCXSearchTool", + "DallETool", + "DatabricksQueryTool", + "DirectoryReadTool", + "DirectorySearchTool", + "EXASearchTool", + "EnterpriseActionTool", + "FileCompressorTool", + "FileReadTool", + "FileWriterTool", + "FirecrawlCrawlWebsiteTool", + "FirecrawlScrapeWebsiteTool", + "FirecrawlSearchTool", + "GenerateCrewaiAutomationTool", + "GithubSearchTool", + "HyperbrowserLoadTool", + "InvokeCrewAIAutomationTool", + "JSONSearchTool", + "JinaScrapeWebsiteTool", + "LinkupSearchTool", + "LlamaIndexTool", + "MCPServerAdapter", + "MDXSearchTool", + "MongoDBVectorSearchConfig", + "MongoDBVectorSearchTool", + "MultiOnTool", + "MySQLSearchTool", + "NL2SQLTool", + "OCRTool", + "OxylabsAmazonProductScraperTool", + "OxylabsAmazonSearchScraperTool", + "OxylabsGoogleSearchScraperTool", + "OxylabsUniversalScraperTool", + "PDFSearchTool", + "ParallelSearchTool", + "PatronusEvalTool", + "PatronusLocalEvaluatorTool", + "PatronusPredefinedCriteriaEvalTool", + "QdrantVectorSearchTool", + "RagTool", + "S3ReaderTool", + "S3WriterTool", + "ScrapeElementFromWebsiteTool", + "ScrapeWebsiteTool", + "ScrapegraphScrapeTool", + "ScrapegraphScrapeToolSchema", + "ScrapflyScrapeWebsiteTool", + "SeleniumScrapingTool", + "SerpApiGoogleSearchTool", + "SerpApiGoogleShoppingTool", + "SerperDevTool", + "SerperScrapeWebsiteTool", + "SerplyJobSearchTool", + "SerplyNewsSearchTool", + "SerplyScholarSearchTool", + "SerplyWebSearchTool", + "SerplyWebpageToMarkdownTool", + "SingleStoreSearchTool", + "SnowflakeConfig", + "SnowflakeSearchTool", + "SpiderTool", + "StagehandTool", + "TXTSearchTool", + "TavilyExtractorTool", + "TavilySearchTool", + "VisionTool", + "WeaviateVectorSearchTool", + "WebsiteSearchTool", + "XMLSearchTool", + "YoutubeChannelSearchTool", + "YoutubeVideoSearchTool", + "ZapierActionTool", + "ZapierActionTools", +] + +__version__ = "1.0.0b3" diff --git a/src/crewai/agents/agent_adapters/__init__.py b/lib/crewai-tools/src/crewai_tools/adapters/__init__.py similarity index 100% rename from src/crewai/agents/agent_adapters/__init__.py rename to lib/crewai-tools/src/crewai_tools/adapters/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/adapters/crewai_rag_adapter.py b/lib/crewai-tools/src/crewai_tools/adapters/crewai_rag_adapter.py new file mode 100644 index 000000000..f23cea85c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/adapters/crewai_rag_adapter.py @@ -0,0 +1,282 @@ +"""Adapter for CrewAI's native RAG system.""" + +import hashlib +from pathlib import Path +from typing import Any, TypeAlias, TypedDict +import uuid + +from crewai.rag.config.types import RagConfigType +from crewai.rag.config.utils import get_rag_client +from crewai.rag.core.base_client import BaseClient +from crewai.rag.factory import create_client +from crewai.rag.qdrant.config import QdrantConfig +from crewai.rag.types import BaseRecord, SearchResult +from pydantic import PrivateAttr +from qdrant_client.models import VectorParams +from typing_extensions import Unpack + +from crewai_tools.rag.data_types import DataType +from crewai_tools.rag.misc import sanitize_metadata_for_chromadb +from crewai_tools.tools.rag.rag_tool import Adapter + + +ContentItem: TypeAlias = str | Path | dict[str, Any] + + +class AddDocumentParams(TypedDict, total=False): + """Parameters for adding documents to the RAG system.""" + + data_type: DataType + metadata: dict[str, Any] + website: str + url: str + file_path: str | Path + github_url: str + youtube_url: str + directory_path: str | Path + + +class CrewAIRagAdapter(Adapter): + """Adapter that uses CrewAI's native RAG system. + + Supports custom vector database configuration through the config parameter. + """ + + collection_name: str = "default" + summarize: bool = False + similarity_threshold: float = 0.6 + limit: int = 5 + config: RagConfigType | None = None + _client: BaseClient | None = PrivateAttr(default=None) + + def model_post_init(self, __context: Any) -> None: + """Initialize the CrewAI RAG client after model initialization.""" + if self.config is not None: + self._client = create_client(self.config) + else: + self._client = get_rag_client() + collection_params: dict[str, Any] = {"collection_name": self.collection_name} + if isinstance(self.config, QdrantConfig) and self.config.vectors_config: + if isinstance(self.config.vectors_config, VectorParams): + collection_params["vectors_config"] = self.config.vectors_config + self._client.get_or_create_collection(**collection_params) + + def query( + self, + question: str, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + """Query the knowledge base with a question. + + Args: + question: The question to ask + similarity_threshold: Minimum similarity score for results (default: 0.6) + limit: Maximum number of results to return (default: 5) + + Returns: + Relevant content from the knowledge base + """ + search_limit = limit if limit is not None else self.limit + search_threshold = ( + similarity_threshold + if similarity_threshold is not None + else self.similarity_threshold + ) + if self._client is None: + raise ValueError("Client is not initialized") + + results: list[SearchResult] = self._client.search( + collection_name=self.collection_name, + query=question, + limit=search_limit, + score_threshold=search_threshold, + ) + + if not results: + return "No relevant content found." + + contents: list[str] = [] + for result in results: + content: str = result.get("content", "") + if content: + contents.append(content) + + return "\n\n".join(contents) + + def add(self, *args: ContentItem, **kwargs: Unpack[AddDocumentParams]) -> None: + """Add content to the knowledge base. + + This method handles various input types and converts them to documents + for the vector database. It supports the data_type parameter for + compatibility with existing tools. + + Args: + *args: Content items to add (strings, paths, or document dicts) + **kwargs: Additional parameters including data_type, metadata, etc. + """ + import os + + from crewai_tools.rag.base_loader import LoaderResult + from crewai_tools.rag.data_types import DataType, DataTypes + from crewai_tools.rag.source_content import SourceContent + + documents: list[BaseRecord] = [] + data_type: DataType | None = kwargs.get("data_type") + base_metadata: dict[str, Any] = kwargs.get("metadata", {}) + + for arg in args: + source_ref: str + if isinstance(arg, dict): + source_ref = str(arg.get("source", arg.get("content", ""))) + else: + source_ref = str(arg) + + if not data_type: + data_type = DataTypes.from_content(source_ref) + + if data_type == DataType.DIRECTORY: + if not os.path.isdir(source_ref): + raise ValueError(f"Directory does not exist: {source_ref}") + + # Define binary and non-text file extensions to skip + binary_extensions = { + ".pyc", + ".pyo", + ".png", + ".jpg", + ".jpeg", + ".gif", + ".bmp", + ".ico", + ".svg", + ".webp", + ".pdf", + ".zip", + ".tar", + ".gz", + ".bz2", + ".7z", + ".rar", + ".exe", + ".dll", + ".so", + ".dylib", + ".bin", + ".dat", + ".db", + ".sqlite", + ".class", + ".jar", + ".war", + ".ear", + } + + for root, dirs, files in os.walk(source_ref): + dirs[:] = [d for d in dirs if not d.startswith(".")] + + for filename in files: + if filename.startswith("."): + continue + + # Skip binary files based on extension + file_ext = os.path.splitext(filename)[1].lower() + if file_ext in binary_extensions: + continue + + # Skip __pycache__ directories + if "__pycache__" in root: + continue + + file_path: str = os.path.join(root, filename) + try: + file_data_type: DataType = DataTypes.from_content(file_path) + file_loader = file_data_type.get_loader() + file_chunker = file_data_type.get_chunker() + + file_source = SourceContent(file_path) + file_result: LoaderResult = file_loader.load(file_source) + + file_chunks = file_chunker.chunk(file_result.content) + + for chunk_idx, file_chunk in enumerate(file_chunks): + file_metadata: dict[str, Any] = base_metadata.copy() + file_metadata.update(file_result.metadata) + file_metadata["data_type"] = str(file_data_type) + file_metadata["file_path"] = file_path + file_metadata["chunk_index"] = chunk_idx + file_metadata["total_chunks"] = len(file_chunks) + + if isinstance(arg, dict): + file_metadata.update(arg.get("metadata", {})) + + chunk_hash = hashlib.sha256( + f"{file_result.doc_id}_{chunk_idx}_{file_chunk}".encode() + ).hexdigest() + chunk_id = str(uuid.UUID(chunk_hash[:32])) + + documents.append( + { + "doc_id": chunk_id, + "content": file_chunk, + "metadata": sanitize_metadata_for_chromadb( + file_metadata + ), + } + ) + except Exception: # noqa: S112 + # Silently skip files that can't be processed + continue + else: + metadata: dict[str, Any] = base_metadata.copy() + + if data_type in [ + DataType.PDF_FILE, + DataType.TEXT_FILE, + DataType.DOCX, + DataType.CSV, + DataType.JSON, + DataType.XML, + DataType.MDX, + ]: + if not os.path.isfile(source_ref): + raise FileNotFoundError(f"File does not exist: {source_ref}") + + loader = data_type.get_loader() + chunker = data_type.get_chunker() + + source_content = SourceContent(source_ref) + loader_result: LoaderResult = loader.load(source_content) + + chunks = chunker.chunk(loader_result.content) + + for i, chunk in enumerate(chunks): + chunk_metadata: dict[str, Any] = metadata.copy() + chunk_metadata.update(loader_result.metadata) + chunk_metadata["data_type"] = str(data_type) + chunk_metadata["chunk_index"] = i + chunk_metadata["total_chunks"] = len(chunks) + chunk_metadata["source"] = source_ref + + if isinstance(arg, dict): + chunk_metadata.update(arg.get("metadata", {})) + + chunk_hash = hashlib.sha256( + f"{loader_result.doc_id}_{i}_{chunk}".encode() + ).hexdigest() + chunk_id = str(uuid.UUID(chunk_hash[:32])) + + documents.append( + { + "doc_id": chunk_id, + "content": chunk, + "metadata": sanitize_metadata_for_chromadb(chunk_metadata), + } + ) + + if documents: + if self._client is None: + raise ValueError("Client is not initialized") + self._client.add_documents( + collection_name=self.collection_name, documents=documents + ) diff --git a/lib/crewai-tools/src/crewai_tools/adapters/enterprise_adapter.py b/lib/crewai-tools/src/crewai_tools/adapters/enterprise_adapter.py new file mode 100644 index 000000000..261c2a38a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/adapters/enterprise_adapter.py @@ -0,0 +1,432 @@ +import json +import os +import re +from typing import Any, Literal, Optional, Union, _SpecialForm, cast, get_origin +import warnings + +from crewai.tools import BaseTool +from pydantic import Field, create_model +import requests + + +def get_enterprise_api_base_url() -> str: + """Get the enterprise API base URL from environment or use default.""" + base_url = os.getenv("CREWAI_PLUS_URL", "https://app.crewai.com") + return f"{base_url}/crewai_plus/api/v1/integrations" + + +ENTERPRISE_API_BASE_URL = get_enterprise_api_base_url() + + +class EnterpriseActionTool(BaseTool): + """A tool that executes a specific enterprise action.""" + + enterprise_action_token: str = Field( + default="", description="The enterprise action token" + ) + action_name: str = Field(default="", description="The name of the action") + action_schema: dict[str, Any] = Field( + default={}, description="The schema of the action" + ) + enterprise_api_base_url: str = Field( + default=ENTERPRISE_API_BASE_URL, description="The base API URL" + ) + + def __init__( + self, + name: str, + description: str, + enterprise_action_token: str, + action_name: str, + action_schema: dict[str, Any], + enterprise_api_base_url: str | None = None, + ): + self._model_registry = {} # type: ignore[var-annotated] + self._base_name = self._sanitize_name(name) + + schema_props, required = self._extract_schema_info(action_schema) + + # Define field definitions for the model + field_definitions = {} + for param_name, param_details in schema_props.items(): + param_desc = param_details.get("description", "") + is_required = param_name in required + + try: + field_type = self._process_schema_type( + param_details, self._sanitize_name(param_name).title() + ) + except Exception: + field_type = str + + # Create field definition based on requirement + field_definitions[param_name] = self._create_field_definition( + field_type, is_required, param_desc + ) + + # Create the model + if field_definitions: + try: + args_schema = create_model( # type: ignore[call-overload] + f"{self._base_name}Schema", **field_definitions + ) + except Exception: + args_schema = create_model( + f"{self._base_name}Schema", + input_text=(str, Field(description="Input for the action")), + ) + else: + # Fallback for empty schema + args_schema = create_model( + f"{self._base_name}Schema", + input_text=(str, Field(description="Input for the action")), + ) + + super().__init__(name=name, description=description, args_schema=args_schema) + self.enterprise_action_token = enterprise_action_token + self.action_name = action_name + self.action_schema = action_schema + self.enterprise_api_base_url = ( + enterprise_api_base_url or get_enterprise_api_base_url() + ) + + def _sanitize_name(self, name: str) -> str: + """Sanitize names to create proper Python class names.""" + sanitized = re.sub(r"[^a-zA-Z0-9_]", "", name) + parts = sanitized.split("_") + return "".join(word.capitalize() for word in parts if word) + + def _extract_schema_info( + self, action_schema: dict[str, Any] + ) -> tuple[dict[str, Any], list[str]]: + """Extract schema properties and required fields from action schema.""" + schema_props = ( + action_schema.get("function", {}) + .get("parameters", {}) + .get("properties", {}) + ) + required = ( + action_schema.get("function", {}).get("parameters", {}).get("required", []) + ) + return schema_props, required + + def _process_schema_type( + self, schema: dict[str, Any], type_name: str + ) -> type[Any] | _SpecialForm: + """Process a JSON schema and return appropriate Python type.""" + if "anyOf" in schema: + any_of_types = schema["anyOf"] + is_nullable = any(t.get("type") == "null" for t in any_of_types) + non_null_types = [t for t in any_of_types if t.get("type") != "null"] + + if non_null_types: + base_type = self._process_schema_type(non_null_types[0], type_name) + return Optional[base_type] if is_nullable else base_type # noqa: UP045 + return cast(type[Any], Optional[str]) # noqa: UP045 + + if "oneOf" in schema: + return self._process_schema_type(schema["oneOf"][0], type_name) + + if "allOf" in schema: + return self._process_schema_type(schema["allOf"][0], type_name) + + json_type = schema.get("type", "string") + + if "enum" in schema: + enum_values = schema["enum"] + if not enum_values: + return self._map_json_type_to_python(json_type) + return Literal[tuple(enum_values)] # type: ignore[return-value] + + if json_type == "array": + items_schema = schema.get("items", {"type": "string"}) + item_type = self._process_schema_type(items_schema, f"{type_name}Item") + return list[item_type] # type: ignore[valid-type] + + if json_type == "object": + return self._create_nested_model(schema, type_name) + + return self._map_json_type_to_python(json_type) + + def _create_nested_model( + self, schema: dict[str, Any], model_name: str + ) -> type[Any]: + """Create a nested Pydantic model for complex objects.""" + full_model_name = f"{self._base_name}{model_name}" + + if full_model_name in self._model_registry: + return self._model_registry[full_model_name] + + properties = schema.get("properties", {}) + required_fields = schema.get("required", []) + + if not properties: + return dict + + field_definitions = {} + for prop_name, prop_schema in properties.items(): + prop_desc = prop_schema.get("description", "") + is_required = prop_name in required_fields + + try: + prop_type = self._process_schema_type( + prop_schema, f"{model_name}{self._sanitize_name(prop_name).title()}" + ) + except Exception: + prop_type = str + + field_definitions[prop_name] = self._create_field_definition( + prop_type, + is_required, + prop_desc, # type: ignore[arg-type] + ) + + try: + nested_model = create_model(full_model_name, **field_definitions) # type: ignore[call-overload] + self._model_registry[full_model_name] = nested_model + return nested_model + except Exception: + return dict + + def _create_field_definition( + self, field_type: type[Any] | _SpecialForm, is_required: bool, description: str + ) -> tuple: + """Create Pydantic field definition based on type and requirement.""" + if is_required: + return (field_type, Field(description=description)) + if get_origin(field_type) is Union: + return (field_type, Field(default=None, description=description)) + return ( + Optional[field_type], # noqa: UP045 + Field(default=None, description=description), + ) + + def _map_json_type_to_python(self, json_type: str) -> type[Any]: + """Map basic JSON schema types to Python types.""" + type_mapping = { + "string": str, + "integer": int, + "number": float, + "boolean": bool, + "array": list, + "object": dict, + "null": type(None), + } + return type_mapping.get(json_type, str) + + def _get_required_nullable_fields(self) -> list[str]: + """Get a list of required nullable fields from the action schema.""" + schema_props, required = self._extract_schema_info(self.action_schema) + + required_nullable_fields = [] + for param_name in required: + param_details = schema_props.get(param_name, {}) + if self._is_nullable_type(param_details): + required_nullable_fields.append(param_name) + + return required_nullable_fields + + def _is_nullable_type(self, schema: dict[str, Any]) -> bool: + """Check if a schema represents a nullable type.""" + if "anyOf" in schema: + return any(t.get("type") == "null" for t in schema["anyOf"]) + return schema.get("type") == "null" + + def _run(self, **kwargs) -> str: + """Execute the specific enterprise action with validated parameters.""" + try: + cleaned_kwargs = {} + for key, value in kwargs.items(): + if value is not None: + cleaned_kwargs[key] = value # noqa: PERF403 + + required_nullable_fields = self._get_required_nullable_fields() + + for field_name in required_nullable_fields: + if field_name not in cleaned_kwargs: + cleaned_kwargs[field_name] = None + + api_url = ( + f"{self.enterprise_api_base_url}/actions/{self.action_name}/execute" + ) + headers = { + "Authorization": f"Bearer {self.enterprise_action_token}", + "Content-Type": "application/json", + } + payload = cleaned_kwargs + + response = requests.post( + url=api_url, headers=headers, json=payload, timeout=60 + ) + + data = response.json() + if not response.ok: + error_message = data.get("error", {}).get("message", json.dumps(data)) + return f"API request failed: {error_message}" + + return json.dumps(data, indent=2) + + except Exception as e: + return f"Error executing action {self.action_name}: {e!s}" + + +class EnterpriseActionKitToolAdapter: + """Adapter that creates BaseTool instances for enterprise actions.""" + + def __init__( + self, + enterprise_action_token: str, + enterprise_api_base_url: str | None = None, + ): + """Initialize the adapter with an enterprise action token.""" + self._set_enterprise_action_token(enterprise_action_token) + self._actions_schema = {} # type: ignore[var-annotated] + self._tools = None + self.enterprise_api_base_url = ( + enterprise_api_base_url or get_enterprise_api_base_url() + ) + + def tools(self) -> list[BaseTool]: + """Get the list of tools created from enterprise actions.""" + if self._tools is None: + self._fetch_actions() + self._create_tools() + return self._tools or [] + + def _fetch_actions(self): + """Fetch available actions from the API.""" + try: + actions_url = f"{self.enterprise_api_base_url}/actions" + headers = {"Authorization": f"Bearer {self.enterprise_action_token}"} + + response = requests.get(actions_url, headers=headers, timeout=30) + response.raise_for_status() + + raw_data = response.json() + if "actions" not in raw_data: + return + + parsed_schema = {} + action_categories = raw_data["actions"] + + for action_list in action_categories.values(): + if isinstance(action_list, list): + for action in action_list: + action_name = action.get("name") + if action_name: + action_schema = { + "function": { + "name": action_name, + "description": action.get( + "description", f"Execute {action_name}" + ), + "parameters": action.get("parameters", {}), + } + } + parsed_schema[action_name] = action_schema + + self._actions_schema = parsed_schema + + except Exception: + import traceback + + traceback.print_exc() + + def _generate_detailed_description( + self, schema: dict[str, Any], indent: int = 0 + ) -> list[str]: + """Generate detailed description for nested schema structures.""" + descriptions = [] + indent_str = " " * indent + + schema_type = schema.get("type", "string") + + if schema_type == "object": + properties = schema.get("properties", {}) + required_fields = schema.get("required", []) + + if properties: + descriptions.append(f"{indent_str}Object with properties:") + for prop_name, prop_schema in properties.items(): + prop_desc = prop_schema.get("description", "") + is_required = prop_name in required_fields + req_str = " (required)" if is_required else " (optional)" + descriptions.append( + f"{indent_str} - {prop_name}: {prop_desc}{req_str}" + ) + + if prop_schema.get("type") == "object": + descriptions.extend( + self._generate_detailed_description(prop_schema, indent + 2) + ) + elif prop_schema.get("type") == "array": + items_schema = prop_schema.get("items", {}) + if items_schema.get("type") == "object": + descriptions.append(f"{indent_str} Array of objects:") + descriptions.extend( + self._generate_detailed_description( + items_schema, indent + 3 + ) + ) + elif "enum" in items_schema: + descriptions.append( + f"{indent_str} Array of enum values: {items_schema['enum']}" + ) + elif "enum" in prop_schema: + descriptions.append( + f"{indent_str} Enum values: {prop_schema['enum']}" + ) + + return descriptions + + def _create_tools(self): + """Create BaseTool instances for each action.""" + tools = [] + + for action_name, action_schema in self._actions_schema.items(): + function_details = action_schema.get("function", {}) + description = function_details.get("description", f"Execute {action_name}") + + parameters = function_details.get("parameters", {}) + param_descriptions = [] + + if parameters.get("properties"): + param_descriptions.append("\nDetailed Parameter Structure:") + param_descriptions.extend( + self._generate_detailed_description(parameters) + ) + + full_description = description + "\n".join(param_descriptions) + + tool = EnterpriseActionTool( + name=action_name.lower().replace(" ", "_"), + description=full_description, + action_name=action_name, + action_schema=action_schema, + enterprise_action_token=self.enterprise_action_token, + enterprise_api_base_url=self.enterprise_api_base_url, + ) + + tools.append(tool) + + self._tools = tools + + def _set_enterprise_action_token(self, enterprise_action_token: str | None): + if enterprise_action_token and not enterprise_action_token.startswith("PK_"): + warnings.warn( + "Legacy token detected, please consider using the new Enterprise Action Auth token. Check out our docs for more information https://docs.crewai.com/en/enterprise/features/integrations.", + DeprecationWarning, + stacklevel=2, + ) + + token = enterprise_action_token or os.environ.get( + "CREWAI_ENTERPRISE_TOOLS_TOKEN" + ) + + self.enterprise_action_token = token + + def __enter__(self): + return self.tools() + + def __exit__(self, exc_type, exc_val, exc_tb): + pass diff --git a/lib/crewai-tools/src/crewai_tools/adapters/lancedb_adapter.py b/lib/crewai-tools/src/crewai_tools/adapters/lancedb_adapter.py new file mode 100644 index 000000000..3fd8d8e2c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/adapters/lancedb_adapter.py @@ -0,0 +1,59 @@ +from collections.abc import Callable +from pathlib import Path +from typing import Any + +from lancedb import ( # type: ignore[import-untyped] + DBConnection as LanceDBConnection, + connect as lancedb_connect, +) +from lancedb.table import Table as LanceDBTable # type: ignore[import-untyped] +from openai import Client as OpenAIClient +from pydantic import Field, PrivateAttr + +from crewai_tools.tools.rag.rag_tool import Adapter + + +def _default_embedding_function(): + client = OpenAIClient() + + def _embedding_function(input): + rs = client.embeddings.create(input=input, model="text-embedding-ada-002") + return [record.embedding for record in rs.data] + + return _embedding_function + + +class LanceDBAdapter(Adapter): + uri: str | Path + table_name: str + embedding_function: Callable = Field(default_factory=_default_embedding_function) + top_k: int = 3 + vector_column_name: str = "vector" + text_column_name: str = "text" + + _db: LanceDBConnection = PrivateAttr() + _table: LanceDBTable = PrivateAttr() + + def model_post_init(self, __context: Any) -> None: + self._db = lancedb_connect(self.uri) + self._table = self._db.open_table(self.table_name) + + super().model_post_init(__context) + + def query(self, question: str) -> str: # type: ignore[override] + query = self.embedding_function([question])[0] + results = ( + self._table.search(query, vector_column_name=self.vector_column_name) + .limit(self.top_k) + .select([self.text_column_name]) + .to_list() + ) + values = [result[self.text_column_name] for result in results] + return "\n".join(values) + + def add( + self, + *args: Any, + **kwargs: Any, + ) -> None: + self._table.add(*args, **kwargs) diff --git a/lib/crewai-tools/src/crewai_tools/adapters/mcp_adapter.py b/lib/crewai-tools/src/crewai_tools/adapters/mcp_adapter.py new file mode 100644 index 000000000..edfb222a3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/adapters/mcp_adapter.py @@ -0,0 +1,163 @@ +"""MCPServer for CrewAI.""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from crewai.tools import BaseTool + +from crewai_tools.adapters.tool_collection import ToolCollection + + +logger = logging.getLogger(__name__) + +if TYPE_CHECKING: + from mcp import StdioServerParameters + from mcpadapt.core import MCPAdapt + from mcpadapt.crewai_adapter import CrewAIAdapter + + +try: + from mcp import StdioServerParameters + from mcpadapt.core import MCPAdapt + from mcpadapt.crewai_adapter import CrewAIAdapter + + MCP_AVAILABLE = True +except ImportError: + MCP_AVAILABLE = False + + +class MCPServerAdapter: + """Manages the lifecycle of an MCP server and make its tools available to CrewAI. + + Note: tools can only be accessed after the server has been started with the + `start()` method. + + Attributes: + tools: The CrewAI tools available from the MCP server. + + Usage: + # context manager + stdio + with MCPServerAdapter(...) as tools: + # tools is now available + + # context manager + sse + with MCPServerAdapter({"url": "http://localhost:8000/sse"}) as tools: + # tools is now available + + # context manager with filtered tools + with MCPServerAdapter(..., "tool1", "tool2") as filtered_tools: + # only tool1 and tool2 are available + + # context manager with custom connect timeout (60 seconds) + with MCPServerAdapter(..., connect_timeout=60) as tools: + # tools is now available with longer timeout + + # manually stop mcp server + try: + mcp_server = MCPServerAdapter(...) + tools = mcp_server.tools # all tools + + # or with filtered tools and custom timeout + mcp_server = MCPServerAdapter(..., "tool1", "tool2", connect_timeout=45) + filtered_tools = mcp_server.tools # only tool1 and tool2 + ... + finally: + mcp_server.stop() + + # Best practice is ensure cleanup is done after use. + mcp_server.stop() # run after crew().kickoff() + """ + + def __init__( + self, + serverparams: StdioServerParameters | dict[str, Any], + *tool_names: str, + connect_timeout: int = 30, + ) -> None: + """Initialize the MCP Server. + + Args: + serverparams: The parameters for the MCP server it supports either a + `StdioServerParameters` or a `dict` respectively for STDIO and SSE. + *tool_names: Optional names of tools to filter. If provided, only tools with + matching names will be available. + connect_timeout: Connection timeout in seconds to the MCP server (default is 30s). + + """ + super().__init__() + self._adapter = None + self._tools = None + self._tool_names = list(tool_names) if tool_names else None + + if not MCP_AVAILABLE: + import click + + if click.confirm( + "You are missing the 'mcp' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run(["uv", "add", "mcp crewai-tools[mcp]"], check=True) # noqa: S607 + + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install mcp package") from e + else: + raise ImportError( + "`mcp` package not found, please run `uv add crewai-tools[mcp]`" + ) + + try: + self._serverparams = serverparams + self._adapter = MCPAdapt( + self._serverparams, CrewAIAdapter(), connect_timeout + ) + self.start() + + except Exception as e: + if self._adapter is not None: + try: + self.stop() + except Exception as stop_e: + logger.error(f"Error during stop cleanup: {stop_e}") + raise RuntimeError(f"Failed to initialize MCP Adapter: {e}") from e + + def start(self): + """Start the MCP server and initialize the tools.""" + self._tools = self._adapter.__enter__() + + def stop(self): + """Stop the MCP server.""" + self._adapter.__exit__(None, None, None) + + @property + def tools(self) -> ToolCollection[BaseTool]: + """The CrewAI tools available from the MCP server. + + Raises: + ValueError: If the MCP server is not started. + + Returns: + The CrewAI tools available from the MCP server. + """ + if self._tools is None: + raise ValueError( + "MCP server not started, run `mcp_server.start()` first before accessing `tools`" + ) + + tools_collection = ToolCollection(self._tools) + if self._tool_names: + return tools_collection.filter_by_names(self._tool_names) + return tools_collection + + def __enter__(self): + """Enter the context manager. Note that `__init__()` already starts the MCP server. + So tools should already be available. + """ + return self.tools + + def __exit__(self, exc_type, exc_value, traceback): + """Exit the context manager.""" + return self._adapter.__exit__(exc_type, exc_value, traceback) diff --git a/lib/crewai-tools/src/crewai_tools/adapters/rag_adapter.py b/lib/crewai-tools/src/crewai_tools/adapters/rag_adapter.py new file mode 100644 index 000000000..19a6fed62 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/adapters/rag_adapter.py @@ -0,0 +1,38 @@ +from typing import Any + +from crewai_tools.rag.core import RAG +from crewai_tools.tools.rag.rag_tool import Adapter + + +class RAGAdapter(Adapter): + def __init__( + self, + collection_name: str = "crewai_knowledge_base", + persist_directory: str | None = None, + embedding_model: str = "text-embedding-3-small", + top_k: int = 5, + embedding_api_key: str | None = None, + **embedding_kwargs, + ): + super().__init__() + + # Prepare embedding configuration + embedding_config = {"api_key": embedding_api_key, **embedding_kwargs} + + self._adapter = RAG( + collection_name=collection_name, + persist_directory=persist_directory, + embedding_model=embedding_model, + top_k=top_k, + embedding_config=embedding_config, + ) + + def query(self, question: str) -> str: # type: ignore[override] + return self._adapter.query(question) + + def add( + self, + *args: Any, + **kwargs: Any, + ) -> None: + self._adapter.add(*args, **kwargs) diff --git a/lib/crewai-tools/src/crewai_tools/adapters/tool_collection.py b/lib/crewai-tools/src/crewai_tools/adapters/tool_collection.py new file mode 100644 index 000000000..76df22dde --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/adapters/tool_collection.py @@ -0,0 +1,79 @@ +from __future__ import annotations + +from collections.abc import Callable +from typing import Generic, TypeVar + +from crewai.tools import BaseTool + + +T = TypeVar("T", bound=BaseTool) + + +class ToolCollection(list, Generic[T]): + """A collection of tools that can be accessed by index or name. + + This class extends the built-in list to provide dictionary-like + access to tools based on their name property. + + Usage: + tools = ToolCollection(list_of_tools) + # Access by index (regular list behavior) + first_tool = tools[0] + # Access by name (new functionality) + search_tool = tools["search"] + """ + + def __init__(self, tools: list[T] | None = None): + super().__init__(tools or []) + self._name_cache: dict[str, T] = {} + self._build_name_cache() + + def _build_name_cache(self) -> None: + self._name_cache = {tool.name.lower(): tool for tool in self} + + def __getitem__(self, key: int | str) -> T: # type: ignore[override] + if isinstance(key, str): + return self._name_cache[key.lower()] + return super().__getitem__(key) + + def append(self, tool: T) -> None: + super().append(tool) + self._name_cache[tool.name.lower()] = tool + + def extend(self, tools: list[T]) -> None: # type: ignore[override] + super().extend(tools) + self._build_name_cache() + + def insert(self, index: int, tool: T) -> None: # type: ignore[override] + super().insert(index, tool) + self._name_cache[tool.name.lower()] = tool + + def remove(self, tool: T) -> None: + super().remove(tool) + if tool.name.lower() in self._name_cache: + del self._name_cache[tool.name.lower()] + + def pop(self, index: int = -1) -> T: # type: ignore[override] + tool = super().pop(index) + if tool.name.lower() in self._name_cache: + del self._name_cache[tool.name.lower()] + return tool + + def filter_by_names(self, names: list[str] | None = None) -> ToolCollection[T]: + if names is None: + return self + + return ToolCollection( + [ + tool + for name in names + if (tool := self._name_cache.get(name.lower())) is not None + ] + ) + + def filter_where(self, func: Callable[[T], bool]) -> ToolCollection[T]: + return ToolCollection([tool for tool in self if func(tool)]) + + def clear(self) -> None: + super().clear() + self._name_cache.clear() diff --git a/lib/crewai-tools/src/crewai_tools/adapters/zapier_adapter.py b/lib/crewai-tools/src/crewai_tools/adapters/zapier_adapter.py new file mode 100644 index 000000000..48eb763c5 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/adapters/zapier_adapter.py @@ -0,0 +1,127 @@ +import logging +import os +from typing import Final, Literal + +from crewai.tools import BaseTool +from pydantic import Field, create_model +import requests + + +ACTIONS_URL: Final[Literal["https://actions.zapier.com/api/v2/ai-actions"]] = ( + "https://actions.zapier.com/api/v2/ai-actions" +) + +logger = logging.getLogger(__name__) + + +class ZapierActionTool(BaseTool): + """A tool that wraps a Zapier action.""" + + name: str = Field(description="Tool name") + description: str = Field(description="Tool description") + action_id: str = Field(description="Zapier action ID") + api_key: str = Field(description="Zapier API key") + + def _run(self, **kwargs) -> str: + """Execute the Zapier action.""" + headers = {"x-api-key": self.api_key, "Content-Type": "application/json"} + + instructions = kwargs.pop( + "instructions", "Execute this action with the provided parameters" + ) + + if not kwargs: + action_params = {"instructions": instructions, "params": {}} + else: + formatted_params = {} + for key, value in kwargs.items(): + formatted_params[key] = { + "value": value, + "mode": "guess", + } + action_params = {"instructions": instructions, "params": formatted_params} + + execute_url = f"{ACTIONS_URL}/{self.action_id}/execute/" + response = requests.request( + "POST", + execute_url, + headers=headers, + json=action_params, + timeout=30, + ) + + response.raise_for_status() + + return response.json() + + +class ZapierActionsAdapter: + """Adapter for Zapier Actions.""" + + def __init__(self, api_key: str | None = None): + self.api_key = api_key or os.getenv("ZAPIER_API_KEY") + if not self.api_key: + logger.error("Zapier Actions API key is required") + raise ValueError("Zapier Actions API key is required") + + def get_zapier_actions(self): + headers = { + "x-api-key": self.api_key, + } + response = requests.request( + "GET", + ACTIONS_URL, + headers=headers, + timeout=30, + ) + response.raise_for_status() + + return response.json() + + def tools(self) -> list[ZapierActionTool]: + """Convert Zapier actions to BaseTool instances.""" + actions_response = self.get_zapier_actions() + tools = [] + + for action in actions_response.get("results", []): + tool_name = ( + action["meta"]["action_label"] + .replace(" ", "_") + .replace(":", "") + .lower() + ) + + params = action.get("params", {}) + args_fields = { + "instructions": ( + str, + Field(description="Instructions for how to execute this action"), + ) + } + + for param_name, param_info in params.items(): + field_type = ( + str # Default to string, could be enhanced based on param_info + ) + field_description = ( + param_info.get("description", "") + if isinstance(param_info, dict) + else "" + ) + args_fields[param_name] = ( + field_type, + Field(description=field_description), + ) + + args_schema = create_model(f"{tool_name.title()}Schema", **args_fields) # type: ignore[call-overload] + + tool = ZapierActionTool( + name=tool_name, + description=action["description"], + action_id=action["id"], + api_key=self.api_key, + args_schema=args_schema, + ) + tools.append(tool) + + return tools diff --git a/lib/crewai-tools/src/crewai_tools/aws/__init__.py b/lib/crewai-tools/src/crewai_tools/aws/__init__.py new file mode 100644 index 000000000..e40f4c3fe --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/__init__.py @@ -0,0 +1,17 @@ +from crewai_tools.aws.bedrock import ( + BedrockInvokeAgentTool, + BedrockKBRetrieverTool, + create_browser_toolkit, + create_code_interpreter_toolkit, +) +from crewai_tools.aws.s3 import S3ReaderTool, S3WriterTool + + +__all__ = [ + "BedrockInvokeAgentTool", + "BedrockKBRetrieverTool", + "S3ReaderTool", + "S3WriterTool", + "create_browser_toolkit", + "create_code_interpreter_toolkit", +] diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/__init__.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/__init__.py new file mode 100644 index 000000000..5a656e853 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/__init__.py @@ -0,0 +1,14 @@ +from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool +from crewai_tools.aws.bedrock.browser import create_browser_toolkit +from crewai_tools.aws.bedrock.code_interpreter import create_code_interpreter_toolkit +from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import ( + BedrockKBRetrieverTool, +) + + +__all__ = [ + "BedrockInvokeAgentTool", + "BedrockKBRetrieverTool", + "create_browser_toolkit", + "create_code_interpreter_toolkit", +] diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/README.md b/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/README.md new file mode 100644 index 000000000..7aa43b65d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/README.md @@ -0,0 +1,181 @@ +# BedrockInvokeAgentTool + +The `BedrockInvokeAgentTool` enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows. + +## Installation + +```bash +pip install 'crewai[tools]' +``` + +## Requirements + +- AWS credentials configured (either through environment variables or AWS CLI) +- `boto3` and `python-dotenv` packages +- Access to Amazon Bedrock Agents + +## Usage + +Here's how to use the tool with a CrewAI agent: + +```python +from crewai import Agent, Task, Crew +from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool + +# Initialize the tool +agent_tool = BedrockInvokeAgentTool( + agent_id="your-agent-id", + agent_alias_id="your-agent-alias-id" +) + +# Create a CrewAI agent that uses the tool +aws_expert = Agent( + role='AWS Service Expert', + goal='Help users understand AWS services and quotas', + backstory='I am an expert in AWS services and can provide detailed information about them.', + tools=[agent_tool], + verbose=True +) + +# Create a task for the agent +quota_task = Task( + description="Find out the current service quotas for EC2 in us-west-2 and explain any recent changes.", + agent=aws_expert +) + +# Create a crew with the agent +crew = Crew( + agents=[aws_expert], + tasks=[quota_task], + verbose=2 +) + +# Run the crew +result = crew.kickoff() +print(result) +``` + +## Tool Arguments + +| Argument | Type | Required | Default | Description | +|----------|------|----------|---------|-------------| +| agent_id | str | Yes | None | The unique identifier of the Bedrock agent | +| agent_alias_id | str | Yes | None | The unique identifier of the agent alias | +| session_id | str | No | timestamp | The unique identifier of the session | +| enable_trace | bool | No | False | Whether to enable trace for debugging | +| end_session | bool | No | False | Whether to end the session after invocation | +| description | str | No | None | Custom description for the tool | + +## Environment Variables + +```bash +BEDROCK_AGENT_ID=your-agent-id # Alternative to passing agent_id +BEDROCK_AGENT_ALIAS_ID=your-agent-alias-id # Alternative to passing agent_alias_id +AWS_REGION=your-aws-region # Defaults to us-west-2 +AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication +AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication +``` + +## Advanced Usage + +### Multi-Agent Workflow with Session Management + +```python +from crewai import Agent, Task, Crew, Process +from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool + +# Initialize tools with session management +initial_tool = BedrockInvokeAgentTool( + agent_id="your-agent-id", + agent_alias_id="your-agent-alias-id", + session_id="custom-session-id" +) + +followup_tool = BedrockInvokeAgentTool( + agent_id="your-agent-id", + agent_alias_id="your-agent-alias-id", + session_id="custom-session-id" +) + +final_tool = BedrockInvokeAgentTool( + agent_id="your-agent-id", + agent_alias_id="your-agent-alias-id", + session_id="custom-session-id", + end_session=True +) + +# Create agents for different stages +researcher = Agent( + role='AWS Service Researcher', + goal='Gather information about AWS services', + backstory='I am specialized in finding detailed AWS service information.', + tools=[initial_tool] +) + +analyst = Agent( + role='Service Compatibility Analyst', + goal='Analyze service compatibility and requirements', + backstory='I analyze AWS services for compatibility and integration possibilities.', + tools=[followup_tool] +) + +summarizer = Agent( + role='Technical Documentation Writer', + goal='Create clear technical summaries', + backstory='I specialize in creating clear, concise technical documentation.', + tools=[final_tool] +) + +# Create tasks +research_task = Task( + description="Find all available AWS services in us-west-2 region.", + agent=researcher +) + +analysis_task = Task( + description="Analyze which services support IPv6 and their implementation requirements.", + agent=analyst +) + +summary_task = Task( + description="Create a summary of IPv6-compatible services and their key features.", + agent=summarizer +) + +# Create a crew with the agents and tasks +crew = Crew( + agents=[researcher, analyst, summarizer], + tasks=[research_task, analysis_task, summary_task], + process=Process.sequential, + verbose=2 +) + +# Run the crew +result = crew.kickoff() +``` + +## Use Cases + +### Hybrid Multi-Agent Collaborations +- Create workflows where CrewAI agents collaborate with managed Bedrock agents running as services in AWS +- Enable scenarios where sensitive data processing happens within your AWS environment while other agents operate externally +- Bridge on-premises CrewAI agents with cloud-based Bedrock agents for distributed intelligence workflows + +### Data Sovereignty and Compliance +- Keep data-sensitive agentic workflows within your AWS environment while allowing external CrewAI agents to orchestrate tasks +- Maintain compliance with data residency requirements by processing sensitive information only within your AWS account +- Enable secure multi-agent collaborations where some agents cannot access your organization's private data + +### Seamless AWS Service Integration +- Access any AWS service through Amazon Bedrock Actions without writing complex integration code +- Enable CrewAI agents to interact with AWS services through natural language requests +- Leverage pre-built Bedrock agent capabilities to interact with AWS services like Bedrock Knowledge Bases, Lambda, and more + +### Scalable Hybrid Agent Architectures +- Offload computationally intensive tasks to managed Bedrock agents while lightweight tasks run in CrewAI +- Scale agent processing by distributing workloads between local CrewAI agents and cloud-based Bedrock agents + +### Cross-Organizational Agent Collaboration +- Enable secure collaboration between your organization's CrewAI agents and partner organizations' Bedrock agents +- Create workflows where external expertise from Bedrock agents can be incorporated without exposing sensitive data +- Build agent ecosystems that span organizational boundaries while maintaining security and data control \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/__init__.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/__init__.py new file mode 100644 index 000000000..372e4dd7c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/__init__.py @@ -0,0 +1,4 @@ +from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool + + +__all__ = ["BedrockInvokeAgentTool"] diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/invoke_agent_tool.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/invoke_agent_tool.py new file mode 100644 index 000000000..f8271dea9 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/invoke_agent_tool.py @@ -0,0 +1,187 @@ +from datetime import datetime, timezone +import json +import os +import time + +from crewai.tools import BaseTool +from dotenv import load_dotenv +from pydantic import BaseModel, Field + +from crewai_tools.aws.bedrock.exceptions import ( + BedrockAgentError, + BedrockValidationError, +) + + +# Load environment variables from .env file +load_dotenv() + + +class BedrockInvokeAgentToolInput(BaseModel): + """Input schema for BedrockInvokeAgentTool.""" + + query: str = Field(..., description="The query to send to the agent") + + +class BedrockInvokeAgentTool(BaseTool): + name: str = "Bedrock Agent Invoke Tool" + description: str = "An agent responsible for policy analysis." + args_schema: type[BaseModel] = BedrockInvokeAgentToolInput + agent_id: str | None = None + agent_alias_id: str | None = None + session_id: str | None = None + enable_trace: bool = False + end_session: bool = False + package_dependencies: list[str] = Field(default_factory=lambda: ["boto3"]) + + def __init__( + self, + agent_id: str | None = None, + agent_alias_id: str | None = None, + session_id: str | None = None, + enable_trace: bool = False, + end_session: bool = False, + description: str | None = None, + **kwargs, + ): + """Initialize the BedrockInvokeAgentTool with agent configuration. + + Args: + agent_id (str): The unique identifier of the Bedrock agent + agent_alias_id (str): The unique identifier of the agent alias + session_id (str): The unique identifier of the session + enable_trace (bool): Whether to enable trace for the agent invocation + end_session (bool): Whether to end the session with the agent + description (Optional[str]): Custom description for the tool + """ + super().__init__(**kwargs) + + # Get values from environment variables if not provided + self.agent_id = agent_id or os.getenv("BEDROCK_AGENT_ID") + self.agent_alias_id = agent_alias_id or os.getenv("BEDROCK_AGENT_ALIAS_ID") + self.session_id = session_id or str( + int(time.time()) + ) # Use timestamp as session ID if not provided + self.enable_trace = enable_trace + self.end_session = end_session + + # Update the description if provided + if description: + self.description = description + + # Validate parameters + self._validate_parameters() + + def _validate_parameters(self): + """Validate the parameters according to AWS API requirements.""" + try: + # Validate agent_id + if not self.agent_id: + raise BedrockValidationError("agent_id cannot be empty") + if not isinstance(self.agent_id, str): + raise BedrockValidationError("agent_id must be a string") + + # Validate agent_alias_id + if not self.agent_alias_id: + raise BedrockValidationError("agent_alias_id cannot be empty") + if not isinstance(self.agent_alias_id, str): + raise BedrockValidationError("agent_alias_id must be a string") + + # Validate session_id if provided + if self.session_id and not isinstance(self.session_id, str): + raise BedrockValidationError("session_id must be a string") + + except BedrockValidationError as e: + raise BedrockValidationError(f"Parameter validation failed: {e!s}") from e + + def _run(self, query: str) -> str: + try: + import boto3 + from botocore.exceptions import ClientError + except ImportError as e: + raise ImportError( + "`boto3` package not found, please run `uv add boto3`" + ) from e + + try: + # Initialize the Bedrock Agent Runtime client + bedrock_agent = boto3.client( + "bedrock-agent-runtime", + region_name=os.getenv( + "AWS_REGION", os.getenv("AWS_DEFAULT_REGION", "us-west-2") + ), + ) + + # Format the prompt with current time + current_utc = datetime.now(timezone.utc) + prompt = f""" +The current time is: {current_utc} + +Below is the users query or task. Complete it and answer it consicely and to the point: +{query} +""" + + # Invoke the agent + response = bedrock_agent.invoke_agent( + agentId=self.agent_id, + agentAliasId=self.agent_alias_id, + sessionId=self.session_id, + inputText=prompt, + enableTrace=self.enable_trace, + endSession=self.end_session, + ) + + # Process the response + completion = "" + + # Check if response contains a completion field + if "completion" in response: + # Process streaming response format + for event in response.get("completion", []): + if "chunk" in event and "bytes" in event["chunk"]: + chunk_bytes = event["chunk"]["bytes"] + if isinstance(chunk_bytes, (bytes, bytearray)): + completion += chunk_bytes.decode("utf-8") + else: + completion += str(chunk_bytes) + + # If no completion found in streaming format, try direct format + if not completion and "chunk" in response and "bytes" in response["chunk"]: + chunk_bytes = response["chunk"]["bytes"] + if isinstance(chunk_bytes, (bytes, bytearray)): + completion = chunk_bytes.decode("utf-8") + else: + completion = str(chunk_bytes) + + # If still no completion, return debug info + if not completion: + debug_info = { + "error": "Could not extract completion from response", + "response_keys": list(response.keys()), + } + + # Add more debug info + if "chunk" in response: + debug_info["chunk_keys"] = list(response["chunk"].keys()) + + raise BedrockAgentError( + f"Failed to extract completion: {json.dumps(debug_info, indent=2)}" + ) + + return completion + + except ClientError as e: + error_code = "Unknown" + error_message = str(e) + + # Try to extract error code if available + if hasattr(e, "response") and "Error" in e.response: + error_code = e.response["Error"].get("Code", "Unknown") + error_message = e.response["Error"].get("Message", str(e)) + + raise BedrockAgentError(f"Error ({error_code}): {error_message}") from e + except BedrockAgentError: + # Re-raise BedrockAgentError exceptions + raise + except Exception as e: + raise BedrockAgentError(f"Unexpected error: {e!s}") from e diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/README.md b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/README.md new file mode 100644 index 000000000..7f0188bbb --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/README.md @@ -0,0 +1,158 @@ +# AWS Bedrock Browser Tools + +This toolkit provides a set of tools for interacting with web browsers through AWS Bedrock Browser. It enables your CrewAI agents to navigate websites, extract content, click elements, and more. + +## Features + +- Navigate to URLs and browse the web +- Extract text and hyperlinks from pages +- Click on elements using CSS selectors +- Navigate back through browser history +- Get information about the current webpage +- Multiple browser sessions with thread-based isolation + +## Installation + +Ensure you have the necessary dependencies: + +```bash +uv add crewai-tools bedrock-agentcore beautifulsoup4 playwright nest-asyncio +``` + +## Usage + +### Basic Usage + +```python +from crewai import Agent, Task, Crew, LLM +from crewai_tools.aws.bedrock.browser import create_browser_toolkit + +# Create the browser toolkit +toolkit, browser_tools = create_browser_toolkit(region="us-west-2") + +# Create the Bedrock LLM +llm = LLM( + model="bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0", + region_name="us-west-2", +) + +# Create a CrewAI agent that uses the browser tools +research_agent = Agent( + role="Web Researcher", + goal="Research and summarize web content", + backstory="You're an expert at finding information online.", + tools=browser_tools, + llm=llm +) + +# Create a task for the agent +research_task = Task( + description="Navigate to https://example.com and extract all text content. Summarize the main points.", + expected_output="A list of bullet points containing the most important information on https://example.com. Plus, a description of the tool calls used, and actions performed to get to the page.", + agent=research_agent +) + +# Create and run the crew +crew = Crew( + agents=[research_agent], + tasks=[research_task] +) +result = crew.kickoff() + +print(f"\n***Final result:***\n\n{result}") + +# Clean up browser resources when done +toolkit.sync_cleanup() +``` + +### Available Tools + +The toolkit provides the following tools: + +1. `navigate_browser` - Navigate to a URL +2. `click_element` - Click on an element using CSS selectors +3. `extract_text` - Extract all text from the current webpage +4. `extract_hyperlinks` - Extract all hyperlinks from the current webpage +5. `get_elements` - Get elements matching a CSS selector +6. `navigate_back` - Navigate to the previous page +7. `current_webpage` - Get information about the current webpage + +### Advanced Usage (with async) + +```python +import asyncio +from crewai import Agent, Task, Crew, LLM +from crewai_tools.aws.bedrock.browser import create_browser_toolkit + +async def main(): + + # Create the browser toolkit with specific AWS region + toolkit, browser_tools = create_browser_toolkit(region="us-west-2") + tools_by_name = toolkit.get_tools_by_name() + + # Create the Bedrock LLM + llm = LLM( + model="bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0", + region_name="us-west-2", + ) + + # Create agents with specific tools + navigator_agent = Agent( + role="Navigator", + goal="Find specific information across websites", + backstory="You navigate through websites to locate information.", + tools=[ + tools_by_name["navigate_browser"], + tools_by_name["click_element"], + tools_by_name["navigate_back"] + ], + llm=llm + ) + + content_agent = Agent( + role="Content Extractor", + goal="Extract and analyze webpage content", + backstory="You extract and analyze content from webpages.", + tools=[ + tools_by_name["extract_text"], + tools_by_name["extract_hyperlinks"], + tools_by_name["get_elements"] + ], + llm=llm + ) + + # Create tasks for the agents + navigation_task = Task( + description="Navigate to https://example.com, then click on the the 'More information...' link.", + expected_output="The status of the tool calls for this task.", + agent=navigator_agent, + ) + + extraction_task = Task( + description="Extract all text from the current page and summarize it.", + expected_output="The summary of the page, and a description of the tool calls used, and actions performed to get to the page.", + agent=content_agent, + ) + + # Create and run the crew + crew = Crew( + agents=[navigator_agent, content_agent], + tasks=[navigation_task, extraction_task] + ) + + result = await crew.kickoff_async() + + # Clean up browser resources when done + toolkit.sync_cleanup() + + return result + +if __name__ == "__main__": + result = asyncio.run(main()) + print(f"\n***Final result:***\n\n{result}") +``` + +## Requirements + +- AWS account with access to Bedrock AgentCore API +- Properly configured AWS credentials \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/__init__.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/__init__.py new file mode 100644 index 000000000..dce429610 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/__init__.py @@ -0,0 +1,7 @@ +from crewai_tools.aws.bedrock.browser.browser_toolkit import ( + BrowserToolkit, + create_browser_toolkit, +) + + +__all__ = ["BrowserToolkit", "create_browser_toolkit"] diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/browser_session_manager.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/browser_session_manager.py new file mode 100644 index 000000000..af273a5d0 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/browser_session_manager.py @@ -0,0 +1,255 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + + +if TYPE_CHECKING: + from bedrock_agentcore.tools.browser_client import BrowserClient + from playwright.async_api import Browser as AsyncBrowser + from playwright.sync_api import Browser as SyncBrowser + +logger = logging.getLogger(__name__) + + +class BrowserSessionManager: + """Manages browser sessions for different threads. + + This class maintains separate browser sessions for different threads, + enabling concurrent usage of browsers in multi-threaded environments. + Browsers are created lazily only when needed by tools. + """ + + def __init__(self, region: str = "us-west-2"): + """Initialize the browser session manager. + + Args: + region: AWS region for browser client + """ + self.region = region + self._async_sessions: dict[str, tuple[BrowserClient, AsyncBrowser]] = {} + self._sync_sessions: dict[str, tuple[BrowserClient, SyncBrowser]] = {} + + async def get_async_browser(self, thread_id: str) -> AsyncBrowser: + """Get or create an async browser for the specified thread. + + Args: + thread_id: Unique identifier for the thread requesting the browser + + Returns: + An async browser instance specific to the thread + """ + if thread_id in self._async_sessions: + return self._async_sessions[thread_id][1] + + return await self._create_async_browser_session(thread_id) + + def get_sync_browser(self, thread_id: str) -> SyncBrowser: + """Get or create a sync browser for the specified thread. + + Args: + thread_id: Unique identifier for the thread requesting the browser + + Returns: + A sync browser instance specific to the thread + """ + if thread_id in self._sync_sessions: + return self._sync_sessions[thread_id][1] + + return self._create_sync_browser_session(thread_id) + + async def _create_async_browser_session(self, thread_id: str) -> AsyncBrowser: + """Create a new async browser session for the specified thread. + + Args: + thread_id: Unique identifier for the thread + + Returns: + The newly created async browser instance + + Raises: + Exception: If browser session creation fails + """ + from bedrock_agentcore.tools.browser_client import BrowserClient + + browser_client = BrowserClient(region=self.region) + + try: + # Start browser session + browser_client.start() + + # Get WebSocket connection info + ws_url, headers = browser_client.generate_ws_headers() + + logger.info( + f"Connecting to async WebSocket endpoint for thread {thread_id}: {ws_url}" + ) + + from playwright.async_api import async_playwright + + # Connect to browser using Playwright + playwright = await async_playwright().start() + browser = await playwright.chromium.connect_over_cdp( + endpoint_url=ws_url, headers=headers, timeout=30000 + ) + logger.info( + f"Successfully connected to async browser for thread {thread_id}" + ) + + # Store session resources + self._async_sessions[thread_id] = (browser_client, browser) + + return browser + + except Exception as e: + logger.error( + f"Failed to create async browser session for thread {thread_id}: {e}" + ) + + # Clean up resources if session creation fails + if browser_client: + try: + browser_client.stop() + except Exception as cleanup_error: + logger.warning(f"Error cleaning up browser client: {cleanup_error}") + + raise + + def _create_sync_browser_session(self, thread_id: str) -> SyncBrowser: + """Create a new sync browser session for the specified thread. + + Args: + thread_id: Unique identifier for the thread + + Returns: + The newly created sync browser instance + + Raises: + Exception: If browser session creation fails + """ + from bedrock_agentcore.tools.browser_client import BrowserClient + + browser_client = BrowserClient(region=self.region) + + try: + # Start browser session + browser_client.start() + + # Get WebSocket connection info + ws_url, headers = browser_client.generate_ws_headers() + + logger.info( + f"Connecting to sync WebSocket endpoint for thread {thread_id}: {ws_url}" + ) + + from playwright.sync_api import sync_playwright + + # Connect to browser using Playwright + playwright = sync_playwright().start() + browser = playwright.chromium.connect_over_cdp( + endpoint_url=ws_url, headers=headers, timeout=30000 + ) + logger.info( + f"Successfully connected to sync browser for thread {thread_id}" + ) + + # Store session resources + self._sync_sessions[thread_id] = (browser_client, browser) + + return browser + + except Exception as e: + logger.error( + f"Failed to create sync browser session for thread {thread_id}: {e}" + ) + + # Clean up resources if session creation fails + if browser_client: + try: + browser_client.stop() + except Exception as cleanup_error: + logger.warning(f"Error cleaning up browser client: {cleanup_error}") + + raise + + async def close_async_browser(self, thread_id: str) -> None: + """Close the async browser session for the specified thread. + + Args: + thread_id: Unique identifier for the thread + """ + if thread_id not in self._async_sessions: + logger.warning(f"No async browser session found for thread {thread_id}") + return + + browser_client, browser = self._async_sessions[thread_id] + + # Close browser + if browser: + try: + await browser.close() + except Exception as e: + logger.warning( + f"Error closing async browser for thread {thread_id}: {e}" + ) + + # Stop browser client + if browser_client: + try: + browser_client.stop() + except Exception as e: + logger.warning( + f"Error stopping browser client for thread {thread_id}: {e}" + ) + + # Remove session from dictionary + del self._async_sessions[thread_id] + logger.info(f"Async browser session cleaned up for thread {thread_id}") + + def close_sync_browser(self, thread_id: str) -> None: + """Close the sync browser session for the specified thread. + + Args: + thread_id: Unique identifier for the thread + """ + if thread_id not in self._sync_sessions: + logger.warning(f"No sync browser session found for thread {thread_id}") + return + + browser_client, browser = self._sync_sessions[thread_id] + + # Close browser + if browser: + try: + browser.close() + except Exception as e: + logger.warning( + f"Error closing sync browser for thread {thread_id}: {e}" + ) + + # Stop browser client + if browser_client: + try: + browser_client.stop() + except Exception as e: + logger.warning( + f"Error stopping browser client for thread {thread_id}: {e}" + ) + + # Remove session from dictionary + del self._sync_sessions[thread_id] + logger.info(f"Sync browser session cleaned up for thread {thread_id}") + + async def close_all_browsers(self) -> None: + """Close all browser sessions.""" + # Close all async browsers + async_thread_ids = list(self._async_sessions.keys()) + for thread_id in async_thread_ids: + await self.close_async_browser(thread_id) + + # Close all sync browsers + sync_thread_ids = list(self._sync_sessions.keys()) + for thread_id in sync_thread_ids: + self.close_sync_browser(thread_id) + + logger.info("All browser sessions closed") diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/browser_toolkit.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/browser_toolkit.py new file mode 100644 index 000000000..2e1ddcc74 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/browser_toolkit.py @@ -0,0 +1,612 @@ +"""Toolkit for navigating web with AWS browser.""" + +import asyncio +import json +import logging +from typing import Any +from urllib.parse import urlparse + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + +from crewai_tools.aws.bedrock.browser.browser_session_manager import ( + BrowserSessionManager, +) +from crewai_tools.aws.bedrock.browser.utils import aget_current_page, get_current_page + + +logger = logging.getLogger(__name__) + + +# Input schemas +class NavigateToolInput(BaseModel): + """Input for NavigateTool.""" + + url: str = Field(description="URL to navigate to") + thread_id: str = Field( + default="default", description="Thread ID for the browser session" + ) + + +class ClickToolInput(BaseModel): + """Input for ClickTool.""" + + selector: str = Field(description="CSS selector for the element to click on") + thread_id: str = Field( + default="default", description="Thread ID for the browser session" + ) + + +class GetElementsToolInput(BaseModel): + """Input for GetElementsTool.""" + + selector: str = Field(description="CSS selector for elements to get") + thread_id: str = Field( + default="default", description="Thread ID for the browser session" + ) + + +class ExtractTextToolInput(BaseModel): + """Input for ExtractTextTool.""" + + thread_id: str = Field( + default="default", description="Thread ID for the browser session" + ) + + +class ExtractHyperlinksToolInput(BaseModel): + """Input for ExtractHyperlinksTool.""" + + thread_id: str = Field( + default="default", description="Thread ID for the browser session" + ) + + +class NavigateBackToolInput(BaseModel): + """Input for NavigateBackTool.""" + + thread_id: str = Field( + default="default", description="Thread ID for the browser session" + ) + + +class CurrentWebPageToolInput(BaseModel): + """Input for CurrentWebPageTool.""" + + thread_id: str = Field( + default="default", description="Thread ID for the browser session" + ) + + +# Base tool class +class BrowserBaseTool(BaseTool): + """Base class for browser tools.""" + + def __init__(self, session_manager: BrowserSessionManager): # type: ignore[call-arg] + """Initialize with a session manager.""" + super().__init__() # type: ignore[call-arg] + self._session_manager = session_manager + + if self._is_in_asyncio_loop() and hasattr(self, "_arun"): + self._original_run = self._run + + # Override _run to use _arun when in an asyncio loop + def patched_run(*args, **kwargs): + try: + import nest_asyncio # type: ignore[import-untyped] + + loop = asyncio.get_event_loop() + nest_asyncio.apply(loop) + return asyncio.get_event_loop().run_until_complete( + self._arun(*args, **kwargs) + ) + except Exception as e: + return f"Error in patched _run: {e!s}" + + self._run = patched_run # type: ignore[method-assign] + + async def get_async_page(self, thread_id: str) -> Any: + """Get or create a page for the specified thread.""" + browser = await self._session_manager.get_async_browser(thread_id) + return await aget_current_page(browser) + + def get_sync_page(self, thread_id: str) -> Any: + """Get or create a page for the specified thread.""" + browser = self._session_manager.get_sync_browser(thread_id) + return get_current_page(browser) + + def _is_in_asyncio_loop(self) -> bool: + """Check if we're currently in an asyncio event loop.""" + try: + loop = asyncio.get_event_loop() + return loop.is_running() + except RuntimeError: + return False + + +# Tool classes +class NavigateTool(BrowserBaseTool): + """Tool for navigating a browser to a URL.""" + + name: str = "navigate_browser" + description: str = "Navigate a browser to the specified URL" + args_schema: type[BaseModel] = NavigateToolInput + + def _run(self, url: str, thread_id: str = "default", **kwargs) -> str: + """Use the sync tool.""" + try: + # Get page for this thread + page = self.get_sync_page(thread_id) + + # Validate URL scheme + parsed_url = urlparse(url) + if parsed_url.scheme not in ("http", "https"): + raise ValueError("URL scheme must be 'http' or 'https'") + + # Navigate to URL + response = page.goto(url) + status = response.status if response else "unknown" + return f"Navigating to {url} returned status code {status}" + except Exception as e: + return f"Error navigating to {url}: {e!s}" + + async def _arun(self, url: str, thread_id: str = "default", **kwargs) -> str: + """Use the async tool.""" + try: + # Get page for this thread + page = await self.get_async_page(thread_id) + + # Validate URL scheme + parsed_url = urlparse(url) + if parsed_url.scheme not in ("http", "https"): + raise ValueError("URL scheme must be 'http' or 'https'") + + # Navigate to URL + response = await page.goto(url) + status = response.status if response else "unknown" + return f"Navigating to {url} returned status code {status}" + except Exception as e: + return f"Error navigating to {url}: {e!s}" + + +class ClickTool(BrowserBaseTool): + """Tool for clicking on an element with the given CSS selector.""" + + name: str = "click_element" + description: str = "Click on an element with the given CSS selector" + args_schema: type[BaseModel] = ClickToolInput + + visible_only: bool = True + """Whether to consider only visible elements.""" + playwright_strict: bool = False + """Whether to employ Playwright's strict mode when clicking on elements.""" + playwright_timeout: float = 1_000 + """Timeout (in ms) for Playwright to wait for element to be ready.""" + + def _selector_effective(self, selector: str) -> str: + if not self.visible_only: + return selector + return f"{selector} >> visible=1" + + def _run(self, selector: str, thread_id: str = "default", **kwargs) -> str: + """Use the sync tool.""" + try: + # Get the current page + page = self.get_sync_page(thread_id) + + # Click on the element + selector_effective = self._selector_effective(selector=selector) + from playwright.sync_api import TimeoutError as PlaywrightTimeoutError + + try: + page.click( + selector_effective, + strict=self.playwright_strict, + timeout=self.playwright_timeout, + ) + except PlaywrightTimeoutError: + return f"Unable to click on element '{selector}'" + except Exception as click_error: + return f"Unable to click on element '{selector}': {click_error!s}" + + return f"Clicked element '{selector}'" + except Exception as e: + return f"Error clicking on element: {e!s}" + + async def _arun(self, selector: str, thread_id: str = "default", **kwargs) -> str: + """Use the async tool.""" + try: + # Get the current page + page = await self.get_async_page(thread_id) + + # Click on the element + selector_effective = self._selector_effective(selector=selector) + from playwright.async_api import TimeoutError as PlaywrightTimeoutError + + try: + await page.click( + selector_effective, + strict=self.playwright_strict, + timeout=self.playwright_timeout, + ) + except PlaywrightTimeoutError: + return f"Unable to click on element '{selector}'" + except Exception as click_error: + return f"Unable to click on element '{selector}': {click_error!s}" + + return f"Clicked element '{selector}'" + except Exception as e: + return f"Error clicking on element: {e!s}" + + +class NavigateBackTool(BrowserBaseTool): + """Tool for navigating back in browser history.""" + + name: str = "navigate_back" + description: str = "Navigate back to the previous page" + args_schema: type[BaseModel] = NavigateBackToolInput + + def _run(self, thread_id: str = "default", **kwargs) -> str: + """Use the sync tool.""" + try: + # Get the current page + page = self.get_sync_page(thread_id) + + # Navigate back + try: + page.go_back() + return "Navigated back to the previous page" + except Exception as nav_error: + return f"Unable to navigate back: {nav_error!s}" + except Exception as e: + return f"Error navigating back: {e!s}" + + async def _arun(self, thread_id: str = "default", **kwargs) -> str: + """Use the async tool.""" + try: + # Get the current page + page = await self.get_async_page(thread_id) + + # Navigate back + try: + await page.go_back() + return "Navigated back to the previous page" + except Exception as nav_error: + return f"Unable to navigate back: {nav_error!s}" + except Exception as e: + return f"Error navigating back: {e!s}" + + +class ExtractTextTool(BrowserBaseTool): + """Tool for extracting text from a webpage.""" + + name: str = "extract_text" + description: str = "Extract all the text on the current webpage" + args_schema: type[BaseModel] = ExtractTextToolInput + + def _run(self, thread_id: str = "default", **kwargs) -> str: + """Use the sync tool.""" + try: + # Import BeautifulSoup + try: + from bs4 import BeautifulSoup + except ImportError: + return ( + "The 'beautifulsoup4' package is required to use this tool." + " Please install it with 'pip install beautifulsoup4'." + ) + + # Get the current page + page = self.get_sync_page(thread_id) + + # Extract text + content = page.content() + soup = BeautifulSoup(content, "html.parser") + return soup.get_text(separator="\n").strip() + except Exception as e: + return f"Error extracting text: {e!s}" + + async def _arun(self, thread_id: str = "default", **kwargs) -> str: + """Use the async tool.""" + try: + # Import BeautifulSoup + try: + from bs4 import BeautifulSoup + except ImportError: + return ( + "The 'beautifulsoup4' package is required to use this tool." + " Please install it with 'pip install beautifulsoup4'." + ) + + # Get the current page + page = await self.get_async_page(thread_id) + + # Extract text + content = await page.content() + soup = BeautifulSoup(content, "html.parser") + return soup.get_text(separator="\n").strip() + except Exception as e: + return f"Error extracting text: {e!s}" + + +class ExtractHyperlinksTool(BrowserBaseTool): + """Tool for extracting hyperlinks from a webpage.""" + + name: str = "extract_hyperlinks" + description: str = "Extract all hyperlinks on the current webpage" + args_schema: type[BaseModel] = ExtractHyperlinksToolInput + + def _run(self, thread_id: str = "default", **kwargs) -> str: + """Use the sync tool.""" + try: + # Import BeautifulSoup + try: + from bs4 import BeautifulSoup + except ImportError: + return ( + "The 'beautifulsoup4' package is required to use this tool." + " Please install it with 'pip install beautifulsoup4'." + ) + + # Get the current page + page = self.get_sync_page(thread_id) + + # Extract hyperlinks + content = page.content() + soup = BeautifulSoup(content, "html.parser") + links = [] + for link in soup.find_all("a", href=True): + text = link.get_text().strip() + href = link["href"] + if href.startswith(("http", "https")): # type: ignore[union-attr] + links.append({"text": text, "url": href}) + + if not links: + return "No hyperlinks found on the current page." + + return json.dumps(links, indent=2) + except Exception as e: + return f"Error extracting hyperlinks: {e!s}" + + async def _arun(self, thread_id: str = "default", **kwargs) -> str: + """Use the async tool.""" + try: + # Import BeautifulSoup + try: + from bs4 import BeautifulSoup + except ImportError: + return ( + "The 'beautifulsoup4' package is required to use this tool." + " Please install it with 'pip install beautifulsoup4'." + ) + + # Get the current page + page = await self.get_async_page(thread_id) + + # Extract hyperlinks + content = await page.content() + soup = BeautifulSoup(content, "html.parser") + links = [] + for link in soup.find_all("a", href=True): + text = link.get_text().strip() + href = link["href"] + if href.startswith(("http", "https")): # type: ignore[union-attr] + links.append({"text": text, "url": href}) + + if not links: + return "No hyperlinks found on the current page." + + return json.dumps(links, indent=2) + except Exception as e: + return f"Error extracting hyperlinks: {e!s}" + + +class GetElementsTool(BrowserBaseTool): + """Tool for getting elements from a webpage.""" + + name: str = "get_elements" + description: str = "Get elements from the webpage using a CSS selector" + args_schema: type[BaseModel] = GetElementsToolInput + + def _run(self, selector: str, thread_id: str = "default", **kwargs) -> str: + """Use the sync tool.""" + try: + # Get the current page + page = self.get_sync_page(thread_id) + + # Get elements + elements = page.query_selector_all(selector) + if not elements: + return f"No elements found with selector '{selector}'" + + elements_text = [] + for i, element in enumerate(elements): + text = element.text_content() + elements_text.append(f"Element {i + 1}: {text.strip()}") + + return "\n".join(elements_text) + except Exception as e: + return f"Error getting elements: {e!s}" + + async def _arun(self, selector: str, thread_id: str = "default", **kwargs) -> str: + """Use the async tool.""" + try: + # Get the current page + page = await self.get_async_page(thread_id) + + # Get elements + elements = await page.query_selector_all(selector) + if not elements: + return f"No elements found with selector '{selector}'" + + elements_text = [] + for i, element in enumerate(elements): + text = await element.text_content() + elements_text.append(f"Element {i + 1}: {text.strip()}") + + return "\n".join(elements_text) + except Exception as e: + return f"Error getting elements: {e!s}" + + +class CurrentWebPageTool(BrowserBaseTool): + """Tool for getting information about the current webpage.""" + + name: str = "current_webpage" + description: str = "Get information about the current webpage" + args_schema: type[BaseModel] = CurrentWebPageToolInput + + def _run(self, thread_id: str = "default", **kwargs) -> str: + """Use the sync tool.""" + try: + # Get the current page + page = self.get_sync_page(thread_id) + + # Get information + url = page.url + title = page.title() + return f"URL: {url}\nTitle: {title}" + except Exception as e: + return f"Error getting current webpage info: {e!s}" + + async def _arun(self, thread_id: str = "default", **kwargs) -> str: + """Use the async tool.""" + try: + # Get the current page + page = await self.get_async_page(thread_id) + + # Get information + url = page.url + title = await page.title() + return f"URL: {url}\nTitle: {title}" + except Exception as e: + return f"Error getting current webpage info: {e!s}" + + +class BrowserToolkit: + """Toolkit for navigating web with AWS Bedrock browser. + + This toolkit provides a set of tools for working with a remote browser + and supports multiple threads by maintaining separate browser sessions + for each thread ID. Browsers are created lazily only when needed. + + Example: + ```python + from crewai import Agent, Task, Crew + from crewai_tools.aws.bedrock.browser import create_browser_toolkit + + # Create the browser toolkit + toolkit, browser_tools = create_browser_toolkit(region="us-west-2") + + # Create a CrewAI agent that uses the browser tools + research_agent = Agent( + role="Web Researcher", + goal="Research and summarize web content", + backstory="You're an expert at finding information online.", + tools=browser_tools, + ) + + # Create a task for the agent + research_task = Task( + description="Navigate to https://example.com and extract all text content. Summarize the main points.", + agent=research_agent, + ) + + # Create and run the crew + crew = Crew(agents=[research_agent], tasks=[research_task]) + result = crew.kickoff() + + # Clean up browser resources when done + import asyncio + + asyncio.run(toolkit.cleanup()) + ``` + """ + + def __init__(self, region: str = "us-west-2"): + """Initialize the toolkit. + + Args: + region: AWS region for the browser client + """ + self.region = region + self.session_manager = BrowserSessionManager(region=region) + self.tools: list[BaseTool] = [] + self._nest_current_loop() + self._setup_tools() + + def _nest_current_loop(self): + """Apply nest_asyncio if we're in an asyncio loop.""" + try: + loop = asyncio.get_event_loop() + if loop.is_running(): + try: + import nest_asyncio + + nest_asyncio.apply(loop) + except Exception as e: + logger.warning(f"Failed to apply nest_asyncio: {e!s}") + except RuntimeError: + pass + + def _setup_tools(self) -> None: + """Initialize tools without creating any browsers.""" + self.tools = [ + NavigateTool(session_manager=self.session_manager), + ClickTool(session_manager=self.session_manager), + NavigateBackTool(session_manager=self.session_manager), + ExtractTextTool(session_manager=self.session_manager), + ExtractHyperlinksTool(session_manager=self.session_manager), + GetElementsTool(session_manager=self.session_manager), + CurrentWebPageTool(session_manager=self.session_manager), + ] + + def get_tools(self) -> list[BaseTool]: + """Get the list of browser tools. + + Returns: + List of CrewAI tools + """ + return self.tools + + def get_tools_by_name(self) -> dict[str, BaseTool]: + """Get a dictionary of tools mapped by their names. + + Returns: + Dictionary of {tool_name: tool} + """ + return {tool.name: tool for tool in self.tools} + + async def cleanup(self) -> None: + """Clean up all browser sessions asynchronously.""" + await self.session_manager.close_all_browsers() + logger.info("All browser sessions cleaned up") + + def sync_cleanup(self) -> None: + """Clean up all browser sessions from synchronous code.""" + import asyncio + + try: + loop = asyncio.get_event_loop() + if loop.is_running(): + asyncio.create_task(self.cleanup()) # noqa: RUF006 + else: + loop.run_until_complete(self.cleanup()) + except RuntimeError: + asyncio.run(self.cleanup()) + + +def create_browser_toolkit( + region: str = "us-west-2", +) -> tuple[BrowserToolkit, list[BaseTool]]: + """Create a BrowserToolkit. + + Args: + region: AWS region for browser client + + Returns: + Tuple of (toolkit, tools) + """ + toolkit = BrowserToolkit(region=region) + tools = toolkit.get_tools() + return toolkit, tools diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/utils.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/utils.py new file mode 100644 index 000000000..14cad3981 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/utils.py @@ -0,0 +1,44 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + + +if TYPE_CHECKING: + from playwright.async_api import Browser as AsyncBrowser, Page as AsyncPage + from playwright.sync_api import Browser as SyncBrowser, Page as SyncPage + + +async def aget_current_page(browser: AsyncBrowser | Any) -> AsyncPage: + """Asynchronously get the current page of the browser. + + Args: + browser: The browser (AsyncBrowser) to get the current page from. + + Returns: + AsyncPage: The current page. + """ + if not browser.contexts: + context = await browser.new_context() + return await context.new_page() + context = browser.contexts[0] + if not context.pages: + return await context.new_page() + return context.pages[-1] + + +def get_current_page(browser: SyncBrowser | Any) -> SyncPage: + """Get the current page of the browser. + + Args: + browser: The browser to get the current page from. + + Returns: + SyncPage: The current page. + """ + if not browser.contexts: + context = browser.new_context() + return context.new_page() + context = browser.contexts[0] + if not context.pages: + return context.new_page() + return context.pages[-1] diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/README.md b/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/README.md new file mode 100644 index 000000000..92e8ec5b2 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/README.md @@ -0,0 +1,217 @@ +# AWS Bedrock Code Interpreter Tools + +This toolkit provides a set of tools for interacting with the AWS Bedrock Code Interpreter environment. It enables your CrewAI agents to execute code, run shell commands, manage files, and perform computational tasks in a secure, isolated environment. + +## Features + +- Execute code in various languages (primarily Python) +- Run shell commands in the environment +- Read, write, list, and delete files +- Manage long-running tasks asynchronously +- Multiple code interpreter sessions with thread-based isolation + +## Installation + +Ensure you have the necessary dependencies: + +```bash +uv add crewai-tools bedrock-agentcore +``` + +## Usage + +### Basic Usage + +```python +from crewai import Agent, Task, Crew, LLM +from crewai_tools.aws import create_code_interpreter_toolkit + +# Create the code interpreter toolkit +toolkit, code_tools = create_code_interpreter_toolkit(region="us-west-2") + +# Create the Bedrock LLM +llm = LLM( + model="bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0", + region_name="us-west-2", +) + +# Create a CrewAI agent that uses the code interpreter tools +developer_agent = Agent( + role="Python Developer", + goal="Create and execute Python code to solve problems.", + backstory="You're a skilled Python developer with expertise in data analysis.", + tools=code_tools, + llm=llm +) + +# Create a task for the agent +coding_task = Task( + description="Write a Python function that calculates the factorial of a number and test it. Do not use any imports from outside the Python standard library.", + expected_output="The Python function created, and the test results.", + agent=developer_agent +) + +# Create and run the crew +crew = Crew( + agents=[developer_agent], + tasks=[coding_task] +) +result = crew.kickoff() + +print(f"\n***Final result:***\n\n{result}") + +# Clean up resources when done +import asyncio +asyncio.run(toolkit.cleanup()) +``` + +### Available Tools + +The toolkit provides the following tools: + +1. `execute_code` - Run code in various languages (primarily Python) +2. `execute_command` - Run shell commands in the environment +3. `read_files` - Read content of files in the environment +4. `list_files` - List files in directories +5. `delete_files` - Remove files from the environment +6. `write_files` - Create or update files +7. `start_command_execution` - Start long-running commands asynchronously +8. `get_task` - Check status of async tasks +9. `stop_task` - Stop running tasks + +### Advanced Usage + +```python +from crewai import Agent, Task, Crew, LLM +from crewai_tools.aws import create_code_interpreter_toolkit + +# Create the code interpreter toolkit +toolkit, code_tools = create_code_interpreter_toolkit(region="us-west-2") +tools_by_name = toolkit.get_tools_by_name() + +# Create the Bedrock LLM +llm = LLM( + model="bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0", + region_name="us-west-2", +) + +# Create agents with specific tools +code_agent = Agent( + role="Code Developer", + goal="Write and execute code", + backstory="You write and test code to solve complex problems.", + tools=[ + # Use specific tools by name + tools_by_name["execute_code"], + tools_by_name["execute_command"], + tools_by_name["read_files"], + tools_by_name["write_files"] + ], + llm=llm +) + +file_agent = Agent( + role="File Manager", + goal="Manage files in the environment", + backstory="You help organize and manage files in the code environment.", + tools=[ + # Use specific tools by name + tools_by_name["list_files"], + tools_by_name["read_files"], + tools_by_name["write_files"], + tools_by_name["delete_files"] + ], + llm=llm +) + +# Create tasks for the agents +coding_task = Task( + description="Write a Python script to analyze data from a CSV file. Do not use any imports from outside the Python standard library.", + expected_output="The Python function created.", + agent=code_agent +) + +file_task = Task( + description="Organize the created files into separate directories.", + agent=file_agent +) + +# Create and run the crew +crew = Crew( + agents=[code_agent, file_agent], + tasks=[coding_task, file_task] +) +result = crew.kickoff() + +print(f"\n***Final result:***\n\n{result}") + +# Clean up code interpreter resources when done +import asyncio +asyncio.run(toolkit.cleanup()) +``` + +### Example: Data Analysis with Python + +```python +from crewai import Agent, Task, Crew, LLM +from crewai_tools.aws import create_code_interpreter_toolkit + +# Create toolkit and tools +toolkit, code_tools = create_code_interpreter_toolkit(region="us-west-2") + +# Create the Bedrock LLM +llm = LLM( + model="bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0", + region_name="us-west-2", +) + +# Create a data analyst agent +analyst_agent = Agent( + role="Data Analyst", + goal="Analyze data using Python", + backstory="You're an expert data analyst who uses Python for data processing.", + tools=code_tools, + llm=llm +) + +# Create a task for the agent +analysis_task = Task( + description=""" + For all of the below, do not use any imports from outside the Python standard library. + 1. Create a sample dataset with random data + 2. Perform statistical analysis on the dataset + 3. Generate visualizations of the results + 4. Save the results and visualizations to files + """, + agent=analyst_agent +) + +# Create and run the crew +crew = Crew( + agents=[analyst_agent], + tasks=[analysis_task] +) +result = crew.kickoff() + +print(f"\n***Final result:***\n\n{result}") + +# Clean up resources +import asyncio +asyncio.run(toolkit.cleanup()) +``` + +## Resource Cleanup + +Always clean up code interpreter resources when done to prevent resource leaks: + +```python +import asyncio + +# Clean up all code interpreter sessions +asyncio.run(toolkit.cleanup()) +``` + +## Requirements + +- AWS account with access to Bedrock AgentCore API +- Properly configured AWS credentials \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/__init__.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/__init__.py new file mode 100644 index 000000000..e491ead43 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/__init__.py @@ -0,0 +1,7 @@ +from crewai_tools.aws.bedrock.code_interpreter.code_interpreter_toolkit import ( + CodeInterpreterToolkit, + create_code_interpreter_toolkit, +) + + +__all__ = ["CodeInterpreterToolkit", "create_code_interpreter_toolkit"] diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/code_interpreter_toolkit.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/code_interpreter_toolkit.py new file mode 100644 index 000000000..240aa6220 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/code_interpreter_toolkit.py @@ -0,0 +1,625 @@ +"""Toolkit for working with AWS Bedrock Code Interpreter.""" + +from __future__ import annotations + +import json +import logging +from typing import TYPE_CHECKING, Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +if TYPE_CHECKING: + from bedrock_agentcore.tools.code_interpreter_client import CodeInterpreter + +logger = logging.getLogger(__name__) + + +def extract_output_from_stream(response): + """Extract output from code interpreter response stream. + + Args: + response: Response from code interpreter execution + + Returns: + Extracted output as string + """ + output = [] + for event in response["stream"]: + if "result" in event: + result = event["result"] + for content_item in result["content"]: + if content_item["type"] == "text": + output.append(content_item["text"]) + if content_item["type"] == "resource": + resource = content_item["resource"] + if "text" in resource: + file_path = resource["uri"].replace("file://", "") + file_content = resource["text"] + output.append(f"==== File: {file_path} ====\n{file_content}\n") + else: + output.append(json.dumps(resource)) + + return "\n".join(output) + + +# Input schemas +class ExecuteCodeInput(BaseModel): + """Input for ExecuteCode.""" + + code: str = Field(description="The code to execute") + language: str = Field( + default="python", description="The programming language of the code" + ) + clear_context: bool = Field( + default=False, description="Whether to clear execution context" + ) + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +class ExecuteCommandInput(BaseModel): + """Input for ExecuteCommand.""" + + command: str = Field(description="The command to execute") + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +class ReadFilesInput(BaseModel): + """Input for ReadFiles.""" + + paths: list[str] = Field(description="List of file paths to read") + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +class ListFilesInput(BaseModel): + """Input for ListFiles.""" + + directory_path: str = Field(default="", description="Path to the directory to list") + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +class DeleteFilesInput(BaseModel): + """Input for DeleteFiles.""" + + paths: list[str] = Field(description="List of file paths to delete") + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +class WriteFilesInput(BaseModel): + """Input for WriteFiles.""" + + files: list[dict[str, str]] = Field( + description="List of dictionaries with path and text fields" + ) + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +class StartCommandInput(BaseModel): + """Input for StartCommand.""" + + command: str = Field(description="The command to execute asynchronously") + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +class GetTaskInput(BaseModel): + """Input for GetTask.""" + + task_id: str = Field(description="The ID of the task to check") + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +class StopTaskInput(BaseModel): + """Input for StopTask.""" + + task_id: str = Field(description="The ID of the task to stop") + thread_id: str = Field( + default="default", description="Thread ID for the code interpreter session" + ) + + +# Tool classes +class ExecuteCodeTool(BaseTool): + """Tool for executing code in various languages.""" + + name: str = "execute_code" + description: str = "Execute code in various languages (primarily Python)" + args_schema: type[BaseModel] = ExecuteCodeInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run( + self, + code: str, + language: str = "python", + clear_context: bool = False, + thread_id: str = "default", + ) -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # Execute code + response = code_interpreter.invoke( + method="executeCode", + params={ + "code": code, + "language": language, + "clearContext": clear_context, + }, + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error executing code: {e!s}" + + async def _arun( + self, + code: str, + language: str = "python", + clear_context: bool = False, + thread_id: str = "default", + ) -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run( + code=code, + language=language, + clear_context=clear_context, + thread_id=thread_id, + ) + + +class ExecuteCommandTool(BaseTool): + """Tool for running shell commands in the code interpreter environment.""" + + name: str = "execute_command" + description: str = "Run shell commands in the code interpreter environment" + args_schema: type[BaseModel] = ExecuteCommandInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run(self, command: str, thread_id: str = "default") -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # Execute command + response = code_interpreter.invoke( + method="executeCommand", params={"command": command} + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error executing command: {e!s}" + + async def _arun(self, command: str, thread_id: str = "default") -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run(command=command, thread_id=thread_id) + + +class ReadFilesTool(BaseTool): + """Tool for reading content of files in the environment.""" + + name: str = "read_files" + description: str = "Read content of files in the environment" + args_schema: type[BaseModel] = ReadFilesInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run(self, paths: list[str], thread_id: str = "default") -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # Read files + response = code_interpreter.invoke( + method="readFiles", params={"paths": paths} + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error reading files: {e!s}" + + async def _arun(self, paths: list[str], thread_id: str = "default") -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run(paths=paths, thread_id=thread_id) + + +class ListFilesTool(BaseTool): + """Tool for listing files in directories in the environment.""" + + name: str = "list_files" + description: str = "List files in directories in the environment" + args_schema: type[BaseModel] = ListFilesInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run(self, directory_path: str = "", thread_id: str = "default") -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # List files + response = code_interpreter.invoke( + method="listFiles", params={"directoryPath": directory_path} + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error listing files: {e!s}" + + async def _arun(self, directory_path: str = "", thread_id: str = "default") -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run(directory_path=directory_path, thread_id=thread_id) + + +class DeleteFilesTool(BaseTool): + """Tool for removing files from the environment.""" + + name: str = "delete_files" + description: str = "Remove files from the environment" + args_schema: type[BaseModel] = DeleteFilesInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run(self, paths: list[str], thread_id: str = "default") -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # Remove files + response = code_interpreter.invoke( + method="removeFiles", params={"paths": paths} + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error deleting files: {e!s}" + + async def _arun(self, paths: list[str], thread_id: str = "default") -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run(paths=paths, thread_id=thread_id) + + +class WriteFilesTool(BaseTool): + """Tool for creating or updating files in the environment.""" + + name: str = "write_files" + description: str = "Create or update files in the environment" + args_schema: type[BaseModel] = WriteFilesInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run(self, files: list[dict[str, str]], thread_id: str = "default") -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # Write files + response = code_interpreter.invoke( + method="writeFiles", params={"content": files} + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error writing files: {e!s}" + + async def _arun( + self, files: list[dict[str, str]], thread_id: str = "default" + ) -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run(files=files, thread_id=thread_id) + + +class StartCommandTool(BaseTool): + """Tool for starting long-running commands asynchronously.""" + + name: str = "start_command_execution" + description: str = "Start long-running commands asynchronously" + args_schema: type[BaseModel] = StartCommandInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run(self, command: str, thread_id: str = "default") -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # Start command execution + response = code_interpreter.invoke( + method="startCommandExecution", params={"command": command} + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error starting command: {e!s}" + + async def _arun(self, command: str, thread_id: str = "default") -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run(command=command, thread_id=thread_id) + + +class GetTaskTool(BaseTool): + """Tool for checking status of async tasks.""" + + name: str = "get_task" + description: str = "Check status of async tasks" + args_schema: type[BaseModel] = GetTaskInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run(self, task_id: str, thread_id: str = "default") -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # Get task status + response = code_interpreter.invoke( + method="getTask", params={"taskId": task_id} + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error getting task status: {e!s}" + + async def _arun(self, task_id: str, thread_id: str = "default") -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run(task_id=task_id, thread_id=thread_id) + + +class StopTaskTool(BaseTool): + """Tool for stopping running tasks.""" + + name: str = "stop_task" + description: str = "Stop running tasks" + args_schema: type[BaseModel] = StopTaskInput + toolkit: Any = Field(default=None, exclude=True) + + def __init__(self, toolkit): + super().__init__() + self.toolkit = toolkit + + def _run(self, task_id: str, thread_id: str = "default") -> str: + try: + # Get or create code interpreter + code_interpreter = self.toolkit._get_or_create_interpreter( + thread_id=thread_id + ) + + # Stop task + response = code_interpreter.invoke( + method="stopTask", params={"taskId": task_id} + ) + + return extract_output_from_stream(response) + except Exception as e: + return f"Error stopping task: {e!s}" + + async def _arun(self, task_id: str, thread_id: str = "default") -> str: + # Use _run as we're working with a synchronous API that's thread-safe + return self._run(task_id=task_id, thread_id=thread_id) + + +class CodeInterpreterToolkit: + """Toolkit for working with AWS Bedrock code interpreter environment. + + This toolkit provides a set of tools for working with a remote code interpreter environment: + + * execute_code - Run code in various languages (primarily Python) + * execute_command - Run shell commands + * read_files - Read content of files in the environment + * list_files - List files in directories + * delete_files - Remove files from the environment + * write_files - Create or update files + * start_command_execution - Start long-running commands asynchronously + * get_task - Check status of async tasks + * stop_task - Stop running tasks + + The toolkit lazily initializes the code interpreter session on first use. + It supports multiple threads by maintaining separate code interpreter sessions for each thread ID. + + Example: + ```python + from crewai import Agent, Task, Crew + from crewai_tools.aws.bedrock.code_interpreter import ( + create_code_interpreter_toolkit, + ) + + # Create the code interpreter toolkit + toolkit, code_tools = create_code_interpreter_toolkit(region="us-west-2") + + # Create a CrewAI agent that uses the code interpreter tools + developer_agent = Agent( + role="Python Developer", + goal="Create and execute Python code to solve problems", + backstory="You're a skilled Python developer with expertise in data analysis.", + tools=code_tools, + ) + + # Create a task for the agent + coding_task = Task( + description="Write a Python function that calculates the factorial of a number and test it.", + agent=developer_agent, + ) + + # Create and run the crew + crew = Crew(agents=[developer_agent], tasks=[coding_task]) + result = crew.kickoff() + + # Clean up resources when done + import asyncio + + asyncio.run(toolkit.cleanup()) + ``` + """ + + def __init__(self, region: str = "us-west-2"): + """Initialize the toolkit. + + Args: + region: AWS region for the code interpreter + """ + self.region = region + self._code_interpreters: dict[str, CodeInterpreter] = {} + self.tools: list[BaseTool] = [] + self._setup_tools() + + def _setup_tools(self) -> None: + """Initialize tools without creating any code interpreter sessions.""" + self.tools = [ + ExecuteCodeTool(self), + ExecuteCommandTool(self), + ReadFilesTool(self), + ListFilesTool(self), + DeleteFilesTool(self), + WriteFilesTool(self), + StartCommandTool(self), + GetTaskTool(self), + StopTaskTool(self), + ] + + def _get_or_create_interpreter(self, thread_id: str = "default") -> CodeInterpreter: + """Get or create a code interpreter for the specified thread. + + Args: + thread_id: Thread ID for the code interpreter session + + Returns: + CodeInterpreter instance + """ + if thread_id in self._code_interpreters: + return self._code_interpreters[thread_id] + + # Create a new code interpreter for this thread + from bedrock_agentcore.tools.code_interpreter_client import CodeInterpreter + + code_interpreter = CodeInterpreter(region=self.region) + code_interpreter.start() + logger.info( + f"Started code interpreter with session_id:{code_interpreter.session_id} for thread:{thread_id}" + ) + + # Store the interpreter + self._code_interpreters[thread_id] = code_interpreter + return code_interpreter + + def get_tools(self) -> list[BaseTool]: + """Get the list of code interpreter tools. + + Returns: + List of CrewAI tools + """ + return self.tools + + def get_tools_by_name(self) -> dict[str, BaseTool]: + """Get a dictionary of tools mapped by their names. + + Returns: + Dictionary of {tool_name: tool} + """ + return {tool.name: tool for tool in self.tools} + + async def cleanup(self, thread_id: str | None = None) -> None: + """Clean up resources. + + Args: + thread_id: Optional thread ID to clean up. If None, cleans up all sessions. + """ + if thread_id: + # Clean up a specific thread's session + if thread_id in self._code_interpreters: + try: + self._code_interpreters[thread_id].stop() + del self._code_interpreters[thread_id] + logger.info( + f"Code interpreter session for thread {thread_id} cleaned up" + ) + except Exception as e: + logger.warning( + f"Error stopping code interpreter for thread {thread_id}: {e}" + ) + else: + # Clean up all sessions + thread_ids = list(self._code_interpreters.keys()) + for tid in thread_ids: + try: + self._code_interpreters[tid].stop() + except Exception as e: # noqa: PERF203 + logger.warning( + f"Error stopping code interpreter for thread {tid}: {e}" + ) + + self._code_interpreters = {} + logger.info("All code interpreter sessions cleaned up") + + +def create_code_interpreter_toolkit( + region: str = "us-west-2", +) -> tuple[CodeInterpreterToolkit, list[BaseTool]]: + """Create a CodeInterpreterToolkit. + + Args: + region: AWS region for code interpreter + + Returns: + Tuple of (toolkit, tools) + """ + toolkit = CodeInterpreterToolkit(region=region) + tools = toolkit.get_tools() + return toolkit, tools diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/exceptions.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/exceptions.py new file mode 100644 index 000000000..4c61a185a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/exceptions.py @@ -0,0 +1,17 @@ +"""Custom exceptions for AWS Bedrock integration.""" + + +class BedrockError(Exception): + """Base exception for Bedrock-related errors.""" + + +class BedrockAgentError(BedrockError): + """Exception raised for errors in the Bedrock Agent operations.""" + + +class BedrockKnowledgeBaseError(BedrockError): + """Exception raised for errors in the Bedrock Knowledge Base operations.""" + + +class BedrockValidationError(BedrockError): + """Exception raised for validation errors in Bedrock operations.""" diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/README.md b/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/README.md new file mode 100644 index 000000000..6da54f848 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/README.md @@ -0,0 +1,159 @@ +# BedrockKBRetrieverTool + +The `BedrockKBRetrieverTool` enables CrewAI agents to retrieve information from Amazon Bedrock Knowledge Bases using natural language queries. + +## Installation + +```bash +pip install 'crewai[tools]' +``` + +## Requirements + +- AWS credentials configured (either through environment variables or AWS CLI) +- `boto3` and `python-dotenv` packages +- Access to Amazon Bedrock Knowledge Base + +## Usage + +Here's how to use the tool with a CrewAI agent: + +```python +from crewai import Agent, Task, Crew +from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import BedrockKBRetrieverTool + +# Initialize the tool +kb_tool = BedrockKBRetrieverTool( + knowledge_base_id="your-kb-id", + number_of_results=5 +) + +# Create a CrewAI agent that uses the tool +researcher = Agent( + role='Knowledge Base Researcher', + goal='Find information about company policies', + backstory='I am a researcher specialized in retrieving and analyzing company documentation.', + tools=[kb_tool], + verbose=True +) + +# Create a task for the agent +research_task = Task( + description="Find our company's remote work policy and summarize the key points.", + agent=researcher +) + +# Create a crew with the agent +crew = Crew( + agents=[researcher], + tasks=[research_task], + verbose=2 +) + +# Run the crew +result = crew.kickoff() +print(result) +``` + +## Tool Arguments + +| Argument | Type | Required | Default | Description | +|----------|------|----------|---------|-------------| +| knowledge_base_id | str | Yes | None | The unique identifier of the knowledge base (0-10 alphanumeric characters) | +| number_of_results | int | No | 5 | Maximum number of results to return | +| retrieval_configuration | dict | No | None | Custom configurations for the knowledge base query | +| guardrail_configuration | dict | No | None | Content filtering settings | +| next_token | str | No | None | Token for pagination | + +## Environment Variables + +```bash +BEDROCK_KB_ID=your-knowledge-base-id # Alternative to passing knowledge_base_id +AWS_REGION=your-aws-region # Defaults to us-east-1 +AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication +AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication +``` + +## Response Format + +The tool returns results in JSON format: + +```json +{ + "results": [ + { + "content": "Retrieved text content", + "content_type": "text", + "source_type": "S3", + "source_uri": "s3://bucket/document.pdf", + "score": 0.95, + "metadata": { + "additional": "metadata" + } + } + ], + "nextToken": "pagination-token", + "guardrailAction": "NONE" +} +``` + +## Advanced Usage + +### Custom Retrieval Configuration + +```python +kb_tool = BedrockKBRetrieverTool( + knowledge_base_id="your-kb-id", + retrieval_configuration={ + "vectorSearchConfiguration": { + "numberOfResults": 10, + "overrideSearchType": "HYBRID" + } + } +) + +policy_expert = Agent( + role='Policy Expert', + goal='Analyze company policies in detail', + backstory='I am an expert in corporate policy analysis with deep knowledge of regulatory requirements.', + tools=[kb_tool] +) +``` + +## Supported Data Sources + +- Amazon S3 +- Confluence +- Salesforce +- SharePoint +- Web pages +- Custom document locations +- Amazon Kendra +- SQL databases + +## Use Cases + +### Enterprise Knowledge Integration +- Enable CrewAI agents to access your organization's proprietary knowledge without exposing sensitive data +- Allow agents to make decisions based on your company's specific policies, procedures, and documentation +- Create agents that can answer questions based on your internal documentation while maintaining data security + +### Specialized Domain Knowledge +- Connect CrewAI agents to domain-specific knowledge bases (legal, medical, technical) without retraining models +- Leverage existing knowledge repositories that are already maintained in your AWS environment +- Combine CrewAI's reasoning with domain-specific information from your knowledge bases + +### Data-Driven Decision Making +- Ground CrewAI agent responses in your actual company data rather than general knowledge +- Ensure agents provide recommendations based on your specific business context and documentation +- Reduce hallucinations by retrieving factual information from your knowledge bases + +### Scalable Information Access +- Access terabytes of organizational knowledge without embedding it all into your models +- Dynamically query only the relevant information needed for specific tasks +- Leverage AWS's scalable infrastructure to handle large knowledge bases efficiently + +### Compliance and Governance +- Ensure CrewAI agents provide responses that align with your company's approved documentation +- Create auditable trails of information sources used by your agents +- Maintain control over what information sources your agents can access \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/__init__.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/__init__.py new file mode 100644 index 000000000..741a636d9 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/__init__.py @@ -0,0 +1,6 @@ +from crewai_tools.aws.bedrock.knowledge_base.retriever_tool import ( + BedrockKBRetrieverTool, +) + + +__all__ = ["BedrockKBRetrieverTool"] diff --git a/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/retriever_tool.py b/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/retriever_tool.py new file mode 100644 index 000000000..d20a0bf51 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/retriever_tool.py @@ -0,0 +1,269 @@ +import json +import os +from typing import Any + +from crewai.tools import BaseTool +from dotenv import load_dotenv +from pydantic import BaseModel, Field + +from crewai_tools.aws.bedrock.exceptions import ( + BedrockKnowledgeBaseError, + BedrockValidationError, +) + + +# Load environment variables from .env file +load_dotenv() + + +class BedrockKBRetrieverToolInput(BaseModel): + """Input schema for BedrockKBRetrieverTool.""" + + query: str = Field( + ..., description="The query to retrieve information from the knowledge base" + ) + + +class BedrockKBRetrieverTool(BaseTool): + name: str = "Bedrock Knowledge Base Retriever Tool" + description: str = ( + "Retrieves information from an Amazon Bedrock Knowledge Base given a query" + ) + args_schema: type[BaseModel] = BedrockKBRetrieverToolInput + knowledge_base_id: str = None # type: ignore[assignment] + number_of_results: int | None = 5 + retrieval_configuration: dict[str, Any] | None = None + guardrail_configuration: dict[str, Any] | None = None + next_token: str | None = None + package_dependencies: list[str] = Field(default_factory=lambda: ["boto3"]) + + def __init__( + self, + knowledge_base_id: str | None = None, + number_of_results: int | None = 5, + retrieval_configuration: dict[str, Any] | None = None, + guardrail_configuration: dict[str, Any] | None = None, + next_token: str | None = None, + **kwargs, + ): + """Initialize the BedrockKBRetrieverTool with knowledge base configuration. + + Args: + knowledge_base_id (str): The unique identifier of the knowledge base to query + number_of_results (Optional[int], optional): The maximum number of results to return. Defaults to 5. + retrieval_configuration (Optional[Dict[str, Any]], optional): Configurations for the knowledge base query and retrieval process. Defaults to None. + guardrail_configuration (Optional[Dict[str, Any]], optional): Guardrail settings. Defaults to None. + next_token (Optional[str], optional): Token for retrieving the next batch of results. Defaults to None. + """ + super().__init__(**kwargs) + + # Get knowledge_base_id from environment variable if not provided + self.knowledge_base_id = knowledge_base_id or os.getenv("BEDROCK_KB_ID") # type: ignore[assignment] + self.number_of_results = number_of_results + self.guardrail_configuration = guardrail_configuration + self.next_token = next_token + + # Initialize retrieval_configuration with provided parameters or use the one provided + if retrieval_configuration is None: + self.retrieval_configuration = self._build_retrieval_configuration() + else: + self.retrieval_configuration = retrieval_configuration + + # Validate parameters + self._validate_parameters() + + # Update the description to include the knowledge base details + self.description = f"Retrieves information from Amazon Bedrock Knowledge Base '{self.knowledge_base_id}' given a query" + + def _build_retrieval_configuration(self) -> dict[str, Any]: + """Build the retrieval configuration based on provided parameters. + + Returns: + Dict[str, Any]: The constructed retrieval configuration + """ + vector_search_config = {} + + # Add number of results if provided + if self.number_of_results is not None: + vector_search_config["numberOfResults"] = self.number_of_results + + return {"vectorSearchConfiguration": vector_search_config} + + def _validate_parameters(self): + """Validate the parameters according to AWS API requirements.""" + try: + # Validate knowledge_base_id + if not self.knowledge_base_id: + raise BedrockValidationError("knowledge_base_id cannot be empty") + if not isinstance(self.knowledge_base_id, str): + raise BedrockValidationError("knowledge_base_id must be a string") + if len(self.knowledge_base_id) > 10: + raise BedrockValidationError( + "knowledge_base_id must be 10 characters or less" + ) + if not all(c.isalnum() for c in self.knowledge_base_id): + raise BedrockValidationError( + "knowledge_base_id must contain only alphanumeric characters" + ) + + # Validate next_token if provided + if self.next_token: + if not isinstance(self.next_token, str): + raise BedrockValidationError("next_token must be a string") + if len(self.next_token) < 1 or len(self.next_token) > 2048: + raise BedrockValidationError( + "next_token must be between 1 and 2048 characters" + ) + if " " in self.next_token: + raise BedrockValidationError("next_token cannot contain spaces") + + # Validate number_of_results if provided + if self.number_of_results is not None: + if not isinstance(self.number_of_results, int): + raise BedrockValidationError("number_of_results must be an integer") + if self.number_of_results < 1: + raise BedrockValidationError( + "number_of_results must be greater than 0" + ) + + except BedrockValidationError as e: + raise BedrockValidationError(f"Parameter validation failed: {e!s}") from e + + def _process_retrieval_result(self, result: dict[str, Any]) -> dict[str, Any]: + """Process a single retrieval result from Bedrock Knowledge Base. + + Args: + result (Dict[str, Any]): Raw result from Bedrock Knowledge Base + + Returns: + Dict[str, Any]: Processed result with standardized format + """ + # Extract content + content_obj = result.get("content", {}) + content = content_obj.get("text", "") + content_type = content_obj.get("type", "text") + + # Extract location information + location = result.get("location", {}) + location_type = location.get("type", "unknown") + source_uri = None + + # Map for location types and their URI fields + location_mapping = { + "s3Location": {"field": "uri", "type": "S3"}, + "confluenceLocation": {"field": "url", "type": "Confluence"}, + "salesforceLocation": {"field": "url", "type": "Salesforce"}, + "sharePointLocation": {"field": "url", "type": "SharePoint"}, + "webLocation": {"field": "url", "type": "Web"}, + "customDocumentLocation": {"field": "id", "type": "CustomDocument"}, + "kendraDocumentLocation": {"field": "uri", "type": "KendraDocument"}, + "sqlLocation": {"field": "query", "type": "SQL"}, + } + + # Extract the URI based on location type + for loc_key, config in location_mapping.items(): + if loc_key in location: + source_uri = location[loc_key].get(config["field"]) + if not location_type or location_type == "unknown": + location_type = config["type"] + break + + # Create result object + result_object = { + "content": content, + "content_type": content_type, + "source_type": location_type, + "source_uri": source_uri, + } + + # Add optional fields if available + if "score" in result: + result_object["score"] = result["score"] + + if "metadata" in result: + result_object["metadata"] = result["metadata"] + + # Handle byte content if present + if "byteContent" in content_obj: + result_object["byte_content"] = content_obj["byteContent"] + + # Handle row content if present + if "row" in content_obj: + result_object["row_content"] = content_obj["row"] + + return result_object + + def _run(self, query: str) -> str: + try: + import boto3 + from botocore.exceptions import ClientError + except ImportError as e: + raise ImportError( + "`boto3` package not found, please run `uv add boto3`" + ) from e + + try: + # Initialize the Bedrock Agent Runtime client + bedrock_agent_runtime = boto3.client( + "bedrock-agent-runtime", + region_name=os.getenv( + "AWS_REGION", os.getenv("AWS_DEFAULT_REGION", "us-east-1") + ), + # AWS SDK will automatically use AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY from environment + ) + + # Prepare the request parameters + retrieve_params = { + "knowledgeBaseId": self.knowledge_base_id, + "retrievalQuery": {"text": query}, + } + + # Add optional parameters if provided + if self.retrieval_configuration: + retrieve_params["retrievalConfiguration"] = self.retrieval_configuration + + if self.guardrail_configuration: + retrieve_params["guardrailConfiguration"] = self.guardrail_configuration + + if self.next_token: + retrieve_params["nextToken"] = self.next_token + + # Make the retrieve API call + response = bedrock_agent_runtime.retrieve(**retrieve_params) + + # Process the response + results = [] + for result in response.get("retrievalResults", []): + processed_result = self._process_retrieval_result(result) + results.append(processed_result) + + # Build the response object + response_object = {} + if results: + response_object["results"] = results + else: + response_object["message"] = "No results found for the given query." # type: ignore[assignment] + + if "nextToken" in response: + response_object["nextToken"] = response["nextToken"] + + if "guardrailAction" in response: + response_object["guardrailAction"] = response["guardrailAction"] + + # Return the results as a JSON string + return json.dumps(response_object, indent=2) + + except ClientError as e: + error_code = "Unknown" + error_message = str(e) + + # Try to extract error code if available + if hasattr(e, "response") and "Error" in e.response: + error_code = e.response["Error"].get("Code", "Unknown") + error_message = e.response["Error"].get("Message", str(e)) + + raise BedrockKnowledgeBaseError( + f"Error ({error_code}): {error_message}" + ) from e + except Exception as e: + raise BedrockKnowledgeBaseError(f"Unexpected error: {e!s}") from e diff --git a/lib/crewai-tools/src/crewai_tools/aws/s3/README.md b/lib/crewai-tools/src/crewai_tools/aws/s3/README.md new file mode 100644 index 000000000..ffd74d88c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/s3/README.md @@ -0,0 +1,52 @@ +# AWS S3 Tools + +## Description + +These tools provide a way to interact with Amazon S3, a cloud storage service. + +## Installation + +Install the crewai_tools package + +```shell +pip install 'crewai[tools]' +``` + +## AWS Connectivity + +The tools use `boto3` to connect to AWS S3. +You can configure your environment to use AWS IAM roles, see [AWS IAM Roles documentation](https://docs.aws.amazon.com/sdk-for-python/v1/developer-guide/iam-roles.html#creating-an-iam-role) + +Set the following environment variables: + +- `CREW_AWS_REGION` +- `CREW_AWS_ACCESS_KEY_ID` +- `CREW_AWS_SEC_ACCESS_KEY` + +## Usage + +To use the AWS S3 tools in your CrewAI agents, import the necessary tools and include them in your agent's configuration: + +```python +from crewai_tools.aws.s3 import S3ReaderTool, S3WriterTool + +# For reading from S3 +@agent +def file_retriever(self) -> Agent: + return Agent( + config=self.agents_config['file_retriever'], + verbose=True, + tools=[S3ReaderTool()] + ) + +# For writing to S3 +@agent +def file_uploader(self) -> Agent: + return Agent( + config=self.agents_config['file_uploader'], + verbose=True, + tools=[S3WriterTool()] + ) +``` + +These tools can be used to read from and write to S3 buckets within your CrewAI workflows. Make sure you have properly configured your AWS credentials as mentioned in the AWS Connectivity section above. diff --git a/lib/crewai-tools/src/crewai_tools/aws/s3/__init__.py b/lib/crewai-tools/src/crewai_tools/aws/s3/__init__.py new file mode 100644 index 000000000..1315ca8e2 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/s3/__init__.py @@ -0,0 +1,2 @@ +from crewai_tools.aws.s3.reader_tool import S3ReaderTool as S3ReaderTool +from crewai_tools.aws.s3.writer_tool import S3WriterTool as S3WriterTool diff --git a/lib/crewai-tools/src/crewai_tools/aws/s3/reader_tool.py b/lib/crewai-tools/src/crewai_tools/aws/s3/reader_tool.py new file mode 100644 index 000000000..30203a434 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/s3/reader_tool.py @@ -0,0 +1,49 @@ +import os + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class S3ReaderToolInput(BaseModel): + """Input schema for S3ReaderTool.""" + + file_path: str = Field( + ..., description="S3 file path (e.g., 's3://bucket-name/file-name')" + ) + + +class S3ReaderTool(BaseTool): + name: str = "S3 Reader Tool" + description: str = "Reads a file from Amazon S3 given an S3 file path" + args_schema: type[BaseModel] = S3ReaderToolInput + package_dependencies: list[str] = Field(default_factory=lambda: ["boto3"]) + + def _run(self, file_path: str) -> str: + try: + import boto3 + from botocore.exceptions import ClientError + except ImportError as e: + raise ImportError( + "`boto3` package not found, please run `uv add boto3`" + ) from e + + try: + bucket_name, object_key = self._parse_s3_path(file_path) + + s3 = boto3.client( + "s3", + region_name=os.getenv("CREW_AWS_REGION", "us-east-1"), + aws_access_key_id=os.getenv("CREW_AWS_ACCESS_KEY_ID"), + aws_secret_access_key=os.getenv("CREW_AWS_SEC_ACCESS_KEY"), + ) + + # Read file content from S3 + response = s3.get_object(Bucket=bucket_name, Key=object_key) + return response["Body"].read().decode("utf-8") + + except ClientError as e: + return f"Error reading file from S3: {e!s}" + + def _parse_s3_path(self, file_path: str) -> tuple: + parts = file_path.replace("s3://", "").split("/", 1) + return parts[0], parts[1] diff --git a/lib/crewai-tools/src/crewai_tools/aws/s3/writer_tool.py b/lib/crewai-tools/src/crewai_tools/aws/s3/writer_tool.py new file mode 100644 index 000000000..87f211dbc --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/aws/s3/writer_tool.py @@ -0,0 +1,50 @@ +import os + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class S3WriterToolInput(BaseModel): + """Input schema for S3WriterTool.""" + + file_path: str = Field( + ..., description="S3 file path (e.g., 's3://bucket-name/file-name')" + ) + content: str = Field(..., description="Content to write to the file") + + +class S3WriterTool(BaseTool): + name: str = "S3 Writer Tool" + description: str = "Writes content to a file in Amazon S3 given an S3 file path" + args_schema: type[BaseModel] = S3WriterToolInput + package_dependencies: list[str] = Field(default_factory=lambda: ["boto3"]) + + def _run(self, file_path: str, content: str) -> str: + try: + import boto3 + from botocore.exceptions import ClientError + except ImportError as e: + raise ImportError( + "`boto3` package not found, please run `uv add boto3`" + ) from e + + try: + bucket_name, object_key = self._parse_s3_path(file_path) + + s3 = boto3.client( + "s3", + region_name=os.getenv("CREW_AWS_REGION", "us-east-1"), + aws_access_key_id=os.getenv("CREW_AWS_ACCESS_KEY_ID"), + aws_secret_access_key=os.getenv("CREW_AWS_SEC_ACCESS_KEY"), + ) + + s3.put_object( + Bucket=bucket_name, Key=object_key, Body=content.encode("utf-8") + ) + return f"Successfully wrote content to {file_path}" + except ClientError as e: + return f"Error writing file to S3: {e!s}" + + def _parse_s3_path(self, file_path: str) -> tuple: + parts = file_path.replace("s3://", "").split("/", 1) + return parts[0], parts[1] diff --git a/lib/crewai-tools/src/crewai_tools/printer.py b/lib/crewai-tools/src/crewai_tools/printer.py new file mode 100644 index 000000000..d50a794fb --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/printer.py @@ -0,0 +1,129 @@ +"""Utility for colored console output.""" + + +class Printer: + """Handles colored console output formatting.""" + + @staticmethod + def print(content: str, color: str | None = None) -> None: + """Prints content with optional color formatting. + + Args: + content: The string to be printed. + color: Optional color name to format the output. If provided, + must match one of the _print_* methods available in this class. + If not provided or if the color is not supported, prints without + formatting. + """ + if hasattr(Printer, f"_print_{color}"): + getattr(Printer, f"_print_{color}")(content) + else: + print(content) # noqa: T201 + + @staticmethod + def _print_bold_purple(content: str) -> None: + """Prints content in bold purple color. + + Args: + content: The string to be printed in bold purple. + """ + print(f"\033[1m\033[95m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_bold_green(content: str) -> None: + """Prints content in bold green color. + + Args: + content: The string to be printed in bold green. + """ + print(f"\033[1m\033[92m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_purple(content: str) -> None: + """Prints content in purple color. + + Args: + content: The string to be printed in purple. + """ + print(f"\033[95m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_red(content: str) -> None: + """Prints content in red color. + + Args: + content: The string to be printed in red. + """ + print(f"\033[91m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_bold_blue(content: str) -> None: + """Prints content in bold blue color. + + Args: + content: The string to be printed in bold blue. + """ + print(f"\033[1m\033[94m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_yellow(content: str) -> None: + """Prints content in yellow color. + + Args: + content: The string to be printed in yellow. + """ + print(f"\033[93m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_bold_yellow(content: str) -> None: + """Prints content in bold yellow color. + + Args: + content: The string to be printed in bold yellow. + """ + print(f"\033[1m\033[93m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_cyan(content: str) -> None: + """Prints content in cyan color. + + Args: + content: The string to be printed in cyan. + """ + print(f"\033[96m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_bold_cyan(content: str) -> None: + """Prints content in bold cyan color. + + Args: + content: The string to be printed in bold cyan. + """ + print(f"\033[1m\033[96m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_magenta(content: str) -> None: + """Prints content in magenta color. + + Args: + content: The string to be printed in magenta. + """ + print(f"\033[35m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_bold_magenta(content: str) -> None: + """Prints content in bold magenta color. + + Args: + content: The string to be printed in bold magenta. + """ + print(f"\033[1m\033[35m {content}\033[00m") # noqa: T201 + + @staticmethod + def _print_green(content: str) -> None: + """Prints content in green color. + + Args: + content: The string to be printed in green. + """ + print(f"\033[32m {content}\033[00m") # noqa: T201 diff --git a/src/crewai/agents/agent_builder/__init__.py b/lib/crewai-tools/src/crewai_tools/py.typed similarity index 100% rename from src/crewai/agents/agent_builder/__init__.py rename to lib/crewai-tools/src/crewai_tools/py.typed diff --git a/lib/crewai-tools/src/crewai_tools/rag/__init__.py b/lib/crewai-tools/src/crewai_tools/rag/__init__.py new file mode 100644 index 000000000..c08ef1a7c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/__init__.py @@ -0,0 +1,9 @@ +from crewai_tools.rag.core import RAG, EmbeddingService +from crewai_tools.rag.data_types import DataType + + +__all__ = [ + "RAG", + "DataType", + "EmbeddingService", +] diff --git a/lib/crewai-tools/src/crewai_tools/rag/base_loader.py b/lib/crewai-tools/src/crewai_tools/rag/base_loader.py new file mode 100644 index 000000000..0ddacf9ec --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/base_loader.py @@ -0,0 +1,40 @@ +from abc import ABC, abstractmethod +from typing import Any + +from pydantic import BaseModel, Field + +from crewai_tools.rag.misc import compute_sha256 +from crewai_tools.rag.source_content import SourceContent + + +class LoaderResult(BaseModel): + content: str = Field(description="The text content of the source") + source: str = Field(description="The source of the content", default="unknown") + metadata: dict[str, Any] = Field( + description="The metadata of the source", default_factory=dict + ) + doc_id: str = Field(description="The id of the document") + + +class BaseLoader(ABC): + def __init__(self, config: dict[str, Any] | None = None) -> None: + self.config = config or {} + + @abstractmethod + def load(self, content: SourceContent, **kwargs) -> LoaderResult: ... + + @staticmethod + def generate_doc_id( + source_ref: str | None = None, content: str | None = None + ) -> str: + """Generate a unique document id based on the source reference and content. + If the source reference is not provided, the content is used as the source reference. + If the content is not provided, the source reference is used as the content. + If both are provided, the source reference is used as the content. + + Both are optional because the TEXT content type does not have a source reference. In this case, the content is used as the source reference. + """ + source_ref = source_ref or "" + content = content or "" + + return compute_sha256(source_ref + content) diff --git a/lib/crewai-tools/src/crewai_tools/rag/chunkers/__init__.py b/lib/crewai-tools/src/crewai_tools/rag/chunkers/__init__.py new file mode 100644 index 000000000..495a1ef06 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/chunkers/__init__.py @@ -0,0 +1,20 @@ +from crewai_tools.rag.chunkers.base_chunker import BaseChunker +from crewai_tools.rag.chunkers.default_chunker import DefaultChunker +from crewai_tools.rag.chunkers.structured_chunker import ( + CsvChunker, + JsonChunker, + XmlChunker, +) +from crewai_tools.rag.chunkers.text_chunker import DocxChunker, MdxChunker, TextChunker + + +__all__ = [ + "BaseChunker", + "CsvChunker", + "DefaultChunker", + "DocxChunker", + "JsonChunker", + "MdxChunker", + "TextChunker", + "XmlChunker", +] diff --git a/lib/crewai-tools/src/crewai_tools/rag/chunkers/base_chunker.py b/lib/crewai-tools/src/crewai_tools/rag/chunkers/base_chunker.py new file mode 100644 index 000000000..b5677ff5e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/chunkers/base_chunker.py @@ -0,0 +1,191 @@ +import re + + +class RecursiveCharacterTextSplitter: + """A text splitter that recursively splits text based on a hierarchy of separators.""" + + def __init__( + self, + chunk_size: int = 4000, + chunk_overlap: int = 200, + separators: list[str] | None = None, + keep_separator: bool = True, + ) -> None: + """Initialize the RecursiveCharacterTextSplitter. + + Args: + chunk_size: Maximum size of each chunk + chunk_overlap: Number of characters to overlap between chunks + separators: List of separators to use for splitting (in order of preference) + keep_separator: Whether to keep the separator in the split text + """ + if chunk_overlap >= chunk_size: + raise ValueError( + f"Chunk overlap ({chunk_overlap}) cannot be >= chunk size ({chunk_size})" + ) + + self._chunk_size = chunk_size + self._chunk_overlap = chunk_overlap + self._keep_separator = keep_separator + + self._separators = separators or [ + "\n\n", + "\n", + " ", + "", + ] + + def split_text(self, text: str) -> list[str]: + """Split the input text into chunks. + + Args: + text: The text to split. + + Returns: + A list of text chunks. + """ + return self._split_text(text, self._separators) + + def _split_text(self, text: str, separators: list[str]) -> list[str]: + separator = separators[-1] + new_separators = [] + + for i, sep in enumerate(separators): + if sep == "": + separator = sep + break + if re.search(re.escape(sep), text): + separator = sep + new_separators = separators[i + 1 :] + break + + splits = self._split_text_with_separator(text, separator) + + good_splits = [] + + for split in splits: + if len(split) < self._chunk_size: + good_splits.append(split) + else: + if new_separators: + other_info = self._split_text(split, new_separators) + good_splits.extend(other_info) + else: + good_splits.extend(self._split_by_characters(split)) + + return self._merge_splits(good_splits, separator) + + def _split_text_with_separator(self, text: str, separator: str) -> list[str]: + if separator == "": + return list(text) + + if self._keep_separator and separator in text: + parts = text.split(separator) + splits = [] + + for i, part in enumerate(parts): + if i == 0: + splits.append(part) + elif i == len(parts) - 1: + if part: + splits.append(separator + part) + else: + if part: + splits.append(separator + part) + else: + if splits: + splits[-1] += separator + + return [s for s in splits if s] + return text.split(separator) + + def _split_by_characters(self, text: str) -> list[str]: + chunks = [] + for i in range(0, len(text), self._chunk_size): + chunks.append(text[i : i + self._chunk_size]) # noqa: PERF401 + return chunks + + def _merge_splits(self, splits: list[str], separator: str) -> list[str]: + """Merge splits into chunks with proper overlap.""" + docs: list[str] = [] + current_doc: list[str] = [] + total = 0 + + for split in splits: + split_len = len(split) + + if total + split_len > self._chunk_size and current_doc: + if separator == "": + doc = "".join(current_doc) + else: + if self._keep_separator and separator == " ": + doc = "".join(current_doc) + else: + doc = separator.join(current_doc) + + if doc: + docs.append(doc) + + # Handle overlap by keeping some of the previous content + while total > self._chunk_overlap and len(current_doc) > 1: + removed = current_doc.pop(0) + total -= len(removed) + if separator != "": + total -= len(separator) + + current_doc.append(split) + total += split_len + if separator != "" and len(current_doc) > 1: + total += len(separator) + + if current_doc: + if separator == "": + doc = "".join(current_doc) + else: + if self._keep_separator and separator == " ": + doc = "".join(current_doc) + else: + doc = separator.join(current_doc) + + if doc: + docs.append(doc) + + return docs + + +class BaseChunker: + def __init__( + self, + chunk_size: int = 1000, + chunk_overlap: int = 200, + separators: list[str] | None = None, + keep_separator: bool = True, + ) -> None: + """Initialize the Chunker. + + Args: + chunk_size: Maximum size of each chunk + chunk_overlap: Number of characters to overlap between chunks + separators: List of separators to use for splitting + keep_separator: Whether to keep separators in the chunks + """ + self._splitter = RecursiveCharacterTextSplitter( + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + separators=separators, + keep_separator=keep_separator, + ) + + def chunk(self, text: str) -> list[str]: + """Chunk the input text into smaller pieces. + + Args: + text: The text to chunk. + + Returns: + A list of text chunks. + """ + if not text or not text.strip(): + return [] + + return self._splitter.split_text(text) diff --git a/lib/crewai-tools/src/crewai_tools/rag/chunkers/default_chunker.py b/lib/crewai-tools/src/crewai_tools/rag/chunkers/default_chunker.py new file mode 100644 index 000000000..7073161b2 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/chunkers/default_chunker.py @@ -0,0 +1,12 @@ +from crewai_tools.rag.chunkers.base_chunker import BaseChunker + + +class DefaultChunker(BaseChunker): + def __init__( + self, + chunk_size: int = 2000, + chunk_overlap: int = 20, + separators: list[str] | None = None, + keep_separator: bool = True, + ): + super().__init__(chunk_size, chunk_overlap, separators, keep_separator) diff --git a/lib/crewai-tools/src/crewai_tools/rag/chunkers/structured_chunker.py b/lib/crewai-tools/src/crewai_tools/rag/chunkers/structured_chunker.py new file mode 100644 index 000000000..4fb4a36df --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/chunkers/structured_chunker.py @@ -0,0 +1,66 @@ +from crewai_tools.rag.chunkers.base_chunker import BaseChunker + + +class CsvChunker(BaseChunker): + def __init__( + self, + chunk_size: int = 1200, + chunk_overlap: int = 100, + separators: list[str] | None = None, + keep_separator: bool = True, + ): + if separators is None: + separators = [ + "\nRow ", # Row boundaries (from CSVLoader format) + "\n", # Line breaks + " | ", # Column separators + ", ", # Comma separators + " ", # Word breaks + "", # Character level + ] + super().__init__(chunk_size, chunk_overlap, separators, keep_separator) + + +class JsonChunker(BaseChunker): + def __init__( + self, + chunk_size: int = 2000, + chunk_overlap: int = 200, + separators: list[str] | None = None, + keep_separator: bool = True, + ): + if separators is None: + separators = [ + "\n\n", # Object/array boundaries + "\n", # Line breaks + "},", # Object endings + "],", # Array endings + ", ", # Property separators + ": ", # Key-value separators + " ", # Word breaks + "", # Character level + ] + super().__init__(chunk_size, chunk_overlap, separators, keep_separator) + + +class XmlChunker(BaseChunker): + def __init__( + self, + chunk_size: int = 2500, + chunk_overlap: int = 250, + separators: list[str] | None = None, + keep_separator: bool = True, + ): + if separators is None: + separators = [ + "\n\n", # Element boundaries + "\n", # Line breaks + ">", # Tag endings + ". ", # Sentence endings (for text content) + "! ", # Exclamation endings + "? ", # Question endings + ", ", # Comma separators + " ", # Word breaks + "", # Character level + ] + super().__init__(chunk_size, chunk_overlap, separators, keep_separator) diff --git a/lib/crewai-tools/src/crewai_tools/rag/chunkers/text_chunker.py b/lib/crewai-tools/src/crewai_tools/rag/chunkers/text_chunker.py new file mode 100644 index 000000000..7b9aae5b0 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/chunkers/text_chunker.py @@ -0,0 +1,76 @@ +from crewai_tools.rag.chunkers.base_chunker import BaseChunker + + +class TextChunker(BaseChunker): + def __init__( + self, + chunk_size: int = 1500, + chunk_overlap: int = 150, + separators: list[str] | None = None, + keep_separator: bool = True, + ): + if separators is None: + separators = [ + "\n\n\n", # Multiple line breaks (sections) + "\n\n", # Paragraph breaks + "\n", # Line breaks + ". ", # Sentence endings + "! ", # Exclamation endings + "? ", # Question endings + "; ", # Semicolon breaks + ", ", # Comma breaks + " ", # Word breaks + "", # Character level + ] + super().__init__(chunk_size, chunk_overlap, separators, keep_separator) + + +class DocxChunker(BaseChunker): + def __init__( + self, + chunk_size: int = 2500, + chunk_overlap: int = 250, + separators: list[str] | None = None, + keep_separator: bool = True, + ): + if separators is None: + separators = [ + "\n\n\n", # Multiple line breaks (major sections) + "\n\n", # Paragraph breaks + "\n", # Line breaks + ". ", # Sentence endings + "! ", # Exclamation endings + "? ", # Question endings + "; ", # Semicolon breaks + ", ", # Comma breaks + " ", # Word breaks + "", # Character level + ] + super().__init__(chunk_size, chunk_overlap, separators, keep_separator) + + +class MdxChunker(BaseChunker): + def __init__( + self, + chunk_size: int = 3000, + chunk_overlap: int = 300, + separators: list[str] | None = None, + keep_separator: bool = True, + ): + if separators is None: + separators = [ + "\n## ", # H2 headers (major sections) + "\n### ", # H3 headers (subsections) + "\n#### ", # H4 headers (sub-subsections) + "\n\n", # Paragraph breaks + "\n```", # Code block boundaries + "\n", # Line breaks + ". ", # Sentence endings + "! ", # Exclamation endings + "? ", # Question endings + "; ", # Semicolon breaks + ", ", # Comma breaks + " ", # Word breaks + "", # Character level + ] + super().__init__(chunk_size, chunk_overlap, separators, keep_separator) diff --git a/lib/crewai-tools/src/crewai_tools/rag/chunkers/web_chunker.py b/lib/crewai-tools/src/crewai_tools/rag/chunkers/web_chunker.py new file mode 100644 index 000000000..cc1a514d3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/chunkers/web_chunker.py @@ -0,0 +1,25 @@ +from crewai_tools.rag.chunkers.base_chunker import BaseChunker + + +class WebsiteChunker(BaseChunker): + def __init__( + self, + chunk_size: int = 2500, + chunk_overlap: int = 250, + separators: list[str] | None = None, + keep_separator: bool = True, + ): + if separators is None: + separators = [ + "\n\n\n", # Major section breaks + "\n\n", # Paragraph breaks + "\n", # Line breaks + ". ", # Sentence endings + "! ", # Exclamation endings + "? ", # Question endings + "; ", # Semicolon breaks + ", ", # Comma breaks + " ", # Word breaks + "", # Character level + ] + super().__init__(chunk_size, chunk_overlap, separators, keep_separator) diff --git a/lib/crewai-tools/src/crewai_tools/rag/core.py b/lib/crewai-tools/src/crewai_tools/rag/core.py new file mode 100644 index 000000000..31e3a283c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/core.py @@ -0,0 +1,231 @@ +import logging +from pathlib import Path +from typing import Any +from uuid import uuid4 + +import chromadb +from pydantic import BaseModel, Field, PrivateAttr + +from crewai_tools.rag.base_loader import BaseLoader +from crewai_tools.rag.chunkers.base_chunker import BaseChunker +from crewai_tools.rag.data_types import DataType +from crewai_tools.rag.embedding_service import EmbeddingService +from crewai_tools.rag.misc import compute_sha256 +from crewai_tools.rag.source_content import SourceContent +from crewai_tools.tools.rag.rag_tool import Adapter + + +logger = logging.getLogger(__name__) + + +class Document(BaseModel): + id: str = Field(default_factory=lambda: str(uuid4())) + content: str + metadata: dict[str, Any] = Field(default_factory=dict) + data_type: DataType = DataType.TEXT + source: str | None = None + + +class RAG(Adapter): + collection_name: str = "crewai_knowledge_base" + persist_directory: str | None = None + embedding_provider: str = "openai" + embedding_model: str = "text-embedding-3-large" + summarize: bool = False + top_k: int = 5 + embedding_config: dict[str, Any] = Field(default_factory=dict) + + _client: Any = PrivateAttr() + _collection: Any = PrivateAttr() + _embedding_service: EmbeddingService = PrivateAttr() + + def model_post_init(self, __context: Any) -> None: + try: + if self.persist_directory: + self._client = chromadb.PersistentClient(path=self.persist_directory) + else: + self._client = chromadb.Client() + + self._collection = self._client.get_or_create_collection( + name=self.collection_name, + metadata={ + "hnsw:space": "cosine", + "description": "CrewAI Knowledge Base", + }, + ) + + self._embedding_service = EmbeddingService( + provider=self.embedding_provider, + model=self.embedding_model, + **self.embedding_config, + ) + except Exception as e: + logger.error(f"Failed to initialize ChromaDB: {e}") + raise + + super().model_post_init(__context) + + def add( + self, + content: str | Path, + data_type: str | DataType | None = None, + metadata: dict[str, Any] | None = None, + loader: BaseLoader | None = None, + chunker: BaseChunker | None = None, + **kwargs: Any, + ) -> None: + source_content = SourceContent(content) + + data_type = self._get_data_type(data_type=data_type, content=source_content) + + if not loader: + loader = data_type.get_loader() + + if not chunker: + chunker = data_type.get_chunker() + + loader_result = loader.load(source_content) + doc_id = loader_result.doc_id + + existing_doc = self._collection.get( + where={"source": source_content.source_ref}, limit=1 + ) + existing_doc_id = ( + existing_doc and existing_doc["metadatas"][0]["doc_id"] + if existing_doc["metadatas"] + else None + ) + + if existing_doc_id == doc_id: + logger.warning( + f"Document with source {loader_result.source} already exists" + ) + return + + # Document with same source ref does exists but the content has changed, deleting the oldest reference + if existing_doc_id and existing_doc_id != loader_result.doc_id: + logger.warning(f"Deleting old document with doc_id {existing_doc_id}") + self._collection.delete(where={"doc_id": existing_doc_id}) + + documents = [] + + chunks = chunker.chunk(loader_result.content) + for i, chunk in enumerate(chunks): + doc_metadata = (metadata or {}).copy() + doc_metadata["chunk_index"] = i + documents.append( + Document( + id=compute_sha256(chunk), + content=chunk, + metadata=doc_metadata, + data_type=data_type, + source=loader_result.source, + ) + ) + + if not documents: + logger.warning("No documents to add") + return + + contents = [doc.content for doc in documents] + try: + embeddings = self._embedding_service.embed_batch(contents) + except Exception as e: + logger.error(f"Failed to generate embeddings: {e}") + return + + ids = [doc.id for doc in documents] + metadatas = [] + + for doc in documents: + doc_metadata = doc.metadata.copy() + doc_metadata.update( + { + "data_type": doc.data_type.value, + "source": doc.source, + "doc_id": doc_id, + } + ) + metadatas.append(doc_metadata) + + try: + self._collection.add( + ids=ids, + embeddings=embeddings, + documents=contents, + metadatas=metadatas, + ) + logger.info(f"Added {len(documents)} documents to knowledge base") + except Exception as e: + logger.error(f"Failed to add documents to ChromaDB: {e}") + + def query(self, question: str, where: dict[str, Any] | None = None) -> str: # type: ignore + try: + question_embedding = self._embedding_service.embed_text(question) + + results = self._collection.query( + query_embeddings=[question_embedding], + n_results=self.top_k, + where=where, + include=["documents", "metadatas", "distances"], + ) + + if ( + not results + or not results.get("documents") + or not results["documents"][0] + ): + return "No relevant content found." + + documents = results["documents"][0] + metadatas = results.get("metadatas", [None])[0] or [] + distances = results.get("distances", [None])[0] or [] + + # Return sources with relevance scores + formatted_results = [] + for i, doc in enumerate(documents): + metadata = metadatas[i] if i < len(metadatas) else {} + distance = distances[i] if i < len(distances) else 1.0 + source = metadata.get("source", "unknown") if metadata else "unknown" + score = ( + 1 - distance if distance is not None else 0 + ) # Convert distance to similarity + formatted_results.append( + f"[Source: {source}, Relevance: {score:.3f}]\n{doc}" + ) + + return "\n\n".join(formatted_results) + except Exception as e: + logger.error(f"Query failed: {e}") + return f"Error querying knowledge base: {e}" + + def delete_collection(self) -> None: + try: + self._client.delete_collection(self.collection_name) + logger.info(f"Deleted collection: {self.collection_name}") + except Exception as e: + logger.error(f"Failed to delete collection: {e}") + + def get_collection_info(self) -> dict[str, Any]: + try: + count = self._collection.count() + return { + "name": self.collection_name, + "count": count, + "embedding_model": self.embedding_model, + } + except Exception as e: + logger.error(f"Failed to get collection info: {e}") + return {"error": str(e)} + + @staticmethod + def _get_data_type( + content: SourceContent, data_type: str | DataType | None = None + ) -> DataType: + try: + if isinstance(data_type, str): + return DataType(data_type) + except Exception: # noqa: S110 + pass + + return content.data_type diff --git a/lib/crewai-tools/src/crewai_tools/rag/data_types.py b/lib/crewai-tools/src/crewai_tools/rag/data_types.py new file mode 100644 index 000000000..3e9cf724b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/data_types.py @@ -0,0 +1,161 @@ +from enum import Enum +import os +from pathlib import Path +from urllib.parse import urlparse + +from crewai_tools.rag.base_loader import BaseLoader +from crewai_tools.rag.chunkers.base_chunker import BaseChunker + + +class DataType(str, Enum): + PDF_FILE = "pdf_file" + TEXT_FILE = "text_file" + CSV = "csv" + JSON = "json" + XML = "xml" + DOCX = "docx" + MDX = "mdx" + + # Database types + MYSQL = "mysql" + POSTGRES = "postgres" + + # Repository types + GITHUB = "github" + DIRECTORY = "directory" + + # Web types + WEBSITE = "website" + DOCS_SITE = "docs_site" + YOUTUBE_VIDEO = "youtube_video" + YOUTUBE_CHANNEL = "youtube_channel" + + # Raw types + TEXT = "text" + + def get_chunker(self) -> BaseChunker: + from importlib import import_module + + chunkers = { + DataType.PDF_FILE: ("text_chunker", "TextChunker"), + DataType.TEXT_FILE: ("text_chunker", "TextChunker"), + DataType.TEXT: ("text_chunker", "TextChunker"), + DataType.DOCX: ("text_chunker", "DocxChunker"), + DataType.MDX: ("text_chunker", "MdxChunker"), + # Structured formats + DataType.CSV: ("structured_chunker", "CsvChunker"), + DataType.JSON: ("structured_chunker", "JsonChunker"), + DataType.XML: ("structured_chunker", "XmlChunker"), + DataType.WEBSITE: ("web_chunker", "WebsiteChunker"), + DataType.DIRECTORY: ("text_chunker", "TextChunker"), + DataType.YOUTUBE_VIDEO: ("text_chunker", "TextChunker"), + DataType.YOUTUBE_CHANNEL: ("text_chunker", "TextChunker"), + DataType.GITHUB: ("text_chunker", "TextChunker"), + DataType.DOCS_SITE: ("text_chunker", "TextChunker"), + DataType.MYSQL: ("text_chunker", "TextChunker"), + DataType.POSTGRES: ("text_chunker", "TextChunker"), + } + + if self not in chunkers: + raise ValueError(f"No chunker defined for {self}") + module_name, class_name = chunkers[self] + module_path = f"crewai_tools.rag.chunkers.{module_name}" + + try: + module = import_module(module_path) + return getattr(module, class_name)() + except Exception as e: + raise ValueError(f"Error loading chunker for {self}: {e}") from e + + def get_loader(self) -> BaseLoader: + from importlib import import_module + + loaders = { + DataType.PDF_FILE: ("pdf_loader", "PDFLoader"), + DataType.TEXT_FILE: ("text_loader", "TextFileLoader"), + DataType.TEXT: ("text_loader", "TextLoader"), + DataType.XML: ("xml_loader", "XMLLoader"), + DataType.WEBSITE: ("webpage_loader", "WebPageLoader"), + DataType.MDX: ("mdx_loader", "MDXLoader"), + DataType.JSON: ("json_loader", "JSONLoader"), + DataType.DOCX: ("docx_loader", "DOCXLoader"), + DataType.CSV: ("csv_loader", "CSVLoader"), + DataType.DIRECTORY: ("directory_loader", "DirectoryLoader"), + DataType.YOUTUBE_VIDEO: ("youtube_video_loader", "YoutubeVideoLoader"), + DataType.YOUTUBE_CHANNEL: ( + "youtube_channel_loader", + "YoutubeChannelLoader", + ), + DataType.GITHUB: ("github_loader", "GithubLoader"), + DataType.DOCS_SITE: ("docs_site_loader", "DocsSiteLoader"), + DataType.MYSQL: ("mysql_loader", "MySQLLoader"), + DataType.POSTGRES: ("postgres_loader", "PostgresLoader"), + } + + if self not in loaders: + raise ValueError(f"No loader defined for {self}") + module_name, class_name = loaders[self] + module_path = f"crewai_tools.rag.loaders.{module_name}" + try: + module = import_module(module_path) + return getattr(module, class_name)() + except Exception as e: + raise ValueError(f"Error loading loader for {self}: {e}") from e + + +class DataTypes: + @staticmethod + def from_content(content: str | Path | None = None) -> DataType: + if content is None: + return DataType.TEXT + + if isinstance(content, Path): + content = str(content) + + is_url = False + if isinstance(content, str): + try: + url = urlparse(content) + is_url = bool(url.scheme and url.netloc) or url.scheme == "file" + except Exception: # noqa: S110 + pass + + def get_file_type(path: str) -> DataType | None: + mapping = { + ".pdf": DataType.PDF_FILE, + ".csv": DataType.CSV, + ".mdx": DataType.MDX, + ".md": DataType.MDX, + ".docx": DataType.DOCX, + ".json": DataType.JSON, + ".xml": DataType.XML, + ".txt": DataType.TEXT_FILE, + } + for ext, dtype in mapping.items(): + if path.endswith(ext): + return dtype + return None + + if is_url: + dtype = get_file_type(url.path) + if dtype: + return dtype + + if "docs" in url.netloc or ("docs" in url.path and url.scheme != "file"): + return DataType.DOCS_SITE + if "github.com" in url.netloc: + return DataType.GITHUB + + return DataType.WEBSITE + + if os.path.isfile(content): + dtype = get_file_type(content) + if dtype: + return dtype + + if os.path.exists(content): + return DataType.TEXT_FILE + elif os.path.isdir(content): + return DataType.DIRECTORY + + return DataType.TEXT diff --git a/lib/crewai-tools/src/crewai_tools/rag/embedding_service.py b/lib/crewai-tools/src/crewai_tools/rag/embedding_service.py new file mode 100644 index 000000000..174273140 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/embedding_service.py @@ -0,0 +1,511 @@ +""" +Enhanced embedding service that leverages CrewAI's existing embedding providers. +This replaces the litellm-based EmbeddingService with a more flexible architecture. +""" + +from __future__ import annotations + +import logging +import os +from typing import Any + +from pydantic import BaseModel, Field + + +logger = logging.getLogger(__name__) + + +class EmbeddingConfig(BaseModel): + """Configuration for embedding providers.""" + + provider: str = Field(description="Embedding provider name") + model: str = Field(description="Model name to use") + api_key: str | None = Field(default=None, description="API key for the provider") + timeout: float | None = Field( + default=30.0, description="Request timeout in seconds" + ) + max_retries: int = Field(default=3, description="Maximum number of retries") + batch_size: int = Field( + default=100, description="Batch size for processing multiple texts" + ) + extra_config: dict[str, Any] = Field( + default_factory=dict, description="Additional provider-specific configuration" + ) + + +class EmbeddingService: + """ + Enhanced embedding service that uses CrewAI's existing embedding providers. + + Supports multiple providers: + - openai: OpenAI embeddings (text-embedding-3-small, text-embedding-3-large, etc.) + - voyageai: Voyage AI embeddings (voyage-2, voyage-large-2, etc.) + - cohere: Cohere embeddings (embed-english-v3.0, embed-multilingual-v3.0, etc.) + - google-generativeai: Google Gemini embeddings (models/embedding-001, etc.) + - google-vertex: Google Vertex embeddings (models/embedding-001, etc.) + - huggingface: Hugging Face embeddings (sentence-transformers/all-MiniLM-L6-v2, etc.) + - jina: Jina embeddings (jina-embeddings-v2-base-en, etc.) + - ollama: Ollama embeddings (nomic-embed-text, etc.) + - openai: OpenAI embeddings (text-embedding-3-small, text-embedding-3-large, etc.) + - roboflow: Roboflow embeddings (roboflow-embeddings-v2-base-en, etc.) + - voyageai: Voyage AI embeddings (voyage-2, voyage-large-2, etc.) + - watsonx: Watson X embeddings (ibm/slate-125m-english-rtrvr, etc.) + - custom: Custom embeddings (embedding_callable, etc.) + - sentence-transformer: Sentence Transformers embeddings (all-MiniLM-L6-v2, etc.) + - text2vec: Text2Vec embeddings (text2vec-base-en, etc.) + - openclip: OpenClip embeddings (openclip-large-v2, etc.) + - instructor: Instructor embeddings (hkunlp/instructor-large, etc.) + - onnx: ONNX embeddings (onnx-large-v2, etc.) + """ + + def __init__( + self, + provider: str = "openai", + model: str = "text-embedding-3-small", + api_key: str | None = None, + **kwargs: Any, + ): + """ + Initialize the embedding service. + + Args: + provider: The embedding provider to use + model: The model name + api_key: API key (if not provided, will look for environment variables) + **kwargs: Additional configuration options + """ + self.config = EmbeddingConfig( + provider=provider, + model=model, + api_key=api_key or self._get_default_api_key(provider), + **kwargs, + ) + + self._embedding_function = None + self._initialize_embedding_function() + + @staticmethod + def _get_default_api_key(provider: str) -> str | None: + """Get default API key from environment variables.""" + env_key_map = { + "azure": "AZURE_OPENAI_API_KEY", + "amazon-bedrock": "AWS_ACCESS_KEY_ID", # or AWS_PROFILE + "cohere": "COHERE_API_KEY", + "google-generativeai": "GOOGLE_API_KEY", + "google-vertex": "GOOGLE_APPLICATION_CREDENTIALS", + "huggingface": "HUGGINGFACE_API_KEY", + "jina": "JINA_API_KEY", + "ollama": None, # Ollama typically runs locally without API key + "openai": "OPENAI_API_KEY", + "roboflow": "ROBOFLOW_API_KEY", + "voyageai": "VOYAGE_API_KEY", + "watsonx": "WATSONX_API_KEY", + } + + env_key = env_key_map.get(provider) + if env_key: + return os.getenv(env_key) + return None + + def _initialize_embedding_function(self): + """Initialize the embedding function using CrewAI's factory.""" + try: + from crewai.rag.embeddings.factory import build_embedder + + # Build the configuration for CrewAI's factory + config = self._build_provider_config() + + # Create the embedding function + self._embedding_function = build_embedder(config) + + logger.info( + f"Initialized {self.config.provider} embedding service with model " + f"{self.config.model}" + ) + + except ImportError as e: + raise ImportError( + f"CrewAI embedding providers not available. " + f"Make sure crewai is installed: {e}" + ) from e + except Exception as e: + logger.error(f"Failed to initialize embedding function: {e}") + raise RuntimeError( + f"Failed to initialize {self.config.provider} embedding service: {e}" + ) from e + + def _build_provider_config(self) -> dict[str, Any]: + """Build configuration dictionary for CrewAI's embedding factory.""" + base_config = {"provider": self.config.provider, "config": {}} + + # Provider-specific configuration mapping + if self.config.provider == "openai": + base_config["config"] = { + "api_key": self.config.api_key, + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "azure": + base_config["config"] = { + "api_key": self.config.api_key, + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "voyageai": + base_config["config"] = { + "api_key": self.config.api_key, + "model": self.config.model, + "max_retries": self.config.max_retries, + "timeout": self.config.timeout, + **self.config.extra_config, + } + elif self.config.provider == "cohere": + base_config["config"] = { + "api_key": self.config.api_key, + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider in ["google-generativeai", "google-vertex"]: + base_config["config"] = { + "api_key": self.config.api_key, + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "amazon-bedrock": + base_config["config"] = { + "aws_access_key_id": self.config.api_key, + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "huggingface": + base_config["config"] = { + "api_key": self.config.api_key, + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "jina": + base_config["config"] = { + "api_key": self.config.api_key, + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "ollama": + base_config["config"] = { + "model": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "sentence-transformer": + base_config["config"] = { + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "instructor": + base_config["config"] = { + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "onnx": + base_config["config"] = { + **self.config.extra_config, + } + elif self.config.provider == "roboflow": + base_config["config"] = { + "api_key": self.config.api_key, + **self.config.extra_config, + } + elif self.config.provider == "openclip": + base_config["config"] = { + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "text2vec": + base_config["config"] = { + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "watsonx": + base_config["config"] = { + "api_key": self.config.api_key, + "model_name": self.config.model, + **self.config.extra_config, + } + elif self.config.provider == "custom": + # Custom provider requires embedding_callable in extra_config + base_config["config"] = { + **self.config.extra_config, + } + else: + # Generic configuration for any unlisted providers + base_config["config"] = { + "api_key": self.config.api_key, + "model": self.config.model, + **self.config.extra_config, + } + + return base_config + + def embed_text(self, text: str) -> list[float]: + """ + Generate embedding for a single text. + + Args: + text: Text to embed + + Returns: + List of floats representing the embedding + + Raises: + RuntimeError: If embedding generation fails + """ + if not text or not text.strip(): + logger.warning("Empty text provided for embedding") + return [] + + try: + # Use ChromaDB's embedding function interface + embeddings = self._embedding_function([text]) # type: ignore + return embeddings[0] if embeddings else [] + + except Exception as e: + logger.error(f"Error generating embedding for text: {e}") + raise RuntimeError(f"Failed to generate embedding: {e}") from e + + def embed_batch(self, texts: list[str]) -> list[list[float]]: + """ + Generate embeddings for multiple texts. + + Args: + texts: List of texts to embed + + Returns: + List of embedding vectors + + Raises: + RuntimeError: If embedding generation fails + """ + if not texts: + return [] + + # Filter out empty texts + valid_texts = [text for text in texts if text and text.strip()] + if not valid_texts: + logger.warning("No valid texts provided for batch embedding") + return [] + + try: + # Process in batches to avoid API limits + all_embeddings = [] + + for i in range(0, len(valid_texts), self.config.batch_size): + batch = valid_texts[i : i + self.config.batch_size] + batch_embeddings = self._embedding_function(batch) # type: ignore + all_embeddings.extend(batch_embeddings) + + return all_embeddings + + except Exception as e: + logger.error(f"Error generating batch embeddings: {e}") + raise RuntimeError(f"Failed to generate batch embeddings: {e}") from e + + def get_embedding_dimension(self) -> int | None: + """ + Get the dimension of embeddings produced by this service. + + Returns: + Embedding dimension or None if unknown + """ + # Try to get dimension by generating a test embedding + try: + test_embedding = self.embed_text("test") + return len(test_embedding) if test_embedding else None + except Exception: + logger.warning("Could not determine embedding dimension") + return None + + def validate_connection(self) -> bool: + """ + Validate that the embedding service is working correctly. + + Returns: + True if the service is working, False otherwise + """ + try: + test_embedding = self.embed_text("test connection") + return len(test_embedding) > 0 + except Exception as e: + logger.error(f"Connection validation failed: {e}") + return False + + def get_service_info(self) -> dict[str, Any]: + """ + Get information about the current embedding service. + + Returns: + Dictionary with service information + """ + return { + "provider": self.config.provider, + "model": self.config.model, + "embedding_dimension": self.get_embedding_dimension(), + "batch_size": self.config.batch_size, + "is_connected": self.validate_connection(), + } + + @classmethod + def list_supported_providers(cls) -> list[str]: + """ + List all supported embedding providers. + + Returns: + List of supported provider names + """ + return [ + "azure", + "amazon-bedrock", + "cohere", + "custom", + "google-generativeai", + "google-vertex", + "huggingface", + "instructor", + "jina", + "ollama", + "onnx", + "openai", + "openclip", + "roboflow", + "sentence-transformer", + "text2vec", + "voyageai", + "watsonx", + ] + + @classmethod + def create_openai_service( + cls, + model: str = "text-embedding-3-small", + api_key: str | None = None, + **kwargs: Any, + ) -> EmbeddingService: + """Create an OpenAI embedding service.""" + return cls(provider="openai", model=model, api_key=api_key, **kwargs) + + @classmethod + def create_voyage_service( + cls, model: str = "voyage-2", api_key: str | None = None, **kwargs: Any + ) -> EmbeddingService: + """Create a Voyage AI embedding service.""" + return cls(provider="voyageai", model=model, api_key=api_key, **kwargs) + + @classmethod + def create_cohere_service( + cls, + model: str = "embed-english-v3.0", + api_key: str | None = None, + **kwargs: Any, + ) -> EmbeddingService: + """Create a Cohere embedding service.""" + return cls(provider="cohere", model=model, api_key=api_key, **kwargs) + + @classmethod + def create_gemini_service( + cls, + model: str = "models/embedding-001", + api_key: str | None = None, + **kwargs: Any, + ) -> EmbeddingService: + """Create a Google Gemini embedding service.""" + return cls( + provider="google-generativeai", model=model, api_key=api_key, **kwargs + ) + + @classmethod + def create_azure_service( + cls, + model: str = "text-embedding-ada-002", + api_key: str | None = None, + **kwargs: Any, + ) -> EmbeddingService: + """Create an Azure OpenAI embedding service.""" + return cls(provider="azure", model=model, api_key=api_key, **kwargs) + + @classmethod + def create_bedrock_service( + cls, + model: str = "amazon.titan-embed-text-v1", + api_key: str | None = None, + **kwargs: Any, + ) -> EmbeddingService: + """Create an Amazon Bedrock embedding service.""" + return cls(provider="amazon-bedrock", model=model, api_key=api_key, **kwargs) + + @classmethod + def create_huggingface_service( + cls, + model: str = "sentence-transformers/all-MiniLM-L6-v2", + api_key: str | None = None, + **kwargs: Any, + ) -> EmbeddingService: + """Create a Hugging Face embedding service.""" + return cls(provider="huggingface", model=model, api_key=api_key, **kwargs) + + @classmethod + def create_sentence_transformer_service( + cls, + model: str = "all-MiniLM-L6-v2", + **kwargs: Any, + ) -> EmbeddingService: + """Create a Sentence Transformers embedding service (local).""" + return cls(provider="sentence-transformer", model=model, **kwargs) + + @classmethod + def create_ollama_service( + cls, + model: str = "nomic-embed-text", + **kwargs: Any, + ) -> EmbeddingService: + """Create an Ollama embedding service (local).""" + return cls(provider="ollama", model=model, **kwargs) + + @classmethod + def create_jina_service( + cls, + model: str = "jina-embeddings-v2-base-en", + api_key: str | None = None, + **kwargs: Any, + ) -> EmbeddingService: + """Create a Jina AI embedding service.""" + return cls(provider="jina", model=model, api_key=api_key, **kwargs) + + @classmethod + def create_instructor_service( + cls, + model: str = "hkunlp/instructor-large", + **kwargs: Any, + ) -> EmbeddingService: + """Create an Instructor embedding service.""" + return cls(provider="instructor", model=model, **kwargs) + + @classmethod + def create_watsonx_service( + cls, + model: str = "ibm/slate-125m-english-rtrvr", + api_key: str | None = None, + **kwargs: Any, + ) -> EmbeddingService: + """Create a Watson X embedding service.""" + return cls(provider="watsonx", model=model, api_key=api_key, **kwargs) + + @classmethod + def create_custom_service( + cls, + embedding_callable: Any, + **kwargs: Any, + ) -> EmbeddingService: + """Create a custom embedding service with your own embedding function.""" + return cls( + provider="custom", + model="custom", + extra_config={"embedding_callable": embedding_callable}, + **kwargs, + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/__init__.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/__init__.py new file mode 100644 index 000000000..f6abce520 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/__init__.py @@ -0,0 +1,27 @@ +from crewai_tools.rag.loaders.csv_loader import CSVLoader +from crewai_tools.rag.loaders.directory_loader import DirectoryLoader +from crewai_tools.rag.loaders.docx_loader import DOCXLoader +from crewai_tools.rag.loaders.json_loader import JSONLoader +from crewai_tools.rag.loaders.mdx_loader import MDXLoader +from crewai_tools.rag.loaders.pdf_loader import PDFLoader +from crewai_tools.rag.loaders.text_loader import TextFileLoader, TextLoader +from crewai_tools.rag.loaders.webpage_loader import WebPageLoader +from crewai_tools.rag.loaders.xml_loader import XMLLoader +from crewai_tools.rag.loaders.youtube_channel_loader import YoutubeChannelLoader +from crewai_tools.rag.loaders.youtube_video_loader import YoutubeVideoLoader + + +__all__ = [ + "CSVLoader", + "DOCXLoader", + "DirectoryLoader", + "JSONLoader", + "MDXLoader", + "PDFLoader", + "TextFileLoader", + "TextLoader", + "WebPageLoader", + "XMLLoader", + "YoutubeChannelLoader", + "YoutubeVideoLoader", +] diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/csv_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/csv_loader.py new file mode 100644 index 000000000..ad1bdff99 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/csv_loader.py @@ -0,0 +1,63 @@ +import csv +from io import StringIO + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.loaders.utils import load_from_url +from crewai_tools.rag.source_content import SourceContent + + +class CSVLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + source_ref = source_content.source_ref + + content_str = source_content.source + if source_content.is_url(): + content_str = load_from_url( + content_str, + kwargs, + accept_header="text/csv, application/csv, text/plain", + loader_name="CSVLoader", + ) + elif source_content.path_exists(): + content_str = self._load_from_file(content_str) + + return self._parse_csv(content_str, source_ref) + + @staticmethod + def _load_from_file(path: str) -> str: + with open(path, encoding="utf-8") as file: + return file.read() + + def _parse_csv(self, content: str, source_ref: str) -> LoaderResult: + try: + csv_reader = csv.DictReader(StringIO(content)) + + text_parts = [] + headers = csv_reader.fieldnames + + if headers: + text_parts.append("Headers: " + " | ".join(headers)) + text_parts.append("-" * 50) + + for row_num, row in enumerate(csv_reader, 1): + row_text = " | ".join([f"{k}: {v}" for k, v in row.items() if v]) + text_parts.append(f"Row {row_num}: {row_text}") + + text = "\n".join(text_parts) + + metadata = { + "format": "csv", + "columns": headers, + "rows": len(text_parts) - 2 if headers else 0, + } + + except Exception as e: + text = content + metadata = {"format": "csv", "parse_error": str(e)} + + return LoaderResult( + content=text, + source=source_ref, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=source_ref, content=text), + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/directory_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/directory_loader.py new file mode 100644 index 000000000..c5420ab4f --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/directory_loader.py @@ -0,0 +1,166 @@ +import os +from pathlib import Path + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class DirectoryLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + """Load and process all files from a directory recursively. + + Args: + source_content: Directory path or URL to a directory listing + **kwargs: Additional options: + - recursive: bool (default True) - Whether to search recursively + - include_extensions: list - Only include files with these extensions + - exclude_extensions: list - Exclude files with these extensions + - max_files: int - Maximum number of files to process + """ + source_ref = source_content.source_ref + + if source_content.is_url(): + raise ValueError( + "URL directory loading is not supported. Please provide a local directory path." + ) + + if not os.path.exists(source_ref): + raise FileNotFoundError(f"Directory does not exist: {source_ref}") + + if not os.path.isdir(source_ref): + raise ValueError(f"Path is not a directory: {source_ref}") + + return self._process_directory(source_ref, kwargs) + + def _process_directory(self, dir_path: str, kwargs: dict) -> LoaderResult: + recursive: bool = kwargs.get("recursive", True) + include_extensions: list[str] | None = kwargs.get("include_extensions", None) + exclude_extensions: list[str] | None = kwargs.get("exclude_extensions", None) + max_files: int | None = kwargs.get("max_files", None) + + files = self._find_files( + dir_path, recursive, include_extensions, exclude_extensions + ) + + if max_files is not None and len(files) > max_files: + files = files[:max_files] + + all_contents = [] + processed_files = [] + errors = [] + + for file_path in files: + try: + result = self._process_single_file(file_path) + if result: + all_contents.append(f"=== File: {file_path} ===\n{result.content}") + processed_files.append( + { + "path": file_path, + "metadata": result.metadata, + "source": result.source, + } + ) + except Exception as e: # noqa: PERF203 + error_msg = f"Error processing {file_path}: {e!s}" + errors.append(error_msg) + all_contents.append(f"=== File: {file_path} (ERROR) ===\n{error_msg}") + + combined_content = "\n\n".join(all_contents) + + metadata = { + "format": "directory", + "directory_path": dir_path, + "total_files": len(files), + "processed_files": len(processed_files), + "errors": len(errors), + "file_details": processed_files, + "error_details": errors, + } + + return LoaderResult( + content=combined_content, + source=dir_path, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=dir_path, content=combined_content), + ) + + def _find_files( + self, + dir_path: str, + recursive: bool, + include_ext: list[str] | None = None, + exclude_ext: list[str] | None = None, + ) -> list[str]: + """Find all files in directory matching criteria.""" + files = [] + + if recursive: + for root, dirs, filenames in os.walk(dir_path): + dirs[:] = [d for d in dirs if not d.startswith(".")] + + for filename in filenames: + if self._should_include_file(filename, include_ext, exclude_ext): + files.append(os.path.join(root, filename)) # noqa: PERF401 + else: + try: + for item in os.listdir(dir_path): + item_path = os.path.join(dir_path, item) + if os.path.isfile(item_path) and self._should_include_file( + item, include_ext, exclude_ext + ): + files.append(item_path) + except PermissionError: + pass + + return sorted(files) + + @staticmethod + def _should_include_file( + filename: str, + include_ext: list[str] | None = None, + exclude_ext: list[str] | None = None, + ) -> bool: + """Determine if a file should be included based on criteria.""" + if filename.startswith("."): + return False + + _, ext = os.path.splitext(filename.lower()) + + if include_ext: + if ext not in [ + e.lower() if e.startswith(".") else f".{e.lower()}" for e in include_ext + ]: + return False + + if exclude_ext: + if ext in [ + e.lower() if e.startswith(".") else f".{e.lower()}" for e in exclude_ext + ]: + return False + + return True + + @staticmethod + def _process_single_file(file_path: str) -> LoaderResult: + from crewai_tools.rag.data_types import DataTypes + + data_type = DataTypes.from_content(Path(file_path)) + + loader = data_type.get_loader() + + result = loader.load(SourceContent(file_path)) + + if result.metadata is None: + result.metadata = {} + + result.metadata.update( + { + "file_path": file_path, + "file_size": os.path.getsize(file_path), + "data_type": str(data_type), + "loader_type": loader.__class__.__name__, + } + ) + + return result diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/docs_site_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/docs_site_loader.py new file mode 100644 index 000000000..4ad2aa5d5 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/docs_site_loader.py @@ -0,0 +1,109 @@ +"""Documentation site loader.""" + +from urllib.parse import urljoin, urlparse + +from bs4 import BeautifulSoup +import requests + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class DocsSiteLoader(BaseLoader): + """Loader for documentation websites.""" + + def load(self, source: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + """Load content from a documentation site. + + Args: + source: Documentation site URL + **kwargs: Additional arguments + + Returns: + LoaderResult with documentation content + """ + docs_url = source.source + + try: + response = requests.get(docs_url, timeout=30) + response.raise_for_status() + except requests.RequestException as e: + raise ValueError( + f"Unable to fetch documentation from {docs_url}: {e}" + ) from e + + soup = BeautifulSoup(response.text, "html.parser") + + for script in soup(["script", "style"]): + script.decompose() + + title = soup.find("title") + title_text = title.get_text(strip=True) if title else "Documentation" + + for selector in [ + "main", + "article", + '[role="main"]', + ".content", + "#content", + ".documentation", + ]: + main_content = soup.select_one(selector) + if main_content: + break + + if not main_content: + main_content = soup.find("body") + + if not main_content: + raise ValueError( + f"Unable to extract content from documentation site: {docs_url}" + ) + + text_parts = [f"Title: {title_text}", ""] + + headings = main_content.find_all(["h1", "h2", "h3"]) + if headings: + text_parts.append("Table of Contents:") + for heading in headings[:15]: + level = int(heading.name[1]) + indent = " " * (level - 1) + text_parts.append(f"{indent}- {heading.get_text(strip=True)}") + text_parts.append("") + + text = main_content.get_text(separator="\n", strip=True) + lines = [line.strip() for line in text.split("\n") if line.strip()] + text_parts.extend(lines) + + nav_links = [] + for nav_selector in ["nav", ".sidebar", ".toc", ".navigation"]: + nav = soup.select_one(nav_selector) + if nav: + links = nav.find_all("a", href=True) + for link in links[:20]: + href = link.get("href", "") + if isinstance(href, str) and not href.startswith( + ("http://", "https://", "mailto:", "#") + ): + full_url = urljoin(docs_url, href) + nav_links.append(f"- {link.get_text(strip=True)}: {full_url}") + + if nav_links: + text_parts.append("") + text_parts.append("Related documentation pages:") + text_parts.extend(nav_links[:10]) + + content = "\n".join(text_parts) + + if len(content) > 100000: + content = content[:100000] + "\n\n[Content truncated...]" + + return LoaderResult( + content=content, + metadata={ + "source": docs_url, + "title": title_text, + "domain": urlparse(docs_url).netloc, + }, + doc_id=self.generate_doc_id(source_ref=docs_url, content=content), + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/docx_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/docx_loader.py new file mode 100644 index 000000000..1433c494c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/docx_loader.py @@ -0,0 +1,86 @@ +import os +import tempfile +from typing import Any + +import requests + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class DOCXLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + try: + from docx import Document as DocxDocument + except ImportError as e: + raise ImportError( + "python-docx is required for DOCX loading. Install with: 'uv pip install python-docx' or pip install crewai-tools[rag]" + ) from e + + source_ref = source_content.source_ref + + if source_content.is_url(): + temp_file = self._download_from_url(source_ref, kwargs) + try: + return self._load_from_file(temp_file, source_ref, DocxDocument) + finally: + os.unlink(temp_file) + elif source_content.path_exists(): + return self._load_from_file(source_ref, source_ref, DocxDocument) + else: + raise ValueError( + f"Source must be a valid file path or URL, got: {source_content.source}" + ) + + @staticmethod + def _download_from_url(url: str, kwargs: dict) -> str: + headers = kwargs.get( + "headers", + { + "Accept": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + "User-Agent": "Mozilla/5.0 (compatible; crewai-tools DOCXLoader)", + }, + ) + + try: + response = requests.get(url, headers=headers, timeout=30) + response.raise_for_status() + + # Create temporary file to save the DOCX content + with tempfile.NamedTemporaryFile(suffix=".docx", delete=False) as temp_file: + temp_file.write(response.content) + return temp_file.name + except Exception as e: + raise ValueError(f"Error fetching content from URL {url}: {e!s}") from e + + def _load_from_file( + self, + file_path: str, + source_ref: str, + DocxDocument: Any, # noqa: N803 + ) -> LoaderResult: + try: + doc = DocxDocument(file_path) + + text_parts = [] + for paragraph in doc.paragraphs: + if paragraph.text.strip(): + text_parts.append(paragraph.text) # noqa: PERF401 + + content = "\n".join(text_parts) + + metadata = { + "format": "docx", + "paragraphs": len(doc.paragraphs), + "tables": len(doc.tables), + } + + return LoaderResult( + content=content, + source=source_ref, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=source_ref, content=content), + ) + + except Exception as e: + raise ValueError(f"Error loading DOCX file: {e!s}") from e diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/github_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/github_loader.py new file mode 100644 index 000000000..b1e729d4e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/github_loader.py @@ -0,0 +1,110 @@ +"""GitHub repository content loader.""" + +from github import Github, GithubException + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class GithubLoader(BaseLoader): + """Loader for GitHub repository content.""" + + def load(self, source: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + """Load content from a GitHub repository. + + Args: + source: GitHub repository URL + **kwargs: Additional arguments including gh_token and content_types + + Returns: + LoaderResult with repository content + """ + metadata = kwargs.get("metadata", {}) + gh_token = metadata.get("gh_token") + content_types = metadata.get("content_types", ["code", "repo"]) + + repo_url = source.source + if not repo_url.startswith("https://github.com/"): + raise ValueError(f"Invalid GitHub URL: {repo_url}") + + parts = repo_url.replace("https://github.com/", "").strip("/").split("/") + if len(parts) < 2: + raise ValueError(f"Invalid GitHub repository URL: {repo_url}") + + repo_name = f"{parts[0]}/{parts[1]}" + + g = Github(gh_token) if gh_token else Github() + + try: + repo = g.get_repo(repo_name) + except GithubException as e: + raise ValueError(f"Unable to access repository {repo_name}: {e}") from e + + all_content = [] + + if "repo" in content_types: + all_content.append(f"Repository: {repo.full_name}") + all_content.append(f"Description: {repo.description or 'No description'}") + all_content.append(f"Language: {repo.language or 'Not specified'}") + all_content.append(f"Stars: {repo.stargazers_count}") + all_content.append(f"Forks: {repo.forks_count}") + all_content.append("") + + if "code" in content_types: + try: + readme = repo.get_readme() + all_content.append("README:") + all_content.append(readme.decoded_content.decode(errors="ignore")) + all_content.append("") + except GithubException: + pass + + try: + contents = repo.get_contents("") + if isinstance(contents, list): + all_content.append("Repository structure:") + for content_file in contents[:20]: + all_content.append( # noqa: PERF401 + f"- {content_file.path} ({content_file.type})" + ) + all_content.append("") + except GithubException: + pass + + if "pr" in content_types: + prs = repo.get_pulls(state="open") + pr_list = list(prs[:5]) + if pr_list: + all_content.append("Recent Pull Requests:") + for pr in pr_list: + all_content.append(f"- PR #{pr.number}: {pr.title}") + if pr.body: + body_preview = pr.body[:200].replace("\n", " ") + all_content.append(f" {body_preview}") + all_content.append("") + + if "issue" in content_types: + issues = repo.get_issues(state="open") + issue_list = [i for i in list(issues[:10]) if not i.pull_request][:5] + if issue_list: + all_content.append("Recent Issues:") + for issue in issue_list: + all_content.append(f"- Issue #{issue.number}: {issue.title}") + if issue.body: + body_preview = issue.body[:200].replace("\n", " ") + all_content.append(f" {body_preview}") + all_content.append("") + + if not all_content: + raise ValueError(f"No content could be loaded from repository: {repo_url}") + + content = "\n".join(all_content) + return LoaderResult( + content=content, + metadata={ + "source": repo_url, + "repo": repo_name, + "content_types": content_types, + }, + doc_id=self.generate_doc_id(source_ref=repo_url, content=content), + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/json_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/json_loader.py new file mode 100644 index 000000000..affce196f --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/json_loader.py @@ -0,0 +1,56 @@ +import json + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.loaders.utils import load_from_url +from crewai_tools.rag.source_content import SourceContent + + +class JSONLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + source_ref = source_content.source_ref + content = source_content.source + + if source_content.is_url(): + content = load_from_url( + source_ref, + kwargs, + accept_header="application/json", + loader_name="JSONLoader", + ) + elif source_content.path_exists(): + content = self._load_from_file(source_ref) + + return self._parse_json(content, source_ref) + + @staticmethod + def _load_from_file(path: str) -> str: + with open(path, encoding="utf-8") as file: + return file.read() + + def _parse_json(self, content: str, source_ref: str) -> LoaderResult: + try: + data = json.loads(content) + if isinstance(data, dict): + text = "\n".join( + f"{k}: {json.dumps(v, indent=0)}" for k, v in data.items() + ) + elif isinstance(data, list): + text = "\n".join(json.dumps(item, indent=0) for item in data) + else: + text = json.dumps(data, indent=0) + + metadata = { + "format": "json", + "type": type(data).__name__, + "size": len(data) if isinstance(data, (list, dict)) else 1, + } + except json.JSONDecodeError as e: + text = content + metadata = {"format": "json", "parse_error": str(e)} + + return LoaderResult( + content=text, + source=source_ref, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=source_ref, content=text), + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/mdx_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/mdx_loader.py new file mode 100644 index 000000000..b4e646b46 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/mdx_loader.py @@ -0,0 +1,61 @@ +import re +from typing import Final + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.loaders.utils import load_from_url +from crewai_tools.rag.source_content import SourceContent + + +_IMPORT_PATTERN: Final[re.Pattern[str]] = re.compile(r"^import\s+.*?\n", re.MULTILINE) +_EXPORT_PATTERN: Final[re.Pattern[str]] = re.compile( + r"^export\s+.*?(?:\n|$)", re.MULTILINE +) +_JSX_TAG_PATTERN: Final[re.Pattern[str]] = re.compile(r"<[^>]+>") +_EXTRA_NEWLINES_PATTERN: Final[re.Pattern[str]] = re.compile(r"\n\s*\n\s*\n") + + +class MDXLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + source_ref = source_content.source_ref + content = source_content.source + + if source_content.is_url(): + content = load_from_url( + source_ref, + kwargs, + accept_header="text/markdown, text/x-markdown, text/plain", + loader_name="MDXLoader", + ) + elif source_content.path_exists(): + content = self._load_from_file(source_ref) + + return self._parse_mdx(content, source_ref) + + @staticmethod + def _load_from_file(path: str) -> str: + with open(path, encoding="utf-8") as file: + return file.read() + + def _parse_mdx(self, content: str, source_ref: str) -> LoaderResult: + cleaned_content = content + + # Remove import statements + cleaned_content = _IMPORT_PATTERN.sub("", cleaned_content) + + # Remove export statements + cleaned_content = _EXPORT_PATTERN.sub("", cleaned_content) + + # Remove JSX tags (simple approach) + cleaned_content = _JSX_TAG_PATTERN.sub("", cleaned_content) + + # Clean up extra whitespace + cleaned_content = _EXTRA_NEWLINES_PATTERN.sub("\n\n", cleaned_content) + cleaned_content = cleaned_content.strip() + + metadata = {"format": "mdx"} + return LoaderResult( + content=cleaned_content, + source=source_ref, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=source_ref, content=cleaned_content), + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/mysql_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/mysql_loader.py new file mode 100644 index 000000000..a8b8f32fd --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/mysql_loader.py @@ -0,0 +1,102 @@ +"""MySQL database loader.""" + +from typing import Any +from urllib.parse import urlparse + +from pymysql import Error, connect +from pymysql.cursors import DictCursor + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class MySQLLoader(BaseLoader): + """Loader for MySQL database content.""" + + def load(self, source: SourceContent, **kwargs: Any) -> LoaderResult: # type: ignore[override] + """Load content from a MySQL database table. + + Args: + source: SQL query (e.g., "SELECT * FROM table_name") + **kwargs: Additional arguments including db_uri + + Returns: + LoaderResult with database content + """ + metadata = kwargs.get("metadata", {}) + db_uri = metadata.get("db_uri") + + if not db_uri: + raise ValueError("Database URI is required for MySQL loader") + + query = source.source + + parsed = urlparse(db_uri) + if parsed.scheme not in ["mysql", "mysql+pymysql"]: + raise ValueError(f"Invalid MySQL URI scheme: {parsed.scheme}") + + connection_params = { + "host": parsed.hostname or "localhost", + "port": parsed.port or 3306, + "user": parsed.username, + "password": parsed.password, + "database": parsed.path.lstrip("/") if parsed.path else None, + "charset": "utf8mb4", + "cursorclass": DictCursor, + } + + if not connection_params["database"]: + raise ValueError("Database name is required in the URI") + + try: + connection = connect(**connection_params) + try: + with connection.cursor() as cursor: + cursor.execute(query) + rows = cursor.fetchall() + + if not rows: + content = "No data found in the table" + return LoaderResult( + content=content, + metadata={"source": query, "row_count": 0}, + doc_id=self.generate_doc_id( + source_ref=query, content=content + ), + ) + + text_parts = [] + + columns = list(rows[0].keys()) + text_parts.append(f"Columns: {', '.join(columns)}") + text_parts.append(f"Total rows: {len(rows)}") + text_parts.append("") + + for i, row in enumerate(rows, 1): + text_parts.append(f"Row {i}:") + for col, val in row.items(): + if val is not None: + text_parts.append(f" {col}: {val}") + text_parts.append("") + + content = "\n".join(text_parts) + + if len(content) > 100000: + content = content[:100000] + "\n\n[Content truncated...]" + + return LoaderResult( + content=content, + metadata={ + "source": query, + "database": connection_params["database"], + "row_count": len(rows), + "columns": columns, + }, + doc_id=self.generate_doc_id(source_ref=query, content=content), + ) + finally: + connection.close() + except Error as e: + raise ValueError(f"MySQL database error: {e}") from e + except Exception as e: + raise ValueError(f"Failed to load data from MySQL: {e}") from e diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/pdf_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/pdf_loader.py new file mode 100644 index 000000000..7e7f0f8e3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/pdf_loader.py @@ -0,0 +1,71 @@ +"""PDF loader for extracting text from PDF files.""" + +import os +from pathlib import Path +from typing import Any + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class PDFLoader(BaseLoader): + """Loader for PDF files.""" + + def load(self, source: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + """Load and extract text from a PDF file. + + Args: + source: The source content containing the PDF file path + + Returns: + LoaderResult with extracted text content + + Raises: + FileNotFoundError: If the PDF file doesn't exist + ImportError: If required PDF libraries aren't installed + """ + try: + import pypdf + except ImportError: + try: + import PyPDF2 as pypdf # type: ignore[import-not-found,no-redef] # noqa: N813 + except ImportError as e: + raise ImportError( + "PDF support requires pypdf or PyPDF2. Install with: uv add pypdf" + ) from e + + file_path = source.source + + if not os.path.isfile(file_path): + raise FileNotFoundError(f"PDF file not found: {file_path}") + + text_content = [] + metadata: dict[str, Any] = { + "source": str(file_path), + "file_name": Path(file_path).name, + "file_type": "pdf", + } + + try: + with open(file_path, "rb") as file: + pdf_reader = pypdf.PdfReader(file) + metadata["num_pages"] = len(pdf_reader.pages) + + for page_num, page in enumerate(pdf_reader.pages, 1): + page_text = page.extract_text() + if page_text.strip(): + text_content.append(f"Page {page_num}:\n{page_text}") + except Exception as e: + raise ValueError(f"Error reading PDF file {file_path}: {e!s}") from e + + if not text_content: + content = f"[PDF file with no extractable text: {Path(file_path).name}]" + else: + content = "\n\n".join(text_content) + + return LoaderResult( + content=content, + source=str(file_path), + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=str(file_path), content=content), + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/postgres_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/postgres_loader.py new file mode 100644 index 000000000..b71e2278c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/postgres_loader.py @@ -0,0 +1,100 @@ +"""PostgreSQL database loader.""" + +from urllib.parse import urlparse + +from psycopg2 import Error, connect +from psycopg2.extras import RealDictCursor + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class PostgresLoader(BaseLoader): + """Loader for PostgreSQL database content.""" + + def load(self, source: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + """Load content from a PostgreSQL database table. + + Args: + source: SQL query (e.g., "SELECT * FROM table_name") + **kwargs: Additional arguments including db_uri + + Returns: + LoaderResult with database content + """ + metadata = kwargs.get("metadata", {}) + db_uri = metadata.get("db_uri") + + if not db_uri: + raise ValueError("Database URI is required for PostgreSQL loader") + + query = source.source + + parsed = urlparse(db_uri) + if parsed.scheme not in ["postgresql", "postgres", "postgresql+psycopg2"]: + raise ValueError(f"Invalid PostgreSQL URI scheme: {parsed.scheme}") + + connection_params = { + "host": parsed.hostname or "localhost", + "port": parsed.port or 5432, + "user": parsed.username, + "password": parsed.password, + "database": parsed.path.lstrip("/") if parsed.path else None, + "cursor_factory": RealDictCursor, + } + + if not connection_params["database"]: + raise ValueError("Database name is required in the URI") + + try: + connection = connect(**connection_params) + try: + with connection.cursor() as cursor: + cursor.execute(query) + rows = cursor.fetchall() + + if not rows: + content = "No data found in the table" + return LoaderResult( + content=content, + metadata={"source": query, "row_count": 0}, + doc_id=self.generate_doc_id( + source_ref=query, content=content + ), + ) + + text_parts = [] + + columns = list(rows[0].keys()) + text_parts.append(f"Columns: {', '.join(columns)}") + text_parts.append(f"Total rows: {len(rows)}") + text_parts.append("") + + for i, row in enumerate(rows, 1): + text_parts.append(f"Row {i}:") + for col, val in row.items(): + if val is not None: + text_parts.append(f" {col}: {val}") + text_parts.append("") + + content = "\n".join(text_parts) + + if len(content) > 100000: + content = content[:100000] + "\n\n[Content truncated...]" + + return LoaderResult( + content=content, + metadata={ + "source": query, + "database": connection_params["database"], + "row_count": len(rows), + "columns": columns, + }, + doc_id=self.generate_doc_id(source_ref=query, content=content), + ) + finally: + connection.close() + except Error as e: + raise ValueError(f"PostgreSQL database error: {e}") from e + except Exception as e: + raise ValueError(f"Failed to load data from PostgreSQL: {e}") from e diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/text_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/text_loader.py new file mode 100644 index 000000000..4c9be1eaa --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/text_loader.py @@ -0,0 +1,29 @@ +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class TextFileLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + source_ref = source_content.source_ref + if not source_content.path_exists(): + raise FileNotFoundError( + f"The following file does not exist: {source_content.source}" + ) + + with open(source_content.source, encoding="utf-8") as file: + content = file.read() + + return LoaderResult( + content=content, + source=source_ref, + doc_id=self.generate_doc_id(source_ref=source_ref, content=content), + ) + + +class TextLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + return LoaderResult( + content=source_content.source, + source=source_content.source_ref, + doc_id=self.generate_doc_id(content=source_content.source), + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/utils.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/utils.py new file mode 100644 index 000000000..f13d06dcf --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/utils.py @@ -0,0 +1,36 @@ +"""Utility functions for RAG loaders.""" + + +def load_from_url( + url: str, kwargs: dict, accept_header: str = "*/*", loader_name: str = "Loader" +) -> str: + """Load content from a URL. + + Args: + url: The URL to fetch content from + kwargs: Additional keyword arguments (can include 'headers' override) + accept_header: The Accept header value for the request + loader_name: The name of the loader for the User-Agent header + + Returns: + The text content from the URL + + Raises: + ValueError: If there's an error fetching the URL + """ + import requests + + headers = kwargs.get( + "headers", + { + "Accept": accept_header, + "User-Agent": f"Mozilla/5.0 (compatible; crewai-tools {loader_name})", + }, + ) + + try: + response = requests.get(url, headers=headers, timeout=30) + response.raise_for_status() + return response.text + except Exception as e: + raise ValueError(f"Error fetching content from URL {url}: {e!s}") from e diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/webpage_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/webpage_loader.py new file mode 100644 index 000000000..c3b02fbaf --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/webpage_loader.py @@ -0,0 +1,59 @@ +import re +from typing import Final + +from bs4 import BeautifulSoup +import requests + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +_SPACES_PATTERN: Final[re.Pattern[str]] = re.compile(r"[ \t]+") +_NEWLINE_PATTERN: Final[re.Pattern[str]] = re.compile(r"\s+\n\s+") + + +class WebPageLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + url = source_content.source + headers = kwargs.get( + "headers", + { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36", + "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", + "Accept-Language": "en-US,en;q=0.9", + }, + ) + + try: + response = requests.get(url, timeout=15, headers=headers) + response.encoding = response.apparent_encoding + + soup = BeautifulSoup(response.text, "html.parser") + + for script in soup(["script", "style"]): + script.decompose() + + text = soup.get_text(" ") + text = _SPACES_PATTERN.sub(" ", text) + text = _NEWLINE_PATTERN.sub("\n", text) + text = text.strip() + + title = ( + soup.title.string.strip() if soup.title and soup.title.string else "" + ) + metadata = { + "url": url, + "title": title, + "status_code": response.status_code, + "content_type": response.headers.get("content-type", ""), + } + + return LoaderResult( + content=text, + source=url, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=url, content=text), + ) + + except Exception as e: + raise ValueError(f"Error loading webpage {url}: {e!s}") from e diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/xml_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/xml_loader.py new file mode 100644 index 000000000..f7f9a6d00 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/xml_loader.py @@ -0,0 +1,63 @@ +from typing import Any +from xml.etree.ElementTree import ParseError, fromstring, parse + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.loaders.utils import load_from_url +from crewai_tools.rag.source_content import SourceContent + + +class XMLLoader(BaseLoader): + def load(self, source_content: SourceContent, **kwargs: Any) -> LoaderResult: # type: ignore[override] + """Load and parse XML content from various sources. + + Args: + source_content: SourceContent: The source content to load. + **kwargs: Additional keyword arguments for loading from URL. + + Returns: + LoaderResult: The result of loading and parsing the XML content. + """ + source_ref = source_content.source_ref + content = source_content.source + + if source_content.is_url(): + content = load_from_url( + source_ref, + kwargs, + accept_header="application/xml, text/xml, text/plain", + loader_name="XMLLoader", + ) + elif source_content.path_exists(): + content = self._load_from_file(source_ref) + + return self._parse_xml(content, source_ref) + + @staticmethod + def _load_from_file(path: str) -> str: + with open(path, encoding="utf-8") as file: + return file.read() + + def _parse_xml(self, content: str, source_ref: str) -> LoaderResult: + try: + if content.strip().startswith("<"): + root = fromstring(content) # noqa: S314 + else: + root = parse(source_ref).getroot() # noqa: S314 + + text_parts = [] + for text_content in root.itertext(): + if text_content and text_content.strip(): + text_parts.append(text_content.strip()) # noqa: PERF401 + + text = "\n".join(text_parts) + metadata = {"format": "xml", "root_tag": root.tag} + except ParseError as e: + text = content + metadata = {"format": "xml", "parse_error": str(e)} + + return LoaderResult( + content=text, + source=source_ref, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=source_ref, content=text), + ) diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/youtube_channel_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/youtube_channel_loader.py new file mode 100644 index 000000000..a4f40d2a7 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/youtube_channel_loader.py @@ -0,0 +1,162 @@ +"""YouTube channel loader for extracting content from YouTube channels.""" + +import re +from typing import Any + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class YoutubeChannelLoader(BaseLoader): + """Loader for YouTube channels.""" + + def load(self, source: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + """Load and extract content from a YouTube channel. + + Args: + source: The source content containing the YouTube channel URL + + Returns: + LoaderResult with channel content + + Raises: + ImportError: If required YouTube libraries aren't installed + ValueError: If the URL is not a valid YouTube channel URL + """ + try: + from pytube import Channel # type: ignore[import-untyped] + except ImportError as e: + raise ImportError( + "YouTube channel support requires pytube. Install with: uv add pytube" + ) from e + + channel_url = source.source + + if not any( + pattern in channel_url + for pattern in [ + "youtube.com/channel/", + "youtube.com/c/", + "youtube.com/@", + "youtube.com/user/", + ] + ): + raise ValueError(f"Invalid YouTube channel URL: {channel_url}") + + metadata: dict[str, Any] = { + "source": channel_url, + "data_type": "youtube_channel", + } + + try: + channel = Channel(channel_url) + + metadata["channel_name"] = channel.channel_name + metadata["channel_id"] = channel.channel_id + + max_videos = kwargs.get("max_videos", 10) + video_urls = list(channel.video_urls)[:max_videos] + metadata["num_videos_loaded"] = len(video_urls) + metadata["total_videos"] = len(list(channel.video_urls)) + + content_parts = [ + f"YouTube Channel: {channel.channel_name}", + f"Channel ID: {channel.channel_id}", + f"Total Videos: {metadata['total_videos']}", + f"Videos Loaded: {metadata['num_videos_loaded']}", + "\n--- Video Summaries ---\n", + ] + + try: + from pytube import YouTube + from youtube_transcript_api import YouTubeTranscriptApi + + for i, video_url in enumerate(video_urls, 1): + try: + video_id = self._extract_video_id(video_url) + if not video_id: + continue + yt = YouTube(video_url) + title = yt.title or f"Video {i}" + description = ( + yt.description[:200] if yt.description else "No description" + ) + + content_parts.append(f"\n{i}. {title}") + content_parts.append(f" URL: {video_url}") + content_parts.append(f" Description: {description}...") + + try: + api = YouTubeTranscriptApi() + transcript_list = api.list(video_id) + + try: + transcript = transcript_list.find_transcript(["en"]) + except Exception: + try: + transcript = ( + transcript_list.find_generated_transcript( + ["en"] + ) + ) + except Exception: + transcript = next(iter(transcript_list)) + + if transcript: + transcript_data = transcript.fetch() + text_parts = [] + char_count = 0 + for entry in transcript_data: + text = ( + entry.text.strip() + if hasattr(entry, "text") + else "" + ) + if text: + text_parts.append(text) + char_count += len(text) + if char_count > 500: + break + + if text_parts: + preview = " ".join(text_parts)[:500] + content_parts.append( + f" Transcript Preview: {preview}..." + ) + except Exception: + content_parts.append(" Transcript: Not available") + + except Exception as e: + content_parts.append(f"\n{i}. Error loading video: {e!s}") + + except ImportError: + for i, video_url in enumerate(video_urls, 1): + content_parts.append(f"\n{i}. {video_url}") + + content = "\n".join(content_parts) + + except Exception as e: + raise ValueError( + f"Unable to load YouTube channel {channel_url}: {e!s}" + ) from e + + return LoaderResult( + content=content, + source=channel_url, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=channel_url, content=content), + ) + + @staticmethod + def _extract_video_id(url: str) -> str | None: + """Extract video ID from YouTube URL.""" + patterns = [ + r"(?:youtube\.com\/watch\?v=|youtu\.be\/|youtube\.com\/embed\/|youtube\.com\/v\/)([^&\n?#]+)", + ] + + for pattern in patterns: + match = re.search(pattern, url) + if match: + return match.group(1) + + return None diff --git a/lib/crewai-tools/src/crewai_tools/rag/loaders/youtube_video_loader.py b/lib/crewai-tools/src/crewai_tools/rag/loaders/youtube_video_loader.py new file mode 100644 index 000000000..f708c9d4a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/loaders/youtube_video_loader.py @@ -0,0 +1,134 @@ +"""YouTube video loader for extracting transcripts from YouTube videos.""" + +import re +from typing import Any +from urllib.parse import parse_qs, urlparse + +from crewai_tools.rag.base_loader import BaseLoader, LoaderResult +from crewai_tools.rag.source_content import SourceContent + + +class YoutubeVideoLoader(BaseLoader): + """Loader for YouTube videos.""" + + def load(self, source: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override] + """Load and extract transcript from a YouTube video. + + Args: + source: The source content containing the YouTube URL + + Returns: + LoaderResult with transcript content + + Raises: + ImportError: If required YouTube libraries aren't installed + ValueError: If the URL is not a valid YouTube video URL + """ + try: + from youtube_transcript_api import YouTubeTranscriptApi + except ImportError as e: + raise ImportError( + "YouTube support requires youtube-transcript-api. " + "Install with: uv add youtube-transcript-api" + ) from e + + video_url = source.source + video_id = self._extract_video_id(video_url) + + if not video_id: + raise ValueError(f"Invalid YouTube URL: {video_url}") + + metadata: dict[str, Any] = { + "source": video_url, + "video_id": video_id, + "data_type": "youtube_video", + } + + try: + api = YouTubeTranscriptApi() + transcript_list = api.list(video_id) + + try: + transcript = transcript_list.find_transcript(["en"]) + except Exception: + try: + transcript = transcript_list.find_generated_transcript(["en"]) + except Exception: + transcript = next(iter(transcript_list)) + + if transcript: + metadata["language"] = transcript.language + metadata["is_generated"] = transcript.is_generated + + transcript_data = transcript.fetch() + + text_content = [] + for entry in transcript_data: + text = entry.text.strip() if hasattr(entry, "text") else "" + if text: + text_content.append(text) + + content = " ".join(text_content) + + try: + from pytube import YouTube # type: ignore[import-untyped] + + yt = YouTube(video_url) + metadata["title"] = yt.title + metadata["author"] = yt.author + metadata["length_seconds"] = yt.length + metadata["description"] = ( + yt.description[:500] if yt.description else None + ) + + if yt.title: + content = f"Title: {yt.title}\n\nAuthor: {yt.author or 'Unknown'}\n\nTranscript:\n{content}" + except Exception: # noqa: S110 + pass + else: + raise ValueError( + f"No transcript available for YouTube video: {video_id}" + ) + + except Exception as e: + raise ValueError( + f"Unable to extract transcript from YouTube video {video_id}: {e!s}" + ) from e + + return LoaderResult( + content=content, + source=video_url, + metadata=metadata, + doc_id=self.generate_doc_id(source_ref=video_url, content=content), + ) + + @staticmethod + def _extract_video_id(url: str) -> str | None: + """Extract video ID from various YouTube URL formats.""" + patterns = [ + r"(?:youtube\.com\/watch\?v=|youtu\.be\/|youtube\.com\/embed\/|youtube\.com\/v\/)([^&\n?#]+)", + ] + + for pattern in patterns: + match = re.search(pattern, url) + if match: + return match.group(1) + + try: + parsed = urlparse(url) + hostname = parsed.hostname + if hostname: + hostname_lower = hostname.lower() + # Allow youtube.com and any subdomain of youtube.com, plus youtu.be shortener + if ( + hostname_lower == "youtube.com" + or hostname_lower.endswith(".youtube.com") + or hostname_lower == "youtu.be" + ): + query_params = parse_qs(parsed.query) + if "v" in query_params: + return query_params["v"][0] + except Exception: # noqa: S110 + pass + + return None diff --git a/lib/crewai-tools/src/crewai_tools/rag/misc.py b/lib/crewai-tools/src/crewai_tools/rag/misc.py new file mode 100644 index 000000000..2f906b8f5 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/misc.py @@ -0,0 +1,39 @@ +import hashlib +from typing import Any + + +def compute_sha256(content: str) -> str: + """Compute the SHA-256 hash of the given content. + + Args: + content: The content to hash. + + Returns: + The SHA-256 hash of the content as a hexadecimal string. + """ + return hashlib.sha256(content.encode()).hexdigest() + + +def sanitize_metadata_for_chromadb(metadata: dict[str, Any]) -> dict[str, Any]: + """Sanitize metadata to ensure ChromaDB compatibility. + + ChromaDB only accepts str, int, float, or bool values in metadata. + This function converts other types to strings. + + Args: + metadata: Dictionary of metadata to sanitize + + Returns: + Sanitized metadata dictionary with only ChromaDB-compatible types + """ + sanitized = {} + for key, value in metadata.items(): + if isinstance(value, (str, int, float, bool)) or value is None: + sanitized[key] = value + elif isinstance(value, (list, tuple)): + # Convert lists/tuples to pipe-separated strings + sanitized[key] = " | ".join(str(v) for v in value) + else: + # Convert other types to string + sanitized[key] = str(value) + return sanitized diff --git a/lib/crewai-tools/src/crewai_tools/rag/source_content.py b/lib/crewai-tools/src/crewai_tools/rag/source_content.py new file mode 100644 index 000000000..096139bdb --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/rag/source_content.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from functools import cached_property +import os +from pathlib import Path +from typing import TYPE_CHECKING +from urllib.parse import urlparse + +from crewai_tools.rag.misc import compute_sha256 + + +if TYPE_CHECKING: + from crewai_tools.rag.data_types import DataType + + +class SourceContent: + def __init__(self, source: str | Path): + self.source = str(source) + + def is_url(self) -> bool: + if not isinstance(self.source, str): + return False + try: + parsed_url = urlparse(self.source) + return bool(parsed_url.scheme and parsed_url.netloc) + except Exception: + return False + + def path_exists(self) -> bool: + return os.path.exists(self.source) + + @cached_property + def data_type(self) -> DataType: + from crewai_tools.rag.data_types import DataTypes + + return DataTypes.from_content(self.source) + + @cached_property + def source_ref(self) -> str: + """ " + Returns the source reference for the content. + If the content is a URL or a local file, returns the source. + Otherwise, returns the hash of the content. + """ + if self.is_url() or self.path_exists(): + return self.source + + return compute_sha256(self.source) diff --git a/lib/crewai-tools/src/crewai_tools/tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/__init__.py new file mode 100644 index 000000000..36806d281 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/__init__.py @@ -0,0 +1,270 @@ +from crewai_tools.tools.ai_mind_tool.ai_mind_tool import AIMindTool +from crewai_tools.tools.apify_actors_tool.apify_actors_tool import ApifyActorsTool +from crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool import ArxivPaperTool +from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool +from crewai_tools.tools.brightdata_tool import ( + BrightDataDatasetTool, + BrightDataSearchTool, + BrightDataWebUnlockerTool, +) +from crewai_tools.tools.browserbase_load_tool.browserbase_load_tool import ( + BrowserbaseLoadTool, +) +from crewai_tools.tools.code_docs_search_tool.code_docs_search_tool import ( + CodeDocsSearchTool, +) +from crewai_tools.tools.code_interpreter_tool.code_interpreter_tool import ( + CodeInterpreterTool, +) +from crewai_tools.tools.composio_tool.composio_tool import ComposioTool +from crewai_tools.tools.contextualai_create_agent_tool.contextual_create_agent_tool import ( + ContextualAICreateAgentTool, +) +from crewai_tools.tools.contextualai_parse_tool.contextual_parse_tool import ( + ContextualAIParseTool, +) +from crewai_tools.tools.contextualai_query_tool.contextual_query_tool import ( + ContextualAIQueryTool, +) +from crewai_tools.tools.contextualai_rerank_tool.contextual_rerank_tool import ( + ContextualAIRerankTool, +) +from crewai_tools.tools.couchbase_tool.couchbase_tool import ( + CouchbaseFTSVectorSearchTool, +) +from crewai_tools.tools.crewai_platform_tools.crewai_platform_tools import ( + CrewaiPlatformTools, +) +from crewai_tools.tools.csv_search_tool.csv_search_tool import CSVSearchTool +from crewai_tools.tools.dalle_tool.dalle_tool import DallETool +from crewai_tools.tools.databricks_query_tool.databricks_query_tool import ( + DatabricksQueryTool, +) +from crewai_tools.tools.directory_read_tool.directory_read_tool import ( + DirectoryReadTool, +) +from crewai_tools.tools.directory_search_tool.directory_search_tool import ( + DirectorySearchTool, +) +from crewai_tools.tools.docx_search_tool.docx_search_tool import DOCXSearchTool +from crewai_tools.tools.exa_tools.exa_search_tool import EXASearchTool +from crewai_tools.tools.file_read_tool.file_read_tool import FileReadTool +from crewai_tools.tools.file_writer_tool.file_writer_tool import FileWriterTool +from crewai_tools.tools.files_compressor_tool.files_compressor_tool import ( + FileCompressorTool, +) +from crewai_tools.tools.firecrawl_crawl_website_tool.firecrawl_crawl_website_tool import ( + FirecrawlCrawlWebsiteTool, +) +from crewai_tools.tools.firecrawl_scrape_website_tool.firecrawl_scrape_website_tool import ( + FirecrawlScrapeWebsiteTool, +) +from crewai_tools.tools.firecrawl_search_tool.firecrawl_search_tool import ( + FirecrawlSearchTool, +) +from crewai_tools.tools.generate_crewai_automation_tool.generate_crewai_automation_tool import ( + GenerateCrewaiAutomationTool, +) +from crewai_tools.tools.github_search_tool.github_search_tool import GithubSearchTool +from crewai_tools.tools.hyperbrowser_load_tool.hyperbrowser_load_tool import ( + HyperbrowserLoadTool, +) +from crewai_tools.tools.invoke_crewai_automation_tool.invoke_crewai_automation_tool import ( + InvokeCrewAIAutomationTool, +) +from crewai_tools.tools.jina_scrape_website_tool.jina_scrape_website_tool import ( + JinaScrapeWebsiteTool, +) +from crewai_tools.tools.json_search_tool.json_search_tool import JSONSearchTool +from crewai_tools.tools.linkup.linkup_search_tool import LinkupSearchTool +from crewai_tools.tools.llamaindex_tool.llamaindex_tool import LlamaIndexTool +from crewai_tools.tools.mdx_search_tool.mdx_search_tool import MDXSearchTool +from crewai_tools.tools.mongodb_vector_search_tool import ( + MongoDBToolSchema, + MongoDBVectorSearchConfig, + MongoDBVectorSearchTool, +) +from crewai_tools.tools.multion_tool.multion_tool import MultiOnTool +from crewai_tools.tools.mysql_search_tool.mysql_search_tool import MySQLSearchTool +from crewai_tools.tools.nl2sql.nl2sql_tool import NL2SQLTool +from crewai_tools.tools.ocr_tool.ocr_tool import OCRTool +from crewai_tools.tools.oxylabs_amazon_product_scraper_tool.oxylabs_amazon_product_scraper_tool import ( + OxylabsAmazonProductScraperTool, +) +from crewai_tools.tools.oxylabs_amazon_search_scraper_tool.oxylabs_amazon_search_scraper_tool import ( + OxylabsAmazonSearchScraperTool, +) +from crewai_tools.tools.oxylabs_google_search_scraper_tool.oxylabs_google_search_scraper_tool import ( + OxylabsGoogleSearchScraperTool, +) +from crewai_tools.tools.oxylabs_universal_scraper_tool.oxylabs_universal_scraper_tool import ( + OxylabsUniversalScraperTool, +) +from crewai_tools.tools.parallel_tools import ParallelSearchTool +from crewai_tools.tools.patronus_eval_tool import ( + PatronusEvalTool, + PatronusLocalEvaluatorTool, + PatronusPredefinedCriteriaEvalTool, +) +from crewai_tools.tools.pdf_search_tool.pdf_search_tool import PDFSearchTool +from crewai_tools.tools.qdrant_vector_search_tool.qdrant_search_tool import ( + QdrantVectorSearchTool, +) +from crewai_tools.tools.rag.rag_tool import RagTool +from crewai_tools.tools.scrape_element_from_website.scrape_element_from_website import ( + ScrapeElementFromWebsiteTool, +) +from crewai_tools.tools.scrape_website_tool.scrape_website_tool import ( + ScrapeWebsiteTool, +) +from crewai_tools.tools.scrapegraph_scrape_tool.scrapegraph_scrape_tool import ( + ScrapegraphScrapeTool, + ScrapegraphScrapeToolSchema, +) +from crewai_tools.tools.scrapfly_scrape_website_tool.scrapfly_scrape_website_tool import ( + ScrapflyScrapeWebsiteTool, +) +from crewai_tools.tools.selenium_scraping_tool.selenium_scraping_tool import ( + SeleniumScrapingTool, +) +from crewai_tools.tools.serpapi_tool.serpapi_google_search_tool import ( + SerpApiGoogleSearchTool, +) +from crewai_tools.tools.serpapi_tool.serpapi_google_shopping_tool import ( + SerpApiGoogleShoppingTool, +) +from crewai_tools.tools.serper_dev_tool.serper_dev_tool import SerperDevTool +from crewai_tools.tools.serper_scrape_website_tool.serper_scrape_website_tool import ( + SerperScrapeWebsiteTool, +) +from crewai_tools.tools.serply_api_tool.serply_job_search_tool import ( + SerplyJobSearchTool, +) +from crewai_tools.tools.serply_api_tool.serply_news_search_tool import ( + SerplyNewsSearchTool, +) +from crewai_tools.tools.serply_api_tool.serply_scholar_search_tool import ( + SerplyScholarSearchTool, +) +from crewai_tools.tools.serply_api_tool.serply_web_search_tool import ( + SerplyWebSearchTool, +) +from crewai_tools.tools.serply_api_tool.serply_webpage_to_markdown_tool import ( + SerplyWebpageToMarkdownTool, +) +from crewai_tools.tools.singlestore_search_tool import SingleStoreSearchTool +from crewai_tools.tools.snowflake_search_tool import ( + SnowflakeConfig, + SnowflakeSearchTool, + SnowflakeSearchToolInput, +) +from crewai_tools.tools.spider_tool.spider_tool import SpiderTool +from crewai_tools.tools.stagehand_tool.stagehand_tool import StagehandTool +from crewai_tools.tools.tavily_extractor_tool.tavily_extractor_tool import ( + TavilyExtractorTool, +) +from crewai_tools.tools.tavily_search_tool.tavily_search_tool import TavilySearchTool +from crewai_tools.tools.txt_search_tool.txt_search_tool import TXTSearchTool +from crewai_tools.tools.vision_tool.vision_tool import VisionTool +from crewai_tools.tools.weaviate_tool.vector_search import WeaviateVectorSearchTool +from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool +from crewai_tools.tools.xml_search_tool.xml_search_tool import XMLSearchTool +from crewai_tools.tools.youtube_channel_search_tool.youtube_channel_search_tool import ( + YoutubeChannelSearchTool, +) +from crewai_tools.tools.youtube_video_search_tool.youtube_video_search_tool import ( + YoutubeVideoSearchTool, +) +from crewai_tools.tools.zapier_action_tool.zapier_action_tool import ZapierActionTools + + +__all__ = [ + "AIMindTool", + "ApifyActorsTool", + "ArxivPaperTool", + "BraveSearchTool", + "BrightDataDatasetTool", + "BrightDataSearchTool", + "BrightDataWebUnlockerTool", + "BrowserbaseLoadTool", + "CSVSearchTool", + "CodeDocsSearchTool", + "CodeInterpreterTool", + "ComposioTool", + "ContextualAICreateAgentTool", + "ContextualAIParseTool", + "ContextualAIQueryTool", + "ContextualAIRerankTool", + "CouchbaseFTSVectorSearchTool", + "CrewaiPlatformTools", + "DOCXSearchTool", + "DallETool", + "DatabricksQueryTool", + "DirectoryReadTool", + "DirectorySearchTool", + "EXASearchTool", + "FileCompressorTool", + "FileReadTool", + "FileWriterTool", + "FirecrawlCrawlWebsiteTool", + "FirecrawlScrapeWebsiteTool", + "FirecrawlSearchTool", + "GenerateCrewaiAutomationTool", + "GithubSearchTool", + "HyperbrowserLoadTool", + "InvokeCrewAIAutomationTool", + "JSONSearchTool", + "JinaScrapeWebsiteTool", + "LinkupSearchTool", + "LlamaIndexTool", + "MDXSearchTool", + "MongoDBToolSchema", + "MongoDBVectorSearchConfig", + "MongoDBVectorSearchTool", + "MultiOnTool", + "MySQLSearchTool", + "NL2SQLTool", + "OCRTool", + "OxylabsAmazonProductScraperTool", + "OxylabsAmazonSearchScraperTool", + "OxylabsGoogleSearchScraperTool", + "OxylabsUniversalScraperTool", + "PDFSearchTool", + "ParallelSearchTool", + "PatronusEvalTool", + "PatronusLocalEvaluatorTool", + "PatronusPredefinedCriteriaEvalTool", + "QdrantVectorSearchTool", + "RagTool", + "ScrapeElementFromWebsiteTool", + "ScrapeWebsiteTool", + "ScrapegraphScrapeTool", + "ScrapegraphScrapeToolSchema", + "ScrapflyScrapeWebsiteTool", + "SeleniumScrapingTool", + "SerpApiGoogleSearchTool", + "SerpApiGoogleShoppingTool", + "SerperDevTool", + "SerperScrapeWebsiteTool", + "SerplyJobSearchTool", + "SerplyNewsSearchTool", + "SerplyScholarSearchTool", + "SerplyWebSearchTool", + "SerplyWebpageToMarkdownTool", + "SingleStoreSearchTool", + "SnowflakeConfig", + "SnowflakeSearchTool", + "SnowflakeSearchToolInput", + "SpiderTool", + "StagehandTool", + "TXTSearchTool", + "TavilyExtractorTool", + "TavilySearchTool", + "VisionTool", + "WeaviateVectorSearchTool", + "WebsiteSearchTool", + "XMLSearchTool", + "YoutubeChannelSearchTool", + "YoutubeVideoSearchTool", + "ZapierActionTools", +] diff --git a/lib/crewai-tools/src/crewai_tools/tools/ai_mind_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/ai_mind_tool/README.md new file mode 100644 index 000000000..95d2deb42 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/ai_mind_tool/README.md @@ -0,0 +1,79 @@ +# AIMind Tool + +## Description + +[Minds](https://mindsdb.com/minds) are AI systems provided by [MindsDB](https://mindsdb.com/) that work similarly to large language models (LLMs) but go beyond by answering any question from any data. + +This is accomplished by selecting the most relevant data for an answer using parametric search, understanding the meaning and providing responses within the correct context through semantic search, and finally, delivering precise answers by analyzing data and using machine learning (ML) models. + +The `AIMindTool` can be used to query data sources in natural language by simply configuring their connection parameters. + +## Installation + +1. Install the `crewai[tools]` package: + +```shell +pip install 'crewai[tools]' +``` + +2. Install the Minds SDK: + +```shell +pip install minds-sdk +``` + +3. Sign for a Minds account [here](https://mdb.ai/register), and obtain an API key. + +4. Set the Minds API key in an environment variable named `MINDS_API_KEY`. + +## Usage + +```python +from crewai_tools import AIMindTool + + +# Initialize the AIMindTool. +aimind_tool = AIMindTool( + datasources=[ + { + "description": "house sales data", + "engine": "postgres", + "connection_data": { + "user": "demo_user", + "password": "demo_password", + "host": "samples.mindsdb.com", + "port": 5432, + "database": "demo", + "schema": "demo_data" + }, + "tables": ["house_sales"] + } + ] +) + +aimind_tool.run("How many 3 bedroom houses were sold in 2008?") +``` + +The `datasources` parameter is a list of dictionaries, each containing the following keys: + +- `description`: A description of the data contained in the datasource. +- `engine`: The engine (or type) of the datasource. Find a list of supported engines in the link below. +- `connection_data`: A dictionary containing the connection parameters for the datasource. Find a list of connection parameters for each engine in the link below. +- `tables`: A list of tables that the data source will use. This is optional and can be omitted if all tables in the data source are to be used. + +A list of supported data sources and their connection parameters can be found [here](https://docs.mdb.ai/docs/data_sources). + +```python +from crewai import Agent +from crewai.project import agent + + +# Define an agent with the AIMindTool. +@agent +def researcher(self) -> Agent: + return Agent( + config=self.agents_config["researcher"], + allow_delegation=False, + tools=[aimind_tool] + ) +``` diff --git a/src/crewai/agents/agent_builder/utilities/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/ai_mind_tool/__init__.py similarity index 100% rename from src/crewai/agents/agent_builder/utilities/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/ai_mind_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/ai_mind_tool/ai_mind_tool.py b/lib/crewai-tools/src/crewai_tools/tools/ai_mind_tool/ai_mind_tool.py new file mode 100644 index 000000000..4d48c3e06 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/ai_mind_tool/ai_mind_tool.py @@ -0,0 +1,102 @@ +import os +import secrets +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from openai import OpenAI +from openai.types.chat import ChatCompletion +from pydantic import BaseModel, Field + + +class AIMindToolConstants: + MINDS_API_BASE_URL = "https://mdb.ai/" + MIND_NAME_PREFIX = "crwai_mind_" + DATASOURCE_NAME_PREFIX = "crwai_ds_" + + +class AIMindToolInputSchema(BaseModel): + """Input for AIMind Tool.""" + + query: str = Field(description="Question in natural language to ask the AI-Mind") + + +class AIMindTool(BaseTool): + name: str = "AIMind Tool" + description: str = ( + "A wrapper around [AI-Minds](https://mindsdb.com/minds). " + "Useful for when you need answers to questions from your data, stored in " + "data sources including PostgreSQL, MySQL, MariaDB, ClickHouse, Snowflake " + "and Google BigQuery. " + "Input should be a question in natural language." + ) + args_schema: type[BaseModel] = AIMindToolInputSchema + api_key: str | None = None + datasources: list[dict[str, Any]] = Field(default_factory=list) + mind_name: str | None = None + package_dependencies: list[str] = Field(default_factory=lambda: ["minds-sdk"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="MINDS_API_KEY", description="API key for AI-Minds", required=True + ), + ] + ) + + def __init__(self, api_key: str | None = None, **kwargs): + super().__init__(**kwargs) + self.api_key = api_key or os.getenv("MINDS_API_KEY") + if not self.api_key: + raise ValueError( + "API key must be provided either through constructor or MINDS_API_KEY environment variable" + ) + + try: + from minds.client import Client # type: ignore + from minds.datasources import DatabaseConfig # type: ignore + except ImportError as e: + raise ImportError( + "`minds_sdk` package not found, please run `pip install minds-sdk`" + ) from e + + minds_client = Client(api_key=self.api_key) + + # Convert the datasources to DatabaseConfig objects. + datasources = [] + for datasource in self.datasources: + config = DatabaseConfig( + name=f"{AIMindToolConstants.DATASOURCE_NAME_PREFIX}_{secrets.token_hex(5)}", + engine=datasource["engine"], + description=datasource["description"], + connection_data=datasource["connection_data"], + tables=datasource["tables"], + ) + datasources.append(config) + + # Generate a random name for the Mind. + name = f"{AIMindToolConstants.MIND_NAME_PREFIX}_{secrets.token_hex(5)}" + + mind = minds_client.minds.create( + name=name, datasources=datasources, replace=True + ) + + self.mind_name = mind.name + + def _run(self, query: str): + # Run the query on the AI-Mind. + # The Minds API is OpenAI compatible and therefore, the OpenAI client can be used. + openai_client = OpenAI( + base_url=AIMindToolConstants.MINDS_API_BASE_URL, api_key=self.api_key + ) + + if self.mind_name is None: + raise ValueError("Mind name is not set.") + + completion = openai_client.chat.completions.create( + model=self.mind_name, + messages=[{"role": "user", "content": query}], + stream=False, + ) + if not isinstance(completion, ChatCompletion): + raise ValueError("Invalid response from AI-Mind") + + return completion.choices[0].message.content diff --git a/lib/crewai-tools/src/crewai_tools/tools/apify_actors_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/apify_actors_tool/README.md new file mode 100644 index 000000000..c00891deb --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/apify_actors_tool/README.md @@ -0,0 +1,96 @@ +# ApifyActorsTool + +Integrate [Apify Actors](https://apify.com/actors) into your CrewAI workflows. + +## Description + +The `ApifyActorsTool` connects [Apify Actors](https://apify.com/actors), cloud-based programs for web scraping and automation, to your CrewAI workflows. +Use any of the 4,000+ Actors on [Apify Store](https://apify.com/store) for use cases such as extracting data from social media, search engines, online maps, e-commerce sites, travel portals, or general websites. + +For details, see the [Apify CrewAI integration](https://docs.apify.com/platform/integrations/crewai) in Apify documentation. + +## Installation + +To use `ApifyActorsTool`, install the necessary packages and set up your Apify API token. Follow the [Apify API documentation](https://docs.apify.com/platform/integrations/api) for steps to obtain the token. + +### Steps + +1. **Install dependencies** + Install `crewai[tools]` and `langchain-apify`: + ```bash + pip install 'crewai[tools]' langchain-apify + ``` + +2. **Set your API token** + Export the token as an environment variable: + ```bash + export APIFY_API_TOKEN='your-api-token-here' + ``` + +## Usage example + +Use the `ApifyActorsTool` manually to run the [RAG Web Browser Actor](https://apify.com/apify/rag-web-browser) to perform a web search: + +```python +from crewai_tools import ApifyActorsTool + +# Initialize the tool with an Apify Actor +tool = ApifyActorsTool(actor_name="apify/rag-web-browser") + +# Run the tool with input parameters +results = tool.run(run_input={"query": "What is CrewAI?", "maxResults": 5}) + +# Process the results +for result in results: + print(f"URL: {result['metadata']['url']}") + print(f"Content: {result.get('markdown', 'N/A')[:100]}...") +``` + +### Expected output + +Here is the output from running the code above: + +```text +URL: https://www.example.com/crewai-intro +Content: CrewAI is a framework for building AI-powered workflows... +URL: https://docs.crewai.com/ +Content: Official documentation for CrewAI... +``` + +The `ApifyActorsTool` automatically fetches the Actor definition and input schema from Apify using the provided `actor_name` and then constructs the tool description and argument schema. This means you need to specify only a valid `actor_name`, and the tool handles the rest when used with agents—no need to specify the `run_input`. Here's how it works: + +```python +from crewai import Agent +from crewai_tools import ApifyActorsTool + +rag_browser = ApifyActorsTool(actor_name="apify/rag-web-browser") + +agent = Agent( + role="Research Analyst", + goal="Find and summarize information about specific topics", + backstory="You are an experienced researcher with attention to detail", + tools=[rag_browser], +) +``` + +You can run other Actors from [Apify Store](https://apify.com/store) simply by changing the `actor_name` and, when using it manually, adjusting the `run_input` based on the Actor input schema. + +For an example of usage with agents, see the [CrewAI Actor template](https://apify.com/templates/python-crewai). + +## Configuration + +The `ApifyActorsTool` requires these inputs to work: + +- **`actor_name`** + The ID of the Apify Actor to run, e.g., `"apify/rag-web-browser"`. Browse all Actors on [Apify Store](https://apify.com/store). +- **`run_input`** + A dictionary of input parameters for the Actor when running the tool manually. + - For example, for the `apify/rag-web-browser` Actor: `{"query": "search term", "maxResults": 5}` + - See the Actor's [input schema](https://apify.com/apify/rag-web-browser/input-schema) for the list of input parameters. + +## Resources + +- **[Apify](https://apify.com/)**: Explore the Apify platform. +- **[How to build an AI agent on Apify](https://blog.apify.com/how-to-build-an-ai-agent/)** - A complete step-by-step guide to creating, publishing, and monetizing AI agents on the Apify platform. +- **[RAG Web Browser Actor](https://apify.com/apify/rag-web-browser)**: A popular Actor for web search for LLMs. +- **[CrewAI Integration Guide](https://docs.apify.com/platform/integrations/crewai)**: Follow the official guide for integrating Apify and CrewAI. diff --git a/src/crewai/cli/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/apify_actors_tool/__init__.py similarity index 100% rename from src/crewai/cli/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/apify_actors_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/apify_actors_tool/apify_actors_tool.py b/lib/crewai-tools/src/crewai_tools/tools/apify_actors_tool/apify_actors_tool.py new file mode 100644 index 000000000..a3bb24c4d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/apify_actors_tool/apify_actors_tool.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +import os +from typing import TYPE_CHECKING, Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import Field + + +if TYPE_CHECKING: + from langchain_apify import ApifyActorsTool as _ApifyActorsTool + + +class ApifyActorsTool(BaseTool): + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="APIFY_API_TOKEN", + description="API token for Apify platform access", + required=True, + ), + ] + ) + """Tool that runs Apify Actors. + + To use, you should have the environment variable `APIFY_API_TOKEN` set + with your API key. + + For details, see https://docs.apify.com/platform/integrations/crewai + + Args: + actor_name (str): The name of the Apify Actor to run. + *args: Variable length argument list passed to BaseTool. + **kwargs: Arbitrary keyword arguments passed to BaseTool. + + Returns: + List[Dict[str, Any]]: Results from the Actor execution. + + Raises: + ValueError: If `APIFY_API_TOKEN` is not set or if the tool is not initialized. + ImportError: If `langchain_apify` package is not installed. + + Example: + .. code-block:: python + from crewai_tools import ApifyActorsTool + + tool = ApifyActorsTool(actor_name="apify/rag-web-browser") + + results = tool.run(run_input={"query": "What is CrewAI?", "maxResults": 5}) + for result in results: + print(f"URL: {result['metadata']['url']}") + print(f"Content: {result.get('markdown', 'N/A')[:100]}...") + """ + actor_tool: _ApifyActorsTool = Field(description="Apify Actor Tool") + package_dependencies: list[str] = Field(default_factory=lambda: ["langchain-apify"]) + + def __init__(self, actor_name: str, *args: Any, **kwargs: Any) -> None: + if not os.environ.get("APIFY_API_TOKEN"): + msg = ( + "APIFY_API_TOKEN environment variable is not set. " + "Please set it to your API key, to learn how to get it, " + "see https://docs.apify.com/platform/integrations/api" + ) + raise ValueError(msg) + + try: + from langchain_apify import ApifyActorsTool as _ApifyActorsTool + except ImportError as e: + raise ImportError( + "Could not import langchain_apify python package. " + "Please install it with `pip install langchain-apify` or `uv add langchain-apify`." + ) from e + actor_tool = _ApifyActorsTool(actor_name) + + kwargs.update( + { + "name": actor_tool.name, + "description": actor_tool.description, + "args_schema": actor_tool.args_schema, + "actor_tool": actor_tool, + } + ) + super().__init__(*args, **kwargs) + + def _run(self, run_input: dict[str, Any]) -> list[dict[str, Any]]: + """Run the Actor tool with the given input. + + Returns: + List[Dict[str, Any]]: Results from the Actor execution. + + Raises: + ValueError: If 'actor_tool' is not initialized. + """ + try: + return self.actor_tool._run(run_input) + except Exception as e: + msg = ( + f"Failed to run ApifyActorsTool {self.name}. " + "Please check your Apify account Actor run logs for more details." + f"Error: {e}" + ) + raise RuntimeError(msg) from e diff --git a/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/Examples.md b/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/Examples.md new file mode 100644 index 000000000..676fa4106 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/Examples.md @@ -0,0 +1,80 @@ +### Example 1: Fetching Research Papers from arXiv with CrewAI + +This example demonstrates how to build a simple CrewAI workflow that automatically searches for and downloads academic papers from [arXiv.org](https://arxiv.org). The setup uses: + +* A custom `ArxivPaperTool` to fetch metadata and download PDFs +* A single `Agent` tasked with locating relevant papers based on a given research topic +* A `Task` to define the data retrieval and download process +* A sequential `Crew` to orchestrate execution + +The downloaded PDFs are saved to a local directory (`./DOWNLOADS`). Filenames are optionally based on sanitized paper titles, ensuring compatibility with your operating system. + +> The saved PDFs can be further used in **downstream tasks**, such as: +> +> * **RAG (Retrieval-Augmented Generation)** +> * **Summarization** +> * **Citation extraction** +> * **Embedding-based search or analysis** + +--- + + +``` +from crewai import Agent, Task, Crew, Process, LLM +from crewai_tools import ArxivPaperTool + + + +llm = LLM( + model="ollama/llama3.1", + base_url="http://localhost:11434", + temperature=0.1 +) + + +topic = "Crew AI" +max_results = 3 +save_dir = "./DOWNLOADS" +use_title_as_filename = True + +tool = ArxivPaperTool( + download_pdfs=True, + save_dir=save_dir, + use_title_as_filename=True +) +tool.result_as_answer = True #Required,otherwise + + +arxiv_paper_fetch = Agent( + role="Arxiv Data Fetcher", + goal=f"Retrieve relevant papers from arXiv based on a research topic {topic} and maximum number of papers to be downloaded is{max_results},try to use title as filename {use_title_as_filename} and download PDFs to {save_dir},", + backstory="An expert in scientific data retrieval, skilled in extracting academic content from arXiv.", + # tools=[ArxivPaperTool()], + llm=llm, + verbose=True, + allow_delegation=False +) +fetch_task = Task( + description=( + f"Search arXiv for the topic '{topic}' and fetch up to {max_results} papers. " + f"Download PDFs for analysis and store them at {save_dir}." + ), + expected_output="PDFs saved to disk for downstream agents.", + agent=arxiv_paper_fetch, + tools=[tool], # Use the actual tool instance here + +) + + +pdf_qa_crew = Crew( + agents=[arxiv_paper_fetch], + tasks=[fetch_task], + process=Process.sequential, + verbose=True, +) + + +result = pdf_qa_crew.kickoff() + +print(f"\n🤖 Answer:\n\n{result.raw}\n") +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/README.md new file mode 100644 index 000000000..f9ef56bdc --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/README.md @@ -0,0 +1,142 @@ +# ArxivPaperTool + + +# 📚 ArxivPaperTool + +The **ArxivPaperTool** is a utility for fetching metadata and optionally downloading PDFs of academic papers from the [arXiv](https://arxiv.org) platform using its public API. It supports configurable queries, batch retrieval, PDF downloading, and clean formatting for summaries and metadata. This tool is particularly useful for researchers, students, academic agents, and AI tools performing automated literature reviews. + +--- + +## Description + +This tool: + +* Accepts a **search query** and retrieves a list of papers from arXiv. +* Allows configuration of the **maximum number of results** to fetch. +* Optionally downloads the **PDFs** of the matched papers. +* Lets you specify whether to name PDF files using the **arXiv ID** or **paper title**. +* Saves downloaded files into a **custom or default directory**. +* Returns structured summaries of all fetched papers including metadata. + +--- + +## Arguments + +| Argument | Type | Required | Description | +| ----------------------- | ------ | -------- | --------------------------------------------------------------------------------- | +| `search_query` | `str` | ✅ | Search query string (e.g., `"transformer neural network"`). | +| `max_results` | `int` | ✅ | Number of results to fetch (between 1 and 100). | +| `download_pdfs` | `bool` | ❌ | Whether to download the corresponding PDFs. Defaults to `False`. | +| `save_dir` | `str` | ❌ | Directory to save PDFs (created if it doesn’t exist). Defaults to `./arxiv_pdfs`. | +| `use_title_as_filename` | `bool` | ❌ | Use the paper title as the filename (sanitized). Defaults to `False`. | + +--- + +## 📄 `ArxivPaperTool` Usage Examples + +This document shows how to use the `ArxivPaperTool` to fetch research paper metadata from arXiv and optionally download PDFs. + +### 🔧 Tool Initialization + +```python +from crewai_tools import ArxivPaperTool +``` + +--- + +### Example 1: Fetch Metadata Only (No Downloads) + +```python +tool = ArxivPaperTool() +result = tool._run( + search_query="deep learning", + max_results=1 +) +print(result) +``` + +--- + +### Example 2: Fetch and Download PDFs (arXiv ID as Filename) + +```python +tool = ArxivPaperTool(download_pdfs=True) +result = tool._run( + search_query="transformer models", + max_results=2 +) +print(result) +``` + +--- + +### Example 3: Download PDFs into a Custom Directory + +```python +tool = ArxivPaperTool( + download_pdfs=True, + save_dir="./my_papers" +) +result = tool._run( + search_query="graph neural networks", + max_results=2 +) +print(result) +``` + +--- + +### Example 4: Use Paper Titles as Filenames + +```python +tool = ArxivPaperTool( + download_pdfs=True, + use_title_as_filename=True +) +result = tool._run( + search_query="vision transformers", + max_results=1 +) +print(result) +``` + +--- + +### Example 5: All Options Combined + +```python +tool = ArxivPaperTool( + download_pdfs=True, + save_dir="./downloads", + use_title_as_filename=True +) +result = tool._run( + search_query="stable diffusion", + max_results=3 +) +print(result) +``` + +--- + +### Run via `__main__` + +Your file can also include: + +```python +if __name__ == "__main__": + tool = ArxivPaperTool( + download_pdfs=True, + save_dir="./downloads2", + use_title_as_filename=False + ) + result = tool._run( + search_query="deep learning", + max_results=1 + ) + print(result) +``` + +--- + + diff --git a/src/crewai/cli/authentication/providers/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/__init__.py similarity index 100% rename from src/crewai/cli/authentication/providers/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/arxiv_paper_tool.py b/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/arxiv_paper_tool.py new file mode 100644 index 000000000..3776f56d6 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/arxiv_paper_tool/arxiv_paper_tool.py @@ -0,0 +1,169 @@ +import logging +from pathlib import Path +import re +import time +from typing import ClassVar +import urllib.error +import urllib.parse +import urllib.request +import xml.etree.ElementTree as ET + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field + + +logger = logging.getLogger(__file__) + + +class ArxivToolInput(BaseModel): + search_query: str = Field( + ..., description="Search query for Arxiv, e.g., 'transformer neural network'" + ) + max_results: int = Field( + 5, ge=1, le=100, description="Max results to fetch; must be between 1 and 100" + ) + + +class ArxivPaperTool(BaseTool): + BASE_API_URL: ClassVar[str] = "http://export.arxiv.org/api/query" + SLEEP_DURATION: ClassVar[int] = 1 + SUMMARY_TRUNCATE_LENGTH: ClassVar[int] = 300 + ATOM_NAMESPACE: ClassVar[str] = "{http://www.w3.org/2005/Atom}" + REQUEST_TIMEOUT: ClassVar[int] = 10 + name: str = "Arxiv Paper Fetcher and Downloader" + description: str = "Fetches metadata from Arxiv based on a search query and optionally downloads PDFs." + args_schema: type[BaseModel] = ArxivToolInput + model_config = ConfigDict(extra="allow") + package_dependencies: list[str] = Field(default_factory=lambda: ["pydantic"]) + env_vars: list[EnvVar] = Field(default_factory=list) + download_pdfs: bool = False + save_dir: str = "./arxiv_pdfs" + use_title_as_filename: bool = False + + def _run(self, search_query: str, max_results: int = 5) -> str: + try: + args = ArxivToolInput(search_query=search_query, max_results=max_results) + logger.info( + f"Running Arxiv tool: query='{args.search_query}', max_results={args.max_results}, " + f"download_pdfs={self.download_pdfs}, save_dir='{self.save_dir}', " + f"use_title_as_filename={self.use_title_as_filename}" + ) + + papers = self.fetch_arxiv_data(args.search_query, args.max_results) + + if self.download_pdfs: + save_dir = self._validate_save_path(self.save_dir) + for paper in papers: + if paper["pdf_url"]: + if self.use_title_as_filename: + safe_title = re.sub( + r'[\\/*?:"<>|]', "_", paper["title"] + ).strip() + filename_base = safe_title or paper["arxiv_id"] + else: + filename_base = paper["arxiv_id"] + filename = f"{filename_base[:500]}.pdf" + save_path = Path(save_dir) / filename + + self.download_pdf(paper["pdf_url"], save_path) # type: ignore[arg-type] + time.sleep(self.SLEEP_DURATION) + + results = [self._format_paper_result(p) for p in papers] + return "\n\n" + "-" * 80 + "\n\n".join(results) + + except Exception as e: + logger.error(f"ArxivTool Error: {e!s}") + return f"Failed to fetch or download Arxiv papers: {e!s}" + + def fetch_arxiv_data(self, search_query: str, max_results: int) -> list[dict]: + api_url = f"{self.BASE_API_URL}?search_query={urllib.parse.quote(search_query)}&start=0&max_results={max_results}" + logger.info(f"Fetching data from Arxiv API: {api_url}") + + try: + with urllib.request.urlopen( # noqa: S310 + api_url, timeout=self.REQUEST_TIMEOUT + ) as response: + if response.status != 200: + raise Exception(f"HTTP {response.status}: {response.reason}") + data = response.read().decode("utf-8") + except urllib.error.URLError as e: + logger.error(f"Error fetching data from Arxiv: {e}") + raise + + root = ET.fromstring(data) # noqa: S314 + papers = [] + + for entry in root.findall(self.ATOM_NAMESPACE + "entry"): + raw_id = self._get_element_text(entry, "id") + arxiv_id = raw_id.split("/")[-1].replace(".", "_") if raw_id else "unknown" + + title = self._get_element_text(entry, "title") or "No Title" + summary = self._get_element_text(entry, "summary") or "No Summary" + published = self._get_element_text(entry, "published") or "No Publish Date" + authors = [ + self._get_element_text(author, "name") or "Unknown" + for author in entry.findall(self.ATOM_NAMESPACE + "author") + ] + + pdf_url = self._extract_pdf_url(entry) + + papers.append( + { + "arxiv_id": arxiv_id, + "title": title, + "summary": summary, + "authors": authors, + "published_date": published, + "pdf_url": pdf_url, + } + ) + + return papers + + @staticmethod + def _get_element_text(entry: ET.Element, element_name: str) -> str | None: + elem = entry.find(f"{ArxivPaperTool.ATOM_NAMESPACE}{element_name}") + return elem.text.strip() if elem is not None and elem.text else None + + def _extract_pdf_url(self, entry: ET.Element) -> str | None: + for link in entry.findall(self.ATOM_NAMESPACE + "link"): + if link.attrib.get("title", "").lower() == "pdf": + return link.attrib.get("href") + for link in entry.findall(self.ATOM_NAMESPACE + "link"): + href = link.attrib.get("href") + if href and "pdf" in href: + return href + return None + + def _format_paper_result(self, paper: dict) -> str: + summary = ( + (paper["summary"][: self.SUMMARY_TRUNCATE_LENGTH] + "...") + if len(paper["summary"]) > self.SUMMARY_TRUNCATE_LENGTH + else paper["summary"] + ) + authors_str = ", ".join(paper["authors"]) + return ( + f"Title: {paper['title']}\n" + f"Authors: {authors_str}\n" + f"Published: {paper['published_date']}\n" + f"PDF: {paper['pdf_url'] or 'N/A'}\n" + f"Summary: {summary}" + ) + + @staticmethod + def _validate_save_path(path: str) -> Path: + save_path = Path(path).resolve() + save_path.mkdir(parents=True, exist_ok=True) + return save_path + + def download_pdf(self, pdf_url: str, save_path: str): + try: + logger.info(f"Downloading PDF from {pdf_url} to {save_path}") + urllib.request.urlretrieve(pdf_url, str(save_path)) # noqa: S310 + logger.info(f"PDF saved: {save_path}") + except urllib.error.URLError as e: + logger.error(f"Network error occurred while downloading {pdf_url}: {e}") + raise + except OSError as e: + logger.error(f"File save error for {save_path}: {e}") + raise diff --git a/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/README.md new file mode 100644 index 000000000..a66210491 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/README.md @@ -0,0 +1,30 @@ +# BraveSearchTool Documentation + +## Description +This tool is designed to perform a web search for a specified query from a text's content across the internet. It utilizes the Brave Web Search API, which is a REST API to query Brave Search and get back search results from the web. The following sections describe how to curate requests, including parameters and headers, to Brave Web Search API and get a JSON response back. + +## Installation +To incorporate this tool into your project, follow the installation instructions below: +```shell +pip install 'crewai[tools]' +``` + +## Example +The following example demonstrates how to initialize the tool and execute a search with a given query: + +```python +from crewai_tools import BraveSearchTool + +# Initialize the tool for internet searching capabilities +tool = BraveSearchTool() +``` + +## Steps to Get Started +To effectively use the `BraveSearchTool`, follow these steps: + +1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment. +2. **API Key Acquisition**: Acquire a API key [here](https://api.search.brave.com/app/keys). +3. **Environment Configuration**: Store your obtained API key in an environment variable named `BRAVE_API_KEY` to facilitate its use by the tool. + +## Conclusion +By integrating the `BraveSearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward. diff --git a/src/crewai/cli/deploy/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/__init__.py similarity index 100% rename from src/crewai/cli/deploy/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_search_tool.py new file mode 100644 index 000000000..e13f3823c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_search_tool.py @@ -0,0 +1,126 @@ +from datetime import datetime +import os +import time +from typing import Any, ClassVar + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +def _save_results_to_file(content: str) -> None: + """Saves the search results to a file.""" + filename = f"search_results_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt" + with open(filename, "w") as file: + file.write(content) + + +class BraveSearchToolSchema(BaseModel): + """Input for BraveSearchTool.""" + + search_query: str = Field( + ..., description="Mandatory search query you want to use to search the internet" + ) + + +class BraveSearchTool(BaseTool): + """BraveSearchTool - A tool for performing web searches using the Brave Search API. + + This module provides functionality to search the internet using Brave's Search API, + supporting customizable result counts and country-specific searches. + + Dependencies: + - requests + - pydantic + - python-dotenv (for API key management) + """ + + name: str = "Brave Web Search the internet" + description: str = ( + "A tool that can be used to search the internet with a search_query." + ) + args_schema: type[BaseModel] = BraveSearchToolSchema + search_url: str = "https://api.search.brave.com/res/v1/web/search" + country: str | None = "" + n_results: int = 10 + save_file: bool = False + _last_request_time: ClassVar[float] = 0 + _min_request_interval: ClassVar[float] = 1.0 # seconds + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="BRAVE_API_KEY", + description="API key for Brave Search", + required=True, + ), + ] + ) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if "BRAVE_API_KEY" not in os.environ: + raise ValueError( + "BRAVE_API_KEY environment variable is required for BraveSearchTool" + ) + + def _run( + self, + **kwargs: Any, + ) -> Any: + current_time = time.time() + if (current_time - self._last_request_time) < self._min_request_interval: + time.sleep( + self._min_request_interval - (current_time - self._last_request_time) + ) + BraveSearchTool._last_request_time = time.time() + try: + search_query = kwargs.get("search_query") or kwargs.get("query") + if not search_query: + raise ValueError("Search query is required") + + save_file = kwargs.get("save_file", self.save_file) + n_results = kwargs.get("n_results", self.n_results) + + payload = {"q": search_query, "count": n_results} + + if self.country != "": + payload["country"] = self.country + + headers = { + "X-Subscription-Token": os.environ["BRAVE_API_KEY"], + "Accept": "application/json", + } + + response = requests.get( + self.search_url, headers=headers, params=payload, timeout=30 + ) + response.raise_for_status() # Handle non-200 responses + results = response.json() + + if "web" in results: + results = results["web"]["results"] + string = [] + for result in results: + try: + string.append( + "\n".join( + [ + f"Title: {result['title']}", + f"Link: {result['url']}", + f"Snippet: {result['description']}", + "---", + ] + ) + ) + except KeyError: # noqa: PERF203 + continue + + content = "\n".join(string) + except requests.RequestException as e: + return f"Error performing search: {e!s}" + except KeyError as e: + return f"Error parsing search results: {e!s}" + if save_file: + _save_results_to_file(content) + return f"\nSearch results: {content}\n" + return content diff --git a/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/README.md new file mode 100644 index 000000000..f16b5ac73 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/README.md @@ -0,0 +1,79 @@ +# BrightData Tools Documentation + +## Description + +A comprehensive suite of CrewAI tools that leverage Bright Data's powerful infrastructure for web scraping, data extraction, and search operations. These tools provide three distinct capabilities: + +- **BrightDataDatasetTool**: Extract structured data from popular data feeds (Amazon, LinkedIn, Instagram, etc.) using pre-built datasets +- **BrightDataSearchTool**: Perform web searches across multiple search engines with geo-targeting and device simulation +- **BrightDataWebUnlockerTool**: Scrape any website content while bypassing bot protection mechanisms + +## Installation + +To incorporate these tools into your project, follow the installation instructions below: + +```shell +pip install crewai[tools] aiohttp requests +``` + +## Examples + +### Dataset Tool - Extract Amazon Product Data +```python +from crewai_tools import BrightDataDatasetTool + +# Initialize with specific dataset and URL +tool = BrightDataDatasetTool( + dataset_type="amazon_product", + url="https://www.amazon.com/dp/B08QB1QMJ5/" +) +result = tool.run() +``` + +### Search Tool - Perform Web Search +```python +from crewai_tools import BrightDataSearchTool + +# Initialize with search query +tool = BrightDataSearchTool( + query="latest AI trends 2025", + search_engine="google", + country="us" +) +result = tool.run() +``` + +### Web Unlocker Tool - Scrape Website Content +```python +from crewai_tools import BrightDataWebUnlockerTool + +# Initialize with target URL +tool = BrightDataWebUnlockerTool( + url="https://example.com", + data_format="markdown" +) +result = tool.run() +``` + +## Steps to Get Started + +To effectively use the BrightData Tools, follow these steps: + +1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment. + +2. **API Key Acquisition**: Register for a Bright Data account at `https://brightdata.com/` and obtain your API credentials from your account settings. + +3. **Environment Configuration**: Set up the required environment variables: + ```bash + export BRIGHT_DATA_API_KEY="your_api_key_here" + export BRIGHT_DATA_ZONE="your_zone_here" + ``` + +4. **Tool Selection**: Choose the appropriate tool based on your needs: + - Use **DatasetTool** for structured data from supported platforms + - Use **SearchTool** for web search operations + - Use **WebUnlockerTool** for general website scraping + +## Conclusion + +By integrating BrightData Tools into your CrewAI agents, you gain access to enterprise-grade web scraping and data extraction capabilities. These tools handle complex challenges like bot protection, geo-restrictions, and data parsing, allowing you to focus on building your applications rather than managing scraping infrastructure. \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/__init__.py new file mode 100644 index 000000000..fa1522048 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/__init__.py @@ -0,0 +1,8 @@ +from crewai_tools.tools.brightdata_tool.brightdata_dataset import BrightDataDatasetTool +from crewai_tools.tools.brightdata_tool.brightdata_serp import BrightDataSearchTool +from crewai_tools.tools.brightdata_tool.brightdata_unlocker import ( + BrightDataWebUnlockerTool, +) + + +__all__ = ["BrightDataDatasetTool", "BrightDataSearchTool", "BrightDataWebUnlockerTool"] diff --git a/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_dataset.py b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_dataset.py new file mode 100644 index 000000000..ddf4a10a1 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_dataset.py @@ -0,0 +1,600 @@ +import asyncio +import os +from typing import Any + +import aiohttp +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field + + +class BrightDataConfig(BaseModel): + API_URL: str = "https://api.brightdata.com" + DEFAULT_TIMEOUT: int = 600 + DEFAULT_POLLING_INTERVAL: int = 1 + + @classmethod + def from_env(cls): + return cls( + API_URL=os.environ.get("BRIGHTDATA_API_URL", "https://api.brightdata.com"), + DEFAULT_TIMEOUT=int(os.environ.get("BRIGHTDATA_DEFAULT_TIMEOUT", "600")), + DEFAULT_POLLING_INTERVAL=int( + os.environ.get("BRIGHTDATA_DEFAULT_POLLING_INTERVAL", "1") + ), + ) + + +class BrightDataDatasetToolException(Exception): # noqa: N818 + """Exception raised for custom error in the application.""" + + def __init__(self, message, error_code): + self.message = message + super().__init__(message) + self.error_code = error_code + + def __str__(self): + return f"{self.message} (Error Code: {self.error_code})" + + +class BrightDataDatasetToolSchema(BaseModel): + """Schema for validating input parameters for the BrightDataDatasetTool. + + Attributes: + dataset_type (str): Required Bright Data Dataset Type used to specify which dataset to access. + format (str): Response format (json by default). Multiple formats exist - json, ndjson, jsonl, csv + url (str): The URL from which structured data needs to be extracted. + zipcode (Optional[str]): An optional ZIP code to narrow down the data geographically. + additional_params (Optional[Dict]): Extra parameters for the Bright Data API call. + """ + + dataset_type: str = Field(..., description="The Bright Data Dataset Type") + format: str | None = Field( + default="json", description="Response format (json by default)" + ) + url: str = Field(..., description="The URL to extract data from") + zipcode: str | None = Field(default=None, description="Optional zipcode") + additional_params: dict[str, Any] | None = Field( + default=None, description="Additional params if any" + ) + + +config = BrightDataConfig.from_env() + +BRIGHTDATA_API_URL = config.API_URL +timeout = config.DEFAULT_TIMEOUT + +datasets = [ + { + "id": "amazon_product", + "dataset_id": "gd_l7q7dkf244hwjntr0", + "description": "\n".join( + [ + "Quickly read structured amazon product data.", + "Requires a valid product URL with /dp/ in it.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "amazon_product_reviews", + "dataset_id": "gd_le8e811kzy4ggddlq", + "description": "\n".join( + [ + "Quickly read structured amazon product review data.", + "Requires a valid product URL with /dp/ in it.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "amazon_product_search", + "dataset_id": "gd_lwdb4vjm1ehb499uxs", + "description": "\n".join( + [ + "Quickly read structured amazon product search data.", + "Requires a valid search keyword and amazon domain URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["keyword", "url", "pages_to_search"], + "defaults": {"pages_to_search": "1"}, + }, + { + "id": "walmart_product", + "dataset_id": "gd_l95fol7l1ru6rlo116", + "description": "\n".join( + [ + "Quickly read structured walmart product data.", + "Requires a valid product URL with /ip/ in it.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "walmart_seller", + "dataset_id": "gd_m7ke48w81ocyu4hhz0", + "description": "\n".join( + [ + "Quickly read structured walmart seller data.", + "Requires a valid walmart seller URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "ebay_product", + "dataset_id": "gd_ltr9mjt81n0zzdk1fb", + "description": "\n".join( + [ + "Quickly read structured ebay product data.", + "Requires a valid ebay product URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "homedepot_products", + "dataset_id": "gd_lmusivh019i7g97q2n", + "description": "\n".join( + [ + "Quickly read structured homedepot product data.", + "Requires a valid homedepot product URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "zara_products", + "dataset_id": "gd_lct4vafw1tgx27d4o0", + "description": "\n".join( + [ + "Quickly read structured zara product data.", + "Requires a valid zara product URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "etsy_products", + "dataset_id": "gd_ltppk0jdv1jqz25mz", + "description": "\n".join( + [ + "Quickly read structured etsy product data.", + "Requires a valid etsy product URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "bestbuy_products", + "dataset_id": "gd_ltre1jqe1jfr7cccf", + "description": "\n".join( + [ + "Quickly read structured bestbuy product data.", + "Requires a valid bestbuy product URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "linkedin_person_profile", + "dataset_id": "gd_l1viktl72bvl7bjuj0", + "description": "\n".join( + [ + "Quickly read structured linkedin people profile data.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "linkedin_company_profile", + "dataset_id": "gd_l1vikfnt1wgvvqz95w", + "description": "\n".join( + [ + "Quickly read structured linkedin company profile data", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "linkedin_job_listings", + "dataset_id": "gd_lpfll7v5hcqtkxl6l", + "description": "\n".join( + [ + "Quickly read structured linkedin job listings data", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "linkedin_posts", + "dataset_id": "gd_lyy3tktm25m4avu764", + "description": "\n".join( + [ + "Quickly read structured linkedin posts data", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "linkedin_people_search", + "dataset_id": "gd_m8d03he47z8nwb5xc", + "description": "\n".join( + [ + "Quickly read structured linkedin people search data", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url", "first_name", "last_name"], + }, + { + "id": "crunchbase_company", + "dataset_id": "gd_l1vijqt9jfj7olije", + "description": "\n".join( + [ + "Quickly read structured crunchbase company data", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "zoominfo_company_profile", + "dataset_id": "gd_m0ci4a4ivx3j5l6nx", + "description": "\n".join( + [ + "Quickly read structured ZoomInfo company profile data.", + "Requires a valid ZoomInfo company URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "instagram_profiles", + "dataset_id": "gd_l1vikfch901nx3by4", + "description": "\n".join( + [ + "Quickly read structured Instagram profile data.", + "Requires a valid Instagram URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "instagram_posts", + "dataset_id": "gd_lk5ns7kz21pck8jpis", + "description": "\n".join( + [ + "Quickly read structured Instagram post data.", + "Requires a valid Instagram URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "instagram_reels", + "dataset_id": "gd_lyclm20il4r5helnj", + "description": "\n".join( + [ + "Quickly read structured Instagram reel data.", + "Requires a valid Instagram URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "instagram_comments", + "dataset_id": "gd_ltppn085pokosxh13", + "description": "\n".join( + [ + "Quickly read structured Instagram comments data.", + "Requires a valid Instagram URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "facebook_posts", + "dataset_id": "gd_lyclm1571iy3mv57zw", + "description": "\n".join( + [ + "Quickly read structured Facebook post data.", + "Requires a valid Facebook post URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "facebook_marketplace_listings", + "dataset_id": "gd_lvt9iwuh6fbcwmx1a", + "description": "\n".join( + [ + "Quickly read structured Facebook marketplace listing data.", + "Requires a valid Facebook marketplace listing URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "facebook_company_reviews", + "dataset_id": "gd_m0dtqpiu1mbcyc2g86", + "description": "\n".join( + [ + "Quickly read structured Facebook company reviews data.", + "Requires a valid Facebook company URL and number of reviews.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url", "num_of_reviews"], + }, + { + "id": "facebook_events", + "dataset_id": "gd_m14sd0to1jz48ppm51", + "description": "\n".join( + [ + "Quickly read structured Facebook events data.", + "Requires a valid Facebook event URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "tiktok_profiles", + "dataset_id": "gd_l1villgoiiidt09ci", + "description": "\n".join( + [ + "Quickly read structured Tiktok profiles data.", + "Requires a valid Tiktok profile URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "tiktok_posts", + "dataset_id": "gd_lu702nij2f790tmv9h", + "description": "\n".join( + [ + "Quickly read structured Tiktok post data.", + "Requires a valid Tiktok post URL.", + "This can be a cache lookup, so it can be more reliable than scraping", + ] + ), + "inputs": ["url"], + }, + { + "id": "tiktok_shop", + "dataset_id": "gd_m45m1u911dsa4274pi", + "description": "\n".join( + [ + "Quickly read structured Tiktok shop data.", + "Requires a valid Tiktok shop product URL.", + "This can be a cache lookup...", + ] + ), + "inputs": ["url"], + }, +] + + +class BrightDataDatasetTool(BaseTool): + """CrewAI-compatible tool for scraping structured data using Bright Data Datasets. + + Attributes: + name (str): Tool name displayed in the CrewAI environment. + description (str): Tool description shown to agents or users. + args_schema (Type[BaseModel]): Pydantic schema for validating input arguments. + """ + + name: str = "Bright Data Dataset Tool" + description: str = "Scrapes structured data using Bright Data Dataset API from a URL and optional input parameters" + args_schema: type[BaseModel] = BrightDataDatasetToolSchema + dataset_type: str | None = None + url: str | None = None + format: str = "json" + zipcode: str | None = None + additional_params: dict[str, Any] | None = None + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="BRIGHT_DATA_API_KEY", + description="API key for Bright Data", + required=True, + ), + ] + ) + + def __init__( + self, + dataset_type: str | None = None, + url: str | None = None, + format: str = "json", + zipcode: str | None = None, + additional_params: dict[str, Any] | None = None, + **kwargs: Any, + ): + super().__init__(**kwargs) + self.dataset_type = dataset_type + self.url = url + self.format = format + self.zipcode = zipcode + self.additional_params = additional_params + + def filter_dataset_by_id(self, target_id): + return [dataset for dataset in datasets if dataset["id"] == target_id] + + async def get_dataset_data_async( + self, + dataset_type: str, + output_format: str, + url: str, + zipcode: str | None = None, + additional_params: dict[str, Any] | None = None, + polling_interval: int = 1, + ) -> str: + """Asynchronously trigger and poll Bright Data dataset scraping. + + Args: + dataset_type (str): Bright Data Dataset Type. + url (str): Target URL to scrape. + zipcode (Optional[str]): Optional ZIP code for geo-specific data. + additional_params (Optional[Dict]): Extra API parameters. + polling_interval (int): Time interval in seconds between polling attempts. + + Returns: + Dict: Structured dataset result from Bright Data. + + Raises: + Exception: If any API step fails or the job fails. + TimeoutError: If polling times out before job completion. + """ + request_data = {"url": url} + if zipcode is not None: + request_data["zipcode"] = zipcode + + # Set additional parameters dynamically depending upon the dataset that is being requested + if additional_params: + request_data.update(additional_params) + + api_key = os.getenv("BRIGHT_DATA_API_KEY") + + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + } + + dataset_id = "" + dataset = self.filter_dataset_by_id(dataset_type) + + if len(dataset) == 1: + dataset_id = dataset[0]["dataset_id"] + else: + raise ValueError( + f"Unable to find the dataset for {dataset_type}. Please make sure to pass a valid one" + ) + + async with aiohttp.ClientSession() as session: + # Step 1: Trigger job + async with session.post( + f"{BRIGHTDATA_API_URL}/datasets/v3/trigger", + params={"dataset_id": dataset_id, "include_errors": "true"}, + json=[request_data], + headers=headers, + ) as trigger_response: + if trigger_response.status != 200: + raise BrightDataDatasetToolException( + f"Trigger failed: {await trigger_response.text()}", + trigger_response.status, + ) + trigger_data = await trigger_response.json() + snapshot_id = trigger_data.get("snapshot_id") + + # Step 2: Poll for completion + elapsed = 0 + while elapsed < timeout: + await asyncio.sleep(polling_interval) + elapsed += polling_interval + + async with session.get( + f"{BRIGHTDATA_API_URL}/datasets/v3/progress/{snapshot_id}", + headers=headers, + ) as status_response: + if status_response.status != 200: + raise BrightDataDatasetToolException( + f"Status check failed: {await status_response.text()}", + status_response.status, + ) + status_data = await status_response.json() + if status_data.get("status") == "ready": + break + if status_data.get("status") == "error": + raise BrightDataDatasetToolException( + f"Job failed: {status_data}", 0 + ) + else: + raise TimeoutError("Polling timed out before job completed.") + + # Step 3: Retrieve result + async with session.get( + f"{BRIGHTDATA_API_URL}/datasets/v3/snapshot/{snapshot_id}", + params={"format": output_format}, + headers=headers, + ) as snapshot_response: + if snapshot_response.status != 200: + raise BrightDataDatasetToolException( + f"Result fetch failed: {await snapshot_response.text()}", + snapshot_response.status, + ) + + return await snapshot_response.text() + + def _run( + self, + url: str | None = None, + dataset_type: str | None = None, + format: str | None = None, + zipcode: str | None = None, + additional_params: dict[str, Any] | None = None, + **kwargs: Any, + ) -> Any: + dataset_type = dataset_type or self.dataset_type + output_format = format or self.format + url = url or self.url + zipcode = zipcode or self.zipcode + additional_params = additional_params or self.additional_params + + if not dataset_type: + raise ValueError( + "dataset_type is required either in constructor or method call" + ) + if not url: + raise ValueError("url is required either in constructor or method call") + + valid_output_formats = {"json", "ndjson", "jsonl", "csv"} + if output_format not in valid_output_formats: + raise ValueError( + f"Unsupported output format: {output_format}. Must be one of {', '.join(valid_output_formats)}." + ) + + api_key = os.getenv("BRIGHT_DATA_API_KEY") + if not api_key: + raise ValueError("BRIGHT_DATA_API_KEY environment variable is required.") + + try: + return asyncio.run( + self.get_dataset_data_async( + dataset_type=dataset_type, + output_format=output_format, + url=url, + zipcode=zipcode, + additional_params=additional_params, + ) + ) + except TimeoutError as e: + return f"Timeout Exception occured in method : get_dataset_data_async. Details - {e!s}" + except BrightDataDatasetToolException as e: + return ( + f"Exception occured in method : get_dataset_data_async. Details - {e!s}" + ) + except Exception as e: + return f"Bright Data API error: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_serp.py b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_serp.py new file mode 100644 index 000000000..e18b4269a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_serp.py @@ -0,0 +1,237 @@ +import os +from typing import Any +import urllib.parse + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +class BrightDataConfig(BaseModel): + API_URL: str = "https://api.brightdata.com/request" + + @classmethod + def from_env(cls): + return cls( + API_URL=os.environ.get( + "BRIGHTDATA_API_URL", "https://api.brightdata.com/request" + ) + ) + + +class BrightDataSearchToolSchema(BaseModel): + """Schema that defines the input arguments for the BrightDataSearchToolSchema. + + Attributes: + query (str): The search query to be executed (e.g., "latest AI news"). + search_engine (Optional[str]): The search engine to use ("google", "bing", "yandex"). Default is "google". + country (Optional[str]): Two-letter country code for geo-targeting (e.g., "us", "in"). Default is "us". + language (Optional[str]): Language code for search results (e.g., "en", "es"). Default is "en". + search_type (Optional[str]): Type of search, such as "isch" (images), "nws" (news), "jobs", etc. + device_type (Optional[str]): Device type to simulate ("desktop", "mobile", "ios", "android"). Default is "desktop". + parse_results (Optional[bool]): If True, results will be returned in structured JSON. If False, raw HTML. Default is True. + """ + + query: str = Field(..., description="Search query to perform") + search_engine: str | None = Field( + default="google", + description="Search engine domain (e.g., 'google', 'bing', 'yandex')", + ) + country: str | None = Field( + default="us", + description="Two-letter country code for geo-targeting (e.g., 'us', 'gb')", + ) + language: str | None = Field( + default="en", + description="Language code (e.g., 'en', 'es') used in the query URL", + ) + search_type: str | None = Field( + default=None, + description="Type of search (e.g., 'isch' for images, 'nws' for news)", + ) + device_type: str | None = Field( + default="desktop", + description="Device type to simulate (e.g., 'mobile', 'desktop', 'ios')", + ) + parse_results: bool | None = Field( + default=True, + description="Whether to parse and return JSON (True) or raw HTML/text (False)", + ) + + +class BrightDataSearchTool(BaseTool): + """A web search tool that utilizes Bright Data's SERP API to perform queries and return either structured results + or raw page content from search engines like Google or Bing. + + Attributes: + name (str): Tool name used by the agent. + description (str): A brief explanation of what the tool does. + args_schema (Type[BaseModel]): Schema class for validating tool arguments. + base_url (str): The Bright Data API endpoint used for making the POST request. + api_key (str): Bright Data API key loaded from the environment variable 'BRIGHT_DATA_API_KEY'. + zone (str): Zone identifier from Bright Data, loaded from the environment variable 'BRIGHT_DATA_ZONE'. + + Raises: + ValueError: If API key or zone environment variables are not set. + """ + + name: str = "Bright Data SERP Search" + description: str = "Tool to perform web search using Bright Data SERP API." + args_schema: type[BaseModel] = BrightDataSearchToolSchema + _config = BrightDataConfig.from_env() + base_url: str = "" + api_key: str = "" + zone: str = "" + query: str | None = None + search_engine: str = "google" + country: str = "us" + language: str = "en" + search_type: str | None = None + device_type: str = "desktop" + parse_results: bool = True + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="BRIGHT_DATA_API_KEY", + description="API key for Bright Data", + required=True, + ), + ] + ) + + def __init__( + self, + query: str | None = None, + search_engine: str = "google", + country: str = "us", + language: str = "en", + search_type: str | None = None, + device_type: str = "desktop", + parse_results: bool = True, + **kwargs: Any, + ): + super().__init__(**kwargs) + self.base_url = self._config.API_URL + self.query = query + self.search_engine = search_engine + self.country = country + self.language = language + self.search_type = search_type + self.device_type = device_type + self.parse_results = parse_results + + self.api_key = os.getenv("BRIGHT_DATA_API_KEY") or "" + self.zone = os.getenv("BRIGHT_DATA_ZONE") or "" + if not self.api_key: + raise ValueError("BRIGHT_DATA_API_KEY environment variable is required.") + if not self.zone: + raise ValueError("BRIGHT_DATA_ZONE environment variable is required.") + + def get_search_url(self, engine: str, query: str): + if engine == "yandex": + return f"https://yandex.com/search/?text=${query}" + if engine == "bing": + return f"https://www.bing.com/search?q=${query}" + return f"https://www.google.com/search?q=${query}" + + def _run( + self, + query: str | None = None, + search_engine: str | None = None, + country: str | None = None, + language: str | None = None, + search_type: str | None = None, + device_type: str | None = None, + parse_results: bool | None = None, + **kwargs, + ) -> Any: + """Executes a search query using Bright Data SERP API and returns results. + + Args: + query (str): The search query string (URL encoded internally). + search_engine (str): The search engine to use (default: "google"). + country (str): Country code for geotargeting (default: "us"). + language (str): Language code for the query (default: "en"). + search_type (str): Optional type of search such as "nws", "isch", "jobs". + device_type (str): Optional device type to simulate (e.g., "mobile", "ios", "desktop"). + parse_results (bool): If True, returns structured data; else raw page (default: True). + results_count (str or int): Number of search results to fetch (default: "10"). + + Returns: + dict or str: Parsed JSON data from Bright Data if available, otherwise error message. + """ + query = query or self.query + search_engine = search_engine or self.search_engine + country = country or self.country + language = language or self.language + search_type = search_type or self.search_type + device_type = device_type or self.device_type + parse_results = ( + parse_results if parse_results is not None else self.parse_results + ) + results_count = kwargs.get("results_count", "10") + + # Validate required parameters + if not query: + raise ValueError("query is required either in constructor or method call") + + # Build the search URL + query = urllib.parse.quote(query) + url = self.get_search_url(search_engine, query) + + # Add parameters to the URL + params = [] + + if country: + params.append(f"gl={country}") + + if language: + params.append(f"hl={language}") + + if results_count: + params.append(f"num={results_count}") + + if parse_results: + params.append("brd_json=1") + + if search_type: + if search_type == "jobs": + params.append("ibp=htl;jobs") + else: + params.append(f"tbm={search_type}") + + if device_type: + if device_type == "mobile": + params.append("brd_mobile=1") + elif device_type == "ios": + params.append("brd_mobile=ios") + elif device_type == "android": + params.append("brd_mobile=android") + + # Combine parameters with the URL + if params: + url += "&" + "&".join(params) + + # Set up the API request parameters + request_params = {"zone": self.zone, "url": url, "format": "raw"} + + request_params = {k: v for k, v in request_params.items() if v is not None} + + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + + try: + response = requests.post( + self.base_url, json=request_params, headers=headers, timeout=30 + ) + + response.raise_for_status() + + return response.text + + except requests.RequestException as e: + return f"Error performing BrightData search: {e!s}" + except Exception as e: + return f"Error fetching results: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_unlocker.py b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_unlocker.py new file mode 100644 index 000000000..897b3cdb6 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_unlocker.py @@ -0,0 +1,146 @@ +import os +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +class BrightDataConfig(BaseModel): + API_URL: str = "https://api.brightdata.com/request" + + @classmethod + def from_env(cls): + return cls( + API_URL=os.environ.get( + "BRIGHTDATA_API_URL", "https://api.brightdata.com/request" + ) + ) + + +class BrightDataUnlockerToolSchema(BaseModel): + """Pydantic schema for input parameters used by the BrightDataWebUnlockerTool. + + This schema defines the structure and validation for parameters passed when performing + a web scraping request using Bright Data's Web Unlocker. + + Attributes: + url (str): The target URL to scrape. + format (Optional[str]): Format of the response returned by Bright Data. Default 'raw' format. + data_format (Optional[str]): Response data format (html by default). markdown is one more option. + """ + + url: str = Field(..., description="URL to perform the web scraping") + format: str | None = Field( + default="raw", description="Response format (raw is standard)" + ) + data_format: str | None = Field( + default="markdown", description="Response data format (html by default)" + ) + + +class BrightDataWebUnlockerTool(BaseTool): + """A tool for performing web scraping using the Bright Data Web Unlocker API. + + This tool allows automated and programmatic access to web pages by routing requests + through Bright Data's unlocking and proxy infrastructure, which can bypass bot + protection mechanisms like CAPTCHA, geo-restrictions, and anti-bot detection. + + Attributes: + name (str): Name of the tool. + description (str): Description of what the tool does. + args_schema (Type[BaseModel]): Pydantic model schema for expected input arguments. + base_url (str): Base URL of the Bright Data Web Unlocker API. + api_key (str): Bright Data API key (must be set in the BRIGHT_DATA_API_KEY environment variable). + zone (str): Bright Data zone identifier (must be set in the BRIGHT_DATA_ZONE environment variable). + + Methods: + _run(**kwargs: Any) -> Any: + Sends a scraping request to Bright Data's Web Unlocker API and returns the result. + """ + + name: str = "Bright Data Web Unlocker Scraping" + description: str = "Tool to perform web scraping using Bright Data Web Unlocker" + args_schema: type[BaseModel] = BrightDataUnlockerToolSchema + _config = BrightDataConfig.from_env() + base_url: str = "" + api_key: str = "" + zone: str = "" + url: str | None = None + format: str = "raw" + data_format: str = "markdown" + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="BRIGHT_DATA_API_KEY", + description="API key for Bright Data", + required=True, + ), + ] + ) + + def __init__( + self, + url: str | None = None, + format: str = "raw", + data_format: str = "markdown", + **kwargs: Any, + ): + super().__init__(**kwargs) + self.base_url = self._config.API_URL + self.url = url + self.format = format + self.data_format = data_format + + self.api_key = os.getenv("BRIGHT_DATA_API_KEY") or "" + self.zone = os.getenv("BRIGHT_DATA_ZONE") or "" + if not self.api_key: + raise ValueError("BRIGHT_DATA_API_KEY environment variable is required.") + if not self.zone: + raise ValueError("BRIGHT_DATA_ZONE environment variable is required.") + + def _run( + self, + url: str | None = None, + format: str | None = None, + data_format: str | None = None, + **kwargs: Any, + ) -> Any: + url = url or self.url + format = format or self.format + data_format = data_format or self.data_format + + if not url: + raise ValueError("url is required either in constructor or method call") + + payload = { + "url": url, + "zone": self.zone, + "format": format, + } + valid_data_formats = {"html", "markdown"} + if data_format not in valid_data_formats: + raise ValueError( + f"Unsupported data format: {data_format}. Must be one of {', '.join(valid_data_formats)}." + ) + + if data_format == "markdown": + payload["data_format"] = "markdown" + + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + + try: + response = requests.post( + self.base_url, json=payload, headers=headers, timeout=30 + ) + response.raise_for_status() + + return response.text + + except requests.RequestException as e: + return f"HTTP Error performing BrightData Web Unlocker Scrape: {e}\nResponse: {getattr(e.response, 'text', '')}" + except Exception as e: + return f"Error fetching results: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/browserbase_load_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/browserbase_load_tool/README.md new file mode 100644 index 000000000..bd562da0d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/browserbase_load_tool/README.md @@ -0,0 +1,38 @@ +# BrowserbaseLoadTool + +## Description + +[Browserbase](https://browserbase.com) is a developer platform to reliably run, manage, and monitor headless browsers. + + Power your AI data retrievals with: + - [Serverless Infrastructure](https://docs.browserbase.com/under-the-hood) providing reliable browsers to extract data from complex UIs + - [Stealth Mode](https://docs.browserbase.com/features/stealth-mode) with included fingerprinting tactics and automatic captcha solving + - [Session Debugger](https://docs.browserbase.com/features/sessions) to inspect your Browser Session with networks timeline and logs + - [Live Debug](https://docs.browserbase.com/guides/session-debug-connection/browser-remote-control) to quickly debug your automation + +## Installation + +- Get an API key and Project ID from [browserbase.com](https://browserbase.com) and set it in environment variables (`BROWSERBASE_API_KEY`, `BROWSERBASE_PROJECT_ID`). +- Install the [Browserbase SDK](http://github.com/browserbase/python-sdk) along with `crewai[tools]` package: + +``` +pip install browserbase 'crewai[tools]' +``` + +## Example + +Utilize the BrowserbaseLoadTool as follows to allow your agent to load websites: + +```python +from crewai_tools import BrowserbaseLoadTool + +tool = BrowserbaseLoadTool() +``` + +## Arguments + +- `api_key` Optional. Browserbase API key. Default is `BROWSERBASE_API_KEY` env variable. +- `project_id` Optional. Browserbase Project ID. Default is `BROWSERBASE_PROJECT_ID` env variable. +- `text_content` Retrieve only text content. Default is `False`. +- `session_id` Optional. Provide an existing Session ID. +- `proxy` Optional. Enable/Disable Proxies." diff --git a/src/crewai/cli/enterprise/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/browserbase_load_tool/__init__.py similarity index 100% rename from src/crewai/cli/enterprise/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/browserbase_load_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/browserbase_load_tool/browserbase_load_tool.py b/lib/crewai-tools/src/crewai_tools/tools/browserbase_load_tool/browserbase_load_tool.py new file mode 100644 index 000000000..f12c1c6ea --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/browserbase_load_tool/browserbase_load_tool.py @@ -0,0 +1,77 @@ +import os +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field + + +class BrowserbaseLoadToolSchema(BaseModel): + url: str = Field(description="Website URL") + + +class BrowserbaseLoadTool(BaseTool): + name: str = "Browserbase web load tool" + description: str = "Load webpages url in a headless browser using Browserbase and return the contents" + args_schema: type[BaseModel] = BrowserbaseLoadToolSchema + api_key: str | None = os.getenv("BROWSERBASE_API_KEY") + project_id: str | None = os.getenv("BROWSERBASE_PROJECT_ID") + text_content: bool | None = False + session_id: str | None = None + proxy: bool | None = None + browserbase: Any | None = None + package_dependencies: list[str] = Field(default_factory=lambda: ["browserbase"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="BROWSERBASE_API_KEY", + description="API key for Browserbase services", + required=False, + ), + EnvVar( + name="BROWSERBASE_PROJECT_ID", + description="Project ID for Browserbase services", + required=False, + ), + ] + ) + + def __init__( + self, + api_key: str | None = None, + project_id: str | None = None, + text_content: bool | None = False, + session_id: str | None = None, + proxy: bool | None = None, + **kwargs, + ): + super().__init__(**kwargs) + if not self.api_key: + raise EnvironmentError( + "BROWSERBASE_API_KEY environment variable is required for initialization" + ) + try: + from browserbase import Browserbase # type: ignore + except ImportError: + import click + + if click.confirm( + "`browserbase` package not found, would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "browserbase"], check=True) # noqa: S607 + from browserbase import Browserbase # type: ignore + else: + raise ImportError( + "`browserbase` package not found, please run `uv add browserbase`" + ) from None + + self.browserbase = Browserbase(api_key=self.api_key) + self.text_content = text_content + self.session_id = session_id + self.proxy = proxy + + def _run(self, url: str): + return self.browserbase.load_url( # type: ignore[union-attr] + url, self.text_content, self.session_id, self.proxy + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/code_docs_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/code_docs_search_tool/README.md new file mode 100644 index 000000000..f90398a11 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/code_docs_search_tool/README.md @@ -0,0 +1,56 @@ +# CodeDocsSearchTool + +## Description +The CodeDocsSearchTool is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within code documentation. It enables users to efficiently find specific information or topics within code documentation. By providing a `docs_url` during initialization, the tool narrows down the search to that particular documentation site. Alternatively, without a specific `docs_url`, it searches across a wide array of code documentation known or discovered throughout its execution, making it versatile for various documentation search needs. + +## Installation +To start using the CodeDocsSearchTool, first, install the crewai_tools package via pip: +```shell +pip install 'crewai[tools]' +``` + +## Example +Utilize the CodeDocsSearchTool as follows to conduct searches within code documentation: +```python +from crewai_tools import CodeDocsSearchTool + +# To search any code documentation content if the URL is known or discovered during its execution: +tool = CodeDocsSearchTool() + +# OR + +# To specifically focus your search on a given documentation site by providing its URL: +tool = CodeDocsSearchTool(docs_url='https://docs.example.com/reference') +``` +Note: Substitute 'https://docs.example.com/reference' with your target documentation URL and 'How to use search tool' with the search query relevant to your needs. + +## Arguments +- `docs_url`: Optional. Specifies the URL of the code documentation to be searched. Providing this during the tool's initialization focuses the search on the specified documentation content. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = CodeDocsSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/src/crewai/cli/settings/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/code_docs_search_tool/__init__.py similarity index 100% rename from src/crewai/cli/settings/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/code_docs_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/code_docs_search_tool/code_docs_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/code_docs_search_tool/code_docs_search_tool.py new file mode 100644 index 000000000..2a9e99e86 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/code_docs_search_tool/code_docs_search_tool.py @@ -0,0 +1,51 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedCodeDocsSearchToolSchema(BaseModel): + """Input for CodeDocsSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the Code Docs content", + ) + + +class CodeDocsSearchToolSchema(FixedCodeDocsSearchToolSchema): + """Input for CodeDocsSearchTool.""" + + docs_url: str = Field(..., description="Mandatory docs_url path you want to search") + + +class CodeDocsSearchTool(RagTool): + name: str = "Search a Code Docs content" + description: str = ( + "A tool that can be used to semantic search a query from a Code Docs content." + ) + args_schema: type[BaseModel] = CodeDocsSearchToolSchema + + def __init__(self, docs_url: str | None = None, **kwargs): + super().__init__(**kwargs) + if docs_url is not None: + self.add(docs_url) + self.description = f"A tool that can be used to semantic search a query the {docs_url} Code Docs content." + self.args_schema = FixedCodeDocsSearchToolSchema + self._generate_description() + + def add(self, docs_url: str) -> None: + super().add(docs_url, data_type=DataType.DOCS_SITE) + + def _run( # type: ignore[override] + self, + search_query: str, + docs_url: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if docs_url is not None: + self.add(docs_url) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/Dockerfile b/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/Dockerfile new file mode 100644 index 000000000..4df22ca58 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/Dockerfile @@ -0,0 +1,6 @@ +FROM python:3.12-alpine + +RUN pip install requests beautifulsoup4 + +# Set the working directory +WORKDIR /workspace diff --git a/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/README.md new file mode 100644 index 000000000..ab0cbf44b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/README.md @@ -0,0 +1,53 @@ +# CodeInterpreterTool + +## Description +This tool is used to give the Agent the ability to run code (Python3) from the code generated by the Agent itself. The code is executed in a sandboxed environment, so it is safe to run any code. + +It is incredible useful since it allows the Agent to generate code, run it in the same environment, get the result and use it to make decisions. + +## Requirements + +- Docker + +## Installation +Install the crewai_tools package +```shell +pip install 'crewai[tools]' +``` + +## Example + +Remember that when using this tool, the code must be generated by the Agent itself. The code must be a Python3 code. And it will take some time for the first time to run because it needs to build the Docker image. + +```python +from crewai_tools import CodeInterpreterTool + +Agent( + ... + tools=[CodeInterpreterTool()], +) +``` + +Or if you need to pass your own Dockerfile just do this + +```python +from crewai_tools import CodeInterpreterTool + +Agent( + ... + tools=[CodeInterpreterTool(user_dockerfile_path="")], +) +``` + +If it is difficult to connect to docker daemon automatically (especially for macOS users), you can do this to setup docker host manually + +```python +from crewai_tools import CodeInterpreterTool + +Agent( + ... + tools=[CodeInterpreterTool(user_docker_base_url="", + user_dockerfile_path="")], +) + +``` diff --git a/src/crewai/cli/shared/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/__init__.py similarity index 100% rename from src/crewai/cli/shared/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/code_interpreter_tool.py b/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/code_interpreter_tool.py new file mode 100644 index 000000000..c4a2093ee --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/code_interpreter_tool.py @@ -0,0 +1,391 @@ +"""Code Interpreter Tool for executing Python code in isolated environments. + +This module provides a tool for executing Python code either in a Docker container for +safe isolation or directly in a restricted sandbox. It includes mechanisms for blocking +potentially unsafe operations and importing restricted modules. +""" + +import importlib.util +import os +import subprocess +from types import ModuleType +from typing import Any, ClassVar, TypedDict + +from crewai.tools import BaseTool +from docker import ( # type: ignore[import-untyped] + DockerClient, + from_env as docker_from_env, +) +from docker.errors import ImageNotFound, NotFound # type: ignore[import-untyped] +from docker.models.containers import Container # type: ignore[import-untyped] +from pydantic import BaseModel, Field +from typing_extensions import Unpack + +from crewai_tools.printer import Printer + + +class RunKwargs(TypedDict, total=False): + """Keyword arguments for the _run method.""" + + code: str + libraries_used: list[str] + + +class CodeInterpreterSchema(BaseModel): + """Schema for defining inputs to the CodeInterpreterTool. + + This schema defines the required parameters for code execution, + including the code to run and any libraries that need to be installed. + """ + + code: str = Field( + ..., + description="Python3 code used to be interpreted in the Docker container. ALWAYS PRINT the final result and the output of the code", + ) + + libraries_used: list[str] = Field( + ..., + description="List of libraries used in the code with proper installing names separated by commas. Example: numpy,pandas,beautifulsoup4", + ) + + +class SandboxPython: + """A restricted Python execution environment for running code safely. + + This class provides methods to safely execute Python code by restricting access to + potentially dangerous modules and built-in functions. It creates a sandboxed + environment where harmful operations are blocked. + """ + + BLOCKED_MODULES: ClassVar[set[str]] = { + "os", + "sys", + "subprocess", + "shutil", + "importlib", + "inspect", + "tempfile", + "sysconfig", + "builtins", + } + + UNSAFE_BUILTINS: ClassVar[set[str]] = { + "exec", + "eval", + "open", + "compile", + "input", + "globals", + "locals", + "vars", + "help", + "dir", + } + + @staticmethod + def restricted_import( + name: str, + custom_globals: dict[str, Any] | None = None, + custom_locals: dict[str, Any] | None = None, + fromlist: list[str] | None = None, + level: int = 0, + ) -> ModuleType: + """A restricted import function that blocks importing of unsafe modules. + + Args: + name: The name of the module to import. + custom_globals: Global namespace to use. + custom_locals: Local namespace to use. + fromlist: List of items to import from the module. + level: The level value passed to __import__. + + Returns: + The imported module if allowed. + + Raises: + ImportError: If the module is in the blocked modules list. + """ + if name in SandboxPython.BLOCKED_MODULES: + raise ImportError(f"Importing '{name}' is not allowed.") + return __import__(name, custom_globals, custom_locals, fromlist or (), level) + + @staticmethod + def safe_builtins() -> dict[str, Any]: + """Creates a dictionary of built-in functions with unsafe ones removed. + + Returns: + A dictionary of safe built-in functions and objects. + """ + import builtins + + safe_builtins = { + k: v + for k, v in builtins.__dict__.items() + if k not in SandboxPython.UNSAFE_BUILTINS + } + safe_builtins["__import__"] = SandboxPython.restricted_import + return safe_builtins + + @staticmethod + def exec(code: str, locals_: dict[str, Any]) -> None: + """Executes Python code in a restricted environment. + + Args: + code: The Python code to execute as a string. + locals_: A dictionary that will be used for local variable storage. + """ + exec(code, {"__builtins__": SandboxPython.safe_builtins()}, locals_) # noqa: S102 + + +class CodeInterpreterTool(BaseTool): + """A tool for executing Python code in isolated environments. + + This tool provides functionality to run Python code either in a Docker container + for safe isolation or directly in a restricted sandbox. It can handle installing + Python packages and executing arbitrary Python code. + """ + + name: str = "Code Interpreter" + description: str = "Interprets Python3 code strings with a final print statement." + args_schema: type[BaseModel] = CodeInterpreterSchema + default_image_tag: str = "code-interpreter:latest" + code: str | None = None + user_dockerfile_path: str | None = None + user_docker_base_url: str | None = None + unsafe_mode: bool = False + + @staticmethod + def _get_installed_package_path() -> str: + """Gets the installation path of the crewai_tools package. + + Returns: + The directory path where the package is installed. + + Raises: + RuntimeError: If the package cannot be found. + """ + spec = importlib.util.find_spec("crewai_tools") + if spec is None or spec.origin is None: + raise RuntimeError("Cannot find crewai_tools package installation path") + return os.path.dirname(spec.origin) + + def _verify_docker_image(self) -> None: + """Verifies if the Docker image is available or builds it if necessary. + + Checks if the required Docker image exists. If not, builds it using either a + user-provided Dockerfile or the default one included with the package. + + Raises: + FileNotFoundError: If the Dockerfile cannot be found. + """ + client = ( + docker_from_env() + if self.user_docker_base_url is None + else DockerClient(base_url=self.user_docker_base_url) + ) + + try: + client.images.get(self.default_image_tag) + + except ImageNotFound: + if self.user_dockerfile_path and os.path.exists(self.user_dockerfile_path): + dockerfile_path = self.user_dockerfile_path + else: + package_path = self._get_installed_package_path() + dockerfile_path = os.path.join( + package_path, "tools/code_interpreter_tool" + ) + if not os.path.exists(dockerfile_path): + raise FileNotFoundError( + f"Dockerfile not found in {dockerfile_path}" + ) from None + + client.images.build( + path=dockerfile_path, + tag=self.default_image_tag, + rm=True, + ) + + def _run(self, **kwargs: Unpack[RunKwargs]) -> str: + """Runs the code interpreter tool with the provided arguments. + + Args: + **kwargs: Keyword arguments that should include 'code' and 'libraries_used'. + + Returns: + The output of the executed code as a string. + """ + code: str | None = kwargs.get("code", self.code) + libraries_used: list[str] = kwargs.get("libraries_used", []) + + if not code: + return "No code provided to execute." + + if self.unsafe_mode: + return self.run_code_unsafe(code, libraries_used) + return self.run_code_safety(code, libraries_used) + + @staticmethod + def _install_libraries(container: Container, libraries: list[str]) -> None: + """Installs required Python libraries in the Docker container. + + Args: + container: The Docker container where libraries will be installed. + libraries: A list of library names to install using pip. + """ + for library in libraries: + container.exec_run(["pip", "install", library]) + + def _init_docker_container(self) -> Container: + """Initializes and returns a Docker container for code execution. + + Stops and removes any existing container with the same name before creating + a new one. Maps the current working directory to /workspace in the container. + + Returns: + A Docker container object ready for code execution. + """ + container_name = "code-interpreter" + client = docker_from_env() + current_path = os.getcwd() + + # Check if the container is already running + try: + existing_container = client.containers.get(container_name) + existing_container.stop() + existing_container.remove() + except NotFound: + pass # Container does not exist, no need to remove + + return client.containers.run( + self.default_image_tag, + detach=True, + tty=True, + working_dir="/workspace", + name=container_name, + volumes={current_path: {"bind": "/workspace", "mode": "rw"}}, # type: ignore + ) + + @staticmethod + def _check_docker_available() -> bool: + """Checks if Docker is available and running on the system. + + Attempts to run the 'docker info' command to verify Docker availability. + Prints appropriate messages if Docker is not installed or not running. + + Returns: + True if Docker is available and running, False otherwise. + """ + + try: + subprocess.run( + ["docker", "info"], # noqa: S607 + check=True, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + timeout=1, + ) + return True + except (subprocess.CalledProcessError, subprocess.TimeoutExpired): + Printer.print( + "Docker is installed but not running or inaccessible.", + color="bold_purple", + ) + return False + except FileNotFoundError: + Printer.print("Docker is not installed", color="bold_purple") + return False + + def run_code_safety(self, code: str, libraries_used: list[str]) -> str: + """Runs code in the safest available environment. + + Attempts to run code in Docker if available, falls back to a restricted + sandbox if Docker is not available. + + Args: + code: The Python code to execute as a string. + libraries_used: A list of Python library names to install before execution. + + Returns: + The output of the executed code as a string. + """ + if self._check_docker_available(): + return self.run_code_in_docker(code, libraries_used) + return self.run_code_in_restricted_sandbox(code) + + def run_code_in_docker(self, code: str, libraries_used: list[str]) -> str: + """Runs Python code in a Docker container for safe isolation. + + Creates a Docker container, installs the required libraries, executes the code, + and then cleans up by stopping and removing the container. + + Args: + code: The Python code to execute as a string. + libraries_used: A list of Python library names to install before execution. + + Returns: + The output of the executed code as a string, or an error message if execution failed. + """ + Printer.print("Running code in Docker environment", color="bold_blue") + self._verify_docker_image() + container = self._init_docker_container() + self._install_libraries(container, libraries_used) + + exec_result = container.exec_run(["python3", "-c", code]) + + container.stop() + container.remove() + + if exec_result.exit_code != 0: + return f"Something went wrong while running the code: \n{exec_result.output.decode('utf-8')}" + return exec_result.output.decode("utf-8") + + @staticmethod + def run_code_in_restricted_sandbox(code: str) -> str: + """Runs Python code in a restricted sandbox environment. + + Executes the code with restricted access to potentially dangerous modules and + built-in functions for basic safety when Docker is not available. + + Args: + code: The Python code to execute as a string. + + Returns: + The value of the 'result' variable from the executed code, + or an error message if execution failed. + """ + Printer.print("Running code in restricted sandbox", color="yellow") + exec_locals: dict[str, Any] = {} + try: + SandboxPython.exec(code=code, locals_=exec_locals) + return exec_locals.get("result", "No result variable found.") + except Exception as e: + return f"An error occurred: {e!s}" + + @staticmethod + def run_code_unsafe(code: str, libraries_used: list[str]) -> str: + """Runs code directly on the host machine without any safety restrictions. + + WARNING: This mode is unsafe and should only be used in trusted environments + with code from trusted sources. + + Args: + code: The Python code to execute as a string. + libraries_used: A list of Python library names to install before execution. + + Returns: + The value of the 'result' variable from the executed code, + or an error message if execution failed. + """ + Printer.print("WARNING: Running code in unsafe mode", color="bold_magenta") + # Install libraries on the host machine + for library in libraries_used: + os.system(f"pip install {library}") # noqa: S605 + + # Execute the code + try: + exec_locals: dict[str, Any] = {} + exec(code, {}, exec_locals) # noqa: S102 + return exec_locals.get("result", "No result variable found.") + except Exception as e: + return f"An error occurred: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/composio_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/composio_tool/README.md new file mode 100644 index 000000000..18045e7f1 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/composio_tool/README.md @@ -0,0 +1,72 @@ +# ComposioTool Documentation + +## Description + +This tools is a wrapper around the composio toolset and gives your agent access to a wide variety of tools from the composio SDK. + +## Installation + +To incorporate this tool into your project, follow the installation instructions below: + +```shell +pip install composio-core +pip install 'crewai[tools]' +``` + +after the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`. + +## Example + +The following example demonstrates how to initialize the tool and execute a github action: + +1. Initialize toolset + +```python +from composio import App +from crewai_tools import ComposioTool +from crewai import Agent, Task + + +tools = [ComposioTool.from_action(action=Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER)] +``` + +If you don't know what action you want to use, use `from_app` and `tags` filter to get relevant actions + +```python +tools = ComposioTool.from_app(App.GITHUB, tags=["important"]) +``` + +or use `use_case` to search relevant actions + +```python +tools = ComposioTool.from_app(App.GITHUB, use_case="Star a github repository") +``` + +2. Define agent + +```python +crewai_agent = Agent( + role="Github Agent", + goal="You take action on Github using Github APIs", + backstory=( + "You are AI agent that is responsible for taking actions on Github " + "on users behalf. You need to take action on Github using Github APIs" + ), + verbose=True, + tools=tools, +) +``` + +3. Execute task + +```python +task = Task( + description="Star a repo ComposioHQ/composio on GitHub", + agent=crewai_agent, + expected_output="if the star happened", +) + +task.execute() +``` + +* More detailed list of tools can be found [here](https://app.composio.dev) diff --git a/src/crewai/cli/templates/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/composio_tool/__init__.py similarity index 100% rename from src/crewai/cli/templates/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/composio_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/composio_tool/composio_tool.py b/lib/crewai-tools/src/crewai_tools/tools/composio_tool/composio_tool.py new file mode 100644 index 000000000..763872f5b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/composio_tool/composio_tool.py @@ -0,0 +1,128 @@ +"""Composio tools wrapper.""" + +import typing as t + +from crewai.tools import BaseTool, EnvVar +from pydantic import Field +import typing_extensions as te + + +class ComposioTool(BaseTool): + """Wrapper for composio tools.""" + + composio_action: t.Callable + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="COMPOSIO_API_KEY", + description="API key for Composio services", + required=True, + ), + ] + ) + + def _run(self, *args: t.Any, **kwargs: t.Any) -> t.Any: + """Run the composio action with given arguments.""" + return self.composio_action(*args, **kwargs) + + @staticmethod + def _check_connected_account(tool: t.Any, toolset: t.Any) -> None: + """Check if connected account is required and if required it exists or not.""" + from composio import Action + from composio.client.collections import ConnectedAccountModel + + tool = t.cast(Action, tool) + if tool.no_auth: + return + + connections = t.cast( + list[ConnectedAccountModel], + toolset.client.connected_accounts.get(), + ) + if tool.app not in [connection.appUniqueId for connection in connections]: + raise RuntimeError( + f"No connected account found for app `{tool.app}`; " + f"Run `composio add {tool.app}` to fix this" + ) + + @classmethod + def from_action( + cls, + action: t.Any, + **kwargs: t.Any, + ) -> te.Self: + """Wrap a composio tool as crewAI tool.""" + from composio import Action, ComposioToolSet + from composio.constants import DEFAULT_ENTITY_ID + from composio.utils.shared import json_schema_to_model + + toolset = ComposioToolSet() + if not isinstance(action, Action): + action = Action(action) + + action = t.cast(Action, action) + cls._check_connected_account( + tool=action, + toolset=toolset, + ) + + (action_schema,) = toolset.get_action_schemas(actions=[action]) + schema = action_schema.model_dump(exclude_none=True) + entity_id = kwargs.pop("entity_id", DEFAULT_ENTITY_ID) + + def function(**kwargs: t.Any) -> dict: + """Wrapper function for composio action.""" + return toolset.execute_action( + action=Action(schema["name"]), + params=kwargs, + entity_id=entity_id, + ) + + function.__name__ = schema["name"] + function.__doc__ = schema["description"] + + return cls( + name=schema["name"], + description=schema["description"], + args_schema=json_schema_to_model( + action_schema.parameters.model_dump( + exclude_none=True, + ) + ), + composio_action=function, + **kwargs, + ) + + @classmethod + def from_app( + cls, + *apps: t.Any, + tags: list[str] | None = None, + use_case: str | None = None, + **kwargs: t.Any, + ) -> list[te.Self]: + """Create toolset from an app.""" + if len(apps) == 0: + raise ValueError("You need to provide at least one app name") + + if use_case is None and tags is None: + raise ValueError("Both `use_case` and `tags` cannot be `None`") + + if use_case is not None and tags is not None: + raise ValueError( + "Cannot use both `use_case` and `tags` to filter the actions" + ) + + from composio import ComposioToolSet + + toolset = ComposioToolSet() + if use_case is not None: + return [ + cls.from_action(action=action, **kwargs) + for action in toolset.find_actions_by_use_case(*apps, use_case=use_case) + ] + + return [ + cls.from_action(action=action, **kwargs) + for action in toolset.find_actions_by_tags(*apps, tags=tags) # type: ignore[arg-type] + ] diff --git a/lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/README.md new file mode 100644 index 000000000..ee08bd23c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/README.md @@ -0,0 +1,58 @@ +# ContextualAICreateAgentTool + +## Description +This tool is designed to integrate Contextual AI's enterprise-grade RAG agents with CrewAI. This tool enables you to create a new Contextual RAG agent. It uploads your documents to create a datastore and returns the Contextual agent ID and datastore ID. + +## Installation +To incorporate this tool into your project, follow the installation instructions below: + +``` +pip install 'crewai[tools]' contextual-client +``` + +**Note**: You'll need a Contextual AI API key. Sign up at [app.contextual.ai](https://app.contextual.ai) to get your free API key. + +## Example + +```python +from crewai_tools import ContextualAICreateAgentTool + +# Initialize the tool +tool = ContextualAICreateAgentTool(api_key="your_api_key_here") + +# Create agent with documents +result = tool._run( + agent_name="Financial Analysis Agent", + agent_description="Agent for analyzing financial documents", + datastore_name="Financial Reports", + document_paths=["/path/to/report1.pdf", "/path/to/report2.pdf"], +) +print(result) +``` + +## Parameters +- `api_key`: Your Contextual AI API key +- `agent_name`: Name for the new agent +- `agent_description`: Description of the agent's purpose +- `datastore_name`: Name for the document datastore +- `document_paths`: List of file paths to upload + +Example result: + +``` +Successfully created agent 'Research Analyst' with ID: {created_agent_ID} and datastore ID: {created_datastore_ID}. Uploaded 5 documents. +``` + +You can use `ContextualAIQueryTool` with the returned IDs to query the knowledge base and retrieve relevant information from your documents. + +## Key Features +- **Complete Pipeline Setup**: Creates datastore, uploads documents, and configures agent in one operation +- **Document Processing**: Leverages Contextual AI's powerful parser to ingest complex PDFs and documents +- **Vector Storage**: Use Contextual AI's datastore for large document collections + +## Use Cases +- Set up new RAG agents from scratch with complete automation +- Upload and organize document collections into structured datastores +- Create specialized domain agents for legal, financial, technical, or research workflows + +For more detailed information about Contextual AI's capabilities, visit the [official documentation](https://docs.contextual.ai). \ No newline at end of file diff --git a/src/crewai/cli/templates/crew/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/__init__.py similarity index 100% rename from src/crewai/cli/templates/crew/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/contextual_create_agent_tool.py b/lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/contextual_create_agent_tool.py new file mode 100644 index 000000000..add80f928 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/contextual_create_agent_tool.py @@ -0,0 +1,81 @@ +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class ContextualAICreateAgentSchema(BaseModel): + """Schema for contextual create agent tool.""" + + agent_name: str = Field(..., description="Name for the new agent") + agent_description: str = Field(..., description="Description for the new agent") + datastore_name: str = Field(..., description="Name for the new datastore") + document_paths: list[str] = Field(..., description="List of file paths to upload") + + +class ContextualAICreateAgentTool(BaseTool): + """Tool to create Contextual AI RAG agents with documents.""" + + name: str = "Contextual AI Create Agent Tool" + description: str = ( + "Create a new Contextual AI RAG agent with documents and datastore" + ) + args_schema: type[BaseModel] = ContextualAICreateAgentSchema + + api_key: str + contextual_client: Any = None + package_dependencies: list[str] = Field( + default_factory=lambda: ["contextual-client"] + ) + + def __init__(self, **kwargs): + super().__init__(**kwargs) + try: + from contextual import ContextualAI + + self.contextual_client = ContextualAI(api_key=self.api_key) + except ImportError as e: + raise ImportError( + "contextual-client package is required. Install it with: pip install contextual-client" + ) from e + + def _run( + self, + agent_name: str, + agent_description: str, + datastore_name: str, + document_paths: list[str], + ) -> str: + """Create a complete RAG pipeline with documents.""" + try: + import os + + # Create datastore + datastore = self.contextual_client.datastores.create(name=datastore_name) + datastore_id = datastore.id + + # Upload documents + document_ids = [] + for doc_path in document_paths: + if not os.path.exists(doc_path): + raise FileNotFoundError(f"Document not found: {doc_path}") + + with open(doc_path, "rb") as f: + ingestion_result = ( + self.contextual_client.datastores.documents.ingest( + datastore_id, file=f + ) + ) + document_ids.append(ingestion_result.id) + + # Create agent + agent = self.contextual_client.agents.create( + name=agent_name, + description=agent_description, + datastore_ids=[datastore_id], + ) + + return f"Successfully created agent '{agent_name}' with ID: {agent.id} and datastore ID: {datastore_id}. Uploaded {len(document_ids)} documents." + + except Exception as e: + return f"Failed to create agent with documents: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/README.md new file mode 100644 index 000000000..da4bc8821 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/README.md @@ -0,0 +1,68 @@ +# ContextualAIParseTool + +## Description +This tool is designed to integrate Contextual AI's enterprise-grade document parsing capabilities with CrewAI, enabling you to leverage advanced AI-powered document understanding for complex layouts, tables, and figures. Use this tool to extract structured content from your documents using Contextual AI's powerful document parser. + +## Installation +To incorporate this tool into your project, follow the installation instructions below: + +``` +pip install 'crewai[tools]' contextual-client +``` + +**Note**: You'll need a Contextual AI API key. Sign up at [app.contextual.ai](https://app.contextual.ai) to get your free API key. + +## Example + +```python +from crewai_tools import ContextualAIParseTool + +tool = ContextualAIParseTool(api_key="your_api_key_here") + +result = tool._run( + file_path="/path/to/document.pdf", + parse_mode="standard", + page_range="0-5", + output_types=["markdown-per-page"] +) +print(result) +``` + +The result will show the parsed contents of your document. For example: +``` +{ + "file_name": "attention_is_all_you_need.pdf", + "status": "completed", + "pages": [ + { + "index": 0, + "markdown": "Provided proper attribution ... + }, + { + "index": 1, + "markdown": "## 1 Introduction ... + }, + ... + ] +} +``` +## Parameters +- `api_key`: Your Contextual AI API key +- `file_path`: Path to document to parse +- `parse_mode`: Parsing mode (default: "standard") +- `figure_caption_mode`: Figure caption handling (default: "concise") +- `enable_document_hierarchy`: Enable hierarchy detection (default: True) +- `page_range`: Pages to parse (e.g., "0-5", None for all) +- `output_types`: Output formats (default: ["markdown-per-page"]) + +## Key Features +- **Advanced Document Understanding**: Handles complex PDF layouts, tables, and multi-column documents +- **Figure and Table Extraction**: Intelligent extraction of figures, charts, and tabular data +- **Page Range Selection**: Parse specific pages or entire documents + +## Use Cases +- Extract structured content from complex PDFs and research papers +- Parse financial reports, legal documents, and technical manuals +- Convert documents to markdown for further processing in RAG pipelines + +For more detailed information about Contextual AI's capabilities, visit the [official documentation](https://docs.contextual.ai). \ No newline at end of file diff --git a/src/crewai/cli/templates/crew/tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/__init__.py similarity index 100% rename from src/crewai/cli/templates/crew/tools/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/contextual_parse_tool.py b/lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/contextual_parse_tool.py new file mode 100644 index 000000000..1a0317172 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/contextual_parse_tool.py @@ -0,0 +1,108 @@ +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class ContextualAIParseSchema(BaseModel): + """Schema for contextual parse tool.""" + + file_path: str = Field(..., description="Path to the document to parse") + parse_mode: str = Field(default="standard", description="Parsing mode") + figure_caption_mode: str = Field( + default="concise", description="Figure caption mode" + ) + enable_document_hierarchy: bool = Field( + default=True, description="Enable document hierarchy" + ) + page_range: str | None = Field( + default=None, description="Page range to parse (e.g., '0-5')" + ) + output_types: list[str] = Field( + default=["markdown-per-page"], description="List of output types" + ) + + +class ContextualAIParseTool(BaseTool): + """Tool to parse documents using Contextual AI's parser.""" + + name: str = "Contextual AI Document Parser" + description: str = "Parse documents using Contextual AI's advanced document parser" + args_schema: type[BaseModel] = ContextualAIParseSchema + + api_key: str + package_dependencies: list[str] = Field( + default_factory=lambda: ["contextual-client"] + ) + + def _run( + self, + file_path: str, + parse_mode: str = "standard", + figure_caption_mode: str = "concise", + enable_document_hierarchy: bool = True, + page_range: str | None = None, + output_types: list[str] | None = None, + ) -> str: + """Parse a document using Contextual AI's parser.""" + if output_types is None: + output_types = ["markdown-per-page"] + try: + import json + import os + from time import sleep + + import requests + + if not os.path.exists(file_path): + raise FileNotFoundError(f"Document not found: {file_path}") + + base_url = "https://api.contextual.ai/v1" + headers = { + "accept": "application/json", + "authorization": f"Bearer {self.api_key}", + } + + # Submit parse job + url = f"{base_url}/parse" + config = { + "parse_mode": parse_mode, + "figure_caption_mode": figure_caption_mode, + "enable_document_hierarchy": enable_document_hierarchy, + } + + if page_range: + config["page_range"] = page_range + + with open(file_path, "rb") as fp: + file = {"raw_file": fp} + result = requests.post( + url, headers=headers, data=config, files=file, timeout=30 + ) + response = json.loads(result.text) + job_id = response["job_id"] + + # Monitor job status + status_url = f"{base_url}/parse/jobs/{job_id}/status" + while True: + result = requests.get(status_url, headers=headers, timeout=30) + parse_response = json.loads(result.text)["status"] + + if parse_response == "completed": + break + if parse_response == "failed": + raise RuntimeError("Document parsing failed") + + sleep(5) + + # Get parse results + results_url = f"{base_url}/parse/jobs/{job_id}/results" + result = requests.get( + results_url, + headers=headers, + params={"output_types": ",".join(output_types)}, + timeout=30, + ) + + return json.dumps(json.loads(result.text), indent=2) + + except Exception as e: + return f"Failed to parse document: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/contextualai_query_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/contextualai_query_tool/README.md new file mode 100644 index 000000000..ef939572b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/contextualai_query_tool/README.md @@ -0,0 +1,54 @@ +# ContextualAIQueryTool + +## Description +This tool is designed to integrate Contextual AI's enterprise-grade RAG agents with CrewAI. Run this tool to query existing Contextual AI RAG agents that have been pre-configured with documents and knowledge bases. + +## Installation +To incorporate this tool into your project, follow the installation instructions below: + +```shell +pip install 'crewai[tools]' contextual-client +``` + +**Note**: You'll need a Contextual AI API key. Sign up at [app.contextual.ai](https://app.contextual.ai) to get your free API key. + +## Example + +Make sure you have already created a Contextual agent and ingested documents into the datastore before using this tool. + +```python +from crewai_tools import ContextualAIQueryTool + +# Initialize the tool +tool = ContextualAIQueryTool(api_key="your_api_key_here") + +# Query the agent with IDs +result = tool._run( + query="What are the key findings in the financial report?", + agent_id="your_agent_id_here", + datastore_id="your_datastore_id_here" # Optional: for document readiness checking +) +print(result) +``` + +The result will contain the generated answer to the user's query. + +## Parameters +**Initialization:** +- `api_key`: Your Contextual AI API key + +**Query (_run method):** +- `query`: The question or query to send to the agent +- `agent_id`: ID of the existing Contextual AI agent to query (required) +- `datastore_id`: Optional datastore ID for document readiness verification (if not provided, document status checking is disabled with a warning) + +## Key Features +- **Document Readiness Checking**: Automatically waits for documents to be processed before querying +- **Grounded Responses**: Built-in grounding ensures factual, source-attributed answers + +## Use Cases +- Query pre-configured RAG agents with document collections +- Access enterprise knowledge bases through user queries +- Build specialized domain experts with access to curated documents + +For more detailed information about Contextual AI's capabilities, visit the [official documentation](https://docs.contextual.ai). \ No newline at end of file diff --git a/src/crewai/cli/templates/flow/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/contextualai_query_tool/__init__.py similarity index 100% rename from src/crewai/cli/templates/flow/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/contextualai_query_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/contextualai_query_tool/contextual_query_tool.py b/lib/crewai-tools/src/crewai_tools/tools/contextualai_query_tool/contextual_query_tool.py new file mode 100644 index 000000000..f4748ef41 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/contextualai_query_tool/contextual_query_tool.py @@ -0,0 +1,119 @@ +import asyncio +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field +import requests + + +class ContextualAIQuerySchema(BaseModel): + """Schema for contextual query tool.""" + + query: str = Field(..., description="Query to send to the Contextual AI agent.") + agent_id: str = Field(..., description="ID of the Contextual AI agent to query") + datastore_id: str | None = Field( + None, description="Optional datastore ID for document readiness verification" + ) + + +class ContextualAIQueryTool(BaseTool): + """Tool to query Contextual AI RAG agents.""" + + name: str = "Contextual AI Query Tool" + description: str = ( + "Use this tool to query a Contextual AI RAG agent with access to your documents" + ) + args_schema: type[BaseModel] = ContextualAIQuerySchema + + api_key: str + contextual_client: Any = None + package_dependencies: list[str] = Field( + default_factory=lambda: ["contextual-client"] + ) + + def __init__(self, **kwargs): + super().__init__(**kwargs) + try: + from contextual import ContextualAI + + self.contextual_client = ContextualAI(api_key=self.api_key) + except ImportError as e: + raise ImportError( + "contextual-client package is required. Install it with: pip install contextual-client" + ) from e + + def _check_documents_ready(self, datastore_id: str) -> bool: + """Synchronous check if all documents are ready.""" + url = f"https://api.contextual.ai/v1/datastores/{datastore_id}/documents" + headers = {"Authorization": f"Bearer {self.api_key}"} + response = requests.get(url, headers=headers, timeout=30) + if response.status_code == 200: + data = response.json() + documents = data.get("documents", []) + return not any( + doc.get("status") in ("processing", "pending") for doc in documents + ) + return True + + async def _wait_for_documents_async( + self, datastore_id: str, max_attempts: int = 20, interval: float = 30.0 + ) -> bool: + """Asynchronously poll until documents are ready, exiting early if possible.""" + for _attempt in range(max_attempts): + ready = await asyncio.to_thread(self._check_documents_ready, datastore_id) + if ready: + return True + await asyncio.sleep(interval) + return True # give up but don't fail hard + + def _run(self, query: str, agent_id: str, datastore_id: str | None = None) -> str: + if not agent_id: + raise ValueError("Agent ID is required to query the Contextual AI agent") + + if datastore_id: + ready = self._check_documents_ready(datastore_id) + if not ready: + try: + # If no running event loop, use asyncio.run + loop = asyncio.get_running_loop() + except RuntimeError: + loop = None + + if loop and loop.is_running(): + # Already inside an event loop + try: + import nest_asyncio # type: ignore[import-untyped] + + nest_asyncio.apply(loop) + loop.run_until_complete( + self._wait_for_documents_async(datastore_id) + ) + except Exception: # noqa: S110 + pass + else: + asyncio.run(self._wait_for_documents_async(datastore_id)) + else: + pass + + try: + response = self.contextual_client.agents.query.create( + agent_id=agent_id, messages=[{"role": "user", "content": query}] + ) + if hasattr(response, "content"): + return response.content + if hasattr(response, "message"): + return ( + response.message.content + if hasattr(response.message, "content") + else str(response.message) + ) + if hasattr(response, "messages") and len(response.messages) > 0: + last_message = response.messages[-1] + return ( + last_message.content + if hasattr(last_message, "content") + else str(last_message) + ) + return str(response) + except Exception as e: + return f"Error querying Contextual AI agent: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/contextualai_rerank_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/contextualai_rerank_tool/README.md new file mode 100644 index 000000000..d8c8a9ed8 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/contextualai_rerank_tool/README.md @@ -0,0 +1,72 @@ +# ContextualAIRerankTool + +## Description +This tool is designed to integrate Contextual AI's enterprise-grade instruction-following reranker with CrewAI, enabling you to intelligently reorder documents based on relevance and custom criteria. Use this tool to enhance search result quality and document retrieval for RAG systems using Contextual AI's reranking models that understand context and follow specific instructions for optimal document ordering. + +## Installation +To incorporate this tool into your project, follow the installation instructions below: + +```shell +pip install 'crewai[tools]' contextual-client +``` + +**Note**: You'll need a Contextual AI API key. Sign up at [app.contextual.ai](https://app.contextual.ai) to get your free API key. + +## Example + +```python +from crewai_tools import ContextualAIRerankTool + +tool = ContextualAIRerankTool(api_key="your_api_key_here") + +result = tool._run( + query="financial performance and revenue metrics", + documents=[ + "Q1 report content with revenue data", + "Q2 report content with growth metrics", + "News article about market trends" + ], + instruction="Prioritize documents with specific financial metrics and quantitative data" +) +print(result) +``` + +The result will contain the document ranking. For example: +``` +Rerank Result: +{ + "results": [ + { + "index": 1, + "relevance_score": 0.88227631 + }, + { + "index": 0, + "relevance_score": 0.61159354 + }, + { + "index": 2, + "relevance_score": 0.28579462 + } + ] +} +``` + +## Parameters +- `api_key`: Your Contextual AI API key +- `query`: Search query for reranking +- `documents`: List of document texts to rerank +- `instruction`: Optional reranking instruction for custom criteria +- `metadata`: Optional metadata for each document +- `model`: Reranker model (default: "ctxl-rerank-en-v1-instruct") + +## Key Features +- **Instruction-Following Reranking**: Follows custom instructions for domain-specific document ordering +- **Metadata Integration**: Incorporates document metadata for enhanced ranking decisions + +## Use Cases +- Improve search result relevance in document collections +- Reorder documents by custom business criteria (recency, authority, relevance) +- Filter and prioritize documents for research and analysis workflows + +For more detailed information about Contextual AI's capabilities, visit the [official documentation](https://docs.contextual.ai). \ No newline at end of file diff --git a/src/crewai/cli/templates/flow/tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/contextualai_rerank_tool/__init__.py similarity index 100% rename from src/crewai/cli/templates/flow/tools/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/contextualai_rerank_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/contextualai_rerank_tool/contextual_rerank_tool.py b/lib/crewai-tools/src/crewai_tools/tools/contextualai_rerank_tool/contextual_rerank_tool.py new file mode 100644 index 000000000..b78e1d907 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/contextualai_rerank_tool/contextual_rerank_tool.py @@ -0,0 +1,81 @@ +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class ContextualAIRerankSchema(BaseModel): + """Schema for contextual rerank tool.""" + + query: str = Field(..., description="The search query to rerank documents against") + documents: list[str] = Field(..., description="List of document texts to rerank") + instruction: str | None = Field( + default=None, description="Optional instruction for reranking behavior" + ) + metadata: list[str] | None = Field( + default=None, description="Optional metadata for each document" + ) + model: str = Field( + default="ctxl-rerank-en-v1-instruct", description="Reranker model to use" + ) + + +class ContextualAIRerankTool(BaseTool): + """Tool to rerank documents using Contextual AI's instruction-following reranker.""" + + name: str = "Contextual AI Document Reranker" + description: str = ( + "Rerank documents using Contextual AI's instruction-following reranker" + ) + args_schema: type[BaseModel] = ContextualAIRerankSchema + + api_key: str + package_dependencies: list[str] = Field( + default_factory=lambda: ["contextual-client"] + ) + + def _run( + self, + query: str, + documents: list[str], + instruction: str | None = None, + metadata: list[str] | None = None, + model: str = "ctxl-rerank-en-v1-instruct", + ) -> str: + """Rerank documents using Contextual AI's instruction-following reranker.""" + try: + import json + + import requests + + base_url = "https://api.contextual.ai/v1" + headers = { + "accept": "application/json", + "content-type": "application/json", + "authorization": f"Bearer {self.api_key}", + } + + payload = {"query": query, "documents": documents, "model": model} + + if instruction: + payload["instruction"] = instruction + + if metadata: + if len(metadata) != len(documents): + raise ValueError( + "Metadata list must have the same length as documents list" + ) + payload["metadata"] = metadata + + rerank_url = f"{base_url}/rerank" + result = requests.post( + rerank_url, json=payload, headers=headers, timeout=30 + ) + + if result.status_code != 200: + raise RuntimeError( + f"Reranker API returned status {result.status_code}: {result.text}" + ) + + return json.dumps(result.json(), indent=2) + + except Exception as e: + return f"Failed to rerank documents: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/couchbase_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/couchbase_tool/README.md new file mode 100644 index 000000000..382f6eae0 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/couchbase_tool/README.md @@ -0,0 +1,62 @@ +# CouchbaseFTSVectorSearchTool +## Description +Couchbase is a NoSQL database with vector search capabilities. Users can store and query vector embeddings. You can learn more about Couchbase vector search here: https://docs.couchbase.com/cloud/vector-search/vector-search.html + +This tool is specifically crafted for performing semantic search using Couchbase. Use this tool to find semantically similar docs to a given query. + +## Installation +Install the crewai_tools package by executing the following command in your terminal: + +```shell +uv pip install 'crewai[tools]' +``` + +## Setup +Before instantiating the tool, you need a Couchbase cluster. +- Create a cluster on [Couchbase Capella](https://docs.couchbase.com/cloud/get-started/create-account.html), Couchbase's cloud database solution. +- Create a [local Couchbase server](https://docs.couchbase.com/server/current/getting-started/start-here.html). + +You will need to create a bucket, scope and collection on the cluster. Then, [follow this guide](https://docs.couchbase.com/python-sdk/current/hello-world/start-using-sdk.html) to create a Couchbase Cluster object and load documents into your collection. + +Follow the docs below to create a vector search index on Couchbase. +- [Create a vector search index on Couchbase Capella.](https://docs.couchbase.com/cloud/vector-search/create-vector-search-index-ui.html) +- [Create a vector search index on your local Couchbase server.](https://docs.couchbase.com/server/current/vector-search/create-vector-search-index-ui.html) + +Ensure that the `Dimension` field in the index matches the embedding model. For example, OpenAI's `text-embedding-3-small` model has an embedding dimension of 1536 dimensions, and so the `Dimension` field must be 1536 in the index. + +## Example +To utilize the CouchbaseFTSVectorSearchTool for different use cases, follow these examples: + +```python +from crewai_tools import CouchbaseFTSVectorSearchTool + +# Instantiate a Couchbase Cluster object from the Couchbase SDK + +tool = CouchbaseFTSVectorSearchTool( + cluster=cluster, + collection_name="collection", + scope_name="scope", + bucket_name="bucket", + index_name="index", + embedding_function=embed_fn +) + +# Adding the tool to an agent +rag_agent = Agent( + name="rag_agent", + role="You are a helpful assistant that can answer questions with the help of the CouchbaseFTSVectorSearchTool.", + llm="gpt-4o-mini", + tools=[tool], +) +``` + +## Arguments +- `cluster`: An initialized Couchbase `Cluster` instance. +- `bucket_name`: The name of the Couchbase bucket. +- `scope_name`: The name of the scope within the bucket. +- `collection_name`: The name of the collection within the scope. +- `index_name`: The name of the search index (vector index). +- `embedding_function`: A function that takes a string and returns its embedding (list of floats). +- `embedding_key`: Name of the field in the search index storing the vector. (Optional, defaults to 'embedding') +- `scoped_index`: Whether the index is scoped (True) or cluster-level (False). (Optional, defaults to True) +- `limit`: The maximum number of search results to return. (Optional, defaults to 3) \ No newline at end of file diff --git a/src/crewai/cli/tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/couchbase_tool/__init__.py similarity index 100% rename from src/crewai/cli/tools/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/couchbase_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/couchbase_tool/couchbase_tool.py b/lib/crewai-tools/src/crewai_tools/tools/couchbase_tool/couchbase_tool.py new file mode 100644 index 000000000..054624139 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/couchbase_tool/couchbase_tool.py @@ -0,0 +1,235 @@ +from collections.abc import Callable +import json +from typing import Any + + +try: + from couchbase.cluster import Cluster # type: ignore[import-untyped] + from couchbase.options import SearchOptions # type: ignore[import-untyped] + import couchbase.search as search # type: ignore[import-untyped] + from couchbase.vector_search import ( # type: ignore[import-untyped] + VectorQuery, + VectorSearch, + ) + + COUCHBASE_AVAILABLE = True +except ImportError: + COUCHBASE_AVAILABLE = False + search = Any + Cluster = Any + SearchOptions = Any + VectorQuery = Any + VectorSearch = Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, ConfigDict, Field, SkipValidation + + +class CouchbaseToolSchema(BaseModel): + """Input for CouchbaseTool.""" + + query: str = Field( + ..., + description="The query to search retrieve relevant information from the Couchbase database. Pass only the query, not the question.", + ) + + +class CouchbaseFTSVectorSearchTool(BaseTool): + """Tool to search the Couchbase database.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + name: str = "CouchbaseFTSVectorSearchTool" + description: str = "A tool to search the Couchbase database for relevant information on internal documents." + args_schema: type[BaseModel] = CouchbaseToolSchema + cluster: SkipValidation[Cluster] = Field( + description="An instance of the Couchbase Cluster connected to the desired Couchbase server.", + ) + collection_name: str = Field( + description="The name of the Couchbase collection to search", + ) + scope_name: str = Field( + description="The name of the Couchbase scope containing the collection to search.", + ) + bucket_name: str = Field( + description="The name of the Couchbase bucket to search", + ) + index_name: str = Field( + description="The name of the Couchbase index to search", + ) + embedding_key: str | None = Field( + default="embedding", + description="Name of the field in the search index that stores the vector", + ) + scoped_index: bool = Field( + default=True, + description="Specify whether the index is scoped. Is True by default.", + ) + limit: int | None = Field(default=3) + embedding_function: SkipValidation[Callable[[str], list[float]]] = Field( + description="A function that takes a string and returns a list of floats. This is used to embed the query before searching the database.", + ) + + def _check_bucket_exists(self) -> bool: + """Check if the bucket exists in the linked Couchbase cluster.""" + bucket_manager = self.cluster.buckets() + try: + bucket_manager.get_bucket(self.bucket_name) + return True + except Exception: + return False + + def _check_scope_and_collection_exists(self) -> bool: + """Check if the scope and collection exists in the linked Couchbase bucket + Raises a ValueError if either is not found. + """ + scope_collection_map: dict[str, Any] = {} + + # Get a list of all scopes in the bucket + for scope in self._bucket.collections().get_all_scopes(): + scope_collection_map[scope.name] = [] + + # Get a list of all the collections in the scope + for collection in scope.collections: + scope_collection_map[scope.name].append(collection.name) + + # Check if the scope exists + if self.scope_name not in scope_collection_map.keys(): + raise ValueError( + f"Scope {self.scope_name} not found in Couchbase " + f"bucket {self.bucket_name}" + ) + + # Check if the collection exists in the scope + if self.collection_name not in scope_collection_map[self.scope_name]: + raise ValueError( + f"Collection {self.collection_name} not found in scope " + f"{self.scope_name} in Couchbase bucket {self.bucket_name}" + ) + + return True + + def _check_index_exists(self) -> bool: + """Check if the Search index exists in the linked Couchbase cluster + Raises a ValueError if the index does not exist. + """ + if self.scoped_index: + all_indexes = [ + index.name for index in self._scope.search_indexes().get_all_indexes() + ] + if self.index_name not in all_indexes: + raise ValueError( + f"Index {self.index_name} does not exist. " + " Please create the index before searching." + ) + else: + if not self.cluster: + raise ValueError("Cluster instance must be provided") + + all_indexes = [ + index.name for index in self.cluster.search_indexes().get_all_indexes() + ] + if self.index_name not in all_indexes: + raise ValueError( + f"Index {self.index_name} does not exist. " + " Please create the index before searching." + ) + + return True + + def __init__(self, **kwargs): + """Initialize the CouchbaseFTSVectorSearchTool. + + Args: + **kwargs: Keyword arguments to pass to the BaseTool constructor and + to configure the Couchbase connection and search parameters. + Requires 'cluster', 'bucket_name', 'scope_name', + 'collection_name', 'index_name', and 'embedding_function'. + + Raises: + ValueError: If required parameters are missing, the Couchbase cluster + cannot be reached, or the specified bucket, scope, + collection, or index does not exist. + """ + super().__init__(**kwargs) + if COUCHBASE_AVAILABLE: + try: + self._bucket = self.cluster.bucket(self.bucket_name) + self._scope = self._bucket.scope(self.scope_name) + self._collection = self._scope.collection(self.collection_name) + except Exception as e: + raise ValueError( + "Error connecting to couchbase. " + "Please check the connection and credentials" + ) from e + + # check if bucket exists + if not self._check_bucket_exists(): + raise ValueError( + f"Bucket {self.bucket_name} does not exist. " + " Please create the bucket before searching." + ) + + self._check_scope_and_collection_exists() + self._check_index_exists() + else: + import click + + if click.confirm( + "The 'couchbase' package is required to use the CouchbaseFTSVectorSearchTool. " + "Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "couchbase"], check=True) # noqa: S607 + else: + raise ImportError( + "The 'couchbase' package is required to use the CouchbaseFTSVectorSearchTool. " + "Please install it with: uv add couchbase" + ) + + def _run(self, query: str) -> str: + """Execute a vector search query against the Couchbase index. + + Args: + query: The search query string. + + Returns: + A JSON string containing the search results. + + Raises: + ValueError: If the search query fails or returns results without fields. + """ + query_embedding = self.embedding_function(query) + fields = ["*"] + + search_req = search.SearchRequest.create( + VectorSearch.from_vector_query( + VectorQuery(self.embedding_key, query_embedding, self.limit) + ) + ) + + try: + if self.scoped_index: + search_iter = self._scope.search( + self.index_name, + search_req, + SearchOptions( + limit=self.limit, + fields=fields, + ), + ) + else: + search_iter = self.cluster.search( + self.index_name, + search_req, + SearchOptions(limit=self.limit, fields=fields), + ) + + json_response = [] + + for row in search_iter.rows(): + json_response.append(row.fields) # noqa: PERF401 + except Exception as e: + return f"Search failed with error: {e}" + + return json.dumps(json_response, indent=2) diff --git a/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/__init__.py new file mode 100644 index 000000000..588414e19 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/__init__.py @@ -0,0 +1,22 @@ +"""CrewAI Platform Tools. + +This module provides tools for integrating with various platform applications +through the CrewAI platform API. +""" + +from crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool import ( + CrewAIPlatformActionTool, +) +from crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder import ( + CrewaiPlatformToolBuilder, +) +from crewai_tools.tools.crewai_platform_tools.crewai_platform_tools import ( + CrewaiPlatformTools, +) + + +__all__ = [ + "CrewAIPlatformActionTool", + "CrewaiPlatformToolBuilder", + "CrewaiPlatformTools", +] diff --git a/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_action_tool.py b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_action_tool.py new file mode 100644 index 000000000..c848cfd21 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_action_tool.py @@ -0,0 +1,446 @@ +"""Crewai Enterprise Tools.""" + +import json +import re +from typing import Any, Optional, Union, cast, get_origin + +from crewai.tools import BaseTool +from pydantic import Field, create_model +import requests + +from crewai_tools.tools.crewai_platform_tools.misc import ( + get_platform_api_base_url, + get_platform_integration_token, +) + + +class AllOfSchemaAnalyzer: + """Helper class to analyze and merge allOf schemas.""" + + def __init__(self, schemas: list[dict[str, Any]]): + self.schemas = schemas + self._explicit_types: list[str] = [] + self._merged_properties: dict[str, Any] = {} + self._merged_required: list[str] = [] + self._analyze_schemas() + + def _analyze_schemas(self) -> None: + """Analyze all schemas and extract relevant information.""" + for schema in self.schemas: + if "type" in schema: + self._explicit_types.append(schema["type"]) + + # Merge object properties + if schema.get("type") == "object" and "properties" in schema: + self._merged_properties.update(schema["properties"]) + if "required" in schema: + self._merged_required.extend(schema["required"]) + + def has_consistent_type(self) -> bool: + """Check if all schemas have the same explicit type.""" + return len(set(self._explicit_types)) == 1 if self._explicit_types else False + + def get_consistent_type(self) -> type[Any]: + """Get the consistent type if all schemas agree.""" + if not self.has_consistent_type(): + raise ValueError("No consistent type found") + + type_mapping = { + "string": str, + "integer": int, + "number": float, + "boolean": bool, + "array": list, + "object": dict, + "null": type(None), + } + return type_mapping.get(self._explicit_types[0], str) + + def has_object_schemas(self) -> bool: + """Check if any schemas are object types with properties.""" + return bool(self._merged_properties) + + def get_merged_properties(self) -> dict[str, Any]: + """Get merged properties from all object schemas.""" + return self._merged_properties + + def get_merged_required_fields(self) -> list[str]: + """Get merged required fields from all object schemas.""" + return list(set(self._merged_required)) # Remove duplicates + + def get_fallback_type(self) -> type[Any]: + """Get a fallback type when merging fails.""" + if self._explicit_types: + # Use the first explicit type + type_mapping = { + "string": str, + "integer": int, + "number": float, + "boolean": bool, + "array": list, + "object": dict, + "null": type(None), + } + return type_mapping.get(self._explicit_types[0], str) + return str + + +class CrewAIPlatformActionTool(BaseTool): + action_name: str = Field(default="", description="The name of the action") + action_schema: dict[str, Any] = Field( + default_factory=dict, description="The schema of the action" + ) + + def __init__( + self, + description: str, + action_name: str, + action_schema: dict[str, Any], + ): + self._model_registry: dict[str, type[Any]] = {} + self._base_name = self._sanitize_name(action_name) + + schema_props, required = self._extract_schema_info(action_schema) + + field_definitions: dict[str, Any] = {} + for param_name, param_details in schema_props.items(): + param_desc = param_details.get("description", "") + is_required = param_name in required + + try: + field_type = self._process_schema_type( + param_details, self._sanitize_name(param_name).title() + ) + except Exception: + field_type = str + + field_definitions[param_name] = self._create_field_definition( + field_type, is_required, param_desc + ) + + if field_definitions: + try: + args_schema = create_model( + f"{self._base_name}Schema", **field_definitions + ) + except Exception: + args_schema = create_model( + f"{self._base_name}Schema", + input_text=(str, Field(description="Input for the action")), + ) + else: + args_schema = create_model( + f"{self._base_name}Schema", + input_text=(str, Field(description="Input for the action")), + ) + + super().__init__( + name=action_name.lower().replace(" ", "_"), + description=description, + args_schema=args_schema, + ) + self.action_name = action_name + self.action_schema = action_schema + + @staticmethod + def _sanitize_name(name: str) -> str: + name = name.lower().replace(" ", "_") + sanitized = re.sub(r"[^a-zA-Z0-9_]", "", name) + parts = sanitized.split("_") + return "".join(word.capitalize() for word in parts if word) + + @staticmethod + def _extract_schema_info( + action_schema: dict[str, Any], + ) -> tuple[dict[str, Any], list[str]]: + schema_props = ( + action_schema.get("function", {}) + .get("parameters", {}) + .get("properties", {}) + ) + required = ( + action_schema.get("function", {}).get("parameters", {}).get("required", []) + ) + return schema_props, required + + def _process_schema_type(self, schema: dict[str, Any], type_name: str) -> type[Any]: + """ + Process a JSON Schema type definition into a Python type. + + Handles complex schema constructs like anyOf, oneOf, allOf, enums, arrays, and objects. + """ + # Handle composite schema types (anyOf, oneOf, allOf) + if composite_type := self._process_composite_schema(schema, type_name): + return composite_type + + # Handle primitive types and simple constructs + return self._process_primitive_schema(schema, type_name) + + def _process_composite_schema( + self, schema: dict[str, Any], type_name: str + ) -> type[Any] | None: + """Process composite schema types: anyOf, oneOf, allOf.""" + if "anyOf" in schema: + return self._process_any_of_schema(schema["anyOf"], type_name) + if "oneOf" in schema: + return self._process_one_of_schema(schema["oneOf"], type_name) + if "allOf" in schema: + return self._process_all_of_schema(schema["allOf"], type_name) + return None + + def _process_any_of_schema( + self, any_of_types: list[dict[str, Any]], type_name: str + ) -> type[Any]: + """Process anyOf schema - creates Union of possible types.""" + is_nullable = any(t.get("type") == "null" for t in any_of_types) + non_null_types = [t for t in any_of_types if t.get("type") != "null"] + + if not non_null_types: + return cast( + type[Any], cast(object, str | None) + ) # fallback for only-null case + + base_type = ( + self._process_schema_type(non_null_types[0], type_name) + if len(non_null_types) == 1 + else self._create_union_type(non_null_types, type_name, "AnyOf") + ) + return base_type | None if is_nullable else base_type # type: ignore[return-value] + + def _process_one_of_schema( + self, one_of_types: list[dict[str, Any]], type_name: str + ) -> type[Any]: + """Process oneOf schema - creates Union of mutually exclusive types.""" + return ( + self._process_schema_type(one_of_types[0], type_name) + if len(one_of_types) == 1 + else self._create_union_type(one_of_types, type_name, "OneOf") + ) + + def _process_all_of_schema( + self, all_of_schemas: list[dict[str, Any]], type_name: str + ) -> type[Any]: + """Process allOf schema - merges schemas that must all be satisfied.""" + if len(all_of_schemas) == 1: + return self._process_schema_type(all_of_schemas[0], type_name) + return self._merge_all_of_schemas(all_of_schemas, type_name) + + def _create_union_type( + self, schemas: list[dict[str, Any]], type_name: str, prefix: str + ) -> type[Any]: + """Create a Union type from multiple schemas.""" + return Union[ # type: ignore # noqa: UP007 + tuple( + self._process_schema_type(schema, f"{type_name}{prefix}{i}") + for i, schema in enumerate(schemas) + ) + ] + + def _process_primitive_schema( + self, schema: dict[str, Any], type_name: str + ) -> type[Any]: + """Process primitive schema types: string, number, array, object, etc.""" + json_type = schema.get("type", "string") + + if "enum" in schema: + return self._process_enum_schema(schema, json_type) + + if json_type == "array": + return self._process_array_schema(schema, type_name) + + if json_type == "object": + return self._create_nested_model(schema, type_name) + + return self._map_json_type_to_python(json_type) + + def _process_enum_schema(self, schema: dict[str, Any], json_type: str) -> type[Any]: + """Process enum schema - currently falls back to base type.""" + enum_values = schema["enum"] + if not enum_values: + return self._map_json_type_to_python(json_type) + + # For Literal types, we need to pass the values directly, not as a tuple + # This is a workaround since we can't dynamically create Literal types easily + # Fall back to the base JSON type for now + return self._map_json_type_to_python(json_type) + + def _process_array_schema( + self, schema: dict[str, Any], type_name: str + ) -> type[Any]: + items_schema = schema.get("items", {"type": "string"}) + item_type = self._process_schema_type(items_schema, f"{type_name}Item") + return list[item_type] # type: ignore + + def _merge_all_of_schemas( + self, schemas: list[dict[str, Any]], type_name: str + ) -> type[Any]: + schema_analyzer = AllOfSchemaAnalyzer(schemas) + + if schema_analyzer.has_consistent_type(): + return schema_analyzer.get_consistent_type() + + if schema_analyzer.has_object_schemas(): + return self._create_merged_object_model( + schema_analyzer.get_merged_properties(), + schema_analyzer.get_merged_required_fields(), + type_name, + ) + + return schema_analyzer.get_fallback_type() + + def _create_merged_object_model( + self, properties: dict[str, Any], required: list[str], model_name: str + ) -> type[Any]: + full_model_name = f"{self._base_name}{model_name}AllOf" + + if full_model_name in self._model_registry: + return self._model_registry[full_model_name] + + if not properties: + return dict + + field_definitions = self._build_field_definitions( + properties, required, model_name + ) + + try: + merged_model = create_model(full_model_name, **field_definitions) + self._model_registry[full_model_name] = merged_model + return merged_model + except Exception: + return dict + + def _build_field_definitions( + self, properties: dict[str, Any], required: list[str], model_name: str + ) -> dict[str, Any]: + field_definitions = {} + + for prop_name, prop_schema in properties.items(): + prop_desc = prop_schema.get("description", "") + is_required = prop_name in required + + try: + prop_type = self._process_schema_type( + prop_schema, f"{model_name}{self._sanitize_name(prop_name).title()}" + ) + except Exception: + prop_type = str + + field_definitions[prop_name] = self._create_field_definition( + prop_type, is_required, prop_desc + ) + + return field_definitions + + def _create_nested_model( + self, schema: dict[str, Any], model_name: str + ) -> type[Any]: + full_model_name = f"{self._base_name}{model_name}" + + if full_model_name in self._model_registry: + return self._model_registry[full_model_name] + + properties = schema.get("properties", {}) + required_fields = schema.get("required", []) + + if not properties: + return dict + + field_definitions = {} + for prop_name, prop_schema in properties.items(): + prop_desc = prop_schema.get("description", "") + is_required = prop_name in required_fields + + try: + prop_type = self._process_schema_type( + prop_schema, f"{model_name}{self._sanitize_name(prop_name).title()}" + ) + except Exception: + prop_type = str + + field_definitions[prop_name] = self._create_field_definition( + prop_type, is_required, prop_desc + ) + + try: + nested_model = create_model(full_model_name, **field_definitions) # type: ignore + self._model_registry[full_model_name] = nested_model + return nested_model + except Exception: + return dict + + def _create_field_definition( + self, field_type: type[Any], is_required: bool, description: str + ) -> tuple: + if is_required: + return (field_type, Field(description=description)) + if get_origin(field_type) is Union: + return (field_type, Field(default=None, description=description)) + return ( + Optional[field_type], # noqa: UP045 + Field(default=None, description=description), + ) + + def _map_json_type_to_python(self, json_type: str) -> type[Any]: + type_mapping = { + "string": str, + "integer": int, + "number": float, + "boolean": bool, + "array": list, + "object": dict, + "null": type(None), + } + return type_mapping.get(json_type, str) + + def _get_required_nullable_fields(self) -> list[str]: + schema_props, required = self._extract_schema_info(self.action_schema) + + required_nullable_fields = [] + for param_name in required: + param_details = schema_props.get(param_name, {}) + if self._is_nullable_type(param_details): + required_nullable_fields.append(param_name) + + return required_nullable_fields + + def _is_nullable_type(self, schema: dict[str, Any]) -> bool: + if "anyOf" in schema: + return any(t.get("type") == "null" for t in schema["anyOf"]) + return schema.get("type") == "null" + + def _run(self, **kwargs) -> str: + try: + cleaned_kwargs = { + key: value for key, value in kwargs.items() if value is not None + } + + required_nullable_fields = self._get_required_nullable_fields() + + for field_name in required_nullable_fields: + if field_name not in cleaned_kwargs: + cleaned_kwargs[field_name] = None + + api_url = ( + f"{get_platform_api_base_url()}/actions/{self.action_name}/execute" + ) + token = get_platform_integration_token() + headers = { + "Authorization": f"Bearer {token}", + "Content-Type": "application/json", + } + payload = cleaned_kwargs + + response = requests.post( + url=api_url, headers=headers, json=payload, timeout=60 + ) + + data = response.json() + if not response.ok: + error_message = data.get("error", {}).get("message", json.dumps(data)) + return f"API request failed: {error_message}" + + return json.dumps(data, indent=2) + + except Exception as e: + return f"Error executing action {self.action_name}: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tool_builder.py b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tool_builder.py new file mode 100644 index 000000000..3bf9cfc7e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tool_builder.py @@ -0,0 +1,144 @@ +from typing import Any + +from crewai.tools import BaseTool +import requests + +from crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool import ( + CrewAIPlatformActionTool, +) +from crewai_tools.tools.crewai_platform_tools.misc import ( + get_platform_api_base_url, + get_platform_integration_token, +) + + +class CrewaiPlatformToolBuilder: + def __init__( + self, + apps: list[str], + ): + self._apps = apps + self._actions_schema = {} # type: ignore[var-annotated] + self._tools = None + + def tools(self) -> list[BaseTool]: + if self._tools is None: + self._fetch_actions() + self._create_tools() + return self._tools if self._tools is not None else [] + + def _fetch_actions(self): + actions_url = f"{get_platform_api_base_url()}/actions" + headers = {"Authorization": f"Bearer {get_platform_integration_token()}"} + + try: + response = requests.get( + actions_url, + headers=headers, + timeout=30, + params={"apps": ",".join(self._apps)}, + ) + response.raise_for_status() + except Exception: + return + + raw_data = response.json() + + self._actions_schema = {} + action_categories = raw_data.get("actions", {}) + + for app, action_list in action_categories.items(): + if isinstance(action_list, list): + for action in action_list: + if action_name := action.get("name"): + action_schema = { + "function": { + "name": action_name, + "description": action.get( + "description", f"Execute {action_name}" + ), + "parameters": action.get("parameters", {}), + "app": app, + } + } + self._actions_schema[action_name] = action_schema + + def _generate_detailed_description( + self, schema: dict[str, Any], indent: int = 0 + ) -> list[str]: + descriptions = [] + indent_str = " " * indent + + schema_type = schema.get("type", "string") + + if schema_type == "object": + properties = schema.get("properties", {}) + required_fields = schema.get("required", []) + + if properties: + descriptions.append(f"{indent_str}Object with properties:") + for prop_name, prop_schema in properties.items(): + prop_desc = prop_schema.get("description", "") + is_required = prop_name in required_fields + req_str = " (required)" if is_required else " (optional)" + descriptions.append( + f"{indent_str} - {prop_name}: {prop_desc}{req_str}" + ) + + if prop_schema.get("type") == "object": + descriptions.extend( + self._generate_detailed_description(prop_schema, indent + 2) + ) + elif prop_schema.get("type") == "array": + items_schema = prop_schema.get("items", {}) + if items_schema.get("type") == "object": + descriptions.append(f"{indent_str} Array of objects:") + descriptions.extend( + self._generate_detailed_description( + items_schema, indent + 3 + ) + ) + elif "enum" in items_schema: + descriptions.append( + f"{indent_str} Array of enum values: {items_schema['enum']}" + ) + elif "enum" in prop_schema: + descriptions.append( + f"{indent_str} Enum values: {prop_schema['enum']}" + ) + + return descriptions + + def _create_tools(self): + tools = [] + + for action_name, action_schema in self._actions_schema.items(): + function_details = action_schema.get("function", {}) + description = function_details.get("description", f"Execute {action_name}") + + parameters = function_details.get("parameters", {}) + param_descriptions = [] + + if parameters.get("properties"): + param_descriptions.append("\nDetailed Parameter Structure:") + param_descriptions.extend( + self._generate_detailed_description(parameters) + ) + + full_description = description + "\n".join(param_descriptions) + + tool = CrewAIPlatformActionTool( + description=full_description, + action_name=action_name, + action_schema=action_schema, + ) + + tools.append(tool) + + self._tools = tools + + def __enter__(self): + return self.tools() + + def __exit__(self, exc_type, exc_val, exc_tb): + pass diff --git a/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tools.py b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tools.py new file mode 100644 index 000000000..83016ddb8 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tools.py @@ -0,0 +1,27 @@ +import logging + +from crewai.tools import BaseTool + +from crewai_tools.adapters.tool_collection import ToolCollection +from crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder import ( + CrewaiPlatformToolBuilder, +) + + +logger = logging.getLogger(__name__) + + +def CrewaiPlatformTools( # noqa: N802 + apps: list[str], +) -> ToolCollection[BaseTool]: + """Factory function that returns crewai platform tools. + + Args: + apps: List of platform apps to get tools that are available on the platform. + + Returns: + A list of BaseTool instances for platform actions + """ + builder = CrewaiPlatformToolBuilder(apps=apps) + + return builder.tools() # type: ignore diff --git a/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/misc.py b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/misc.py new file mode 100644 index 000000000..06cf7147d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/misc.py @@ -0,0 +1,17 @@ +import os + + +def get_platform_api_base_url() -> str: + """Get the platform API base URL from environment or use default.""" + base_url = os.getenv("CREWAI_PLUS_URL", "https://app.crewai.com") + return f"{base_url}/crewai_plus/api/v1/integrations" + + +def get_platform_integration_token() -> str: + """Get the platform API base URL from environment or use default.""" + token = os.getenv("CREWAI_PLATFORM_INTEGRATION_TOKEN") or "" + if not token: + raise ValueError( + "No platform integration token found, please set the CREWAI_PLATFORM_INTEGRATION_TOKEN environment variable" + ) + return token # TODO: Use context manager to get token diff --git a/lib/crewai-tools/src/crewai_tools/tools/csv_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/csv_search_tool/README.md new file mode 100644 index 000000000..c0bcbae3d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/csv_search_tool/README.md @@ -0,0 +1,59 @@ +# CSVSearchTool + +## Description + +This tool is used to perform a RAG (Retrieval-Augmented Generation) search within a CSV file's content. It allows users to semantically search for queries in the content of a specified CSV file. This feature is particularly useful for extracting information from large CSV datasets where traditional search methods might be inefficient. All tools with "Search" in their name, including CSVSearchTool, are RAG tools designed for searching different sources of data. + +## Installation + +Install the crewai_tools package + +```shell +pip install 'crewai[tools]' +``` + +## Example + +```python +from crewai_tools import CSVSearchTool + +# Initialize the tool with a specific CSV file. This setup allows the agent to only search the given CSV file. +tool = CSVSearchTool(csv='path/to/your/csvfile.csv') + +# OR + +# Initialize the tool without a specific CSV file. Agent will need to provide the CSV path at runtime. +tool = CSVSearchTool() +``` + +## Arguments + +- `csv` : The path to the CSV file you want to search. This is a mandatory argument if the tool was initialized without a specific CSV file; otherwise, it is optional. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = CSVSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/src/crewai/events/listeners/tracing/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/csv_search_tool/__init__.py similarity index 100% rename from src/crewai/events/listeners/tracing/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/csv_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/csv_search_tool/csv_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/csv_search_tool/csv_search_tool.py new file mode 100644 index 000000000..e441ced56 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/csv_search_tool/csv_search_tool.py @@ -0,0 +1,51 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedCSVSearchToolSchema(BaseModel): + """Input for CSVSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the CSV's content", + ) + + +class CSVSearchToolSchema(FixedCSVSearchToolSchema): + """Input for CSVSearchTool.""" + + csv: str = Field(..., description="File path or URL of a CSV file to be searched") + + +class CSVSearchTool(RagTool): + name: str = "Search a CSV's content" + description: str = ( + "A tool that can be used to semantic search a query from a CSV's content." + ) + args_schema: type[BaseModel] = CSVSearchToolSchema + + def __init__(self, csv: str | None = None, **kwargs): + super().__init__(**kwargs) + if csv is not None: + self.add(csv) + self.description = f"A tool that can be used to semantic search a query the {csv} CSV's content." + self.args_schema = FixedCSVSearchToolSchema + self._generate_description() + + def add(self, csv: str) -> None: + super().add(csv, data_type=DataType.CSV) + + def _run( # type: ignore[override] + self, + search_query: str, + csv: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if csv is not None: + self.add(csv) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/dalle_tool/README.MD b/lib/crewai-tools/src/crewai_tools/tools/dalle_tool/README.MD new file mode 100644 index 000000000..8f65e78e5 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/dalle_tool/README.MD @@ -0,0 +1,41 @@ +# DALL-E Tool + +## Description +This tool is used to give the Agent the ability to generate images using the DALL-E model. It is a transformer-based model that generates images from textual descriptions. This tool allows the Agent to generate images based on the text input provided by the user. + +## Installation +Install the crewai_tools package +```shell +pip install 'crewai[tools]' +``` + +## Example + +Remember that when using this tool, the text must be generated by the Agent itself. The text must be a description of the image you want to generate. + +```python +from crewai_tools import DallETool + +Agent( + ... + tools=[DallETool()], +) +``` + +If needed you can also tweak the parameters of the DALL-E model by passing them as arguments to the `DallETool` class. For example: + +```python +from crewai_tools import DallETool + +dalle_tool = DallETool(model="dall-e-3", + size="1024x1024", + quality="standard", + n=1) + +Agent( + ... + tools=[dalle_tool] +) +``` + +The parameters are based on the `client.images.generate` method from the OpenAI API. For more information on the parameters, please refer to the [OpenAI API documentation](https://platform.openai.com/docs/guides/images/introduction?lang=python). diff --git a/src/crewai/knowledge/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/dalle_tool/__init__.py similarity index 100% rename from src/crewai/knowledge/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/dalle_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/dalle_tool/dalle_tool.py b/lib/crewai-tools/src/crewai_tools/tools/dalle_tool/dalle_tool.py new file mode 100644 index 000000000..a94b11a87 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/dalle_tool/dalle_tool.py @@ -0,0 +1,75 @@ +import json +from typing import Literal + +from crewai.tools import BaseTool, EnvVar +from openai import Omit, OpenAI +from pydantic import BaseModel, Field + + +class ImagePromptSchema(BaseModel): + """Input for Dall-E Tool.""" + + image_description: str = Field( + description="Description of the image to be generated by Dall-E." + ) + + +class DallETool(BaseTool): + name: str = "Dall-E Tool" + description: str = "Generates images using OpenAI's Dall-E model." + args_schema: type[BaseModel] = ImagePromptSchema + + model: str = "dall-e-3" + size: ( + Literal[ + "auto", + "1024x1024", + "1536x1024", + "1024x1536", + "256x256", + "512x512", + "1792x1024", + "1024x1792", + ] + | None + ) = "1024x1024" + quality: ( + Literal["standard", "hd", "low", "medium", "high", "auto"] | None | Omit + ) = "standard" + n: int = 1 + + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="OPENAI_API_KEY", + description="API key for OpenAI services", + required=True, + ), + ] + ) + + def _run(self, **kwargs) -> str: + client = OpenAI() + + image_description = kwargs.get("image_description") + + if not image_description: + return "Image description is required." + + response = client.images.generate( + model=self.model, + prompt=image_description, + size=self.size, + quality=self.quality, + n=self.n, + ) + + if not response or not response.data: + return "Failed to generate image." + + return json.dumps( + { + "image_url": response.data[0].url, + "image_description": response.data[0].revised_prompt, + } + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/README.md new file mode 100644 index 000000000..b5f4880c6 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/README.md @@ -0,0 +1,66 @@ +# Databricks Query Tool + +## Description + +This tool allows AI agents to execute SQL queries against Databricks workspace tables and retrieve the results. It provides a simple interface for querying data from Databricks tables using SQL, making it easy for agents to access and analyze data stored in Databricks. + +## Installation + +Install the crewai_tools package with the databricks extra: + +```shell +pip install 'crewai[tools]' 'databricks-sdk' +``` + +## Authentication + +The tool requires Databricks authentication credentials. You can provide these in two ways: + +1. **Using Databricks CLI profile**: + - Set the `DATABRICKS_CONFIG_PROFILE` environment variable to your profile name. + +2. **Using direct credentials**: + - Set both `DATABRICKS_HOST` and `DATABRICKS_TOKEN` environment variables. + +Example: +```shell +export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com" +export DATABRICKS_TOKEN="dapi1234567890abcdef" +``` + +## Usage + +```python +from crewai_tools import DatabricksQueryTool + +# Basic usage +databricks_tool = DatabricksQueryTool() + +# With default parameters for catalog, schema, and warehouse +databricks_tool = DatabricksQueryTool( + default_catalog="my_catalog", + default_schema="my_schema", + default_warehouse_id="warehouse_id" +) + +# Example in a CrewAI agent +@agent +def data_analyst(self) -> Agent: + return Agent( + config=self.agents_config["data_analyst"], + allow_delegation=False, + tools=[databricks_tool] + ) +``` + +## Parameters + +When executing queries, you can provide the following parameters: + +- `query` (required): SQL query to execute against the Databricks workspace +- `catalog` (optional): Databricks catalog name +- `schema` (optional): Databricks schema name +- `warehouse_id` (optional): Databricks SQL warehouse ID +- `row_limit` (optional): Maximum number of rows to return (default: 1000) + +If not provided, the tool will use the default values set during initialization. \ No newline at end of file diff --git a/src/crewai/knowledge/source/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/__init__.py similarity index 100% rename from src/crewai/knowledge/source/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/databricks_query_tool.py b/lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/databricks_query_tool.py new file mode 100644 index 000000000..ba9a375bd --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/databricks_query_tool.py @@ -0,0 +1,850 @@ +from __future__ import annotations + +import os +import time +from typing import TYPE_CHECKING, Any, TypeGuard, TypedDict + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field, model_validator + + +if TYPE_CHECKING: + from databricks.sdk import WorkspaceClient + + +class ExecutionContext(TypedDict, total=False): + catalog: str + schema: str + + +def _has_data_array(result: Any) -> TypeGuard[Any]: + """Type guard to check if result has data_array attribute. + + Args: + result: The result object to check. + + Returns: + True if result.result.data_array exists and is not None. + """ + return ( + hasattr(result, "result") + and result.result is not None + and hasattr(result.result, "data_array") + and result.result.data_array is not None + ) + + +class DatabricksQueryToolSchema(BaseModel): + """Input schema for DatabricksQueryTool.""" + + query: str = Field( + ..., description="SQL query to execute against the Databricks workspace table" + ) + catalog: str | None = Field( + None, + description="Databricks catalog name (optional, defaults to configured catalog)", + ) + db_schema: str | None = Field( + None, + description="Databricks schema name (optional, defaults to configured schema)", + ) + warehouse_id: str | None = Field( + None, + description="Databricks SQL warehouse ID (optional, defaults to configured warehouse)", + ) + row_limit: int | None = Field( + 1000, description="Maximum number of rows to return (default: 1000)" + ) + + @model_validator(mode="after") + def validate_input(self) -> DatabricksQueryToolSchema: + """Validate the input parameters.""" + # Ensure the query is not empty + if not self.query or not self.query.strip(): + raise ValueError("Query cannot be empty") + + # Add a LIMIT clause to the query if row_limit is provided and query doesn't have one + if self.row_limit and "limit" not in self.query.lower(): + self.query = f"{self.query.rstrip(';')} LIMIT {self.row_limit};" + + return self + + +class DatabricksQueryTool(BaseTool): + """A tool for querying Databricks workspace tables using SQL. + + This tool executes SQL queries against Databricks tables and returns the results. + It requires Databricks authentication credentials to be set as environment variables. + + Authentication can be provided via: + - Databricks CLI profile: Set DATABRICKS_CONFIG_PROFILE environment variable + - Direct credentials: Set DATABRICKS_HOST and DATABRICKS_TOKEN environment variables + + Example: + >>> tool = DatabricksQueryTool() + >>> results = tool.run(query="SELECT * FROM my_table LIMIT 10") + """ + + name: str = "Databricks SQL Query" + description: str = ( + "Execute SQL queries against Databricks workspace tables and return the results." + " Provide a 'query' parameter with the SQL query to execute." + ) + args_schema: type[BaseModel] = DatabricksQueryToolSchema + + # Optional default parameters + default_catalog: str | None = None + default_schema: str | None = None + default_warehouse_id: str | None = None + + _workspace_client: WorkspaceClient | None = None + package_dependencies: list[str] = Field(default_factory=lambda: ["databricks-sdk"]) + + def __init__( + self, + default_catalog: str | None = None, + default_schema: str | None = None, + default_warehouse_id: str | None = None, + **kwargs: Any, + ) -> None: + """Initialize the DatabricksQueryTool. + + Args: + default_catalog (Optional[str]): Default catalog to use for queries. + default_schema (Optional[str]): Default schema to use for queries. + default_warehouse_id (Optional[str]): Default SQL warehouse ID to use. + **kwargs: Additional keyword arguments passed to BaseTool. + """ + super().__init__(**kwargs) + self.default_catalog = default_catalog + self.default_schema = default_schema + self.default_warehouse_id = default_warehouse_id + self._validate_credentials() + + def _validate_credentials(self) -> None: + """Validate that Databricks credentials are available.""" + has_profile = "DATABRICKS_CONFIG_PROFILE" in os.environ + has_direct_auth = ( + "DATABRICKS_HOST" in os.environ and "DATABRICKS_TOKEN" in os.environ + ) + + if not (has_profile or has_direct_auth): + raise ValueError( + "Databricks authentication credentials are required. " + "Set either DATABRICKS_CONFIG_PROFILE or both DATABRICKS_HOST and DATABRICKS_TOKEN environment variables." + ) + + @property + def workspace_client(self) -> WorkspaceClient: + """Get or create a Databricks WorkspaceClient instance.""" + if self._workspace_client is None: + try: + from databricks.sdk import WorkspaceClient + + self._workspace_client = WorkspaceClient() + except ImportError as e: + raise ImportError( + "`databricks-sdk` package not found, please run `uv add databricks-sdk`" + ) from e + return self._workspace_client + + def _format_results(self, results: list[dict[str, Any]]) -> str: + """Format query results as a readable string.""" + if not results: + return "Query returned no results." + + # Get column names from the first row + if not results[0]: + return "Query returned empty rows with no columns." + + columns = list(results[0].keys()) + + # If we have rows but they're all empty, handle that case + if not columns: + return "Query returned rows but with no column data." + + # Calculate column widths based on data + col_widths = {col: len(col) for col in columns} + for row in results: + for col in columns: + # Convert value to string and get its length + # Handle None values gracefully + value_str = str(row[col]) if row[col] is not None else "NULL" + col_widths[col] = max(col_widths[col], len(value_str)) + + # Create header row + header = " | ".join(f"{col:{col_widths[col]}}" for col in columns) + separator = "-+-".join("-" * col_widths[col] for col in columns) + + # Format data rows + data_rows = [] + for row in results: + # Handle None values by displaying "NULL" + row_values = { + col: str(row[col]) if row[col] is not None else "NULL" + for col in columns + } + data_row = " | ".join( + f"{row_values[col]:{col_widths[col]}}" for col in columns + ) + data_rows.append(data_row) + + # Add row count information + result_info = f"({len(results)} row{'s' if len(results) != 1 else ''} returned)" + + # Combine all parts + return f"{header}\n{separator}\n" + "\n".join(data_rows) + f"\n\n{result_info}" + + def _run( + self, + **kwargs: Any, + ) -> str: + """Execute a SQL query against Databricks and return the results. + + Args: + query (str): SQL query to execute + catalog (Optional[str]): Databricks catalog name + db_schema (Optional[str]): Databricks schema name + warehouse_id (Optional[str]): SQL warehouse ID + row_limit (Optional[int]): Maximum number of rows to return + + Returns: + str: Formatted query results + """ + try: + # Get parameters with fallbacks to default values + query = kwargs.get("query") + catalog = kwargs.get("catalog") or self.default_catalog + db_schema = kwargs.get("db_schema") or self.default_schema + warehouse_id = kwargs.get("warehouse_id") or self.default_warehouse_id + row_limit = kwargs.get("row_limit", 1000) + + # Validate schema and query + validated_input = DatabricksQueryToolSchema( + query=query, + catalog=catalog, + db_schema=db_schema, + warehouse_id=warehouse_id, + row_limit=row_limit, + ) + + # Extract validated parameters + query = validated_input.query + catalog = validated_input.catalog + db_schema = validated_input.db_schema + warehouse_id = validated_input.warehouse_id + + if warehouse_id is None: + return "SQL warehouse ID must be provided either as a parameter or as a default." + + # Setup SQL context with catalog/schema if provided + + context: ExecutionContext = {} + if catalog: + context["catalog"] = catalog + if db_schema: + context["schema"] = db_schema + + # Execute query + statement = self.workspace_client.statement_execution + + try: + # Execute the statement + execution = statement.execute_statement( + warehouse_id=warehouse_id, statement=query, **context + ) + + statement_id = execution.statement_id + except Exception as execute_error: + # Handle immediate execution errors + return f"Error starting query execution: {execute_error!s}" + + # Poll for results with better error handling + + result = None + timeout = 300 # 5 minutes timeout + start_time = time.time() + poll_count = 0 + previous_state = None # Track previous state to detect changes + + if statement_id is None: + return "Failed to retrieve statement ID after execution." + + while time.time() - start_time < timeout: + poll_count += 1 + try: + # Get statement status + result = statement.get_statement(statement_id) + + # Check if finished - be very explicit about state checking + if hasattr(result, "status") and hasattr(result.status, "state"): + state_value = str( + result.status.state # type: ignore[union-attr] + ) # Convert to string to handle both string and enum + + # Track state changes for debugging + if previous_state != state_value: + previous_state = state_value + + # Check if state indicates completion + if "SUCCEEDED" in state_value: + break + if "FAILED" in state_value: + # Extract error message with more robust handling + error_info = "No detailed error info" + try: + # First try direct access to error.message + if ( + hasattr(result.status, "error") + and result.status.error # type: ignore[union-attr] + ): + if hasattr(result.status.error, "message"): # type: ignore[union-attr] + error_info = result.status.error.message # type: ignore[union-attr,assignment] + # Some APIs may have a different structure + elif hasattr(result.status.error, "error_message"): # type: ignore[union-attr] + error_info = result.status.error.error_message # type: ignore[union-attr] + # Last resort, try to convert the whole error object to string + else: + error_info = str(result.status.error) # type: ignore[union-attr] + except Exception as err_extract_error: + # If all else fails, try to get any info we can + error_info = ( + f"Error details unavailable: {err_extract_error!s}" + ) + + # Return immediately on first FAILED state detection + return f"Query execution failed: {error_info}" + if "CANCELED" in state_value: + return "Query was canceled" + + except Exception as poll_error: + # Don't immediately fail - try again a few times + if poll_count > 3: + return f"Error checking query status: {poll_error!s}" + + # Wait before polling again + time.sleep(2) + + # Check if we timed out + if result is None: + return "Query returned no result (likely timed out or failed)" + + if not hasattr(result, "status") or not hasattr(result.status, "state"): + return "Query completed but returned an invalid result structure" + + # Convert state to string for comparison + state_value = str(result.status.state) # type: ignore[union-attr] + if not any( + state in state_value for state in ["SUCCEEDED", "FAILED", "CANCELED"] + ): + return f"Query timed out after 5 minutes (last state: {state_value})" + + # Get results - adapt this based on the actual structure of the result object + chunk_results = [] + + # Check if we have results and a schema in a very defensive way + has_schema = ( + hasattr(result, "manifest") + and result.manifest is not None + and hasattr(result.manifest, "schema") + and result.manifest.schema is not None + ) + has_result = hasattr(result, "result") and result.result is not None + + if has_schema and has_result: + try: + # Get schema for column names + columns = [col.name for col in result.manifest.schema.columns] # type: ignore[union-attr] + + # Debug info for schema + + # Keep track of all dynamic columns we create + all_columns = set(columns) + + # Dump the raw structure of result data to help troubleshoot + if _has_data_array(result): + # Add defensive check for None data_array + if result.result.data_array is None: + # Return empty result handling rather than trying to process null data + return "Query executed successfully (no data returned)" + + # IMPROVED DETECTION LOGIC: Check if we're possibly dealing with rows where each item + # contains a single value or character (which could indicate incorrect row structure) + is_likely_incorrect_row_structure = False + + # Only try to analyze sample if data_array exists and has content + if ( + _has_data_array(result) + and len(result.result.data_array) > 0 + and len(result.result.data_array[0]) > 0 + ): + sample_size = min(20, len(result.result.data_array[0])) + + if sample_size > 0: + single_char_count = 0 + single_digit_count = 0 + total_items = 0 + + for i in range(sample_size): + val = result.result.data_array[0][i] + total_items += 1 + if ( + isinstance(val, str) + and len(val) == 1 + and not val.isdigit() + ): + single_char_count += 1 + elif ( + isinstance(val, str) + and len(val) == 1 + and val.isdigit() + ): + single_digit_count += 1 + + # If a significant portion of the first values are single characters or digits, + # this likely indicates data is being incorrectly structured + if ( + total_items > 0 + and (single_char_count + single_digit_count) + / total_items + > 0.5 + ): + is_likely_incorrect_row_structure = True + + # Additional check: if many rows have just 1 item when we expect multiple columns + rows_with_single_item = 0 + if ( + hasattr(result.result, "data_array") + and result.result.data_array # type: ignore[union-attr] + and len(result.result.data_array) > 0 # type: ignore[union-attr] + ): + sample_size_for_rows = ( + min(sample_size, len(result.result.data_array[0])) # type: ignore[union-attr] + if "sample_size" in locals() + else min(20, len(result.result.data_array[0])) # type: ignore[union-attr] + ) + rows_with_single_item = sum( + 1 # type: ignore[misc] + for row in result.result.data_array[0][ # type: ignore[union-attr] + :sample_size_for_rows + ] + if isinstance(row, list) and len(row) == 1 + ) + if ( + rows_with_single_item > sample_size_for_rows * 0.5 + and len(columns) > 1 + ): + is_likely_incorrect_row_structure = True + + # Check if we're getting primarily single characters or the data structure seems off, + # we should use special handling + if ( + "is_likely_incorrect_row_structure" in locals() + and is_likely_incorrect_row_structure + ): + needs_special_string_handling = True + else: + needs_special_string_handling = False + + # Process results differently based on detection + if ( + "needs_special_string_handling" in locals() + and needs_special_string_handling + ): + # We're dealing with data where the rows may be incorrectly structured + + # Collect all values into a flat list + all_values: list[Any] = [] + if ( + hasattr(result.result, "data_array") + and result.result.data_array # type: ignore[union-attr] + ): + # Flatten all values into a single list + for chunk in result.result.data_array: # type: ignore[union-attr] + for item in chunk: + if isinstance(item, (list, tuple)): + all_values.extend(item) + else: + all_values.append(item) + + # Get the expected column count from schema + expected_column_count = len(columns) + + # Try to reconstruct rows using pattern recognition + reconstructed_rows = [] + + # PATTERN RECOGNITION APPROACH + # Look for likely indicators of row boundaries in the data + # For Netflix data, we expect IDs as numbers, titles as text strings, etc. + + # Use regex pattern to identify ID columns that likely start a new row + import re + + id_pattern = re.compile( + r"^\d{5,9}$" + ) # Netflix IDs are often 5-9 digits + id_indices = [] + + for i, val in enumerate(all_values): + if isinstance(val, str) and id_pattern.match(val): + # This value looks like an ID, might be the start of a row + if i < len(all_values) - 1: + next_few_values = all_values[i + 1 : i + 5] + # If following values look like they could be part of a title + if any( + isinstance(v, str) and len(v) > 1 + for v in next_few_values + ): + id_indices.append(i) + + if id_indices: + # If we found potential row starts, use them to extract rows + for i in range(len(id_indices)): + start_idx = id_indices[i] + end_idx = ( + id_indices[i + 1] + if i + 1 < len(id_indices) + else len(all_values) + ) + + # Extract values for this row + row_values = all_values[start_idx:end_idx] + + # Special handling for Netflix title data + # Titles might be split into individual characters + if ( + "Title" in columns + and len(row_values) > expected_column_count + ): + # Try to reconstruct by looking for patterns + # We know ID is first, then Title (which may be split) + # Then other fields like Genre, etc. + + # Take first value as ID + row_dict = {columns[0]: row_values[0]} + + # Look for Genre or other non-title fields to determine where title ends + title_end_idx = 1 + for j in range(2, min(100, len(row_values))): + val = row_values[j] + # Check for common genres or non-title markers + if isinstance(val, str) and val in [ + "Comedy", + "Drama", + "Action", + "Horror", + "Thriller", + "Documentary", + ]: + # Likely found the Genre field + title_end_idx = j + break + + # Reconstruct title from individual characters + if title_end_idx > 1: + title_chars = row_values[1:title_end_idx] + # Check if they're individual characters + if all( + isinstance(c, str) and len(c) == 1 + for c in title_chars + ): + title = "".join(title_chars) + row_dict["Title"] = title + + # Assign remaining values to columns + remaining_values = row_values[ + title_end_idx: + ] + for j, col_name in enumerate( + columns[2:], 2 + ): + if j - 2 < len(remaining_values): + row_dict[col_name] = ( + remaining_values[j - 2] + ) + else: + row_dict[col_name] = None + else: + # Fallback: simple mapping + for j, col_name in enumerate(columns): + if j < len(row_values): + row_dict[col_name] = row_values[j] + else: + row_dict[col_name] = None + else: + # Standard mapping + row_dict = {} + for j, col_name in enumerate(columns): + if j < len(row_values): + row_dict[col_name] = row_values[j] + else: + row_dict[col_name] = None + + reconstructed_rows.append(row_dict) + else: + # More intelligent chunking - try to detect where columns like Title might be split + title_idx = ( + columns.index("Title") if "Title" in columns else -1 + ) + + if title_idx >= 0: + # Try to detect if title is split across multiple values + i = 0 + while i < len(all_values): + # Check if this could be an ID (start of a row) + if isinstance( + all_values[i], str + ) and id_pattern.match(all_values[i]): + row_dict = {columns[0]: all_values[i]} + i += 1 + + # Try to reconstruct title if it appears to be split + title_chars = [] + while ( + i < len(all_values) + and isinstance(all_values[i], str) + and len(all_values[i]) <= 1 + and len(title_chars) < 100 + ): # Cap title length + title_chars.append(all_values[i]) + i += 1 + + if title_chars: + row_dict[columns[title_idx]] = "".join( + title_chars + ) + + # Add remaining fields + for j in range(title_idx + 1, len(columns)): + if i < len(all_values): + row_dict[columns[j]] = all_values[i] + i += 1 + else: + row_dict[columns[j]] = None + + reconstructed_rows.append(row_dict) + else: + i += 1 + + # If we still don't have rows, use simple chunking as fallback + if not reconstructed_rows: + chunks = [ + all_values[i : i + expected_column_count] + for i in range( + 0, len(all_values), expected_column_count + ) + ] + + for chunk in chunks: + # Skip chunks that seem to be partial/incomplete rows + if ( + len(chunk) < expected_column_count * 0.75 + ): # Allow for some missing values + continue + + row_dict = {} + + # Map values to column names + for i, col in enumerate(columns): + if i < len(chunk): + row_dict[col] = chunk[i] + else: + row_dict[col] = None + + reconstructed_rows.append(row_dict) + + # Apply post-processing to fix known issues + if reconstructed_rows and "Title" in columns: + for row in reconstructed_rows: + # Fix titles that might still have issues + if ( + isinstance(row.get("Title"), str) + and len(row.get("Title")) <= 1 # type: ignore[arg-type] + ): + # This is likely still a fragmented title - mark as potentially incomplete + row["Title"] = f"[INCOMPLETE] {row.get('Title')}" + + # Ensure we respect the row limit + if row_limit and len(reconstructed_rows) > row_limit: + reconstructed_rows = reconstructed_rows[:row_limit] + + chunk_results = reconstructed_rows + else: + # Process normal result structure as before + + # Check different result structures + if ( + hasattr(result.result, "data_array") + and result.result.data_array # type: ignore[union-attr] + ): + # Check if data appears to be malformed within chunks + for _chunk_idx, chunk in enumerate( + result.result.data_array # type: ignore[union-attr] + ): + # Check if chunk might actually contain individual columns of a single row + # This is another way data might be malformed - check the first few values + if len(chunk) > 0 and len(columns) > 1: + # If there seems to be a mismatch between chunk structure and expected columns + first_few_values = chunk[: min(5, len(chunk))] + if all( + isinstance(val, (str, int, float)) + and not isinstance(val, (list, dict)) + for val in first_few_values + ): + if ( + len(chunk) > len(columns) * 3 + ): # Heuristic: if chunk has way more items than columns + # This chunk might actually be values of multiple rows - try to reconstruct + values = chunk # All values in this chunk + reconstructed_rows = [] + + # Try to create rows based on expected column count + for i in range( + 0, len(values), len(columns) + ): + if i + len(columns) <= len( + values + ): # Ensure we have enough values + row_values = values[ + i : i + len(columns) + ] + row_dict = { + col: val + for col, val in zip( + columns, + row_values, + strict=False, + ) + } + reconstructed_rows.append(row_dict) + + if reconstructed_rows: + chunk_results.extend(reconstructed_rows) + continue # Skip normal processing for this chunk + + # Special case: when chunk contains exactly the right number of values for a single row + # This handles the case where instead of a list of rows, we just got all values in a flat list + if all( + isinstance(val, (str, int, float)) + and not isinstance(val, (list, dict)) + for val in chunk + ): + if len(chunk) == len(columns) or ( + len(chunk) > 0 + and len(chunk) % len(columns) == 0 + ): + # Process flat list of values as rows + for i in range(0, len(chunk), len(columns)): + row_values = chunk[i : i + len(columns)] + if len(row_values) == len( + columns + ): # Only process complete rows + row_dict = { + col: val + for col, val in zip( + columns, + row_values, + strict=False, + ) + } + chunk_results.append(row_dict) + + # Skip regular row processing for this chunk + continue + + # Normal processing for typical row structure + for _row_idx, row in enumerate(chunk): + # Ensure row is actually a collection of values + if not isinstance(row, (list, tuple, dict)): + # This might be a single value; skip it or handle specially + continue + + # Convert each row to a dictionary with column names as keys + row_dict = {} + + # Handle dict rows directly + if isinstance(row, dict): + # Use the existing column mapping + row_dict = dict(row) + elif isinstance(row, (list, tuple)): + # Map list of values to columns + for i, val in enumerate(row): + if ( + i < len(columns) + ): # Only process if we have a matching column + row_dict[columns[i]] = val + else: + # Extra values without column names + dynamic_col = f"Column_{i}" + row_dict[dynamic_col] = val + all_columns.add(dynamic_col) + + # If we have fewer values than columns, set missing values to None + for col in columns: + if col not in row_dict: + row_dict[col] = None + + chunk_results.append(row_dict) + + elif hasattr(result.result, "data") and result.result.data: # type: ignore[union-attr] + # Alternative data structure + + for _row_idx, row in enumerate(result.result.data): # type: ignore[union-attr] + # Debug info + + # Safely create dictionary matching column names to values + row_dict = {} + for i, val in enumerate(row): + if i < len( + columns + ): # Only process if we have a matching column + row_dict[columns[i]] = val + else: + # Extra values without column names + dynamic_col = f"Column_{i}" + row_dict[dynamic_col] = val + all_columns.add(dynamic_col) + + # If we have fewer values than columns, set missing values to None + for i, col in enumerate(columns): + if i >= len(row): + row_dict[col] = None + + chunk_results.append(row_dict) + + # After processing all rows, ensure all rows have all columns + normalized_results = [] + for row in chunk_results: + # Create a new row with all columns, defaulting to None for missing ones + normalized_row = { + col: row.get(col, None) for col in all_columns + } + normalized_results.append(normalized_row) + + # Replace the original results with normalized ones + chunk_results = normalized_results + + except Exception as results_error: + # Enhanced error message with more context + import traceback + + error_details = traceback.format_exc() + return f"Error processing query results: {results_error!s}\n\nDetails:\n{error_details}" + + # If we have no results but the query succeeded (e.g., for DDL statements) + if not chunk_results and hasattr(result, "status"): + state_value = str(result.status.state) # type: ignore[union-attr] + if "SUCCEEDED" in state_value: + return "Query executed successfully (no results to display)" + + # Format and return results + return self._format_results(chunk_results) # type: ignore[arg-type] + + except Exception as e: + # Include more details in the error message to help with debugging + import traceback + + error_details = traceback.format_exc() + return ( + f"Error executing Databricks query: {e!s}\n\nDetails:\n{error_details}" + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/directory_read_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/directory_read_tool/README.md new file mode 100644 index 000000000..9305fd1a3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/directory_read_tool/README.md @@ -0,0 +1,40 @@ +```markdown +# DirectoryReadTool + +## Description +The DirectoryReadTool is a highly efficient utility designed for the comprehensive listing of directory contents. It recursively navigates through the specified directory, providing users with a detailed enumeration of all files, including those nested within subdirectories. This tool is indispensable for tasks requiring a thorough inventory of directory structures or for validating the organization of files within directories. + +## Installation +Install the `crewai_tools` package to use the DirectoryReadTool in your project. If you haven't added this package to your environment, you can easily install it with pip using the following command: + +```shell +pip install 'crewai[tools]' +``` + +This installs the latest version of the `crewai_tools` package, allowing access to the DirectoryReadTool and other utilities. + +## Example +The DirectoryReadTool is simple to use. The code snippet below shows how to set up and use the tool to list the contents of a specified directory: + +```python +from crewai_tools import DirectoryReadTool + +# Initialize the tool with the directory you want to explore +tool = DirectoryReadTool(directory='/path/to/your/directory') + +# Use the tool to list the contents of the specified directory +directory_contents = tool.run() +print(directory_contents) +``` + +This example demonstrates the essential steps to utilize the DirectoryReadTool effectively, highlighting its simplicity and user-friendly design. + +## Arguments +The DirectoryReadTool requires minimal configuration for use. The essential argument for this tool is as follows: + +- `directory`: A mandatory argument that specifies the path to the directory whose contents you wish to list. It accepts both absolute and relative paths, guiding the tool to the desired directory for content listing. + +The DirectoryReadTool provides a user-friendly and efficient way to list directory contents, making it an invaluable tool for managing and inspecting directory structures. +``` + +This revised documentation for the DirectoryReadTool maintains the structure and content requirements as outlined, with adjustments made for clarity, consistency, and adherence to the high-quality standards exemplified in the provided documentation example. diff --git a/src/crewai/knowledge/storage/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/directory_read_tool/__init__.py similarity index 100% rename from src/crewai/knowledge/storage/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/directory_read_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/directory_read_tool/directory_read_tool.py b/lib/crewai-tools/src/crewai_tools/tools/directory_read_tool/directory_read_tool.py new file mode 100644 index 000000000..a59e2c19e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/directory_read_tool/directory_read_tool.py @@ -0,0 +1,50 @@ +import os +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class FixedDirectoryReadToolSchema(BaseModel): + """Input for DirectoryReadTool.""" + + +class DirectoryReadToolSchema(FixedDirectoryReadToolSchema): + """Input for DirectoryReadTool.""" + + directory: str = Field(..., description="Mandatory directory to list content") + + +class DirectoryReadTool(BaseTool): + name: str = "List files in directory" + description: str = ( + "A tool that can be used to recursively list a directory's content." + ) + args_schema: type[BaseModel] = DirectoryReadToolSchema + directory: str | None = None + + def __init__(self, directory: str | None = None, **kwargs): + super().__init__(**kwargs) + if directory is not None: + self.directory = directory + self.description = f"A tool that can be used to list {directory}'s content." + self.args_schema = FixedDirectoryReadToolSchema + self._generate_description() + + def _run( + self, + **kwargs: Any, + ) -> Any: + directory: str | None = kwargs.get("directory", self.directory) + if directory is None: + raise ValueError("Directory must be provided.") + + if directory[-1] == "/": + directory = directory[:-1] + files_list = [ + f"{directory}/{(os.path.join(root, filename).replace(directory, '').lstrip(os.path.sep))}" + for root, dirs, files in os.walk(directory) + for filename in files + ] + files = "\n- ".join(files_list) + return f"File paths: \n-{files}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/directory_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/directory_search_tool/README.md new file mode 100644 index 000000000..b39e9fe96 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/directory_search_tool/README.md @@ -0,0 +1,55 @@ +# DirectorySearchTool + +## Description +This tool is designed to perform a semantic search for queries within the content of a specified directory. Utilizing the RAG (Retrieval-Augmented Generation) methodology, it offers a powerful means to semantically navigate through the files of a given directory. The tool can be dynamically set to search any directory specified at runtime or can be pre-configured to search within a specific directory upon initialization. + +## Installation +To start using the DirectorySearchTool, you need to install the crewai_tools package. Execute the following command in your terminal: + +```shell +pip install 'crewai[tools]' +``` + +## Example +The following examples demonstrate how to initialize the DirectorySearchTool for different use cases and how to perform a search: + +```python +from crewai_tools import DirectorySearchTool + +# To enable searching within any specified directory at runtime +tool = DirectorySearchTool() + +# Alternatively, to restrict searches to a specific directory +tool = DirectorySearchTool(directory='/path/to/directory') +``` + +## Arguments +- `directory` : This string argument specifies the directory within which to search. It is mandatory if the tool has not been initialized with a directory; otherwise, the tool will only search within the initialized directory. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = DirectorySearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/src/crewai/memory/contextual/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/directory_search_tool/__init__.py similarity index 100% rename from src/crewai/memory/contextual/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/directory_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/directory_search_tool/directory_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/directory_search_tool/directory_search_tool.py new file mode 100644 index 000000000..0ccc1673f --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/directory_search_tool/directory_search_tool.py @@ -0,0 +1,51 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedDirectorySearchToolSchema(BaseModel): + """Input for DirectorySearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the directory's content", + ) + + +class DirectorySearchToolSchema(FixedDirectorySearchToolSchema): + """Input for DirectorySearchTool.""" + + directory: str = Field(..., description="Mandatory directory you want to search") + + +class DirectorySearchTool(RagTool): + name: str = "Search a directory's content" + description: str = ( + "A tool that can be used to semantic search a query from a directory's content." + ) + args_schema: type[BaseModel] = DirectorySearchToolSchema + + def __init__(self, directory: str | None = None, **kwargs): + super().__init__(**kwargs) + if directory is not None: + self.add(directory) + self.description = f"A tool that can be used to semantic search a query the {directory} directory's content." + self.args_schema = FixedDirectorySearchToolSchema + self._generate_description() + + def add(self, directory: str) -> None: + super().add(directory, data_type=DataType.DIRECTORY) + + def _run( # type: ignore[override] + self, + search_query: str, + directory: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if directory is not None: + self.add(directory) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/docx_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/docx_search_tool/README.md new file mode 100644 index 000000000..c99a4984e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/docx_search_tool/README.md @@ -0,0 +1,57 @@ +# DOCXSearchTool + +## Description +The DOCXSearchTool is a RAG tool designed for semantic searching within DOCX documents. It enables users to effectively search and extract relevant information from DOCX files using query-based searches. This tool is invaluable for data analysis, information management, and research tasks, streamlining the process of finding specific information within large document collections. + +## Installation +Install the crewai_tools package by running the following command in your terminal: + +```shell +pip install 'crewai[tools]' +``` + +## Example +The following example demonstrates initializing the DOCXSearchTool to search within any DOCX file's content or with a specific DOCX file path. + +```python +from crewai_tools import DOCXSearchTool + +# Initialize the tool to search within any DOCX file's content +tool = DOCXSearchTool() + +# OR + +# Initialize the tool with a specific DOCX file, so the agent can only search the content of the specified DOCX file +tool = DOCXSearchTool(docx='path/to/your/document.docx') +``` + +## Arguments +- `docx`: An optional file path to a specific DOCX document you wish to search. If not provided during initialization, the tool allows for later specification of any DOCX file's content path for searching. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = DOCXSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/src/crewai/memory/entity/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/docx_search_tool/__init__.py similarity index 100% rename from src/crewai/memory/entity/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/docx_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/docx_search_tool/docx_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/docx_search_tool/docx_search_tool.py new file mode 100644 index 000000000..eb4f73354 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/docx_search_tool/docx_search_tool.py @@ -0,0 +1,59 @@ +from typing import Any + +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedDOCXSearchToolSchema(BaseModel): + """Input for DOCXSearchTool.""" + + docx: str | None = Field( + ..., description="File path or URL of a DOCX file to be searched" + ) + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the DOCX's content", + ) + + +class DOCXSearchToolSchema(FixedDOCXSearchToolSchema): + """Input for DOCXSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the DOCX's content", + ) + + +class DOCXSearchTool(RagTool): + name: str = "Search a DOCX's content" + description: str = ( + "A tool that can be used to semantic search a query from a DOCX's content." + ) + args_schema: type[BaseModel] = DOCXSearchToolSchema + + def __init__(self, docx: str | None = None, **kwargs): + super().__init__(**kwargs) + if docx is not None: + self.add(docx) + self.description = f"A tool that can be used to semantic search a query the {docx} DOCX's content." + self.args_schema = FixedDOCXSearchToolSchema + self._generate_description() + + def add(self, docx: str) -> None: + super().add(docx, data_type=DataType.DOCX) + + def _run( # type: ignore[override] + self, + search_query: str, + docx: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> Any: + if docx is not None: + self.add(docx) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/exa_tools/README.md b/lib/crewai-tools/src/crewai_tools/tools/exa_tools/README.md new file mode 100644 index 000000000..1d1d20150 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/exa_tools/README.md @@ -0,0 +1,30 @@ +# EXASearchTool Documentation + +## Description +This tool is designed to perform a semantic search for a specified query from a text's content across the internet. It utilizes the `https://exa.ai/` API to fetch and display the most relevant search results based on the query provided by the user. + +## Installation +To incorporate this tool into your project, follow the installation instructions below: +```shell +uv add crewai[tools] exa_py +``` + +## Example +The following example demonstrates how to initialize the tool and execute a search with a given query: + +```python +from crewai_tools import EXASearchTool + +# Initialize the tool for internet searching capabilities +tool = EXASearchTool(api_key="your_api_key") +``` + +## Steps to Get Started +To effectively use the `EXASearchTool`, follow these steps: + +1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment. +2. **API Key Acquisition**: Acquire a `https://exa.ai/` API key by registering for a free account at `https://exa.ai/`. +3. **Environment Configuration**: Store your obtained API key in an environment variable named `EXA_API_KEY` to facilitate its use by the tool. + +## Conclusion +By integrating the `EXASearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward. diff --git a/src/crewai/memory/external/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/exa_tools/__init__.py similarity index 100% rename from src/crewai/memory/external/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/exa_tools/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/exa_tools/exa_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/exa_tools/exa_search_tool.py new file mode 100644 index 000000000..b3187adb1 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/exa_tools/exa_search_tool.py @@ -0,0 +1,135 @@ +from __future__ import annotations + +from builtins import type as type_ +import os +from typing import Any, TypedDict + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field +from typing_extensions import Required + + +class SearchParams(TypedDict, total=False): + """Parameters for Exa search API.""" + + type: Required[str | None] + start_published_date: str + end_published_date: str + include_domains: list[str] + + +class EXABaseToolSchema(BaseModel): + search_query: str = Field( + ..., description="Mandatory search query you want to use to search the internet" + ) + start_published_date: str | None = Field( + None, description="Start date for the search" + ) + end_published_date: str | None = Field(None, description="End date for the search") + include_domains: list[str] | None = Field( + None, description="List of domains to include in the search" + ) + + +class EXASearchTool(BaseTool): + model_config = ConfigDict(arbitrary_types_allowed=True) + name: str = "EXASearchTool" + description: str = "Search the internet using Exa" + args_schema: type_[BaseModel] = EXABaseToolSchema + client: Any | None = None + content: bool | None = False + summary: bool | None = False + type: str | None = "auto" + package_dependencies: list[str] = Field(default_factory=lambda: ["exa_py"]) + api_key: str | None = Field( + default_factory=lambda: os.getenv("EXA_API_KEY"), + description="API key for Exa services", + json_schema_extra={"required": False}, + ) + base_url: str | None = Field( + default_factory=lambda: os.getenv("EXA_BASE_URL"), + description="API server url", + json_schema_extra={"required": False}, + ) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="EXA_API_KEY", + description="API key for Exa services", + required=False, + ), + EnvVar( + name="EXA_BASE_URL", + description="API url for the Exa services", + required=False, + ), + ] + ) + + def __init__( + self, + content: bool | None = False, + summary: bool | None = False, + type: str | None = "auto", + **kwargs, + ): + super().__init__( + **kwargs, + ) + try: + from exa_py import Exa + except ImportError as e: + import click + + if click.confirm( + "You are missing the 'exa_py' package. Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "exa_py"], check=True) # noqa: S607 + + # Re-import after installation + from exa_py import Exa + else: + raise ImportError( + "You are missing the 'exa_py' package. Would you like to install it?" + ) from e + + client_kwargs: dict[str, str] = {} + if self.api_key: + client_kwargs["api_key"] = self.api_key + if self.base_url: + client_kwargs["base_url"] = self.base_url + self.client = Exa(**client_kwargs) + self.content = content + self.summary = summary + self.type = type + + def _run( + self, + search_query: str, + start_published_date: str | None = None, + end_published_date: str | None = None, + include_domains: list[str] | None = None, + ) -> Any: + if self.client is None: + raise ValueError("Client not initialized") + + search_params: SearchParams = { + "type": self.type, + } + + if start_published_date: + search_params["start_published_date"] = start_published_date + if end_published_date: + search_params["end_published_date"] = end_published_date + if include_domains: + search_params["include_domains"] = include_domains + + if self.content: + results = self.client.search_and_contents( + search_query, summary=self.summary, **search_params + ) + else: + results = self.client.search(search_query, **search_params) + return results diff --git a/lib/crewai-tools/src/crewai_tools/tools/file_read_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/file_read_tool/README.md new file mode 100644 index 000000000..7b8a15488 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/file_read_tool/README.md @@ -0,0 +1,40 @@ +# FileReadTool + +## Description + +The FileReadTool is a versatile component of the crewai_tools package, designed to streamline the process of reading and retrieving content from files. It is particularly useful in scenarios such as batch text file processing, runtime configuration file reading, and data importation for analytics. This tool supports various text-based file formats including `.txt`, `.csv`, `.json`, and adapts its functionality based on the file type, for instance, converting JSON content into a Python dictionary for easy use. + +The tool also supports reading specific chunks of a file by specifying a starting line and the number of lines to read, which is helpful when working with large files that don't need to be loaded entirely into memory. + +## Installation + +Install the crewai_tools package to use the FileReadTool in your projects: + +```shell +pip install 'crewai[tools]' +``` + +## Example + +To get started with the FileReadTool: + +```python +from crewai_tools import FileReadTool + +# Initialize the tool to read any files the agents knows or lean the path for +file_read_tool = FileReadTool() + +# OR + +# Initialize the tool with a specific file path, so the agent can only read the content of the specified file +file_read_tool = FileReadTool(file_path='path/to/your/file.txt') + +# Read a specific chunk of the file (lines 100-149) +partial_content = file_read_tool.run(file_path='path/to/your/file.txt', start_line=100, line_count=50) +``` + +## Arguments + +- `file_path`: The path to the file you want to read. It accepts both absolute and relative paths. Ensure the file exists and you have the necessary permissions to access it. +- `start_line`: (Optional) The line number to start reading from (1-indexed). Defaults to 1 (the first line). +- `line_count`: (Optional) The number of lines to read. If not provided, reads from the start_line to the end of the file. diff --git a/src/crewai/memory/long_term/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/file_read_tool/__init__.py similarity index 100% rename from src/crewai/memory/long_term/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/file_read_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/file_read_tool/file_read_tool.py b/lib/crewai-tools/src/crewai_tools/tools/file_read_tool/file_read_tool.py new file mode 100644 index 000000000..2c56a70cd --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/file_read_tool/file_read_tool.py @@ -0,0 +1,102 @@ +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class FileReadToolSchema(BaseModel): + """Input for FileReadTool.""" + + file_path: str = Field(..., description="Mandatory file full path to read the file") + start_line: int | None = Field( + 1, description="Line number to start reading from (1-indexed)" + ) + line_count: int | None = Field( + None, description="Number of lines to read. If None, reads the entire file" + ) + + +class FileReadTool(BaseTool): + """A tool for reading file contents. + + This tool inherits its schema handling from BaseTool to avoid recursive schema + definition issues. The args_schema is set to FileReadToolSchema which defines + the required file_path parameter. The schema should not be overridden in the + constructor as it would break the inheritance chain and cause infinite loops. + + The tool supports two ways of specifying the file path: + 1. At construction time via the file_path parameter + 2. At runtime via the file_path parameter in the tool's input + + Args: + file_path (Optional[str]): Path to the file to be read. If provided, + this becomes the default file path for the tool. + **kwargs: Additional keyword arguments passed to BaseTool. + + Example: + >>> tool = FileReadTool(file_path="/path/to/file.txt") + >>> content = tool.run() # Reads /path/to/file.txt + >>> content = tool.run(file_path="/path/to/other.txt") # Reads other.txt + >>> content = tool.run( + ... file_path="/path/to/file.txt", start_line=100, line_count=50 + ... ) # Reads lines 100-149 + """ + + name: str = "Read a file's content" + description: str = "A tool that reads the content of a file. To use this tool, provide a 'file_path' parameter with the path to the file you want to read. Optionally, provide 'start_line' to start reading from a specific line and 'line_count' to limit the number of lines read." + args_schema: type[BaseModel] = FileReadToolSchema + file_path: str | None = None + + def __init__(self, file_path: str | None = None, **kwargs: Any) -> None: + """Initialize the FileReadTool. + + Args: + file_path (Optional[str]): Path to the file to be read. If provided, + this becomes the default file path for the tool. + **kwargs: Additional keyword arguments passed to BaseTool. + """ + if file_path is not None: + kwargs["description"] = ( + f"A tool that reads file content. The default file is {file_path}, but you can provide a different 'file_path' parameter to read another file. You can also specify 'start_line' and 'line_count' to read specific parts of the file." + ) + + super().__init__(**kwargs) + self.file_path = file_path + + def _run( + self, + file_path: str | None = None, + start_line: int | None = 1, + line_count: int | None = None, + ) -> str: + file_path = file_path or self.file_path + start_line = start_line or 1 + line_count = line_count or None + + if file_path is None: + return "Error: No file path provided. Please provide a file path either in the constructor or as an argument." + + try: + with open(file_path, "r") as file: + if start_line == 1 and line_count is None: + return file.read() + + start_idx = max(start_line - 1, 0) + + selected_lines = [ + line + for i, line in enumerate(file) + if i >= start_idx + and (line_count is None or i < start_idx + line_count) + ] + + if not selected_lines and start_idx > 0: + return f"Error: Start line {start_line} exceeds the number of lines in the file." + + return "".join(selected_lines) + except FileNotFoundError: + return f"Error: File not found at path: {file_path}" + except PermissionError: + return f"Error: Permission denied when trying to read file: {file_path}" + except Exception as e: + return f"Error: Failed to read file {file_path}. {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/README.md new file mode 100644 index 000000000..e93e5c682 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/README.md @@ -0,0 +1,35 @@ +Here's the rewritten README for the `FileWriterTool`: + +# FileWriterTool Documentation + +## Description +The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files. It is particularly useful in scenarios such as generating reports, saving logs, creating configuration files, and more. This tool supports creating new directories if they don't exist, making it easier to organize your output. + +## Installation +Install the crewai_tools package to use the `FileWriterTool` in your projects: + +```shell +pip install 'crewai[tools]' +``` + +## Example +To get started with the `FileWriterTool`: + +```python +from crewai_tools import FileWriterTool + +# Initialize the tool +file_writer_tool = FileWriterTool() + +# Write content to a file in a specified directory +result = file_writer_tool._run('example.txt', 'This is a test content.', 'test_directory') +print(result) +``` + +## Arguments +- `filename`: The name of the file you want to create or overwrite. +- `content`: The content to write into the file. +- `directory` (optional): The path to the directory where the file will be created. Defaults to the current directory (`.`). If the directory does not exist, it will be created. + +## Conclusion +By integrating the `FileWriterTool` into your crews, the agents can execute the process of writing content to files and creating directories. This tool is essential for tasks that require saving output data, creating structured file systems, and more. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is straightforward and efficient. diff --git a/src/crewai/memory/short_term/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/__init__.py similarity index 100% rename from src/crewai/memory/short_term/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/file_writer_tool.py b/lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/file_writer_tool.py new file mode 100644 index 000000000..33b43985d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/file_writer_tool/file_writer_tool.py @@ -0,0 +1,59 @@ +import os +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel + + +def strtobool(val) -> bool: + if isinstance(val, bool): + return val + val = val.lower() + if val in ("y", "yes", "t", "true", "on", "1"): + return True + if val in ("n", "no", "f", "false", "off", "0"): + return False + raise ValueError(f"invalid value to cast to bool: {val!r}") + + +class FileWriterToolInput(BaseModel): + filename: str + directory: str | None = "./" + overwrite: str | bool = False + content: str + + +class FileWriterTool(BaseTool): + name: str = "File Writer Tool" + description: str = "A tool to write content to a specified file. Accepts filename, content, and optionally a directory path and overwrite flag as input." + args_schema: type[BaseModel] = FileWriterToolInput + + def _run(self, **kwargs: Any) -> str: + try: + # Create the directory if it doesn't exist + if kwargs.get("directory") and not os.path.exists(kwargs["directory"]): + os.makedirs(kwargs["directory"]) + + # Construct the full path + filepath = os.path.join(kwargs.get("directory") or "", kwargs["filename"]) + + # Convert overwrite to boolean + kwargs["overwrite"] = strtobool(kwargs["overwrite"]) + + # Check if file exists and overwrite is not allowed + if os.path.exists(filepath) and not kwargs["overwrite"]: + return f"File {filepath} already exists and overwrite option was not passed." + + # Write content to the file + mode = "w" if kwargs["overwrite"] else "x" + with open(filepath, mode) as file: + file.write(kwargs["content"]) + return f"Content successfully written to {filepath}" + except FileExistsError: + return ( + f"File {filepath} already exists and overwrite option was not passed." + ) + except KeyError as e: + return f"An error occurred while accessing key: {e!s}" + except Exception as e: + return f"An error occurred while writing to the file: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/files_compressor_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/files_compressor_tool/README.md new file mode 100644 index 000000000..01fdeee7d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/files_compressor_tool/README.md @@ -0,0 +1,119 @@ +# 📦 FileCompressorTool + +The **FileCompressorTool** is a utility for compressing individual files or entire directories (including nested subdirectories) into different archive formats, such as `.zip` or `.tar` (including `.tar.gz`, `.tar.bz2`, and `.tar.xz`). This tool is useful for archiving logs, documents, datasets, or backups in a compact format, and ensures flexibility in how the archives are created. + +--- + +## Description + +This tool: +- Accepts a **file or directory** as input. +- Supports **recursive compression** of subdirectories. +- Lets you define a **custom output archive path** or defaults to the current directory. +- Handles **overwrite protection** to avoid unintentional data loss. +- Supports multiple compression formats: `.zip`, `.tar`, `.tar.gz`, `.tar.bz2`, and `.tar.xz`. + +--- + +## Arguments + +| Argument | Type | Required | Description | +|---------------|-----------|----------|-----------------------------------------------------------------------------| +| `input_path` | `str` | ✅ | Path to the file or directory you want to compress. | +| `output_path` | `str` | ❌ | Optional path for the resulting archive file. Defaults to `./.`. | +| `overwrite` | `bool` | ❌ | Whether to overwrite an existing archive file. Defaults to `False`. | +| `format` | `str` | ❌ | Compression format to use. Can be one of `zip`, `tar`, `tar.gz`, `tar.bz2`, `tar.xz`. Defaults to `zip`. | + +--- + + +## Usage Example + +```python +from crewai_tools import FileCompressorTool + +# Initialize the tool +tool = FileCompressorTool() + +# Compress a directory with subdirectories and files into a zip archive +result = tool._run( + input_path="./data/project_docs", # Folder containing subfolders & files + output_path="./output/project_docs.zip", # Optional output path (defaults to zip format) + overwrite=True # Allow overwriting if file exists +) +print(result) +# Example output: Successfully compressed './data/project_docs' into './output/project_docs.zip' + +``` + +--- + +## Example Scenarios + +### Compress a single file into a zip archive: +```python +# Compress a single file into a zip archive +result = tool._run(input_path="report.pdf") +# Example output: Successfully compressed 'report.pdf' into './report.zip' +``` + +### Compress a directory with nested folders into a zip archive: +```python +# Compress a directory containing nested subdirectories and files +result = tool._run(input_path="./my_data", overwrite=True) +# Example output: Successfully compressed 'my_data' into './my_data.zip' +``` + +### Use a custom output path with a zip archive: +```python +# Compress a directory and specify a custom zip output location +result = tool._run(input_path="./my_data", output_path="./backups/my_data_backup.zip", overwrite=True) +# Example output: Successfully compressed 'my_data' into './backups/my_data_backup.zip' +``` + +### Prevent overwriting an existing zip file: +```python +# Try to compress a directory without overwriting an existing zip file +result = tool._run(input_path="./my_data", output_path="./backups/my_data_backup.zip", overwrite=False) +# Example output: Output zip './backups/my_data_backup.zip' already exists and overwrite is set to False. +``` + +### Compress into a tar archive: +```python +# Compress a directory into a tar archive +result = tool._run(input_path="./my_data", format="tar", overwrite=True) +# Example output: Successfully compressed 'my_data' into './my_data.tar' +``` + +### Compress into a tar.gz archive: +```python +# Compress a directory into a tar.gz archive +result = tool._run(input_path="./my_data", format="tar.gz", overwrite=True) +# Example output: Successfully compressed 'my_data' into './my_data.tar.gz' +``` + +### Compress into a tar.bz2 archive: +```python +# Compress a directory into a tar.bz2 archive +result = tool._run(input_path="./my_data", format="tar.bz2", overwrite=True) +# Example output: Successfully compressed 'my_data' into './my_data.tar.bz2' +``` + +### Compress into a tar.xz archive: +```python +# Compress a directory into a tar.xz archive +result = tool._run(input_path="./my_data", format="tar.xz", overwrite=True) +# Example output: Successfully compressed 'my_data' into './my_data.tar.xz' +``` + +--- + +## Error Handling and Validations + +- **File Extension Validation**: The tool ensures that the output file extension matches the selected format (e.g., `.zip` for `zip` format, `.tar` for `tar` format, etc.). +- **File/Directory Existence**: If the input path does not exist, an error message will be returned. +- **Overwrite Protection**: If a file already exists at the output path, the tool checks the `overwrite` flag before proceeding. If `overwrite=False`, it prevents overwriting the existing file. + +--- + +This tool provides a flexible and robust way to handle file and directory compression across multiple formats for efficient storage and backups. diff --git a/src/crewai/rag/chromadb/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/files_compressor_tool/__init__.py similarity index 100% rename from src/crewai/rag/chromadb/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/files_compressor_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/files_compressor_tool/files_compressor_tool.py b/lib/crewai-tools/src/crewai_tools/tools/files_compressor_tool/files_compressor_tool.py new file mode 100644 index 000000000..cdea23b2f --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/files_compressor_tool/files_compressor_tool.py @@ -0,0 +1,138 @@ +import os +import tarfile +import zipfile + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class FileCompressorToolInput(BaseModel): + """Input schema for FileCompressorTool.""" + + input_path: str = Field( + ..., description="Path to the file or directory to compress." + ) + output_path: str | None = Field( + default=None, description="Optional output archive filename." + ) + overwrite: bool = Field( + default=False, + description="Whether to overwrite the archive if it already exists.", + ) + format: str = Field( + default="zip", + description="Compression format ('zip', 'tar', 'tar.gz', 'tar.bz2', 'tar.xz').", + ) + + +class FileCompressorTool(BaseTool): + name: str = "File Compressor Tool" + description: str = ( + "Compresses a file or directory into an archive (.zip currently supported). " + "Useful for archiving logs, documents, or backups." + ) + args_schema: type[BaseModel] = FileCompressorToolInput + + def _run( + self, + input_path: str, + output_path: str | None = None, + overwrite: bool = False, + format: str = "zip", + ) -> str: + if not os.path.exists(input_path): + return f"Input path '{input_path}' does not exist." + + if not output_path: + output_path = self._generate_output_path(input_path, format) + + format_extension = { + "zip": ".zip", + "tar": ".tar", + "tar.gz": ".tar.gz", + "tar.bz2": ".tar.bz2", + "tar.xz": ".tar.xz", + } + + if format not in format_extension: + return f"Compression format '{format}' is not supported. Allowed formats: {', '.join(format_extension.keys())}" + if not output_path.endswith(format_extension[format]): + return f"Error: If '{format}' format is chosen, output file must have a '{format_extension[format]}' extension." + if not self._prepare_output(output_path, overwrite): + return ( + f"Output '{output_path}' already exists and overwrite is set to False." + ) + + try: + format_compression = { + "zip": self._compress_zip, + "tar": self._compress_tar, + "tar.gz": self._compress_tar, + "tar.bz2": self._compress_tar, + "tar.xz": self._compress_tar, + } + if format == "zip": + format_compression[format](input_path, output_path) # type: ignore[operator] + else: + format_compression[format](input_path, output_path, format) # type: ignore[operator] + + return f"Successfully compressed '{input_path}' into '{output_path}'" + except FileNotFoundError: + return f"Error: File not found at path: {input_path}" + except PermissionError: + return f"Error: Permission denied when accessing '{input_path}' or writing '{output_path}'" + except Exception as e: + return f"An unexpected error occurred during compression: {e!s}" + + @staticmethod + def _generate_output_path(input_path: str, format: str) -> str: + """Generates output path based on input path and format.""" + if os.path.isfile(input_path): + base_name = os.path.splitext(os.path.basename(input_path))[ + 0 + ] # Remove extension + else: + base_name = os.path.basename(os.path.normpath(input_path)) # Directory name + return os.path.join(os.getcwd(), f"{base_name}.{format}") + + @staticmethod + def _prepare_output(output_path: str, overwrite: bool) -> bool: + """Ensures output path is ready for writing.""" + output_dir = os.path.dirname(output_path) + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir) + if os.path.exists(output_path) and not overwrite: + return False + return True + + @staticmethod + def _compress_zip(input_path: str, output_path: str): + """Compresses input into a zip archive.""" + with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zipf: + if os.path.isfile(input_path): + zipf.write(input_path, os.path.basename(input_path)) + else: + for root, _, files in os.walk(input_path): + for file in files: + full_path = os.path.join(root, file) + arcname = os.path.relpath(full_path, start=input_path) + zipf.write(full_path, arcname) + + @staticmethod + def _compress_tar(input_path: str, output_path: str, format: str): + """Compresses input into a tar archive with the given format.""" + format_mode = { + "tar": "w", + "tar.gz": "w:gz", + "tar.bz2": "w:bz2", + "tar.xz": "w:xz", + } + + if format not in format_mode: + raise ValueError(f"Unsupported tar format: {format}") + + mode = format_mode[format] + + with tarfile.open(output_path, mode) as tarf: # type: ignore[call-overload] + arcname = os.path.basename(input_path) + tarf.add(input_path, arcname=arcname) diff --git a/lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/README.md new file mode 100644 index 000000000..3edb73f02 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/README.md @@ -0,0 +1,60 @@ +# FirecrawlCrawlWebsiteTool + +## Description + +[Firecrawl](https://firecrawl.dev) is a platform for crawling and convert any website into clean markdown or structured data. + +## Version Compatibility + +This implementation is compatible with FireCrawl API v1 + +## Installation + +- Get an API key from [firecrawl.dev](https://firecrawl.dev) and set it in environment variables (`FIRECRAWL_API_KEY`). +- Install the [Firecrawl SDK](https://github.com/mendableai/firecrawl) along with `crewai[tools]` package: + +``` +pip install firecrawl-py 'crewai[tools]' +``` + +## Example + +Utilize the FirecrawlScrapeFromWebsiteTool as follows to allow your agent to load websites: + +```python +from crewai_tools import FirecrawlCrawlWebsiteTool +from firecrawl import ScrapeOptions + +tool = FirecrawlCrawlWebsiteTool( + config={ + "limit": 100, + "scrape_options": ScrapeOptions(formats=["markdown", "html"]), + "poll_interval": 30, + } +) +tool.run(url="firecrawl.dev") +``` + +## Arguments + +- `api_key`: Optional. Specifies Firecrawl API key. Defaults is the `FIRECRAWL_API_KEY` environment variable. +- `config`: Optional. It contains Firecrawl API parameters. + +This is the default configuration + +```python +from firecrawl import ScrapeOptions + +{ + "max_depth": 2, + "ignore_sitemap": True, + "limit": 100, + "allow_backward_links": False, + "allow_external_links": False, + "scrape_options": ScrapeOptions( + formats=["markdown", "screenshot", "links"], + only_main_content=True, + timeout=30000, + ), +} +``` diff --git a/src/crewai/tools/cache_tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/__init__.py similarity index 100% rename from src/crewai/tools/cache_tools/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/firecrawl_crawl_website_tool.py b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/firecrawl_crawl_website_tool.py new file mode 100644 index 000000000..9745616d9 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_crawl_website_tool/firecrawl_crawl_website_tool.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field, PrivateAttr + + +if TYPE_CHECKING: + from firecrawl import FirecrawlApp # type: ignore[import-untyped] + +try: + from firecrawl import FirecrawlApp # type: ignore[import-untyped] + + FIRECRAWL_AVAILABLE = True +except ImportError: + FIRECRAWL_AVAILABLE = False + + +class FirecrawlCrawlWebsiteToolSchema(BaseModel): + url: str = Field(description="Website URL") + + +class FirecrawlCrawlWebsiteTool(BaseTool): + """Tool for crawling websites using Firecrawl. To run this tool, you need to have a Firecrawl API key. + + Args: + api_key (str): Your Firecrawl API key. + config (dict): Optional. It contains Firecrawl API parameters. + + Default configuration options: + max_depth (int): Maximum depth to crawl. Default: 2 + ignore_sitemap (bool): Whether to ignore sitemap. Default: True + limit (int): Maximum number of pages to crawl. Default: 100 + allow_backward_links (bool): Allow crawling backward links. Default: False + allow_external_links (bool): Allow crawling external links. Default: False + scrape_options (ScrapeOptions): Options for scraping content + - formats (list[str]): Content formats to return. Default: ["markdown", "screenshot", "links"] + - only_main_content (bool): Only return main content. Default: True + - timeout (int): Timeout in milliseconds. Default: 30000 + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, validate_assignment=True, frozen=False + ) + name: str = "Firecrawl web crawl tool" + description: str = "Crawl webpages using Firecrawl and return the contents" + args_schema: type[BaseModel] = FirecrawlCrawlWebsiteToolSchema + api_key: str | None = None + config: dict[str, Any] | None = Field( + default_factory=lambda: { + "maxDepth": 2, + "ignoreSitemap": True, + "limit": 10, + "allowBackwardLinks": False, + "allowExternalLinks": False, + "scrapeOptions": { + "formats": ["markdown", "screenshot", "links"], + "onlyMainContent": True, + "timeout": 10000, + }, + } + ) + _firecrawl: FirecrawlApp | None = PrivateAttr(None) + package_dependencies: list[str] = Field(default_factory=lambda: ["firecrawl-py"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="FIRECRAWL_API_KEY", + description="API key for Firecrawl services", + required=True, + ), + ] + ) + + def __init__(self, api_key: str | None = None, **kwargs): + super().__init__(**kwargs) + self.api_key = api_key + self._initialize_firecrawl() + + def _initialize_firecrawl(self) -> None: + try: + from firecrawl import FirecrawlApp # type: ignore + + self._firecrawl = FirecrawlApp(api_key=self.api_key) + except ImportError: + import click + + if click.confirm( + "You are missing the 'firecrawl-py' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run(["uv", "add", "firecrawl-py"], check=True) # noqa: S607 + from firecrawl import FirecrawlApp + + self._firecrawl = FirecrawlApp(api_key=self.api_key) + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install firecrawl-py package") from e + else: + raise ImportError( + "`firecrawl-py` package not found, please run `uv add firecrawl-py`" + ) from None + + def _run(self, url: str): + if not self._firecrawl: + raise RuntimeError("FirecrawlApp not properly initialized") + + return self._firecrawl.crawl_url(url, poll_interval=2, params=self.config) + + +try: + from firecrawl import FirecrawlApp + + # Only rebuild if the class hasn't been initialized yet + if not hasattr(FirecrawlCrawlWebsiteTool, "_model_rebuilt"): + FirecrawlCrawlWebsiteTool.model_rebuild() + FirecrawlCrawlWebsiteTool._model_rebuilt = True # type: ignore[attr-defined] +except ImportError: + """ + When this tool is not used, then exception can be ignored. + """ diff --git a/lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/README.md new file mode 100644 index 000000000..ebcea2f53 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/README.md @@ -0,0 +1,46 @@ +# FirecrawlScrapeWebsiteTool + +## Description + +[Firecrawl](https://firecrawl.dev) is a platform for crawling and convert any website into clean markdown or structured data. + +## Installation + +- Get an API key from [firecrawl.dev](https://firecrawl.dev) and set it in environment variables (`FIRECRAWL_API_KEY`). +- Install the [Firecrawl SDK](https://github.com/mendableai/firecrawl) along with `crewai[tools]` package: + +``` +pip install firecrawl-py 'crewai[tools]' +``` + +## Example + +Utilize the FirecrawlScrapeWebsiteTool as follows to allow your agent to load websites: + +```python +from crewai_tools import FirecrawlScrapeWebsiteTool + +tool = FirecrawlScrapeWebsiteTool(config={"formats": ['html']}) +tool.run(url="firecrawl.dev") +``` + +## Arguments + +- `api_key`: Optional. Specifies Firecrawl API key. Defaults is the `FIRECRAWL_API_KEY` environment variable. +- `config`: Optional. It contains Firecrawl API parameters. + + +This is the default configuration + +```python +{ + "formats": ["markdown"], + "only_main_content": True, + "include_tags": [], + "exclude_tags": [], + "headers": {}, + "wait_for": 0, +} +``` + + diff --git a/src/crewai/types/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/__init__.py similarity index 100% rename from src/crewai/types/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/firecrawl_scrape_website_tool.py b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/firecrawl_scrape_website_tool.py new file mode 100644 index 000000000..81be0c7d1 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/firecrawl_scrape_website_tool.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field, PrivateAttr + + +if TYPE_CHECKING: + from firecrawl import FirecrawlApp # type: ignore[import-untyped] + +try: + from firecrawl import FirecrawlApp # type: ignore[import-untyped] + + FIRECRAWL_AVAILABLE = True +except ImportError: + FIRECRAWL_AVAILABLE = False + + +class FirecrawlScrapeWebsiteToolSchema(BaseModel): + url: str = Field(description="Website URL") + + +class FirecrawlScrapeWebsiteTool(BaseTool): + """Tool for scraping webpages using Firecrawl. To run this tool, you need to have a Firecrawl API key. + + Args: + api_key (str): Your Firecrawl API key. + config (dict): Optional. It contains Firecrawl API parameters. + + Default configuration options: + formats (list[str]): Content formats to return. Default: ["markdown"] + onlyMainContent (bool): Only return main content. Default: True + includeTags (list[str]): Tags to include. Default: [] + excludeTags (list[str]): Tags to exclude. Default: [] + headers (dict): Headers to include. Default: {} + waitFor (int): Time to wait for page to load in ms. Default: 0 + json_options (dict): Options for JSON extraction. Default: None + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, validate_assignment=True, frozen=False + ) + name: str = "Firecrawl web scrape tool" + description: str = "Scrape webpages using Firecrawl and return the contents" + args_schema: type[BaseModel] = FirecrawlScrapeWebsiteToolSchema + api_key: str | None = None + config: dict[str, Any] = Field( + default_factory=lambda: { + "formats": ["markdown"], + "onlyMainContent": True, + "includeTags": [], + "excludeTags": [], + "headers": {}, + "waitFor": 0, + } + ) + + _firecrawl: FirecrawlApp | None = PrivateAttr(None) + package_dependencies: list[str] = Field(default_factory=lambda: ["firecrawl-py"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="FIRECRAWL_API_KEY", + description="API key for Firecrawl services", + required=True, + ), + ] + ) + + def __init__(self, api_key: str | None = None, **kwargs): + super().__init__(**kwargs) + try: + from firecrawl import FirecrawlApp # type: ignore + except ImportError: + import click + + if click.confirm( + "You are missing the 'firecrawl-py' package. Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "firecrawl-py"], check=True) # noqa: S607 + from firecrawl import ( + FirecrawlApp, + ) + else: + raise ImportError( + "`firecrawl-py` package not found, please run `uv add firecrawl-py`" + ) from None + + self._firecrawl = FirecrawlApp(api_key=api_key) + + def _run(self, url: str): + if not self._firecrawl: + raise RuntimeError("FirecrawlApp not properly initialized") + + return self._firecrawl.scrape_url(url, params=self.config) + + +try: + from firecrawl import FirecrawlApp + + # Must rebuild model after class is defined + if not hasattr(FirecrawlScrapeWebsiteTool, "_model_rebuilt"): + FirecrawlScrapeWebsiteTool.model_rebuild() + FirecrawlScrapeWebsiteTool._model_rebuilt = True # type: ignore[attr-defined] +except ImportError: + """ + When this tool is not used, then exception can be ignored. + """ diff --git a/lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/README.md new file mode 100644 index 000000000..a2037e951 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/README.md @@ -0,0 +1,44 @@ +# FirecrawlSearchTool + +## Description + +[Firecrawl](https://firecrawl.dev) is a platform for crawling and convert any website into clean markdown or structured data. + +## Installation + +- Get an API key from [firecrawl.dev](https://firecrawl.dev) and set it in environment variables (`FIRECRAWL_API_KEY`). +- Install the [Firecrawl SDK](https://github.com/mendableai/firecrawl) along with `crewai[tools]` package: + +``` +pip install firecrawl-py 'crewai[tools]' +``` + +## Example + +Utilize the FirecrawlSearchTool as follows to allow your agent to load websites: + +```python +from crewai_tools import FirecrawlSearchTool + +tool = FirecrawlSearchTool(config={"limit": 5}) +tool.run(query="firecrawl web scraping") +``` + +## Arguments + +- `api_key`: Optional. Specifies Firecrawl API key. Defaults is the `FIRECRAWL_API_KEY` environment variable. +- `config`: Optional. It contains Firecrawl API parameters. + + +This is the default configuration + +```python +{ + "limit": 5, + "tbs": None, + "lang": "en", + "country": "us", + "location": None, + "timeout": 60000, +} +``` diff --git a/tests/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/__init__.py similarity index 100% rename from tests/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/firecrawl_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/firecrawl_search_tool.py new file mode 100644 index 000000000..19ee5cef0 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/firecrawl_search_tool/firecrawl_search_tool.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field, PrivateAttr + + +if TYPE_CHECKING: + from firecrawl import FirecrawlApp # type: ignore[import-untyped] + + +try: + from firecrawl import FirecrawlApp # type: ignore[import-untyped] + + FIRECRAWL_AVAILABLE = True +except ImportError: + FIRECRAWL_AVAILABLE = False + + +class FirecrawlSearchToolSchema(BaseModel): + query: str = Field(description="Search query") + + +class FirecrawlSearchTool(BaseTool): + """Tool for searching webpages using Firecrawl. To run this tool, you need to have a Firecrawl API key. + + Args: + api_key (str): Your Firecrawl API key. + config (dict): Optional. It contains Firecrawl API parameters. + + Default configuration options: + limit (int): Maximum number of pages to crawl. Default: 5 + tbs (str): Time before search. Default: None + lang (str): Language. Default: "en" + country (str): Country. Default: "us" + location (str): Location. Default: None + timeout (int): Timeout in milliseconds. Default: 60000 + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, validate_assignment=True, frozen=False + ) + name: str = "Firecrawl web search tool" + description: str = "Search webpages using Firecrawl and return the results" + args_schema: type[BaseModel] = FirecrawlSearchToolSchema + api_key: str | None = None + config: dict[str, Any] | None = Field( + default_factory=lambda: { + "limit": 5, + "tbs": None, + "lang": "en", + "country": "us", + "location": None, + "timeout": 60000, + } + ) + _firecrawl: FirecrawlApp | None = PrivateAttr(None) + package_dependencies: list[str] = Field(default_factory=lambda: ["firecrawl-py"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="FIRECRAWL_API_KEY", + description="API key for Firecrawl services", + required=True, + ), + ] + ) + + def __init__(self, api_key: str | None = None, **kwargs): + super().__init__(**kwargs) + self.api_key = api_key + self._initialize_firecrawl() + + def _initialize_firecrawl(self) -> None: + try: + from firecrawl import FirecrawlApp # type: ignore + + self._firecrawl = FirecrawlApp(api_key=self.api_key) + except ImportError: + import click + + if click.confirm( + "You are missing the 'firecrawl-py' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run(["uv", "add", "firecrawl-py"], check=True) # noqa: S607 + from firecrawl import FirecrawlApp + + self._firecrawl = FirecrawlApp(api_key=self.api_key) + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install firecrawl-py package") from e + else: + raise ImportError( + "`firecrawl-py` package not found, please run `uv add firecrawl-py`" + ) from None + + def _run( + self, + query: str, + ) -> Any: + if not self._firecrawl: + raise RuntimeError("FirecrawlApp not properly initialized") + + return self._firecrawl.search( + query=query, + params=self.config, + ) + + +try: + from firecrawl import FirecrawlApp # type: ignore + + # Only rebuild if the class hasn't been initialized yet + if not hasattr(FirecrawlSearchTool, "_model_rebuilt"): + FirecrawlSearchTool.model_rebuild() + FirecrawlSearchTool._model_rebuilt = True # type: ignore[attr-defined] +except ImportError: + """ + When this tool is not used, then exception can be ignored. + """ diff --git a/lib/crewai-tools/src/crewai_tools/tools/generate_crewai_automation_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/generate_crewai_automation_tool/README.md new file mode 100644 index 000000000..4e5e8a580 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/generate_crewai_automation_tool/README.md @@ -0,0 +1,50 @@ +# GenerateCrewaiAutomationTool + +## Description + +The GenerateCrewaiAutomationTool integrates with CrewAI Studio API to generate complete CrewAI automations from natural language descriptions. It translates high-level requirements into functional CrewAI implementations and returns direct links to Studio projects. + +## Environment Variables + +Set your CrewAI Personal Access Token (CrewAI AMP > Settings > Account > Personal Access Token): + +```bash +export CREWAI_PERSONAL_ACCESS_TOKEN="your_personal_access_token_here" +export CREWAI_PLUS_URL="https://app.crewai.com" # optional +``` + +## Example + +```python +from crewai_tools import GenerateCrewaiAutomationTool +from crewai import Agent, Task, Crew + +# Initialize tool +tool = GenerateCrewaiAutomationTool() + +# Generate automation +result = tool.run( + prompt="Generate a CrewAI automation that scrapes websites and stores data in a database", + organization_id="org_123" # optional but recommended +) + +print(result) +# Output: Generated CrewAI Studio project URL: https://studio.crewai.com/project/abc123 + +# Use with agent +agent = Agent( + role="Automation Architect", + goal="Generate CrewAI automations", + backstory="Expert at creating automated workflows", + tools=[tool] +) + +task = Task( + description="Create a lead qualification automation", + agent=agent, + expected_output="Studio project URL" +) + +crew = Crew(agents=[agent], tasks=[task]) +result = crew.kickoff() +``` \ No newline at end of file diff --git a/tests/agents/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/generate_crewai_automation_tool/__init__.py similarity index 100% rename from tests/agents/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/generate_crewai_automation_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/generate_crewai_automation_tool/generate_crewai_automation_tool.py b/lib/crewai-tools/src/crewai_tools/tools/generate_crewai_automation_tool/generate_crewai_automation_tool.py new file mode 100644 index 000000000..4fd13b978 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/generate_crewai_automation_tool/generate_crewai_automation_tool.py @@ -0,0 +1,71 @@ +import os + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +class GenerateCrewaiAutomationToolSchema(BaseModel): + prompt: str = Field( + description="The prompt to generate the CrewAI automation, e.g. 'Generate a CrewAI automation that will scrape the website and store the data in a database.'" + ) + organization_id: str | None = Field( + default=None, + description="The identifier for the CrewAI AMP organization. If not specified, a default organization will be used.", + ) + + +class GenerateCrewaiAutomationTool(BaseTool): + name: str = "Generate CrewAI Automation" + description: str = ( + "A tool that leverages CrewAI Studio's capabilities to automatically generate complete CrewAI " + "automations based on natural language descriptions. It translates high-level requirements into " + "functional CrewAI implementations." + ) + args_schema: type[BaseModel] = GenerateCrewaiAutomationToolSchema + crewai_enterprise_url: str = Field( + default_factory=lambda: os.getenv("CREWAI_PLUS_URL", "https://app.crewai.com"), + description="The base URL of CrewAI AMP. If not provided, it will be loaded from the environment variable CREWAI_PLUS_URL with default https://app.crewai.com.", + ) + personal_access_token: str | None = Field( + default_factory=lambda: os.getenv("CREWAI_PERSONAL_ACCESS_TOKEN"), + description="The user's Personal Access Token to access CrewAI AMP API. If not provided, it will be loaded from the environment variable CREWAI_PERSONAL_ACCESS_TOKEN.", + ) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="CREWAI_PERSONAL_ACCESS_TOKEN", + description="Personal Access Token for CrewAI Enterprise API", + required=True, + ), + EnvVar( + name="CREWAI_PLUS_URL", + description="Base URL for CrewAI Enterprise API", + required=False, + ), + ] + ) + + def _run(self, **kwargs) -> str: + input_data = GenerateCrewaiAutomationToolSchema(**kwargs) + response = requests.post( # noqa: S113 + f"{self.crewai_enterprise_url}/crewai_plus/api/v1/studio", + headers=self._get_headers(input_data.organization_id), + json={"prompt": input_data.prompt}, + ) + + response.raise_for_status() + studio_project_url = response.json().get("url") + return f"Generated CrewAI Studio project URL: {studio_project_url}" + + def _get_headers(self, organization_id: str | None = None) -> dict: + headers = { + "Authorization": f"Bearer {self.personal_access_token}", + "Content-Type": "application/json", + "Accept": "application/json", + } + + if organization_id: + headers["X-Crewai-Organization-Id"] = organization_id + + return headers diff --git a/lib/crewai-tools/src/crewai_tools/tools/github_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/github_search_tool/README.md new file mode 100644 index 000000000..c77e494c8 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/github_search_tool/README.md @@ -0,0 +1,67 @@ +# GithubSearchTool + +## Description +The GithubSearchTool is a Retrieval Augmented Generation (RAG) tool specifically designed for conducting semantic searches within GitHub repositories. Utilizing advanced semantic search capabilities, it sifts through code, pull requests, issues, and repositories, making it an essential tool for developers, researchers, or anyone in need of precise information from GitHub. + +## Installation +To use the GithubSearchTool, first ensure the crewai_tools package is installed in your Python environment: + +```shell +pip install 'crewai[tools]' +``` + +This command installs the necessary package to run the GithubSearchTool along with any other tools included in the crewai_tools package. + +## Example +Here’s how you can use the GithubSearchTool to perform semantic searches within a GitHub repository: +```python +from crewai_tools import GithubSearchTool + +# Initialize the tool for semantic searches within a specific GitHub repository +tool = GithubSearchTool( + gh_token='...', + github_repo='https://github.com/example/repo', + content_types=['code', 'issue'] # Options: code, repo, pr, issue +) + +# OR + +# Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution +tool = GithubSearchTool( + gh_token='...', + content_types=['code', 'issue'] # Options: code, repo, pr, issue +) +``` + +## Arguments +- `gh_token` : The GitHub token used to authenticate the search. This is a mandatory field and allows the tool to access the GitHub API for conducting searches. +- `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search. +- `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code, `repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues. This field is mandatory and allows tailoring the search to specific content types within the GitHub repository. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = GithubSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/tests/cli/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/github_search_tool/__init__.py similarity index 100% rename from tests/cli/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/github_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/github_search_tool/github_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/github_search_tool/github_search_tool.py new file mode 100644 index 000000000..4edbebc7e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/github_search_tool/github_search_tool.py @@ -0,0 +1,78 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedGithubSearchToolSchema(BaseModel): + """Input for GithubSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the github repo's content", + ) + + +class GithubSearchToolSchema(FixedGithubSearchToolSchema): + """Input for GithubSearchTool.""" + + github_repo: str = Field(..., description="Mandatory github you want to search") + content_types: list[str] = Field( + ..., + description="Mandatory content types you want to be included search, options: [code, repo, pr, issue]", + ) + + +class GithubSearchTool(RagTool): + name: str = "Search a github repo's content" + description: str = "A tool that can be used to semantic search a query from a github repo's content. This is not the GitHub API, but instead a tool that can provide semantic search capabilities." + summarize: bool = False + gh_token: str + args_schema: type[BaseModel] = GithubSearchToolSchema + content_types: list[str] = Field( + default_factory=lambda: ["code", "repo", "pr", "issue"], + description="Content types you want to be included search, options: [code, repo, pr, issue]", + ) + + def __init__( + self, + github_repo: str | None = None, + content_types: list[str] | None = None, + **kwargs, + ): + super().__init__(**kwargs) + + if github_repo and content_types: + self.add(repo=github_repo, content_types=content_types) + self.description = f"A tool that can be used to semantic search a query the {github_repo} github repo's content. This is not the GitHub API, but instead a tool that can provide semantic search capabilities." + self.args_schema = FixedGithubSearchToolSchema + self._generate_description() + + def add( + self, + repo: str, + content_types: list[str] | None = None, + ) -> None: + content_types = content_types or self.content_types + super().add( + f"https://github.com/{repo}", + data_type=DataType.GITHUB, + metadata={"content_types": content_types, "gh_token": self.gh_token}, + ) + + def _run( # type: ignore[override] + self, + search_query: str, + github_repo: str | None = None, + content_types: list[str] | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if github_repo: + self.add( + repo=github_repo, + content_types=content_types, + ) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/hyperbrowser_load_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/hyperbrowser_load_tool/README.md new file mode 100644 index 000000000..e95864f5a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/hyperbrowser_load_tool/README.md @@ -0,0 +1,42 @@ +# HyperbrowserLoadTool + +## Description + +[Hyperbrowser](https://hyperbrowser.ai) is a platform for running and scaling headless browsers. It lets you launch and manage browser sessions at scale and provides easy to use solutions for any webscraping needs, such as scraping a single page or crawling an entire site. + +Key Features: +- Instant Scalability - Spin up hundreds of browser sessions in seconds without infrastructure headaches +- Simple Integration - Works seamlessly with popular tools like Puppeteer and Playwright +- Powerful APIs - Easy to use APIs for scraping/crawling any site, and much more +- Bypass Anti-Bot Measures - Built-in stealth mode, ad blocking, automatic CAPTCHA solving, and rotating proxies + +For more information about Hyperbrowser, please visit the [Hyperbrowser website](https://hyperbrowser.ai) or if you want to check out the docs, you can visit the [Hyperbrowser docs](https://docs.hyperbrowser.ai). + +## Installation + +- Head to [Hyperbrowser](https://app.hyperbrowser.ai/) to sign up and generate an API key. Once you've done this set the `HYPERBROWSER_API_KEY` environment variable or you can pass it to the `HyperbrowserLoadTool` constructor. +- Install the [Hyperbrowser SDK](https://github.com/hyperbrowserai/python-sdk): + +``` +pip install hyperbrowser 'crewai[tools]' +``` + +## Example + +Utilize the HyperbrowserLoadTool as follows to allow your agent to load websites: + +```python +from crewai_tools import HyperbrowserLoadTool + +tool = HyperbrowserLoadTool() +``` + +## Arguments + +`__init__` arguments: +- `api_key`: Optional. Specifies Hyperbrowser API key. Defaults to the `HYPERBROWSER_API_KEY` environment variable. + +`run` arguments: +- `url`: The base URL to start scraping or crawling from. +- `operation`: Optional. Specifies the operation to perform on the website. Either 'scrape' or 'crawl'. Defaults is 'scrape'. +- `params`: Optional. Specifies the params for the operation. For more information on the supported params, visit https://docs.hyperbrowser.ai/reference/sdks/python/scrape#start-scrape-job-and-wait or https://docs.hyperbrowser.ai/reference/sdks/python/crawl#start-crawl-job-and-wait. diff --git a/tests/cli/authentication/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/hyperbrowser_load_tool/__init__.py similarity index 100% rename from tests/cli/authentication/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/hyperbrowser_load_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/hyperbrowser_load_tool/hyperbrowser_load_tool.py b/lib/crewai-tools/src/crewai_tools/tools/hyperbrowser_load_tool/hyperbrowser_load_tool.py new file mode 100644 index 000000000..6dd2eca28 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/hyperbrowser_load_tool/hyperbrowser_load_tool.py @@ -0,0 +1,137 @@ +import os +from typing import Any, Literal + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field + + +class HyperbrowserLoadToolSchema(BaseModel): + url: str = Field(description="Website URL") + operation: Literal["scrape", "crawl"] = Field( + description="Operation to perform on the website. Either 'scrape' or 'crawl'" + ) + params: dict | None = Field( + description="Optional params for scrape or crawl. For more information on the supported params, visit https://docs.hyperbrowser.ai/reference/sdks/python/scrape#start-scrape-job-and-wait or https://docs.hyperbrowser.ai/reference/sdks/python/crawl#start-crawl-job-and-wait" + ) + + +class HyperbrowserLoadTool(BaseTool): + """HyperbrowserLoadTool. + + Scrape or crawl web pages and load the contents with optional parameters for configuring content extraction. + Requires the `hyperbrowser` package. + Get your API Key from https://app.hyperbrowser.ai/ + + Args: + api_key: The Hyperbrowser API key, can be set as an environment variable `HYPERBROWSER_API_KEY` or passed directly + """ + + name: str = "Hyperbrowser web load tool" + description: str = "Scrape or crawl a website using Hyperbrowser and return the contents in properly formatted markdown or html" + args_schema: type[BaseModel] = HyperbrowserLoadToolSchema + api_key: str | None = None + hyperbrowser: Any | None = None + package_dependencies: list[str] = Field(default_factory=lambda: ["hyperbrowser"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="HYPERBROWSER_API_KEY", + description="API key for Hyperbrowser services", + required=False, + ), + ] + ) + + def __init__(self, api_key: str | None = None, **kwargs): + super().__init__(**kwargs) + self.api_key = api_key or os.getenv("HYPERBROWSER_API_KEY") + if not api_key: + raise ValueError( + "`api_key` is required, please set the `HYPERBROWSER_API_KEY` environment variable or pass it directly" + ) + + try: + from hyperbrowser import Hyperbrowser # type: ignore[import-untyped] + except ImportError as e: + raise ImportError( + "`hyperbrowser` package not found, please run `pip install hyperbrowser`" + ) from e + + if not self.api_key: + raise ValueError( + "HYPERBROWSER_API_KEY is not set. Please provide it either via the constructor with the `api_key` argument or by setting the HYPERBROWSER_API_KEY environment variable." + ) + + self.hyperbrowser = Hyperbrowser(api_key=self.api_key) + + @staticmethod + def _prepare_params(params: dict) -> dict: + """Prepare session and scrape options parameters.""" + try: + from hyperbrowser.models.scrape import ( # type: ignore[import-untyped] + ScrapeOptions, + ) + from hyperbrowser.models.session import ( # type: ignore[import-untyped] + CreateSessionParams, + ) + except ImportError as e: + raise ImportError( + "`hyperbrowser` package not found, please run `pip install hyperbrowser`" + ) from e + + if "scrape_options" in params: + if "formats" in params["scrape_options"]: + formats = params["scrape_options"]["formats"] + if not all(fmt in ["markdown", "html"] for fmt in formats): + raise ValueError("formats can only contain 'markdown' or 'html'") + + if "session_options" in params: + params["session_options"] = CreateSessionParams(**params["session_options"]) + if "scrape_options" in params: + params["scrape_options"] = ScrapeOptions(**params["scrape_options"]) + return params + + def _extract_content(self, data: Any | None): + """Extract content from response data.""" + content = "" + if data: + content = data.markdown or data.html or "" + return content + + def _run( + self, + url: str, + operation: Literal["scrape", "crawl"] = "scrape", + params: dict | None = None, + ): + if params is None: + params = {} + try: + from hyperbrowser.models.crawl import ( # type: ignore[import-untyped] + StartCrawlJobParams, + ) + from hyperbrowser.models.scrape import ( # type: ignore[import-untyped] + StartScrapeJobParams, + ) + except ImportError as e: + raise ImportError( + "`hyperbrowser` package not found, please run `pip install hyperbrowser`" + ) from e + + params = self._prepare_params(params) + + if operation == "scrape": + scrape_params = StartScrapeJobParams(url=url, **params) + scrape_resp = self.hyperbrowser.scrape.start_and_wait(scrape_params) # type: ignore[union-attr] + return self._extract_content(scrape_resp.data) + crawl_params = StartCrawlJobParams(url=url, **params) + crawl_resp = self.hyperbrowser.crawl.start_and_wait(crawl_params) # type: ignore[union-attr] + content = "" + if crawl_resp.data: + for page in crawl_resp.data: + page_content = self._extract_content(page) + if page_content: + content += ( + f"\n{'-' * 50}\nUrl: {page.url}\nContent:\n{page_content}\n" + ) + return content diff --git a/lib/crewai-tools/src/crewai_tools/tools/invoke_crewai_automation_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/invoke_crewai_automation_tool/README.md new file mode 100644 index 000000000..58ab4bbcc --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/invoke_crewai_automation_tool/README.md @@ -0,0 +1,159 @@ +# InvokeCrewAIAutomationTool + +## Description + +The InvokeCrewAIAutomationTool provides CrewAI Platform API integration with external crew services. This tool allows you to invoke and interact with CrewAI Platform automations from within your CrewAI agents, enabling seamless integration between different crew workflows. + +## Features + +- **Dynamic Input Schema**: Configure custom input parameters for different crew automations +- **Automatic Polling**: Automatically polls for task completion with configurable timeout +- **Bearer Token Authentication**: Secure API authentication using bearer tokens +- **Comprehensive Error Handling**: Robust error handling for API failures and timeouts +- **Flexible Configuration**: Support for both simple and complex crew automation workflows + +## Installation + +Install the required dependencies: + +```shell +pip install 'crewai[tools]' +``` + +## Example + +### Basic Usage + +```python +from crewai_tools import InvokeCrewAIAutomationTool + +# Basic crew automation tool +tool = InvokeCrewAIAutomationTool( + crew_api_url="https://data-analysis-crew-[...].crewai.com", + crew_bearer_token="your_bearer_token_here", + crew_name="Data Analysis Crew", + crew_description="Analyzes data and generates insights" +) + +# Use the tool +result = tool.run() +``` + +### Advanced Usage with Custom Inputs + +```python +from crewai_tools import InvokeCrewAIAutomationTool +from pydantic import Field + +# Define custom input schema +custom_inputs = { + "year": Field(..., description="Year to retrieve the report for (integer)"), + "region": Field(default="global", description="Geographic region for analysis"), + "format": Field(default="summary", description="Report format (summary, detailed, raw)") +} + +# Create tool with custom inputs +tool = InvokeCrewAIAutomationTool( + crew_api_url="https://state-of-ai-report-crew-[...].crewai.com", + crew_bearer_token="your_bearer_token_here", + crew_name="State of AI Report", + crew_description="Retrieves a comprehensive report on state of AI for a given year and region", + crew_inputs=custom_inputs, + max_polling_time=15 * 60 # 15 minutes timeout +) + +# Use with custom parameters +result = tool.run(year=2024, region="north-america", format="detailed") +``` + +### Integration with CrewAI Agents + +```python +from crewai import Agent, Task, Crew +from crewai_tools import InvokeCrewAIAutomationTool + +# Create the automation tool +market_research_tool = InvokeCrewAIAutomationTool( + crew_api_url="https://market-research-automation-crew-[...].crewai.com", + crew_bearer_token="your_bearer_token_here", + crew_name="Market Research Automation", + crew_description="Conducts comprehensive market research analysis", + inputs={ + "year": Field(..., description="Year to use for the market research"), + } +) + +# Create an agent with the tool +research_agent = Agent( + role="Research Coordinator", + goal="Coordinate and execute market research tasks", + backstory="You are an expert at coordinating research tasks and leveraging automation tools.", + tools=[market_research_tool], + verbose=True +) + +# Create and execute a task +research_task = Task( + description="Conduct market research on AI tools market for 2024", + agent=research_agent, + expected_output="Comprehensive market research report" +) + +crew = Crew( + agents=[research_agent], + tasks=[research_task] +) + +result = crew.kickoff() +``` + +## Arguments + +### Required Parameters + +- `crew_api_url` (str): Base URL of the CrewAI Platform automation API +- `crew_bearer_token` (str): Bearer token for API authentication +- `crew_name` (str): Name of the crew automation +- `crew_description` (str): Description of what the crew automation does + +### Optional Parameters + +- `max_polling_time` (int): Maximum time in seconds to wait for task completion (default: 600 seconds = 10 minutes) +- `crew_inputs` (dict): Dictionary defining custom input schema fields using Pydantic Field objects + +## Custom Input Schema + +When defining `crew_inputs`, use Pydantic Field objects to specify the input parameters. These have to be compatible with the crew automation you are invoking: + +```python +from pydantic import Field + +crew_inputs = { + "required_param": Field(..., description="This parameter is required"), + "optional_param": Field(default="default_value", description="This parameter is optional"), + "typed_param": Field(..., description="Integer parameter", ge=1, le=100) # With validation +} +``` + +## Error Handling + +The tool provides comprehensive error handling for common scenarios: + +- **API Connection Errors**: Network connectivity issues +- **Authentication Errors**: Invalid or expired bearer tokens +- **Timeout Errors**: Tasks that exceed the maximum polling time +- **Task Failures**: Crew automations that fail during execution + +## API Endpoints + +The tool interacts with two main API endpoints: + +- `POST {crew_api_url}/kickoff`: Starts a new crew automation task +- `GET {crew_api_url}/status/{crew_id}`: Checks the status of a running task + +## Notes + +- The tool automatically polls the status endpoint every second until completion or timeout +- Successful tasks return the result directly, while failed tasks return error information +- The bearer token should be kept secure and not hardcoded in production environments +- Consider using environment variables for sensitive configuration like bearer tokens \ No newline at end of file diff --git a/tests/cli/authentication/providers/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/invoke_crewai_automation_tool/__init__.py similarity index 100% rename from tests/cli/authentication/providers/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/invoke_crewai_automation_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/invoke_crewai_automation_tool/invoke_crewai_automation_tool.py b/lib/crewai-tools/src/crewai_tools/tools/invoke_crewai_automation_tool/invoke_crewai_automation_tool.py new file mode 100644 index 000000000..065e5e14c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/invoke_crewai_automation_tool/invoke_crewai_automation_tool.py @@ -0,0 +1,184 @@ +import time +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field, create_model +import requests + + +class InvokeCrewAIAutomationInput(BaseModel): + """Input schema for InvokeCrewAIAutomationTool.""" + + prompt: str = Field(..., description="The prompt or query to send to the crew") + + +class InvokeCrewAIAutomationTool(BaseTool): + """A CrewAI tool for invoking external crew/flows APIs. + + This tool provides CrewAI Platform API integration with external crew services, supporting: + - Dynamic input schema configuration + - Automatic polling for task completion + - Bearer token authentication + - Comprehensive error handling + + Example: + Basic usage: + >>> tool = InvokeCrewAIAutomationTool( + ... crew_api_url="https://api.example.com", + ... crew_bearer_token="your_token", + ... crew_name="My Crew", + ... crew_description="Description of what the crew does", + ... ) + + With custom inputs: + >>> custom_inputs = { + ... "param1": Field(..., description="Description of param1"), + ... "param2": Field( + ... default="default_value", description="Description of param2" + ... ), + ... } + >>> tool = InvokeCrewAIAutomationTool( + ... crew_api_url="https://api.example.com", + ... crew_bearer_token="your_token", + ... crew_name="My Crew", + ... crew_description="Description of what the crew does", + ... crew_inputs=custom_inputs, + ... ) + + Example: + >>> tools = [ + ... InvokeCrewAIAutomationTool( + ... crew_api_url="https://canary-crew-[...].crewai.com", + ... crew_bearer_token="[Your token: abcdef012345]", + ... crew_name="State of AI Report", + ... crew_description="Retrieves a report on state of AI for a given year.", + ... crew_inputs={ + ... "year": Field( + ... ..., description="Year to retrieve the report for (integer)" + ... ) + ... }, + ... ) + ... ] + """ + + name: str = "invoke_amp_automation" + description: str = "Invokes an CrewAI Platform Automation using API" + args_schema: type[BaseModel] = InvokeCrewAIAutomationInput + + crew_api_url: str + crew_bearer_token: str + max_polling_time: int = 10 * 60 # 10 minutes + + def __init__( + self, + crew_api_url: str, + crew_bearer_token: str, + crew_name: str, + crew_description: str, + max_polling_time: int = 10 * 60, + crew_inputs: dict[str, Any] | None = None, + ): + """Initialize the InvokeCrewAIAutomationTool. + + Args: + crew_api_url: Base URL of the crew API service + crew_bearer_token: Bearer token for API authentication + crew_name: Name of the crew to invoke + crew_description: Description of the crew to invoke + max_polling_time: Maximum time in seconds to wait for task completion (default: 600 seconds = 10 minutes) + crew_inputs: Optional dictionary defining custom input schema fields + """ + # Create dynamic args_schema if custom inputs provided + if crew_inputs: + # Start with the base prompt field + fields = {} + + # Add custom fields + for field_name, field_def in crew_inputs.items(): + if isinstance(field_def, tuple): + fields[field_name] = field_def + else: + # Assume it's a Field object, extract type from annotation if available + fields[field_name] = (str, field_def) + + # Create dynamic model + args_schema = create_model("DynamicInvokeCrewAIAutomationInput", **fields) # type: ignore[call-overload] + else: + args_schema = InvokeCrewAIAutomationInput + + # Initialize the parent class with proper field values + super().__init__( + name=crew_name, + description=crew_description, + args_schema=args_schema, + crew_api_url=crew_api_url, + crew_bearer_token=crew_bearer_token, + max_polling_time=max_polling_time, + ) + + def _kickoff_crew(self, inputs: dict[str, Any]) -> dict[str, Any]: + """Start a new crew task. + + Args: + inputs: Dictionary containing the query and other input parameters + + Returns: + Dictionary containing the crew task response. The response will contain the crew id which needs to be returned to check the status of the crew. + """ + response = requests.post( + f"{self.crew_api_url}/kickoff", + headers={ + "Authorization": f"Bearer {self.crew_bearer_token}", + "Content-Type": "application/json", + }, + json={"inputs": inputs}, + timeout=30, + ) + return response.json() + + def _get_crew_status(self, crew_id: str) -> dict[str, Any]: + """Get the status of a crew task. + + Args: + crew_id: The ID of the crew task to check + + Returns: + Dictionary containing the crew task status + """ + response = requests.get( + f"{self.crew_api_url}/status/{crew_id}", + headers={ + "Authorization": f"Bearer {self.crew_bearer_token}", + "Content-Type": "application/json", + }, + timeout=30, + ) + return response.json() + + def _run(self, **kwargs) -> str: + """Execute the crew invocation tool.""" + if kwargs is None: + kwargs = {} + + # Start the crew + response = self._kickoff_crew(inputs=kwargs) + kickoff_id: str | None = response.get("kickoff_id") + + if kickoff_id is None: + return f"Error: Failed to kickoff crew. Response: {response}" + + # Poll for completion + for i in range(self.max_polling_time): + try: + status_response = self._get_crew_status(crew_id=kickoff_id) + if status_response.get("state", "").lower() == "success": + return status_response.get("result", "No result returned") + if status_response.get("state", "").lower() == "failed": + return f"Error: Crew task failed. Response: {status_response}" + except Exception as e: + if i == self.max_polling_time - 1: # Last attempt + return f"Error: Failed to get crew status after {self.max_polling_time} attempts. Last error: {e}" + + time.sleep(1) + + return f"Error: Crew did not complete within {self.max_polling_time} seconds" diff --git a/lib/crewai-tools/src/crewai_tools/tools/jina_scrape_website_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/jina_scrape_website_tool/README.md new file mode 100644 index 000000000..0278e5aa0 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/jina_scrape_website_tool/README.md @@ -0,0 +1,38 @@ +# JinaScrapeWebsiteTool + +## Description +A tool designed to extract and read the content of a specified website by using Jina.ai reader. It is capable of handling various types of web pages by making HTTP requests and parsing the received HTML content. This tool can be particularly useful for web scraping tasks, data collection, or extracting specific information from websites. + +## Installation +Install the crewai_tools package +```shell +pip install 'crewai[tools]' +``` + +## Example +```python +from crewai_tools import JinaScrapeWebsiteTool + +# To enable scraping any website it finds during its execution +tool = JinaScrapeWebsiteTool(api_key='YOUR_API_KEY') + +# Initialize the tool with the website URL, so the agent can only scrape the content of the specified website +tool = JinaScrapeWebsiteTool(website_url='https://www.example.com') + +# With custom headers +tool = JinaScrapeWebsiteTool( + website_url='https://www.example.com', + custom_headers={'X-Target-Selector': 'body, .class, #id'} +) +``` + +## Authentication +The tool uses Jina.ai's reader service. While it can work without an API key, Jina.ai may apply rate limiting or blocking to unauthenticated requests. For production use, it's recommended to provide an API key. + +## Arguments +- `website_url`: Mandatory website URL to read the file. This is the primary input for the tool, specifying which website's content should be scraped and read. +- `api_key`: Optional Jina.ai API key for authenticated access to the reader service. +- `custom_headers`: Optional dictionary of HTTP headers to use when making requests. + +## Note +This tool is an alternative to the standard `ScrapeWebsiteTool` that specifically uses Jina.ai's reader service for enhanced content extraction. Choose this tool when you need more sophisticated content parsing capabilities. \ No newline at end of file diff --git a/tests/cli/enterprise/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/jina_scrape_website_tool/__init__.py similarity index 100% rename from tests/cli/enterprise/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/jina_scrape_website_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/jina_scrape_website_tool/jina_scrape_website_tool.py b/lib/crewai-tools/src/crewai_tools/tools/jina_scrape_website_tool/jina_scrape_website_tool.py new file mode 100644 index 000000000..62561b5e2 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/jina_scrape_website_tool/jina_scrape_website_tool.py @@ -0,0 +1,50 @@ +from crewai.tools import BaseTool +from pydantic import BaseModel, Field +import requests + + +class JinaScrapeWebsiteToolInput(BaseModel): + """Input schema for JinaScrapeWebsiteTool.""" + + website_url: str = Field(..., description="Mandatory website url to read the file") + + +class JinaScrapeWebsiteTool(BaseTool): + name: str = "JinaScrapeWebsiteTool" + description: str = "A tool that can be used to read a website content using Jina.ai reader and return markdown content." + args_schema: type[BaseModel] = JinaScrapeWebsiteToolInput + website_url: str | None = None + api_key: str | None = None + headers: dict = Field(default_factory=dict) + + def __init__( + self, + website_url: str | None = None, + api_key: str | None = None, + custom_headers: dict | None = None, + **kwargs, + ): + super().__init__(**kwargs) + if website_url is not None: + self.website_url = website_url + self.description = f"A tool that can be used to read {website_url}'s content and return markdown content." + self._generate_description() + + if custom_headers is not None: + self.headers = custom_headers + + if api_key is not None: + self.headers["Authorization"] = f"Bearer {api_key}" + + def _run(self, website_url: str | None = None) -> str: + url = website_url or self.website_url + if not url: + raise ValueError( + "Website URL must be provided either during initialization or execution" + ) + + response = requests.get( + f"https://r.jina.ai/{url}", headers=self.headers, timeout=15 + ) + response.raise_for_status() + return response.text diff --git a/lib/crewai-tools/src/crewai_tools/tools/json_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/json_search_tool/README.md new file mode 100644 index 000000000..51510932e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/json_search_tool/README.md @@ -0,0 +1,55 @@ +# JSONSearchTool + +## Description +This tool is used to perform a RAG search within a JSON file's content. It allows users to initiate a search with a specific JSON path, focusing the search operation within that particular JSON file. If the path is provided at initialization, the tool restricts its search scope to the specified JSON file, thereby enhancing the precision of search results. + +## Installation +Install the crewai_tools package by executing the following command in your terminal: + +```shell +pip install 'crewai[tools]' +``` + +## Example +Below are examples demonstrating how to use the JSONSearchTool for searching within JSON files. You can either search any JSON content or restrict the search to a specific JSON file. + +```python +from crewai_tools import JSONSearchTool + +# Example 1: Initialize the tool for a general search across any JSON content. This is useful when the path is known or can be discovered during execution. +tool = JSONSearchTool() + +# Example 2: Initialize the tool with a specific JSON path, limiting the search to a particular JSON file. +tool = JSONSearchTool(json_path='./path/to/your/file.json') +``` + +## Arguments +- `json_path` (str): An optional argument that defines the path to the JSON file to be searched. This parameter is only necessary if the tool is initialized without a specific JSON path. Providing this argument restricts the search to the specified JSON file. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = JSONSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/tests/cli/tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/json_search_tool/__init__.py similarity index 100% rename from tests/cli/tools/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/json_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/json_search_tool/json_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/json_search_tool/json_search_tool.py new file mode 100644 index 000000000..a6716f758 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/json_search_tool/json_search_tool.py @@ -0,0 +1,49 @@ +from pydantic import BaseModel, Field + +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedJSONSearchToolSchema(BaseModel): + """Input for JSONSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the JSON's content", + ) + + +class JSONSearchToolSchema(FixedJSONSearchToolSchema): + """Input for JSONSearchTool.""" + + json_path: str = Field( + ..., description="File path or URL of a JSON file to be searched" + ) + + +class JSONSearchTool(RagTool): + name: str = "Search a JSON's content" + description: str = ( + "A tool that can be used to semantic search a query from a JSON's content." + ) + args_schema: type[BaseModel] = JSONSearchToolSchema + + def __init__(self, json_path: str | None = None, **kwargs): + super().__init__(**kwargs) + if json_path is not None: + self.add(json_path) + self.description = f"A tool that can be used to semantic search a query the {json_path} JSON's content." + self.args_schema = FixedJSONSearchToolSchema + self._generate_description() + + def _run( # type: ignore[override] + self, + search_query: str, + json_path: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if json_path is not None: + self.add(json_path) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/linkup/README.md b/lib/crewai-tools/src/crewai_tools/tools/linkup/README.md new file mode 100644 index 000000000..c51946a11 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/linkup/README.md @@ -0,0 +1,98 @@ +# Linkup Search Tool + +## Description + +The `LinkupSearchTool` is a tool designed for integration with the CrewAI framework. It provides the ability to query the Linkup API for contextual information and retrieve structured results. This tool is ideal for enriching workflows with up-to-date and reliable information from Linkup. + +--- + +## Features + +- Perform API queries to the Linkup platform using customizable parameters (`query`, `depth`, `output_type`). +- Gracefully handles API errors and provides structured feedback. +- Returns well-structured results for seamless integration into CrewAI processes. + +--- + +## Installation + +### Prerequisites + +- Linkup API Key + +### Steps + +1. ```shell + pip install 'crewai[tools]' + ``` + +2. Create a `.env` file in your project root and add your Linkup API Key: + ```plaintext + LINKUP_API_KEY=your_linkup_api_key + ``` + +--- + +## Usage + +### Basic Example + +Here is how to use the `LinkupSearchTool` in a CrewAI project: + +1. **Import and Initialize**: + ```python + from tools.linkup_tools import LinkupSearchTool + import os + from dotenv import load_dotenv + + load_dotenv() + + linkup_tool = LinkupSearchTool(api_key=os.getenv("LINKUP_API_KEY")) + ``` + +2. **Set Up an Agent and Task**: + ```python + from crewai import Agent, Task, Crew + + # Define the agent + research_agent = Agent( + role="Information Researcher", + goal="Fetch relevant results from Linkup.", + backstory="An expert in online information retrieval...", + tools=[linkup_tool], + verbose=True + ) + + # Define the task + search_task = Task( + expected_output="A detailed list of Nobel Prize-winning women in physics with their achievements.", + description="Search for women who have won the Nobel Prize in Physics.", + agent=research_agent + ) + + # Create and run the crew + crew = Crew( + agents=[research_agent], + tasks=[search_task] + ) + + result = crew.kickoff() + print(result) + ``` + +### Advanced Configuration + +You can customize the parameters for the `LinkupSearchTool`: + +- `query`: The search term or phrase. +- `depth`: The search depth (`"standard"` by default). +- `output_type`: The type of output (`"searchResults"` by default). + +Example: +```python +response = linkup_tool._run( + query="Women Nobel Prize Physics", + depth="standard", + output_type="searchResults" +) +``` \ No newline at end of file diff --git a/tests/experimental/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/linkup/__init__.py similarity index 100% rename from tests/experimental/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/linkup/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/linkup/assets/icon.png 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b/lib/crewai-tools/src/crewai_tools/tools/linkup/linkup_search_tool.py @@ -0,0 +1,81 @@ +import os +from typing import Any, Literal + +from crewai.tools import BaseTool, EnvVar + + +try: + from linkup import LinkupClient + + LINKUP_AVAILABLE = True +except ImportError: + LINKUP_AVAILABLE = False + LinkupClient = Any # type: ignore[misc,assignment] # type placeholder when package is not available + +from pydantic import Field, PrivateAttr + + +class LinkupSearchTool(BaseTool): + name: str = "Linkup Search Tool" + description: str = ( + "Performs an API call to Linkup to retrieve contextual information." + ) + _client: LinkupClient = PrivateAttr() # type: ignore + package_dependencies: list[str] = Field(default_factory=lambda: ["linkup-sdk"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="LINKUP_API_KEY", description="API key for Linkup", required=True + ), + ] + ) + + def __init__(self, api_key: str | None = None) -> None: + """Initialize the tool with an API key.""" + super().__init__() # type: ignore[call-arg] + try: + from linkup import LinkupClient + except ImportError: + import click + + if click.confirm( + "You are missing the 'linkup-sdk' package. Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "linkup-sdk"], check=True) # noqa: S607 + from linkup import LinkupClient + + else: + raise ImportError( + "The 'linkup-sdk' package is required to use the LinkupSearchTool. " + "Please install it with: uv add linkup-sdk" + ) from None + self._client = LinkupClient(api_key=api_key or os.getenv("LINKUP_API_KEY")) + + def _run( + self, + query: str, + depth: Literal["standard", "deep"] = "standard", + output_type: Literal[ + "searchResults", "sourcedAnswer", "structured" + ] = "searchResults", + ) -> dict: + """Executes a search using the Linkup API. + + :param query: The query to search for. + :param depth: Search depth (default is "standard"). + :param output_type: Desired result type (default is "searchResults"). + :return: A dictionary containing the results or an error message. + """ + try: + response = self._client.search( + query=query, depth=depth, output_type=output_type + ) + results = [ + {"name": result.name, "url": result.url, "content": result.content} + for result in response.results + ] + return {"success": True, "results": results} + except Exception as e: + return {"success": False, "error": str(e)} diff --git a/lib/crewai-tools/src/crewai_tools/tools/llamaindex_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/llamaindex_tool/README.md new file mode 100644 index 000000000..cd8f4cd99 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/llamaindex_tool/README.md @@ -0,0 +1,53 @@ +# LlamaIndexTool Documentation + +## Description +This tool is designed to be a general wrapper around LlamaIndex tools and query engines, enabling you to leverage LlamaIndex resources +in terms of RAG/agentic pipelines as tools to plug into CrewAI agents. + +## Installation +To incorporate this tool into your project, follow the installation instructions below: +```shell +pip install 'crewai[tools]' +``` + +## Example +The following example demonstrates how to initialize the tool and execute a search with a given query: + +```python +from crewai_tools import LlamaIndexTool + +# Initialize the tool from a LlamaIndex Tool + +## Example 1: Initialize from FunctionTool +from llama_index.core.tools import FunctionTool + +your_python_function = lambda ...: ... +og_tool = FunctionTool.from_defaults(your_python_function, name="", description='') +tool = LlamaIndexTool.from_tool(og_tool) + +## Example 2: Initialize from LlamaHub Tools +from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec +wolfram_spec = WolframAlphaToolSpec(app_id="") +wolfram_tools = wolfram_spec.to_tool_list() +tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools] + + +# Initialize Tool from a LlamaIndex Query Engine + +## NOTE: LlamaIndex has a lot of query engines, define whatever query engine you want +query_engine = index.as_query_engine() +query_tool = LlamaIndexTool.from_query_engine( + query_engine, + name="Uber 2019 10K Query Tool", + description="Use this tool to lookup the 2019 Uber 10K Annual Report" +) + +``` + +## Steps to Get Started +To effectively use the `LlamaIndexTool`, follow these steps: + +1. **Install CrewAI**: Confirm that the `crewai[tools]` package is installed in your Python environment. +2. **Install and use LlamaIndex**: Follow LlamaIndex documentation (https://docs.llamaindex.ai/) to setup a RAG/agent pipeline. + + diff --git a/tests/experimental/evaluation/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/llamaindex_tool/__init__.py similarity index 100% rename from tests/experimental/evaluation/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/llamaindex_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/llamaindex_tool/llamaindex_tool.py b/lib/crewai-tools/src/crewai_tools/tools/llamaindex_tool/llamaindex_tool.py new file mode 100644 index 000000000..730ebd020 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/llamaindex_tool/llamaindex_tool.py @@ -0,0 +1,92 @@ +from __future__ import annotations + +from typing import Any, cast + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +class LlamaIndexTool(BaseTool): + """Tool to wrap LlamaIndex tools/query engines.""" + + llama_index_tool: Any + + def _run( + self, + *args: Any, + **kwargs: Any, + ) -> Any: + """Run tool.""" + from llama_index.core.tools import ( # type: ignore[import-not-found] + BaseTool as LlamaBaseTool, + ) + + tool = cast(LlamaBaseTool, self.llama_index_tool) + + if self.result_as_answer: + return tool(*args, **kwargs).content + + return tool(*args, **kwargs) + + @classmethod + def from_tool(cls, tool: Any, **kwargs: Any) -> LlamaIndexTool: + from llama_index.core.tools import ( # type: ignore[import-not-found] + BaseTool as LlamaBaseTool, + ) + + if not isinstance(tool, LlamaBaseTool): + raise ValueError(f"Expected a LlamaBaseTool, got {type(tool)}") + tool = cast(LlamaBaseTool, tool) + + if tool.metadata.fn_schema is None: + raise ValueError( + "The LlamaIndex tool does not have an fn_schema specified." + ) + args_schema = cast(type[BaseModel], tool.metadata.fn_schema) + + return cls( + name=tool.metadata.name, + description=tool.metadata.description, + args_schema=args_schema, + llama_index_tool=tool, + **kwargs, + ) + + @classmethod + def from_query_engine( + cls, + query_engine: Any, + name: str | None = None, + description: str | None = None, + return_direct: bool = False, + **kwargs: Any, + ) -> LlamaIndexTool: + from llama_index.core.query_engine import ( # type: ignore[import-not-found] + BaseQueryEngine, + ) + from llama_index.core.tools import ( # type: ignore[import-not-found] + QueryEngineTool, + ) + + if not isinstance(query_engine, BaseQueryEngine): + raise ValueError(f"Expected a BaseQueryEngine, got {type(query_engine)}") + + # NOTE: by default the schema expects an `input` variable. However this + # confuses crewAI so we are renaming to `query`. + class QueryToolSchema(BaseModel): + """Schema for query tool.""" + + query: str = Field(..., description="Search query for the query tool.") + + # NOTE: setting `resolve_input_errors` to True is important because the schema expects `input` but we are using `query` + query_engine_tool = QueryEngineTool.from_defaults( + query_engine, + name=name, + description=description, + return_direct=return_direct, + resolve_input_errors=True, + ) + # HACK: we are replacing the schema with our custom schema + query_engine_tool.metadata.fn_schema = QueryToolSchema + + return cls.from_tool(query_engine_tool, **kwargs) diff --git a/lib/crewai-tools/src/crewai_tools/tools/mdx_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/mdx_search_tool/README.md new file mode 100644 index 000000000..71b58131a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/mdx_search_tool/README.md @@ -0,0 +1,57 @@ +# MDXSearchTool + +## Description +The MDX Search Tool, a key component of the `crewai_tools` package, is designed for advanced market data extraction, offering invaluable support to researchers and analysts requiring immediate market insights in the AI sector. With its ability to interface with various data sources and tools, it streamlines the process of acquiring, reading, and organizing market data efficiently. + +## Installation +To utilize the MDX Search Tool, ensure the `crewai_tools` package is installed. If not already present, install it using the following command: + +```shell +pip install 'crewai[tools]' +``` + +## Example +Configuring and using the MDX Search Tool involves setting up environment variables and utilizing the tool within a crewAI project for market research. Here's a simple example: + +```python +from crewai_tools import MDXSearchTool + +# Initialize the tool so the agent can search any MDX content if it learns about during its execution +tool = MDXSearchTool() + +# OR + +# Initialize the tool with a specific MDX file path for exclusive search within that document +tool = MDXSearchTool(mdx='path/to/your/document.mdx') +``` + +## Arguments +- mdx: **Optional** The MDX path for the search. Can be provided at initialization + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = MDXSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/tests/experimental/evaluation/metrics/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/mdx_search_tool/__init__.py similarity index 100% rename from tests/experimental/evaluation/metrics/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/mdx_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/mdx_search_tool/mdx_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/mdx_search_tool/mdx_search_tool.py new file mode 100644 index 000000000..dd201b3c0 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/mdx_search_tool/mdx_search_tool.py @@ -0,0 +1,51 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedMDXSearchToolSchema(BaseModel): + """Input for MDXSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the MDX's content", + ) + + +class MDXSearchToolSchema(FixedMDXSearchToolSchema): + """Input for MDXSearchTool.""" + + mdx: str = Field(..., description="File path or URL of a MDX file to be searched") + + +class MDXSearchTool(RagTool): + name: str = "Search a MDX's content" + description: str = ( + "A tool that can be used to semantic search a query from a MDX's content." + ) + args_schema: type[BaseModel] = MDXSearchToolSchema + + def __init__(self, mdx: str | None = None, **kwargs): + super().__init__(**kwargs) + if mdx is not None: + self.add(mdx) + self.description = f"A tool that can be used to semantic search a query the {mdx} MDX's content." + self.args_schema = FixedMDXSearchToolSchema + self._generate_description() + + def add(self, mdx: str) -> None: + super().add(mdx, data_type=DataType.MDX) + + def _run( # type: ignore[override] + self, + search_query: str, + mdx: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if mdx is not None: + self.add(mdx) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/README.md new file mode 100644 index 000000000..c66dfcf43 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/README.md @@ -0,0 +1,87 @@ +# MongoDBVectorSearchTool + +## Description +This tool is specifically crafted for conducting vector searches within docs within a MongoDB database. Use this tool to find semantically similar docs to a given query. + +MongoDB can act as a vector database that is used to store and query vector embeddings. You can follow the docs here: +https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/ + +## Installation +Install the crewai_tools package with MongoDB support by executing the following command in your terminal: + +```shell +pip install crewai-tools[mongodb] +``` + +or + +``` +uv add crewai-tools --extra mongodb +``` + +## Example +To utilize the MongoDBVectorSearchTool for different use cases, follow these examples: + +```python +from crewai_tools import MongoDBVectorSearchTool + +# To enable the tool to search any website the agent comes across or learns about during its operation +tool = MongoDBVectorSearchTool( + database_name="example_database', + collection_name='example_collections', + connection_string="", +) +``` + +or + +```python +from crewai_tools import MongoDBVectorSearchConfig, MongoDBVectorSearchTool + +# Setup custom embedding model and customize the parameters. +query_config = MongoDBVectorSearchConfig(limit=10, oversampling_factor=2) +tool = MongoDBVectorSearchTool( + database_name="example_database', + collection_name='example_collections', + connection_string="", + query_config=query_config, + index_name="my_vector_index", + generative_model="gpt-4o-mini" +) + +# Adding the tool to an agent +rag_agent = Agent( + name="rag_agent", + role="You are a helpful assistant that can answer questions with the help of the MongoDBVectorSearchTool.", + goal="...", + backstory="...", + llm="gpt-4o-mini", + tools=[tool], +) +``` + +Preloading the MongoDB database with documents: + +```python +from crewai_tools import MongoDBVectorSearchTool + +# Generate the documents and add them to the MongoDB database +test_docs = client.collections.get("example_collections") + +# Create the tool. +tool = MongoDBVectorSearchTool( + database_name="example_database', + collection_name='example_collections', + connection_string="", +) + +# Add the text from a set of CrewAI knowledge documents. +texts = [] +for d in os.listdir("knowledge"): + with open(os.path.join("knowledge", d), "r") as f: + texts.append(f.read()) +tool.add_texts(text) + +# Create the vector search index (if it wasn't already created in Atlas). +tool.create_vector_search_index(dimensions=3072) +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/__init__.py new file mode 100644 index 000000000..8ce630d33 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/__init__.py @@ -0,0 +1,12 @@ +from crewai_tools.tools.mongodb_vector_search_tool.vector_search import ( + MongoDBToolSchema, + MongoDBVectorSearchConfig, + MongoDBVectorSearchTool, +) + + +__all__ = [ + "MongoDBToolSchema", + "MongoDBVectorSearchConfig", + "MongoDBVectorSearchTool", +] diff --git a/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/utils.py b/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/utils.py new file mode 100644 index 000000000..c1a025094 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/utils.py @@ -0,0 +1,122 @@ +from __future__ import annotations + +from collections.abc import Callable +from time import monotonic, sleep +from typing import TYPE_CHECKING, Any + + +if TYPE_CHECKING: + from pymongo.collection import Collection + + +def _vector_search_index_definition( + dimensions: int, + path: str, + similarity: str, + filters: list[str] | None = None, + **kwargs: Any, +) -> dict[str, Any]: + # https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/ + fields = [ + { + "numDimensions": dimensions, + "path": path, + "similarity": similarity, + "type": "vector", + }, + ] + if filters: + for field in filters: + fields.append({"type": "filter", "path": field}) # noqa: PERF401 + definition = {"fields": fields} + definition.update(kwargs) + return definition + + +def create_vector_search_index( + collection: Collection, + index_name: str, + dimensions: int, + path: str, + similarity: str, + filters: list[str] | None = None, + *, + wait_until_complete: float | None = None, + **kwargs: Any, +) -> None: + """Experimental Utility function to create a vector search index. + + Args: + collection (Collection): MongoDB Collection + index_name (str): Name of Index + dimensions (int): Number of dimensions in embedding + path (str): field with vector embedding + similarity (str): The similarity score used for the index + filters (List[str]): Fields/paths to index to allow filtering in $vectorSearch + wait_until_complete (Optional[float]): If provided, number of seconds to wait + until search index is ready. + kwargs: Keyword arguments supplying any additional options to SearchIndexModel. + """ + from pymongo.operations import SearchIndexModel + + if collection.name not in collection.database.list_collection_names(): + collection.database.create_collection(collection.name) + + collection.create_search_index( + SearchIndexModel( + definition=_vector_search_index_definition( + dimensions=dimensions, + path=path, + similarity=similarity, + filters=filters, + **kwargs, + ), + name=index_name, + type="vectorSearch", + ) + ) + + if wait_until_complete: + _wait_for_predicate( + predicate=lambda: _is_index_ready(collection, index_name), + err=f"{index_name=} did not complete in {wait_until_complete}!", + timeout=wait_until_complete, + ) + + +def _is_index_ready(collection: Collection, index_name: str) -> bool: + """Check for the index name in the list of available search indexes to see if the + specified index is of status READY. + + Args: + collection (Collection): MongoDB Collection to for the search indexes + index_name (str): Vector Search Index name + + Returns: + bool : True if the index is present and READY false otherwise + """ + for index in collection.list_search_indexes(index_name): + if index["status"] == "READY": + return True + return False + + +def _wait_for_predicate( + predicate: Callable, err: str, timeout: float = 120, interval: float = 0.5 +) -> None: + """Generic to block until the predicate returns true. + + Args: + predicate (Callable[, bool]): A function that returns a boolean value + err (str): Error message to raise if nothing occurs + timeout (float, optional): Wait time for predicate. Defaults to TIMEOUT. + interval (float, optional): Interval to check predicate. Defaults to DELAY. + + Raises: + TimeoutError: _description_ + """ + start = monotonic() + while not predicate(): + if monotonic() - start > timeout: + raise TimeoutError(err) + sleep(interval) diff --git a/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/vector_search.py b/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/vector_search.py new file mode 100644 index 000000000..a8273cdac --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/mongodb_vector_search_tool/vector_search.py @@ -0,0 +1,330 @@ +from collections.abc import Iterable +from importlib.metadata import version +from logging import getLogger +import os +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from openai import AzureOpenAI, Client +from pydantic import BaseModel, Field + +from crewai_tools.tools.mongodb_vector_search_tool.utils import ( + create_vector_search_index, +) + + +try: + import pymongo # noqa: F401 + + MONGODB_AVAILABLE = True +except ImportError: + MONGODB_AVAILABLE = False + +logger = getLogger(__name__) + + +class MongoDBVectorSearchConfig(BaseModel): + """Configuration for MongoDB vector search queries.""" + + limit: int | None = Field(default=4, description="number of documents to return.") + pre_filter: dict[str, Any] | None = Field( + default=None, + description="List of MQL match expressions comparing an indexed field", + ) + post_filter_pipeline: list[dict] | None = Field( + default=None, + description="Pipeline of MongoDB aggregation stages to filter/process results after $vectorSearch.", + ) + oversampling_factor: int = Field( + default=10, + description="Multiple of limit used when generating number of candidates at each step in the HNSW Vector Search", + ) + include_embeddings: bool = Field( + default=False, + description="Whether to include the embedding vector of each result in metadata.", + ) + + +class MongoDBToolSchema(BaseModel): + """Input for MongoDBTool.""" + + query: str = Field( + ..., + description="The query to search retrieve relevant information from the MongoDB database. Pass only the query, not the question.", + ) + + +class MongoDBVectorSearchTool(BaseTool): + """Tool to perfrom a vector search the MongoDB database.""" + + name: str = "MongoDBVectorSearchTool" + description: str = "A tool to perfrom a vector search on a MongoDB database for relevant information on internal documents." + + args_schema: type[BaseModel] = MongoDBToolSchema + query_config: MongoDBVectorSearchConfig | None = Field( + default=None, description="MongoDB Vector Search query configuration" + ) + embedding_model: str = Field( + default="text-embedding-3-large", + description="Text OpenAI embedding model to use", + ) + vector_index_name: str = Field( + default="vector_index", description="Name of the Atlas Search vector index" + ) + text_key: str = Field( + default="text", + description="MongoDB field that will contain the text for each document", + ) + embedding_key: str = Field( + default="embedding", + description="Field that will contain the embedding for each document", + ) + database_name: str = Field(..., description="The name of the MongoDB database") + collection_name: str = Field(..., description="The name of the MongoDB collection") + connection_string: str = Field( + ..., + description="The connection string of the MongoDB cluster", + ) + dimensions: int = Field( + default=1536, + description="Number of dimensions in the embedding vector", + ) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="BROWSERBASE_API_KEY", + description="API key for Browserbase services", + required=False, + ), + EnvVar( + name="BROWSERBASE_PROJECT_ID", + description="Project ID for Browserbase services", + required=False, + ), + ] + ) + package_dependencies: list[str] = Field(default_factory=lambda: ["mongdb"]) + + def __init__(self, **kwargs): + super().__init__(**kwargs) + if not MONGODB_AVAILABLE: + import click + + if click.confirm( + "You are missing the 'mongodb' crewai tool. Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "pymongo"], check=True) # noqa: S607 + + else: + raise ImportError("You are missing the 'mongodb' crewai tool.") + + if "AZURE_OPENAI_ENDPOINT" in os.environ: + self._openai_client = AzureOpenAI() + elif "OPENAI_API_KEY" in os.environ: + self._openai_client = Client() + else: + raise ValueError( + "OPENAI_API_KEY environment variable is required for MongoDBVectorSearchTool and it is mandatory to use the tool." + ) + + from pymongo import MongoClient + from pymongo.driver_info import DriverInfo + + self._client = MongoClient( + self.connection_string, + driver=DriverInfo(name="CrewAI", version=version("crewai-tools")), + ) + self._coll = self._client[self.database_name][self.collection_name] + + def create_vector_search_index( + self, + *, + dimensions: int, + relevance_score_fn: str = "cosine", + auto_index_timeout: int = 15, + ) -> None: + """Convenience function to create a vector search index. + + Args: + dimensions: Number of dimensions in embedding. If the value is set and + the index does not exist, an index will be created. + relevance_score_fn: The similarity score used for the index + Currently supported: 'euclidean', 'cosine', and 'dotProduct' + auto_index_timeout: Timeout in seconds to wait for an auto-created index + to be ready. + """ + create_vector_search_index( + collection=self._coll, + index_name=self.vector_index_name, + dimensions=dimensions, + path=self.embedding_key, + similarity=relevance_score_fn, + wait_until_complete=auto_index_timeout, + ) + + def add_texts( + self, + texts: Iterable[str], + metadatas: list[dict[str, Any]] | None = None, + ids: list[str] | None = None, + batch_size: int = 100, + **kwargs: Any, + ) -> list[str]: + """Add texts, create embeddings, and add to the Collection and index. + + Important notes on ids: + - If _id or id is a key in the metadatas dicts, one must + pop them and provide as separate list. + - They must be unique. + - If they are not provided, the VectorStore will create unique ones, + stored as bson.ObjectIds internally, and strings in Langchain. + These will appear in Document.metadata with key, '_id'. + + Args: + texts: Iterable of strings to add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + ids: Optional list of unique ids that will be used as index in VectorStore. + See note on ids. + batch_size: Number of documents to insert at a time. + Tuning this may help with performance and sidestep MongoDB limits. + + Returns: + List of ids added to the vectorstore. + """ + from bson import ObjectId + + _metadatas = metadatas or [{} for _ in texts] + ids = [str(ObjectId()) for _ in range(len(list(texts)))] + + result_ids = [] + texts_batch = [] + metadatas_batch = [] + size = 0 + i = 0 + for j, (text, metadata) in enumerate(zip(texts, _metadatas, strict=False)): + size += len(text) + len(metadata) + texts_batch.append(text) + metadatas_batch.append(metadata) + if (j + 1) % batch_size == 0 or size >= 47_000_000: + batch_res = self._bulk_embed_and_insert_texts( + texts_batch, metadatas_batch, ids[i : j + 1] + ) + result_ids.extend(batch_res) + texts_batch = [] + metadatas_batch = [] + size = 0 + i = j + 1 + if texts_batch: + batch_res = self._bulk_embed_and_insert_texts( + texts_batch, metadatas_batch, ids[i : j + 1] + ) + result_ids.extend(batch_res) + return result_ids + + def _embed_texts(self, texts: list[str]) -> list[list[float]]: + return [ + i.embedding + for i in self._openai_client.embeddings.create( + input=texts, + model=self.embedding_model, + dimensions=self.dimensions, + ).data + ] + + def _bulk_embed_and_insert_texts( + self, + texts: list[str], + metadatas: list[dict], + ids: list[str], + ) -> list[str]: + """Bulk insert single batch of texts, embeddings, and ids.""" + from bson import ObjectId + from pymongo.operations import ReplaceOne + + if not texts: + return [] + # Compute embedding vectors + embeddings = self._embed_texts(texts) + docs = [ + { + "_id": ObjectId(i), + self.text_key: t, + self.embedding_key: embedding, + **m, + } + for i, t, m, embedding in zip( + ids, texts, metadatas, embeddings, strict=False + ) + ] + operations = [ReplaceOne({"_id": doc["_id"]}, doc, upsert=True) for doc in docs] + # insert the documents in MongoDB Atlas + result = self._coll.bulk_write(operations) + if result.upserted_ids is None: + raise ValueError("No documents were inserted.") + return [str(_id) for _id in result.upserted_ids.values()] + + def _run(self, query: str) -> str: + from bson import json_util + + try: + query_config = self.query_config or MongoDBVectorSearchConfig() + limit = query_config.limit + oversampling_factor = query_config.oversampling_factor + pre_filter = query_config.pre_filter + include_embeddings = query_config.include_embeddings + post_filter_pipeline = query_config.post_filter_pipeline + + # Create the embedding for the query + query_vector = self._embed_texts([query])[0] + + # Atlas Vector Search, potentially with filter + stage = { + "index": self.vector_index_name, + "path": self.embedding_key, + "queryVector": query_vector, + "numCandidates": limit * oversampling_factor, # type: ignore[operator] + "limit": limit, + } + if pre_filter: + stage["filter"] = pre_filter + + pipeline = [ + {"$vectorSearch": stage}, + {"$set": {"score": {"$meta": "vectorSearchScore"}}}, + ] + + # Remove embeddings unless requested + if not include_embeddings: + pipeline.append({"$project": {self.embedding_key: 0}}) + + # Post-processing + if post_filter_pipeline is not None: + pipeline.extend(post_filter_pipeline) + + # Execution + cursor = self._coll.aggregate(pipeline) # type: ignore[arg-type] + docs = [] + + # Format + for doc in cursor: + docs.append(doc) # noqa: PERF402 + return json_util.dumps(docs) + except Exception as e: + logger.error(f"Error: {e}") + return "" + + def __del__(self): + """Cleanup clients on deletion.""" + try: + if hasattr(self, "_client") and self._client: + self._client.close() + except Exception as e: + logger.error(f"Error: {e}") + + try: + if hasattr(self, "_openai_client") and self._openai_client: + self._openai_client.close() + except Exception as e: + logger.error(f"Error: {e}") diff --git a/lib/crewai-tools/src/crewai_tools/tools/multion_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/README.md new file mode 100644 index 000000000..da92a0682 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/README.md @@ -0,0 +1,53 @@ +# MultiOnTool Documentation + +## Description +The MultiOnTool, integrated within the crewai_tools package, empowers CrewAI agents with the capability to navigate and interact with the web through natural language instructions. Leveraging the Multion API, this tool facilitates seamless web browsing, making it an essential asset for projects requiring dynamic web data interaction. + +## Installation +Ensure the `crewai[tools]` package is installed in your environment to use the MultiOnTool. If it's not already installed, you can add it using the command below: +```shell +pip install 'crewai[tools]' +``` + +## Example +The following example demonstrates how to initialize the tool and execute a search with a given query: + +```python +from crewai import Agent, Task, Crew +from crewai_tools import MultiOnTool + +# Initialize the tool from a MultiOn Tool +multion_tool = MultiOnTool(api_key= "YOUR_MULTION_API_KEY", local=False) + +Browser = Agent( + role="Browser Agent", + goal="control web browsers using natural language ", + backstory="An expert browsing agent.", + tools=[multion_remote_tool], + verbose=True, +) + +# example task to search and summarize news +browse = Task( + description="Summarize the top 3 trending AI News headlines", + expected_output="A summary of the top 3 trending AI News headlines", + agent=Browser, +) + +crew = Crew(agents=[Browser], tasks=[browse]) + +crew.kickoff() +``` + +## Arguments + +- `api_key`: Specifies MultiOn API key. Default is the `MULTION_API_KEY` environment variable. +- `local`: Use the local flag set as "true" to run the agent locally on your browser. Make sure the multion browser extension is installed and API Enabled is checked. +- `max_steps`: Optional. Set the max_steps the multion agent can take for a command + +## Steps to Get Started +To effectively use the `MultiOnTool`, follow these steps: + +1. **Install CrewAI**: Confirm that the `crewai[tools]` package is installed in your Python environment. +2. **Install and use MultiOn**: Follow MultiOn documentation for installing the MultiOn Browser Extension (https://docs.multion.ai/learn/browser-extension). +3. **Enable API Usage**: Click on the MultiOn extension in the extensions folder of your browser (not the hovering MultiOn icon on the web page) to open the extension configurations. Click the API Enabled toggle to enable the API diff --git a/tests/knowledge/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/__init__.py similarity index 100% rename from tests/knowledge/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/multion_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/multion_tool/example.py b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/example.py new file mode 100644 index 000000000..28354efa3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/example.py @@ -0,0 +1,30 @@ +import os + +from crewai import Agent, Crew, Task +from multion_tool import MultiOnTool # type: ignore[import-not-found] + + +os.environ["OPENAI_API_KEY"] = "Your Key" + +multion_browse_tool = MultiOnTool(api_key="Your Key") + +# Create a new agent +Browser = Agent( + role="Browser Agent", + goal="control web browsers using natural language ", + backstory="An expert browsing agent.", + tools=[multion_browse_tool], + verbose=True, +) + +# Define tasks +browse = Task( + description="Summarize the top 3 trending AI News headlines", + expected_output="A summary of the top 3 trending AI News headlines", + agent=Browser, +) + + +crew = Crew(agents=[Browser], tasks=[browse]) + +crew.kickoff() diff --git a/lib/crewai-tools/src/crewai_tools/tools/multion_tool/multion_tool.py b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/multion_tool.py new file mode 100644 index 000000000..7368ddd2d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/multion_tool/multion_tool.py @@ -0,0 +1,81 @@ +"""Multion tool spec.""" + +import os +import subprocess +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import Field + + +class MultiOnTool(BaseTool): + """Tool to wrap MultiOn Browse Capabilities.""" + + name: str = "Multion Browse Tool" + description: str = """Multion gives the ability for LLMs to control web browsers using natural language instructions. + If the status is 'CONTINUE', reissue the same instruction to continue execution + """ + multion: Any | None = None + session_id: str | None = None + local: bool = False + max_steps: int = 3 + package_dependencies: list[str] = Field(default_factory=lambda: ["multion"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="MULTION_API_KEY", description="API key for Multion", required=True + ), + ] + ) + + def __init__( + self, + api_key: str | None = None, + **kwargs, + ): + super().__init__(**kwargs) + try: + from multion.client import MultiOn # type: ignore + except ImportError: + import click + + if click.confirm( + "You are missing the 'multion' package. Would you like to install it?" + ): + subprocess.run(["uv", "add", "multion"], check=True) # noqa: S607 + from multion.client import MultiOn + else: + raise ImportError( + "`multion` package not found, please run `uv add multion`" + ) from None + self.session_id = None + self.multion = MultiOn(api_key=api_key or os.getenv("MULTION_API_KEY")) + + def _run( + self, + cmd: str, + *args: Any, + **kwargs: Any, + ) -> str: + """Run the Multion client with the given command. + + Args: + cmd (str): The detailed and specific natural language instructrion for web browsing + + *args (Any): Additional arguments to pass to the Multion client + **kwargs (Any): Additional keyword arguments to pass to the Multion client + """ + if self.multion is None: + raise ValueError("Multion client is not initialized.") + + browse = self.multion.browse( + cmd=cmd, + session_id=self.session_id, + local=self.local, + max_steps=self.max_steps, + *args, # noqa: B026 + **kwargs, + ) + self.session_id = browse.session_id + + return browse.message + "\n\n STATUS: " + browse.status diff --git a/lib/crewai-tools/src/crewai_tools/tools/mysql_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/mysql_search_tool/README.md new file mode 100644 index 000000000..b31d7120b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/mysql_search_tool/README.md @@ -0,0 +1,56 @@ +# MySQLSearchTool + +## Description +This tool is designed to facilitate semantic searches within MySQL database tables. Leveraging the RAG (Retrieve and Generate) technology, the MySQLSearchTool provides users with an efficient means of querying database table content, specifically tailored for MySQL databases. It simplifies the process of finding relevant data through semantic search queries, making it an invaluable resource for users needing to perform advanced queries on extensive datasets within a MySQL database. + +## Installation +To install the `crewai_tools` package and utilize the MySQLSearchTool, execute the following command in your terminal: + +```shell +pip install 'crewai[tools]' +``` + +## Example +Below is an example showcasing how to use the MySQLSearchTool to conduct a semantic search on a table within a MySQL database: + +```python +from crewai_tools import MySQLSearchTool + +# Initialize the tool with the database URI and the target table name +tool = MySQLSearchTool(db_uri='mysql://user:password@localhost:3306/mydatabase', table_name='employees') + +``` + +## Arguments +The MySQLSearchTool requires the following arguments for its operation: + +- `db_uri`: A string representing the URI of the MySQL database to be queried. This argument is mandatory and must include the necessary authentication details and the location of the database. +- `table_name`: A string specifying the name of the table within the database on which the semantic search will be performed. This argument is mandatory. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = MySQLSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/tests/pipeline/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/mysql_search_tool/__init__.py similarity index 100% rename from tests/pipeline/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/mysql_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/mysql_search_tool/mysql_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/mysql_search_tool/mysql_search_tool.py new file mode 100644 index 000000000..34921b0d4 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/mysql_search_tool/mysql_search_tool.py @@ -0,0 +1,46 @@ +from typing import Any + +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class MySQLSearchToolSchema(BaseModel): + """Input for MySQLSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory semantic search query you want to use to search the database's content", + ) + + +class MySQLSearchTool(RagTool): + name: str = "Search a database's table content" + description: str = "A tool that can be used to semantic search a query from a database table's content." + args_schema: type[BaseModel] = MySQLSearchToolSchema + db_uri: str = Field(..., description="Mandatory database URI") + + def __init__(self, table_name: str, **kwargs): + super().__init__(**kwargs) + self.add(table_name, data_type=DataType.MYSQL, metadata={"db_uri": self.db_uri}) + self.description = f"A tool that can be used to semantic search a query the {table_name} database table's content." + self._generate_description() + + def add( + self, + table_name: str, + **kwargs: Any, + ) -> None: + super().add(f"SELECT * FROM {table_name};", **kwargs) # noqa: S608 + + def _run( # type: ignore[override] + self, + search_query: str, + similarity_threshold: float | None = None, + limit: int | None = None, + **kwargs: Any, + ) -> Any: + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/nl2sql/README.md b/lib/crewai-tools/src/crewai_tools/tools/nl2sql/README.md new file mode 100644 index 000000000..932867c90 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/nl2sql/README.md @@ -0,0 +1,73 @@ +# NL2SQL Tool + +## Description + +This tool is used to convert natural language to SQL queries. When passed to the agent it will generate queries and then use them to interact with the database. + +This enables multiple workflows like having an Agent to access the database fetch information based on the goal and then use the information to generate a response, report or any other output. Along with that provides the ability for the Agent to update the database based on its goal. + +**Attention**: Make sure that the Agent has access to a Read-Replica or that is okay for the Agent to run insert/update queries on the database. + +## Requirements + +- SqlAlchemy +- Any DB compatible library (e.g. psycopg2, mysql-connector-python) + +## Installation +Install the crewai_tools package +```shell +pip install 'crewai[tools]' +``` + +## Usage + +In order to use the NL2SQLTool, you need to pass the database URI to the tool. The URI should be in the format `dialect+driver://username:password@host:port/database`. + +```python +from crewai_tools import NL2SQLTool + +# psycopg2 was installed to run this example with PostgreSQL +nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db") + +@agent +def researcher(self) -> Agent: + return Agent( + config=self.agents_config["researcher"], + allow_delegation=False, + tools=[nl2sql] + ) +``` + +## Example + +The primary task goal was: + +"Retrieve the average, maximum, and minimum monthly revenue for each city, but only include cities that have more than one user. Also, count the number of users in each city and sort the results by the average monthly revenue in descending order" + +So the Agent tried to get information from the DB, the first one is wrong so the Agent tries again and gets the correct information and passes to the next agent. + +![alt text](images/image-2.png) +![alt text](images/image-3.png) + + +The second task goal was: + +"Review the data and create a detailed report, and then create the table on the database with the fields based on the data provided. +Include information on the average, maximum, and minimum monthly revenue for each city, but only include cities that have more than one user. Also, count the number of users in each city and sort the results by the average monthly revenue in descending order." + +Now things start to get interesting, the Agent generates the SQL query to not only create the table but also insert the data into the table. And in the end the Agent still returns the final report which is exactly what was in the database. + +![alt text](images/image-4.png) +![alt text](images/image-5.png) + +![alt text](images/image-9.png) +![alt text](images/image-7.png) + + +This is a simple example of how the NL2SQLTool can be used to interact with the database and generate reports based on the data in the database. + +The Tool provides endless possibilities on the logic of the Agent and how it can interact with the database. + +``` + DB -> Agent -> ... -> Agent -> DB +``` diff --git a/tests/rag/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/nl2sql/__init__.py similarity index 100% rename from tests/rag/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/nl2sql/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/nl2sql/images/image-2.png b/lib/crewai-tools/src/crewai_tools/tools/nl2sql/images/image-2.png new file mode 100644 index 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b/lib/crewai-tools/src/crewai_tools/tools/nl2sql/nl2sql_tool.py new file mode 100644 index 000000000..3ddea477b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/nl2sql/nl2sql_tool.py @@ -0,0 +1,97 @@ +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +try: + from sqlalchemy import create_engine, text + from sqlalchemy.orm import sessionmaker + + SQLALCHEMY_AVAILABLE = True +except ImportError: + SQLALCHEMY_AVAILABLE = False + + +class NL2SQLToolInput(BaseModel): + sql_query: str = Field( + title="SQL Query", + description="The SQL query to execute.", + ) + + +class NL2SQLTool(BaseTool): + name: str = "NL2SQLTool" + description: str = "Converts natural language to SQL queries and executes them." + db_uri: str = Field( + title="Database URI", + description="The URI of the database to connect to.", + ) + tables: list = Field(default_factory=list) + columns: dict = Field(default_factory=dict) + args_schema: type[BaseModel] = NL2SQLToolInput + + def model_post_init(self, __context: Any) -> None: + if not SQLALCHEMY_AVAILABLE: + raise ImportError( + "sqlalchemy is not installed. Please install it with `pip install crewai-tools[sqlalchemy]`" + ) + + data = {} + tables = self._fetch_available_tables() + + for table in tables: + table_columns = self._fetch_all_available_columns(table["table_name"]) + data[f"{table['table_name']}_columns"] = table_columns + + self.tables = tables + self.columns = data + + def _fetch_available_tables(self): + return self.execute_sql( + "SELECT table_name FROM information_schema.tables WHERE table_schema = 'public';" + ) + + def _fetch_all_available_columns(self, table_name: str): + return self.execute_sql( + f"SELECT column_name, data_type FROM information_schema.columns WHERE table_name = '{table_name}';" # noqa: S608 + ) + + def _run(self, sql_query: str): + try: + data = self.execute_sql(sql_query) + except Exception as exc: + data = ( + f"Based on these tables {self.tables} and columns {self.columns}, " + "you can create SQL queries to retrieve data from the database." + f"Get the original request {sql_query} and the error {exc} and create the correct SQL query." + ) + + return data + + def execute_sql(self, sql_query: str) -> list | str: + if not SQLALCHEMY_AVAILABLE: + raise ImportError( + "sqlalchemy is not installed. Please install it with `pip install crewai-tools[sqlalchemy]`" + ) + + engine = create_engine(self.db_uri) + Session = sessionmaker(bind=engine) # noqa: N806 + session = Session() + try: + result = session.execute(text(sql_query)) + session.commit() + + if result.returns_rows: # type: ignore[attr-defined] + columns = result.keys() + return [ + dict(zip(columns, row, strict=False)) for row in result.fetchall() + ] + return f"Query {sql_query} executed successfully" + + except Exception as e: + session.rollback() + raise e + + finally: + session.close() diff --git a/lib/crewai-tools/src/crewai_tools/tools/ocr_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/ocr_tool/README.md new file mode 100644 index 000000000..f5375ca18 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/ocr_tool/README.md @@ -0,0 +1,42 @@ +# OCR Tool + +## Description + +This tool performs Optical Character Recognition (OCR) on images using supported LLMs. It can extract text from both local image files and images available via URLs. The tool leverages the LLM's vision capabilities to provide accurate text extraction from images. + +## Installation +Install the crewai_tools package +```shell +pip install 'crewai[tools]' +``` + +## Supported LLMs + +Any LLM that supports the `vision` feature should work. It must accept image_url as a user message. +The tool has been tested with: +- OpenAI's `gpt-4o` +- Gemini's `gemini/gemini-1.5-pro` + +## Usage + +In order to use the OCRTool, make sure your LLM supports the `vision` feature and the appropriate API key is set in the environment (e.g., `OPENAI_API_KEY` for OpenAI). + +```python +from crewai_tools import OCRTool + +selected_llm = LLM(model="gpt-4o") # select your LLM, the tool has been tested with gpt-4o and gemini/gemini-1.5-pro + +ocr_tool = OCRTool(llm=selected_llm) + +@agent +def researcher(self) -> Agent: + return Agent( + config=self.agents_config["researcher"], + allow_delegation=False, + tools=[ocr_tool] + ) +``` + +The tool accepts either a local file path or a URL to the image: +- For local files, provide the absolute or relative path +- For remote images, provide the complete URL starting with 'http' or 'https' diff --git a/tests/rag/chromadb/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/ocr_tool/__init__.py similarity index 100% rename from tests/rag/chromadb/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/ocr_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/ocr_tool/ocr_tool.py b/lib/crewai-tools/src/crewai_tools/tools/ocr_tool/ocr_tool.py new file mode 100644 index 000000000..654af9ad1 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/ocr_tool/ocr_tool.py @@ -0,0 +1,101 @@ +"""Optical Character Recognition (OCR) Tool. + +This tool provides functionality for extracting text from images using supported LLMs. Make sure your model supports the `vision` feature. +""" + +import base64 + +from crewai.llm import LLM +from crewai.tools.base_tool import BaseTool +from crewai.utilities.types import LLMMessage +from pydantic import BaseModel, Field + + +class OCRToolSchema(BaseModel): + """Input schema for Optical Character Recognition Tool. + + Attributes: + image_path_url (str): Path to a local image file or URL of an image. + For local files, provide the absolute or relative path. + For remote images, provide the complete URL starting with 'http' or 'https'. + """ + + image_path_url: str = Field(description="The image path or URL.") + + +class OCRTool(BaseTool): + """A tool for performing Optical Character Recognition on images. + + This tool leverages LLMs to extract text from images. It can process + both local image files and images available via URLs. + + Attributes: + name (str): Name of the tool. + description (str): Description of the tool's functionality. + args_schema (Type[BaseModel]): Pydantic schema for input validation. + + Private Attributes: + _llm (Optional[LLM]): Language model instance for making API calls. + """ + + name: str = "Optical Character Recognition Tool" + description: str = "This tool uses an LLM's API to extract text from an image file." + llm: LLM = Field(default_factory=lambda: LLM(model="gpt-4o", temperature=0.7)) + args_schema: type[BaseModel] = OCRToolSchema + + def _run(self, **kwargs) -> str: + """Execute the OCR operation on the provided image. + + Args: + **kwargs: Keyword arguments containing the image_path_url. + + Returns: + str: Extracted text from the image. + If no image path/URL is provided, returns an error message. + + Note: + The method handles both local image files and remote URLs: + - For local files: The image is read and encoded to base64 + - For URLs: The URL is passed directly to the Vision API + """ + image_path_url = kwargs.get("image_path_url") + + if not image_path_url: + return "Image Path or URL is required." + + if image_path_url.startswith("http"): + image_data = image_path_url + else: + base64_image = self._encode_image(image_path_url) + image_data = f"data:image/jpeg;base64,{base64_image}" + + messages: list[LLMMessage] = [ + { + "role": "system", + "content": "You are an expert OCR specialist. Extract complete text from the provided image. Provide the result as a raw text.", + }, + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": image_data}, + } + ], + }, + ] + + return self.llm.call(messages=messages) + + @staticmethod + def _encode_image(image_path: str): + """Encode an image file to base64 format. + + Args: + image_path (str): Path to the local image file. + + Returns: + str: Base64-encoded image data as a UTF-8 string. + """ + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode() diff --git a/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_product_scraper_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_product_scraper_tool/README.md new file mode 100644 index 000000000..f87c70c19 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_product_scraper_tool/README.md @@ -0,0 +1,55 @@ +# OxylabsAmazonProductScraperTool + +Scrape any website with `OxylabsAmazonProductScraperTool` + +## Installation + +``` +pip install 'crewai[tools]' oxylabs +``` + +## Example + +```python +from crewai_tools import OxylabsAmazonProductScraperTool + +# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set +tool = OxylabsAmazonProductScraperTool() + +result = tool.run(query="AAAAABBBBCC") + +print(result) +``` + +## Arguments + +- `username`: Oxylabs username. +- `password`: Oxylabs password. + +Get the credentials by creating an Oxylabs Account [here](https://oxylabs.io). + +## Advanced example + +Check out the Oxylabs [documentation](https://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/amazon/product) to get the full list of parameters. + +```python +from crewai_tools import OxylabsAmazonProductScraperTool + +# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set +tool = OxylabsAmazonProductScraperTool( + config={ + "domain": "com", + "parse": True, + "context": [ + { + "key": "autoselect_variant", + "value": True + } + ] + } +) + +result = tool.run(query="AAAAABBBBCC") + +print(result) +``` diff --git a/tests/security/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_product_scraper_tool/__init__.py similarity index 100% rename from tests/security/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_product_scraper_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_product_scraper_tool/oxylabs_amazon_product_scraper_tool.py b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_product_scraper_tool/oxylabs_amazon_product_scraper_tool.py new file mode 100644 index 000000000..b257d797f --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_product_scraper_tool/oxylabs_amazon_product_scraper_tool.py @@ -0,0 +1,167 @@ +from importlib.metadata import version +import json +import os +from platform import architecture, python_version +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field + + +try: + from oxylabs import RealtimeClient # type: ignore[import-untyped] + from oxylabs.sources.response import ( # type: ignore[import-untyped] + Response as OxylabsResponse, + ) + + OXYLABS_AVAILABLE = True +except ImportError: + RealtimeClient = Any + OxylabsResponse = Any + + OXYLABS_AVAILABLE = False + + +__all__ = ["OxylabsAmazonProductScraperConfig", "OxylabsAmazonProductScraperTool"] + + +class OxylabsAmazonProductScraperArgs(BaseModel): + query: str = Field(description="Amazon product ASIN") + + +class OxylabsAmazonProductScraperConfig(BaseModel): + """Amazon Product Scraper configuration options: + https://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/amazon/product. + """ + + domain: str | None = Field( + None, description="The domain to limit the search results to." + ) + geo_location: str | None = Field(None, description="The Deliver to location.") + user_agent_type: str | None = Field(None, description="Device type and browser.") + render: str | None = Field(None, description="Enables JavaScript rendering.") + callback_url: str | None = Field(None, description="URL to your callback endpoint.") + context: list | None = Field( + None, + description="Additional advanced settings and controls for specialized requirements.", + ) + parse: bool | None = Field(None, description="True will return structured data.") + parsing_instructions: dict | None = Field( + None, description="Instructions for parsing the results." + ) + + +class OxylabsAmazonProductScraperTool(BaseTool): + """Scrape Amazon product pages with OxylabsAmazonProductScraperTool. + + Get Oxylabs account: + https://dashboard.oxylabs.io/en + + Args: + username (str): Oxylabs username. + password (str): Oxylabs password. + config: Configuration options. See ``OxylabsAmazonProductScraperConfig`` + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, + validate_assignment=True, + ) + name: str = "Oxylabs Amazon Product Scraper tool" + description: str = "Scrape Amazon product pages with Oxylabs Amazon Product Scraper" + args_schema: type[BaseModel] = OxylabsAmazonProductScraperArgs + + oxylabs_api: RealtimeClient + config: OxylabsAmazonProductScraperConfig + package_dependencies: list[str] = Field(default_factory=lambda: ["oxylabs"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="OXYLABS_USERNAME", + description="Username for Oxylabs", + required=True, + ), + EnvVar( + name="OXYLABS_PASSWORD", + description="Password for Oxylabs", + required=True, + ), + ] + ) + + def __init__( + self, + username: str | None = None, + password: str | None = None, + config: OxylabsAmazonProductScraperConfig | dict | None = None, + **kwargs, + ) -> None: + bits, _ = architecture() + sdk_type = ( + f"oxylabs-crewai-sdk-python/" + f"{version('crewai')} " + f"({python_version()}; {bits})" + ) + + if username is None or password is None: + username, password = self._get_credentials_from_env() + + if OXYLABS_AVAILABLE: + # import RealtimeClient to make it accessible for the current scope + from oxylabs import RealtimeClient + + kwargs["oxylabs_api"] = RealtimeClient( + username=username, + password=password, + sdk_type=sdk_type, + ) + else: + import click + + if click.confirm( + "You are missing the 'oxylabs' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run(["uv", "add", "oxylabs"], check=True) # noqa: S607 + from oxylabs import RealtimeClient + + kwargs["oxylabs_api"] = RealtimeClient( + username=username, + password=password, + sdk_type=sdk_type, + ) + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install oxylabs package") from e + else: + raise ImportError( + "`oxylabs` package not found, please run `uv add oxylabs`" + ) + + if config is None: + config = OxylabsAmazonProductScraperConfig() + super().__init__(config=config, **kwargs) + + def _get_credentials_from_env(self) -> tuple[str, str]: + username = os.environ.get("OXYLABS_USERNAME") + password = os.environ.get("OXYLABS_PASSWORD") + if not username or not password: + raise ValueError( + "You must pass oxylabs username and password when instantiating the tool " + "or specify OXYLABS_USERNAME and OXYLABS_PASSWORD environment variables" + ) + return username, password + + def _run(self, query: str) -> str: + response = self.oxylabs_api.amazon.scrape_product( + query, + **self.config.model_dump(exclude_none=True), + ) + + content = response.results[0].content + + if isinstance(content, dict): + return json.dumps(content) + + return content diff --git a/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_search_scraper_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_search_scraper_tool/README.md new file mode 100644 index 000000000..b0e2ef7b0 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_search_scraper_tool/README.md @@ -0,0 +1,54 @@ +# OxylabsAmazonSearchScraperTool + +Scrape any website with `OxylabsAmazonSearchScraperTool` + +## Installation + +``` +pip install 'crewai[tools]' oxylabs +``` + +## Example + +```python +from crewai_tools import OxylabsAmazonSearchScraperTool + +# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set +tool = OxylabsAmazonSearchScraperTool() + +result = tool.run(query="headsets") + +print(result) +``` + +## Arguments + +- `username`: Oxylabs username. +- `password`: Oxylabs password. + +Get the credentials by creating an Oxylabs Account [here](https://oxylabs.io). + +## Advanced example + +Check out the Oxylabs [documentation](https://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/amazon/search) to get the full list of parameters. + +```python +from crewai_tools import OxylabsAmazonSearchScraperTool + +# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set +tool = OxylabsAmazonSearchScraperTool( + config={ + "domain": 'nl', + "start_page": 2, + "pages": 2, + "parse": True, + "context": [ + {'key': 'category_id', 'value': 16391693031} + ], + } +) + +result = tool.run(query='nirvana tshirt') + +print(result) +``` diff --git a/tests/telemetry/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_search_scraper_tool/__init__.py similarity index 100% rename from tests/telemetry/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_search_scraper_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_search_scraper_tool/oxylabs_amazon_search_scraper_tool.py b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_search_scraper_tool/oxylabs_amazon_search_scraper_tool.py new file mode 100644 index 000000000..f8fdd0763 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_amazon_search_scraper_tool/oxylabs_amazon_search_scraper_tool.py @@ -0,0 +1,169 @@ +from importlib.metadata import version +import json +import os +from platform import architecture, python_version +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field + + +try: + from oxylabs import RealtimeClient # type: ignore[import-untyped] + from oxylabs.sources.response import ( # type: ignore[import-untyped] + Response as OxylabsResponse, + ) + + OXYLABS_AVAILABLE = True +except ImportError: + RealtimeClient = Any + OxylabsResponse = Any + + OXYLABS_AVAILABLE = False + + +__all__ = ["OxylabsAmazonSearchScraperConfig", "OxylabsAmazonSearchScraperTool"] + + +class OxylabsAmazonSearchScraperArgs(BaseModel): + query: str = Field(description="Amazon search term") + + +class OxylabsAmazonSearchScraperConfig(BaseModel): + """Amazon Search Scraper configuration options: + https://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/amazon/search. + """ + + domain: str | None = Field( + None, description="The domain to limit the search results to." + ) + start_page: int | None = Field(None, description="The starting page number.") + pages: int | None = Field(None, description="The number of pages to scrape.") + geo_location: str | None = Field(None, description="The Deliver to location.") + user_agent_type: str | None = Field(None, description="Device type and browser.") + render: str | None = Field(None, description="Enables JavaScript rendering.") + callback_url: str | None = Field(None, description="URL to your callback endpoint.") + context: list | None = Field( + None, + description="Additional advanced settings and controls for specialized requirements.", + ) + parse: bool | None = Field(None, description="True will return structured data.") + parsing_instructions: dict | None = Field( + None, description="Instructions for parsing the results." + ) + + +class OxylabsAmazonSearchScraperTool(BaseTool): + """Scrape Amazon search results with OxylabsAmazonSearchScraperTool. + + Get Oxylabs account: + https://dashboard.oxylabs.io/en + + Args: + username (str): Oxylabs username. + password (str): Oxylabs password. + config: Configuration options. See ``OxylabsAmazonSearchScraperConfig`` + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, + validate_assignment=True, + ) + name: str = "Oxylabs Amazon Search Scraper tool" + description: str = "Scrape Amazon search results with Oxylabs Amazon Search Scraper" + args_schema: type[BaseModel] = OxylabsAmazonSearchScraperArgs + + oxylabs_api: RealtimeClient + config: OxylabsAmazonSearchScraperConfig + package_dependencies: list[str] = Field(default_factory=lambda: ["oxylabs"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="OXYLABS_USERNAME", + description="Username for Oxylabs", + required=True, + ), + EnvVar( + name="OXYLABS_PASSWORD", + description="Password for Oxylabs", + required=True, + ), + ] + ) + + def __init__( + self, + username: str | None = None, + password: str | None = None, + config: OxylabsAmazonSearchScraperConfig | dict | None = None, + **kwargs, + ): + bits, _ = architecture() + sdk_type = ( + f"oxylabs-crewai-sdk-python/" + f"{version('crewai')} " + f"({python_version()}; {bits})" + ) + + if username is None or password is None: + username, password = self._get_credentials_from_env() + + if OXYLABS_AVAILABLE: + # import RealtimeClient to make it accessible for the current scope + from oxylabs import RealtimeClient + + kwargs["oxylabs_api"] = RealtimeClient( + username=username, + password=password, + sdk_type=sdk_type, + ) + else: + import click + + if click.confirm( + "You are missing the 'oxylabs' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run(["uv", "add", "oxylabs"], check=True) # noqa: S607 + from oxylabs import RealtimeClient + + kwargs["oxylabs_api"] = RealtimeClient( + username=username, + password=password, + sdk_type=sdk_type, + ) + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install oxylabs package") from e + else: + raise ImportError( + "`oxylabs` package not found, please run `uv add oxylabs`" + ) + + if config is None: + config = OxylabsAmazonSearchScraperConfig() + super().__init__(config=config, **kwargs) + + def _get_credentials_from_env(self) -> tuple[str, str]: + username = os.environ.get("OXYLABS_USERNAME") + password = os.environ.get("OXYLABS_PASSWORD") + if not username or not password: + raise ValueError( + "You must pass oxylabs username and password when instantiating the tool " + "or specify OXYLABS_USERNAME and OXYLABS_PASSWORD environment variables" + ) + return username, password + + def _run(self, query: str) -> str: + response = self.oxylabs_api.amazon.scrape_search( + query, + **self.config.model_dump(exclude_none=True), + ) + + content = response.results[0].content + + if isinstance(content, dict): + return json.dumps(content) + + return content diff --git a/lib/crewai-tools/src/crewai_tools/tools/oxylabs_google_search_scraper_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_google_search_scraper_tool/README.md new file mode 100644 index 000000000..e9448d2db --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_google_search_scraper_tool/README.md @@ -0,0 +1,50 @@ +# OxylabsGoogleSearchScraperTool + +Scrape any website with `OxylabsGoogleSearchScraperTool` + +## Installation + +``` +pip install 'crewai[tools]' oxylabs +``` + +## Example + +```python +from crewai_tools import OxylabsGoogleSearchScraperTool + +# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set +tool = OxylabsGoogleSearchScraperTool() + +result = tool.run(query="iPhone 16") + +print(result) +``` + +## Arguments + +- `username`: Oxylabs username. +- `password`: Oxylabs password. + +Get the credentials by creating an Oxylabs Account [here](https://oxylabs.io). + +## Advanced example + +Check out the Oxylabs [documentation](https://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/google/search/search) to get the full list of parameters. + +```python +from crewai_tools import OxylabsGoogleSearchScraperTool + +# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set +tool = OxylabsGoogleSearchScraperTool( + config={ + "parse": True, + "geo_location": "Paris, France", + "user_agent_type": "tablet", + } +) + +result = tool.run(query="iPhone 16") + +print(result) +``` diff --git a/tests/tools/agent_tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_google_search_scraper_tool/__init__.py similarity index 100% rename from tests/tools/agent_tools/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/oxylabs_google_search_scraper_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/oxylabs_google_search_scraper_tool/oxylabs_google_search_scraper_tool.py b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_google_search_scraper_tool/oxylabs_google_search_scraper_tool.py new file mode 100644 index 000000000..fbeee6dd1 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_google_search_scraper_tool/oxylabs_google_search_scraper_tool.py @@ -0,0 +1,172 @@ +from importlib.metadata import version +import json +import os +from platform import architecture, python_version +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field + + +try: + from oxylabs import RealtimeClient # type: ignore[import-untyped] + from oxylabs.sources.response import ( # type: ignore[import-untyped] + Response as OxylabsResponse, + ) + + OXYLABS_AVAILABLE = True +except ImportError: + RealtimeClient = Any + OxylabsResponse = Any + + OXYLABS_AVAILABLE = False + + +__all__ = ["OxylabsGoogleSearchScraperConfig", "OxylabsGoogleSearchScraperTool"] + + +class OxylabsGoogleSearchScraperArgs(BaseModel): + query: str = Field(description="Search query") + + +class OxylabsGoogleSearchScraperConfig(BaseModel): + """Google Search Scraper configuration options: + https://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/google/search/search. + """ + + domain: str | None = Field( + None, description="The domain to limit the search results to." + ) + start_page: int | None = Field(None, description="The starting page number.") + pages: int | None = Field(None, description="The number of pages to scrape.") + limit: int | None = Field( + None, description="Number of results to retrieve in each page." + ) + geo_location: str | None = Field(None, description="The Deliver to location.") + user_agent_type: str | None = Field(None, description="Device type and browser.") + render: str | None = Field(None, description="Enables JavaScript rendering.") + callback_url: str | None = Field(None, description="URL to your callback endpoint.") + context: list | None = Field( + None, + description="Additional advanced settings and controls for specialized requirements.", + ) + parse: bool | None = Field(None, description="True will return structured data.") + parsing_instructions: dict | None = Field( + None, description="Instructions for parsing the results." + ) + + +class OxylabsGoogleSearchScraperTool(BaseTool): + """Scrape Google Search results with OxylabsGoogleSearchScraperTool. + + Get Oxylabs account: + https://dashboard.oxylabs.io/en + + Args: + username (str): Oxylabs username. + password (str): Oxylabs password. + config: Configuration options. See ``OxylabsGoogleSearchScraperConfig`` + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, + validate_assignment=True, + ) + name: str = "Oxylabs Google Search Scraper tool" + description: str = "Scrape Google Search results with Oxylabs Google Search Scraper" + args_schema: type[BaseModel] = OxylabsGoogleSearchScraperArgs + + oxylabs_api: RealtimeClient + config: OxylabsGoogleSearchScraperConfig + package_dependencies: list[str] = Field(default_factory=lambda: ["oxylabs"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="OXYLABS_USERNAME", + description="Username for Oxylabs", + required=True, + ), + EnvVar( + name="OXYLABS_PASSWORD", + description="Password for Oxylabs", + required=True, + ), + ] + ) + + def __init__( + self, + username: str | None = None, + password: str | None = None, + config: OxylabsGoogleSearchScraperConfig | dict | None = None, + **kwargs, + ): + bits, _ = architecture() + sdk_type = ( + f"oxylabs-crewai-sdk-python/" + f"{version('crewai')} " + f"({python_version()}; {bits})" + ) + + if username is None or password is None: + username, password = self._get_credentials_from_env() + + if OXYLABS_AVAILABLE: + # import RealtimeClient to make it accessible for the current scope + from oxylabs import RealtimeClient + + kwargs["oxylabs_api"] = RealtimeClient( + username=username, + password=password, + sdk_type=sdk_type, + ) + else: + import click + + if click.confirm( + "You are missing the 'oxylabs' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run(["uv", "add", "oxylabs"], check=True) # noqa: S607 + from oxylabs import RealtimeClient + + kwargs["oxylabs_api"] = RealtimeClient( + username=username, + password=password, + sdk_type=sdk_type, + ) + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install oxylabs package") from e + else: + raise ImportError( + "`oxylabs` package not found, please run `uv add oxylabs`" + ) + + if config is None: + config = OxylabsGoogleSearchScraperConfig() + super().__init__(config=config, **kwargs) + + def _get_credentials_from_env(self) -> tuple[str, str]: + username = os.environ.get("OXYLABS_USERNAME") + password = os.environ.get("OXYLABS_PASSWORD") + if not username or not password: + raise ValueError( + "You must pass oxylabs username and password when instantiating the tool " + "or specify OXYLABS_USERNAME and OXYLABS_PASSWORD environment variables" + ) + return username, password + + def _run(self, query: str, **kwargs) -> str: + response = self.oxylabs_api.google.scrape_search( + query, + **self.config.model_dump(exclude_none=True), + ) + + content = response.results[0].content + + if isinstance(content, dict): + return json.dumps(content) + + return content diff --git a/lib/crewai-tools/src/crewai_tools/tools/oxylabs_universal_scraper_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_universal_scraper_tool/README.md new file mode 100644 index 000000000..82f345a65 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_universal_scraper_tool/README.md @@ -0,0 +1,69 @@ +# OxylabsUniversalScraperTool + +Scrape any website with `OxylabsUniversalScraperTool` + +## Installation + +``` +pip install 'crewai[tools]' oxylabs +``` + +## Example + +```python +from crewai_tools import OxylabsUniversalScraperTool + +# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set +tool = OxylabsUniversalScraperTool() + +result = tool.run(url="https://ip.oxylabs.io") + +print(result) +``` + +## Arguments + +- `username`: Oxylabs username. +- `password`: Oxylabs password. + +Get the credentials by creating an Oxylabs Account [here](https://oxylabs.io). + +## Advanced example + +Check out the Oxylabs [documentation](https://developers.oxylabs.io/scraper-apis/web-scraper-api/other-websites) to get the full list of parameters. + +```python +from crewai_tools import OxylabsUniversalScraperTool + +# make sure OXYLABS_USERNAME and OXYLABS_PASSWORD variables are set +tool = OxylabsUniversalScraperTool( + config={ + "render": "html", + "user_agent_type": "mobile", + "context": [ + {"key": "force_headers", "value": True}, + {"key": "force_cookies", "value": True}, + { + "key": "headers", + "value": { + "Custom-Header-Name": "custom header content", + }, + }, + { + "key": "cookies", + "value": [ + {"key": "NID", "value": "1234567890"}, + {"key": "1P JAR", "value": "0987654321"}, + ], + }, + {"key": "http_method", "value": "get"}, + {"key": "follow_redirects", "value": True}, + {"key": "successful_status_codes", "value": [808, 909]}, + ], + } +) + +result = tool.run(url="https://ip.oxylabs.io") + +print(result) +``` diff --git a/tests/tracing/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_universal_scraper_tool/__init__.py similarity index 100% rename from tests/tracing/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/oxylabs_universal_scraper_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/oxylabs_universal_scraper_tool/oxylabs_universal_scraper_tool.py b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_universal_scraper_tool/oxylabs_universal_scraper_tool.py new file mode 100644 index 000000000..fefc5008b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/oxylabs_universal_scraper_tool/oxylabs_universal_scraper_tool.py @@ -0,0 +1,163 @@ +from importlib.metadata import version +import json +import os +from platform import architecture, python_version +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field + + +try: + from oxylabs import RealtimeClient # type: ignore[import-untyped] + from oxylabs.sources.response import ( # type: ignore[import-untyped] + Response as OxylabsResponse, + ) + + OXYLABS_AVAILABLE = True +except ImportError: + RealtimeClient = Any + OxylabsResponse = Any + + OXYLABS_AVAILABLE = False + +__all__ = ["OxylabsUniversalScraperConfig", "OxylabsUniversalScraperTool"] + + +class OxylabsUniversalScraperArgs(BaseModel): + url: str = Field(description="Website URL") + + +class OxylabsUniversalScraperConfig(BaseModel): + """Universal Scraper configuration options: + https://developers.oxylabs.io/scraper-apis/web-scraper-api/other-websites. + """ + + geo_location: str | None = Field(None, description="The Deliver to location.") + user_agent_type: str | None = Field(None, description="Device type and browser.") + render: str | None = Field(None, description="Enables JavaScript rendering.") + callback_url: str | None = Field(None, description="URL to your callback endpoint.") + context: list | None = Field( + None, + description="Additional advanced settings and controls for specialized requirements.", + ) + parse: bool | None = Field(None, description="True will return structured data.") + parsing_instructions: dict | None = Field( + None, description="Instructions for parsing the results." + ) + + +class OxylabsUniversalScraperTool(BaseTool): + """Scrape any website with OxylabsUniversalScraperTool. + + Get Oxylabs account: + https://dashboard.oxylabs.io/en + + Args: + username (str): Oxylabs username. + password (str): Oxylabs password. + config: Configuration options. See ``OxylabsUniversalScraperConfig`` + """ + + model_config = ConfigDict( + arbitrary_types_allowed=True, + validate_assignment=True, + ) + name: str = "Oxylabs Universal Scraper tool" + description: str = "Scrape any url with Oxylabs Universal Scraper" + args_schema: type[BaseModel] = OxylabsUniversalScraperArgs + + oxylabs_api: RealtimeClient + config: OxylabsUniversalScraperConfig + package_dependencies: list[str] = Field(default_factory=lambda: ["oxylabs"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="OXYLABS_USERNAME", + description="Username for Oxylabs", + required=True, + ), + EnvVar( + name="OXYLABS_PASSWORD", + description="Password for Oxylabs", + required=True, + ), + ] + ) + + def __init__( + self, + username: str | None = None, + password: str | None = None, + config: OxylabsUniversalScraperConfig | dict | None = None, + **kwargs, + ): + bits, _ = architecture() + sdk_type = ( + f"oxylabs-crewai-sdk-python/" + f"{version('crewai')} " + f"({python_version()}; {bits})" + ) + + if username is None or password is None: + username, password = self._get_credentials_from_env() + + if OXYLABS_AVAILABLE: + # import RealtimeClient to make it accessible for the current scope + from oxylabs import RealtimeClient + + kwargs["oxylabs_api"] = RealtimeClient( + username=username, + password=password, + sdk_type=sdk_type, + ) + else: + import click + + if click.confirm( + "You are missing the 'oxylabs' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run(["uv", "add", "oxylabs"], check=True) # noqa: S607 + from oxylabs import RealtimeClient + + kwargs["oxylabs_api"] = RealtimeClient( + username=username, + password=password, + sdk_type=sdk_type, + ) + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install oxylabs package") from e + else: + raise ImportError( + "`oxylabs` package not found, please run `uv add oxylabs`" + ) + + if config is None: + config = OxylabsUniversalScraperConfig() + super().__init__(config=config, **kwargs) + + def _get_credentials_from_env(self) -> tuple[str, str]: + username = os.environ.get("OXYLABS_USERNAME") + password = os.environ.get("OXYLABS_PASSWORD") + if not username or not password: + raise ValueError( + "You must pass oxylabs username and password when instantiating the tool " + "or specify OXYLABS_USERNAME and OXYLABS_PASSWORD environment variables" + ) + return username, password + + def _run(self, url: str) -> str: + response = self.oxylabs_api.universal.scrape_url( + url, + **self.config.model_dump(exclude_none=True), + ) + + content = response.results[0].content + + if isinstance(content, dict): + return json.dumps(content) + + return content diff --git a/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/README.md b/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/README.md new file mode 100644 index 000000000..37f413561 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/README.md @@ -0,0 +1,153 @@ +# ParallelSearchTool + +Unified Parallel web search tool using the Parallel Search API (v1beta). Returns ranked results with compressed excerpts optimized for LLMs. + +- **Quickstart**: see the official docs: [Search API Quickstart](https://docs.parallel.ai/search-api/search-quickstart) +- **Processors**: guidance on `base` vs `pro`: [Processors](https://docs.parallel.ai/search-api/processors) + +## Why this tool + +- **Single-call pipeline**: Replaces search → scrape → extract with a single, low‑latency API call. +- **LLM‑ready**: Returns compressed excerpts that feed directly into LLM prompts (fewer tokens, less pre/post‑processing). +- **Flexible**: Control result count and excerpt length; optionally restrict sources via `source_policy`. + +## Environment + +- `PARALLEL_API_KEY` (required) + +Optional (for the agent example): +- `OPENAI_API_KEY` or other LLM provider keys supported by CrewAI + +## Parameters + +- `objective` (str, optional): Natural‑language research goal (≤ 5000 chars) +- `search_queries` (list[str], optional): Up to 5 keyword queries (each ≤ 200 chars) +- `processor` (str, default `base`): `base` (fast/low cost) or `pro` (freshness/quality) +- `max_results` (int, default 10): ≤ 40 (subject to processor limits) +- `max_chars_per_result` (int, default 6000): ≥ 100; values > 30000 not guaranteed +- `source_policy` (dict, optional): Source policy for domain inclusion/exclusion + +Notes: +- API is in beta; default rate limit is 600 RPM. Contact support for production capacity. + +## Direct usage (when published) + +```python +from crewai_tools import ParallelSearchTool + +tool = ParallelSearchTool() +resp_json = tool.run( + objective="When was the United Nations established? Prefer UN's websites.", + search_queries=["Founding year UN", "Year of founding United Nations"], + processor="base", + max_results=5, + max_chars_per_result=1500, +) +print(resp_json) # => {"search_id": ..., "results": [{"url", "title", "excerpts": [...]}, ...]} +``` + +### Parameters you can pass + +Call `run(...)` with any of the following (at least one of `objective` or `search_queries` is required): + +```python +tool.run( + objective: str | None = None, # ≤ 5000 chars + search_queries: list[str] | None = None, # up to 5 items, each ≤ 200 chars + processor: str = "base", # "base" (fast) or "pro" (freshness/quality) + max_results: int = 10, # ≤ 40 (processor limits apply) + max_chars_per_result: int = 6000, # ≥ 100 (values > 30000 not guaranteed) + source_policy: dict | None = None, # optional SourcePolicy config +) +``` + +Example with `source_policy`: + +```python +source_policy = { + "allow": {"domains": ["un.org"]}, + # "deny": {"domains": ["example.com"]}, # optional +} + +resp_json = tool.run( + objective="When was the United Nations established?", + processor="base", + max_results=5, + max_chars_per_result=1500, + source_policy=source_policy, +) +``` + +## Example with agents + +Here’s a minimal example that calls `ParallelSearchTool` to fetch sources and has an LLM produce a short, cited answer. + +```python +import os +from crewai import Agent, Task, Crew, LLM, Process +from crewai_tools import ParallelSearchTool + +# LLM +llm = LLM( + model="gemini/gemini-2.0-flash", + temperature=0.5, + api_key=os.getenv("GEMINI_API_KEY") +) + +# Parallel Search +search = ParallelSearchTool() + +# User query +query = "find all the recent concerns about AI evals? please cite the sources" + +# Researcher agent +researcher = Agent( + role="Web Researcher", + backstory="You are an expert web researcher", + goal="Find cited, high-quality sources and provide a brief answer.", + tools=[search], + llm=llm, + verbose=True, +) + +# Research task +task = Task( + description=f"Research the {query} and produce a short, cited answer.", + expected_output="A concise, sourced answer to the question. The answer should be in this format: [query]: [answer] - [source]", + agent=researcher, + output_file="answer.mdx", +) + +# Crew +crew = Crew( + agents=[researcher], + tasks=[task], + verbose=True, + process=Process.sequential, +) + +# Run the crew +result = crew.kickoff(inputs={'query': query}) +print(result) +``` + +Output from the agent above: + +```md +Recent concerns about AI evaluations include: the rise of AI-related incidents alongside a lack of standardized Responsible AI (RAI) evaluations among major industrial model developers - [https://hai.stanford.edu/ai-index/2025-ai-index-report]; flawed benchmark datasets that fail to account for critical factors, leading to unrealistic estimates of AI model abilities - [https://www.nature.com/articles/d41586-025-02462-5]; the need for multi-metric, context-aware evaluations in medical imaging AI to ensure reliability and clinical relevance - [https://www.sciencedirect.com/science/article/pii/S3050577125000283]; challenges related to data sets (insufficient, imbalanced, or poor quality), communication gaps, and misaligned expectations in AI model training - [https://www.oracle.com/artificial-intelligence/ai-model-training-challenges/]; the argument that LLM agents should be evaluated primarily on their riskiness, not just performance, due to unreliability, hallucinations, and brittleness - [https://www.technologyreview.com/2025/06/24/1119187/fix-ai-evaluation-crisis/]; the fact that the AI industry's embraced benchmarks may be close to meaningless, with top makers of AI models picking and choosing different responsible AI benchmarks, complicating efforts to systematically compare risks and limitations - [https://themarkup.org/artificial-intelligence/2024/07/17/everyone-is-judging-ai-by-these-tests-but-experts-say-theyre-close-to-meaningless]; and the difficulty of building robust and reliable model evaluations, as many existing evaluation suites are limited in their ability to serve as accurate indicators of model capabilities or safety - [https://www.anthropic.com/research/evaluating-ai-systems]. +``` + +Tips: +- Ensure your LLM provider keys are set (e.g., `GEMINI_API_KEY`) and CrewAI model config is in place. +- For longer analyses, raise `max_chars_per_result` or use `processor="pro"` (higher quality, higher latency). + +## Behavior + +- Single‑request web research; no scraping/post‑processing required. +- Returns `search_id` and ranked `results` with compressed `excerpts`. +- Clear error handling on HTTP/timeouts. + +## References + +- Search API Quickstart: https://docs.parallel.ai/search-api/search-quickstart +- Processors: https://docs.parallel.ai/search-api/processors diff --git a/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/__init__.py new file mode 100644 index 000000000..bdb07b309 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/__init__.py @@ -0,0 +1,6 @@ +from crewai_tools.tools.parallel_tools.parallel_search_tool import ParallelSearchTool + + +__all__ = [ + "ParallelSearchTool", +] diff --git a/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/parallel_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/parallel_search_tool.py new file mode 100644 index 000000000..b015ec695 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/parallel_tools/parallel_search_tool.py @@ -0,0 +1,125 @@ +import os +from typing import Annotated, Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +class ParallelSearchInput(BaseModel): + """Input schema for ParallelSearchTool using the Search API (v1beta). + + At least one of objective or search_queries is required. + """ + + objective: str | None = Field( + None, + description="Natural-language goal for the web research (<=5000 chars)", + max_length=5000, + ) + search_queries: list[Annotated[str, Field(max_length=200)]] | None = Field( + default=None, + description="Optional list of keyword queries (<=5 items, each <=200 chars)", + min_length=1, + max_length=5, + ) + processor: str = Field( + default="base", + description="Search processor: 'base' (fast/low cost) or 'pro' (higher quality/freshness)", + pattern=r"^(base|pro)$", + ) + max_results: int = Field( + default=10, + ge=1, + le=40, + description="Maximum number of search results to return (processor limits apply)", + ) + max_chars_per_result: int = Field( + default=6000, + ge=100, + description="Maximum characters per result excerpt (values >30000 not guaranteed)", + ) + source_policy: dict[str, Any] | None = Field( + default=None, description="Optional source policy configuration" + ) + + +class ParallelSearchTool(BaseTool): + name: str = "Parallel Web Search Tool" + description: str = ( + "Search the web using Parallel's Search API (v1beta). Returns ranked results with " + "compressed excerpts optimized for LLMs." + ) + args_schema: type[BaseModel] = ParallelSearchInput + + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="PARALLEL_API_KEY", + description="API key for Parallel", + required=True, + ), + ] + ) + package_dependencies: list[str] = Field(default_factory=lambda: ["requests"]) + + search_url: str = "https://api.parallel.ai/v1beta/search" + + def _run( + self, + objective: str | None = None, + search_queries: list[str] | None = None, + processor: str = "base", + max_results: int = 10, + max_chars_per_result: int = 6000, + source_policy: dict[str, Any] | None = None, + **_: Any, + ) -> str: + api_key = os.environ.get("PARALLEL_API_KEY") + if not api_key: + return "Error: PARALLEL_API_KEY environment variable is required" + + if not objective and not search_queries: + return "Error: Provide at least one of 'objective' or 'search_queries'" + + headers = { + "x-api-key": api_key, + "Content-Type": "application/json", + } + + try: + payload: dict[str, Any] = { + "processor": processor, + "max_results": max_results, + "max_chars_per_result": max_chars_per_result, + } + if objective is not None: + payload["objective"] = objective + if search_queries is not None: + payload["search_queries"] = search_queries + if source_policy is not None: + payload["source_policy"] = source_policy + + request_timeout = 90 if processor == "pro" else 30 + resp = requests.post( + self.search_url, json=payload, headers=headers, timeout=request_timeout + ) + if resp.status_code >= 300: + return ( + f"Parallel Search API error: {resp.status_code} {resp.text[:200]}" + ) + data = resp.json() + return self._format_output(data) + except requests.Timeout: + return "Parallel Search API timeout. Please try again later." + except Exception as exc: + return f"Unexpected error calling Parallel Search API: {exc}" + + def _format_output(self, result: dict[str, Any]) -> str: + # Return the full JSON payload (search_id + results) as a compact JSON string + try: + import json + + return json.dumps(result or {}, ensure_ascii=False) + except Exception: + return str(result or {}) diff --git a/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/__init__.py new file mode 100644 index 000000000..46415a4f3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/__init__.py @@ -0,0 +1,9 @@ +from crewai_tools.tools.patronus_eval_tool.patronus_eval_tool import ( + PatronusEvalTool as PatronusEvalTool, +) +from crewai_tools.tools.patronus_eval_tool.patronus_local_evaluator_tool import ( + PatronusLocalEvaluatorTool as PatronusLocalEvaluatorTool, +) +from crewai_tools.tools.patronus_eval_tool.patronus_predefined_criteria_eval_tool import ( + PatronusPredefinedCriteriaEvalTool as PatronusPredefinedCriteriaEvalTool, +) diff --git a/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/example.py b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/example.py new file mode 100644 index 000000000..949fae1fd --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/example.py @@ -0,0 +1,61 @@ +import random + +from crewai import Agent, Crew, Task +from patronus import ( # type: ignore[import-not-found,import-untyped] + Client, + EvaluationResult, +) +from patronus_local_evaluator_tool import ( # type: ignore[import-not-found,import-untyped] + PatronusLocalEvaluatorTool, +) + + +# Test the PatronusLocalEvaluatorTool where agent uses the local evaluator +client = Client() + + +# Example of an evaluator that returns a random pass/fail result +@client.register_local_evaluator("random_evaluator") +def random_evaluator(**kwargs): + score = random.random() # noqa: S311 + return EvaluationResult( + score_raw=score, + pass_=score >= 0.5, + explanation="example explanation", # Optional justification for LLM judges + ) + + +# 1. Uses PatronusEvalTool: agent can pick the best evaluator and criteria +# patronus_eval_tool = PatronusEvalTool() + +# 2. Uses PatronusPredefinedCriteriaEvalTool: agent uses the defined evaluator and criteria +# patronus_eval_tool = PatronusPredefinedCriteriaEvalTool( +# evaluators=[{"evaluator": "judge", "criteria": "contains-code"}] +# ) + +# 3. Uses PatronusLocalEvaluatorTool: agent uses user defined evaluator +patronus_eval_tool = PatronusLocalEvaluatorTool( + patronus_client=client, + evaluator="random_evaluator", + evaluated_model_gold_answer="example label", +) + +# Create a new agent +coding_agent = Agent( + role="Coding Agent", + goal="Generate high quality code and verify that the output is code by using Patronus AI's evaluation tool.", + backstory="You are an experienced coder who can generate high quality python code. You can follow complex instructions accurately and effectively.", + tools=[patronus_eval_tool], + verbose=True, +) + +# Define tasks +generate_code = Task( + description="Create a simple program to generate the first N numbers in the Fibonacci sequence. Select the most appropriate evaluator and criteria for evaluating your output.", + expected_output="Program that generates the first N numbers in the Fibonacci sequence.", + agent=coding_agent, +) + +crew = Crew(agents=[coding_agent], tasks=[generate_code]) + +crew.kickoff() diff --git a/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_eval_tool.py b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_eval_tool.py new file mode 100644 index 000000000..d5056f36a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_eval_tool.py @@ -0,0 +1,156 @@ +import json +import os +from typing import Any +import warnings + +from crewai.tools import BaseTool, EnvVar +from pydantic import Field +import requests + + +class PatronusEvalTool(BaseTool): + name: str = "Patronus Evaluation Tool" + evaluate_url: str = "https://api.patronus.ai/v1/evaluate" + evaluators: list[dict[str, str]] = Field(default_factory=list) + criteria: list[dict[str, str]] = Field(default_factory=list) + description: str = "" + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="PATRONUS_API_KEY", + description="API key for Patronus evaluation services", + required=True, + ), + ] + ) + + def __init__(self, **kwargs: Any): + super().__init__(**kwargs) + temp_evaluators, temp_criteria = self._init_run() + self.evaluators = temp_evaluators + self.criteria = temp_criteria + warnings.warn( + "You are allowing the agent to select the best evaluator and criteria when you use the `PatronusEvalTool`. If this is not intended then please use `PatronusPredefinedCriteriaEvalTool` instead.", + stacklevel=2, + ) + + def _init_run(self): + evaluators_set = json.loads( + requests.get( + "https://api.patronus.ai/v1/evaluators", + headers={ + "accept": "application/json", + "X-API-KEY": os.environ["PATRONUS_API_KEY"], + }, + timeout=30, + ).text + )["evaluators"] + ids, evaluators = set(), [] + for ev in evaluators_set: + if not ev["deprecated"] and ev["id"] not in ids: + evaluators.append( + { + "id": ev["id"], + "name": ev["name"], + "description": ev["description"], + "aliases": ev["aliases"], + } + ) + ids.add(ev["id"]) + + criteria_set = json.loads( + requests.get( + "https://api.patronus.ai/v1/evaluator-criteria", + headers={ + "accept": "application/json", + "X-API-KEY": os.environ["PATRONUS_API_KEY"], + }, + timeout=30, + ).text + )["evaluator_criteria"] + criteria = [] + for cr in criteria_set: + if cr["config"].get("pass_criteria", None): + if cr["config"].get("rubric", None): + criteria.append( + { + "evaluator": cr["evaluator_family"], + "name": cr["name"], + "pass_criteria": cr["config"]["pass_criteria"], + "rubric": cr["config"]["rubric"], + } + ) + else: + criteria.append( + { + "evaluator": cr["evaluator_family"], + "name": cr["name"], + "pass_criteria": cr["config"]["pass_criteria"], + } + ) + elif cr["description"]: + criteria.append( + { + "evaluator": cr["evaluator_family"], + "name": cr["name"], + "description": cr["description"], + } + ) + + return evaluators, criteria + + def _generate_description(self) -> None: + criteria = "\n".join([json.dumps(i) for i in self.criteria]) + self.description = f"""This tool calls the Patronus Evaluation API that takes the following arguments: + 1. evaluated_model_input: str: The agent's task description in simple text + 2. evaluated_model_output: str: The agent's output of the task + 3. evaluated_model_retrieved_context: str: The agent's context + 4. evaluators: This is a list of dictionaries containing one of the following evaluators and the corresponding criteria. An example input for this field: [{{"evaluator": "Judge", "criteria": "patronus:is-code"}}] + + Evaluators: + {criteria} + + You must ONLY choose the most appropriate evaluator and criteria based on the "pass_criteria" or "description" fields for your evaluation task and nothing from outside of the options present.""" + + def _run( + self, + evaluated_model_input: str | None, + evaluated_model_output: str | None, + evaluated_model_retrieved_context: str | None, + evaluators: list[dict[str, str]], + ) -> Any: + # Assert correct format of evaluators + evals = [] + for ev in evaluators: + evals.append( # noqa: PERF401 + { + "evaluator": ev["evaluator"].lower(), + "criteria": ev["name"] if "name" in ev else ev["criteria"], + } + ) + + data = { + "evaluated_model_input": evaluated_model_input, + "evaluated_model_output": evaluated_model_output, + "evaluated_model_retrieved_context": evaluated_model_retrieved_context, + "evaluators": evals, + } + + headers = { + "X-API-KEY": os.getenv("PATRONUS_API_KEY"), + "accept": "application/json", + "content-type": "application/json", + } + + response = requests.post( + self.evaluate_url, + headers=headers, + data=json.dumps(data), + timeout=30, + ) + if response.status_code != 200: + raise Exception( + f"Failed to evaluate model input and output. Response status code: {response.status_code}. Reason: {response.text}" + ) + + return response.json() diff --git a/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_local_evaluator_tool.py b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_local_evaluator_tool.py new file mode 100644 index 000000000..4eee439df --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_local_evaluator_tool.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, ConfigDict, Field + + +if TYPE_CHECKING: + from patronus import Client, EvaluationResult # type: ignore[import-untyped] + +try: + import patronus # noqa: F401 + + PYPATRONUS_AVAILABLE = True +except ImportError: + PYPATRONUS_AVAILABLE = False + + +class FixedLocalEvaluatorToolSchema(BaseModel): + evaluated_model_input: str = Field( + ..., description="The agent's task description in simple text" + ) + evaluated_model_output: str = Field( + ..., description="The agent's output of the task" + ) + evaluated_model_retrieved_context: str = Field( + ..., description="The agent's context" + ) + evaluated_model_gold_answer: str = Field( + ..., description="The agent's gold answer only if available" + ) + evaluator: str = Field(..., description="The registered local evaluator") + + +class PatronusLocalEvaluatorTool(BaseTool): + name: str = "Patronus Local Evaluator Tool" + description: str = "This tool is used to evaluate the model input and output using custom function evaluators." + args_schema: type[BaseModel] = FixedLocalEvaluatorToolSchema + client: Client = None + evaluator: str + evaluated_model_gold_answer: str + + model_config = ConfigDict(arbitrary_types_allowed=True) + package_dependencies: list[str] = Field(default_factory=lambda: ["patronus"]) + + def __init__( + self, + patronus_client: Client = None, + evaluator: str = "", + evaluated_model_gold_answer: str = "", + **kwargs: Any, + ): + super().__init__(**kwargs) + self.evaluator = evaluator + self.evaluated_model_gold_answer = evaluated_model_gold_answer + self._initialize_patronus(patronus_client) + + def _initialize_patronus(self, patronus_client: Client) -> None: + try: + if PYPATRONUS_AVAILABLE: + self.client = patronus_client + self._generate_description() + else: + raise ImportError + except ImportError: + import click + + if click.confirm( + "You are missing the 'patronus' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run(["uv", "add", "patronus"], check=True) # noqa: S607 + self.client = patronus_client + self._generate_description() + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install 'patronus' package") from e + else: + raise ImportError( + "`patronus` package not found, please run `uv add patronus`" + ) from None + + def _run( + self, + **kwargs: Any, + ) -> Any: + evaluated_model_input = kwargs.get("evaluated_model_input") + evaluated_model_output = kwargs.get("evaluated_model_output") + evaluated_model_retrieved_context = kwargs.get( + "evaluated_model_retrieved_context" + ) + evaluated_model_gold_answer = self.evaluated_model_gold_answer + evaluator = self.evaluator + + result: EvaluationResult = self.client.evaluate( + evaluator=evaluator, + evaluated_model_input=evaluated_model_input, + evaluated_model_output=evaluated_model_output, + evaluated_model_retrieved_context=evaluated_model_retrieved_context, + evaluated_model_gold_answer=evaluated_model_gold_answer, + tags={}, # Optional metadata, supports arbitrary key-value pairs + ) + return f"Evaluation result: {result.pass_}, Explanation: {result.explanation}" + + +try: + # Only rebuild if the class hasn't been initialized yet + if not hasattr(PatronusLocalEvaluatorTool, "_model_rebuilt"): + PatronusLocalEvaluatorTool.model_rebuild() + PatronusLocalEvaluatorTool._model_rebuilt = True # type: ignore[attr-defined] +except Exception: # noqa: S110 + pass diff --git a/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_predefined_criteria_eval_tool.py b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_predefined_criteria_eval_tool.py new file mode 100644 index 000000000..57eb091a8 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/patronus_eval_tool/patronus_predefined_criteria_eval_tool.py @@ -0,0 +1,105 @@ +import json +import os +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field +import requests + + +class FixedBaseToolSchema(BaseModel): + evaluated_model_input: dict = Field( + ..., description="The agent's task description in simple text" + ) + evaluated_model_output: dict = Field( + ..., description="The agent's output of the task" + ) + evaluated_model_retrieved_context: dict = Field( + ..., description="The agent's context" + ) + evaluated_model_gold_answer: dict = Field( + ..., description="The agent's gold answer only if available" + ) + evaluators: list[dict[str, str]] = Field( + ..., + description="List of dictionaries containing the evaluator and criteria to evaluate the model input and output. An example input for this field: [{'evaluator': '[evaluator-from-user]', 'criteria': '[criteria-from-user]'}]", + ) + + +class PatronusPredefinedCriteriaEvalTool(BaseTool): + """PatronusEvalTool is a tool to automatically evaluate and score agent interactions. + + Results are logged to the Patronus platform at app.patronus.ai + """ + + name: str = "Call Patronus API tool for evaluation of model inputs and outputs" + description: str = """This tool calls the Patronus Evaluation API that takes the following arguments:""" + evaluate_url: str = "https://api.patronus.ai/v1/evaluate" + args_schema: type[BaseModel] = FixedBaseToolSchema + evaluators: list[dict[str, str]] = Field(default_factory=list) + + def __init__(self, evaluators: list[dict[str, str]], **kwargs: Any): + super().__init__(**kwargs) + if evaluators: + self.evaluators = evaluators + self.description = f"This tool calls the Patronus Evaluation API that takes an additional argument in addition to the following new argument:\n evaluators={evaluators}" + self._generate_description() + + def _run( + self, + **kwargs: Any, + ) -> Any: + evaluated_model_input = kwargs.get("evaluated_model_input") + evaluated_model_output = kwargs.get("evaluated_model_output") + evaluated_model_retrieved_context = kwargs.get( + "evaluated_model_retrieved_context" + ) + evaluated_model_gold_answer = kwargs.get("evaluated_model_gold_answer") + evaluators = self.evaluators + + headers = { + "X-API-KEY": os.getenv("PATRONUS_API_KEY"), + "accept": "application/json", + "content-type": "application/json", + } + + data = { + "evaluated_model_input": ( + evaluated_model_input + if isinstance(evaluated_model_input, str) + else evaluated_model_input.get("description") # type: ignore[union-attr] + ), + "evaluated_model_output": ( + evaluated_model_output + if isinstance(evaluated_model_output, str) + else evaluated_model_output.get("description") # type: ignore[union-attr] + ), + "evaluated_model_retrieved_context": ( + evaluated_model_retrieved_context + if isinstance(evaluated_model_retrieved_context, str) + else evaluated_model_retrieved_context.get("description") # type: ignore[union-attr] + ), + "evaluated_model_gold_answer": ( + evaluated_model_gold_answer + if isinstance(evaluated_model_gold_answer, str) + else evaluated_model_gold_answer.get("description") # type: ignore[union-attr] + ), + "evaluators": ( + evaluators + if isinstance(evaluators, list) + else evaluators.get("description") + ), + } + + response = requests.post( + self.evaluate_url, + headers=headers, + data=json.dumps(data), + timeout=30, + ) + if response.status_code != 200: + raise Exception( + f"Failed to evaluate model input and output. Status code: {response.status_code}. Reason: {response.text}" + ) + + return response.json() diff --git a/lib/crewai-tools/src/crewai_tools/tools/pdf_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/pdf_search_tool/README.md new file mode 100644 index 000000000..a4bf5d8ed --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/pdf_search_tool/README.md @@ -0,0 +1,57 @@ +# PDFSearchTool + +## Description +The PDFSearchTool is a RAG tool designed for semantic searches within PDF content. It allows for inputting a search query and a PDF document, leveraging advanced search techniques to find relevant content efficiently. This capability makes it especially useful for extracting specific information from large PDF files quickly. + +## Installation +To get started with the PDFSearchTool, first, ensure the crewai_tools package is installed with the following command: + +```shell +pip install 'crewai[tools]' +``` + +## Example +Here's how to use the PDFSearchTool to search within a PDF document: + +```python +from crewai_tools import PDFSearchTool + +# Initialize the tool allowing for any PDF content search if the path is provided during execution +tool = PDFSearchTool() + +# OR + +# Initialize the tool with a specific PDF path for exclusive search within that document +tool = PDFSearchTool(pdf='path/to/your/document.pdf') +``` + +## Arguments +- `pdf`: **Optinal** The PDF path for the search. Can be provided at initialization or within the `run` method's arguments. If provided at initialization, the tool confines its search to the specified document. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = PDFSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/tests/utilities/crew/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/pdf_search_tool/__init__.py similarity index 100% rename from tests/utilities/crew/__init__.py rename to lib/crewai-tools/src/crewai_tools/tools/pdf_search_tool/__init__.py diff --git a/lib/crewai-tools/src/crewai_tools/tools/pdf_search_tool/pdf_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/pdf_search_tool/pdf_search_tool.py new file mode 100644 index 000000000..049745d45 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/pdf_search_tool/pdf_search_tool.py @@ -0,0 +1,50 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedPDFSearchToolSchema(BaseModel): + """Input for PDFSearchTool.""" + + query: str = Field( + ..., description="Mandatory query you want to use to search the PDF's content" + ) + + +class PDFSearchToolSchema(FixedPDFSearchToolSchema): + """Input for PDFSearchTool.""" + + pdf: str = Field(..., description="File path or URL of a PDF file to be searched") + + +class PDFSearchTool(RagTool): + name: str = "Search a PDF's content" + description: str = ( + "A tool that can be used to semantic search a query from a PDF's content." + ) + args_schema: type[BaseModel] = PDFSearchToolSchema + + def __init__(self, pdf: str | None = None, **kwargs): + super().__init__(**kwargs) + if pdf is not None: + self.add(pdf) + self.description = f"A tool that can be used to semantic search a query the {pdf} PDF's content." + self.args_schema = FixedPDFSearchToolSchema + self._generate_description() + + def add(self, pdf: str) -> None: + super().add(pdf, data_type=DataType.PDF_FILE) + + def _run( # type: ignore[override] + self, + query: str, + pdf: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if pdf is not None: + self.add(pdf) + return super()._run( + query=query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/README.md new file mode 100644 index 000000000..26ad9a15f --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/README.md @@ -0,0 +1,49 @@ +# QdrantVectorSearchTool + +## Description + +This tool is specifically crafted for conducting semantic searches within docs within a Qdrant vector database. Use this tool to find semantically similar docs to a given query. + +Qdrant is a vector database that is used to store and query vector embeddings. You can follow their docs here: https://qdrant.tech/documentation/ + +## Installation + +Install the crewai_tools package by executing the following command in your terminal: + +```shell +uv pip install 'crewai[tools] qdrant-client openai' +``` + +## Example + +To utilize the QdrantVectorSearchTool for different use cases, follow these examples: Default model is openai. + +```python +from crewai_tools import QdrantVectorSearchTool + +# To enable the tool to search any website the agent comes across or learns about during its operation +tool = QdrantVectorSearchTool( + collection_name="example_collections", + limit=3, + qdrant_url="https://your-qdrant-cluster-url.com", + qdrant_api_key="your-qdrant-api-key", # (optional) +) + + +# Adding the tool to an agent +rag_agent = Agent( + name="rag_agent", + role="You are a helpful assistant that can answer questions with the help of the QdrantVectorSearchTool. Retrieve the most relevant docs from the Qdrant database.", + llm="gpt-4o-mini", + tools=[tool], +) +``` + +## Arguments + +- `collection_name` : The name of the collection to search within. (Required) +- `qdrant_url` : The URL of the Qdrant cluster. (Required) +- `qdrant_api_key` : The API key for the Qdrant cluster. (Optional) +- `limit` : The number of results to return. (Optional) +- `vectorizer` : The vectorizer to use. (Optional) + diff --git a/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py new file mode 100644 index 000000000..c1a88114e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py @@ -0,0 +1,189 @@ +from collections.abc import Callable +import json +import os +from typing import Any + + +try: + from qdrant_client import QdrantClient + from qdrant_client.http.models import FieldCondition, Filter, MatchValue + + QDRANT_AVAILABLE = True +except ImportError: + QDRANT_AVAILABLE = False + QdrantClient = Any # type: ignore[assignment,misc] # type placeholder + Filter = Any # type: ignore[assignment,misc] + FieldCondition = Any # type: ignore[assignment,misc] + MatchValue = Any # type: ignore[assignment,misc] + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field + + +class QdrantToolSchema(BaseModel): + """Input for QdrantTool.""" + + query: str = Field( + ..., + description="The query to search retrieve relevant information from the Qdrant database. Pass only the query, not the question.", + ) + filter_by: str | None = Field( + default=None, + description="Filter by properties. Pass only the properties, not the question.", + ) + filter_value: str | None = Field( + default=None, + description="Filter by value. Pass only the value, not the question.", + ) + + +class QdrantVectorSearchTool(BaseTool): + """Tool to query and filter results from a Qdrant database. + + This tool enables vector similarity search on internal documents stored in Qdrant, + with optional filtering capabilities. + + Attributes: + client: Configured QdrantClient instance + collection_name: Name of the Qdrant collection to search + limit: Maximum number of results to return + score_threshold: Minimum similarity score threshold + qdrant_url: Qdrant server URL + qdrant_api_key: Authentication key for Qdrant + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + client: QdrantClient = None # type: ignore[assignment] + name: str = "QdrantVectorSearchTool" + description: str = "A tool to search the Qdrant database for relevant information on internal documents." + args_schema: type[BaseModel] = QdrantToolSchema + query: str | None = None + filter_by: str | None = None + filter_value: str | None = None + collection_name: str | None = None + limit: int | None = Field(default=3) + score_threshold: float = Field(default=0.35) + qdrant_url: str = Field( + ..., + description="The URL of the Qdrant server", + ) + qdrant_api_key: str | None = Field( + default=None, + description="The API key for the Qdrant server", + ) + custom_embedding_fn: Callable | None = Field( + default=None, + description="A custom embedding function to use for vectorization. If not provided, the default model will be used.", + ) + package_dependencies: list[str] = Field(default_factory=lambda: ["qdrant-client"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="OPENAI_API_KEY", description="API key for OpenAI", required=True + ) + ] + ) + + def __init__(self, **kwargs): + super().__init__(**kwargs) + if QDRANT_AVAILABLE: + self.client = QdrantClient( + url=self.qdrant_url, + api_key=self.qdrant_api_key if self.qdrant_api_key else None, + ) + else: + import click + + if click.confirm( + "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. " + "Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "qdrant-client"], check=True) # noqa: S607 + else: + raise ImportError( + "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. " + "Please install it with: uv add qdrant-client" + ) + + def _run( + self, + query: str, + filter_by: str | None = None, + filter_value: str | None = None, + ) -> str: + """Execute vector similarity search on Qdrant. + + Args: + query: Search query to vectorize and match + filter_by: Optional metadata field to filter on + filter_value: Optional value to filter by + + Returns: + JSON string containing search results with metadata and scores + + Raises: + ImportError: If qdrant-client is not installed + ValueError: If Qdrant credentials are missing + """ + if not self.qdrant_url: + raise ValueError("QDRANT_URL is not set") + + # Create filter if filter parameters are provided + search_filter = None + if filter_by and filter_value: + search_filter = Filter( + must=[ + FieldCondition(key=filter_by, match=MatchValue(value=filter_value)) + ] + ) + + # Search in Qdrant using the built-in query method + query_vector = ( + self._vectorize_query(query, embedding_model="text-embedding-3-large") + if not self.custom_embedding_fn + else self.custom_embedding_fn(query) + ) + search_results = self.client.query_points( + collection_name=self.collection_name, # type: ignore[arg-type] + query=query_vector, + query_filter=search_filter, + limit=self.limit, # type: ignore[arg-type] + score_threshold=self.score_threshold, + ) + + # Format results similar to storage implementation + results = [] + # Extract the list of ScoredPoint objects from the tuple + for point in search_results: + result = { + "metadata": point[1][0].payload.get("metadata", {}), + "context": point[1][0].payload.get("text", ""), + "distance": point[1][0].score, + } + results.append(result) + + return json.dumps(results, indent=2) + + def _vectorize_query(self, query: str, embedding_model: str) -> list[float]: + """Default vectorization function with openai. + + Args: + query (str): The query to vectorize + embedding_model (str): The embedding model to use + + Returns: + list[float]: The vectorized query + """ + import openai + + client = openai.Client(api_key=os.getenv("OPENAI_API_KEY")) + return ( + client.embeddings.create( + input=[query], + model=embedding_model, + ) + .data[0] + .embedding + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/rag/README.md b/lib/crewai-tools/src/crewai_tools/tools/rag/README.md new file mode 100644 index 000000000..b432a1a69 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/rag/README.md @@ -0,0 +1,61 @@ +# RagTool: A Dynamic Knowledge Base Tool + +RagTool is designed to answer questions by leveraging the power of RAG by leveraging (EmbedChain). It integrates seamlessly with the CrewAI ecosystem, offering a versatile and powerful solution for information retrieval. + +## **Overview** + +RagTool enables users to dynamically query a knowledge base, making it an ideal tool for applications requiring access to a vast array of information. Its flexible design allows for integration with various data sources, including files, directories, web pages, yoututbe videos and custom configurations. + +## **Usage** + +RagTool can be instantiated with data from different sources, including: + +- 📰 PDF file +- 📊 CSV file +- 📃 JSON file +- 📝 Text +- 📁 Directory/ Folder +- 🌐 HTML Web page +- 📽️ Youtube Channel +- 📺 Youtube Video +- 📚 Docs website +- 📝 MDX file +- 📄 DOCX file +- 🧾 XML file +- 📬 Gmail +- 📝 Github +- 🐘 Postgres +- 🐬 MySQL +- 🤖 Slack +- 💬 Discord +- 🗨️ Discourse +- 📝 Substack +- 🐝 Beehiiv +- 💾 Dropbox +- 🖼️ Image +- ⚙️ Custom + +#### **Creating an Instance** + +```python +from crewai_tools.tools.rag_tool import RagTool + +# Example: Loading from a file +rag_tool = RagTool().from_file('path/to/your/file.txt') + +# Example: Loading from a directory +rag_tool = RagTool().from_directory('path/to/your/directory') + +# Example: Loading from a web page +rag_tool = RagTool().from_web_page('https://example.com') +``` + +## **Contribution** + +Contributions to RagTool and the broader CrewAI tools ecosystem are welcome. To contribute, please follow the standard GitHub workflow for forking the repository, making changes, and submitting a pull request. + +## **License** + +RagTool is open-source and available under the MIT license. + +Thank you for considering RagTool for your knowledge base needs. Your contributions and feedback are invaluable to making RagTool even better. diff --git a/lib/crewai-tools/src/crewai_tools/tools/rag/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/rag/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/rag/rag_tool.py b/lib/crewai-tools/src/crewai_tools/tools/rag/rag_tool.py new file mode 100644 index 000000000..743946226 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/rag/rag_tool.py @@ -0,0 +1,202 @@ +from abc import ABC, abstractmethod +import os +from typing import Any, cast + +from crewai.rag.embeddings.factory import get_embedding_function +from crewai.tools import BaseTool +from pydantic import BaseModel, ConfigDict, Field, model_validator + + +class Adapter(BaseModel, ABC): + model_config = ConfigDict(arbitrary_types_allowed=True) + + @abstractmethod + def query( + self, + question: str, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + """Query the knowledge base with a question and return the answer.""" + + @abstractmethod + def add( + self, + *args: Any, + **kwargs: Any, + ) -> None: + """Add content to the knowledge base.""" + + +class RagTool(BaseTool): + class _AdapterPlaceholder(Adapter): + def query( + self, + question: str, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + raise NotImplementedError + + def add(self, *args: Any, **kwargs: Any) -> None: + raise NotImplementedError + + name: str = "Knowledge base" + description: str = "A knowledge base that can be used to answer questions." + summarize: bool = False + similarity_threshold: float = 0.6 + limit: int = 5 + adapter: Adapter = Field(default_factory=_AdapterPlaceholder) + config: Any | None = None + + @model_validator(mode="after") + def _set_default_adapter(self): + if isinstance(self.adapter, RagTool._AdapterPlaceholder): + from crewai_tools.adapters.crewai_rag_adapter import CrewAIRagAdapter + + parsed_config = self._parse_config(self.config) + + self.adapter = CrewAIRagAdapter( + collection_name="rag_tool_collection", + summarize=self.summarize, + similarity_threshold=self.similarity_threshold, + limit=self.limit, + config=parsed_config, + ) + + return self + + def _parse_config(self, config: Any) -> Any: + """Parse complex config format to extract provider-specific config. + + Raises: + ValueError: If the config format is invalid or uses unsupported providers. + """ + if config is None: + return None + + if isinstance(config, dict) and "provider" in config: + return config + + if isinstance(config, dict): + if "vectordb" in config: + vectordb_config = config["vectordb"] + if isinstance(vectordb_config, dict) and "provider" in vectordb_config: + provider = vectordb_config["provider"] + provider_config = vectordb_config.get("config", {}) + + supported_providers = ["chromadb", "qdrant"] + if provider not in supported_providers: + raise ValueError( + f"Unsupported vector database provider: '{provider}'. " + f"CrewAI RAG currently supports: {', '.join(supported_providers)}." + ) + + embedding_config = config.get("embedding_model") + embedding_function = None + if embedding_config and isinstance(embedding_config, dict): + embedding_function = self._create_embedding_function( + embedding_config, provider + ) + + return self._create_provider_config( + provider, provider_config, embedding_function + ) + return None + embedding_config = config.get("embedding_model") + embedding_function = None + if embedding_config and isinstance(embedding_config, dict): + embedding_function = self._create_embedding_function( + embedding_config, "chromadb" + ) + + return self._create_provider_config("chromadb", {}, embedding_function) + return config + + @staticmethod + def _create_embedding_function(embedding_config: dict, provider: str) -> Any: + """Create embedding function for the specified vector database provider.""" + embedding_provider = embedding_config.get("provider") + embedding_model_config = embedding_config.get("config", {}).copy() + + if "model" in embedding_model_config: + embedding_model_config["model_name"] = embedding_model_config.pop("model") + + factory_config = {"provider": embedding_provider, **embedding_model_config} + + if embedding_provider == "openai" and "api_key" not in factory_config: + api_key = os.getenv("OPENAI_API_KEY") + if api_key: + factory_config["api_key"] = api_key + + if provider == "chromadb": + return get_embedding_function(factory_config) # type: ignore[call-overload] + + if provider == "qdrant": + chromadb_func = get_embedding_function(factory_config) # type: ignore[call-overload] + + def qdrant_embed_fn(text: str) -> list[float]: + """Embed text using ChromaDB function and convert to list of floats for Qdrant. + + Args: + text: The input text to embed. + + Returns: + A list of floats representing the embedding. + """ + embeddings = chromadb_func([text]) + return embeddings[0] if embeddings and len(embeddings) > 0 else [] + + return cast(Any, qdrant_embed_fn) + + return None + + @staticmethod + def _create_provider_config( + provider: str, provider_config: dict, embedding_function: Any + ) -> Any: + """Create proper provider config object.""" + if provider == "chromadb": + from crewai.rag.chromadb.config import ChromaDBConfig + + config_kwargs = {} + if embedding_function: + config_kwargs["embedding_function"] = embedding_function + + config_kwargs.update(provider_config) + + return ChromaDBConfig(**config_kwargs) + + if provider == "qdrant": + from crewai.rag.qdrant.config import QdrantConfig + + config_kwargs = {} + if embedding_function: + config_kwargs["embedding_function"] = embedding_function + + config_kwargs.update(provider_config) + + return QdrantConfig(**config_kwargs) + + return None + + def add( + self, + *args: Any, + **kwargs: Any, + ) -> None: + self.adapter.add(*args, **kwargs) + + def _run( + self, + query: str, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + threshold = ( + similarity_threshold + if similarity_threshold is not None + else self.similarity_threshold + ) + result_limit = limit if limit is not None else self.limit + return f"Relevant Content:\n{self.adapter.query(query, similarity_threshold=threshold, limit=result_limit)}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrape_element_from_website/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/scrape_element_from_website/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrape_element_from_website/scrape_element_from_website.py b/lib/crewai-tools/src/crewai_tools/tools/scrape_element_from_website/scrape_element_from_website.py new file mode 100644 index 000000000..0f20142aa --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/scrape_element_from_website/scrape_element_from_website.py @@ -0,0 +1,92 @@ +import os +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field +import requests + + +try: + from bs4 import BeautifulSoup + + BEAUTIFULSOUP_AVAILABLE = True +except ImportError: + BEAUTIFULSOUP_AVAILABLE = False + + +class FixedScrapeElementFromWebsiteToolSchema(BaseModel): + """Input for ScrapeElementFromWebsiteTool.""" + + +class ScrapeElementFromWebsiteToolSchema(FixedScrapeElementFromWebsiteToolSchema): + """Input for ScrapeElementFromWebsiteTool.""" + + website_url: str = Field(..., description="Mandatory website url to read the file") + css_element: str = Field( + ..., + description="Mandatory css reference for element to scrape from the website", + ) + + +class ScrapeElementFromWebsiteTool(BaseTool): + name: str = "Read a website content" + description: str = "A tool that can be used to read a website content." + args_schema: type[BaseModel] = ScrapeElementFromWebsiteToolSchema + website_url: str | None = None + cookies: dict | None = None + css_element: str | None = None + headers: dict | None = Field( + default_factory=lambda: { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36", + "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", + "Accept-Language": "en-US,en;q=0.9", + "Referer": "https://www.google.com/", + "Connection": "keep-alive", + "Upgrade-Insecure-Requests": "1", + "Accept-Encoding": "gzip, deflate, br", + } + ) + + def __init__( + self, + website_url: str | None = None, + cookies: dict | None = None, + css_element: str | None = None, + **kwargs, + ): + super().__init__(**kwargs) + if website_url is not None: + self.website_url = website_url + self.css_element = css_element + self.description = ( + f"A tool that can be used to read {website_url}'s content." + ) + self.args_schema = FixedScrapeElementFromWebsiteToolSchema + self._generate_description() + if cookies is not None: + self.cookies = {cookies["name"]: os.getenv(cookies["value"])} + + def _run( + self, + **kwargs: Any, + ) -> Any: + if not BEAUTIFULSOUP_AVAILABLE: + raise ImportError( + "beautifulsoup4 is not installed. Please install it with `pip install crewai-tools[beautifulsoup4]`" + ) + + website_url = kwargs.get("website_url", self.website_url) + css_element = kwargs.get("css_element", self.css_element) + + if website_url is None or css_element is None: + raise ValueError("Both website_url and css_element must be provided.") + + page = requests.get( + website_url, + headers=self.headers, + cookies=self.cookies if self.cookies else {}, + timeout=30, + ) + parsed = BeautifulSoup(page.content, "html.parser") + elements = parsed.select(css_element) + return "\n".join([element.get_text() for element in elements]) diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrape_website_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/scrape_website_tool/README.md new file mode 100644 index 000000000..6a933c355 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/scrape_website_tool/README.md @@ -0,0 +1,24 @@ +# ScrapeWebsiteTool + +## Description +A tool designed to extract and read the content of a specified website. It is capable of handling various types of web pages by making HTTP requests and parsing the received HTML content. This tool can be particularly useful for web scraping tasks, data collection, or extracting specific information from websites. + +## Installation +Install the crewai_tools package +```shell +pip install 'crewai[tools]' +``` + +## Example +```python +from crewai_tools import ScrapeWebsiteTool + +# To enable scrapping any website it finds during it's execution +tool = ScrapeWebsiteTool() + +# Initialize the tool with the website URL, so the agent can only scrap the content of the specified website +tool = ScrapeWebsiteTool(website_url='https://www.example.com') +``` + +## Arguments +- `website_url` : Mandatory website URL to read the file. This is the primary input for the tool, specifying which website's content should be scraped and read. \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrape_website_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/scrape_website_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrape_website_tool/scrape_website_tool.py b/lib/crewai-tools/src/crewai_tools/tools/scrape_website_tool/scrape_website_tool.py new file mode 100644 index 000000000..c539d16cb --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/scrape_website_tool/scrape_website_tool.py @@ -0,0 +1,89 @@ +import os +import re +from typing import Any + +from pydantic import Field +import requests + + +try: + from bs4 import BeautifulSoup + + BEAUTIFULSOUP_AVAILABLE = True +except ImportError: + BEAUTIFULSOUP_AVAILABLE = False +from crewai.tools import BaseTool +from pydantic import BaseModel + + +class FixedScrapeWebsiteToolSchema(BaseModel): + """Input for ScrapeWebsiteTool.""" + + +class ScrapeWebsiteToolSchema(FixedScrapeWebsiteToolSchema): + """Input for ScrapeWebsiteTool.""" + + website_url: str = Field(..., description="Mandatory website url to read the file") + + +class ScrapeWebsiteTool(BaseTool): + name: str = "Read website content" + description: str = "A tool that can be used to read a website content." + args_schema: type[BaseModel] = ScrapeWebsiteToolSchema + website_url: str | None = None + cookies: dict | None = None + headers: dict | None = Field( + default_factory=lambda: { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36", + "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", + "Accept-Language": "en-US,en;q=0.9", + "Referer": "https://www.google.com/", + "Connection": "keep-alive", + "Upgrade-Insecure-Requests": "1", + } + ) + + def __init__( + self, + website_url: str | None = None, + cookies: dict | None = None, + **kwargs, + ): + super().__init__(**kwargs) + if not BEAUTIFULSOUP_AVAILABLE: + raise ImportError( + "beautifulsoup4 is not installed. Please install it with `pip install crewai-tools[beautifulsoup4]`" + ) + + if website_url is not None: + self.website_url = website_url + self.description = ( + f"A tool that can be used to read {website_url}'s content." + ) + self.args_schema = FixedScrapeWebsiteToolSchema + self._generate_description() + if cookies is not None: + self.cookies = {cookies["name"]: os.getenv(cookies["value"])} + + def _run( + self, + **kwargs: Any, + ) -> Any: + website_url: str | None = kwargs.get("website_url", self.website_url) + if website_url is None: + raise ValueError("Website URL must be provided.") + + page = requests.get( + website_url, + timeout=15, + headers=self.headers, + cookies=self.cookies if self.cookies else {}, + ) + + page.encoding = page.apparent_encoding + parsed = BeautifulSoup(page.text, "html.parser") + + text = "The following text is scraped website content:\n\n" + text += parsed.get_text(" ") + text = re.sub("[ \t]+", " ", text) + return re.sub("\\s+\n\\s+", "\n", text) diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrapegraph_scrape_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/scrapegraph_scrape_tool/README.md new file mode 100644 index 000000000..e006c0ff9 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/scrapegraph_scrape_tool/README.md @@ -0,0 +1,84 @@ +# ScrapegraphScrapeTool + +## Description +A tool that leverages Scrapegraph AI's SmartScraper API to intelligently extract content from websites. This tool provides advanced web scraping capabilities with AI-powered content extraction, making it ideal for targeted data collection and content analysis tasks. + +## Installation +Install the required packages: +```shell +pip install 'crewai[tools]' +``` + +## Example Usage + +### Basic Usage +```python +from crewai_tools import ScrapegraphScrapeTool + +# Basic usage with API key +tool = ScrapegraphScrapeTool(api_key="your_api_key") +result = tool.run( + website_url="https://www.example.com", + user_prompt="Extract the main heading and summary" +) +``` + +### Fixed Website URL +```python +# Initialize with a fixed website URL +tool = ScrapegraphScrapeTool( + website_url="https://www.example.com", + api_key="your_api_key" +) +result = tool.run() +``` + +### Custom Prompt +```python +# With custom prompt +tool = ScrapegraphScrapeTool( + api_key="your_api_key", + user_prompt="Extract all product prices and descriptions" +) +result = tool.run(website_url="https://www.example.com") +``` + +### Error Handling +```python +try: + tool = ScrapegraphScrapeTool(api_key="your_api_key") + result = tool.run( + website_url="https://www.example.com", + user_prompt="Extract the main heading" + ) +except ValueError as e: + print(f"Configuration error: {e}") # Handles invalid URLs or missing API keys +except RuntimeError as e: + print(f"Scraping error: {e}") # Handles API or network errors +``` + +## Arguments +- `website_url`: The URL of the website to scrape (required if not set during initialization) +- `user_prompt`: Custom instructions for content extraction (optional) +- `api_key`: Your Scrapegraph API key (required, can be set via SCRAPEGRAPH_API_KEY environment variable) + +## Environment Variables +- `SCRAPEGRAPH_API_KEY`: Your Scrapegraph API key, you can obtain one [here](https://scrapegraphai.com) + +## Rate Limiting +The Scrapegraph API has rate limits that vary based on your subscription plan. Consider the following best practices: +- Implement appropriate delays between requests when processing multiple URLs +- Handle rate limit errors gracefully in your application +- Check your API plan limits on the Scrapegraph dashboard + +## Error Handling +The tool may raise the following exceptions: +- `ValueError`: When API key is missing or URL format is invalid +- `RuntimeError`: When scraping operation fails (network issues, API errors) +- `RateLimitError`: When API rate limits are exceeded + +## Best Practices +1. Always validate URLs before making requests +2. Implement proper error handling as shown in examples +3. Consider caching results for frequently accessed pages +4. Monitor your API usage through the Scrapegraph dashboard diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrapegraph_scrape_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/scrapegraph_scrape_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrapegraph_scrape_tool/scrapegraph_scrape_tool.py b/lib/crewai-tools/src/crewai_tools/tools/scrapegraph_scrape_tool/scrapegraph_scrape_tool.py new file mode 100644 index 000000000..d65df160c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/scrapegraph_scrape_tool/scrapegraph_scrape_tool.py @@ -0,0 +1,197 @@ +from __future__ import annotations + +import os +from typing import TYPE_CHECKING, Any +from urllib.parse import urlparse + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, ConfigDict, Field, field_validator + + +# Type checking import +if TYPE_CHECKING: + from scrapegraph_py import Client # type: ignore[import-untyped] + + +class ScrapegraphError(Exception): + """Base exception for Scrapegraph-related errors.""" + + +class RateLimitError(ScrapegraphError): + """Raised when API rate limits are exceeded.""" + + +class FixedScrapegraphScrapeToolSchema(BaseModel): + """Input for ScrapegraphScrapeTool when website_url is fixed.""" + + +class ScrapegraphScrapeToolSchema(FixedScrapegraphScrapeToolSchema): + """Input for ScrapegraphScrapeTool.""" + + website_url: str = Field(..., description="Mandatory website url to scrape") + user_prompt: str = Field( + default="Extract the main content of the webpage", + description="Prompt to guide the extraction of content", + ) + + @field_validator("website_url") + @classmethod + def validate_url(cls, v): + """Validate URL format.""" + try: + result = urlparse(v) + if not all([result.scheme, result.netloc]): + raise ValueError + return v + except Exception as e: + raise ValueError( + "Invalid URL format. URL must include scheme (http/https) and domain" + ) from e + + +class ScrapegraphScrapeTool(BaseTool): + """A tool that uses Scrapegraph AI to intelligently scrape website content. + + Raises: + ValueError: If API key is missing or URL format is invalid + RateLimitError: If API rate limits are exceeded + RuntimeError: If scraping operation fails + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + + name: str = "Scrapegraph website scraper" + description: str = ( + "A tool that uses Scrapegraph AI to intelligently scrape website content." + ) + args_schema: type[BaseModel] = ScrapegraphScrapeToolSchema + website_url: str | None = None + user_prompt: str | None = None + api_key: str | None = None + enable_logging: bool = False + _client: Client | None = None + package_dependencies: list[str] = Field(default_factory=lambda: ["scrapegraph-py"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SCRAPEGRAPH_API_KEY", + description="API key for Scrapegraph AI services", + required=False, + ), + ] + ) + + def __init__( + self, + website_url: str | None = None, + user_prompt: str | None = None, + api_key: str | None = None, + enable_logging: bool = False, + **kwargs, + ): + super().__init__(**kwargs) + try: + from scrapegraph_py import Client # type: ignore[import-not-found] + from scrapegraph_py.logger import ( # type: ignore[import-not-found] + sgai_logger, + ) + + except ImportError: + import click + + if click.confirm( + "You are missing the 'scrapegraph-py' package. Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "scrapegraph-py"], check=True) # noqa: S607 + from scrapegraph_py import Client # type: ignore[import-untyped] + from scrapegraph_py.logger import ( # type: ignore[import-untyped] + sgai_logger, + ) + + else: + raise ImportError( + "`scrapegraph-py` package not found, please run `uv add scrapegraph-py`" + ) from None + + self.api_key = api_key or os.getenv("SCRAPEGRAPH_API_KEY") + self._client = Client(api_key=self.api_key) + + if not self.api_key: + raise ValueError("Scrapegraph API key is required") + + if website_url is not None: + self._validate_url(website_url) + self.website_url = website_url + self.description = f"A tool that uses Scrapegraph AI to intelligently scrape {website_url}'s content." + self.args_schema = FixedScrapegraphScrapeToolSchema + + if user_prompt is not None: + self.user_prompt = user_prompt + + # Configure logging only if enabled + if self.enable_logging: + sgai_logger.set_logging(level="INFO") + + @staticmethod + def _validate_url(url: str) -> None: + """Validate URL format.""" + try: + result = urlparse(url) + if not all([result.scheme, result.netloc]): + raise ValueError + except Exception as e: + raise ValueError( + "Invalid URL format. URL must include scheme (http/https) and domain" + ) from e + + def _handle_api_response(self, response: dict) -> str: + """Handle and validate API response.""" + if not response: + raise RuntimeError("Empty response from Scrapegraph API") + + if "error" in response: + error_msg = response.get("error", {}).get("message", "Unknown error") + if "rate limit" in error_msg.lower(): + raise RateLimitError(f"Rate limit exceeded: {error_msg}") + raise RuntimeError(f"API error: {error_msg}") + + if "result" not in response: + raise RuntimeError("Invalid response format from Scrapegraph API") + + return response["result"] + + def _run( + self, + **kwargs: Any, + ) -> Any: + website_url = kwargs.get("website_url", self.website_url) + user_prompt = ( + kwargs.get("user_prompt", self.user_prompt) + or "Extract the main content of the webpage" + ) + + if not website_url: + raise ValueError("website_url is required") + + # Validate URL format + self._validate_url(website_url) + + try: + # Make the SmartScraper request + if self._client is None: + raise RuntimeError("Client not initialized") + return self._client.smartscraper( + website_url=website_url, + user_prompt=user_prompt, + ) + + except RateLimitError: + raise # Re-raise rate limit errors + except Exception as e: + raise RuntimeError(f"Scraping failed: {e!s}") from e + finally: + # Always close the client + if self._client is not None: + self._client.close() diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrapfly_scrape_website_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/scrapfly_scrape_website_tool/README.md new file mode 100644 index 000000000..6ab9c9d52 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/scrapfly_scrape_website_tool/README.md @@ -0,0 +1,57 @@ +# ScrapflyScrapeWebsiteTool + +## Description +[ScrapFly](https://scrapfly.io/) is a web scraping API with headless browser capabilities, proxies, and anti-bot bypass. It allows for extracting web page data into accessible LLM markdown or text. + +## Setup and Installation +1. **Install ScrapFly Python SDK**: Install `scrapfly-sdk` Python package is installed to use the ScrapFly Web Loader. Install it via pip with the following command: + + ```bash + pip install scrapfly-sdk + ``` + +2. **API Key**: Register for free from [scrapfly.io/register](https://www.scrapfly.io/register/) to obtain your API key. + +## Example Usage + +Utilize the ScrapflyScrapeWebsiteTool as follows to retrieve a web page data as text, markdown (LLM accissible) or HTML: + +```python +from crewai_tools import ScrapflyScrapeWebsiteTool + +tool = ScrapflyScrapeWebsiteTool( + api_key="Your ScrapFly API key" +) + +result = tool._run( + url="https://web-scraping.dev/products", + scrape_format="markdown", + ignore_scrape_failures=True +) +``` + +## Additional Arguments +The ScrapflyScrapeWebsiteTool also allows passigng ScrapeConfig object for customizing the scrape request. See the [API params documentation](https://scrapfly.io/docs/scrape-api/getting-started) for the full feature details and their API params: +```python +from crewai_tools import ScrapflyScrapeWebsiteTool + +tool = ScrapflyScrapeWebsiteTool( + api_key="Your ScrapFly API key" +) + +scrapfly_scrape_config = { + "asp": True, # Bypass scraping blocking and solutions, like Cloudflare + "render_js": True, # Enable JavaScript rendering with a cloud headless browser + "proxy_pool": "public_residential_pool", # Select a proxy pool (datacenter or residnetial) + "country": "us", # Select a proxy location + "auto_scroll": True, # Auto scroll the page + "js": "" # Execute custom JavaScript code by the headless browser +} + +result = tool._run( + url="https://web-scraping.dev/products", + scrape_format="markdown", + ignore_scrape_failures=True, + scrape_config=scrapfly_scrape_config +) +``` \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrapfly_scrape_website_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/scrapfly_scrape_website_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/scrapfly_scrape_website_tool/scrapfly_scrape_website_tool.py b/lib/crewai-tools/src/crewai_tools/tools/scrapfly_scrape_website_tool/scrapfly_scrape_website_tool.py new file mode 100644 index 000000000..af3db8410 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/scrapfly_scrape_website_tool/scrapfly_scrape_website_tool.py @@ -0,0 +1,85 @@ +import logging +import os +from typing import Any, Literal + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field + + +logger = logging.getLogger(__file__) + + +class ScrapflyScrapeWebsiteToolSchema(BaseModel): + url: str = Field(description="Webpage URL") + scrape_format: Literal["raw", "markdown", "text"] | None = Field( + default="markdown", description="Webpage extraction format" + ) + scrape_config: dict[str, Any] | None = Field( + default=None, description="Scrapfly request scrape config" + ) + ignore_scrape_failures: bool | None = Field( + default=None, description="whether to ignore failures" + ) + + +class ScrapflyScrapeWebsiteTool(BaseTool): + name: str = "Scrapfly web scraping API tool" + description: str = ( + "Scrape a webpage url using Scrapfly and return its content as markdown or text" + ) + args_schema: type[BaseModel] = ScrapflyScrapeWebsiteToolSchema + api_key: str | None = None + scrapfly: Any | None = None + package_dependencies: list[str] = Field(default_factory=lambda: ["scrapfly-sdk"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SCRAPFLY_API_KEY", + description="API key for Scrapfly", + required=True, + ), + ] + ) + + def __init__(self, api_key: str): + super().__init__( + name="Scrapfly web scraping API tool", + description="Scrape a webpage url using Scrapfly and return its content as markdown or text", + ) + try: + from scrapfly import ScrapflyClient # type: ignore[import-untyped] + except ImportError: + import click + + if click.confirm( + "You are missing the 'scrapfly-sdk' package. Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "scrapfly-sdk"], check=True) # noqa: S607 + else: + raise ImportError( + "`scrapfly-sdk` package not found, please run `uv add scrapfly-sdk`" + ) from None + self.scrapfly = ScrapflyClient(key=api_key or os.getenv("SCRAPFLY_API_KEY")) + + def _run( + self, + url: str, + scrape_format: str = "markdown", + scrape_config: dict[str, Any] | None = None, + ignore_scrape_failures: bool | None = None, + ): + from scrapfly import ScrapeApiResponse, ScrapeConfig + + scrape_config = scrape_config if scrape_config is not None else {} + try: + response: ScrapeApiResponse = self.scrapfly.scrape( # type: ignore[union-attr] + ScrapeConfig(url, format=scrape_format, **scrape_config) + ) + return response.scrape_result["content"] + except Exception as e: + if ignore_scrape_failures: + logger.error(f"Error fetching data from {url}, exception: {e}") + return None + raise e diff --git a/lib/crewai-tools/src/crewai_tools/tools/selenium_scraping_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/selenium_scraping_tool/README.md new file mode 100644 index 000000000..2d54eb970 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/selenium_scraping_tool/README.md @@ -0,0 +1,44 @@ +# SeleniumScrapingTool + +## Description +This tool is designed for efficient web scraping, enabling users to extract content from web pages. It supports targeted scraping by allowing the specification of a CSS selector for desired elements. The flexibility of the tool enables it to be used on any website URL provided by the user, making it a versatile tool for various web scraping needs. + +## Installation +Install the crewai_tools package +``` +pip install 'crewai[tools]' +``` + +## Example +```python +from crewai_tools import SeleniumScrapingTool + +# Example 1: Scrape any website it finds during its execution +tool = SeleniumScrapingTool() + +# Example 2: Scrape the entire webpage +tool = SeleniumScrapingTool(website_url='https://example.com') + +# Example 3: Scrape a specific CSS element from the webpage +tool = SeleniumScrapingTool(website_url='https://example.com', css_element='.main-content') + +# Example 4: Scrape using optional parameters for customized scraping +tool = SeleniumScrapingTool(website_url='https://example.com', css_element='.main-content', cookie={'name': 'user', 'value': 'John Doe'}) + +# Example 5: Scrape content in HTML format +tool = SeleniumScrapingTool(website_url='https://example.com', return_html=True) +result = tool._run() +# Returns HTML content like: ['
Hello World
', '
Copyright 2024
'] + +# Example 6: Scrape content in text format (default) +tool = SeleniumScrapingTool(website_url='https://example.com', return_html=False) +result = tool._run() +# Returns text content like: ['Hello World', 'Copyright 2024'] +``` + +## Arguments +- `website_url`: Mandatory. The URL of the website to scrape. +- `css_element`: Mandatory. The CSS selector for a specific element to scrape from the website. +- `cookie`: Optional. A dictionary containing cookie information. This parameter allows the tool to simulate a session with cookie information, providing access to content that may be restricted to logged-in users. +- `wait_time`: Optional. The number of seconds the tool waits after loading the website and after setting a cookie, before scraping the content. This allows for dynamic content to load properly. +- `return_html`: Optional. If True, the tool returns HTML content. If False, the tool returns text content. diff --git a/lib/crewai-tools/src/crewai_tools/tools/selenium_scraping_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/selenium_scraping_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/selenium_scraping_tool/selenium_scraping_tool.py b/lib/crewai-tools/src/crewai_tools/tools/selenium_scraping_tool/selenium_scraping_tool.py new file mode 100644 index 000000000..2ebfd0d9c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/selenium_scraping_tool/selenium_scraping_tool.py @@ -0,0 +1,198 @@ +import re +import time +from typing import Any +from urllib.parse import urlparse + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field, field_validator + + +class FixedSeleniumScrapingToolSchema(BaseModel): + """Input for SeleniumScrapingTool.""" + + +class SeleniumScrapingToolSchema(FixedSeleniumScrapingToolSchema): + """Input for SeleniumScrapingTool.""" + + website_url: str = Field( + ..., + description="Mandatory website url to read the file. Must start with http:// or https://", + ) + css_element: str = Field( + ..., + description="Mandatory css reference for element to scrape from the website", + ) + + @field_validator("website_url") + @classmethod + def validate_website_url(cls, v): + if not v: + raise ValueError("Website URL cannot be empty") + + if len(v) > 2048: # Common maximum URL length + raise ValueError("URL is too long (max 2048 characters)") + + if not re.match(r"^https?://", v): + raise ValueError("URL must start with http:// or https://") + + try: + result = urlparse(v) + if not all([result.scheme, result.netloc]): + raise ValueError("Invalid URL format") + except Exception as e: + raise ValueError(f"Invalid URL: {e!s}") from e + + if re.search(r"\s", v): + raise ValueError("URL cannot contain whitespace") + + return v + + +class SeleniumScrapingTool(BaseTool): + name: str = "Read a website content" + description: str = "A tool that can be used to read a website content." + args_schema: type[BaseModel] = SeleniumScrapingToolSchema + website_url: str | None = None + driver: Any | None = None + cookie: dict | None = None + wait_time: int | None = 3 + css_element: str | None = None + return_html: bool | None = False + _by: Any | None = None + package_dependencies: list[str] = Field( + default_factory=lambda: ["selenium", "webdriver-manager"] + ) + + def __init__( + self, + website_url: str | None = None, + cookie: dict | None = None, + css_element: str | None = None, + **kwargs, + ): + super().__init__(**kwargs) + try: + from selenium import webdriver # type: ignore[import-not-found] + from selenium.webdriver.chrome.options import ( # type: ignore[import-not-found] + Options, + ) + from selenium.webdriver.common.by import ( # type: ignore[import-not-found] + By, + ) + except ImportError: + import click + + if click.confirm( + "You are missing the 'selenium' and 'webdriver-manager' packages. Would you like to install it?" + ): + import subprocess + + subprocess.run( + ["uv", "pip", "install", "selenium", "webdriver-manager"], # noqa: S607 + check=True, + ) + from selenium import webdriver # type: ignore[import-not-found] + from selenium.webdriver.chrome.options import ( # type: ignore[import-not-found] + Options, + ) + from selenium.webdriver.common.by import ( # type: ignore[import-not-found] + By, + ) + else: + raise ImportError( + "`selenium` and `webdriver-manager` package not found, please run `uv add selenium webdriver-manager`" + ) from None + + if "driver" not in kwargs: + if "options" not in kwargs: + options: Options = Options() + options.add_argument("--headless") + else: + options = kwargs["options"] + self.driver = webdriver.Chrome(options=options) + else: + self.driver = kwargs["driver"] + + self._by = By + if cookie is not None: + self.cookie = cookie + + if css_element is not None: + self.css_element = css_element + + if website_url is not None: + self.website_url = website_url + self.description = ( + f"A tool that can be used to read {website_url}'s content." + ) + self.args_schema = FixedSeleniumScrapingToolSchema + + self._generate_description() + + def _run( + self, + **kwargs: Any, + ) -> Any: + website_url = kwargs.get("website_url", self.website_url) + css_element = kwargs.get("css_element", self.css_element) + return_html = kwargs.get("return_html", self.return_html) + try: + self._make_request(website_url, self.cookie, self.wait_time) + content = self._get_content(css_element, return_html) + return "\n".join(content) + except Exception as e: + return f"Error scraping website: {e!s}" + finally: + if self.driver is not None: + self.driver.close() + + def _get_content(self, css_element, return_html): + content = [] + + if self._is_css_element_empty(css_element): + content.append(self._get_body_content(return_html)) + else: + content.extend(self._get_elements_content(css_element, return_html)) + + return content + + def _is_css_element_empty(self, css_element): + return css_element is None or css_element.strip() == "" + + def _get_body_content(self, return_html): + body_element = self.driver.find_element(self._by.TAG_NAME, "body") + + return ( + body_element.get_attribute("outerHTML") + if return_html + else body_element.text + ) + + def _get_elements_content(self, css_element, return_html): + elements_content = [] + + for element in self.driver.find_elements(self._by.CSS_SELECTOR, css_element): + elements_content.append( # noqa: PERF401 + element.get_attribute("outerHTML") if return_html else element.text + ) + + return elements_content + + def _make_request(self, url, cookie, wait_time): + if not url: + raise ValueError("URL cannot be empty") + + # Validate URL format + if not re.match(r"^https?://", url): + raise ValueError("URL must start with http:// or https://") + + self.driver.get(url) + time.sleep(wait_time) + if cookie: + self.driver.add_cookie(cookie) + time.sleep(wait_time) + self.driver.get(url) + time.sleep(wait_time) + + def close(self): + self.driver.close() diff --git a/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/README.md new file mode 100644 index 000000000..d81b851f8 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/README.md @@ -0,0 +1,32 @@ +# SerpApi Tools + +## Description +[SerpApi](https://serpapi.com/) tools are built for searching information in the internet. It currently supports: +- Google Search +- Google Shopping + +To successfully make use of SerpApi tools, you have to have `SERPAPI_API_KEY` set in the environment. To get the API key, register a free account at [SerpApi](https://serpapi.com/). + +## Installation +To start using the SerpApi Tools, you must first install the `crewai_tools` package. This can be easily done with the following command: + +```shell +pip install 'crewai[tools]' +``` + +## Examples +The following example demonstrates how to initialize the tool + +### Google Search +```python +from crewai_tools import SerpApiGoogleSearchTool + +tool = SerpApiGoogleSearchTool() +``` + +### Google Shopping +```python +from crewai_tools import SerpApiGoogleShoppingTool + +tool = SerpApiGoogleShoppingTool() +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_base_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_base_tool.py new file mode 100644 index 000000000..18fcf442d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_base_tool.py @@ -0,0 +1,61 @@ +import os +import re +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import Field + + +class SerpApiBaseTool(BaseTool): + """Base class for SerpApi functionality with shared capabilities.""" + + package_dependencies: list[str] = Field(default_factory=lambda: ["serpapi"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SERPAPI_API_KEY", + description="API key for SerpApi searches", + required=True, + ), + ] + ) + + client: Any | None = None + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + try: + from serpapi import Client # type: ignore + except ImportError: + import click + + if click.confirm( + "You are missing the 'serpapi' package. Would you like to install it?" + ): + import subprocess + + subprocess.run(["uv", "add", "serpapi"], check=True) # noqa: S607 + from serpapi import Client # type: ignore[import-untyped] + else: + raise ImportError( + "`serpapi` package not found, please install with `uv add serpapi`" + ) from None + api_key = os.getenv("SERPAPI_API_KEY") + if not api_key: + raise ValueError( + "Missing API key, you can get the key from https://serpapi.com/manage-api-key" + ) + self.client = Client(api_key=api_key) + + def _omit_fields(self, data: dict | list, omit_patterns: list[str]) -> None: + if isinstance(data, dict): + for field in list(data.keys()): + if any(re.compile(p).match(field) for p in omit_patterns): + data.pop(field, None) + else: + if isinstance(data[field], (dict, list)): + self._omit_fields(data[field], omit_patterns) + elif isinstance(data, list): + for item in data: + self._omit_fields(item, omit_patterns) diff --git a/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_google_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_google_search_tool.py new file mode 100644 index 000000000..e70bb68c7 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_google_search_tool.py @@ -0,0 +1,61 @@ +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from crewai_tools.tools.serpapi_tool.serpapi_base_tool import SerpApiBaseTool + + +try: + from serpapi import HTTPError # type: ignore[import-untyped] +except ImportError: + HTTPError = Any + + +class SerpApiGoogleSearchToolSchema(BaseModel): + """Input for Google Search.""" + + search_query: str = Field( + ..., description="Mandatory search query you want to use to Google search." + ) + location: str | None = Field( + None, description="Location you want the search to be performed in." + ) + + +class SerpApiGoogleSearchTool(SerpApiBaseTool): + model_config = ConfigDict( + arbitrary_types_allowed=True, validate_assignment=True, frozen=False + ) + name: str = "Google Search" + description: str = ( + "A tool to perform to perform a Google search with a search_query." + ) + args_schema: type[BaseModel] = SerpApiGoogleSearchToolSchema + + def _run( + self, + **kwargs: Any, + ) -> Any: + try: + results = self.client.search( # type: ignore[union-attr] + { + "q": kwargs.get("search_query"), + "location": kwargs.get("location"), + } + ).as_dict() + + self._omit_fields( + results, + [ + r"search_metadata", + r"search_parameters", + r"serpapi_.+", + r".+_token", + r"displayed_link", + r"pagination", + ], + ) + + return results + except HTTPError as e: + return f"An error occurred: {e!s}. Some parameters may be invalid." diff --git a/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_google_shopping_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_google_shopping_tool.py new file mode 100644 index 000000000..9300a9d9e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serpapi_tool/serpapi_google_shopping_tool.py @@ -0,0 +1,61 @@ +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from crewai_tools.tools.serpapi_tool.serpapi_base_tool import SerpApiBaseTool + + +try: + from serpapi import HTTPError # type: ignore[import-untyped] +except ImportError: + HTTPError = Any + + +class SerpApiGoogleShoppingToolSchema(BaseModel): + """Input for Google Shopping.""" + + search_query: str = Field( + ..., description="Mandatory search query you want to use to Google shopping." + ) + location: str | None = Field( + None, description="Location you want the search to be performed in." + ) + + +class SerpApiGoogleShoppingTool(SerpApiBaseTool): + model_config = ConfigDict( + arbitrary_types_allowed=True, validate_assignment=True, frozen=False + ) + name: str = "Google Shopping" + description: str = ( + "A tool to perform search on Google shopping with a search_query." + ) + args_schema: type[BaseModel] = SerpApiGoogleShoppingToolSchema + + def _run( + self, + **kwargs: Any, + ) -> Any: + try: + results = self.client.search( # type: ignore[union-attr] + { + "engine": "google_shopping", + "q": kwargs.get("search_query"), + "location": kwargs.get("location"), + } + ).as_dict() + + self._omit_fields( + results, + [ + r"search_metadata", + r"search_parameters", + r"serpapi_.+", + r"filters", + r"pagination", + ], + ) + + return results + except HTTPError as e: + return f"An error occurred: {e!s}. Some parameters may be invalid." diff --git a/lib/crewai-tools/src/crewai_tools/tools/serper_dev_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/serper_dev_tool/README.md new file mode 100644 index 000000000..06f1abd56 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serper_dev_tool/README.md @@ -0,0 +1,52 @@ +# SerperDevTool Documentation + +## Description +The SerperDevTool is a powerful search tool that interfaces with the `serper.dev` API to perform internet searches. It supports multiple search types including general search and news search, with features like knowledge graph integration, organic results, "People Also Ask" questions, and related searches. + +## Features +- Multiple search types: 'search' (default) and 'news' +- Knowledge graph integration for enhanced search context +- Organic search results with sitelinks +- "People Also Ask" questions and answers +- Related searches suggestions +- News search with date, source, and image information +- Configurable number of results +- Optional result saving to file + +## Installation +```shell +pip install 'crewai[tools]' +``` + +## Usage +```python +from crewai_tools import SerperDevTool + +# Initialize the tool +tool = SerperDevTool( + n_results=10, # Optional: Number of results to return (default: 10) + save_file=False, # Optional: Save results to file (default: False) + search_type="search", # Optional: Type of search - "search" or "news" (default: "search") + country="us", # Optional: Country for search (default: "") + location="New York", # Optional: Location for search (default: "") + locale="en-US" # Optional: Locale for search (default: "") +) + +# Execute a search +results = tool._run(search_query="your search query") +``` + +## Configuration +1. **API Key Setup**: + - Sign up for an account at `serper.dev` + - Obtain your API key + - Set the environment variable: `SERPER_API_KEY` + +## Response Format +The tool returns structured data including: +- Search parameters +- Knowledge graph data (for general search) +- Organic search results +- "People Also Ask" questions +- Related searches +- News results (for news search type) diff --git a/lib/crewai-tools/src/crewai_tools/tools/serper_dev_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/serper_dev_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/serper_dev_tool/serper_dev_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serper_dev_tool/serper_dev_tool.py new file mode 100644 index 000000000..9fb538e19 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serper_dev_tool/serper_dev_tool.py @@ -0,0 +1,342 @@ +import datetime +import json +import logging +import os +from typing import Any, TypedDict + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +logger = logging.getLogger(__name__) + + +class KnowledgeGraph(TypedDict, total=False): + """Knowledge graph data from search results.""" + + title: str + type: str + website: str + imageUrl: str + description: str + descriptionSource: str + descriptionLink: str + attributes: dict[str, Any] + + +class Sitelink(TypedDict): + """Sitelink data for organic search results.""" + + title: str + link: str + + +class OrganicResult(TypedDict, total=False): + """Organic search result data.""" + + title: str + link: str + snippet: str + position: int | None + sitelinks: list[Sitelink] + + +class PeopleAlsoAskResult(TypedDict): + """People Also Ask result data.""" + + question: str + snippet: str + title: str + link: str + + +class RelatedSearchResult(TypedDict): + """Related search result data.""" + + query: str + + +class NewsResult(TypedDict): + """News search result data.""" + + title: str + link: str + snippet: str + date: str + source: str + imageUrl: str + + +class SearchParameters(TypedDict, total=False): + """Search parameters used for the query.""" + + q: str + type: str + + +class FormattedResults(TypedDict, total=False): + """Formatted search results from Serper API.""" + + searchParameters: SearchParameters + knowledgeGraph: KnowledgeGraph + organic: list[OrganicResult] + peopleAlsoAsk: list[PeopleAlsoAskResult] + relatedSearches: list[RelatedSearchResult] + news: list[NewsResult] + credits: int + + +def _save_results_to_file(content: str) -> None: + """Saves the search results to a file.""" + try: + filename = f"search_results_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt" + with open(filename, "w") as file: + file.write(content) + logger.info(f"Results saved to {filename}") + except IOError as e: + logger.error(f"Failed to save results to file: {e}") + raise + + +class SerperDevToolSchema(BaseModel): + """Input for SerperDevTool.""" + + search_query: str = Field( + ..., description="Mandatory search query you want to use to search the internet" + ) + + +class SerperDevTool(BaseTool): + name: str = "Search the internet with Serper" + description: str = ( + "A tool that can be used to search the internet with a search_query. " + "Supports different search types: 'search' (default), 'news'" + ) + args_schema: type[BaseModel] = SerperDevToolSchema + base_url: str = "https://google.serper.dev" + n_results: int = 10 + save_file: bool = False + search_type: str = "search" + country: str | None = "" + location: str | None = "" + locale: str | None = "" + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SERPER_API_KEY", description="API key for Serper", required=True + ), + ] + ) + + def _get_search_url(self, search_type: str) -> str: + """Get the appropriate endpoint URL based on search type.""" + search_type = search_type.lower() + allowed_search_types = ["search", "news"] + if search_type not in allowed_search_types: + raise ValueError( + f"Invalid search type: {search_type}. Must be one of: {', '.join(allowed_search_types)}" + ) + return f"{self.base_url}/{search_type}" + + @staticmethod + def _process_knowledge_graph(kg: dict[str, Any]) -> KnowledgeGraph: + """Process knowledge graph data from search results.""" + return { + "title": kg.get("title", ""), + "type": kg.get("type", ""), + "website": kg.get("website", ""), + "imageUrl": kg.get("imageUrl", ""), + "description": kg.get("description", ""), + "descriptionSource": kg.get("descriptionSource", ""), + "descriptionLink": kg.get("descriptionLink", ""), + "attributes": kg.get("attributes", {}), + } + + def _process_organic_results( + self, organic_results: list[dict[str, Any]] + ) -> list[OrganicResult]: + """Process organic search results.""" + processed_results: list[OrganicResult] = [] + for result in organic_results[: self.n_results]: + try: + result_data: OrganicResult = { # type: ignore[typeddict-item] + "title": result["title"], + "link": result["link"], + "snippet": result.get("snippet", ""), + "position": result.get("position"), + } + + if "sitelinks" in result: + result_data["sitelinks"] = [ # type: ignore[typeddict-unknown-key] + { + "title": sitelink.get("title", ""), + "link": sitelink.get("link", ""), + } + for sitelink in result["sitelinks"] + ] + + processed_results.append(result_data) + except KeyError: # noqa: PERF203 + logger.warning(f"Skipping malformed organic result: {result}") + continue + return processed_results # type: ignore[return-value] + + def _process_people_also_ask( + self, paa_results: list[dict[str, Any]] + ) -> list[PeopleAlsoAskResult]: + """Process 'People Also Ask' results.""" + processed_results: list[PeopleAlsoAskResult] = [] + for result in paa_results[: self.n_results]: + try: + result_data: PeopleAlsoAskResult = { # type: ignore[typeddict-item] + "question": result["question"], + "snippet": result.get("snippet", ""), + "title": result.get("title", ""), + "link": result.get("link", ""), + } + processed_results.append(result_data) + except KeyError: # noqa: PERF203 + logger.warning(f"Skipping malformed PAA result: {result}") + continue + return processed_results # type: ignore[return-value] + + def _process_related_searches( + self, related_results: list[dict[str, Any]] + ) -> list[RelatedSearchResult]: + """Process related search results.""" + processed_results: list[RelatedSearchResult] = [] + for result in related_results[: self.n_results]: + try: + processed_results.append({"query": result["query"]}) # type: ignore[typeddict-item] + except KeyError: # noqa: PERF203 + logger.warning(f"Skipping malformed related search result: {result}") + continue + return processed_results # type: ignore[return-value] + + def _process_news_results( + self, news_results: list[dict[str, Any]] + ) -> list[NewsResult]: + """Process news search results.""" + processed_results: list[NewsResult] = [] + for result in news_results[: self.n_results]: + try: + result_data: NewsResult = { # type: ignore[typeddict-item] + "title": result["title"], + "link": result["link"], + "snippet": result.get("snippet", ""), + "date": result.get("date", ""), + "source": result.get("source", ""), + "imageUrl": result.get("imageUrl", ""), + } + processed_results.append(result_data) + except KeyError: # noqa: PERF203 + logger.warning(f"Skipping malformed news result: {result}") + continue + return processed_results # type: ignore[return-value] + + def _make_api_request(self, search_query: str, search_type: str) -> dict[str, Any]: + """Make API request to Serper.""" + search_url = self._get_search_url(search_type) + payload = {"q": search_query, "num": self.n_results} + + if self.country != "": + payload["gl"] = self.country + if self.location != "": + payload["location"] = self.location + if self.locale != "": + payload["hl"] = self.locale + + headers = { + "X-API-KEY": os.environ["SERPER_API_KEY"], + "content-type": "application/json", + } + + response = None + try: + response = requests.post( + search_url, headers=headers, json=payload, timeout=10 + ) + response.raise_for_status() + results = response.json() + if not results: + logger.error("Empty response from Serper API") + raise ValueError("Empty response from Serper API") + return results + except requests.exceptions.RequestException as e: + error_msg = f"Error making request to Serper API: {e}" + if response is not None and hasattr(response, "content"): + error_msg += f"\nResponse content: {response.content.decode('utf-8', errors='replace')}" + logger.error(error_msg) + raise + except json.JSONDecodeError as e: + if response is not None and hasattr(response, "content"): + logger.error(f"Error decoding JSON response: {e}") + logger.error( + f"Response content: {response.content.decode('utf-8', errors='replace')}" + ) + else: + logger.error( + f"Error decoding JSON response: {e} (No response content available)" + ) + raise + + def _process_search_results( + self, results: dict[str, Any], search_type: str + ) -> dict[str, Any]: + """Process search results based on search type.""" + formatted_results: dict[str, Any] = {} + + if search_type == "search": + if "knowledgeGraph" in results: + formatted_results["knowledgeGraph"] = self._process_knowledge_graph( + results["knowledgeGraph"] + ) + + if "organic" in results: + formatted_results["organic"] = self._process_organic_results( + results["organic"] + ) + + if "peopleAlsoAsk" in results: + formatted_results["peopleAlsoAsk"] = self._process_people_also_ask( + results["peopleAlsoAsk"] + ) + + if "relatedSearches" in results: + formatted_results["relatedSearches"] = self._process_related_searches( + results["relatedSearches"] + ) + + elif search_type == "news": + if "news" in results: + formatted_results["news"] = self._process_news_results(results["news"]) + + return formatted_results + + def _run(self, **kwargs: Any) -> FormattedResults: + """Execute the search operation.""" + search_query: str | None = kwargs.get("search_query") or kwargs.get("query") + search_type: str = kwargs.get("search_type", self.search_type) + save_file = kwargs.get("save_file", self.save_file) + + if not search_query: + raise ValueError("search_query is required") + + results = self._make_api_request(search_query, search_type) + + formatted_results = { + "searchParameters": { + "q": search_query, + "type": search_type, + **results.get("searchParameters", {}), + } + } + + formatted_results.update(self._process_search_results(results, search_type)) + formatted_results["credits"] = results.get("credits", 1) + + if save_file: + _save_results_to_file(json.dumps(formatted_results, indent=2)) + + return formatted_results # type: ignore[return-value] diff --git a/lib/crewai-tools/src/crewai_tools/tools/serper_scrape_website_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/serper_scrape_website_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/serper_scrape_website_tool/serper_scrape_website_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serper_scrape_website_tool/serper_scrape_website_tool.py new file mode 100644 index 000000000..6889fdf4e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serper_scrape_website_tool/serper_scrape_website_tool.py @@ -0,0 +1,83 @@ +import json +import os + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +class SerperScrapeWebsiteInput(BaseModel): + """Input schema for SerperScrapeWebsite.""" + + url: str = Field(..., description="The URL of the website to scrape") + include_markdown: bool = Field( + default=True, + description="Whether to include markdown formatting in the scraped content", + ) + + +class SerperScrapeWebsiteTool(BaseTool): + name: str = "serper_scrape_website" + description: str = ( + "Scrapes website content using Serper's scraping API. " + "This tool can extract clean, readable content from any website URL, " + "optionally including markdown formatting for better structure." + ) + args_schema: type[BaseModel] = SerperScrapeWebsiteInput + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SERPER_API_KEY", description="API key for Serper", required=True + ), + ] + ) + + def _run(self, url: str, include_markdown: bool = True) -> str: + """Scrape website content using Serper API. + + Args: + url: The URL to scrape + include_markdown: Whether to include markdown formatting + + Returns: + Scraped website content as a string + """ + try: + # Serper API endpoint + api_url = "https://scrape.serper.dev" + + # Get API key from environment variable for security + api_key = os.getenv("SERPER_API_KEY") + + # Prepare the payload + payload = json.dumps({"url": url, "includeMarkdown": include_markdown}) + + # Set headers + headers = {"X-API-KEY": api_key, "Content-Type": "application/json"} + + # Make the API request + response = requests.post( + api_url, + headers=headers, + data=payload, + timeout=30, + ) + + # Check if request was successful + if response.status_code == 200: + result = response.json() + + # Extract the scraped content + if "text" in result: + return result["text"] + return f"Successfully scraped {url}, but no text content found in response: {response.text}" + return ( + f"Error scraping {url}: HTTP {response.status_code} - {response.text}" + ) + + except requests.exceptions.RequestException as e: + return f"Network error while scraping {url}: {e!s}" + except json.JSONDecodeError as e: + return f"Error parsing JSON response while scraping {url}: {e!s}" + except Exception as e: + return f"Unexpected error while scraping {url}: {e!s}" diff --git a/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/README.md new file mode 100644 index 000000000..5c6b9395e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/README.md @@ -0,0 +1,117 @@ +# Serply API Documentation + +## Description +This tool is designed to perform a web/news/scholar search for a specified query from a text's content across the internet. It utilizes the [Serply.io](https://serply.io) API to fetch and display the most relevant search results based on the query provided by the user. + +## Installation + +To incorporate this tool into your project, follow the installation instructions below: +```shell +pip install 'crewai[tools]' +``` + +## Examples + +## Web Search +The following example demonstrates how to initialize the tool and execute a search the web with a given query: + +```python +from crewai_tools import SerplyWebSearchTool + +# Initialize the tool for internet searching capabilities +tool = SerplyWebSearchTool() + +# increase search limits to 100 results +tool = SerplyWebSearchTool(limit=100) + + +# change results language (fr - French) +tool = SerplyWebSearchTool(hl="fr") +``` + +## News Search +The following example demonstrates how to initialize the tool and execute a search news with a given query: + +```python +from crewai_tools import SerplyNewsSearchTool + +# Initialize the tool for internet searching capabilities +tool = SerplyNewsSearchTool() + +# change country news (JP - Japan) +tool = SerplyNewsSearchTool(proxy_location="JP") +``` + +## Scholar Search +The following example demonstrates how to initialize the tool and execute a search scholar articles a given query: + +```python +from crewai_tools import SerplyScholarSearchTool + +# Initialize the tool for internet searching capabilities +tool = SerplyScholarSearchTool() + +# change country news (GB - Great Britain) +tool = SerplyScholarSearchTool(proxy_location="GB") +``` + +## Job Search +The following example demonstrates how to initialize the tool and searching for jobs in the USA: + +```python +from crewai_tools import SerplyJobSearchTool + +# Initialize the tool for internet searching capabilities +tool = SerplyJobSearchTool() +``` + + +## Web Page To Markdown +The following example demonstrates how to initialize the tool and fetch a web page and convert it to markdown: + +```python +from crewai_tools import SerplyWebpageToMarkdownTool + +# Initialize the tool for internet searching capabilities +tool = SerplyWebpageToMarkdownTool() + +# change country make request from (DE - Germany) +tool = SerplyWebpageToMarkdownTool(proxy_location="DE") +``` + +## Combining Multiple Tools + +The following example demonstrates performing a Google search to find relevant articles. Then, convert those articles to markdown format for easier extraction of key points. + +```python +from crewai import Agent +from crewai_tools import SerplyWebSearchTool, SerplyWebpageToMarkdownTool + +search_tool = SerplyWebSearchTool() +convert_to_markdown = SerplyWebpageToMarkdownTool() + +# Creating a senior researcher agent with memory and verbose mode +researcher = Agent( + role='Senior Researcher', + goal='Uncover groundbreaking technologies in {topic}', + verbose=True, + memory=True, + backstory=( + "Driven by curiosity, you're at the forefront of" + "innovation, eager to explore and share knowledge that could change" + "the world." + ), + tools=[search_tool, convert_to_markdown], + allow_delegation=True +) +``` + +## Steps to Get Started +To effectively use the `SerplyApiTool`, follow these steps: + +1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment. +2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at [Serply.io](https://serply.io). +3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPLY_API_KEY` to facilitate its use by the tool. + +## Conclusion +By integrating the `SerplyApiTool` into Python projects, users gain the ability to conduct real-time searches, relevant news across the internet directly from their applications. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward. diff --git a/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_job_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_job_search_tool.py new file mode 100644 index 000000000..88ea4a93f --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_job_search_tool.py @@ -0,0 +1,94 @@ +import os +from urllib.parse import urlencode + +from crewai.tools import EnvVar +from pydantic import BaseModel, Field +import requests + +from crewai_tools.tools.rag.rag_tool import RagTool + + +class SerplyJobSearchToolSchema(BaseModel): + """Input for Job Search.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to fetch jobs postings.", + ) + + +class SerplyJobSearchTool(RagTool): + name: str = "Job Search" + description: str = ( + "A tool to perform to perform a job search in the US with a search_query." + ) + args_schema: type[BaseModel] = SerplyJobSearchToolSchema + request_url: str = "https://api.serply.io/v1/job/search/" + proxy_location: str | None = "US" + """ + proxy_location: (str): Where to get jobs, specifically for a specific country results. + - Currently only supports US + """ + headers: dict | None = Field(default_factory=dict) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SERPLY_API_KEY", + description="API key for Serply services", + required=True, + ), + ] + ) + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.headers = { + "X-API-KEY": os.environ["SERPLY_API_KEY"], + "User-Agent": "crew-tools", + "X-Proxy-Location": self.proxy_location, + } + + def _run( # type: ignore[override] + self, + query: str | None = None, + search_query: str | None = None, + ) -> str: + query_payload = {} + + if query is not None: + query_payload["q"] = query + elif search_query is not None: + query_payload["q"] = search_query + + # build the url + url = f"{self.request_url}{urlencode(query_payload)}" + + response = requests.request("GET", url, headers=self.headers, timeout=30) + + jobs = response.json().get("jobs", "") + + if not jobs: + return "" + + string = [] + for job in jobs: + try: + string.append( + "\n".join( + [ + f"Position: {job['position']}", + f"Employer: {job['employer']}", + f"Location: {job['location']}", + f"Link: {job['link']}", + f"""Highest: {", ".join([h for h in job["highlights"]])}""", + f"Is Remote: {job['is_remote']}", + f"Is Hybrid: {job['is_remote']}", + "---", + ] + ) + ) + except KeyError: # noqa: PERF203 + continue + + content = "\n".join(string) + return f"\nSearch results: {content}\n" diff --git a/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_news_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_news_search_tool.py new file mode 100644 index 000000000..98802b4e6 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_news_search_tool.py @@ -0,0 +1,101 @@ +import os +from typing import Any +from urllib.parse import urlencode + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +class SerplyNewsSearchToolSchema(BaseModel): + """Input for Serply News Search.""" + + search_query: str = Field( + ..., description="Mandatory search query you want to use to fetch news articles" + ) + + +class SerplyNewsSearchTool(BaseTool): + name: str = "News Search" + description: str = "A tool to perform News article search with a search_query." + args_schema: type[BaseModel] = SerplyNewsSearchToolSchema + search_url: str = "https://api.serply.io/v1/news/" + proxy_location: str | None = "US" + headers: dict | None = Field(default_factory=dict) + limit: int | None = 10 + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SERPLY_API_KEY", + description="API key for Serply services", + required=True, + ), + ] + ) + + def __init__( + self, limit: int | None = 10, proxy_location: str | None = "US", **kwargs + ): + """param: limit (int): The maximum number of results to return [10-100, defaults to 10] + proxy_location: (str): Where to get news, specifically for a specific country results. + ['US', 'CA', 'IE', 'GB', 'FR', 'DE', 'SE', 'IN', 'JP', 'KR', 'SG', 'AU', 'BR'] (defaults to US). + """ + super().__init__(**kwargs) + self.limit = limit + self.proxy_location = proxy_location + self.headers = { + "X-API-KEY": os.environ["SERPLY_API_KEY"], + "User-Agent": "crew-tools", + "X-Proxy-Location": proxy_location, + } + + def _run( + self, + **kwargs: Any, + ) -> Any: + # build query parameters + query_payload = {} + + if "query" in kwargs: + query_payload["q"] = kwargs["query"] + elif "search_query" in kwargs: + query_payload["q"] = kwargs["search_query"] + + # build the url + url = f"{self.search_url}{urlencode(query_payload)}" + + response = requests.request( + "GET", + url, + headers=self.headers, + timeout=30, + ) + results = response.json() + if "entries" in results: + results = results["entries"] + string = [] + for result in results[: self.limit]: + try: + # follow url + r = requests.get( + result["link"], + timeout=30, + ) + final_link = r.history[-1].headers["Location"] + string.append( + "\n".join( + [ + f"Title: {result['title']}", + f"Link: {final_link}", + f"Source: {result['source']['title']}", + f"Published: {result['published']}", + "---", + ] + ) + ) + except KeyError: # noqa: PERF203 + continue + + content = "\n".join(string) + return f"\nSearch results: {content}\n" + return results diff --git a/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_scholar_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_scholar_search_tool.py new file mode 100644 index 000000000..c8e3a1ccd --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_scholar_search_tool.py @@ -0,0 +1,103 @@ +import os +from typing import Any +from urllib.parse import urlencode + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +class SerplyScholarSearchToolSchema(BaseModel): + """Input for Serply Scholar Search.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to fetch scholarly literature", + ) + + +class SerplyScholarSearchTool(BaseTool): + name: str = "Scholar Search" + description: str = ( + "A tool to perform scholarly literature search with a search_query." + ) + args_schema: type[BaseModel] = SerplyScholarSearchToolSchema + search_url: str = "https://api.serply.io/v1/scholar/" + hl: str | None = "us" + proxy_location: str | None = "US" + headers: dict | None = Field(default_factory=dict) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SERPLY_API_KEY", + description="API key for Serply services", + required=True, + ), + ] + ) + + def __init__(self, hl: str = "us", proxy_location: str | None = "US", **kwargs): + """param: hl (str): host Language code to display results in + (reference https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages) + proxy_location: (str): Specify the proxy location for the search, specifically for a specific country results. + ['US', 'CA', 'IE', 'GB', 'FR', 'DE', 'SE', 'IN', 'JP', 'KR', 'SG', 'AU', 'BR'] (defaults to US). + """ + super().__init__(**kwargs) + self.hl = hl + self.proxy_location = proxy_location + self.headers = { + "X-API-KEY": os.environ["SERPLY_API_KEY"], + "User-Agent": "crew-tools", + "X-Proxy-Location": proxy_location, + } + + def _run( + self, + **kwargs: Any, + ) -> Any: + query_payload = {"hl": self.hl} + + if "query" in kwargs: + query_payload["q"] = kwargs["query"] + elif "search_query" in kwargs: + query_payload["q"] = kwargs["search_query"] + + # build the url + url = f"{self.search_url}{urlencode(query_payload)}" + + response = requests.request( + "GET", + url, + headers=self.headers, + timeout=30, + ) + articles = response.json().get("articles", "") + + if not articles: + return "" + + string = [] + for article in articles: + try: + if "doc" in article: + link = article["doc"]["link"] + else: + link = article["link"] + authors = [author["name"] for author in article["author"]["authors"]] + string.append( + "\n".join( + [ + f"Title: {article['title']}", + f"Link: {link}", + f"Description: {article['description']}", + f"Cite: {article['cite']}", + f"Authors: {', '.join(authors)}", + "---", + ] + ) + ) + except KeyError: # noqa: PERF203 + continue + + content = "\n".join(string) + return f"\nSearch results: {content}\n" diff --git a/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_web_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_web_search_tool.py new file mode 100644 index 000000000..690d795c2 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_web_search_tool.py @@ -0,0 +1,113 @@ +import os +from typing import Any +from urllib.parse import urlencode + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field +import requests + + +class SerplyWebSearchToolSchema(BaseModel): + """Input for Serply Web Search.""" + + search_query: str = Field( + ..., description="Mandatory search query you want to use to Google search" + ) + + +class SerplyWebSearchTool(BaseTool): + name: str = "Google Search" + description: str = "A tool to perform Google search with a search_query." + args_schema: type[BaseModel] = SerplyWebSearchToolSchema + search_url: str = "https://api.serply.io/v1/search/" + hl: str | None = "us" + limit: int | None = 10 + device_type: str | None = "desktop" + proxy_location: str | None = "US" + query_payload: dict | None = Field(default_factory=dict) + headers: dict | None = Field(default_factory=dict) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SERPLY_API_KEY", + description="API key for Serply services", + required=True, + ), + ] + ) + + def __init__( + self, + hl: str = "us", + limit: int = 10, + device_type: str = "desktop", + proxy_location: str = "US", + **kwargs, + ): + """param: query (str): The query to search for + param: hl (str): host Language code to display results in + (reference https://developers.google.com/custom-search/docs/xml_results?hl=en#wsInterfaceLanguages) + param: limit (int): The maximum number of results to return [10-100, defaults to 10] + param: device_type (str): desktop/mobile results (defaults to desktop) + proxy_location: (str): Where to perform the search, specifically for local/regional results. + ['US', 'CA', 'IE', 'GB', 'FR', 'DE', 'SE', 'IN', 'JP', 'KR', 'SG', 'AU', 'BR'] (defaults to US). + """ + super().__init__(**kwargs) + + self.limit = limit + self.device_type = device_type + self.proxy_location = proxy_location + + # build query parameters + self.query_payload = { + "num": limit, + "gl": proxy_location.upper(), + "hl": hl.lower(), + } + self.headers = { + "X-API-KEY": os.environ["SERPLY_API_KEY"], + "X-User-Agent": device_type, + "User-Agent": "crew-tools", + "X-Proxy-Location": proxy_location, + } + + def _run( + self, + **kwargs: Any, + ) -> Any: + if "query" in kwargs: + self.query_payload["q"] = kwargs["query"] # type: ignore[index] + elif "search_query" in kwargs: + self.query_payload["q"] = kwargs["search_query"] # type: ignore[index] + + # build the url + url = f"{self.search_url}{urlencode(self.query_payload)}" # type: ignore[arg-type] + + response = requests.request( + "GET", + url, + headers=self.headers, + timeout=30, + ) + results = response.json() + if "results" in results: + results = results["results"] + string = [] + for result in results: + try: + string.append( + "\n".join( + [ + f"Title: {result['title']}", + f"Link: {result['link']}", + f"Description: {result['description'].strip()}", + "---", + ] + ) + ) + except KeyError: # noqa: PERF203 + continue + + content = "\n".join(string) + return f"\nSearch results: {content}\n" + return results diff --git a/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_webpage_to_markdown_tool.py b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_webpage_to_markdown_tool.py new file mode 100644 index 000000000..f3a4729f2 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/serply_api_tool/serply_webpage_to_markdown_tool.py @@ -0,0 +1,59 @@ +import os +from typing import Any, Literal + +from crewai.tools import EnvVar +from pydantic import BaseModel, Field +import requests + +from crewai_tools.tools.rag.rag_tool import RagTool + + +class SerplyWebpageToMarkdownToolSchema(BaseModel): + """Input for Serply Search.""" + + url: str = Field( + ..., + description="Mandatory url you want to use to fetch and convert to markdown", + ) + + +class SerplyWebpageToMarkdownTool(RagTool): + name: str = "Webpage to Markdown" + description: str = "A tool to perform convert a webpage to markdown to make it easier for LLMs to understand" + args_schema: type[BaseModel] = SerplyWebpageToMarkdownToolSchema + request_url: str = "https://api.serply.io/v1/request" + proxy_location: Literal[ + "US", "CA", "IE", "GB", "FR", "DE", "SE", "IN", "JP", "KR", "SG", "AU", "BR" + ] = "US" + headers: dict[str, Any] = Field( + default_factory=lambda: { + "X-API-KEY": os.environ["SERPLY_API_KEY"], + "User-Agent": "crew-tools", + } + ) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SERPLY_API_KEY", + description="API key for Serply services", + required=True, + ), + ] + ) + + def _run( # type: ignore[override] + self, + url: str, + ) -> str: + if self.proxy_location and not self.headers.get("X-Proxy-Location"): + self.headers["X-Proxy-Location"] = self.proxy_location + + data = {"url": url, "method": "GET", "response_type": "markdown"} + response = requests.request( + "POST", + self.request_url, + headers=self.headers, + json=data, + timeout=30, + ) + return response.text diff --git a/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/README.md new file mode 100644 index 000000000..954264683 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/README.md @@ -0,0 +1,299 @@ +# SingleStoreSearchTool + +## Description +The SingleStoreSearchTool is designed to facilitate semantic searches and SQL queries within SingleStore database tables. This tool provides a secure interface for executing SELECT and SHOW queries against SingleStore databases, with built-in connection pooling for optimal performance. It supports various connection methods and allows you to work with specific table subsets within your database. + +## Installation +To install the `crewai_tools` package with SingleStore support, execute the following command: + +```shell +pip install 'crewai[tools]' +``` + +Or install with the SingleStore extra for the latest dependencies: + +```shell +uv sync --extra singlestore +``` + +Or install the required dependencies manually: + +```shell +pip install singlestoredb>=1.12.4 SQLAlchemy>=2.0.40 +``` + +## Features + +- 🔒 **Secure Query Execution**: Only SELECT and SHOW queries are allowed for security +- 🚀 **Connection Pooling**: Built-in connection pooling for optimal performance +- 📊 **Table Subset Support**: Work with specific tables or all tables in the database +- 🔧 **Flexible Configuration**: Multiple connection methods supported +- 🛡️ **SSL/TLS Support**: Comprehensive SSL configuration options +- ⚡ **Efficient Resource Management**: Automatic connection lifecycle management + +## Basic Usage + +### Simple Connection + +```python +from crewai_tools import SingleStoreSearchTool + +# Basic connection using host/user/password +tool = SingleStoreSearchTool( + host='localhost', + user='your_username', + password='your_password', + database='your_database', + port=3306 +) + +# Execute a search query +result = tool._run("SELECT * FROM employees WHERE department = 'Engineering' LIMIT 10") +print(result) +``` + +### Working with Specific Tables + +```python +# Initialize tool for specific tables only +tool = SingleStoreSearchTool( + tables=['employees', 'departments'], # Only work with these tables + host='your_host', + user='your_username', + password='your_password', + database='your_database' +) +``` + +## Complete CrewAI Integration Example + +Here's a complete example showing how to use the SingleStoreSearchTool with CrewAI agents and tasks: + +```python +from crewai import Agent, Task, Crew +from crewai_tools import SingleStoreSearchTool + +# Initialize the SingleStore search tool +singlestore_tool = SingleStoreSearchTool( + tables=["products", "sales", "customers"], # Specify the tables you want to search + host="localhost", + port=3306, + user="root", + password="pass", + database="crewai", +) + +# Create an agent that uses this tool +data_analyst = Agent( + role="Business Analyst", + goal="Analyze and answer business questions using SQL data", + backstory="Expert in interpreting business needs and transforming them into data queries.", + tools=[singlestore_tool], + verbose=True, + embedder={ + "provider": "ollama", + "config": { + "model": "nomic-embed-text", + }, + }, +) + +# Define a task +task = Task( + description="List the top 2 customers by total sales amount.", + agent=data_analyst, + expected_output="A ranked list of top 2 customers that have the highest total sales amount, including their names and total sales figures.", +) + +# Run the crew +crew = Crew(tasks=[task], verbose=True) +result = crew.kickoff() +``` + +### Advanced CrewAI Example with Multiple Agents + +```python +from crewai import Agent, Task, Crew +from crewai_tools import SingleStoreSearchTool + +# Initialize the tool with connection URL +singlestore_tool = SingleStoreSearchTool( + host="user:password@localhost:3306/ecommerce_db", + tables=["orders", "products", "customers", "order_items"] +) + +# Data Analyst Agent +data_analyst = Agent( + role="Senior Data Analyst", + goal="Extract insights from database queries and provide data-driven recommendations", + backstory="You are an experienced data analyst with expertise in SQL and business intelligence.", + tools=[singlestore_tool], + verbose=True +) + +# Business Intelligence Agent +bi_specialist = Agent( + role="Business Intelligence Specialist", + goal="Transform data insights into actionable business recommendations", + backstory="You specialize in translating complex data analysis into clear business strategies.", + verbose=True +) + +# Define multiple tasks +data_extraction_task = Task( + description=""" + Analyze the sales data to find: + 1. Top 5 best-selling products by quantity + 2. Monthly sales trends for the last 6 months + 3. Customer segments by purchase frequency + """, + agent=data_analyst, + expected_output="Detailed SQL query results with sales analysis including product rankings, trends, and customer segments." +) + +insights_task = Task( + description=""" + Based on the sales data analysis, provide business recommendations for: + 1. Inventory management for top products + 2. Marketing strategies for different customer segments + 3. Sales forecasting insights + """, + agent=bi_specialist, + expected_output="Strategic business recommendations with actionable insights based on the data analysis.", + context=[data_extraction_task] +) + +# Create and run the crew +analytics_crew = Crew( + agents=[data_analyst, bi_specialist], + tasks=[data_extraction_task, insights_task], + verbose=True +) + +result = analytics_crew.kickoff() +``` + +## Connection Methods + +SingleStore supports multiple connection methods. Choose the one that best fits your environment: + +### 1. Standard Connection + +```python +tool = SingleStoreSearchTool( + host='your_host', + user='your_username', + password='your_password', + database='your_database', + port=3306 +) +``` + +### 2. Connection URL (Recommended) + +You can use a complete connection URL in the `host` parameter for simplified configuration: + +```python +# Using connection URL in host parameter +tool = SingleStoreSearchTool( + host='user:password@localhost:3306/database_name' +) + +# Or for SingleStore Cloud +tool = SingleStoreSearchTool( + host='user:password@your_cloud_host:3333/database_name?ssl_disabled=false' +) +``` + +### 3. Environment Variable Configuration + +Set the `SINGLESTOREDB_URL` environment variable and initialize the tool without any connection arguments: + +```bash +# Set the environment variable +export SINGLESTOREDB_URL="singlestoredb://user:password@localhost:3306/database_name" + +# Or for cloud connections +export SINGLESTOREDB_URL="singlestoredb://user:password@your_cloud_host:3333/database_name?ssl_disabled=false" +``` + +```python +# No connection arguments needed when using environment variable +tool = SingleStoreSearchTool() + +# Or specify only table subset +tool = SingleStoreSearchTool(tables=['employees', 'departments']) +``` + +### 4. Connection with SSL + +```python +tool = SingleStoreSearchTool( + host='your_host', + user='your_username', + password='your_password', + database='your_database', + ssl_ca='/path/to/ca-cert.pem', + ssl_cert='/path/to/client-cert.pem', + ssl_key='/path/to/client-key.pem' +) +``` + +### 5. Advanced Configuration + +```python +tool = SingleStoreSearchTool( + host='your_host', + user='your_username', + password='your_password', + database='your_database', + # Connection pool settings + pool_size=10, + max_overflow=20, + timeout=60, + # Advanced options + charset='utf8mb4', + autocommit=True, + connect_timeout=30, + results_format='tuple', + # Custom connection attributes + conn_attrs={ + 'program_name': 'MyApp', + 'custom_attr': 'value' + } +) +``` + +## Configuration Parameters + +### Basic Connection Parameters +- `host`: Database host address or complete connection URL +- `user`: Database username +- `password`: Database password +- `port`: Database port (default: 3306) +- `database`: Database name +- `tables`: List of specific tables to work with (optional) + +### Connection Pool Parameters +- `pool_size`: Maximum number of connections in the pool (default: 5) +- `max_overflow`: Maximum overflow connections beyond pool_size (default: 10) +- `timeout`: Connection timeout in seconds (default: 30) + +### SSL/TLS Parameters +- `ssl_key`: Path to client private key file +- `ssl_cert`: Path to client certificate file +- `ssl_ca`: Path to certificate authority file +- `ssl_disabled`: Disable SSL (default: None) +- `ssl_verify_cert`: Verify server certificate +- `ssl_verify_identity`: Verify server identity + +### Advanced Parameters +- `charset`: Character set for the connection +- `autocommit`: Enable autocommit mode +- `connect_timeout`: Connection timeout in seconds +- `results_format`: Format for query results ('tuple', 'dict', etc.) +- `vector_data_format`: Format for vector data ('binary', 'json') +- `parse_json`: Parse JSON columns automatically + + +For more detailed connection options and advanced configurations, refer to the [SingleStore Python SDK documentation](https://singlestoredb-python.labs.singlestore.com/getting-started.html). diff --git a/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/__init__.py new file mode 100644 index 000000000..09f73f4ae --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/__init__.py @@ -0,0 +1,10 @@ +from crewai_tools.tools.singlestore_search_tool.singlestore_search_tool import ( + SingleStoreSearchTool, + SingleStoreSearchToolSchema, +) + + +__all__ = [ + "SingleStoreSearchTool", + "SingleStoreSearchToolSchema", +] diff --git a/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/singlestore_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/singlestore_search_tool.py new file mode 100644 index 000000000..889838f18 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/singlestore_search_tool/singlestore_search_tool.py @@ -0,0 +1,437 @@ +from collections.abc import Callable +from typing import Any + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field + + +try: + from singlestoredb import connect + from sqlalchemy.pool import QueuePool + + SINGLSTORE_AVAILABLE = True + +except ImportError: + SINGLSTORE_AVAILABLE = False + + +class SingleStoreSearchToolSchema(BaseModel): + """Input schema for SingleStoreSearchTool. + + This schema defines the expected input format for the search tool, + ensuring that only valid SELECT and SHOW queries are accepted. + """ + + search_query: str = Field( + ..., + description=( + "Mandatory semantic search query you want to use to search the database's content. " + "Only SELECT and SHOW queries are supported." + ), + ) + + +class SingleStoreSearchTool(BaseTool): + """A tool for performing semantic searches on SingleStore database tables. + + This tool provides a safe interface for executing SELECT and SHOW queries + against a SingleStore database with connection pooling for optimal performance. + """ + + name: str = "Search a database's table(s) content" + description: str = ( + "A tool that can be used to semantic search a query from a database." + ) + args_schema: type[BaseModel] = SingleStoreSearchToolSchema + + package_dependencies: list[str] = Field( + default_factory=lambda: ["singlestoredb", "SQLAlchemy"] + ) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SINGLESTOREDB_URL", + description="A comprehensive URL string that can encapsulate host, port," + " username, password, and database information, often used in environments" + " like SingleStore notebooks or specific frameworks." + " For example: 'me:p455w0rd@s2-host.com/my_db'", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_HOST", + description="Specifies the hostname, IP address, or URL of" + " the SingleStoreDB workspace or cluster", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_PORT", + description="Defines the port number on which the" + " SingleStoreDB server is listening", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_USER", + description="Specifies the database user name", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_PASSWORD", + description="Specifies the database user password", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_DATABASE", + description="Name of the database to connect to", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_SSL_KEY", + description="File containing SSL key", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_SSL_CERT", + description="File containing SSL certificate", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_SSL_CA", + description="File containing SSL certificate authority", + required=False, + default=None, + ), + EnvVar( + name="SINGLESTOREDB_CONNECT_TIMEOUT", + description="The timeout for connecting to the database in seconds", + required=False, + default=None, + ), + ] + ) + + connection_args: dict = Field(default_factory=dict) + connection_pool: Any | None = None + + def __init__( + self, + tables: list[str] | None = None, + # Basic connection parameters + host: str | None = None, + user: str | None = None, + password: str | None = None, + port: int | None = None, + database: str | None = None, + driver: str | None = None, + # Connection behavior options + pure_python: bool | None = None, + local_infile: bool | None = None, + charset: str | None = None, + # SSL/TLS configuration + ssl_key: str | None = None, + ssl_cert: str | None = None, + ssl_ca: str | None = None, + ssl_disabled: bool | None = None, + ssl_cipher: str | None = None, + ssl_verify_cert: bool | None = None, + tls_sni_servername: str | None = None, + ssl_verify_identity: bool | None = None, + # Advanced connection options + conv: dict[int, Callable[..., Any]] | None = None, + credential_type: str | None = None, + autocommit: bool | None = None, + # Result formatting options + results_type: str | None = None, + buffered: bool | None = None, + results_format: str | None = None, + program_name: str | None = None, + conn_attrs: dict[str, str] | None = None, + # Query execution options + multi_statements: bool | None = None, + client_found_rows: bool | None = None, + connect_timeout: int | None = None, + # Data type handling + nan_as_null: bool | None = None, + inf_as_null: bool | None = None, + encoding_errors: str | None = None, + track_env: bool | None = None, + enable_extended_data_types: bool | None = None, + vector_data_format: str | None = None, + parse_json: bool | None = None, + # Connection pool configuration + pool_size: int | None = 5, + max_overflow: int | None = 10, + timeout: float | None = 30, + **kwargs, + ): + """Initialize the SingleStore search tool. + + Args: + tables: List of table names to work with. If empty, all tables will be used. + host: Database host address + user: Database username + password: Database password + port: Database port number + database: Database name + pool_size: Maximum number of connections in the pool + max_overflow: Maximum overflow connections beyond pool_size + timeout: Connection timeout in seconds + **kwargs: Additional arguments passed to the parent class + """ + if conn_attrs is None: + conn_attrs = {} + if tables is None: + tables = [] + if not SINGLSTORE_AVAILABLE: + import click + + if click.confirm( + "You are missing the 'singlestore' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run( + ["uv", "add", "crewai-tools[singlestore]"], # noqa: S607 + check=True, + ) + + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install singlestore package") from e + else: + raise ImportError( + "`singlestore` package not found, please run `uv add crewai-tools[singlestore]`" + ) + + # Set the data type for the parent class + kwargs["data_type"] = "singlestore" + super().__init__(**kwargs) + + # Build connection arguments dictionary with sensible defaults + self.connection_args = { + # Basic connection parameters + "host": host, + "user": user, + "password": password, + "port": port, + "database": database, + "driver": driver, + # Connection behavior + "pure_python": pure_python, + "local_infile": local_infile, + "charset": charset, + # SSL/TLS settings + "ssl_key": ssl_key, + "ssl_cert": ssl_cert, + "ssl_ca": ssl_ca, + "ssl_disabled": ssl_disabled, + "ssl_cipher": ssl_cipher, + "ssl_verify_cert": ssl_verify_cert, + "tls_sni_servername": tls_sni_servername, + "ssl_verify_identity": ssl_verify_identity, + # Advanced options + "conv": conv or {}, + "credential_type": credential_type, + "autocommit": autocommit, + # Result formatting + "results_type": results_type, + "buffered": buffered, + "results_format": results_format, + "program_name": program_name, + "conn_attrs": conn_attrs or {}, + # Query execution + "multi_statements": multi_statements, + "client_found_rows": client_found_rows, + "connect_timeout": connect_timeout or 10, # Default: 10 seconds + # Data type handling with defaults + "nan_as_null": nan_as_null or False, + "inf_as_null": inf_as_null or False, + "encoding_errors": encoding_errors or "strict", + "track_env": track_env or False, + "enable_extended_data_types": enable_extended_data_types or False, + "vector_data_format": vector_data_format or "binary", + "parse_json": parse_json or True, + } + + # Ensure connection attributes are properly initialized + if "conn_attrs" not in self.connection_args or not self.connection_args.get( + "conn_attrs" + ): + self.connection_args["conn_attrs"] = dict() + + # Add tool identification to connection attributes + self.connection_args["conn_attrs"]["_connector_name"] = ( + "crewAI SingleStore Tool" + ) + self.connection_args["conn_attrs"]["_connector_version"] = "1.0" + + # Initialize connection pool for efficient connection management + self.connection_pool = QueuePool( + creator=self._create_connection, # type: ignore[arg-type] + pool_size=pool_size or 5, + max_overflow=max_overflow or 10, + timeout=timeout or 30.0, + ) + + # Validate database schema and initialize table information + self._initialize_tables(tables) + + def _initialize_tables(self, tables: list[str]) -> None: + """Initialize and validate the tables that this tool will work with. + + Args: + tables: List of table names to validate and use + + Raises: + ValueError: If no tables exist or specified tables don't exist + """ + conn = self._get_connection() + try: + with conn.cursor() as cursor: + # Get all existing tables in the database + cursor.execute("SHOW TABLES") + existing_tables = {table[0] for table in cursor.fetchall()} + + # Validate that the database has tables + if not existing_tables or len(existing_tables) == 0: + raise ValueError( + "No tables found in the database. " + "Please ensure the database is initialized with the required tables." + ) + + # Use all tables if none specified + if not tables or len(tables) == 0: + tables = list(existing_tables) + + # Build table definitions for description + table_definitions = [] + for table in tables: + if table not in existing_tables: + raise ValueError( + f"Table {table} does not exist in the database. " + f"Please ensure the table is created." + ) + + # Get column information for each table + cursor.execute(f"SHOW COLUMNS FROM {table}") + columns = cursor.fetchall() + column_info = ", ".join(f"{row[0]} {row[1]}" for row in columns) + table_definitions.append(f"{table}({column_info})") + finally: + # Ensure the connection is returned to the pool + conn.close() + + # Update the tool description with actual table information + self.description = ( + f"A tool that can be used to semantic search a query from a SingleStore " + f"database's {', '.join(table_definitions)} table(s) content." + ) + self._generate_description() + + def _get_connection(self) -> Any: + """Get a connection from the connection pool. + + Returns: + Connection: A SingleStore database connection + + Raises: + Exception: If connection cannot be established + """ + try: + return self.connection_pool.connect() # type: ignore[union-attr] + except Exception: + # Re-raise the exception to be handled by the caller + raise + + def _create_connection(self) -> Any: + """Create a new SingleStore connection. + + This method is used by the connection pool to create new connections + when needed. + + Returns: + Connection: A new SingleStore database connection + + Raises: + Exception: If connection cannot be created + """ + try: + return connect(**self.connection_args) + except Exception: + # Re-raise the exception to be handled by the caller + raise + + def _validate_query(self, search_query: str) -> tuple[bool, str]: + """Validate the search query to ensure it's safe to execute. + + Only SELECT and SHOW statements are allowed for security reasons. + + Args: + search_query: The SQL query to validate + + Returns: + tuple: (is_valid: bool, message: str) + """ + # Check if the input is a string + if not isinstance(search_query, str): + return False, "Search query must be a string." + + # Remove leading/trailing whitespace and convert to lowercase for checking + query_lower = search_query.strip().lower() + + # Allow only SELECT and SHOW statements + if not (query_lower.startswith(("select", "show"))): + return ( + False, + "Only SELECT and SHOW queries are supported for security reasons.", + ) + + return True, "Valid query" + + def _run(self, search_query: str) -> Any: + """Execute the search query against the SingleStore database. + + Args: + search_query: The SQL query to execute + **kwargs: Additional keyword arguments (unused) + + Returns: + str: Formatted search results or error message + """ + # Validate the query before execution + valid, message = self._validate_query(search_query) + if not valid: + return f"Invalid search query: {message}" + + # Execute the query using a connection from the pool + conn = self._get_connection() + try: + with conn.cursor() as cursor: + try: + # Execute the validated search query + cursor.execute(search_query) + results = cursor.fetchall() + + # Handle empty results + if not results: + return "No results found." + + # Format the results for readable output + formatted_results = "\n".join( + [", ".join([str(item) for item in row]) for row in results] + ) + return f"Search Results:\n{formatted_results}" + + except Exception as e: + return f"Error executing search query: {e}" + + finally: + # Ensure the connection is returned to the pool + conn.close() diff --git a/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/README.md new file mode 100644 index 000000000..fc0b845c3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/README.md @@ -0,0 +1,155 @@ +# Snowflake Search Tool + +A tool for executing queries on Snowflake data warehouse with built-in connection pooling, retry logic, and async execution support. + +## Installation + +```bash +uv sync --extra snowflake + +OR +uv pip install snowflake-connector-python>=3.5.0 snowflake-sqlalchemy>=1.5.0 cryptography>=41.0.0 + +OR +pip install snowflake-connector-python>=3.5.0 snowflake-sqlalchemy>=1.5.0 cryptography>=41.0.0 +``` + +## Quick Start + +```python +import asyncio +from crewai_tools import SnowflakeSearchTool, SnowflakeConfig + +# Create configuration +config = SnowflakeConfig( + account="your_account", + user="your_username", + password="your_password", + warehouse="COMPUTE_WH", + database="your_database", + snowflake_schema="your_schema" # Note: Uses snowflake_schema instead of schema +) + +# Initialize tool +tool = SnowflakeSearchTool( + config=config, + pool_size=5, + max_retries=3, + enable_caching=True +) + +# Execute query +async def main(): + results = await tool._run( + query="SELECT * FROM your_table LIMIT 10", + timeout=300 + ) + print(f"Retrieved {len(results)} rows") + +if __name__ == "__main__": + asyncio.run(main()) +``` + +## Features + +- ✨ Asynchronous query execution +- 🚀 Connection pooling for better performance +- 🔄 Automatic retries for transient failures +- 💾 Query result caching (optional) +- 🔒 Support for both password and key-pair authentication +- 📝 Comprehensive error handling and logging + +## Configuration Options + +### SnowflakeConfig Parameters + +| Parameter | Required | Description | +|-----------|----------|-------------| +| account | Yes | Snowflake account identifier | +| user | Yes | Snowflake username | +| password | Yes* | Snowflake password | +| private_key_path | No* | Path to private key file (alternative to password) | +| warehouse | Yes | Snowflake warehouse name | +| database | Yes | Default database | +| snowflake_schema | Yes | Default schema | +| role | No | Snowflake role | +| session_parameters | No | Custom session parameters dict | + +\* Either password or private_key_path must be provided + +### Tool Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| pool_size | 5 | Number of connections in the pool | +| max_retries | 3 | Maximum retry attempts for failed queries | +| retry_delay | 1.0 | Delay between retries in seconds | +| enable_caching | True | Enable/disable query result caching | + +## Advanced Usage + +### Using Key-Pair Authentication + +```python +config = SnowflakeConfig( + account="your_account", + user="your_username", + private_key_path="/path/to/private_key.p8", + warehouse="your_warehouse", + database="your_database", + snowflake_schema="your_schema" +) +``` + +### Custom Session Parameters + +```python +config = SnowflakeConfig( + # ... other config parameters ... + session_parameters={ + "QUERY_TAG": "my_app", + "TIMEZONE": "America/Los_Angeles" + } +) +``` + +## Best Practices + +1. **Error Handling**: Always wrap query execution in try-except blocks +2. **Logging**: Enable logging to track query execution and errors +3. **Connection Management**: Use appropriate pool sizes for your workload +4. **Timeouts**: Set reasonable query timeouts to prevent hanging +5. **Security**: Use key-pair auth in production and never hardcode credentials + +## Example with Logging + +```python +import logging + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + +async def main(): + try: + # ... tool initialization ... + results = await tool._run(query="SELECT * FROM table LIMIT 10") + logger.info(f"Query completed successfully. Retrieved {len(results)} rows") + except Exception as e: + logger.error(f"Query failed: {str(e)}") + raise +``` + +## Error Handling + +The tool automatically handles common Snowflake errors: +- DatabaseError +- OperationalError +- ProgrammingError +- Network timeouts +- Connection issues + +Errors are logged and retried based on your retry configuration. \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/__init__.py new file mode 100644 index 000000000..8eb8e9c71 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/__init__.py @@ -0,0 +1,12 @@ +from crewai_tools.tools.snowflake_search_tool.snowflake_search_tool import ( + SnowflakeConfig, + SnowflakeSearchTool, + SnowflakeSearchToolInput, +) + + +__all__ = [ + "SnowflakeConfig", + "SnowflakeSearchTool", + "SnowflakeSearchToolInput", +] diff --git a/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/snowflake_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/snowflake_search_tool.py new file mode 100644 index 000000000..485e15ba3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/snowflake_search_tool/snowflake_search_tool.py @@ -0,0 +1,289 @@ +from __future__ import annotations + +import asyncio +from concurrent.futures import ThreadPoolExecutor +import logging +from typing import TYPE_CHECKING, Any + +from crewai.tools.base_tool import BaseTool +from pydantic import BaseModel, ConfigDict, Field, SecretStr + + +if TYPE_CHECKING: + # Import types for type checking only + from snowflake.connector.connection import ( # type: ignore[import-not-found] + SnowflakeConnection, + ) + from snowflake.connector.errors import ( # type: ignore[import-not-found] + DatabaseError, + OperationalError, + ) + +try: + from cryptography.hazmat.backends import default_backend + from cryptography.hazmat.primitives import serialization + import snowflake.connector # type: ignore[import-not-found] + + SNOWFLAKE_AVAILABLE = True +except ImportError: + SNOWFLAKE_AVAILABLE = False + +# Configure logging +logger = logging.getLogger(__name__) + +# Cache for query results +_query_cache: dict[str, list[dict[str, Any]]] = {} + + +class SnowflakeConfig(BaseModel): + """Configuration for Snowflake connection.""" + + model_config = ConfigDict(protected_namespaces=()) + + account: str = Field( + ..., description="Snowflake account identifier", pattern=r"^[a-zA-Z0-9\-_]+$" + ) + user: str = Field(..., description="Snowflake username") + password: SecretStr | None = Field(None, description="Snowflake password") + private_key_path: str | None = Field(None, description="Path to private key file") + warehouse: str | None = Field(None, description="Snowflake warehouse") + database: str | None = Field(None, description="Default database") + snowflake_schema: str | None = Field(None, description="Default schema") + role: str | None = Field(None, description="Snowflake role") + session_parameters: dict[str, Any] | None = Field( + default_factory=dict, description="Session parameters" + ) + + @property + def has_auth(self) -> bool: + return bool(self.password or self.private_key_path) + + def model_post_init(self, *args, **kwargs): + if not self.has_auth: + raise ValueError("Either password or private_key_path must be provided") + + +class SnowflakeSearchToolInput(BaseModel): + """Input schema for SnowflakeSearchTool.""" + + model_config = ConfigDict(protected_namespaces=()) + + query: str = Field(..., description="SQL query or semantic search query to execute") + database: str | None = Field(None, description="Override default database") + snowflake_schema: str | None = Field(None, description="Override default schema") + timeout: int | None = Field(300, description="Query timeout in seconds") + + +class SnowflakeSearchTool(BaseTool): + """Tool for executing queries and semantic search on Snowflake.""" + + name: str = "Snowflake Database Search" + description: str = ( + "Execute SQL queries or semantic search on Snowflake data warehouse. " + "Supports both raw SQL and natural language queries." + ) + args_schema: type[BaseModel] = SnowflakeSearchToolInput + + # Define Pydantic fields + config: SnowflakeConfig = Field( + ..., description="Snowflake connection configuration" + ) + pool_size: int = Field(default=5, description="Size of connection pool") + max_retries: int = Field(default=3, description="Maximum retry attempts") + retry_delay: float = Field( + default=1.0, description="Delay between retries in seconds" + ) + enable_caching: bool = Field( + default=True, description="Enable query result caching" + ) + + model_config = ConfigDict( + arbitrary_types_allowed=True, validate_assignment=True, frozen=False + ) + + _connection_pool: list[SnowflakeConnection] | None = None + _pool_lock: asyncio.Lock | None = None + _thread_pool: ThreadPoolExecutor | None = None + _model_rebuilt: bool = False + package_dependencies: list[str] = Field( + default_factory=lambda: [ + "snowflake-connector-python", + "snowflake-sqlalchemy", + "cryptography", + ] + ) + + def __init__(self, **data): + """Initialize SnowflakeSearchTool.""" + super().__init__(**data) + self._initialize_snowflake() + + def _initialize_snowflake(self) -> None: + try: + if SNOWFLAKE_AVAILABLE: + self._connection_pool = [] + self._pool_lock = asyncio.Lock() + self._thread_pool = ThreadPoolExecutor(max_workers=self.pool_size) + else: + raise ImportError + except ImportError: + import click + + if click.confirm( + "You are missing the 'snowflake-connector-python' package. Would you like to install it?" + ): + import subprocess + + try: + subprocess.run( + [ # noqa: S607 + "uv", + "add", + "cryptography", + "snowflake-connector-python", + "snowflake-sqlalchemy", + ], + check=True, + ) + + self._connection_pool = [] + self._pool_lock = asyncio.Lock() + self._thread_pool = ThreadPoolExecutor(max_workers=self.pool_size) + except subprocess.CalledProcessError as e: + raise ImportError("Failed to install Snowflake dependencies") from e + else: + raise ImportError( + "Snowflake dependencies not found. Please install them by running " + "`uv add cryptography snowflake-connector-python snowflake-sqlalchemy`" + ) from None + + async def _get_connection(self) -> SnowflakeConnection: + """Get a connection from the pool or create a new one.""" + if self._pool_lock is None: + raise RuntimeError("Pool lock not initialized") + if self._connection_pool is None: + raise RuntimeError("Connection pool not initialized") + async with self._pool_lock: + if not self._connection_pool: + conn = await asyncio.get_event_loop().run_in_executor( + self._thread_pool, self._create_connection + ) + self._connection_pool.append(conn) + return self._connection_pool.pop() + + def _create_connection(self) -> SnowflakeConnection: + """Create a new Snowflake connection.""" + conn_params: dict[str, Any] = { + "account": self.config.account, + "user": self.config.user, + "warehouse": self.config.warehouse, + "database": self.config.database, + "schema": self.config.snowflake_schema, + "role": self.config.role, + "session_parameters": self.config.session_parameters, + } + + if self.config.password: + conn_params["password"] = self.config.password.get_secret_value() + elif self.config.private_key_path and serialization: + with open(self.config.private_key_path, "rb") as key_file: + p_key = serialization.load_pem_private_key( + key_file.read(), password=None, backend=default_backend() + ) + conn_params["private_key"] = p_key + + return snowflake.connector.connect(**conn_params) + + def _get_cache_key(self, query: str, timeout: int) -> str: + """Generate a cache key for the query.""" + return f"{self.config.account}:{self.config.database}:{self.config.snowflake_schema}:{query}:{timeout}" + + async def _execute_query( + self, query: str, timeout: int = 300 + ) -> list[dict[str, Any]]: + """Execute a query with retries and return results.""" + if self.enable_caching: + cache_key = self._get_cache_key(query, timeout) + if cache_key in _query_cache: + logger.info("Returning cached result") + return _query_cache[cache_key] + + for attempt in range(self.max_retries): + try: + conn = await self._get_connection() + try: + cursor = conn.cursor() + cursor.execute(query, timeout=timeout) + + if not cursor.description: + return [] + + columns = [col[0] for col in cursor.description] + results = [ + dict(zip(columns, row, strict=False)) + for row in cursor.fetchall() + ] + + if self.enable_caching: + _query_cache[self._get_cache_key(query, timeout)] = results + + return results + finally: + cursor.close() + if ( + self._pool_lock is not None + and self._connection_pool is not None + ): + async with self._pool_lock: + self._connection_pool.append(conn) + except (DatabaseError, OperationalError) as e: # noqa: PERF203 + if attempt == self.max_retries - 1: + raise + await asyncio.sleep(self.retry_delay * (2**attempt)) + logger.warning(f"Query failed, attempt {attempt + 1}: {e!s}") + continue + raise RuntimeError("Query failed after all retries") + + async def _run( + self, + query: str, + database: str | None = None, + snowflake_schema: str | None = None, + timeout: int = 300, + **kwargs: Any, + ) -> Any: + """Execute the search query.""" + try: + # Override database/schema if provided + if database: + await self._execute_query(f"USE DATABASE {database}") + if snowflake_schema: + await self._execute_query(f"USE SCHEMA {snowflake_schema}") + + return await self._execute_query(query, timeout) + except Exception as e: + logger.error(f"Error executing query: {e!s}") + raise + + def __del__(self): + """Cleanup connections on deletion.""" + try: + if self._connection_pool: + for conn in self._connection_pool: + try: + conn.close() + except Exception: # noqa: PERF203, S110 + pass + if self._thread_pool: + self._thread_pool.shutdown() + except Exception: # noqa: S110 + pass + + +try: + # Only rebuild if the class hasn't been initialized yet + if not hasattr(SnowflakeSearchTool, "_model_rebuilt"): + SnowflakeSearchTool.model_rebuild() + SnowflakeSearchTool._model_rebuilt = True +except Exception: # noqa: S110 + pass diff --git a/lib/crewai-tools/src/crewai_tools/tools/spider_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/spider_tool/README.md new file mode 100644 index 000000000..482c7c830 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/spider_tool/README.md @@ -0,0 +1,87 @@ +# SpiderTool + +## Description +[Spider](https://spider.cloud/?ref=crewai) is a high-performance web scraping and crawling tool that delivers optimized markdown for LLMs and AI agents. It intelligently switches between HTTP requests and JavaScript rendering based on page requirements. Perfect for both single-page scraping and website crawling—making it ideal for content extraction and data collection. + +## Installation +To use the Spider API you need to download the [Spider SDK](https://pypi.org/project/spider-client/) and the crewai[tools] SDK, too: + +```python +pip install spider-client 'crewai[tools]' +``` + +## Example +This example shows you how you can use the Spider tool to enable your agent to scrape and crawl websites. The data returned from the Spider API is LLM-ready. + +```python +from crewai_tools import SpiderTool + +# To enable scraping any website it finds during its execution +spider_tool = SpiderTool(api_key='YOUR_API_KEY') + +# Initialize the tool with the website URL, so the agent can only scrape the content of the specified website +spider_tool = SpiderTool(website_url='https://spider.cloud') + +# Pass in custom parameters, see below for more details +spider_tool = SpiderTool( + website_url='https://spider.cloud', + custom_params={"depth": 2, "anti_bot": True, "proxy_enabled": True} +) + +# Advanced usage using css query selector to extract content +css_extraction_map = { + "/": [ # pass in path (main index in this case) + { + "name": "headers", # give it a name for this element + "selectors": [ + "h1" + ] + } + ] +} + +spider_tool = SpiderTool( + website_url='https://spider.cloud', + custom_params={"anti_bot": True, "proxy_enabled": True, "metadata": True, "css_extraction_map": css_extraction_map} +) + +### Response (extracted text will be in the metadata) +"css_extracted": { + "headers": [ + "The Web Crawler for AI Agents and LLMs!" + ] +} +``` +## Agent setup +```yaml +researcher: + role: > + You're a researcher that is tasked with researching a website and it's content (use crawl mode). The website is to crawl is: {website_url}. +``` + +## Arguments + +- `api_key` (string, optional): Specifies Spider API key. If not specified, it looks for `SPIDER_API_KEY` in environment variables. +- `website_url` (string): The website URL. Will be used as a fallback if passed when the tool is initialized. +- `log_failures` (bool): Log scrape failures or fail silently. Defaults to `true`. +- `custom_params` (object, optional): Optional parameters for the request. + - `return_format` (string): The return format of the website's content. Defaults to `markdown`. + - `request` (string): The request type to perform. Possible values are `http`, `chrome`, and `smart`. Use `smart` to perform an HTTP request by default until JavaScript rendering is needed for the HTML. + - `limit` (int): The maximum number of pages allowed to crawl per website. Remove the value or set it to `0` to crawl all pages. + - `depth` (int): The crawl limit for maximum depth. If `0`, no limit will be applied. + - `locale` (string): The locale to use for request, example `en-US`. + - `cookies` (string): Add HTTP cookies to use for request. + - `stealth` (bool): Use stealth mode for headless chrome request to help prevent being blocked. The default is `true` on chrome. + - `headers` (object): Forward HTTP headers to use for all request. The object is expected to be a map of key value pairs. + - `metadata` (bool): Boolean to store metadata about the pages and content found. Defaults to `false`. + - `subdomains` (bool): Allow subdomains to be included. Default is `false`. + - `user_agent` (string): Add a custom HTTP user agent to the request. By default this is set to a random agent. + - `proxy_enabled` (bool): Enable high performance premium proxies for the request to prevent being blocked at the network level. + - `css_extraction_map` (object): Use CSS or XPath selectors to scrape contents from the web page. Set the paths and the extraction object map to perform extractions per path or page. + - `request_timeout` (int): The timeout to use for request. Timeouts can be from `5-60`. The default is `30` seconds. + - `return_headers` (bool): Return the HTTP response headers with the results. Defaults to `false`. + - `filter_output_main_only` (bool): Filter the nav, aside, and footer from the output. + - `headers` (object): Forward HTTP headers to use for all request. The object is expected to be a map of key value pairs. + +Learn other parameters that can be used: [https://spider.cloud/docs/api](https://spider.cloud/docs/api) + diff --git a/lib/crewai-tools/src/crewai_tools/tools/spider_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/spider_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/spider_tool/spider_tool.py b/lib/crewai-tools/src/crewai_tools/tools/spider_tool/spider_tool.py new file mode 100644 index 000000000..c72b1e96d --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/spider_tool/spider_tool.py @@ -0,0 +1,218 @@ +import logging +import subprocess +from typing import Any, Literal +from urllib.parse import unquote, urlparse + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field + + +logger = logging.getLogger(__file__) + + +class SpiderToolSchema(BaseModel): + """Input schema for SpiderTool.""" + + website_url: str = Field( + ..., description="Mandatory website URL to scrape or crawl" + ) + mode: Literal["scrape", "crawl"] = Field( + default="scrape", + description="The mode of the SpiderTool. The only two allowed modes are `scrape` or `crawl`. Crawl mode will follow up to 5 links and return their content in markdown format.", + ) + + +class SpiderToolConfig(BaseModel): + """Configuration settings for SpiderTool. + + Contains all default values and constants used by SpiderTool. + Centralizes configuration management for easier maintenance. + """ + + # Crawling settings + DEFAULT_CRAWL_LIMIT: int = 5 + DEFAULT_RETURN_FORMAT: str = "markdown" + + # Request parameters + DEFAULT_REQUEST_MODE: str = "smart" + FILTER_SVG: bool = True + + +class SpiderTool(BaseTool): + """Tool for scraping and crawling websites. + This tool provides functionality to either scrape a single webpage or crawl multiple + pages, returning content in a format suitable for LLM processing. + """ + + name: str = "SpiderTool" + description: str = ( + "A tool to scrape or crawl a website and return LLM-ready content." + ) + args_schema: type[BaseModel] = SpiderToolSchema + custom_params: dict[str, Any] | None = None + website_url: str | None = None + api_key: str | None = None + spider: Any = None + log_failures: bool = True + config: SpiderToolConfig = SpiderToolConfig() + package_dependencies: list[str] = Field(default_factory=lambda: ["spider-client"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="SPIDER_API_KEY", + description="API key for Spider.cloud", + required=True, + ), + ] + ) + + def __init__( + self, + api_key: str | None = None, + website_url: str | None = None, + custom_params: dict[str, Any] | None = None, + log_failures: bool = True, + **kwargs, + ): + """Initialize SpiderTool for web scraping and crawling. + + Args: + api_key (Optional[str]): Spider API key for authentication. Required for production use. + website_url (Optional[str]): Default website URL to scrape/crawl. Can be overridden during execution. + custom_params (Optional[Dict[str, Any]]): Additional parameters to pass to Spider API. + These override any parameters set by the LLM. + log_failures (bool): If True, logs errors. Defaults to True. + **kwargs: Additional arguments passed to BaseTool. + + Raises: + ImportError: If spider-client package is not installed. + RuntimeError: If Spider client initialization fails. + """ + super().__init__(**kwargs) + if website_url is not None: + self.website_url = website_url + + self.log_failures = log_failures + self.custom_params = custom_params + + try: + from spider import Spider # type: ignore + + except ImportError: + import click + + if click.confirm( + "You are missing the 'spider-client' package. Would you like to install it?" + ): + subprocess.run(["uv", "pip", "install", "spider-client"], check=True) # noqa: S607 + from spider import Spider # type: ignore[import-untyped] + else: + raise ImportError( + "`spider-client` package not found, please run `uv add spider-client`" + ) from None + self.spider = Spider(api_key=api_key) + + def _validate_url(self, url: str) -> bool: + """Validate URL format and security constraints. + + Args: + url (str): URL to validate. Must be a properly formatted HTTP(S) URL + + Returns: + bool: True if URL is valid and meets security requirements, False otherwise. + """ + try: + url = url.strip() + decoded_url = unquote(url) + + result = urlparse(decoded_url) + if not all([result.scheme, result.netloc]): + return False + + if result.scheme not in ["http", "https"]: + return False + + return True + except Exception: + return False + + def _run( + self, + website_url: str, + mode: Literal["scrape", "crawl"] = "scrape", + ) -> str | None: + """Execute the spider tool to scrape or crawl the specified website. + + Args: + website_url (str): The URL to process. Must be a valid HTTP(S) URL. + mode (Literal["scrape", "crawl"]): Operation mode. + - "scrape": Extract content from single page + - "crawl": Follow links and extract content from multiple pages + + Returns: + Optional[str]: Extracted content in markdown format, or None if extraction fails + and log_failures is True. + + Raises: + ValueError: If URL is invalid or missing, or if mode is invalid. + ImportError: If spider-client package is not properly installed. + ConnectionError: If network connection fails while accessing the URL. + Exception: For other runtime errors. + """ + try: + params = {} + url = website_url or self.website_url + + if not url: + raise ValueError( + "Website URL must be provided either during initialization or execution" + ) + + if not self._validate_url(url): + raise ValueError(f"Invalid URL format: {url}") + + if mode not in ["scrape", "crawl"]: + raise ValueError( + f"Invalid mode: {mode}. Must be either 'scrape' or 'crawl'" + ) + + params = { + "request": self.config.DEFAULT_REQUEST_MODE, + "filter_output_svg": self.config.FILTER_SVG, + "return_format": self.config.DEFAULT_RETURN_FORMAT, + } + + if mode == "crawl": + params["limit"] = self.config.DEFAULT_CRAWL_LIMIT + + if self.custom_params: + params.update(self.custom_params) + + action = ( + self.spider.scrape_url if mode == "scrape" else self.spider.crawl_url + ) + return action(url=url, params=params) + + except ValueError as ve: + if self.log_failures: + logger.error(f"Validation error for URL {url}: {ve!s}") + return None + raise ve + + except ImportError as ie: + logger.error(f"Spider client import error: {ie!s}") + raise ie + + except ConnectionError as ce: + if self.log_failures: + logger.error(f"Connection error while accessing {url}: {ce!s}") + return None + raise ce + + except Exception as e: + if self.log_failures: + logger.error( + f"Unexpected error during {mode} operation on {url}: {e!s}" + ) + return None + raise e diff --git a/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/.env.example b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/.env.example new file mode 100644 index 000000000..7a4d2890a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/.env.example @@ -0,0 +1,5 @@ +ANTHROPIC_API_KEY="your_anthropic_api_key" +OPENAI_API_KEY="your_openai_api_key" +MODEL_API_KEY="your_model_api_key" +BROWSERBASE_API_KEY="your_browserbase_api_key" +BROWSERBASE_PROJECT_ID="your_browserbase_project_id" \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/README.md new file mode 100644 index 000000000..707b99343 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/README.md @@ -0,0 +1,273 @@ +# Stagehand Web Automation Tool + +This tool integrates the [Stagehand](https://docs.stagehand.dev/) framework with CrewAI, allowing agents to interact with websites and automate browser tasks using natural language instructions. + +## Description + +Stagehand is a powerful browser automation framework built by Browserbase that allows AI agents to: + +- Navigate to websites +- Click buttons, links, and other elements +- Fill in forms +- Extract data from web pages +- Observe and identify elements +- Perform complex workflows + +The StagehandTool wraps the Stagehand Python SDK to provide CrewAI agents with the ability to control a real web browser and interact with websites using three core primitives: + +1. **Act**: Perform actions like clicking, typing, or navigating +2. **Extract**: Extract structured data from web pages +3. **Observe**: Identify and analyze elements on the page + +## Requirements + +Before using this tool, you will need: + +1. A [Browserbase](https://www.browserbase.com/) account with API key and project ID +2. An API key for an LLM (OpenAI or Anthropic Claude) +3. The Stagehand Python SDK installed + +Install the dependencies: + +```bash +pip install stagehand-py +``` + +## Usage + +### Basic Usage + +The StagehandTool can be used in two ways: + +1. **Using a context manager (recommended)**: +```python +from crewai import Agent, Task, Crew +from crewai_tools import StagehandTool +from stagehand.schemas import AvailableModel + +# Initialize the tool with your API keys using a context manager +with StagehandTool( + api_key="your-browserbase-api-key", + project_id="your-browserbase-project-id", + model_api_key="your-llm-api-key", # OpenAI or Anthropic API key + model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST, # Optional: specify which model to use +) as stagehand_tool: + # Create an agent with the tool + researcher = Agent( + role="Web Researcher", + goal="Find and summarize information from websites", + backstory="I'm an expert at finding information online.", + verbose=True, + tools=[stagehand_tool], + ) + + # Create a task that uses the tool + research_task = Task( + description="Go to https://www.example.com and tell me what you see on the homepage.", + agent=researcher, + ) + + # Run the crew + crew = Crew( + agents=[researcher], + tasks=[research_task], + verbose=True, + ) + + result = crew.kickoff() + print(result) + # Resources are automatically cleaned up when exiting the context +``` + +2. **Manual resource management**: +```python +from crewai import Agent, Task, Crew +from crewai_tools import StagehandTool +from stagehand.schemas import AvailableModel + +# Initialize the tool with your API keys +stagehand_tool = StagehandTool( + api_key="your-browserbase-api-key", + project_id="your-browserbase-project-id", + model_api_key="your-llm-api-key", + model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST, +) + +try: + # Create an agent with the tool + researcher = Agent( + role="Web Researcher", + goal="Find and summarize information from websites", + backstory="I'm an expert at finding information online.", + verbose=True, + tools=[stagehand_tool], + ) + + # Create a task that uses the tool + research_task = Task( + description="Go to https://www.example.com and tell me what you see on the homepage.", + agent=researcher, + ) + + # Run the crew + crew = Crew( + agents=[researcher], + tasks=[research_task], + verbose=True, + ) + + result = crew.kickoff() + print(result) +finally: + # Explicitly clean up resources + stagehand_tool.close() +``` + +The context manager approach (option 1) is recommended as it ensures proper cleanup of resources even if exceptions occur. However, both approaches are valid and will properly manage the browser session. + +## Command Types + +The StagehandTool supports three different command types, each designed for specific web automation tasks: + +### 1. Act - Perform Actions on a Page + +The `act` command type (default) allows the agent to perform actions on a webpage, such as clicking buttons, filling forms, navigating, and more. + +**When to use**: Use `act` when you need to interact with a webpage by performing actions like clicking, typing, scrolling, or navigating. + +**Example usage**: +```python +# Perform an action (default behavior) +result = stagehand_tool.run( + instruction="Click the login button", + url="https://example.com", + command_type="act" # Default, so can be omitted +) + +# Fill out a form +result = stagehand_tool.run( + instruction="Fill the contact form with name 'John Doe', email 'john@example.com', and message 'Hello world'", + url="https://example.com/contact" +) + +# Multiple actions in sequence +result = stagehand_tool.run( + instruction="Search for 'AI tools' in the search box and press Enter", + url="https://example.com" +) +``` + +### 2. Extract - Get Data from a Page + +The `extract` command type allows the agent to extract structured data from a webpage, such as product information, article text, or table data. + +**When to use**: Use `extract` when you need to retrieve specific information from a webpage in a structured format. + +**Example usage**: +```python +# Extract all product information +result = stagehand_tool.run( + instruction="Extract all product names, prices, and descriptions", + url="https://example.com/products", + command_type="extract" +) + +# Extract specific information with a selector +result = stagehand_tool.run( + instruction="Extract the main article title and content", + url="https://example.com/blog/article", + command_type="extract", + selector=".article-container" # Optional CSS selector to limit extraction scope +) + +# Extract tabular data +result = stagehand_tool.run( + instruction="Extract the data from the pricing table as a structured list of plans with their features and costs", + url="https://example.com/pricing", + command_type="extract", + selector=".pricing-table" +) +``` + +### 3. Observe - Identify Elements on a Page + +The `observe` command type allows the agent to identify and analyze specific elements on a webpage, returning information about their attributes, location, and suggested actions. + +**When to use**: Use `observe` when you need to identify UI elements, understand page structure, or determine what actions are possible. + +**Example usage**: +```python +# Find interactive elements +result = stagehand_tool.run( + instruction="Find all interactive elements in the navigation menu", + url="https://example.com", + command_type="observe" +) + +# Identify form fields +result = stagehand_tool.run( + instruction="Identify all the input fields in the registration form", + url="https://example.com/register", + command_type="observe", + selector="#registration-form" +) + +# Analyze page structure +result = stagehand_tool.run( + instruction="Find the main content sections of this page", + url="https://example.com/about", + command_type="observe" +) +``` + +## Advanced Configuration + +You can customize the behavior of the StagehandTool by specifying different parameters: + +```python +stagehand_tool = StagehandTool( + api_key="your-browserbase-api-key", + project_id="your-browserbase-project-id", + model_api_key="your-llm-api-key", + model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST, + dom_settle_timeout_ms=5000, # Wait longer for DOM to settle + headless=True, # Run browser in headless mode (no visible window) + self_heal=True, # Attempt to recover from errors + wait_for_captcha_solves=True, # Wait for CAPTCHA solving + verbose=1, # Control logging verbosity (0-3) +) +``` + +## Tips for Effective Use + +1. **Be specific in instructions**: The more specific your instructions, the better the results. For example, instead of "click the button," use "click the 'Submit' button at the bottom of the contact form." + +2. **Use the right command type**: Choose the appropriate command type based on your task: + - Use `act` for interactions and navigation + - Use `extract` for gathering information + - Use `observe` for understanding page structure + +3. **Leverage selectors**: When extracting data or observing elements, use CSS selectors to narrow the scope and improve accuracy. + +4. **Handle multi-step processes**: For complex workflows, break them down into multiple tool calls, each handling a specific step. + +5. **Error handling**: Implement appropriate error handling in your agent's logic to deal with potential issues like elements not found or pages not loading. + +## Troubleshooting + +- **Session not starting**: Ensure you have valid API keys for both Browserbase and your LLM provider. +- **Elements not found**: Try increasing the `dom_settle_timeout_ms` parameter to give the page more time to load. +- **Actions not working**: Make sure your instructions are clear and specific. You may need to use `observe` first to identify the correct elements. +- **Extract returning incomplete data**: Try refining your instruction or providing a more specific selector. + +## Resources + +- [Stagehand Documentation](https://docs.stagehand.dev/reference/introduction) - Complete reference for the Stagehand framework +- [Browserbase](https://www.browserbase.com) - Browser automation platform +- [Join Slack Community](https://stagehand.dev/slack) - Get help and connect with other users of Stagehand + +## Contact + +For more information about Stagehand, visit [the Stagehand documentation](https://docs.stagehand.dev/). + +For questions about the CrewAI integration, join our [Slack](https://stagehand.dev/slack) or open an issue in this repository. \ No newline at end of file diff --git a/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/__init__.py new file mode 100644 index 000000000..3ee79b45e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/__init__.py @@ -0,0 +1,4 @@ +from crewai_tools.tools.stagehand_tool.stagehand_tool import StagehandTool + + +__all__ = ["StagehandTool"] diff --git a/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/example.py b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/example.py new file mode 100644 index 000000000..a14df60df --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/example.py @@ -0,0 +1,121 @@ +""" +StagehandTool Example + +This example demonstrates how to use the StagehandTool in a CrewAI workflow. +It shows how to use the three main primitives: act, extract, and observe. + +Prerequisites: +1. A Browserbase account with API key and project ID +2. An LLM API key (OpenAI or Anthropic) +3. Installed dependencies: crewai, crewai-tools, stagehand-py + +Usage: +- Set your API keys in environment variables (recommended) +- Or modify the script to include your API keys directly +- Run the script: python stagehand_example.py +""" + +import os + +from crewai.utilities.printer import Printer +from dotenv import load_dotenv +from stagehand.schemas import AvailableModel # type: ignore[import-untyped] + +from crewai import Agent, Crew, Process, Task +from crewai_tools import StagehandTool + + +_printer = Printer() + + +# Load environment variables from .env file +load_dotenv() + +# Get API keys from environment variables +# You can set these in your shell or in a .env file +browserbase_api_key = os.environ.get("BROWSERBASE_API_KEY") +browserbase_project_id = os.environ.get("BROWSERBASE_PROJECT_ID") +model_api_key = os.environ.get("OPENAI_API_KEY") # or OPENAI_API_KEY + +# Initialize the StagehandTool with your credentials and use context manager +with StagehandTool( + api_key=browserbase_api_key, # New parameter naming + project_id=browserbase_project_id, # New parameter naming + model_api_key=model_api_key, + model_name=AvailableModel.GPT_4O, # Using the enum from schemas +) as stagehand_tool: + # Create a web researcher agent with the StagehandTool + researcher = Agent( + role="Web Researcher", + goal="Find and extract information from websites using different Stagehand primitives", + backstory=( + "You are an expert web automation agent equipped with the StagehandTool. " + "Your primary function is to interact with websites based on natural language instructions. " + "You must carefully choose the correct command (`command_type`) for each task:\n" + "- Use 'act' (the default) for general interactions like clicking buttons ('Click the login button'), " + "filling forms ('Fill the form with username user and password pass'), scrolling, or navigating within the site.\n" + "- Use 'navigate' specifically when you need to go to a new web page; you MUST provide the target URL " + "in the `url` parameter along with the instruction (e.g., instruction='Go to Google', url='https://google.com').\n" + "- Use 'extract' when the goal is to pull structured data from the page. Provide a clear `instruction` " + "describing what data to extract (e.g., 'Extract all product names and prices').\n" + "- Use 'observe' to identify and analyze elements on the current page based on an `instruction` " + "(e.g., 'Find all images in the main content area').\n\n" + "Remember to break down complex tasks into simple, sequential steps in your `instruction`. For example, " + "instead of 'Search for OpenAI on Google and click the first result', use multiple steps with the tool:\n" + "1. Use 'navigate' with url='https://google.com'.\n" + "2. Use 'act' with instruction='Type OpenAI in the search bar'.\n" + "3. Use 'act' with instruction='Click the search button'.\n" + "4. Use 'act' with instruction='Click the first search result link for OpenAI'.\n\n" + "Always be precise in your instructions and choose the most appropriate command and parameters (`instruction`, `url`, `command_type`, `selector`) for the task at hand." + ), + llm="gpt-4o", + verbose=True, + allow_delegation=False, + tools=[stagehand_tool], + ) + + # Define a research task that demonstrates all three primitives + research_task = Task( + description=( + "Demonstrate Stagehand capabilities by performing the following steps:\n" + "1. Go to https://www.stagehand.dev\n" + "2. Extract all the text content from the page\n" + "3. Find the Docs link and click on it\n" + "4. Go to https://httpbin.org/forms/post and observe what elements are available on the page\n" + "5. Provide a summary of what you learned about using these different commands" + ), + expected_output=( + "A demonstration of all three Stagehand primitives (act, extract, observe) " + "with examples of how each was used and what information was gathered." + ), + agent=researcher, + ) + + # Alternative task: Real research using the primitives + web_research_task = Task( + description=( + "Go to google.com and search for 'Stagehand'.\n" + "Then extract the first search result." + ), + expected_output=( + "A summary report about Stagehand's capabilities and pricing, demonstrating how " + "the different primitives can be used together for effective web research." + ), + agent=researcher, + ) + + # Set up the crew + crew = Crew( + agents=[researcher], + tasks=[research_task], # You can switch this to web_research_task if you prefer + verbose=True, + process=Process.sequential, + ) + + # Run the crew and get the result + result = crew.kickoff() + + _printer.print("\n==== RESULTS ====\n", color="cyan") + _printer.print(str(result)) + +# Resources are automatically cleaned up when exiting the context manager diff --git a/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/stagehand_tool.py b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/stagehand_tool.py new file mode 100644 index 000000000..c97c78e66 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/stagehand_tool/stagehand_tool.py @@ -0,0 +1,723 @@ +import asyncio +import json +import os +import re +from typing import Any + +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + + +# Define a flag to track whether stagehand is available +_HAS_STAGEHAND = False + +try: + from stagehand import ( # type: ignore[import-untyped] + Stagehand, + StagehandConfig, + StagehandPage, + configure_logging, + ) + from stagehand.schemas import ( # type: ignore[import-untyped] + ActOptions, + AvailableModel, + ExtractOptions, + ObserveOptions, + ) + + _HAS_STAGEHAND = True +except ImportError: + # Define type stubs for when stagehand is not installed + Stagehand = Any + StagehandPage = Any + StagehandConfig = Any + ActOptions = Any + ExtractOptions = Any + ObserveOptions = Any + + # Mock configure_logging function + def configure_logging(level=None, remove_logger_name=None, quiet_dependencies=None): + pass + + # Define only what's needed for class defaults + class AvailableModel: # type: ignore[no-redef] + CLAUDE_3_7_SONNET_LATEST = "anthropic.claude-3-7-sonnet-20240607" + + +class StagehandResult(BaseModel): + """Result from a Stagehand operation. + + Attributes: + success: Whether the operation completed successfully + data: The result data from the operation + error: Optional error message if the operation failed + """ + + success: bool = Field( + ..., description="Whether the operation completed successfully" + ) + data: str | dict | list = Field( + ..., description="The result data from the operation" + ) + error: str | None = Field( + None, description="Optional error message if the operation failed" + ) + + +class StagehandToolSchema(BaseModel): + """Input for StagehandTool.""" + + instruction: str | None = Field( + None, + description="Single atomic action with location context. For reliability on complex pages, use ONE specific action with location hints. Good examples: 'Click the search input field in the header', 'Type Italy in the focused field', 'Press Enter', 'Click the first link in the results area'. Avoid combining multiple actions. For 'navigate' command type, this can be omitted if only URL is provided.", + ) + url: str | None = Field( + None, + description="The URL to navigate to before executing the instruction. MUST be used with 'navigate' command. ", + ) + command_type: str | None = Field( + "act", + description="""The type of command to execute (choose one): + - 'act': Perform an action like clicking buttons, filling forms, etc. (default) + - 'navigate': Specifically navigate to a URL + - 'extract': Extract structured data from the page + - 'observe': Identify and analyze elements on the page + """, + ) + + +class StagehandTool(BaseTool): + """A tool that uses Stagehand to automate web browser interactions using natural language with atomic action handling. + + Stagehand allows AI agents to interact with websites through a browser, + performing actions like clicking buttons, filling forms, and extracting data. + + The tool supports four main command types: + 1. act - Perform actions like clicking, typing, scrolling, or navigating + 2. navigate - Specifically navigate to a URL (shorthand for act with navigation) + 3. extract - Extract structured data from web pages + 4. observe - Identify and analyze elements on a page + + Usage examples: + - Navigate to a website: instruction="Go to the homepage", url="https://example.com" + - Click a button: instruction="Click the login button" + - Fill a form: instruction="Fill the login form with username 'user' and password 'pass'" + - Extract data: instruction="Extract all product prices and names", command_type="extract" + - Observe elements: instruction="Find all navigation menu items", command_type="observe" + - Complex tasks: instruction="Step 1: Navigate to https://example.com; Step 2: Scroll down to the 'Features' section; Step 3: Click 'Learn More'", command_type="act" + + Example of breaking down "Search for OpenAI" into multiple steps: + 1. First navigation: instruction="Go to Google", url="https://google.com", command_type="navigate" + 2. Enter search term: instruction="Type 'OpenAI' in the search box", command_type="act" + 3. Submit search: instruction="Press the Enter key or click the search button", command_type="act" + 4. Click on result: instruction="Click on the OpenAI website link in the search results", command_type="act" + """ + + name: str = "Web Automation Tool" + description: str = """Use this tool to control a web browser and interact with websites using natural language. + + Capabilities: + - Navigate to websites and follow links + - Click buttons, links, and other elements + - Fill in forms and input fields + - Search within websites + - Extract information from web pages + - Identify and analyze elements on a page + + To use this tool, provide a natural language instruction describing what you want to do. + For reliability on complex pages, use specific, atomic instructions with location hints: + - Good: "Click the search box in the header" + - Good: "Type 'Italy' in the focused field" + - Bad: "Search for Italy and click the first result" + + For different types of tasks, specify the command_type: + - 'act': For performing one atomic action (default) + - 'navigate': For navigating to a URL + - 'extract': For getting data from a specific page section + - 'observe': For finding elements in a specific area + """ + args_schema: type[BaseModel] = StagehandToolSchema + + # Stagehand configuration + api_key: str | None = None + project_id: str | None = None + model_api_key: str | None = None + model_name: AvailableModel | None = AvailableModel.CLAUDE_3_7_SONNET_LATEST + server_url: str | None = "https://api.stagehand.browserbase.com/v1" + headless: bool = False + dom_settle_timeout_ms: int = 3000 + self_heal: bool = True + wait_for_captcha_solves: bool = True + verbose: int = 1 + + # Token management settings + max_retries_on_token_limit: int = 3 + use_simplified_dom: bool = True + + # Instance variables + _stagehand: Stagehand | None = None + _page: StagehandPage | None = None + _session_id: str | None = None + _testing: bool = False + + def __init__( + self, + api_key: str | None = None, + project_id: str | None = None, + model_api_key: str | None = None, + model_name: str | None = None, + server_url: str | None = None, + session_id: str | None = None, + headless: bool | None = None, + dom_settle_timeout_ms: int | None = None, + self_heal: bool | None = None, + wait_for_captcha_solves: bool | None = None, + verbose: int | None = None, + _testing: bool = False, + **kwargs, + ): + # Set testing flag early so that other init logic can rely on it + self._testing = _testing + super().__init__(**kwargs) + + # Set up logger + import logging + + self._logger = logging.getLogger(__name__) + + # Set configuration from parameters or environment + self.api_key = api_key or os.getenv("BROWSERBASE_API_KEY") + self.project_id = project_id or os.getenv("BROWSERBASE_PROJECT_ID") + + if model_api_key: + self.model_api_key = model_api_key + if model_name: + self.model_name = model_name + if server_url: + self.server_url = server_url + if headless is not None: + self.headless = headless + if dom_settle_timeout_ms is not None: + self.dom_settle_timeout_ms = dom_settle_timeout_ms + if self_heal is not None: + self.self_heal = self_heal + if wait_for_captcha_solves is not None: + self.wait_for_captcha_solves = wait_for_captcha_solves + if verbose is not None: + self.verbose = verbose + + self._session_id = session_id + + # Configure logging based on verbosity level + if not self._testing: + log_level = {1: "INFO", 2: "WARNING", 3: "DEBUG"}.get(self.verbose, "ERROR") + configure_logging( + level=log_level, remove_logger_name=True, quiet_dependencies=True + ) + + self._check_required_credentials() + + def _check_required_credentials(self): + """Validate that required credentials are present.""" + if not self._testing and not _HAS_STAGEHAND: + raise ImportError( + "`stagehand` package not found, please run `uv add stagehand`" + ) + + if not self.api_key: + raise ValueError("api_key is required (or set BROWSERBASE_API_KEY in env).") + if not self.project_id: + raise ValueError( + "project_id is required (or set BROWSERBASE_PROJECT_ID in env)." + ) + + def __del__(self): + """Ensure cleanup on deletion.""" + try: + self.close() + except Exception: # noqa: S110 + pass + + def _get_model_api_key(self): + """Get the appropriate API key based on the model being used.""" + # Check model type and get appropriate key + model_str = str(self.model_name) + if "gpt" in model_str.lower(): + return self.model_api_key or os.getenv("OPENAI_API_KEY") + if "claude" in model_str.lower() or "anthropic" in model_str.lower(): + return self.model_api_key or os.getenv("ANTHROPIC_API_KEY") + if "gemini" in model_str.lower(): + return self.model_api_key or os.getenv("GOOGLE_API_KEY") + # Default to trying OpenAI, then Anthropic + return ( + self.model_api_key + or os.getenv("OPENAI_API_KEY") + or os.getenv("ANTHROPIC_API_KEY") + ) + + async def _setup_stagehand(self, session_id: str | None = None): + """Initialize Stagehand if not already set up.""" + # If we're in testing mode, return mock objects + if self._testing: + if not self._stagehand: + # Create mock objects for testing + class MockPage: + async def act(self, options): + mock_result = type("MockResult", (), {})() + mock_result.model_dump = lambda: { + "message": "Action completed successfully" + } + return mock_result + + async def goto(self, url): + return None + + async def extract(self, options): + mock_result = type("MockResult", (), {})() + mock_result.model_dump = lambda: {"data": "Extracted content"} + return mock_result + + async def observe(self, options): + mock_result1 = type( + "MockResult", + (), + {"description": "Test element", "method": "click"}, + )() + return [mock_result1] + + async def wait_for_load_state(self, state): + return None + + class MockStagehand: + def __init__(self): + self.page = MockPage() + self.session_id = "test-session-id" + + async def init(self): + return None + + async def close(self): + return None + + self._stagehand = MockStagehand() + await self._stagehand.init() + self._page = self._stagehand.page + self._session_id = self._stagehand.session_id + + return self._stagehand, self._page + + # Normal initialization for non-testing mode + if not self._stagehand: + # Get the appropriate API key based on model type + model_api_key = self._get_model_api_key() + + if not model_api_key: + raise ValueError( + "No appropriate API key found for model. Please set OPENAI_API_KEY, ANTHROPIC_API_KEY, or GOOGLE_API_KEY" + ) + + # Build the StagehandConfig with proper parameter names + config = StagehandConfig( + env="BROWSERBASE", + apiKey=self.api_key, # Browserbase API key (camelCase) + projectId=self.project_id, # Browserbase project ID (camelCase) + modelApiKey=model_api_key, # LLM API key - auto-detected based on model + modelName=self.model_name, + apiUrl=self.server_url + if self.server_url + else "https://api.stagehand.browserbase.com/v1", + domSettleTimeoutMs=self.dom_settle_timeout_ms, + selfHeal=self.self_heal, + waitForCaptchaSolves=self.wait_for_captcha_solves, + verbose=self.verbose, + browserbaseSessionID=session_id or self._session_id, + ) + + # Initialize Stagehand with config + self._stagehand = Stagehand(config=config) + + # Initialize the Stagehand instance + await self._stagehand.init() + self._page = self._stagehand.page + self._session_id = self._stagehand.session_id + + return self._stagehand, self._page + + def _extract_steps(self, instruction: str) -> list[str]: + """Extract individual steps from multi-step instructions.""" + # Check for numbered steps (Step 1:, Step 2:, etc.) + if re.search(r"Step \d+:", instruction, re.IGNORECASE): + steps = re.findall( + r"Step \d+:\s*([^;]+?)(?=Step \d+:|$)", + instruction, + re.IGNORECASE | re.DOTALL, + ) + return [step.strip() for step in steps if step.strip()] + # Check for semicolon-separated instructions + if ";" in instruction: + return [step.strip() for step in instruction.split(";") if step.strip()] + return [instruction] + + def _simplify_instruction(self, instruction: str) -> str: + """Simplify complex instructions to basic actions.""" + # Extract the core action from complex instructions + instruction_lower = instruction.lower() + + if "search" in instruction_lower and "click" in instruction_lower: + # For search tasks, focus on the search action first + if "type" in instruction_lower or "enter" in instruction_lower: + return "click on the search input field" + return "search for content on the page" + if "click" in instruction_lower: + # Extract what to click + if "button" in instruction_lower: + return "click the button" + if "link" in instruction_lower: + return "click the link" + if "search" in instruction_lower: + return "click the search field" + return "click on the element" + if "type" in instruction_lower or "enter" in instruction_lower: + return "type in the input field" + return instruction # Return as-is if can't simplify + + async def _async_run( + self, + instruction: str | None = None, + url: str | None = None, + command_type: str = "act", + ): + """Override _async_run with improved atomic action handling.""" + # Handle missing instruction based on command type + if not instruction: + if command_type == "navigate" and url: + instruction = f"Navigate to {url}" + elif command_type == "observe": + instruction = "Observe elements on the page" + elif command_type == "extract": + instruction = "Extract information from the page" + else: + instruction = "Perform the requested action" + + # For testing mode, return mock result directly without calling parent + if self._testing: + mock_data = { + "message": f"Mock {command_type} completed successfully", + "instruction": instruction, + } + if url: + mock_data["url"] = url + return self._format_result(True, mock_data) + + try: + _, page = await self._setup_stagehand(self._session_id) + + self._logger.info( + f"Executing {command_type} with instruction: {instruction}" + ) + + # Get the API key to pass to model operations + model_api_key = self._get_model_api_key() + model_client_options = {"apiKey": model_api_key} + + # Always navigate first if URL is provided and we're doing actions + if url and command_type.lower() == "act": + self._logger.info(f"Navigating to {url} before performing actions") + await page.goto(url) + await page.wait_for_load_state("networkidle") + # Small delay to ensure page is fully loaded + await asyncio.sleep(1) + + # Process according to command type + if command_type.lower() == "act": + # Extract steps from complex instructions + steps = self._extract_steps(instruction) + self._logger.info(f"Extracted {len(steps)} steps: {steps}") + + results = [] + for i, step in enumerate(steps): + self._logger.info(f"Executing step {i + 1}/{len(steps)}: {step}") + + try: + # Create act options with API key for each step + from stagehand.schemas import ActOptions + + act_options = ActOptions( + action=step, + modelName=self.model_name, + domSettleTimeoutMs=self.dom_settle_timeout_ms, + modelClientOptions=model_client_options, + ) + + result = await page.act(act_options) + results.append(result.model_dump()) + + # Small delay between steps to let DOM settle + if i < len(steps) - 1: # Don't delay after last step + await asyncio.sleep(0.5) + + except Exception as step_error: + error_msg = f"Step failed: {step_error}" + self._logger.warning(f"Step {i + 1} failed: {error_msg}") + + # Try with simplified instruction + try: + simplified = self._simplify_instruction(step) + if simplified != step: + self._logger.info( + f"Retrying with simplified instruction: {simplified}" + ) + + act_options = ActOptions( + action=simplified, + modelName=self.model_name, + domSettleTimeoutMs=self.dom_settle_timeout_ms, + modelClientOptions=model_client_options, + ) + + result = await page.act(act_options) + results.append(result.model_dump()) + else: + # If we can't simplify or retry fails, record the error + results.append({"error": error_msg, "step": step}) + except Exception as retry_error: + self._logger.error(f"Retry also failed: {retry_error}") + results.append({"error": str(retry_error), "step": step}) + + # Return combined results + if len(results) == 1: + # Single step, return as-is + if "error" in results[0]: + return self._format_result( + False, results[0], results[0]["error"] + ) + return self._format_result(True, results[0]) + # Multiple steps, return all results + has_errors = any("error" in result for result in results) + return self._format_result(not has_errors, {"steps": results}) + + if command_type.lower() == "navigate": + # For navigation, use the goto method directly + if not url: + error_msg = "No URL provided for navigation. Please provide a URL." + self._logger.error(error_msg) + return self._format_result(False, {}, error_msg) + + result = await page.goto(url) + self._logger.info(f"Navigate operation completed to {url}") + return self._format_result( + True, + { + "url": url, + "message": f"Successfully navigated to {url}", + }, + ) + + if command_type.lower() == "extract": + # Create extract options with API key + from stagehand.schemas import ExtractOptions + + extract_options = ExtractOptions( + instruction=instruction, + modelName=self.model_name, + domSettleTimeoutMs=self.dom_settle_timeout_ms, + useTextExtract=True, + modelClientOptions=model_client_options, # Add API key here + ) + + result = await page.extract(extract_options) + self._logger.info(f"Extract operation completed successfully {result}") + return self._format_result(True, result.model_dump()) + + if command_type.lower() == "observe": + # Create observe options with API key + from stagehand.schemas import ObserveOptions + + observe_options = ObserveOptions( + instruction=instruction, + modelName=self.model_name, + onlyVisible=True, + domSettleTimeoutMs=self.dom_settle_timeout_ms, + modelClientOptions=model_client_options, # Add API key here + ) + + results = await page.observe(observe_options) + + # Format the observation results + formatted_results = [] + for i, result in enumerate(results): + formatted_results.append( + { + "index": i + 1, + "description": result.description, + "method": result.method, + } + ) + + self._logger.info( + f"Observe operation completed with {len(formatted_results)} elements found" + ) + return self._format_result(True, formatted_results) + + error_msg = f"Unknown command type: {command_type}" + self._logger.error(error_msg) + return self._format_result(False, {}, error_msg) + + except Exception as e: + error_msg = f"Error using Stagehand: {e!s}" + self._logger.error(f"Operation failed: {error_msg}") + return self._format_result(False, {}, error_msg) + + def _format_result(self, success, data, error=None): + """Helper to format results consistently.""" + return StagehandResult(success=success, data=data, error=error) + + def _run( + self, + instruction: str | None = None, + url: str | None = None, + command_type: str = "act", + ) -> str: + """Run the Stagehand tool with the given instruction. + + Args: + instruction: Natural language instruction for browser automation + url: Optional URL to navigate to before executing the instruction + command_type: Type of command to execute ('act', 'extract', or 'observe') + + Returns: + The result of the browser automation task + """ + # Handle missing instruction based on command type + if not instruction: + if command_type == "navigate" and url: + instruction = f"Navigate to {url}" + elif command_type == "observe": + instruction = "Observe elements on the page" + elif command_type == "extract": + instruction = "Extract information from the page" + else: + instruction = "Perform the requested action" + # Create an event loop if we're not already in one + try: + loop = asyncio.get_event_loop() + if loop.is_running(): + # We're in an existing event loop, use it + import concurrent.futures + + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit( + asyncio.run, self._async_run(instruction, url, command_type) + ) + result = future.result() + else: + # We have a loop but it's not running + result = loop.run_until_complete( + self._async_run(instruction, url, command_type) + ) + + # Format the result for output + if result.success: + if command_type.lower() == "act": + if isinstance(result.data, dict) and "steps" in result.data: + # Multiple steps + step_messages = [] + for i, step in enumerate(result.data["steps"]): + if "error" in step: + step_messages.append( + f"Step {i + 1}: Failed - {step['error']}" + ) + else: + step_messages.append( + f"Step {i + 1}: {step.get('message', 'Completed')}" + ) + return "\n".join(step_messages) + return f"Action result: {result.data.get('message', 'Completed')}" + if command_type.lower() == "extract": + return f"Extracted data: {json.dumps(result.data, indent=2)}" + if command_type.lower() == "observe": + formatted_results = [] + for element in result.data: + formatted_results.append( + f"Element {element['index']}: {element['description']}" + ) + if element.get("method"): + formatted_results.append( + f"Suggested action: {element['method']}" + ) + return "\n".join(formatted_results) + return json.dumps(result.data, indent=2) + return f"Error: {result.error}" + + except RuntimeError: + # No event loop exists, create one + result = asyncio.run(self._async_run(instruction, url, command_type)) + + if result.success: + if isinstance(result.data, dict): + return json.dumps(result.data, indent=2) + return str(result.data) + return f"Error: {result.error}" + + async def _async_close(self): + """Asynchronously clean up Stagehand resources.""" + # Skip for test mode + if self._testing: + self._stagehand = None + self._page = None + return + + if self._stagehand: + await self._stagehand.close() + self._stagehand = None + if self._page: + self._page = None + + def close(self): + """Clean up Stagehand resources.""" + # Skip actual closing for testing mode + if self._testing: + self._stagehand = None + self._page = None + return + + if self._stagehand: + try: + # Handle both synchronous and asynchronous cases + if hasattr(self._stagehand, "close"): + if asyncio.iscoroutinefunction(self._stagehand.close): + try: + loop = asyncio.get_event_loop() + if loop.is_running(): + import concurrent.futures + + with ( + concurrent.futures.ThreadPoolExecutor() as executor + ): + future = executor.submit( + asyncio.run, self._async_close() + ) + future.result() + else: + loop.run_until_complete(self._async_close()) + except RuntimeError: + asyncio.run(self._async_close()) + else: + # Handle non-async close method (for mocks) + self._stagehand.close() + except Exception: # noqa: S110 + # Log but don't raise - we're cleaning up + pass + + self._stagehand = None + + if self._page: + self._page = None + + def __enter__(self): + """Enter the context manager.""" + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """Exit the context manager and clean up resources.""" + self.close() diff --git a/lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/README.md new file mode 100644 index 000000000..8e2794dd1 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/README.md @@ -0,0 +1,99 @@ +# TavilyExtractorTool + +## Description + +The `TavilyExtractorTool` allows CrewAI agents to extract structured content from web pages using the Tavily API. It can process single URLs or lists of URLs and provides options for controlling the extraction depth and including images. + +## Installation + +To use the `TavilyExtractorTool`, you need to install the `tavily-python` library: + +```shell +pip install 'crewai[tools]' tavily-python +``` + +You also need to set your Tavily API key as an environment variable: + +```bash +export TAVILY_API_KEY='your-tavily-api-key' +``` + +## Example + +Here's how to initialize and use the `TavilyExtractorTool` within a CrewAI agent: + +```python +import os +from crewai import Agent, Task, Crew +from crewai_tools import TavilyExtractorTool + +# Ensure TAVILY_API_KEY is set in your environment +# os.environ["TAVILY_API_KEY"] = "YOUR_API_KEY" + +# Initialize the tool +tavily_tool = TavilyExtractorTool() + +# Create an agent that uses the tool +extractor_agent = Agent( + role='Web Content Extractor', + goal='Extract key information from specified web pages', + backstory='You are an expert at extracting relevant content from websites using the Tavily API.', + tools=[tavily_tool], + verbose=True +) + +# Define a task for the agent +extract_task = Task( + description='Extract the main content from the URL https://example.com using basic extraction depth.', + expected_output='A JSON string containing the extracted content from the URL.', + agent=extractor_agent, + tool_inputs={ + 'urls': 'https://example.com', + 'extract_depth': 'basic' + } +) + +# Create and run the crew +crew = Crew( + agents=[extractor_agent], + tasks=[extract_task], + verbose=2 +) + +result = crew.kickoff() +print(result) + +# Example with multiple URLs and advanced extraction +extract_multiple_task = Task( + description='Extract content from https://example.com and https://anotherexample.org using advanced extraction.', + expected_output='A JSON string containing the extracted content from both URLs.', + agent=extractor_agent, + tool_inputs={ + 'urls': ['https://example.com', 'https://anotherexample.org'], + 'extract_depth': 'advanced', + 'include_images': True + } +) + +result_multiple = crew.kickoff(inputs={'urls': ['https://example.com', 'https://anotherexample.org'], 'extract_depth': 'advanced', 'include_images': True}) # If task doesn't specify inputs directly +print(result_multiple) + +``` + +## Arguments + +The `TavilyExtractorTool` accepts the following arguments during initialization or when running the tool: + +- `api_key` (Optional[str]): Your Tavily API key. If not provided during initialization, it defaults to the `TAVILY_API_KEY` environment variable. +- `proxies` (Optional[dict[str, str]]): Proxies to use for the API requests. Defaults to `None`. + +When running the tool (`_run` or `_arun` methods, or via agent execution), it uses the `TavilyExtractorToolSchema` and expects the following inputs: + +- `urls` (Union[List[str], str]): **Required**. A single URL string or a list of URL strings to extract data from. +- `include_images` (Optional[bool]): Whether to include images in the extraction results. Defaults to `False`. +- `extract_depth` (Literal["basic", "advanced"]): The depth of extraction. Use `"basic"` for faster, surface-level extraction or `"advanced"` for more comprehensive extraction. Defaults to `"basic"`. +- `timeout` (int): The maximum time in seconds to wait for the extraction request to complete. Defaults to `60`. + +## Response Format + +The tool returns a JSON string representing the structured data extracted from the provided URL(s). The exact structure depends on the content of the pages and the `extract_depth` used. Refer to the [Tavily API documentation](https://docs.tavily.com/docs/tavily-api/python-sdk#extract) for details on the response structure. diff --git a/lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/tavily_extractor_tool.py b/lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/tavily_extractor_tool.py new file mode 100644 index 000000000..785057b1c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/tavily_extractor_tool.py @@ -0,0 +1,176 @@ +import json +import os +from typing import Any, Literal + +from crewai.tools import BaseTool, EnvVar +from dotenv import load_dotenv +from pydantic import BaseModel, ConfigDict, Field + + +load_dotenv() +try: + from tavily import AsyncTavilyClient, TavilyClient # type: ignore[import-untyped] + + TAVILY_AVAILABLE = True +except ImportError: + TAVILY_AVAILABLE = False + TavilyClient = Any + AsyncTavilyClient = Any + + +class TavilyExtractorToolSchema(BaseModel): + """Input schema for TavilyExtractorTool.""" + + urls: list[str] | str = Field( + ..., + description="The URL(s) to extract data from. Can be a single URL or a list of URLs.", + ) + + +class TavilyExtractorTool(BaseTool): + package_dependencies: list[str] = Field(default_factory=lambda: ["tavily-python"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="TAVILY_API_KEY", + description="API key for Tavily extraction service", + required=True, + ), + ] + ) + """ + Tool that uses the Tavily API to extract content from web pages. + + Attributes: + client: Synchronous Tavily client. + async_client: Asynchronous Tavily client. + name: The name of the tool. + description: The description of the tool. + args_schema: The schema for the tool's arguments. + api_key: The Tavily API key. + proxies: Optional proxies for the API requests. + include_images: Whether to include images in the extraction. + extract_depth: The depth of extraction. + timeout: The timeout for the extraction request in seconds. + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + client: TavilyClient | None = None + async_client: AsyncTavilyClient | None = None + name: str = "TavilyExtractorTool" + description: str = "Extracts content from one or more web pages using the Tavily API. Returns structured data." + args_schema: type[BaseModel] = TavilyExtractorToolSchema + api_key: str | None = Field( + default_factory=lambda: os.getenv("TAVILY_API_KEY"), + description="The Tavily API key. If not provided, it will be loaded from the environment variable TAVILY_API_KEY.", + ) + proxies: dict[str, str] | None = Field( + default=None, + description="Optional proxies to use for the Tavily API requests.", + ) + include_images: bool = Field( + default=False, + description="Whether to include images in the extraction.", + ) + extract_depth: Literal["basic", "advanced"] = Field( + default="basic", + description="The depth of extraction. 'basic' for basic extraction, 'advanced' for advanced extraction.", + ) + timeout: int = Field( + default=60, + description="The timeout for the extraction request in seconds.", + ) + + def __init__(self, **kwargs: Any): + """Initializes the TavilyExtractorTool. + + Args: + **kwargs: Additional keyword arguments. + """ + super().__init__(**kwargs) + if TAVILY_AVAILABLE: + self.client = TavilyClient(api_key=self.api_key, proxies=self.proxies) + self.async_client = AsyncTavilyClient( + api_key=self.api_key, proxies=self.proxies + ) + else: + try: + import subprocess + + import click + except ImportError: + raise ImportError( + "The 'tavily-python' package is required. 'click' and 'subprocess' are also needed to assist with installation if the package is missing. " + "Please install 'tavily-python' manually (e.g., 'uv add tavily-python') and ensure 'click' and 'subprocess' are available." + ) from None + + if click.confirm( + "You are missing the 'tavily-python' package, which is required for TavilyExtractorTool. Would you like to install it?" + ): + try: + subprocess.run(["uv pip", "install", "tavily-python"], check=True) # noqa: S607 + raise ImportError( + "'tavily-python' has been installed. Please restart your Python application to use the TavilyExtractorTool." + ) + except subprocess.CalledProcessError as e: + raise ImportError( + f"Attempted to install 'tavily-python' but failed: {e}. " + f"Please install it manually to use the TavilyExtractorTool." + ) from e + else: + raise ImportError( + "The 'tavily-python' package is required to use the TavilyExtractorTool. " + "Please install it with: uv add tavily-python" + ) + + def _run( + self, + urls: list[str] | str, + ) -> str: + """Synchronously extracts content from the given URL(s). + + Args: + urls: The URL(s) to extract data from. + + Returns: + A JSON string containing the extracted data. + """ + if not self.client: + raise ValueError( + "Tavily client is not initialized. Ensure 'tavily-python' is installed and API key is set." + ) + + return json.dumps( + self.client.extract( + urls=urls, + extract_depth=self.extract_depth, + include_images=self.include_images, + timeout=self.timeout, + ), + indent=2, + ) + + async def _arun( + self, + urls: list[str] | str, + ) -> str: + """Asynchronously extracts content from the given URL(s). + + Args: + urls: The URL(s) to extract data from. + + Returns: + A JSON string containing the extracted data. + """ + if not self.async_client: + raise ValueError( + "Tavily async client is not initialized. Ensure 'tavily-python' is installed and API key is set." + ) + + results = await self.async_client.extract( + urls=urls, + extract_depth=self.extract_depth, + include_images=self.include_images, + timeout=self.timeout, + ) + return json.dumps(results, indent=2) diff --git a/lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/README.md new file mode 100644 index 000000000..185b19887 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/README.md @@ -0,0 +1,115 @@ +# Tavily Search Tool + +## Description + +The `TavilySearchTool` provides an interface to the Tavily Search API, enabling CrewAI agents to perform comprehensive web searches. It allows for specifying search depth, topics, time ranges, included/excluded domains, and whether to include direct answers, raw content, or images in the results. The tool returns the search results as a JSON string. + +## Installation + +To use the `TavilySearchTool`, you need to install the `tavily-python` library: + +```shell +pip install 'crewai[tools]' tavily-python +``` + +## Environment Variables + +Ensure your Tavily API key is set as an environment variable: + +```bash +export TAVILY_API_KEY='your_tavily_api_key' +``` + +## Example + +Here's how to initialize and use the `TavilySearchTool` within a CrewAI agent: + +```python +import os +from crewai import Agent, Task, Crew +from crewai_tools import TavilySearchTool + +# Ensure the TAVILY_API_KEY environment variable is set +# os.environ["TAVILY_API_KEY"] = "YOUR_TAVILY_API_KEY" + +# Initialize the tool +tavily_tool = TavilySearchTool() + +# Create an agent that uses the tool +researcher = Agent( + role='Market Researcher', + goal='Find information about the latest AI trends', + backstory='An expert market researcher specializing in technology.', + tools=[tavily_tool], + verbose=True +) + +# Create a task for the agent +research_task = Task( + description='Search for the top 3 AI trends in 2024.', + expected_output='A JSON report summarizing the top 3 AI trends found.', + agent=researcher +) + +# Form the crew and kick it off +crew = Crew( + agents=[researcher], + tasks=[research_task], + verbose=2 +) + +result = crew.kickoff() +print(result) + +# Example of using specific parameters +detailed_search_result = tavily_tool.run( + query="What are the recent advancements in large language models?", + search_depth="advanced", + topic="general", + max_results=5, + include_answer=True +) +print(detailed_search_result) +``` + +## Arguments + +The `TavilySearchTool` accepts the following arguments during initialization or when calling the `run` method: + +- `query` (str): **Required**. The search query string. +- `search_depth` (Literal["basic", "advanced"], optional): The depth of the search. Defaults to `"basic"`. +- `topic` (Literal["general", "news", "finance"], optional): The topic to focus the search on. Defaults to `"general"`. +- `time_range` (Literal["day", "week", "month", "year"], optional): The time range for the search. Defaults to `None`. +- `days` (int, optional): The number of days to search back. Relevant if `time_range` is not set. Defaults to `7`. +- `max_results` (int, optional): The maximum number of search results to return. Defaults to `5`. +- `include_domains` (Sequence[str], optional): A list of domains to prioritize in the search. Defaults to `None`. +- `exclude_domains` (Sequence[str], optional): A list of domains to exclude from the search. Defaults to `None`. +- `include_answer` (Union[bool, Literal["basic", "advanced"]], optional): Whether to include a direct answer synthesized from the search results. Defaults to `False`. +- `include_raw_content` (bool, optional): Whether to include the raw HTML content of the searched pages. Defaults to `False`. +- `include_images` (bool, optional): Whether to include image results. Defaults to `False`. +- `timeout` (int, optional): The request timeout in seconds. Defaults to `60`. +- `api_key` (str, optional): Your Tavily API key. If not provided, it's read from the `TAVILY_API_KEY` environment variable. +- `proxies` (dict[str, str], optional): A dictionary of proxies to use for the API request. Defaults to `None`. + +## Custom Configuration + +You can configure the tool during initialization: + +```python +# Example: Initialize with a default max_results and specific API key +custom_tavily_tool = TavilySearchTool( + api_key="YOUR_SPECIFIC_TAVILY_KEY", + config={ + 'max_results': 10, + 'search_depth': 'advanced' + } +) + +# The agent will use these defaults unless overridden in the task input +agent_with_custom_tool = Agent( + # ... agent configuration ... + tools=[custom_tavily_tool] +) +``` + +Note: The `config` dictionary allows setting default values for the arguments defined in `TavilySearchToolSchema`. These defaults can be overridden when the tool is executed if the specific parameters are provided in the agent's action input. diff --git a/lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/tavily_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/tavily_search_tool.py new file mode 100644 index 000000000..c94518732 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/tavily_search_tool/tavily_search_tool.py @@ -0,0 +1,256 @@ +from collections.abc import Sequence +import json +import os +from typing import Any, Literal + +from crewai.tools import BaseTool, EnvVar +from dotenv import load_dotenv +from pydantic import BaseModel, ConfigDict, Field + + +load_dotenv() +try: + from tavily import AsyncTavilyClient, TavilyClient # type: ignore[import-untyped] + + TAVILY_AVAILABLE = True +except ImportError: + TAVILY_AVAILABLE = False + TavilyClient = Any + AsyncTavilyClient = Any + + +class TavilySearchToolSchema(BaseModel): + """Input schema for TavilySearchTool.""" + + query: str = Field(..., description="The search query string.") + + +class TavilySearchTool(BaseTool): + """Tool that uses the Tavily Search API to perform web searches. + + Attributes: + client: An instance of TavilyClient. + async_client: An instance of AsyncTavilyClient. + name: The name of the tool. + description: A description of the tool's purpose. + args_schema: The schema for the tool's arguments. + api_key: The Tavily API key. + proxies: Optional proxies for the API requests. + search_depth: The depth of the search. + topic: The topic to focus the search on. + time_range: The time range for the search. + days: The number of days to search back. + max_results: The maximum number of results to return. + include_domains: A list of domains to include in the search. + exclude_domains: A list of domains to exclude from the search. + include_answer: Whether to include a direct answer to the query. + include_raw_content: Whether to include the raw content of the search results. + include_images: Whether to include images in the search results. + timeout: The timeout for the search request in seconds. + max_content_length_per_result: Maximum length for the 'content' of each search result. + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + client: TavilyClient | None = None + async_client: AsyncTavilyClient | None = None + name: str = "Tavily Search" + description: str = ( + "A tool that performs web searches using the Tavily Search API. " + "It returns a JSON object containing the search results." + ) + args_schema: type[BaseModel] = TavilySearchToolSchema + api_key: str | None = Field( + default_factory=lambda: os.getenv("TAVILY_API_KEY"), + description="The Tavily API key. If not provided, it will be loaded from the environment variable TAVILY_API_KEY.", + ) + proxies: dict[str, str] | None = Field( + default=None, + description="Optional proxies to use for the Tavily API requests.", + ) + search_depth: Literal["basic", "advanced"] = Field( + default="basic", description="The depth of the search." + ) + topic: Literal["general", "news", "finance"] = Field( + default="general", description="The topic to focus the search on." + ) + time_range: Literal["day", "week", "month", "year"] | None = Field( + default=None, description="The time range for the search." + ) + days: int = Field(default=7, description="The number of days to search back.") + max_results: int = Field( + default=5, description="The maximum number of results to return." + ) + include_domains: Sequence[str] | None = Field( + default=None, description="A list of domains to include in the search." + ) + exclude_domains: Sequence[str] | None = Field( + default=None, description="A list of domains to exclude from the search." + ) + include_answer: bool | Literal["basic", "advanced"] = Field( + default=False, description="Whether to include a direct answer to the query." + ) + include_raw_content: bool = Field( + default=False, + description="Whether to include the raw content of the search results.", + ) + include_images: bool = Field( + default=False, description="Whether to include images in the search results." + ) + timeout: int = Field( + default=60, description="The timeout for the search request in seconds." + ) + max_content_length_per_result: int = Field( + default=1000, + description="Maximum length for the 'content' of each search result to avoid context window issues.", + ) + package_dependencies: list[str] = Field(default_factory=lambda: ["tavily-python"]) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="TAVILY_API_KEY", + description="API key for Tavily search service", + required=True, + ), + ] + ) + + def __init__(self, **kwargs: Any): + super().__init__(**kwargs) + if TAVILY_AVAILABLE: + self.client = TavilyClient(api_key=self.api_key, proxies=self.proxies) + self.async_client = AsyncTavilyClient( + api_key=self.api_key, proxies=self.proxies + ) + else: + try: + import subprocess + + import click + except ImportError as e: + raise ImportError( + "The 'tavily-python' package is required. 'click' and 'subprocess' are also needed to assist with installation if the package is missing. " + "Please install 'tavily-python' manually (e.g., 'pip install tavily-python') and ensure 'click' and 'subprocess' are available." + ) from e + + if click.confirm( + "You are missing the 'tavily-python' package, which is required for TavilySearchTool. Would you like to install it?" + ): + try: + subprocess.run(["uv", "add", "tavily-python"], check=True) # noqa: S607 + raise ImportError( + "'tavily-python' has been installed. Please restart your Python application to use the TavilySearchTool." + ) + except subprocess.CalledProcessError as e: + raise ImportError( + f"Attempted to install 'tavily-python' but failed: {e}. " + f"Please install it manually to use the TavilySearchTool." + ) from e + else: + raise ImportError( + "The 'tavily-python' package is required to use the TavilySearchTool. " + "Please install it with: uv add tavily-python" + ) + + def _run( + self, + query: str, + ) -> str: + """Synchronously performs a search using the Tavily API. + Content of each result is truncated to `max_content_length_per_result`. + + Args: + query: The search query string. + + Returns: + A JSON string containing the search results with truncated content. + """ + if not self.client: + raise ValueError( + "Tavily client is not initialized. Ensure 'tavily-python' is installed and API key is set." + ) + + raw_results = self.client.search( + query=query, + search_depth=self.search_depth, + topic=self.topic, + time_range=self.time_range, + days=self.days, + max_results=self.max_results, + include_domains=self.include_domains, + exclude_domains=self.exclude_domains, + include_answer=self.include_answer, + include_raw_content=self.include_raw_content, + include_images=self.include_images, + timeout=self.timeout, + ) + + if ( + isinstance(raw_results, dict) + and "results" in raw_results + and isinstance(raw_results["results"], list) + ): + for item in raw_results["results"]: + if ( + isinstance(item, dict) + and "content" in item + and isinstance(item["content"], str) + ): + if len(item["content"]) > self.max_content_length_per_result: + item["content"] = ( + item["content"][: self.max_content_length_per_result] + + "..." + ) + + return json.dumps(raw_results, indent=2) + + async def _arun( + self, + query: str, + ) -> str: + """Asynchronously performs a search using the Tavily API. + Content of each result is truncated to `max_content_length_per_result`. + + Args: + query: The search query string. + + Returns: + A JSON string containing the search results with truncated content. + """ + if not self.async_client: + raise ValueError( + "Tavily async client is not initialized. Ensure 'tavily-python' is installed and API key is set." + ) + + raw_results = await self.async_client.search( + query=query, + search_depth=self.search_depth, + topic=self.topic, + time_range=self.time_range, + days=self.days, + max_results=self.max_results, + include_domains=self.include_domains, + exclude_domains=self.exclude_domains, + include_answer=self.include_answer, + include_raw_content=self.include_raw_content, + include_images=self.include_images, + timeout=self.timeout, + ) + + if ( + isinstance(raw_results, dict) + and "results" in raw_results + and isinstance(raw_results["results"], list) + ): + for item in raw_results["results"]: + if ( + isinstance(item, dict) + and "content" in item + and isinstance(item["content"], str) + ): + if len(item["content"]) > self.max_content_length_per_result: + item["content"] = ( + item["content"][: self.max_content_length_per_result] + + "..." + ) + + return json.dumps(raw_results, indent=2) diff --git a/lib/crewai-tools/src/crewai_tools/tools/txt_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/txt_search_tool/README.md new file mode 100644 index 000000000..aaf68c291 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/txt_search_tool/README.md @@ -0,0 +1,59 @@ +# TXTSearchTool + +## Description +This tool is used to perform a RAG (Retrieval-Augmented Generation) search within the content of a text file. It allows for semantic searching of a query within a specified text file's content, making it an invaluable resource for quickly extracting information or finding specific sections of text based on the query provided. + +## Installation +To use the TXTSearchTool, you first need to install the crewai_tools package. This can be done using pip, a package manager for Python. Open your terminal or command prompt and enter the following command: + +```shell +pip install 'crewai[tools]' +``` + +This command will download and install the TXTSearchTool along with any necessary dependencies. + +## Example +The following example demonstrates how to use the TXTSearchTool to search within a text file. This example shows both the initialization of the tool with a specific text file and the subsequent search within that file's content. + +```python +from crewai_tools import TXTSearchTool + +# Initialize the tool to search within any text file's content the agent learns about during its execution +tool = TXTSearchTool() + +# OR + +# Initialize the tool with a specific text file, so the agent can search within the given text file's content +tool = TXTSearchTool(txt='path/to/text/file.txt') +``` + +## Arguments +- `txt` (str): **Optinal**. The path to the text file you want to search. This argument is only required if the tool was not initialized with a specific text file; otherwise, the search will be conducted within the initially provided text file. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = TXTSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/txt_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/txt_search_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/txt_search_tool/txt_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/txt_search_tool/txt_search_tool.py new file mode 100644 index 000000000..12bb00a18 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/txt_search_tool/txt_search_tool.py @@ -0,0 +1,47 @@ +from pydantic import BaseModel, Field + +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedTXTSearchToolSchema(BaseModel): + """Input for TXTSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the txt's content", + ) + + +class TXTSearchToolSchema(FixedTXTSearchToolSchema): + """Input for TXTSearchTool.""" + + txt: str = Field(..., description="File path or URL of a TXT file to be searched") + + +class TXTSearchTool(RagTool): + name: str = "Search a txt's content" + description: str = ( + "A tool that can be used to semantic search a query from a txt's content." + ) + args_schema: type[BaseModel] = TXTSearchToolSchema + + def __init__(self, txt: str | None = None, **kwargs): + super().__init__(**kwargs) + if txt is not None: + self.add(txt) + self.description = f"A tool that can be used to semantic search a query the {txt} txt's content." + self.args_schema = FixedTXTSearchToolSchema + self._generate_description() + + def _run( # type: ignore[override] + self, + search_query: str, + txt: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if txt is not None: + self.add(txt) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/vision_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/vision_tool/README.md new file mode 100644 index 000000000..bf7ab7486 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/vision_tool/README.md @@ -0,0 +1,30 @@ +# Vision Tool + +## Description + +This tool is used to extract text from images. When passed to the agent it will extract the text from the image and then use it to generate a response, report or any other output. The URL or the PATH of the image should be passed to the Agent. + + +## Installation +Install the crewai_tools package +```shell +pip install 'crewai[tools]' +``` + +## Usage + +In order to use the VisionTool, the OpenAI API key should be set in the environment variable `OPENAI_API_KEY`. + +```python +from crewai_tools import VisionTool + +vision_tool = VisionTool() + +@agent +def researcher(self) -> Agent: + return Agent( + config=self.agents_config["researcher"], + allow_delegation=False, + tools=[vision_tool] + ) +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/vision_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/vision_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/vision_tool/vision_tool.py b/lib/crewai-tools/src/crewai_tools/tools/vision_tool/vision_tool.py new file mode 100644 index 000000000..0dfe28fc3 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/vision_tool/vision_tool.py @@ -0,0 +1,136 @@ +import base64 +from pathlib import Path + +from crewai import LLM +from crewai.tools import BaseTool, EnvVar +from crewai.utilities.types import LLMMessage +from pydantic import BaseModel, Field, PrivateAttr, field_validator + + +class ImagePromptSchema(BaseModel): + """Input for Vision Tool.""" + + image_path_url: str = "The image path or URL." + + @field_validator("image_path_url") + @classmethod + def validate_image_path_url(cls, v: str) -> str: + if v.startswith("http"): + return v + + path = Path(v) + if not path.exists(): + raise ValueError(f"Image file does not exist: {v}") + + # Validate supported formats + valid_extensions = {".jpg", ".jpeg", ".png", ".gif", ".webp"} + if path.suffix.lower() not in valid_extensions: + raise ValueError( + f"Unsupported image format. Supported formats: {valid_extensions}" + ) + + return v + + +class VisionTool(BaseTool): + """Tool for analyzing images using vision models. + + Args: + llm: Optional LLM instance to use + model: Model identifier to use if no LLM is provided + """ + + name: str = "Vision Tool" + description: str = ( + "This tool uses OpenAI's Vision API to describe the contents of an image." + ) + args_schema: type[BaseModel] = ImagePromptSchema + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="OPENAI_API_KEY", + description="API key for OpenAI services", + required=True, + ), + ] + ) + + _model: str = PrivateAttr(default="gpt-4o-mini") + _llm: LLM | None = PrivateAttr(default=None) + + def __init__(self, llm: LLM | None = None, model: str = "gpt-4o-mini", **kwargs): + """Initialize the vision tool. + + Args: + llm: Optional LLM instance to use + model: Model identifier to use if no LLM is provided + **kwargs: Additional arguments for the base tool + """ + super().__init__(**kwargs) + self._model = model + self._llm = llm + + @property + def model(self) -> str: + """Get the current model identifier.""" + return self._model + + @model.setter + def model(self, value: str) -> None: + """Set the model identifier and reset LLM if it was auto-created.""" + self._model = value + if self._llm is not None and getattr(self._llm, "model", None) != value: + self._llm = None + + @property + def llm(self) -> LLM: + """Get the LLM instance, creating one if needed.""" + if self._llm is None: + self._llm = LLM(model=self._model, stop=["STOP", "END"]) + return self._llm + + def _run(self, **kwargs) -> str: + try: + image_path_url = kwargs.get("image_path_url") + if not image_path_url: + return "Image Path or URL is required." + + ImagePromptSchema(image_path_url=image_path_url) + + if image_path_url.startswith("http"): + image_data = image_path_url + else: + try: + base64_image = self._encode_image(image_path_url) + image_data = f"data:image/jpeg;base64,{base64_image}" + except Exception as e: + return f"Error processing image: {e!s}" + + messages: list[LLMMessage] = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What's in this image?"}, + { + "type": "image_url", + "image_url": {"url": image_data}, + }, + ], + }, + ] + return self.llm.call(messages=messages) + except Exception as e: + return f"An error occurred: {e!s}" + + @staticmethod + def _encode_image(image_path: str) -> str: + """Encode an image file as base64. + + Args: + image_path: Path to the image file + + Returns: + Base64-encoded image data + """ + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode() diff --git a/lib/crewai-tools/src/crewai_tools/tools/weaviate_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/weaviate_tool/README.md new file mode 100644 index 000000000..c48f2f70a --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/weaviate_tool/README.md @@ -0,0 +1,80 @@ +# WeaviateVectorSearchTool + +## Description +This tool is specifically crafted for conducting semantic searches within docs within a Weaviate vector database. Use this tool to find semantically similar docs to a given query. + +Weaviate is a vector database that is used to store and query vector embeddings. You can follow their docs here: https://weaviate.io/developers/wcs/connect + +## Installation +Install the crewai_tools package by executing the following command in your terminal: + +```shell +uv pip install 'crewai[tools]' +``` + +## Example +To utilize the WeaviateVectorSearchTool for different use cases, follow these examples: + +```python +from crewai_tools import WeaviateVectorSearchTool + +# To enable the tool to search any website the agent comes across or learns about during its operation +tool = WeaviateVectorSearchTool( + collection_name='example_collections', + limit=3, + weaviate_cluster_url="https://your-weaviate-cluster-url.com", + weaviate_api_key="your-weaviate-api-key", +) + +# or + +# Setup custom model for vectorizer and generative model +tool = WeaviateVectorSearchTool( + collection_name='example_collections', + limit=3, + vectorizer=Configure.Vectorizer.text2vec_openai(model="nomic-embed-text"), + generative_model=Configure.Generative.openai(model="gpt-4o-mini"), + weaviate_cluster_url="https://your-weaviate-cluster-url.com", + weaviate_api_key="your-weaviate-api-key", +) + +# Adding the tool to an agent +rag_agent = Agent( + name="rag_agent", + role="You are a helpful assistant that can answer questions with the help of the WeaviateVectorSearchTool.", + llm="gpt-4o-mini", + tools=[tool], +) +``` + +## Arguments +- `collection_name` : The name of the collection to search within. (Required) +- `weaviate_cluster_url` : The URL of the Weaviate cluster. (Required) +- `weaviate_api_key` : The API key for the Weaviate cluster. (Required) +- `limit` : The number of results to return. (Optional) +- `vectorizer` : The vectorizer to use. (Optional) +- `generative_model` : The generative model to use. (Optional) + +Preloading the Weaviate database with documents: + +```python +from crewai_tools import WeaviateVectorSearchTool + +# Use before hooks to generate the documents and add them to the Weaviate database. Follow the weaviate docs: https://weaviate.io/developers/wcs/connect +test_docs = client.collections.get("example_collections") + + +docs_to_load = os.listdir("knowledge") +with test_docs.batch.dynamic() as batch: + for d in docs_to_load: + with open(os.path.join("knowledge", d), "r") as f: + content = f.read() + batch.add_object( + { + "content": content, + "year": d.split("_")[0], + } + ) +tool = WeaviateVectorSearchTool(collection_name='example_collections', limit=3) + +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/weaviate_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/weaviate_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/weaviate_tool/vector_search.py b/lib/crewai-tools/src/crewai_tools/tools/weaviate_tool/vector_search.py new file mode 100644 index 000000000..96b395c2c --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/weaviate_tool/vector_search.py @@ -0,0 +1,138 @@ +import json +import os +import subprocess +from typing import Any + +import click + + +try: + import weaviate + from weaviate.classes.config import Configure, Vectorizers + from weaviate.classes.init import Auth + + WEAVIATE_AVAILABLE = True +except ImportError: + WEAVIATE_AVAILABLE = False + weaviate = Any # type: ignore[assignment,misc] # type placeholder + Configure = Any # type: ignore[assignment,misc] + Vectorizers = Any # type: ignore[assignment,misc] + Auth = Any # type: ignore[assignment,misc] + +from crewai.tools import BaseTool, EnvVar +from pydantic import BaseModel, Field + + +class WeaviateToolSchema(BaseModel): + """Input for WeaviateTool.""" + + query: str = Field( + ..., + description="The query to search retrieve relevant information from the Weaviate database. Pass only the query, not the question.", + ) + + +def _set_generative_model() -> Any: + """Set the generative model based on the provided model name.""" + from weaviate.classes.config import Configure + + return Configure.Generative.openai( + model="gpt-4o", + ) + + +def _set_vectorizer() -> Any: + """Set the vectorizer based on the provided model name.""" + from weaviate.classes.config import Configure + + return Configure.Vectorizer.text2vec_openai( + model="nomic-embed-text", + ) + + +class WeaviateVectorSearchTool(BaseTool): + """Tool to search the Weaviate database.""" + + package_dependencies: list[str] = Field(default_factory=lambda: ["weaviate-client"]) + name: str = "WeaviateVectorSearchTool" + description: str = "A tool to search the Weaviate database for relevant information on internal documents." + args_schema: type[BaseModel] = WeaviateToolSchema + query: str | None = None + vectorizer: Any = Field(default_factory=_set_vectorizer) + generative_model: Any = Field(default_factory=_set_generative_model) + collection_name: str = Field( + description="The name of the Weaviate collection to search", + ) + limit: int | None = Field(default=3) + headers: dict | None = None + alpha: float = Field(default=0.75) + env_vars: list[EnvVar] = Field( + default_factory=lambda: [ + EnvVar( + name="OPENAI_API_KEY", + description="OpenAI API key for embedding generation and retrieval", + required=True, + ), + ] + ) + weaviate_cluster_url: str = Field( + ..., + description="The URL of the Weaviate cluster", + ) + weaviate_api_key: str = Field( + ..., + description="The API key for the Weaviate cluster", + ) + + def __init__(self, **kwargs: Any) -> None: + super().__init__(**kwargs) + if WEAVIATE_AVAILABLE: + openai_api_key = os.environ.get("OPENAI_API_KEY") + if not openai_api_key: + raise ValueError( + "OPENAI_API_KEY environment variable is required for WeaviateVectorSearchTool and it is mandatory to use the tool." + ) + self.headers = {"X-OpenAI-Api-Key": openai_api_key} + else: + if click.confirm( + "You are missing the 'weaviate-client' package. Would you like to install it?" + ): + subprocess.run(["uv", "pip", "install", "weaviate-client"], check=True) # noqa: S607 + + else: + raise ImportError( + "You are missing the 'weaviate-client' package. Would you like to install it?" + ) + + def _run(self, query: str) -> str: + if not WEAVIATE_AVAILABLE: + raise ImportError( + "You are missing the 'weaviate-client' package. Would you like to install it?" + ) + + if not self.weaviate_cluster_url or not self.weaviate_api_key: + raise ValueError("WEAVIATE_URL or WEAVIATE_API_KEY is not set") + + client = weaviate.connect_to_weaviate_cloud( + cluster_url=self.weaviate_cluster_url, + auth_credentials=Auth.api_key(self.weaviate_api_key), + headers=self.headers, + ) + internal_docs = client.collections.get(self.collection_name) + + if not internal_docs: + internal_docs = client.collections.create( + name=self.collection_name, + vectorizer_config=self.vectorizer, # type: ignore + generative_config=self.generative_model, + ) + + response = internal_docs.query.hybrid( + query=query, limit=self.limit, alpha=self.alpha + ) + json_response = "" + for obj in response.objects: + json_response += json.dumps(obj.properties, indent=2) + + client.close() + return json_response diff --git a/lib/crewai-tools/src/crewai_tools/tools/website_search/README.md b/lib/crewai-tools/src/crewai_tools/tools/website_search/README.md new file mode 100644 index 000000000..a86c75b45 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/website_search/README.md @@ -0,0 +1,57 @@ +# WebsiteSearchTool + +## Description +This tool is specifically crafted for conducting semantic searches within the content of a particular website. Leveraging a Retrieval-Augmented Generation (RAG) model, it navigates through the information provided on a given URL. Users have the flexibility to either initiate a search across any website known or discovered during its usage or to concentrate the search on a predefined, specific website. + +## Installation +Install the crewai_tools package by executing the following command in your terminal: + +```shell +pip install 'crewai[tools]' +``` + +## Example +To utilize the WebsiteSearchTool for different use cases, follow these examples: + +```python +from crewai_tools import WebsiteSearchTool + +# To enable the tool to search any website the agent comes across or learns about during its operation +tool = WebsiteSearchTool() + +# OR + +# To restrict the tool to only search within the content of a specific website. +tool = WebsiteSearchTool(website='https://example.com') +``` + +## Arguments +- `website` : An optional argument that specifies the valid website URL to perform the search on. This becomes necessary if the tool is initialized without a specific website. In the `WebsiteSearchToolSchema`, this argument is mandatory. However, in the `FixedWebsiteSearchToolSchema`, it becomes optional if a website is provided during the tool's initialization, as it will then only search within the predefined website's content. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = WebsiteSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/website_search/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/website_search/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/website_search/website_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/website_search/website_search_tool.py new file mode 100644 index 000000000..cba8891ae --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/website_search/website_search_tool.py @@ -0,0 +1,51 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedWebsiteSearchToolSchema(BaseModel): + """Input for WebsiteSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search a specific website", + ) + + +class WebsiteSearchToolSchema(FixedWebsiteSearchToolSchema): + """Input for WebsiteSearchTool.""" + + website: str = Field( + ..., description="Mandatory valid website URL you want to search on" + ) + + +class WebsiteSearchTool(RagTool): + name: str = "Search in a specific website" + description: str = "A tool that can be used to semantic search a query from a specific URL content." + args_schema: type[BaseModel] = WebsiteSearchToolSchema + + def __init__(self, website: str | None = None, **kwargs): + super().__init__(**kwargs) + if website is not None: + self.add(website) + self.description = f"A tool that can be used to semantic search a query from {website} website content." + self.args_schema = FixedWebsiteSearchToolSchema + self._generate_description() + + def add(self, website: str) -> None: + super().add(website, data_type=DataType.WEBSITE) + + def _run( # type: ignore[override] + self, + search_query: str, + website: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if website is not None: + self.add(website) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/xml_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/xml_search_tool/README.md new file mode 100644 index 000000000..a019d9e15 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/xml_search_tool/README.md @@ -0,0 +1,57 @@ +# XMLSearchTool + +## Description +The XMLSearchTool is a cutting-edge RAG tool engineered for conducting semantic searches within XML files. Ideal for users needing to parse and extract information from XML content efficiently, this tool supports inputting a search query and an optional XML file path. By specifying an XML path, users can target their search more precisely to the content of that file, thereby obtaining more relevant search outcomes. + +## Installation +To start using the XMLSearchTool, you must first install the crewai_tools package. This can be easily done with the following command: + +```shell +pip install 'crewai[tools]' +``` + +## Example +Here are two examples demonstrating how to use the XMLSearchTool. The first example shows searching within a specific XML file, while the second example illustrates initiating a search without predefining an XML path, providing flexibility in search scope. + +```python +from crewai_tools.tools.xml_search_tool import XMLSearchTool + +# Allow agents to search within any XML file's content as it learns about their paths during execution +tool = XMLSearchTool() + +# OR + +# Initialize the tool with a specific XML file path for exclusive search within that document +tool = XMLSearchTool(xml='path/to/your/xmlfile.xml') +``` + +## Arguments +- `xml`: This is the path to the XML file you wish to search. It is an optional parameter during the tool's initialization but must be provided either at initialization or as part of the `run` method's arguments to execute a search. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = XMLSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/xml_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/xml_search_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/xml_search_tool/xml_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/xml_search_tool/xml_search_tool.py new file mode 100644 index 000000000..561d1fa21 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/xml_search_tool/xml_search_tool.py @@ -0,0 +1,47 @@ +from pydantic import BaseModel, Field + +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedXMLSearchToolSchema(BaseModel): + """Input for XMLSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the XML's content", + ) + + +class XMLSearchToolSchema(FixedXMLSearchToolSchema): + """Input for XMLSearchTool.""" + + xml: str = Field(..., description="File path or URL of a XML file to be searched") + + +class XMLSearchTool(RagTool): + name: str = "Search a XML's content" + description: str = ( + "A tool that can be used to semantic search a query from a XML's content." + ) + args_schema: type[BaseModel] = XMLSearchToolSchema + + def __init__(self, xml: str | None = None, **kwargs): + super().__init__(**kwargs) + if xml is not None: + self.add(xml) + self.description = f"A tool that can be used to semantic search a query the {xml} XML's content." + self.args_schema = FixedXMLSearchToolSchema + self._generate_description() + + def _run( # type: ignore[override] + self, + search_query: str, + xml: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if xml is not None: + self.add(xml) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/youtube_channel_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/youtube_channel_search_tool/README.md new file mode 100644 index 000000000..090684f48 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/youtube_channel_search_tool/README.md @@ -0,0 +1,57 @@ +# YoutubeChannelSearchTool + +## Description +This tool is designed to perform semantic searches within a specific Youtube channel's content. Leveraging the RAG (Retrieval-Augmented Generation) methodology, it provides relevant search results, making it invaluable for extracting information or finding specific content without the need to manually sift through videos. It streamlines the search process within Youtube channels, catering to researchers, content creators, and viewers seeking specific information or topics. + +## Installation +To utilize the YoutubeChannelSearchTool, the `crewai_tools` package must be installed. Execute the following command in your shell to install: + +```shell +pip install 'crewai[tools]' +``` + +## Example +To begin using the YoutubeChannelSearchTool, follow the example below. This demonstrates initializing the tool with a specific Youtube channel handle and conducting a search within that channel's content. + +```python +from crewai_tools import YoutubeChannelSearchTool + +# Initialize the tool to search within any Youtube channel's content the agent learns about during its execution +tool = YoutubeChannelSearchTool() + +# OR + +# Initialize the tool with a specific Youtube channel handle to target your search +tool = YoutubeChannelSearchTool(youtube_channel_handle='@exampleChannel') +``` + +## Arguments +- `youtube_channel_handle` : A mandatory string representing the Youtube channel handle. This parameter is crucial for initializing the tool to specify the channel you want to search within. The tool is designed to only search within the content of the provided channel handle. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = YoutubeChannelSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/youtube_channel_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/youtube_channel_search_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/youtube_channel_search_tool/youtube_channel_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/youtube_channel_search_tool/youtube_channel_search_tool.py new file mode 100644 index 000000000..90b48f252 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/youtube_channel_search_tool/youtube_channel_search_tool.py @@ -0,0 +1,56 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedYoutubeChannelSearchToolSchema(BaseModel): + """Input for YoutubeChannelSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the Youtube Channels content", + ) + + +class YoutubeChannelSearchToolSchema(FixedYoutubeChannelSearchToolSchema): + """Input for YoutubeChannelSearchTool.""" + + youtube_channel_handle: str = Field( + ..., description="Mandatory youtube_channel_handle path you want to search" + ) + + +class YoutubeChannelSearchTool(RagTool): + name: str = "Search a Youtube Channels content" + description: str = "A tool that can be used to semantic search a query from a Youtube Channels content." + args_schema: type[BaseModel] = YoutubeChannelSearchToolSchema + + def __init__(self, youtube_channel_handle: str | None = None, **kwargs): + super().__init__(**kwargs) + if youtube_channel_handle is not None: + self.add(youtube_channel_handle) + self.description = f"A tool that can be used to semantic search a query the {youtube_channel_handle} Youtube Channels content." + self.args_schema = FixedYoutubeChannelSearchToolSchema + self._generate_description() + + def add( + self, + youtube_channel_handle: str, + ) -> None: + if not youtube_channel_handle.startswith("@"): + youtube_channel_handle = f"@{youtube_channel_handle}" + super().add(youtube_channel_handle, data_type=DataType.YOUTUBE_CHANNEL) + + def _run( # type: ignore[override] + self, + search_query: str, + youtube_channel_handle: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if youtube_channel_handle is not None: + self.add(youtube_channel_handle) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/youtube_video_search_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/youtube_video_search_tool/README.md new file mode 100644 index 000000000..8b84613b4 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/youtube_video_search_tool/README.md @@ -0,0 +1,60 @@ +# YoutubeVideoSearchTool + +## Description + +This tool is part of the `crewai_tools` package and is designed to perform semantic searches within Youtube video content, utilizing Retrieval-Augmented Generation (RAG) techniques. It is one of several "Search" tools in the package that leverage RAG for different sources. The YoutubeVideoSearchTool allows for flexibility in searches; users can search across any Youtube video content without specifying a video URL, or they can target their search to a specific Youtube video by providing its URL. + +## Installation + +To utilize the YoutubeVideoSearchTool, you must first install the `crewai_tools` package. This package contains the YoutubeVideoSearchTool among other utilities designed to enhance your data analysis and processing tasks. Install the package by executing the following command in your terminal: + +``` +pip install 'crewai[tools]' +``` + +## Example + +To integrate the YoutubeVideoSearchTool into your Python projects, follow the example below. This demonstrates how to use the tool both for general Youtube content searches and for targeted searches within a specific video's content. + +```python +from crewai_tools import YoutubeVideoSearchTool + +# General search across Youtube content without specifying a video URL, so the agent can search within any Youtube video content it learns about irs url during its operation +tool = YoutubeVideoSearchTool() + +# Targeted search within a specific Youtube video's content +tool = YoutubeVideoSearchTool(youtube_video_url='https://youtube.com/watch?v=example') +``` +## Arguments + +The YoutubeVideoSearchTool accepts the following initialization arguments: + +- `youtube_video_url`: An optional argument at initialization but required if targeting a specific Youtube video. It specifies the Youtube video URL path you want to search within. + +## Custom model and embeddings + +By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows: + +```python +tool = YoutubeVideoSearchTool( + config=dict( + llm=dict( + provider="ollama", # or google, openai, anthropic, llama2, ... + config=dict( + model="llama2", + # temperature=0.5, + # top_p=1, + # stream=true, + ), + ), + embedder=dict( + provider="google", + config=dict( + model="models/embedding-001", + task_type="retrieval_document", + # title="Embeddings", + ), + ), + ) +) +``` diff --git a/lib/crewai-tools/src/crewai_tools/tools/youtube_video_search_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/youtube_video_search_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/youtube_video_search_tool/youtube_video_search_tool.py b/lib/crewai-tools/src/crewai_tools/tools/youtube_video_search_tool/youtube_video_search_tool.py new file mode 100644 index 000000000..6a7fa23c9 --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/youtube_video_search_tool/youtube_video_search_tool.py @@ -0,0 +1,51 @@ +from pydantic import BaseModel, Field + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools.rag.rag_tool import RagTool + + +class FixedYoutubeVideoSearchToolSchema(BaseModel): + """Input for YoutubeVideoSearchTool.""" + + search_query: str = Field( + ..., + description="Mandatory search query you want to use to search the Youtube Video content", + ) + + +class YoutubeVideoSearchToolSchema(FixedYoutubeVideoSearchToolSchema): + """Input for YoutubeVideoSearchTool.""" + + youtube_video_url: str = Field( + ..., description="Mandatory youtube_video_url path you want to search" + ) + + +class YoutubeVideoSearchTool(RagTool): + name: str = "Search a Youtube Video content" + description: str = "A tool that can be used to semantic search a query from a Youtube Video content." + args_schema: type[BaseModel] = YoutubeVideoSearchToolSchema + + def __init__(self, youtube_video_url: str | None = None, **kwargs): + super().__init__(**kwargs) + if youtube_video_url is not None: + self.add(youtube_video_url) + self.description = f"A tool that can be used to semantic search a query the {youtube_video_url} Youtube Video content." + self.args_schema = FixedYoutubeVideoSearchToolSchema + self._generate_description() + + def add(self, youtube_video_url: str) -> None: + super().add(youtube_video_url, data_type=DataType.YOUTUBE_VIDEO) + + def _run( # type: ignore[override] + self, + search_query: str, + youtube_video_url: str | None = None, + similarity_threshold: float | None = None, + limit: int | None = None, + ) -> str: + if youtube_video_url is not None: + self.add(youtube_video_url) + return super()._run( + query=search_query, similarity_threshold=similarity_threshold, limit=limit + ) diff --git a/lib/crewai-tools/src/crewai_tools/tools/zapier_action_tool/README.md b/lib/crewai-tools/src/crewai_tools/tools/zapier_action_tool/README.md new file mode 100644 index 000000000..5a6dad43b --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/zapier_action_tool/README.md @@ -0,0 +1,91 @@ +# Zapier Action Tools + +## Description + +This tool enables CrewAI agents to interact with Zapier actions, allowing them to automate workflows and integrate with hundreds of applications through Zapier's platform. The tool dynamically creates BaseTool instances for each available Zapier action, making it easy to incorporate automation into your AI workflows. + +## Installation + +Install the crewai_tools package by executing the following command in your terminal: + +```shell +uv pip install 'crewai[tools]' +``` + +## Example + +To utilize the ZapierActionTools for different use cases, follow these examples: + +```python +from crewai_tools import ZapierActionTools +from crewai import Agent + +# Get all available Zapier actions you are connected to. +tools = ZapierActionTools( + zapier_api_key="your-zapier-api-key" +) + +# Or specify only certain actions you want to use +tools = ZapierActionTools( + zapier_api_key="your-zapier-api-key", + action_list=["gmail_find_email", "slack_send_message", "google_sheets_create_row"] +) + +# Adding the tools to an agent +zapier_agent = Agent( + name="zapier_agent", + role="You are a helpful assistant that can automate tasks using Zapier integrations.", + llm="gpt-4o-mini", + tools=tools, + goal="Automate workflows and integrate with various applications", + backstory="You are a Zapier automation expert that helps users connect and automate their favorite apps.", + verbose=True, +) + +# Example usage +result = zapier_agent.kickoff( + "Find emails from john@example.com in Gmail" +) +``` + +## Arguments + +- `zapier_api_key` : Your Zapier API key for authentication. Can also be set via `ZAPIER_API_KEY` environment variable. (Required) +- `action_list` : A list of specific Zapier action names to include. If not provided, all available actions will be returned. (Optional) + +## Environment Variables + +You can set your Zapier API key as an environment variable instead of passing it directly: + +```bash +export ZAPIER_API_KEY="your-zapier-api-key" +``` + +Then use the tool without explicitly passing the API key: + +```python +from crewai_tools import ZapierActionTools + +# API key will be automatically loaded from environment +tools = ZapierActionTools( + action_list=["gmail_find_email", "slack_send_message"] +) +``` + +## Getting Your Zapier API Key + +1. Log in to your Zapier account +2. Go to https://zapier.com/app/developer/ +3. Create a new app or use an existing one +4. Navigate to the "Authentication" section +5. Copy your API key + +## Available Actions + +The tool will dynamically discover all available Zapier actions associated with your API key. Common actions include: + +- Gmail operations (find emails, send emails) +- Slack messaging +- Google Sheets operations +- Calendar events +- And hundreds more depending on your Zapier integrations diff --git a/lib/crewai-tools/src/crewai_tools/tools/zapier_action_tool/__init__.py b/lib/crewai-tools/src/crewai_tools/tools/zapier_action_tool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/src/crewai_tools/tools/zapier_action_tool/zapier_action_tool.py b/lib/crewai-tools/src/crewai_tools/tools/zapier_action_tool/zapier_action_tool.py new file mode 100644 index 000000000..48320739e --- /dev/null +++ b/lib/crewai-tools/src/crewai_tools/tools/zapier_action_tool/zapier_action_tool.py @@ -0,0 +1,33 @@ +import logging +import os + +from crewai_tools.adapters.zapier_adapter import ZapierActionTool, ZapierActionsAdapter + + +logger = logging.getLogger(__name__) + + +def ZapierActionTools( # noqa: N802 + zapier_api_key: str | None = None, action_list: list[str] | None = None +) -> list[ZapierActionTool]: + """Factory function that returns Zapier action tools. + + Args: + zapier_api_key: The API key for Zapier. + action_list: Optional list of specific tool names to include. + + Returns: + A list of Zapier action tools. + """ + if zapier_api_key is None: + zapier_api_key = os.getenv("ZAPIER_API_KEY") + if zapier_api_key is None: + logger.error("ZAPIER_API_KEY is not set") + raise ValueError("ZAPIER_API_KEY is not set") + adapter = ZapierActionsAdapter(zapier_api_key) + all_tools = adapter.tools() + + if action_list is None: + return all_tools + + return [tool for tool in all_tools if tool.name in action_list] diff --git a/lib/crewai-tools/tests/__init__.py b/lib/crewai-tools/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/tests/adapters/mcp_adapter_test.py b/lib/crewai-tools/tests/adapters/mcp_adapter_test.py new file mode 100644 index 000000000..188f86699 --- /dev/null +++ b/lib/crewai-tools/tests/adapters/mcp_adapter_test.py @@ -0,0 +1,239 @@ +from textwrap import dedent +from unittest.mock import MagicMock, patch + +from crewai_tools import MCPServerAdapter +from crewai_tools.adapters.tool_collection import ToolCollection +from mcp import StdioServerParameters +import pytest + + +@pytest.fixture +def echo_server_script(): + return dedent( + ''' + from mcp.server.fastmcp import FastMCP + + mcp = FastMCP("Echo Server") + + @mcp.tool() + def echo_tool(text: str) -> str: + """Echo the input text""" + return f"Echo: {text}" + + @mcp.tool() + def calc_tool(a: int, b: int) -> int: + """Calculate a + b""" + return a + b + + mcp.run() + ''' + ) + + +@pytest.fixture +def echo_server_sse_script(): + return dedent( + ''' + from mcp.server.fastmcp import FastMCP + + mcp = FastMCP("Echo Server", host="127.0.0.1", port=8000) + + @mcp.tool() + def echo_tool(text: str) -> str: + """Echo the input text""" + return f"Echo: {text}" + + @mcp.tool() + def calc_tool(a: int, b: int) -> int: + """Calculate a + b""" + return a + b + + mcp.run("sse") + ''' + ) + + +@pytest.fixture +def echo_sse_server(echo_server_sse_script): + import subprocess + import time + + # Start the SSE server process with its own process group + process = subprocess.Popen( + ["python", "-c", echo_server_sse_script], + ) + + # Give the server a moment to start up + time.sleep(1) + + try: + yield {"url": "http://127.0.0.1:8000/sse"} + finally: + # Clean up the process when test is done + process.kill() + process.wait() + + +def test_context_manager_syntax(echo_server_script): + serverparams = StdioServerParameters( + command="uv", args=["run", "python", "-c", echo_server_script] + ) + with MCPServerAdapter(serverparams) as tools: + assert isinstance(tools, ToolCollection) + assert len(tools) == 2 + assert tools[0].name == "echo_tool" + assert tools[1].name == "calc_tool" + assert tools[0].run(text="hello") == "Echo: hello" + assert tools[1].run(a=5, b=3) == "8" + + +def test_context_manager_syntax_sse(echo_sse_server): + sse_serverparams = echo_sse_server + with MCPServerAdapter(sse_serverparams) as tools: + assert len(tools) == 2 + assert tools[0].name == "echo_tool" + assert tools[1].name == "calc_tool" + assert tools[0].run(text="hello") == "Echo: hello" + assert tools[1].run(a=5, b=3) == "8" + + +def test_try_finally_syntax(echo_server_script): + serverparams = StdioServerParameters( + command="uv", args=["run", "python", "-c", echo_server_script] + ) + try: + mcp_server_adapter = MCPServerAdapter(serverparams) + tools = mcp_server_adapter.tools + assert len(tools) == 2 + assert tools[0].name == "echo_tool" + assert tools[1].name == "calc_tool" + assert tools[0].run(text="hello") == "Echo: hello" + assert tools[1].run(a=5, b=3) == "8" + finally: + mcp_server_adapter.stop() + + +def test_try_finally_syntax_sse(echo_sse_server): + sse_serverparams = echo_sse_server + mcp_server_adapter = MCPServerAdapter(sse_serverparams) + try: + tools = mcp_server_adapter.tools + assert len(tools) == 2 + assert tools[0].name == "echo_tool" + assert tools[1].name == "calc_tool" + assert tools[0].run(text="hello") == "Echo: hello" + assert tools[1].run(a=5, b=3) == "8" + finally: + mcp_server_adapter.stop() + + +def test_context_manager_with_filtered_tools(echo_server_script): + serverparams = StdioServerParameters( + command="uv", args=["run", "python", "-c", echo_server_script] + ) + # Only select the echo_tool + with MCPServerAdapter(serverparams, "echo_tool") as tools: + assert isinstance(tools, ToolCollection) + assert len(tools) == 1 + assert tools[0].name == "echo_tool" + assert tools[0].run(text="hello") == "Echo: hello" + # Check that calc_tool is not present + with pytest.raises(IndexError): + _ = tools[1] + with pytest.raises(KeyError): + _ = tools["calc_tool"] + + +def test_context_manager_sse_with_filtered_tools(echo_sse_server): + sse_serverparams = echo_sse_server + # Only select the calc_tool + with MCPServerAdapter(sse_serverparams, "calc_tool") as tools: + assert isinstance(tools, ToolCollection) + assert len(tools) == 1 + assert tools[0].name == "calc_tool" + assert tools[0].run(a=10, b=5) == "15" + # Check that echo_tool is not present + with pytest.raises(IndexError): + _ = tools[1] + with pytest.raises(KeyError): + _ = tools["echo_tool"] + + +def test_try_finally_with_filtered_tools(echo_server_script): + serverparams = StdioServerParameters( + command="uv", args=["run", "python", "-c", echo_server_script] + ) + try: + # Select both tools but in reverse order + mcp_server_adapter = MCPServerAdapter(serverparams, "calc_tool", "echo_tool") + tools = mcp_server_adapter.tools + assert len(tools) == 2 + # The order of tools is based on filter_by_names which preserves + # the original order from the collection + assert tools[0].name == "calc_tool" + assert tools[1].name == "echo_tool" + finally: + mcp_server_adapter.stop() + + +def test_filter_with_nonexistent_tool(echo_server_script): + serverparams = StdioServerParameters( + command="uv", args=["run", "python", "-c", echo_server_script] + ) + # Include a tool that doesn't exist + with MCPServerAdapter(serverparams, "echo_tool", "nonexistent_tool") as tools: + # Only echo_tool should be in the result + assert len(tools) == 1 + assert tools[0].name == "echo_tool" + + +def test_filter_with_only_nonexistent_tools(echo_server_script): + serverparams = StdioServerParameters( + command="uv", args=["run", "python", "-c", echo_server_script] + ) + # All requested tools don't exist + with MCPServerAdapter(serverparams, "nonexistent1", "nonexistent2") as tools: + # Should return an empty tool collection + assert isinstance(tools, ToolCollection) + assert len(tools) == 0 + + +def test_connect_timeout_parameter(echo_server_script): + serverparams = StdioServerParameters( + command="uv", args=["run", "python", "-c", echo_server_script] + ) + with MCPServerAdapter(serverparams, connect_timeout=60) as tools: + assert isinstance(tools, ToolCollection) + assert len(tools) == 2 + assert tools[0].name == "echo_tool" + assert tools[1].name == "calc_tool" + assert tools[0].run(text="hello") == "Echo: hello" + + +def test_connect_timeout_with_filtered_tools(echo_server_script): + serverparams = StdioServerParameters( + command="uv", args=["run", "python", "-c", echo_server_script] + ) + with MCPServerAdapter(serverparams, "echo_tool", connect_timeout=45) as tools: + assert isinstance(tools, ToolCollection) + assert len(tools) == 1 + assert tools[0].name == "echo_tool" + assert tools[0].run(text="timeout test") == "Echo: timeout test" + + +@patch("crewai_tools.adapters.mcp_adapter.MCPAdapt") +def test_connect_timeout_passed_to_mcpadapt(mock_mcpadapt): + mock_adapter_instance = MagicMock() + mock_mcpadapt.return_value = mock_adapter_instance + + serverparams = StdioServerParameters(command="uv", args=["run", "echo", "test"]) + + MCPServerAdapter(serverparams) + mock_mcpadapt.assert_called_once() + assert mock_mcpadapt.call_args[0][2] == 30 + + mock_mcpadapt.reset_mock() + + MCPServerAdapter(serverparams, connect_timeout=5) + mock_mcpadapt.assert_called_once() + assert mock_mcpadapt.call_args[0][2] == 5 diff --git a/lib/crewai-tools/tests/base_tool_test.py b/lib/crewai-tools/tests/base_tool_test.py new file mode 100644 index 000000000..6b7c5e6af --- /dev/null +++ b/lib/crewai-tools/tests/base_tool_test.py @@ -0,0 +1,104 @@ +from collections.abc import Callable + +from crewai.tools import BaseTool, tool +from crewai.tools.base_tool import to_langchain + + +def test_creating_a_tool_using_annotation(): + @tool("Name of my tool") + def my_tool(question: str) -> str: + """Clear description for what this tool is useful for, you agent will need this information to use it.""" + return question + + # Assert all the right attributes were defined + assert my_tool.name == "Name of my tool" + assert ( + my_tool.description + == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it." + ) + assert my_tool.args_schema.model_json_schema()["properties"] == { + "question": {"title": "Question", "type": "string"} + } + assert ( + my_tool.func("What is the meaning of life?") == "What is the meaning of life?" + ) + + # Assert the langchain tool conversion worked as expected + converted_tool = to_langchain([my_tool])[0] + assert converted_tool.name == "Name of my tool" + assert ( + converted_tool.description + == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it." + ) + assert converted_tool.args_schema.model_json_schema()["properties"] == { + "question": {"title": "Question", "type": "string"} + } + assert ( + converted_tool.func("What is the meaning of life?") + == "What is the meaning of life?" + ) + + +def test_creating_a_tool_using_baseclass(): + class MyCustomTool(BaseTool): + name: str = "Name of my tool" + description: str = "Clear description for what this tool is useful for, you agent will need this information to use it." + + def _run(self, question: str) -> str: + return question + + my_tool = MyCustomTool() + # Assert all the right attributes were defined + assert my_tool.name == "Name of my tool" + assert ( + my_tool.description + == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it." + ) + assert my_tool.args_schema.model_json_schema()["properties"] == { + "question": {"title": "Question", "type": "string"} + } + assert ( + my_tool._run("What is the meaning of life?") == "What is the meaning of life?" + ) + + # Assert the langchain tool conversion worked as expected + converted_tool = to_langchain([my_tool])[0] + assert converted_tool.name == "Name of my tool" + assert ( + converted_tool.description + == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it." + ) + assert converted_tool.args_schema.model_json_schema()["properties"] == { + "question": {"title": "Question", "type": "string"} + } + assert ( + converted_tool.invoke({"question": "What is the meaning of life?"}) + == "What is the meaning of life?" + ) + + +def test_setting_cache_function(): + class MyCustomTool(BaseTool): + name: str = "Name of my tool" + description: str = "Clear description for what this tool is useful for, you agent will need this information to use it." + cache_function: Callable = lambda: False + + def _run(self, question: str) -> str: + return question + + my_tool = MyCustomTool() + # Assert all the right attributes were defined + assert not my_tool.cache_function() + + +def test_default_cache_function_is_true(): + class MyCustomTool(BaseTool): + name: str = "Name of my tool" + description: str = "Clear description for what this tool is useful for, you agent will need this information to use it." + + def _run(self, question: str) -> str: + return question + + my_tool = MyCustomTool() + # Assert all the right attributes were defined + assert my_tool.cache_function() diff --git a/lib/crewai-tools/tests/file_read_tool_test.py b/lib/crewai-tools/tests/file_read_tool_test.py new file mode 100644 index 000000000..174b32229 --- /dev/null +++ b/lib/crewai-tools/tests/file_read_tool_test.py @@ -0,0 +1,165 @@ +import os +from unittest.mock import mock_open, patch + +from crewai_tools import FileReadTool + + +def test_file_read_tool_constructor(): + """Test FileReadTool initialization with file_path.""" + # Create a temporary test file + test_file = "/tmp/test_file.txt" + test_content = "Hello, World!" + with open(test_file, "w") as f: + f.write(test_content) + + # Test initialization with file_path + tool = FileReadTool(file_path=test_file) + assert tool.file_path == test_file + assert "test_file.txt" in tool.description + + # Clean up + os.remove(test_file) + + +def test_file_read_tool_run(): + """Test FileReadTool _run method with file_path at runtime.""" + test_file = "/tmp/test_file.txt" + test_content = "Hello, World!" + + # Use mock_open to mock file operations + with patch("builtins.open", mock_open(read_data=test_content)): + # Test reading file with runtime file_path + tool = FileReadTool() + result = tool._run(file_path=test_file) + assert result == test_content + + +def test_file_read_tool_error_handling(): + """Test FileReadTool error handling.""" + # Test missing file path + tool = FileReadTool() + result = tool._run() + assert "Error: No file path provided" in result + + # Test non-existent file + result = tool._run(file_path="/nonexistent/file.txt") + assert "Error: File not found at path:" in result + + # Test permission error + with patch("builtins.open", side_effect=PermissionError()): + result = tool._run(file_path="/tmp/no_permission.txt") + assert "Error: Permission denied" in result + + +def test_file_read_tool_constructor_and_run(): + """Test FileReadTool using both constructor and runtime file paths.""" + test_file1 = "/tmp/test1.txt" + test_file2 = "/tmp/test2.txt" + content1 = "File 1 content" + content2 = "File 2 content" + + # First test with content1 + with patch("builtins.open", mock_open(read_data=content1)): + tool = FileReadTool(file_path=test_file1) + result = tool._run() + assert result == content1 + + # Then test with content2 (should override constructor file_path) + with patch("builtins.open", mock_open(read_data=content2)): + result = tool._run(file_path=test_file2) + assert result == content2 + + +def test_file_read_tool_chunk_reading(): + """Test FileReadTool reading specific chunks of a file.""" + test_file = "/tmp/multiline_test.txt" + lines = [ + "Line 1\n", + "Line 2\n", + "Line 3\n", + "Line 4\n", + "Line 5\n", + "Line 6\n", + "Line 7\n", + "Line 8\n", + "Line 9\n", + "Line 10\n", + ] + file_content = "".join(lines) + + with patch("builtins.open", mock_open(read_data=file_content)): + tool = FileReadTool() + + # Test reading a specific chunk (lines 3-5) + result = tool._run(file_path=test_file, start_line=3, line_count=3) + expected = "".join(lines[2:5]) # Lines are 0-indexed in the array + assert result == expected + + # Test reading from a specific line to the end + result = tool._run(file_path=test_file, start_line=8) + expected = "".join(lines[7:]) + assert result == expected + + # Test with default values (should read entire file) + result = tool._run(file_path=test_file) + expected = "".join(lines) + assert result == expected + + # Test when start_line is 1 but line_count is specified + result = tool._run(file_path=test_file, start_line=1, line_count=5) + expected = "".join(lines[0:5]) + assert result == expected + + +def test_file_read_tool_chunk_error_handling(): + """Test error handling for chunk reading.""" + test_file = "/tmp/short_test.txt" + lines = ["Line 1\n", "Line 2\n", "Line 3\n"] + file_content = "".join(lines) + + with patch("builtins.open", mock_open(read_data=file_content)): + tool = FileReadTool() + + # Test start_line exceeding file length + result = tool._run(file_path=test_file, start_line=10) + assert "Error: Start line 10 exceeds the number of lines in the file" in result + + # Test reading partial chunk when line_count exceeds available lines + result = tool._run(file_path=test_file, start_line=2, line_count=10) + expected = "".join(lines[1:]) # Should return from line 2 to end + assert result == expected + + +def test_file_read_tool_zero_or_negative_start_line(): + """Test that start_line values of 0 or negative read from the start of the file.""" + test_file = "/tmp/negative_test.txt" + lines = ["Line 1\n", "Line 2\n", "Line 3\n", "Line 4\n", "Line 5\n"] + file_content = "".join(lines) + + with patch("builtins.open", mock_open(read_data=file_content)): + tool = FileReadTool() + + # Test with start_line = None + result = tool._run(file_path=test_file, start_line=None) + expected = "".join(lines) # Should read the entire file + assert result == expected + + # Test with start_line = 0 + result = tool._run(file_path=test_file, start_line=0) + expected = "".join(lines) # Should read the entire file + assert result == expected + + # Test with start_line = 0 and limited line count + result = tool._run(file_path=test_file, start_line=0, line_count=3) + expected = "".join(lines[0:3]) # Should read first 3 lines + assert result == expected + + # Test with negative start_line + result = tool._run(file_path=test_file, start_line=-5) + expected = "".join(lines) # Should read the entire file + assert result == expected + + # Test with negative start_line and limited line count + result = tool._run(file_path=test_file, start_line=-10, line_count=2) + expected = "".join(lines[0:2]) # Should read first 2 lines + assert result == expected diff --git a/lib/crewai-tools/tests/it/tools/__init__.py b/lib/crewai-tools/tests/it/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/tests/it/tools/conftest.py b/lib/crewai-tools/tests/it/tools/conftest.py new file mode 100644 index 000000000..a633c22c7 --- /dev/null +++ b/lib/crewai-tools/tests/it/tools/conftest.py @@ -0,0 +1,21 @@ +import pytest + + +def pytest_configure(config): + """Register custom markers.""" + config.addinivalue_line("markers", "integration: mark test as an integration test") + config.addinivalue_line("markers", "asyncio: mark test as an async test") + + # Set the asyncio loop scope through ini configuration + config.inicfg["asyncio_mode"] = "auto" + + +@pytest.fixture(scope="function") +def event_loop(): + """Create an instance of the default event loop for each test case.""" + import asyncio + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + yield loop + loop.close() diff --git a/lib/crewai-tools/tests/rag/__init__.py b/lib/crewai-tools/tests/rag/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/tests/rag/test_csv_loader.py b/lib/crewai-tools/tests/rag/test_csv_loader.py new file mode 100644 index 000000000..2081eead4 --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_csv_loader.py @@ -0,0 +1,130 @@ +import os +import tempfile +from unittest.mock import Mock, patch + +from crewai_tools.rag.base_loader import LoaderResult +from crewai_tools.rag.loaders.csv_loader import CSVLoader +from crewai_tools.rag.source_content import SourceContent +import pytest + + +@pytest.fixture +def temp_csv_file(): + created_files = [] + + def _create(content: str): + f = tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) + f.write(content) + f.close() + created_files.append(f.name) + return f.name + + yield _create + + for path in created_files: + os.unlink(path) + + +class TestCSVLoader: + def test_load_csv_from_file(self, temp_csv_file): + path = temp_csv_file("name,age,city\nJohn,25,New York\nJane,30,Chicago") + loader = CSVLoader() + result = loader.load(SourceContent(path)) + + assert isinstance(result, LoaderResult) + assert "Headers: name | age | city" in result.content + assert "Row 1: name: John | age: 25 | city: New York" in result.content + assert "Row 2: name: Jane | age: 30 | city: Chicago" in result.content + assert result.metadata == { + "format": "csv", + "columns": ["name", "age", "city"], + "rows": 2, + } + assert result.source == path + assert result.doc_id + + def test_load_csv_with_empty_values(self, temp_csv_file): + path = temp_csv_file("name,age,city\nJohn,,New York\n,30,") + result = CSVLoader().load(SourceContent(path)) + + assert "Row 1: name: John | city: New York" in result.content + assert "Row 2: age: 30" in result.content + assert result.metadata["rows"] == 2 + + def test_load_csv_malformed(self, temp_csv_file): + path = temp_csv_file('invalid,csv\nunclosed quote "missing') + result = CSVLoader().load(SourceContent(path)) + + assert "Headers: invalid | csv" in result.content + assert 'Row 1: invalid: unclosed quote "missing' in result.content + assert result.metadata["columns"] == ["invalid", "csv"] + + def test_load_csv_empty_file(self, temp_csv_file): + path = temp_csv_file("") + result = CSVLoader().load(SourceContent(path)) + + assert result.content == "" + assert result.metadata["rows"] == 0 + + def test_load_csv_text_input(self): + raw_csv = "col1,col2\nvalue1,value2\nvalue3,value4" + result = CSVLoader().load(SourceContent(raw_csv)) + + assert "Headers: col1 | col2" in result.content + assert "Row 1: col1: value1 | col2: value2" in result.content + assert "Row 2: col1: value3 | col2: value4" in result.content + assert result.metadata["columns"] == ["col1", "col2"] + assert result.metadata["rows"] == 2 + + def test_doc_id_is_deterministic(self, temp_csv_file): + path = temp_csv_file("name,value\ntest,123") + loader = CSVLoader() + + result1 = loader.load(SourceContent(path)) + result2 = loader.load(SourceContent(path)) + + assert result1.doc_id == result2.doc_id + + @patch("requests.get") + def test_load_csv_from_url(self, mock_get): + mock_get.return_value = Mock( + text="name,value\ntest,123", raise_for_status=Mock(return_value=None) + ) + + result = CSVLoader().load(SourceContent("https://example.com/data.csv")) + + assert "Headers: name | value" in result.content + assert "Row 1: name: test | value: 123" in result.content + headers = mock_get.call_args[1]["headers"] + assert "text/csv" in headers["Accept"] + assert "crewai-tools CSVLoader" in headers["User-Agent"] + + @patch("requests.get") + def test_load_csv_with_custom_headers(self, mock_get): + mock_get.return_value = Mock( + text="data,value\ntest,456", raise_for_status=Mock(return_value=None) + ) + headers = {"Authorization": "Bearer token", "Custom-Header": "value"} + result = CSVLoader().load( + SourceContent("https://example.com/data.csv"), headers=headers + ) + + assert "Headers: data | value" in result.content + assert mock_get.call_args[1]["headers"] == headers + + @patch("requests.get") + def test_csv_loader_handles_network_errors(self, mock_get): + mock_get.side_effect = Exception("Network error") + loader = CSVLoader() + + with pytest.raises(ValueError, match="Error fetching content from URL"): + loader.load(SourceContent("https://example.com/data.csv")) + + @patch("requests.get") + def test_csv_loader_handles_http_error(self, mock_get): + mock_get.return_value = Mock() + mock_get.return_value.raise_for_status.side_effect = Exception("404 Not Found") + loader = CSVLoader() + + with pytest.raises(ValueError, match="Error fetching content from URL"): + loader.load(SourceContent("https://example.com/notfound.csv")) diff --git a/lib/crewai-tools/tests/rag/test_directory_loader.py b/lib/crewai-tools/tests/rag/test_directory_loader.py new file mode 100644 index 000000000..d1e1efee2 --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_directory_loader.py @@ -0,0 +1,160 @@ +import os +import tempfile + +from crewai_tools.rag.base_loader import LoaderResult +from crewai_tools.rag.loaders.directory_loader import DirectoryLoader +from crewai_tools.rag.source_content import SourceContent +import pytest + + +@pytest.fixture +def temp_directory(): + with tempfile.TemporaryDirectory() as temp_dir: + yield temp_dir + + +class TestDirectoryLoader: + def _create_file(self, directory, filename, content="test content"): + path = os.path.join(directory, filename) + with open(path, "w") as f: + f.write(content) + return path + + def test_load_non_recursive(self, temp_directory): + self._create_file(temp_directory, "file1.txt") + self._create_file(temp_directory, "file2.txt") + subdir = os.path.join(temp_directory, "subdir") + os.makedirs(subdir) + self._create_file(subdir, "file3.txt") + + loader = DirectoryLoader() + result = loader.load(SourceContent(temp_directory), recursive=False) + + assert isinstance(result, LoaderResult) + assert "file1.txt" in result.content + assert "file2.txt" in result.content + assert "file3.txt" not in result.content + assert result.metadata["total_files"] == 2 + + def test_load_recursive(self, temp_directory): + self._create_file(temp_directory, "file1.txt") + nested = os.path.join(temp_directory, "subdir", "nested") + os.makedirs(nested) + self._create_file(os.path.join(temp_directory, "subdir"), "file2.txt") + self._create_file(nested, "file3.txt") + + loader = DirectoryLoader() + result = loader.load(SourceContent(temp_directory), recursive=True) + + assert all(f"file{i}.txt" in result.content for i in range(1, 4)) + + def test_include_and_exclude_extensions(self, temp_directory): + self._create_file(temp_directory, "a.txt") + self._create_file(temp_directory, "b.py") + self._create_file(temp_directory, "c.md") + + loader = DirectoryLoader() + result = loader.load( + SourceContent(temp_directory), include_extensions=[".txt", ".py"] + ) + assert "a.txt" in result.content + assert "b.py" in result.content + assert "c.md" not in result.content + + result2 = loader.load( + SourceContent(temp_directory), exclude_extensions=[".py", ".md"] + ) + assert "a.txt" in result2.content + assert "b.py" not in result2.content + assert "c.md" not in result2.content + + def test_max_files_limit(self, temp_directory): + for i in range(5): + self._create_file(temp_directory, f"file{i}.txt") + + loader = DirectoryLoader() + result = loader.load(SourceContent(temp_directory), max_files=3) + + assert result.metadata["total_files"] == 3 + assert all(f"file{i}.txt" in result.content for i in range(3)) + + def test_hidden_files_and_dirs_excluded(self, temp_directory): + self._create_file(temp_directory, "visible.txt", "visible") + self._create_file(temp_directory, ".hidden.txt", "hidden") + + hidden_dir = os.path.join(temp_directory, ".hidden") + os.makedirs(hidden_dir) + self._create_file(hidden_dir, "inside_hidden.txt") + + loader = DirectoryLoader() + result = loader.load(SourceContent(temp_directory), recursive=True) + + assert "visible.txt" in result.content + assert ".hidden.txt" not in result.content + assert "inside_hidden.txt" not in result.content + + def test_directory_does_not_exist(self): + loader = DirectoryLoader() + with pytest.raises(FileNotFoundError, match="Directory does not exist"): + loader.load(SourceContent("/path/does/not/exist")) + + def test_path_is_not_a_directory(self): + with tempfile.NamedTemporaryFile() as f: + loader = DirectoryLoader() + with pytest.raises(ValueError, match="Path is not a directory"): + loader.load(SourceContent(f.name)) + + def test_url_not_supported(self): + loader = DirectoryLoader() + with pytest.raises(ValueError, match="URL directory loading is not supported"): + loader.load(SourceContent("https://example.com")) + + def test_processing_error_handling(self, temp_directory): + self._create_file(temp_directory, "valid.txt") + self._create_file(temp_directory, "error.txt") + + loader = DirectoryLoader() + original_method = loader._process_single_file + + def mock(file_path): + if "error" in file_path: + raise ValueError("Mock error") + return original_method(file_path) + + loader._process_single_file = mock + result = loader.load(SourceContent(temp_directory)) + + assert "valid.txt" in result.content + assert "error.txt (ERROR)" in result.content + assert result.metadata["errors"] == 1 + assert len(result.metadata["error_details"]) == 1 + + def test_metadata_structure(self, temp_directory): + self._create_file(temp_directory, "test.txt", "Sample") + + loader = DirectoryLoader() + result = loader.load(SourceContent(temp_directory)) + metadata = result.metadata + + expected_keys = { + "format", + "directory_path", + "total_files", + "processed_files", + "errors", + "file_details", + "error_details", + } + + assert expected_keys.issubset(metadata) + assert all( + k in metadata["file_details"][0] for k in ("path", "metadata", "source") + ) + + def test_empty_directory(self, temp_directory): + loader = DirectoryLoader() + result = loader.load(SourceContent(temp_directory)) + + assert result.content == "" + assert result.metadata["total_files"] == 0 + assert result.metadata["processed_files"] == 0 diff --git a/lib/crewai-tools/tests/rag/test_docx_loader.py b/lib/crewai-tools/tests/rag/test_docx_loader.py new file mode 100644 index 000000000..51222c829 --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_docx_loader.py @@ -0,0 +1,150 @@ +import tempfile +from unittest.mock import Mock, patch + +from crewai_tools.rag.base_loader import LoaderResult +from crewai_tools.rag.loaders.docx_loader import DOCXLoader +from crewai_tools.rag.source_content import SourceContent +import pytest + + +class TestDOCXLoader: + @patch("docx.Document") + def test_load_docx_from_file(self, mock_docx_class): + mock_doc = Mock() + mock_doc.paragraphs = [ + Mock(text="First paragraph"), + Mock(text="Second paragraph"), + Mock(text=" "), # Blank paragraph + ] + mock_doc.tables = [] + mock_docx_class.return_value = mock_doc + + with tempfile.NamedTemporaryFile(suffix=".docx") as f: + loader = DOCXLoader() + result = loader.load(SourceContent(f.name)) + + assert isinstance(result, LoaderResult) + assert result.content == "First paragraph\nSecond paragraph" + assert result.metadata == {"format": "docx", "paragraphs": 3, "tables": 0} + assert result.source == f.name + + @patch("docx.Document") + def test_load_docx_with_tables(self, mock_docx_class): + mock_doc = Mock() + mock_doc.paragraphs = [Mock(text="Document with table")] + mock_doc.tables = [Mock(), Mock()] + mock_docx_class.return_value = mock_doc + + with tempfile.NamedTemporaryFile(suffix=".docx") as f: + loader = DOCXLoader() + result = loader.load(SourceContent(f.name)) + + assert result.metadata["tables"] == 2 + + @patch("requests.get") + @patch("docx.Document") + @patch("tempfile.NamedTemporaryFile") + @patch("os.unlink") + def test_load_docx_from_url( + self, mock_unlink, mock_tempfile, mock_docx_class, mock_get + ): + mock_get.return_value = Mock( + content=b"fake docx content", raise_for_status=Mock() + ) + + mock_temp = Mock(name="/tmp/temp_docx_file.docx") + mock_temp.__enter__ = Mock(return_value=mock_temp) + mock_temp.__exit__ = Mock(return_value=None) + mock_tempfile.return_value = mock_temp + + mock_doc = Mock() + mock_doc.paragraphs = [Mock(text="Content from URL")] + mock_doc.tables = [] + mock_docx_class.return_value = mock_doc + + loader = DOCXLoader() + result = loader.load(SourceContent("https://example.com/test.docx")) + + assert "Content from URL" in result.content + assert result.source == "https://example.com/test.docx" + + headers = mock_get.call_args[1]["headers"] + assert ( + "application/vnd.openxmlformats-officedocument.wordprocessingml.document" + in headers["Accept"] + ) + assert "crewai-tools DOCXLoader" in headers["User-Agent"] + + mock_temp.write.assert_called_once_with(b"fake docx content") + + @patch("requests.get") + @patch("docx.Document") + def test_load_docx_from_url_with_custom_headers(self, mock_docx_class, mock_get): + mock_get.return_value = Mock( + content=b"fake docx content", raise_for_status=Mock() + ) + mock_docx_class.return_value = Mock(paragraphs=[], tables=[]) + + loader = DOCXLoader() + custom_headers = {"Authorization": "Bearer token"} + + with patch("tempfile.NamedTemporaryFile"), patch("os.unlink"): + loader.load( + SourceContent("https://example.com/test.docx"), headers=custom_headers + ) + + assert mock_get.call_args[1]["headers"] == custom_headers + + @patch("requests.get") + def test_load_docx_url_download_error(self, mock_get): + mock_get.side_effect = Exception("Network error") + + loader = DOCXLoader() + with pytest.raises(ValueError, match="Error fetching content from URL"): + loader.load(SourceContent("https://example.com/test.docx")) + + @patch("requests.get") + def test_load_docx_url_http_error(self, mock_get): + mock_get.return_value = Mock( + raise_for_status=Mock(side_effect=Exception("404 Not Found")) + ) + + loader = DOCXLoader() + with pytest.raises(ValueError, match="Error fetching content from URL"): + loader.load(SourceContent("https://example.com/notfound.docx")) + + def test_load_docx_invalid_source(self): + loader = DOCXLoader() + with pytest.raises(ValueError, match="Source must be a valid file path or URL"): + loader.load(SourceContent("not_a_file_or_url")) + + @patch("docx.Document") + def test_load_docx_parsing_error(self, mock_docx_class): + mock_docx_class.side_effect = Exception("Invalid DOCX file") + + with tempfile.NamedTemporaryFile(suffix=".docx") as f: + loader = DOCXLoader() + with pytest.raises(ValueError, match="Error loading DOCX file"): + loader.load(SourceContent(f.name)) + + @patch("docx.Document") + def test_load_docx_empty_document(self, mock_docx_class): + mock_docx_class.return_value = Mock(paragraphs=[], tables=[]) + + with tempfile.NamedTemporaryFile(suffix=".docx") as f: + loader = DOCXLoader() + result = loader.load(SourceContent(f.name)) + + assert result.content == "" + assert result.metadata == {"paragraphs": 0, "tables": 0, "format": "docx"} + + @patch("docx.Document") + def test_docx_doc_id_generation(self, mock_docx_class): + mock_docx_class.return_value = Mock( + paragraphs=[Mock(text="Consistent content")], tables=[] + ) + + with tempfile.NamedTemporaryFile(suffix=".docx") as f: + loader = DOCXLoader() + source = SourceContent(f.name) + assert loader.load(source).doc_id == loader.load(source).doc_id diff --git a/lib/crewai-tools/tests/rag/test_embedding_service.py b/lib/crewai-tools/tests/rag/test_embedding_service.py new file mode 100644 index 000000000..c6c74fdf1 --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_embedding_service.py @@ -0,0 +1,342 @@ +""" +Tests for the enhanced embedding service. +""" + +import os +import pytest +from unittest.mock import Mock, patch + +from crewai_tools.rag.embedding_service import EmbeddingService, EmbeddingConfig + + +class TestEmbeddingConfig: + """Test the EmbeddingConfig model.""" + + def test_default_config(self): + """Test default configuration values.""" + config = EmbeddingConfig(provider="openai", model="text-embedding-3-small") + + assert config.provider == "openai" + assert config.model == "text-embedding-3-small" + assert config.api_key is None + assert config.timeout == 30.0 + assert config.max_retries == 3 + assert config.batch_size == 100 + assert config.extra_config == {} + + def test_custom_config(self): + """Test custom configuration values.""" + config = EmbeddingConfig( + provider="voyageai", + model="voyage-2", + api_key="test-key", + timeout=60.0, + max_retries=5, + batch_size=50, + extra_config={"input_type": "document"} + ) + + assert config.provider == "voyageai" + assert config.model == "voyage-2" + assert config.api_key == "test-key" + assert config.timeout == 60.0 + assert config.max_retries == 5 + assert config.batch_size == 50 + assert config.extra_config == {"input_type": "document"} + + +class TestEmbeddingService: + """Test the EmbeddingService class.""" + + def test_list_supported_providers(self): + """Test listing supported providers.""" + providers = EmbeddingService.list_supported_providers() + expected_providers = [ + "openai", "azure", "voyageai", "cohere", "google-generativeai", + "amazon-bedrock", "huggingface", "jina", "ollama", "sentence-transformer", + "instructor", "watsonx", "custom" + ] + + assert isinstance(providers, list) + assert len(providers) >= 15 # Should have at least 15 providers + assert all(provider in providers for provider in expected_providers) + + def test_get_default_api_key(self): + """Test getting default API keys from environment.""" + service = EmbeddingService.__new__(EmbeddingService) # Create without __init__ + + # Test with environment variable set + with patch.dict(os.environ, {"OPENAI_API_KEY": "test-openai-key"}): + api_key = service._get_default_api_key("openai") + assert api_key == "test-openai-key" + + # Test with no environment variable + with patch.dict(os.environ, {}, clear=True): + api_key = service._get_default_api_key("openai") + assert api_key is None + + # Test unknown provider + api_key = service._get_default_api_key("unknown-provider") + assert api_key is None + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_initialization_success(self, mock_build_embedder): + """Test successful initialization.""" + # Mock the embedding function + mock_embedding_function = Mock() + mock_build_embedder.return_value = mock_embedding_function + + service = EmbeddingService( + provider="openai", + model="text-embedding-3-small", + api_key="test-key" + ) + + assert service.config.provider == "openai" + assert service.config.model == "text-embedding-3-small" + assert service.config.api_key == "test-key" + assert service._embedding_function == mock_embedding_function + + # Verify build_embedder was called with correct config + mock_build_embedder.assert_called_once() + call_args = mock_build_embedder.call_args[0][0] + assert call_args["provider"] == "openai" + assert call_args["config"]["api_key"] == "test-key" + assert call_args["config"]["model_name"] == "text-embedding-3-small" + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_initialization_import_error(self, mock_build_embedder): + """Test initialization with import error.""" + mock_build_embedder.side_effect = ImportError("CrewAI not installed") + + with pytest.raises(ImportError, match="CrewAI embedding providers not available"): + EmbeddingService(provider="openai", model="test-model", api_key="test-key") + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_embed_text_success(self, mock_build_embedder): + """Test successful text embedding.""" + # Mock the embedding function + mock_embedding_function = Mock() + mock_embedding_function.return_value = [[0.1, 0.2, 0.3]] + mock_build_embedder.return_value = mock_embedding_function + + service = EmbeddingService(provider="openai", model="test-model", api_key="test-key") + + result = service.embed_text("test text") + + assert result == [0.1, 0.2, 0.3] + mock_embedding_function.assert_called_once_with(["test text"]) + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_embed_text_empty_input(self, mock_build_embedder): + """Test embedding empty text.""" + mock_embedding_function = Mock() + mock_build_embedder.return_value = mock_embedding_function + + service = EmbeddingService(provider="openai", model="test-model", api_key="test-key") + + result = service.embed_text("") + assert result == [] + + result = service.embed_text(" ") + assert result == [] + + # Embedding function should not be called for empty text + mock_embedding_function.assert_not_called() + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_embed_batch_success(self, mock_build_embedder): + """Test successful batch embedding.""" + # Mock the embedding function + mock_embedding_function = Mock() + mock_embedding_function.return_value = [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]] + mock_build_embedder.return_value = mock_embedding_function + + service = EmbeddingService(provider="openai", model="test-model", api_key="test-key") + + texts = ["text1", "text2", "text3"] + result = service.embed_batch(texts) + + assert result == [[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]] + mock_embedding_function.assert_called_once_with(texts) + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_embed_batch_empty_input(self, mock_build_embedder): + """Test batch embedding with empty input.""" + mock_embedding_function = Mock() + mock_build_embedder.return_value = mock_embedding_function + + service = EmbeddingService(provider="openai", model="test-model", api_key="test-key") + + # Empty list + result = service.embed_batch([]) + assert result == [] + + # List with empty strings + result = service.embed_batch(["", " ", ""]) + assert result == [] + + # Embedding function should not be called for empty input + mock_embedding_function.assert_not_called() + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_validate_connection(self, mock_build_embedder): + """Test connection validation.""" + # Mock successful embedding + mock_embedding_function = Mock() + mock_embedding_function.return_value = [[0.1, 0.2, 0.3]] + mock_build_embedder.return_value = mock_embedding_function + + service = EmbeddingService(provider="openai", model="test-model", api_key="test-key") + + assert service.validate_connection() is True + + # Mock failed embedding + mock_embedding_function.side_effect = Exception("Connection failed") + assert service.validate_connection() is False + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_get_service_info(self, mock_build_embedder): + """Test getting service information.""" + # Mock the embedding function + mock_embedding_function = Mock() + mock_embedding_function.return_value = [[0.1, 0.2, 0.3]] + mock_build_embedder.return_value = mock_embedding_function + + service = EmbeddingService(provider="openai", model="test-model", api_key="test-key") + + info = service.get_service_info() + + assert info["provider"] == "openai" + assert info["model"] == "test-model" + assert info["embedding_dimension"] == 3 + assert info["batch_size"] == 100 + assert info["is_connected"] is True + + def test_create_openai_service(self): + """Test OpenAI service creation.""" + with patch('crewai.rag.embeddings.factory.build_embedder'): + service = EmbeddingService.create_openai_service( + model="text-embedding-3-large", + api_key="test-key" + ) + + assert service.config.provider == "openai" + assert service.config.model == "text-embedding-3-large" + assert service.config.api_key == "test-key" + + def test_create_voyage_service(self): + """Test Voyage AI service creation.""" + with patch('crewai.rag.embeddings.factory.build_embedder'): + service = EmbeddingService.create_voyage_service( + model="voyage-large-2", + api_key="test-key" + ) + + assert service.config.provider == "voyageai" + assert service.config.model == "voyage-large-2" + assert service.config.api_key == "test-key" + + def test_create_cohere_service(self): + """Test Cohere service creation.""" + with patch('crewai.rag.embeddings.factory.build_embedder'): + service = EmbeddingService.create_cohere_service( + model="embed-multilingual-v3.0", + api_key="test-key" + ) + + assert service.config.provider == "cohere" + assert service.config.model == "embed-multilingual-v3.0" + assert service.config.api_key == "test-key" + + def test_create_gemini_service(self): + """Test Gemini service creation.""" + with patch('crewai.rag.embeddings.factory.build_embedder'): + service = EmbeddingService.create_gemini_service( + model="models/embedding-001", + api_key="test-key" + ) + + assert service.config.provider == "google-generativeai" + assert service.config.model == "models/embedding-001" + assert service.config.api_key == "test-key" + + +class TestProviderConfigurations: + """Test provider-specific configurations.""" + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_openai_config(self, mock_build_embedder): + """Test OpenAI configuration mapping.""" + mock_build_embedder.return_value = Mock() + + service = EmbeddingService( + provider="openai", + model="text-embedding-3-small", + api_key="test-key", + extra_config={"dimensions": 1024} + ) + + # Check the configuration passed to build_embedder + call_args = mock_build_embedder.call_args[0][0] + assert call_args["provider"] == "openai" + assert call_args["config"]["api_key"] == "test-key" + assert call_args["config"]["model_name"] == "text-embedding-3-small" + assert call_args["config"]["dimensions"] == 1024 + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_voyageai_config(self, mock_build_embedder): + """Test Voyage AI configuration mapping.""" + mock_build_embedder.return_value = Mock() + + service = EmbeddingService( + provider="voyageai", + model="voyage-2", + api_key="test-key", + timeout=60.0, + max_retries=5, + extra_config={"input_type": "document"} + ) + + # Check the configuration passed to build_embedder + call_args = mock_build_embedder.call_args[0][0] + assert call_args["provider"] == "voyageai" + assert call_args["config"]["api_key"] == "test-key" + assert call_args["config"]["model"] == "voyage-2" + assert call_args["config"]["timeout"] == 60.0 + assert call_args["config"]["max_retries"] == 5 + assert call_args["config"]["input_type"] == "document" + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_cohere_config(self, mock_build_embedder): + """Test Cohere configuration mapping.""" + mock_build_embedder.return_value = Mock() + + service = EmbeddingService( + provider="cohere", + model="embed-english-v3.0", + api_key="test-key" + ) + + # Check the configuration passed to build_embedder + call_args = mock_build_embedder.call_args[0][0] + assert call_args["provider"] == "cohere" + assert call_args["config"]["api_key"] == "test-key" + assert call_args["config"]["model_name"] == "embed-english-v3.0" + + @patch('crewai.rag.embeddings.factory.build_embedder') + def test_gemini_config(self, mock_build_embedder): + """Test Gemini configuration mapping.""" + mock_build_embedder.return_value = Mock() + + service = EmbeddingService( + provider="google-generativeai", + model="models/embedding-001", + api_key="test-key" + ) + + # Check the configuration passed to build_embedder + call_args = mock_build_embedder.call_args[0][0] + assert call_args["provider"] == "google-generativeai" + assert call_args["config"]["api_key"] == "test-key" + assert call_args["config"]["model_name"] == "models/embedding-001" diff --git a/lib/crewai-tools/tests/rag/test_json_loader.py b/lib/crewai-tools/tests/rag/test_json_loader.py new file mode 100644 index 000000000..1787b029a --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_json_loader.py @@ -0,0 +1,189 @@ +import json +import os +import tempfile +from unittest.mock import Mock, patch + +from crewai_tools.rag.base_loader import LoaderResult +from crewai_tools.rag.loaders.json_loader import JSONLoader +from crewai_tools.rag.source_content import SourceContent +import pytest + + +class TestJSONLoader: + def _create_temp_json_file(self, data) -> str: + """Helper to write JSON data to a temporary file and return its path.""" + with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: + json.dump(data, f) + return f.name + + def _create_temp_raw_file(self, content: str) -> str: + """Helper to write raw content to a temporary file and return its path.""" + with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: + f.write(content) + return f.name + + def _load_from_path(self, path) -> LoaderResult: + loader = JSONLoader() + return loader.load(SourceContent(path)) + + def test_load_json_dict(self): + path = self._create_temp_json_file( + {"name": "John", "age": 30, "items": ["a", "b", "c"]} + ) + try: + result = self._load_from_path(path) + assert isinstance(result, LoaderResult) + assert all(k in result.content for k in ["name", "John", "age", "30"]) + assert result.metadata == {"format": "json", "type": "dict", "size": 3} + assert result.source == path + finally: + os.unlink(path) + + def test_load_json_list(self): + path = self._create_temp_json_file( + [ + {"id": 1, "name": "Item 1"}, + {"id": 2, "name": "Item 2"}, + ] + ) + try: + result = self._load_from_path(path) + assert result.metadata["type"] == "list" + assert result.metadata["size"] == 2 + assert all(item in result.content for item in ["Item 1", "Item 2"]) + finally: + os.unlink(path) + + @pytest.mark.parametrize( + "value, expected_type", + [ + ("simple string value", "str"), + (42, "int"), + ], + ) + def test_load_json_primitives(self, value, expected_type): + path = self._create_temp_json_file(value) + try: + result = self._load_from_path(path) + assert result.metadata["type"] == expected_type + assert result.metadata["size"] == 1 + assert str(value) in result.content + finally: + os.unlink(path) + + def test_load_malformed_json(self): + path = self._create_temp_raw_file('{"invalid": json,}') + try: + result = self._load_from_path(path) + assert result.metadata["format"] == "json" + assert "parse_error" in result.metadata + assert result.content == '{"invalid": json,}' + finally: + os.unlink(path) + + def test_load_empty_file(self): + path = self._create_temp_raw_file("") + try: + result = self._load_from_path(path) + assert "parse_error" in result.metadata + assert result.content == "" + finally: + os.unlink(path) + + def test_load_text_input(self): + json_text = '{"message": "hello", "count": 5}' + loader = JSONLoader() + result = loader.load(SourceContent(json_text)) + assert all( + part in result.content for part in ["message", "hello", "count", "5"] + ) + assert result.metadata["type"] == "dict" + assert result.metadata["size"] == 2 + + def test_load_complex_nested_json(self): + data = { + "users": [ + {"id": 1, "profile": {"name": "Alice", "settings": {"theme": "dark"}}}, + {"id": 2, "profile": {"name": "Bob", "settings": {"theme": "light"}}}, + ], + "meta": {"total": 2, "version": "1.0"}, + } + path = self._create_temp_json_file(data) + try: + result = self._load_from_path(path) + for value in ["Alice", "Bob", "dark", "light"]: + assert value in result.content + assert result.metadata["size"] == 2 # top-level keys + finally: + os.unlink(path) + + def test_consistent_doc_id(self): + path = self._create_temp_json_file({"test": "data"}) + try: + result1 = self._load_from_path(path) + result2 = self._load_from_path(path) + assert result1.doc_id == result2.doc_id + finally: + os.unlink(path) + + # ------------------------------ + # URL-based tests + # ------------------------------ + + @patch("requests.get") + def test_url_response_valid_json(self, mock_get): + mock_get.return_value = Mock( + text='{"key": "value", "number": 123}', + json=Mock(return_value={"key": "value", "number": 123}), + raise_for_status=Mock(), + ) + + loader = JSONLoader() + result = loader.load(SourceContent("https://api.example.com/data.json")) + + assert all(val in result.content for val in ["key", "value", "number", "123"]) + headers = mock_get.call_args[1]["headers"] + assert "application/json" in headers["Accept"] + assert "crewai-tools JSONLoader" in headers["User-Agent"] + + @patch("requests.get") + def test_url_response_not_json(self, mock_get): + mock_get.return_value = Mock( + text='{"key": "value"}', + json=Mock(side_effect=ValueError("Not JSON")), + raise_for_status=Mock(), + ) + + loader = JSONLoader() + result = loader.load(SourceContent("https://example.com/data.json")) + assert all(part in result.content for part in ["key", "value"]) + + @patch("requests.get") + def test_url_with_custom_headers(self, mock_get): + mock_get.return_value = Mock( + text='{"data": "test"}', + json=Mock(return_value={"data": "test"}), + raise_for_status=Mock(), + ) + headers = {"Authorization": "Bearer token", "Custom-Header": "value"} + + loader = JSONLoader() + loader.load(SourceContent("https://api.example.com/data.json"), headers=headers) + + assert mock_get.call_args[1]["headers"] == headers + + @patch("requests.get") + def test_url_network_failure(self, mock_get): + mock_get.side_effect = Exception("Network error") + loader = JSONLoader() + with pytest.raises(ValueError, match="Error fetching content from URL"): + loader.load(SourceContent("https://api.example.com/data.json")) + + @patch("requests.get") + def test_url_http_error(self, mock_get): + mock_get.return_value = Mock( + raise_for_status=Mock(side_effect=Exception("404")) + ) + loader = JSONLoader() + with pytest.raises(ValueError, match="Error fetching content from URL"): + loader.load(SourceContent("https://api.example.com/404.json")) diff --git a/lib/crewai-tools/tests/rag/test_mdx_loader.py b/lib/crewai-tools/tests/rag/test_mdx_loader.py new file mode 100644 index 000000000..c519c8527 --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_mdx_loader.py @@ -0,0 +1,208 @@ +import os +import tempfile +from unittest.mock import Mock, patch + +from crewai_tools.rag.base_loader import LoaderResult +from crewai_tools.rag.loaders.mdx_loader import MDXLoader +from crewai_tools.rag.source_content import SourceContent +import pytest + + +class TestMDXLoader: + def _write_temp_mdx(self, content): + f = tempfile.NamedTemporaryFile(mode="w", suffix=".mdx", delete=False) + f.write(content) + f.close() + return f.name + + def _load_from_file(self, content): + path = self._write_temp_mdx(content) + try: + loader = MDXLoader() + return loader.load(SourceContent(path)), path + finally: + os.unlink(path) + + def test_load_basic_mdx_file(self): + content = """ +import Component from './Component' +export const meta = { title: 'Test' } + +# Test MDX File + +This is a **markdown** file with JSX. + + + +Some more content. + +
+

Nested content

+
+""" + result, path = self._load_from_file(content) + + assert isinstance(result, LoaderResult) + assert all( + tag not in result.content + for tag in ["import", "export", ""] + ) + assert all( + text in result.content + for text in [ + "# Test MDX File", + "markdown", + "Some more content", + "Nested content", + ] + ) + assert result.metadata["format"] == "mdx" + assert result.source == path + + def test_mdx_multiple_imports_exports(self): + content = """ +import React from 'react' +import { useState } from 'react' +import CustomComponent from './custom' + +export default function Layout() { return null } +export const config = { test: true } + +# Content + +Regular markdown content here. +""" + result, _ = self._load_from_file(content) + assert "# Content" in result.content + assert "Regular markdown content here." in result.content + assert "import" not in result.content and "export" not in result.content + + def test_complex_jsx_cleanup(self): + content = """ +# MDX with Complex JSX + +
+ Info: This is important information. +
  • Item 1
  • Item 2
+
+ +Regular paragraph text. + +Nested content inside component +""" + result, _ = self._load_from_file(content) + assert all( + tag not in result.content + for tag in ["", "
    ", " +

    Only JSX content

    +

    No markdown here

    +
+""" + result, _ = self._load_from_file(content) + assert all(tag not in result.content for tag in ["
", "

", "

"]) + assert "Only JSX content" in result.content + assert "No markdown here" in result.content + + @patch("requests.get") + def test_load_mdx_from_url(self, mock_get): + mock_get.return_value = Mock( + text="# MDX from URL\n\nContent here.\n\n", + raise_for_status=lambda: None, + ) + loader = MDXLoader() + result = loader.load(SourceContent("https://example.com/content.mdx")) + assert "# MDX from URL" in result.content + assert "" not in result.content + + @patch("requests.get") + def test_load_mdx_with_custom_headers(self, mock_get): + mock_get.return_value = Mock( + text="# Custom headers test", raise_for_status=lambda: None + ) + loader = MDXLoader() + loader.load( + SourceContent("https://example.com"), + headers={"Authorization": "Bearer token"}, + ) + assert mock_get.call_args[1]["headers"] == {"Authorization": "Bearer token"} + + @patch("requests.get") + def test_mdx_url_fetch_error(self, mock_get): + mock_get.side_effect = Exception("Network error") + with pytest.raises(ValueError, match="Error fetching content from URL https://example.com: Network error"): + MDXLoader().load(SourceContent("https://example.com")) + + def test_load_inline_mdx_text(self): + content = """# Inline MDX\n\nimport Something from 'somewhere'\n\nContent with .\n\nexport const meta = { title: 'Test' }""" + loader = MDXLoader() + result = loader.load(SourceContent(content)) + assert "# Inline MDX" in result.content + assert "Content with ." in result.content + + def test_empty_result_after_cleaning(self): + content = """ +import Something from 'somewhere' +export const config = {} +

+""" + result, _ = self._load_from_file(content) + assert result.content.strip() == "" + + def test_edge_case_parsing(self): + content = """ +# Title + + +Multi-line +JSX content + + +import { a, b } from 'module' + +export { x, y } + +Final text. +""" + result, _ = self._load_from_file(content) + assert "# Title" in result.content + assert "JSX content" in result.content + assert "Final text." in result.content + assert all( + phrase not in result.content + for phrase in ["import {", "export {", ""] + ) diff --git a/lib/crewai-tools/tests/rag/test_text_loaders.py b/lib/crewai-tools/tests/rag/test_text_loaders.py new file mode 100644 index 000000000..5b7eaee97 --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_text_loaders.py @@ -0,0 +1,162 @@ +import hashlib +import os +import tempfile + +from crewai_tools.rag.base_loader import LoaderResult +from crewai_tools.rag.loaders.text_loader import TextFileLoader, TextLoader +from crewai_tools.rag.source_content import SourceContent +import pytest + + +def write_temp_file(content, suffix=".txt", encoding="utf-8"): + with tempfile.NamedTemporaryFile( + mode="w", suffix=suffix, delete=False, encoding=encoding + ) as f: + f.write(content) + return f.name + + +def cleanup_temp_file(path): + try: + os.unlink(path) + except FileNotFoundError: + pass + + +class TestTextFileLoader: + def test_basic_text_file(self): + content = "This is test content\nWith multiple lines\nAnd more text" + path = write_temp_file(content) + try: + result = TextFileLoader().load(SourceContent(path)) + assert isinstance(result, LoaderResult) + assert result.content == content + assert result.source == path + assert result.doc_id + assert result.metadata in (None, {}) + finally: + cleanup_temp_file(path) + + def test_empty_file(self): + path = write_temp_file("") + try: + result = TextFileLoader().load(SourceContent(path)) + assert result.content == "" + finally: + cleanup_temp_file(path) + + def test_unicode_content(self): + content = "Hello 世界 🌍 émojis 🎉 åäö" + path = write_temp_file(content) + try: + result = TextFileLoader().load(SourceContent(path)) + assert content in result.content + finally: + cleanup_temp_file(path) + + def test_large_file(self): + content = "\n".join(f"Line {i}" for i in range(100)) + path = write_temp_file(content) + try: + result = TextFileLoader().load(SourceContent(path)) + assert "Line 0" in result.content + assert "Line 99" in result.content + assert result.content.count("\n") == 99 + finally: + cleanup_temp_file(path) + + def test_missing_file(self): + with pytest.raises(FileNotFoundError): + TextFileLoader().load(SourceContent("/nonexistent/path.txt")) + + def test_permission_denied(self): + path = write_temp_file("Some content") + os.chmod(path, 0o000) + try: + with pytest.raises(PermissionError): + TextFileLoader().load(SourceContent(path)) + finally: + os.chmod(path, 0o644) + cleanup_temp_file(path) + + def test_doc_id_consistency(self): + content = "Consistent content" + path = write_temp_file(content) + try: + loader = TextFileLoader() + result1 = loader.load(SourceContent(path)) + result2 = loader.load(SourceContent(path)) + expected_id = hashlib.sha256((path + content).encode("utf-8")).hexdigest() + assert result1.doc_id == result2.doc_id == expected_id + finally: + cleanup_temp_file(path) + + def test_various_extensions(self): + content = "Same content" + for ext in [".txt", ".md", ".log", ".json"]: + path = write_temp_file(content, suffix=ext) + try: + result = TextFileLoader().load(SourceContent(path)) + assert result.content == content + finally: + cleanup_temp_file(path) + + +class TestTextLoader: + def test_basic_text(self): + content = "Raw text" + result = TextLoader().load(SourceContent(content)) + expected_hash = hashlib.sha256(content.encode("utf-8")).hexdigest() + assert result.content == content + assert result.source == expected_hash + assert result.doc_id == expected_hash + + def test_multiline_text(self): + content = "Line 1\nLine 2\nLine 3" + result = TextLoader().load(SourceContent(content)) + assert "Line 2" in result.content + + def test_empty_text(self): + result = TextLoader().load(SourceContent("")) + assert result.content == "" + assert result.source == hashlib.sha256("".encode("utf-8")).hexdigest() + + def test_unicode_text(self): + content = "世界 🌍 émojis 🎉 åäö" + result = TextLoader().load(SourceContent(content)) + assert content in result.content + + def test_special_characters(self): + content = "!@#$$%^&*()_+-=~`{}[]\\|;:'\",.<>/?" + result = TextLoader().load(SourceContent(content)) + assert result.content == content + + def test_doc_id_uniqueness(self): + result1 = TextLoader().load(SourceContent("A")) + result2 = TextLoader().load(SourceContent("B")) + assert result1.doc_id != result2.doc_id + + def test_whitespace_text(self): + content = " \n\t " + result = TextLoader().load(SourceContent(content)) + assert result.content == content + + def test_long_text(self): + content = "A" * 10000 + result = TextLoader().load(SourceContent(content)) + assert len(result.content) == 10000 + + +class TestTextLoadersIntegration: + def test_consistency_between_loaders(self): + content = "Consistent content" + text_result = TextLoader().load(SourceContent(content)) + file_path = write_temp_file(content) + try: + file_result = TextFileLoader().load(SourceContent(file_path)) + + assert text_result.content == file_result.content + assert text_result.source != file_result.source + assert text_result.doc_id != file_result.doc_id + finally: + cleanup_temp_file(file_path) diff --git a/lib/crewai-tools/tests/rag/test_webpage_loader.py b/lib/crewai-tools/tests/rag/test_webpage_loader.py new file mode 100644 index 000000000..c9debe6a1 --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_webpage_loader.py @@ -0,0 +1,167 @@ +from unittest.mock import Mock, patch + +from crewai_tools.rag.base_loader import LoaderResult +from crewai_tools.rag.loaders.webpage_loader import WebPageLoader +from crewai_tools.rag.source_content import SourceContent +import pytest + + +class TestWebPageLoader: + def setup_mock_response(self, text, status_code=200, content_type="text/html"): + response = Mock() + response.text = text + response.apparent_encoding = "utf-8" + response.status_code = status_code + response.headers = {"content-type": content_type} + return response + + def setup_mock_soup(self, text, title=None, script_style_elements=None): + soup = Mock() + soup.get_text.return_value = text + soup.title = Mock(string=title) if title is not None else None + soup.return_value = script_style_elements or [] + return soup + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_load_basic_webpage(self, mock_bs, mock_get): + mock_get.return_value = self.setup_mock_response( + "Test Page

Test content

" + ) + mock_bs.return_value = self.setup_mock_soup("Test content", title="Test Page") + + loader = WebPageLoader() + result = loader.load(SourceContent("https://example.com")) + + assert isinstance(result, LoaderResult) + assert result.content == "Test content" + assert result.metadata["title"] == "Test Page" + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_load_webpage_with_scripts_and_styles(self, mock_bs, mock_get): + html = """ + Page with Scripts +

Visible content

+ """ + mock_get.return_value = self.setup_mock_response(html) + scripts = [Mock(), Mock()] + styles = [Mock()] + for el in scripts + styles: + el.decompose = Mock() + mock_bs.return_value = self.setup_mock_soup( + "Page with Scripts Visible content", + title="Page with Scripts", + script_style_elements=scripts + styles, + ) + + loader = WebPageLoader() + result = loader.load(SourceContent("https://example.com/with-scripts")) + + assert "Visible content" in result.content + for el in scripts + styles: + el.decompose.assert_called_once() + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_text_cleaning_and_title_handling(self, mock_bs, mock_get): + mock_get.return_value = self.setup_mock_response( + "

Messy text

" + ) + mock_bs.return_value = self.setup_mock_soup( + "Text with extra spaces\n\n More\t text \n\n", title=None + ) + + loader = WebPageLoader() + result = loader.load(SourceContent("https://example.com/messy-text")) + assert result.content is not None + assert result.metadata["title"] == "" + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_empty_or_missing_title(self, mock_bs, mock_get): + for title in [None, ""]: + mock_get.return_value = self.setup_mock_response( + "Content" + ) + mock_bs.return_value = self.setup_mock_soup("Content", title=title) + + loader = WebPageLoader() + result = loader.load(SourceContent("https://example.com")) + assert result.metadata["title"] == "" + + @patch("requests.get") + def test_custom_and_default_headers(self, mock_get): + mock_get.return_value = self.setup_mock_response( + "Test" + ) + custom_headers = { + "User-Agent": "Bot", + "Authorization": "Bearer xyz", + "Accept": "text/html", + } + + with patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") as mock_bs: + mock_bs.return_value = self.setup_mock_soup("Test") + WebPageLoader().load( + SourceContent("https://example.com"), headers=custom_headers + ) + + assert mock_get.call_args[1]["headers"] == custom_headers + + @patch("requests.get") + def test_error_handling(self, mock_get): + for error in [Exception("Fail"), ValueError("Bad"), ImportError("Oops")]: + mock_get.side_effect = error + with pytest.raises(ValueError, match="Error loading webpage"): + WebPageLoader().load(SourceContent("https://example.com")) + + @patch("requests.get") + def test_timeout_and_http_error(self, mock_get): + import requests + + mock_get.side_effect = requests.Timeout("Timeout") + with pytest.raises(ValueError): + WebPageLoader().load(SourceContent("https://example.com")) + + mock_response = Mock() + mock_response.raise_for_status.side_effect = requests.HTTPError("404") + mock_get.side_effect = None + mock_get.return_value = mock_response + with pytest.raises(ValueError): + WebPageLoader().load(SourceContent("https://example.com/404")) + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_doc_id_consistency(self, mock_bs, mock_get): + mock_get.return_value = self.setup_mock_response( + "Doc" + ) + mock_bs.return_value = self.setup_mock_soup("Doc") + + loader = WebPageLoader() + result1 = loader.load(SourceContent("https://example.com")) + result2 = loader.load(SourceContent("https://example.com")) + + assert result1.doc_id == result2.doc_id + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_status_code_and_content_type(self, mock_bs, mock_get): + for status in [200, 201, 301]: + mock_get.return_value = self.setup_mock_response( + f"Status {status}", status_code=status + ) + mock_bs.return_value = self.setup_mock_soup(f"Status {status}") + result = WebPageLoader().load( + SourceContent(f"https://example.com/{status}") + ) + assert result.metadata["status_code"] == status + + for ctype in ["text/html", "text/plain", "application/xhtml+xml"]: + mock_get.return_value = self.setup_mock_response( + "Content", content_type=ctype + ) + mock_bs.return_value = self.setup_mock_soup("Content") + result = WebPageLoader().load(SourceContent("https://example.com")) + assert result.metadata["content_type"] == ctype diff --git a/lib/crewai-tools/tests/rag/test_xml_loader.py b/lib/crewai-tools/tests/rag/test_xml_loader.py new file mode 100644 index 000000000..c9debe6a1 --- /dev/null +++ b/lib/crewai-tools/tests/rag/test_xml_loader.py @@ -0,0 +1,167 @@ +from unittest.mock import Mock, patch + +from crewai_tools.rag.base_loader import LoaderResult +from crewai_tools.rag.loaders.webpage_loader import WebPageLoader +from crewai_tools.rag.source_content import SourceContent +import pytest + + +class TestWebPageLoader: + def setup_mock_response(self, text, status_code=200, content_type="text/html"): + response = Mock() + response.text = text + response.apparent_encoding = "utf-8" + response.status_code = status_code + response.headers = {"content-type": content_type} + return response + + def setup_mock_soup(self, text, title=None, script_style_elements=None): + soup = Mock() + soup.get_text.return_value = text + soup.title = Mock(string=title) if title is not None else None + soup.return_value = script_style_elements or [] + return soup + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_load_basic_webpage(self, mock_bs, mock_get): + mock_get.return_value = self.setup_mock_response( + "Test Page

Test content

" + ) + mock_bs.return_value = self.setup_mock_soup("Test content", title="Test Page") + + loader = WebPageLoader() + result = loader.load(SourceContent("https://example.com")) + + assert isinstance(result, LoaderResult) + assert result.content == "Test content" + assert result.metadata["title"] == "Test Page" + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_load_webpage_with_scripts_and_styles(self, mock_bs, mock_get): + html = """ + Page with Scripts +

Visible content

+ """ + mock_get.return_value = self.setup_mock_response(html) + scripts = [Mock(), Mock()] + styles = [Mock()] + for el in scripts + styles: + el.decompose = Mock() + mock_bs.return_value = self.setup_mock_soup( + "Page with Scripts Visible content", + title="Page with Scripts", + script_style_elements=scripts + styles, + ) + + loader = WebPageLoader() + result = loader.load(SourceContent("https://example.com/with-scripts")) + + assert "Visible content" in result.content + for el in scripts + styles: + el.decompose.assert_called_once() + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_text_cleaning_and_title_handling(self, mock_bs, mock_get): + mock_get.return_value = self.setup_mock_response( + "

Messy text

" + ) + mock_bs.return_value = self.setup_mock_soup( + "Text with extra spaces\n\n More\t text \n\n", title=None + ) + + loader = WebPageLoader() + result = loader.load(SourceContent("https://example.com/messy-text")) + assert result.content is not None + assert result.metadata["title"] == "" + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_empty_or_missing_title(self, mock_bs, mock_get): + for title in [None, ""]: + mock_get.return_value = self.setup_mock_response( + "Content" + ) + mock_bs.return_value = self.setup_mock_soup("Content", title=title) + + loader = WebPageLoader() + result = loader.load(SourceContent("https://example.com")) + assert result.metadata["title"] == "" + + @patch("requests.get") + def test_custom_and_default_headers(self, mock_get): + mock_get.return_value = self.setup_mock_response( + "Test" + ) + custom_headers = { + "User-Agent": "Bot", + "Authorization": "Bearer xyz", + "Accept": "text/html", + } + + with patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") as mock_bs: + mock_bs.return_value = self.setup_mock_soup("Test") + WebPageLoader().load( + SourceContent("https://example.com"), headers=custom_headers + ) + + assert mock_get.call_args[1]["headers"] == custom_headers + + @patch("requests.get") + def test_error_handling(self, mock_get): + for error in [Exception("Fail"), ValueError("Bad"), ImportError("Oops")]: + mock_get.side_effect = error + with pytest.raises(ValueError, match="Error loading webpage"): + WebPageLoader().load(SourceContent("https://example.com")) + + @patch("requests.get") + def test_timeout_and_http_error(self, mock_get): + import requests + + mock_get.side_effect = requests.Timeout("Timeout") + with pytest.raises(ValueError): + WebPageLoader().load(SourceContent("https://example.com")) + + mock_response = Mock() + mock_response.raise_for_status.side_effect = requests.HTTPError("404") + mock_get.side_effect = None + mock_get.return_value = mock_response + with pytest.raises(ValueError): + WebPageLoader().load(SourceContent("https://example.com/404")) + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_doc_id_consistency(self, mock_bs, mock_get): + mock_get.return_value = self.setup_mock_response( + "Doc" + ) + mock_bs.return_value = self.setup_mock_soup("Doc") + + loader = WebPageLoader() + result1 = loader.load(SourceContent("https://example.com")) + result2 = loader.load(SourceContent("https://example.com")) + + assert result1.doc_id == result2.doc_id + + @patch("requests.get") + @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup") + def test_status_code_and_content_type(self, mock_bs, mock_get): + for status in [200, 201, 301]: + mock_get.return_value = self.setup_mock_response( + f"Status {status}", status_code=status + ) + mock_bs.return_value = self.setup_mock_soup(f"Status {status}") + result = WebPageLoader().load( + SourceContent(f"https://example.com/{status}") + ) + assert result.metadata["status_code"] == status + + for ctype in ["text/html", "text/plain", "application/xhtml+xml"]: + mock_get.return_value = self.setup_mock_response( + "Content", content_type=ctype + ) + mock_bs.return_value = self.setup_mock_soup("Content") + result = WebPageLoader().load(SourceContent("https://example.com")) + assert result.metadata["content_type"] == ctype diff --git a/lib/crewai-tools/tests/test_generate_tool_specs.py b/lib/crewai-tools/tests/test_generate_tool_specs.py new file mode 100644 index 000000000..18c2dfe8d --- /dev/null +++ b/lib/crewai-tools/tests/test_generate_tool_specs.py @@ -0,0 +1,191 @@ +import json +from unittest import mock + +from crewai.tools.base_tool import BaseTool, EnvVar +from generate_tool_specs import ToolSpecExtractor +from pydantic import BaseModel, Field +import pytest + + +class MockToolSchema(BaseModel): + query: str = Field(..., description="The query parameter") + count: int = Field(5, description="Number of results to return") + filters: list[str] | None = Field(None, description="Optional filters to apply") + + +class MockTool(BaseTool): + name: str = "Mock Search Tool" + description: str = "A tool that mocks search functionality" + args_schema: type[BaseModel] = MockToolSchema + + another_parameter: str = Field( + "Another way to define a default value", description="" + ) + my_parameter: str = Field("This is default value", description="What a description") + my_parameter_bool: bool = Field(False) + package_dependencies: list[str] = Field( + ["this-is-a-required-package", "another-required-package"], description="" + ) + env_vars: list[EnvVar] = [ + EnvVar( + name="SERPER_API_KEY", + description="API key for Serper", + required=True, + default=None, + ), + EnvVar( + name="API_RATE_LIMIT", + description="API rate limit", + required=False, + default="100", + ), + ] + + +@pytest.fixture +def extractor(): + ext = ToolSpecExtractor() + return ext + + +def test_unwrap_schema(extractor): + nested_schema = { + "type": "function-after", + "schema": {"type": "default", "schema": {"type": "str", "value": "test"}}, + } + result = extractor._unwrap_schema(nested_schema) + assert result["type"] == "str" + assert result["value"] == "test" + + +@pytest.fixture +def mock_tool_extractor(extractor): + with ( + mock.patch("generate_tool_specs.dir", return_value=["MockTool"]), + mock.patch("generate_tool_specs.getattr", return_value=MockTool), + ): + extractor.extract_all_tools() + assert len(extractor.tools_spec) == 1 + return extractor.tools_spec[0] + + +def test_extract_basic_tool_info(mock_tool_extractor): + tool_info = mock_tool_extractor + + assert tool_info.keys() == { + "name", + "humanized_name", + "description", + "run_params_schema", + "env_vars", + "init_params_schema", + "package_dependencies", + } + + assert tool_info["name"] == "MockTool" + assert tool_info["humanized_name"] == "Mock Search Tool" + assert tool_info["description"] == "A tool that mocks search functionality" + + +def test_extract_init_params_schema(mock_tool_extractor): + tool_info = mock_tool_extractor + init_params_schema = tool_info["init_params_schema"] + + assert init_params_schema.keys() == { + "$defs", + "properties", + "title", + "type", + } + + another_parameter = init_params_schema["properties"]["another_parameter"] + assert another_parameter["description"] == "" + assert another_parameter["default"] == "Another way to define a default value" + assert another_parameter["type"] == "string" + + my_parameter = init_params_schema["properties"]["my_parameter"] + assert my_parameter["description"] == "What a description" + assert my_parameter["default"] == "This is default value" + assert my_parameter["type"] == "string" + + my_parameter_bool = init_params_schema["properties"]["my_parameter_bool"] + assert not my_parameter_bool["default"] + assert my_parameter_bool["type"] == "boolean" + + +def test_extract_env_vars(mock_tool_extractor): + tool_info = mock_tool_extractor + + assert len(tool_info["env_vars"]) == 2 + api_key_var, rate_limit_var = tool_info["env_vars"] + assert api_key_var["name"] == "SERPER_API_KEY" + assert api_key_var["description"] == "API key for Serper" + assert api_key_var["required"] + assert api_key_var["default"] is None + + assert rate_limit_var["name"] == "API_RATE_LIMIT" + assert rate_limit_var["description"] == "API rate limit" + assert not rate_limit_var["required"] + assert rate_limit_var["default"] == "100" + + +def test_extract_run_params_schema(mock_tool_extractor): + tool_info = mock_tool_extractor + + run_params_schema = tool_info["run_params_schema"] + assert run_params_schema.keys() == { + "properties", + "required", + "title", + "type", + } + + query_param = run_params_schema["properties"]["query"] + assert query_param["description"] == "The query parameter" + assert query_param["type"] == "string" + + count_param = run_params_schema["properties"]["count"] + assert count_param["type"] == "integer" + assert count_param["default"] == 5 + + filters_param = run_params_schema["properties"]["filters"] + assert filters_param["description"] == "Optional filters to apply" + assert filters_param["default"] is None + assert filters_param["anyOf"] == [ + {"items": {"type": "string"}, "type": "array"}, + {"type": "null"}, + ] + + +def test_extract_package_dependencies(mock_tool_extractor): + tool_info = mock_tool_extractor + assert tool_info["package_dependencies"] == [ + "this-is-a-required-package", + "another-required-package", + ] + + +def test_save_to_json(extractor, tmp_path): + extractor.tools_spec = [ + { + "name": "TestTool", + "humanized_name": "Test Tool", + "description": "A test tool", + "run_params_schema": [ + {"name": "param1", "description": "Test parameter", "type": "str"} + ], + } + ] + + file_path = tmp_path / "output.json" + extractor.save_to_json(str(file_path)) + + assert file_path.exists() + + with open(file_path, "r") as f: + data = json.load(f) + + assert "tools" in data + assert len(data["tools"]) == 1 + assert data["tools"][0]["humanized_name"] == "Test Tool" + assert data["tools"][0]["run_params_schema"][0]["name"] == "param1" diff --git a/lib/crewai-tools/tests/test_optional_dependencies.py b/lib/crewai-tools/tests/test_optional_dependencies.py new file mode 100644 index 000000000..366b1b024 --- /dev/null +++ b/lib/crewai-tools/tests/test_optional_dependencies.py @@ -0,0 +1,45 @@ +from pathlib import Path +import subprocess +import tempfile + +import pytest + + +@pytest.fixture +def temp_project(): + temp_dir = tempfile.TemporaryDirectory() + project_dir = Path(temp_dir.name) / "test_project" + project_dir.mkdir() + + pyproject_content = """ + [project] + name = "test-project" + version = "0.1.0" + description = "Test project" + requires-python = ">=3.10" + """ + + (project_dir / "pyproject.toml").write_text(pyproject_content) + run_command( + ["uv", "add", "--editable", f"file://{Path.cwd().absolute()}"], project_dir + ) + run_command(["uv", "sync"], project_dir) + yield project_dir + + +def run_command(cmd, cwd): + return subprocess.run(cmd, cwd=cwd, capture_output=True, text=True) + + +def test_no_optional_dependencies_in_init(temp_project): + """ + Test that crewai-tools can be imported without optional dependencies. + + The package defines optional dependencies in pyproject.toml, but the base + package should be importable without any of these optional dependencies + being installed. + """ + result = run_command( + ["uv", "run", "python", "-c", "import crewai_tools"], temp_project + ) + assert result.returncode == 0, f"Import failed with error: {result.stderr}" diff --git a/lib/crewai-tools/tests/tools/__init__.py b/lib/crewai-tools/tests/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai-tools/tests/tools/arxiv_paper_tool_test.py b/lib/crewai-tools/tests/tools/arxiv_paper_tool_test.py new file mode 100644 index 000000000..9a2f0a36d --- /dev/null +++ b/lib/crewai-tools/tests/tools/arxiv_paper_tool_test.py @@ -0,0 +1,130 @@ +from pathlib import Path +from unittest.mock import MagicMock, patch +import urllib.error +import xml.etree.ElementTree as ET + +from crewai_tools import ArxivPaperTool +import pytest + + +@pytest.fixture +def tool(): + return ArxivPaperTool(download_pdfs=False) + + +def mock_arxiv_response(): + return """ + + + http://arxiv.org/abs/1234.5678 + Sample Paper + This is a summary of the sample paper. + 2022-01-01T00:00:00Z + John Doe + + + """ + + +@patch("urllib.request.urlopen") +def test_fetch_arxiv_data(mock_urlopen, tool): + mock_response = MagicMock() + mock_response.status = 200 + mock_response.read.return_value = mock_arxiv_response().encode("utf-8") + mock_urlopen.return_value.__enter__.return_value = mock_response + + results = tool.fetch_arxiv_data("transformer", 1) + assert isinstance(results, list) + assert results[0]["title"] == "Sample Paper" + + +@patch("urllib.request.urlopen", side_effect=urllib.error.URLError("Timeout")) +def test_fetch_arxiv_data_network_error(mock_urlopen, tool): + with pytest.raises(urllib.error.URLError): + tool.fetch_arxiv_data("transformer", 1) + + +@patch("urllib.request.urlretrieve") +def test_download_pdf_success(mock_urlretrieve): + tool = ArxivPaperTool() + tool.download_pdf("http://arxiv.org/pdf/1234.5678.pdf", Path("test.pdf")) + mock_urlretrieve.assert_called_once() + + +@patch("urllib.request.urlretrieve", side_effect=OSError("Permission denied")) +def test_download_pdf_oserror(mock_urlretrieve): + tool = ArxivPaperTool() + with pytest.raises(OSError): + tool.download_pdf( + "http://arxiv.org/pdf/1234.5678.pdf", Path("/restricted/test.pdf") + ) + + +@patch("urllib.request.urlopen") +@patch("urllib.request.urlretrieve") +def test_run_with_download(mock_urlretrieve, mock_urlopen): + mock_response = MagicMock() + mock_response.status = 200 + mock_response.read.return_value = mock_arxiv_response().encode("utf-8") + mock_urlopen.return_value.__enter__.return_value = mock_response + + tool = ArxivPaperTool(download_pdfs=True) + output = tool._run("transformer", 1) + assert "Title: Sample Paper" in output + mock_urlretrieve.assert_called_once() + + +@patch("urllib.request.urlopen") +def test_run_no_download(mock_urlopen): + mock_response = MagicMock() + mock_response.status = 200 + mock_response.read.return_value = mock_arxiv_response().encode("utf-8") + mock_urlopen.return_value.__enter__.return_value = mock_response + + tool = ArxivPaperTool(download_pdfs=False) + result = tool._run("transformer", 1) + assert "Title: Sample Paper" in result + + +@patch("pathlib.Path.mkdir") +def test_validate_save_path_creates_directory(mock_mkdir): + path = ArxivPaperTool._validate_save_path("new_folder") + mock_mkdir.assert_called_once_with(parents=True, exist_ok=True) + assert isinstance(path, Path) + + +@patch("urllib.request.urlopen") +def test_run_handles_exception(mock_urlopen): + mock_urlopen.side_effect = Exception("API failure") + tool = ArxivPaperTool() + result = tool._run("transformer", 1) + assert "Failed to fetch or download Arxiv papers" in result + + +@patch("urllib.request.urlopen") +def test_invalid_xml_response(mock_urlopen, tool): + mock_response = MagicMock() + mock_response.read.return_value = b"" + mock_response.status = 200 + mock_urlopen.return_value.__enter__.return_value = mock_response + + with pytest.raises(ET.ParseError): + tool.fetch_arxiv_data("quantum", 1) + + +@patch.object(ArxivPaperTool, "fetch_arxiv_data") +def test_run_with_max_results(mock_fetch, tool): + mock_fetch.return_value = [ + { + "arxiv_id": f"test_{i}", + "title": f"Title {i}", + "summary": "Summary", + "authors": ["Author"], + "published_date": "2023-01-01", + "pdf_url": None, + } + for i in range(100) + ] + + result = tool._run(search_query="test", max_results=100) + assert result.count("Title:") == 100 diff --git a/lib/crewai-tools/tests/tools/brave_search_tool_test.py b/lib/crewai-tools/tests/tools/brave_search_tool_test.py new file mode 100644 index 000000000..c1c32d830 --- /dev/null +++ b/lib/crewai-tools/tests/tools/brave_search_tool_test.py @@ -0,0 +1,48 @@ +from unittest.mock import patch + +from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool +import pytest + + +@pytest.fixture +def brave_tool(): + return BraveSearchTool(n_results=2) + + +def test_brave_tool_initialization(): + tool = BraveSearchTool() + assert tool.n_results == 10 + assert tool.save_file is False + + +@patch("requests.get") +def test_brave_tool_search(mock_get, brave_tool): + mock_response = { + "web": { + "results": [ + { + "title": "Test Title", + "url": "http://test.com", + "description": "Test Description", + } + ] + } + } + mock_get.return_value.json.return_value = mock_response + + result = brave_tool.run(search_query="test") + assert "Test Title" in result + assert "http://test.com" in result + + +def test_brave_tool(): + tool = BraveSearchTool( + n_results=2, + ) + tool.run(search_query="ChatGPT") + + +if __name__ == "__main__": + test_brave_tool() + test_brave_tool_initialization() + # test_brave_tool_search(brave_tool) diff --git a/lib/crewai-tools/tests/tools/brightdata_serp_tool_test.py b/lib/crewai-tools/tests/tools/brightdata_serp_tool_test.py new file mode 100644 index 000000000..11ca018e8 --- /dev/null +++ b/lib/crewai-tools/tests/tools/brightdata_serp_tool_test.py @@ -0,0 +1,54 @@ +import unittest +from unittest.mock import MagicMock, patch + +from crewai_tools.tools.brightdata_tool.brightdata_serp import BrightDataSearchTool + + +class TestBrightDataSearchTool(unittest.TestCase): + @patch.dict( + "os.environ", + {"BRIGHT_DATA_API_KEY": "test_api_key", "BRIGHT_DATA_ZONE": "test_zone"}, + ) + def setUp(self): + self.tool = BrightDataSearchTool() + + @patch("requests.post") + def test_run_successful_search(self, mock_post): + # Sample mock JSON response + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.text = "mock response text" + mock_post.return_value = mock_response + + # Define search input + input_data = { + "query": "latest AI news", + "search_engine": "google", + "country": "us", + "language": "en", + "search_type": "nws", + "device_type": "desktop", + "parse_results": True, + "save_file": False, + } + + result = self.tool._run(**input_data) + + # Assertions + self.assertIsInstance(result, str) # Your tool returns response.text (string) + mock_post.assert_called_once() + + @patch("requests.post") + def test_run_with_request_exception(self, mock_post): + mock_post.side_effect = Exception("Timeout") + + result = self.tool._run(query="AI", search_engine="google") + self.assertIn("Error", result) + + def tearDown(self): + # Clean up env vars + pass + + +if __name__ == "__main__": + unittest.main() diff --git a/lib/crewai-tools/tests/tools/brightdata_webunlocker_tool_test.py b/lib/crewai-tools/tests/tools/brightdata_webunlocker_tool_test.py new file mode 100644 index 000000000..cba42904a --- /dev/null +++ b/lib/crewai-tools/tests/tools/brightdata_webunlocker_tool_test.py @@ -0,0 +1,61 @@ +from unittest.mock import Mock, patch + +from crewai_tools.tools.brightdata_tool.brightdata_unlocker import ( + BrightDataWebUnlockerTool, +) +import requests + + +@patch.dict( + "os.environ", + {"BRIGHT_DATA_API_KEY": "test_api_key", "BRIGHT_DATA_ZONE": "test_zone"}, +) +@patch("crewai_tools.tools.brightdata_tool.brightdata_unlocker.requests.post") +def test_run_success_html(mock_post): + mock_response = Mock() + mock_response.status_code = 200 + mock_response.text = "Test" + mock_response.raise_for_status = Mock() + mock_post.return_value = mock_response + + tool = BrightDataWebUnlockerTool() + tool._run(url="https://example.com", format="html", save_file=False) + + +@patch.dict( + "os.environ", + {"BRIGHT_DATA_API_KEY": "test_api_key", "BRIGHT_DATA_ZONE": "test_zone"}, +) +@patch("crewai_tools.tools.brightdata_tool.brightdata_unlocker.requests.post") +def test_run_success_json(mock_post): + mock_response = Mock() + mock_response.status_code = 200 + mock_response.text = "mock response text" + mock_response.raise_for_status = Mock() + mock_post.return_value = mock_response + + tool = BrightDataWebUnlockerTool() + result = tool._run(url="https://example.com", format="json") + + assert isinstance(result, str) + + +@patch.dict( + "os.environ", + {"BRIGHT_DATA_API_KEY": "test_api_key", "BRIGHT_DATA_ZONE": "test_zone"}, +) +@patch("crewai_tools.tools.brightdata_tool.brightdata_unlocker.requests.post") +def test_run_http_error(mock_post): + mock_response = Mock() + mock_response.status_code = 403 + mock_response.text = "Forbidden" + mock_response.raise_for_status.side_effect = requests.HTTPError( + response=mock_response + ) + mock_post.return_value = mock_response + + tool = BrightDataWebUnlockerTool() + result = tool._run(url="https://example.com") + + assert "HTTP Error" in result + assert "Forbidden" in result diff --git a/lib/crewai-tools/tests/tools/cassettes/test_search_tools/test_csv_search_tool.yaml b/lib/crewai-tools/tests/tools/cassettes/test_search_tools/test_csv_search_tool.yaml new file mode 100644 index 000000000..4247ba7bb --- /dev/null +++ b/lib/crewai-tools/tests/tools/cassettes/test_search_tools/test_csv_search_tool.yaml @@ -0,0 +1,251 @@ +interactions: +- request: + body: '{"input": ["name: test, description: This is a test CSV file"], "model": + "text-embedding-ada-002", "encoding_format": "base64"}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '127' + content-type: + - application/json + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.66.3 + x-stainless-arch: + - arm64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.66.3 + x-stainless-read-timeout: + - '600' + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.12.9 + method: POST + uri: https://api.openai.com/v1/embeddings + response: + content: "{\n \"object\": \"list\",\n \"data\": [\n {\n \"object\": + \"embedding\",\n \"index\": 0,\n \"embedding\": 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+ \ }\n ],\n \"model\": \"text-embedding-ada-002-v2\",\n \"usage\": {\n \"prompt_tokens\": + 2,\n \"total_tokens\": 2\n }\n}\n" + headers: + CF-RAY: + - 936f933fe9eb7e0a-GRU + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Sun, 27 Apr 2025 16:07:51 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + access-control-allow-origin: + - '*' + access-control-expose-headers: + - X-Request-ID + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-model: + - text-embedding-ada-002-v2 + openai-organization: + - crewai-iuxna1 + openai-processing-ms: + - '179' + openai-version: + - '2020-10-01' + strict-transport-security: + - max-age=31536000; includeSubDomains; preload + via: + - envoy-router-7bbfccd4b9-p6rt4 + x-envoy-upstream-service-time: + - '105' + x-ratelimit-limit-requests: + - '10000' + x-ratelimit-limit-tokens: + - '10000000' + x-ratelimit-remaining-requests: + - '9999' + x-ratelimit-remaining-tokens: + - '9999998' + x-ratelimit-reset-requests: + - 6ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_b1ab10d1ad4421252a7eb1b01ad92f5b + http_version: HTTP/1.1 + status_code: 200 +version: 1 diff --git a/lib/crewai-tools/tests/tools/couchbase_tool_test.py b/lib/crewai-tools/tests/tools/couchbase_tool_test.py new file mode 100644 index 000000000..851f17580 --- /dev/null +++ b/lib/crewai-tools/tests/tools/couchbase_tool_test.py @@ -0,0 +1,450 @@ +from unittest.mock import MagicMock, patch + +import pytest + + +# Mock the couchbase library before importing the tool +# This prevents ImportErrors if couchbase isn't installed in the test environment +mock_couchbase = MagicMock() +mock_couchbase.search = MagicMock() +mock_couchbase.cluster = MagicMock() +mock_couchbase.options = MagicMock() +mock_couchbase.vector_search = MagicMock() + +# Simulate the structure needed for checks +mock_couchbase.cluster.Cluster = MagicMock() +mock_couchbase.options.SearchOptions = MagicMock() +mock_couchbase.vector_search.VectorQuery = MagicMock() +mock_couchbase.vector_search.VectorSearch = MagicMock() +mock_couchbase.search.SearchRequest = MagicMock() # Mock the class itself +mock_couchbase.search.SearchRequest.create = MagicMock() # Mock the class method + + +# Add necessary exception types if needed for testing error handling +class MockCouchbaseException(Exception): + pass + + +mock_couchbase.exceptions = MagicMock() +mock_couchbase.exceptions.BucketNotFoundException = MockCouchbaseException +mock_couchbase.exceptions.ScopeNotFoundException = MockCouchbaseException +mock_couchbase.exceptions.CollectionNotFoundException = MockCouchbaseException +mock_couchbase.exceptions.IndexNotFoundException = MockCouchbaseException + + +import sys + + +sys.modules["couchbase"] = mock_couchbase +sys.modules["couchbase.search"] = mock_couchbase.search +sys.modules["couchbase.cluster"] = mock_couchbase.cluster +sys.modules["couchbase.options"] = mock_couchbase.options +sys.modules["couchbase.vector_search"] = mock_couchbase.vector_search +sys.modules["couchbase.exceptions"] = mock_couchbase.exceptions + +# Now import the tool +from crewai_tools.tools.couchbase_tool.couchbase_tool import ( + CouchbaseFTSVectorSearchTool, +) + + +# --- Test Fixtures --- +@pytest.fixture(autouse=True) +def reset_global_mocks(): + """Reset call counts for globally defined mocks before each test.""" + # Reset the specific mock causing the issue + mock_couchbase.vector_search.VectorQuery.reset_mock() + # It's good practice to also reset other related global mocks + # that might be called in your tests to prevent similar issues: + mock_couchbase.vector_search.VectorSearch.from_vector_query.reset_mock() + mock_couchbase.search.SearchRequest.create.reset_mock() + + +# Additional fixture to handle import pollution in full test suite +@pytest.fixture(autouse=True) +def ensure_couchbase_mocks(): + """Ensure that couchbase imports are properly mocked even when other tests have run first.""" + # This fixture ensures our mocks are in place regardless of import order + original_modules = {} + + # Store any existing modules + for module_name in [ + "couchbase", + "couchbase.search", + "couchbase.cluster", + "couchbase.options", + "couchbase.vector_search", + "couchbase.exceptions", + ]: + if module_name in sys.modules: + original_modules[module_name] = sys.modules[module_name] + + # Ensure our mocks are active + sys.modules["couchbase"] = mock_couchbase + sys.modules["couchbase.search"] = mock_couchbase.search + sys.modules["couchbase.cluster"] = mock_couchbase.cluster + sys.modules["couchbase.options"] = mock_couchbase.options + sys.modules["couchbase.vector_search"] = mock_couchbase.vector_search + sys.modules["couchbase.exceptions"] = mock_couchbase.exceptions + + yield + + # Restore original modules if they existed + for module_name, original_module in original_modules.items(): + if original_module is not None: + sys.modules[module_name] = original_module + + +@pytest.fixture +def mock_cluster(): + cluster = MagicMock() + bucket_manager = MagicMock() + search_index_manager = MagicMock() + bucket = MagicMock() + scope = MagicMock() + collection = MagicMock() + scope_search_index_manager = MagicMock() + + # Setup mock return values for checks + cluster.buckets.return_value = bucket_manager + cluster.search_indexes.return_value = search_index_manager + cluster.bucket.return_value = bucket + bucket.scope.return_value = scope + scope.collection.return_value = collection + scope.search_indexes.return_value = scope_search_index_manager + + # Mock bucket existence check + bucket_manager.get_bucket.return_value = True + + # Mock scope/collection existence check + mock_scope_spec = MagicMock() + mock_scope_spec.name = "test_scope" + mock_collection_spec = MagicMock() + mock_collection_spec.name = "test_collection" + mock_scope_spec.collections = [mock_collection_spec] + bucket.collections.return_value.get_all_scopes.return_value = [mock_scope_spec] + + # Mock index existence check + mock_index_def = MagicMock() + mock_index_def.name = "test_index" + scope_search_index_manager.get_all_indexes.return_value = [mock_index_def] + search_index_manager.get_all_indexes.return_value = [mock_index_def] + + return cluster + + +@pytest.fixture +def mock_embedding_function(): + # Simple mock embedding function + # return lambda query: [0.1] * 10 # Example embedding vector + return MagicMock(return_value=[0.1] * 10) + + +@pytest.fixture +def tool_config(mock_cluster, mock_embedding_function): + return { + "cluster": mock_cluster, + "bucket_name": "test_bucket", + "scope_name": "test_scope", + "collection_name": "test_collection", + "index_name": "test_index", + "embedding_function": mock_embedding_function, + "limit": 5, + "embedding_key": "test_embedding", + "scoped_index": True, + } + + +@pytest.fixture +def couchbase_tool(tool_config): + # Patch COUCHBASE_AVAILABLE to True for these tests + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", True + ): + tool = CouchbaseFTSVectorSearchTool(**tool_config) + return tool + + +@pytest.fixture +def mock_search_iter(): + mock_iter = MagicMock() + # Simulate search results with a 'fields' attribute + mock_row1 = MagicMock() + mock_row1.fields = {"id": "doc1", "text": "content 1", "test_embedding": [0.1] * 10} + mock_row2 = MagicMock() + mock_row2.fields = {"id": "doc2", "text": "content 2", "test_embedding": [0.2] * 10} + mock_iter.rows.return_value = [mock_row1, mock_row2] + return mock_iter + + +# --- Test Cases --- + + +def test_initialization_success(couchbase_tool, tool_config): + """Test successful initialization with valid config.""" + assert couchbase_tool.cluster == tool_config["cluster"] + assert couchbase_tool.bucket_name == "test_bucket" + assert couchbase_tool.scope_name == "test_scope" + assert couchbase_tool.collection_name == "test_collection" + assert couchbase_tool.index_name == "test_index" + assert couchbase_tool.embedding_function is not None + assert couchbase_tool.limit == 5 + assert couchbase_tool.embedding_key == "test_embedding" + assert couchbase_tool.scoped_index + + # Check if helper methods were called during init (via mocks in fixture) + couchbase_tool.cluster.buckets().get_bucket.assert_called_once_with("test_bucket") + couchbase_tool.cluster.bucket().collections().get_all_scopes.assert_called_once() + couchbase_tool.cluster.bucket().scope().search_indexes().get_all_indexes.assert_called_once() + + +def test_initialization_missing_required_args(mock_cluster, mock_embedding_function): + """Test initialization fails when required arguments are missing.""" + base_config = { + "cluster": mock_cluster, + "bucket_name": "b", + "scope_name": "s", + "collection_name": "c", + "index_name": "i", + "embedding_function": mock_embedding_function, + } + required_keys = base_config.keys() + + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", True + ): + for key in required_keys: + incomplete_config = base_config.copy() + del incomplete_config[key] + with pytest.raises(ValueError): + CouchbaseFTSVectorSearchTool(**incomplete_config) + + +def test_initialization_couchbase_unavailable(): + """Test behavior when couchbase library is not available.""" + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", False + ): + with patch("click.confirm", return_value=False) as mock_confirm: + with pytest.raises( + ImportError, match="The 'couchbase' package is required" + ): + CouchbaseFTSVectorSearchTool( + cluster=MagicMock(), + bucket_name="b", + scope_name="s", + collection_name="c", + index_name="i", + embedding_function=MagicMock(), + ) + mock_confirm.assert_called_once() # Ensure user was prompted + + +def test_run_success_scoped_index( + couchbase_tool, mock_search_iter, tool_config, mock_embedding_function +): + """Test successful _run execution with a scoped index.""" + query = "find relevant documents" + # expected_embedding = mock_embedding_function(query) + + # Mock the scope search method + couchbase_tool._scope.search = MagicMock(return_value=mock_search_iter) + # Mock the VectorQuery/VectorSearch/SearchRequest creation using runtime patching + with ( + patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.VectorQuery" + ) as mock_vq, + patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.VectorSearch" + ) as mock_vs, + patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.search.SearchRequest" + ) as mock_sr, + patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.SearchOptions" + ) as mock_so, + ): + # Set up the mock objects and their return values + mock_vector_query = MagicMock() + mock_vector_search = MagicMock() + mock_search_req = MagicMock() + mock_search_options = MagicMock() + + mock_vq.return_value = mock_vector_query + mock_vs.from_vector_query.return_value = mock_vector_search + mock_sr.create.return_value = mock_search_req + mock_so.return_value = mock_search_options + + result = couchbase_tool._run(query=query) + + # Check embedding function call + tool_config["embedding_function"].assert_called_once_with(query) + + # Check VectorQuery call + mock_vq.assert_called_once_with( + tool_config["embedding_key"], + mock_embedding_function.return_value, + tool_config["limit"], + ) + # Check VectorSearch call + mock_vs.from_vector_query.assert_called_once_with(mock_vector_query) + # Check SearchRequest creation + mock_sr.create.assert_called_once_with(mock_vector_search) + # Check SearchOptions creation + mock_so.assert_called_once_with(limit=tool_config["limit"], fields=["*"]) + + # Check that scope search was called correctly + couchbase_tool._scope.search.assert_called_once_with( + tool_config["index_name"], mock_search_req, mock_search_options + ) + + # Check cluster search was NOT called + couchbase_tool.cluster.search.assert_not_called() + + # Check result format (simple check for JSON structure) + assert '"id": "doc1"' in result + assert '"id": "doc2"' in result + assert result.startswith("[") # Should be valid JSON after concatenation + + +def test_run_success_global_index( + tool_config, mock_search_iter, mock_embedding_function +): + """Test successful _run execution with a global (non-scoped) index.""" + tool_config["scoped_index"] = False + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", True + ): + couchbase_tool = CouchbaseFTSVectorSearchTool(**tool_config) + + query = "find global documents" + # expected_embedding = mock_embedding_function(query) + + # Mock the cluster search method + couchbase_tool.cluster.search = MagicMock(return_value=mock_search_iter) + # Mock the VectorQuery/VectorSearch/SearchRequest creation using runtime patching + with ( + patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.VectorQuery" + ) as mock_vq, + patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.VectorSearch" + ) as mock_vs, + patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.search.SearchRequest" + ) as mock_sr, + patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.SearchOptions" + ) as mock_so, + ): + # Set up the mock objects and their return values + mock_vector_query = MagicMock() + mock_vector_search = MagicMock() + mock_search_req = MagicMock() + mock_search_options = MagicMock() + + mock_vq.return_value = mock_vector_query + mock_vs.from_vector_query.return_value = mock_vector_search + mock_sr.create.return_value = mock_search_req + mock_so.return_value = mock_search_options + + result = couchbase_tool._run(query=query) + + # Check embedding function call + tool_config["embedding_function"].assert_called_once_with(query) + + # Check VectorQuery/Search call + mock_vq.assert_called_once_with( + tool_config["embedding_key"], + mock_embedding_function.return_value, + tool_config["limit"], + ) + mock_sr.create.assert_called_once_with(mock_vector_search) + # Check SearchOptions creation + mock_so.assert_called_once_with(limit=tool_config["limit"], fields=["*"]) + + # Check that cluster search was called correctly + couchbase_tool.cluster.search.assert_called_once_with( + tool_config["index_name"], mock_search_req, mock_search_options + ) + + # Check scope search was NOT called + couchbase_tool._scope.search.assert_not_called() + + # Check result format + assert '"id": "doc1"' in result + assert '"id": "doc2"' in result + + +def test_check_bucket_exists_fail(tool_config): + """Test check for bucket non-existence.""" + mock_cluster = tool_config["cluster"] + mock_cluster.buckets().get_bucket.side_effect = ( + mock_couchbase.exceptions.BucketNotFoundException("Bucket not found") + ) + + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", True + ): + with pytest.raises(ValueError, match="Bucket test_bucket does not exist."): + CouchbaseFTSVectorSearchTool(**tool_config) + + +def test_check_scope_exists_fail(tool_config): + """Test check for scope non-existence.""" + mock_cluster = tool_config["cluster"] + # Simulate scope not being in the list returned + mock_scope_spec = MagicMock() + mock_scope_spec.name = "wrong_scope" + mock_cluster.bucket().collections().get_all_scopes.return_value = [mock_scope_spec] + + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", True + ): + with pytest.raises(ValueError, match="Scope test_scope not found"): + CouchbaseFTSVectorSearchTool(**tool_config) + + +def test_check_collection_exists_fail(tool_config): + """Test check for collection non-existence.""" + mock_cluster = tool_config["cluster"] + # Simulate collection not being in the scope's list + mock_scope_spec = MagicMock() + mock_scope_spec.name = "test_scope" + mock_collection_spec = MagicMock() + mock_collection_spec.name = "wrong_collection" + mock_scope_spec.collections = [mock_collection_spec] # Only has wrong collection + mock_cluster.bucket().collections().get_all_scopes.return_value = [mock_scope_spec] + + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", True + ): + with pytest.raises(ValueError, match="Collection test_collection not found"): + CouchbaseFTSVectorSearchTool(**tool_config) + + +def test_check_index_exists_fail_scoped(tool_config): + """Test check for scoped index non-existence.""" + mock_cluster = tool_config["cluster"] + # Simulate index not being in the list returned by scope manager + mock_cluster.bucket().scope().search_indexes().get_all_indexes.return_value = [] + + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", True + ): + with pytest.raises(ValueError, match="Index test_index does not exist"): + CouchbaseFTSVectorSearchTool(**tool_config) + + +def test_check_index_exists_fail_global(tool_config): + """Test check for global index non-existence.""" + tool_config["scoped_index"] = False + mock_cluster = tool_config["cluster"] + # Simulate index not being in the list returned by cluster manager + mock_cluster.search_indexes().get_all_indexes.return_value = [] + + with patch( + "crewai_tools.tools.couchbase_tool.couchbase_tool.COUCHBASE_AVAILABLE", True + ): + with pytest.raises(ValueError, match="Index test_index does not exist"): + CouchbaseFTSVectorSearchTool(**tool_config) diff --git a/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_action_tool.py b/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_action_tool.py new file mode 100644 index 000000000..6f1df9e8a --- /dev/null +++ b/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_action_tool.py @@ -0,0 +1,251 @@ +from typing import Union, get_args, get_origin + +from crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool import ( + CrewAIPlatformActionTool, +) + + +class TestSchemaProcessing: + + def setup_method(self): + self.base_action_schema = { + "function": { + "parameters": { + "properties": {}, + "required": [] + } + } + } + + def create_test_tool(self, action_name="test_action"): + return CrewAIPlatformActionTool( + description="Test tool", + action_name=action_name, + action_schema=self.base_action_schema + ) + + def test_anyof_multiple_types(self): + tool = self.create_test_tool() + + test_schema = { + "anyOf": [ + {"type": "string"}, + {"type": "number"}, + {"type": "integer"} + ] + } + + result_type = tool._process_schema_type(test_schema, "TestField") + + assert get_origin(result_type) is Union + + args = get_args(result_type) + expected_types = (str, float, int) + + for expected_type in expected_types: + assert expected_type in args + + def test_anyof_with_null(self): + tool = self.create_test_tool() + + test_schema = { + "anyOf": [ + {"type": "string"}, + {"type": "number"}, + {"type": "null"} + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldNullable") + + assert get_origin(result_type) is Union + + args = get_args(result_type) + assert type(None) in args + assert str in args + assert float in args + + def test_anyof_single_type(self): + tool = self.create_test_tool() + + test_schema = { + "anyOf": [ + {"type": "string"} + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldSingle") + + assert result_type is str + + def test_oneof_multiple_types(self): + tool = self.create_test_tool() + + test_schema = { + "oneOf": [ + {"type": "string"}, + {"type": "boolean"} + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldOneOf") + + assert get_origin(result_type) is Union + + args = get_args(result_type) + expected_types = (str, bool) + + for expected_type in expected_types: + assert expected_type in args + + def test_oneof_single_type(self): + tool = self.create_test_tool() + + test_schema = { + "oneOf": [ + {"type": "integer"} + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldOneOfSingle") + + assert result_type is int + + def test_basic_types(self): + tool = self.create_test_tool() + + test_cases = [ + ({"type": "string"}, str), + ({"type": "integer"}, int), + ({"type": "number"}, float), + ({"type": "boolean"}, bool), + ({"type": "array", "items": {"type": "string"}}, list), + ] + + for schema, expected_type in test_cases: + result_type = tool._process_schema_type(schema, "TestField") + if schema["type"] == "array": + assert get_origin(result_type) is list + else: + assert result_type is expected_type + + def test_enum_handling(self): + tool = self.create_test_tool() + + test_schema = { + "type": "string", + "enum": ["option1", "option2", "option3"] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldEnum") + + assert result_type is str + + def test_nested_anyof(self): + tool = self.create_test_tool() + + test_schema = { + "anyOf": [ + {"type": "string"}, + { + "anyOf": [ + {"type": "integer"}, + {"type": "boolean"} + ] + } + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldNested") + + assert get_origin(result_type) is Union + args = get_args(result_type) + + assert str in args + + if len(args) == 3: + assert int in args + assert bool in args + else: + nested_union = next(arg for arg in args if get_origin(arg) is Union) + nested_args = get_args(nested_union) + assert int in nested_args + assert bool in nested_args + + def test_allof_same_types(self): + tool = self.create_test_tool() + + test_schema = { + "allOf": [ + {"type": "string"}, + {"type": "string", "maxLength": 100} + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldAllOfSame") + + assert result_type is str + + def test_allof_object_merge(self): + tool = self.create_test_tool() + + test_schema = { + "allOf": [ + { + "type": "object", + "properties": { + "name": {"type": "string"}, + "age": {"type": "integer"} + }, + "required": ["name"] + }, + { + "type": "object", + "properties": { + "email": {"type": "string"}, + "age": {"type": "integer"} + }, + "required": ["email"] + } + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldAllOfMerged") + + # Should create a merged model with all properties + # The implementation might fall back to dict if model creation fails + # Let's just verify it's not a basic scalar type + assert result_type is not str + assert result_type is not int + assert result_type is not bool + # It could be dict (fallback) or a proper model class + assert result_type in (dict, type) or hasattr(result_type, '__name__') + + def test_allof_single_schema(self): + """Test that allOf with single schema works correctly.""" + tool = self.create_test_tool() + + test_schema = { + "allOf": [ + {"type": "boolean"} + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldAllOfSingle") + + # Should be just bool + assert result_type is bool + + def test_allof_mixed_types(self): + tool = self.create_test_tool() + + test_schema = { + "allOf": [ + {"type": "string"}, + {"type": "integer"} + ] + } + + result_type = tool._process_schema_type(test_schema, "TestFieldAllOfMixed") + + assert result_type is str diff --git a/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_tool_builder.py b/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_tool_builder.py new file mode 100644 index 000000000..7e6453fd4 --- /dev/null +++ b/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_tool_builder.py @@ -0,0 +1,260 @@ +import unittest +from unittest.mock import Mock, patch + +from crewai_tools.tools.crewai_platform_tools import ( + CrewAIPlatformActionTool, + CrewaiPlatformToolBuilder, +) +import pytest + + +class TestCrewaiPlatformToolBuilder(unittest.TestCase): + @pytest.fixture + def platform_tool_builder(self): + """Create a CrewaiPlatformToolBuilder instance for testing""" + return CrewaiPlatformToolBuilder(apps=["github", "slack"]) + + @pytest.fixture + def mock_api_response(self): + return { + "actions": { + "github": [ + { + "name": "create_issue", + "description": "Create a GitHub issue", + "parameters": { + "type": "object", + "properties": { + "title": { + "type": "string", + "description": "Issue title", + }, + "body": {"type": "string", "description": "Issue body"}, + }, + "required": ["title"], + }, + } + ], + "slack": [ + { + "name": "send_message", + "description": "Send a Slack message", + "parameters": { + "type": "object", + "properties": { + "channel": { + "type": "string", + "description": "Channel name", + }, + "text": { + "type": "string", + "description": "Message text", + }, + }, + "required": ["channel", "text"], + }, + } + ], + } + } + + @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}) + @patch( + "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get" + ) + def test_fetch_actions_success(self, mock_get): + mock_api_response = { + "actions": { + "github": [ + { + "name": "create_issue", + "description": "Create a GitHub issue", + "parameters": { + "type": "object", + "properties": { + "title": { + "type": "string", + "description": "Issue title", + } + }, + "required": ["title"], + }, + } + ] + } + } + + builder = CrewaiPlatformToolBuilder(apps=["github", "slack/send_message"]) + + mock_response = Mock() + mock_response.raise_for_status.return_value = None + mock_response.json.return_value = mock_api_response + mock_get.return_value = mock_response + + builder._fetch_actions() + + mock_get.assert_called_once() + args, kwargs = mock_get.call_args + + assert "/actions" in args[0] + assert kwargs["headers"]["Authorization"] == "Bearer test_token" + assert kwargs["params"]["apps"] == "github,slack/send_message" + + assert "create_issue" in builder._actions_schema + assert ( + builder._actions_schema["create_issue"]["function"]["name"] + == "create_issue" + ) + + def test_fetch_actions_no_token(self): + builder = CrewaiPlatformToolBuilder(apps=["github"]) + + with patch.dict("os.environ", {}, clear=True): + with self.assertRaises(ValueError) as context: + builder._fetch_actions() + assert "No platform integration token found" in str(context.exception) + + @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}) + @patch( + "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get" + ) + def test_create_tools(self, mock_get): + mock_api_response = { + "actions": { + "github": [ + { + "name": "create_issue", + "description": "Create a GitHub issue", + "parameters": { + "type": "object", + "properties": { + "title": { + "type": "string", + "description": "Issue title", + } + }, + "required": ["title"], + }, + } + ], + "slack": [ + { + "name": "send_message", + "description": "Send a Slack message", + "parameters": { + "type": "object", + "properties": { + "channel": { + "type": "string", + "description": "Channel name", + } + }, + "required": ["channel"], + }, + } + ], + } + } + + builder = CrewaiPlatformToolBuilder(apps=["github", "slack"]) + + mock_response = Mock() + mock_response.raise_for_status.return_value = None + mock_response.json.return_value = mock_api_response + mock_get.return_value = mock_response + + tools = builder.tools() + + assert len(tools) == 2 + assert all(isinstance(tool, CrewAIPlatformActionTool) for tool in tools) + + tool_names = [tool.action_name for tool in tools] + assert "create_issue" in tool_names + assert "send_message" in tool_names + + github_tool = next((t for t in tools if t.action_name == "create_issue"), None) + slack_tool = next((t for t in tools if t.action_name == "send_message"), None) + + assert github_tool is not None + assert slack_tool is not None + assert "Create a GitHub issue" in github_tool.description + assert "Send a Slack message" in slack_tool.description + + def test_tools_caching(self): + builder = CrewaiPlatformToolBuilder(apps=["github"]) + + cached_tools = [] + + def mock_create_tools(): + builder._tools = cached_tools + + with ( + patch.object(builder, "_fetch_actions") as mock_fetch, + patch.object( + builder, "_create_tools", side_effect=mock_create_tools + ) as mock_create, + ): + tools1 = builder.tools() + assert mock_fetch.call_count == 1 + assert mock_create.call_count == 1 + + tools2 = builder.tools() + assert mock_fetch.call_count == 1 + assert mock_create.call_count == 1 + + assert tools1 is tools2 + + @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}) + def test_empty_apps_list(self): + builder = CrewaiPlatformToolBuilder(apps=[]) + + with patch( + "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get" + ) as mock_get: + mock_response = Mock() + mock_response.raise_for_status.return_value = None + mock_response.json.return_value = {"actions": {}} + mock_get.return_value = mock_response + + tools = builder.tools() + + assert isinstance(tools, list) + assert len(tools) == 0 + + _, kwargs = mock_get.call_args + assert kwargs["params"]["apps"] == "" + + def test_detailed_description_generation(self): + builder = CrewaiPlatformToolBuilder(apps=["test"]) + + complex_schema = { + "type": "object", + "properties": { + "simple_string": {"type": "string", "description": "A simple string"}, + "nested_object": { + "type": "object", + "properties": { + "inner_prop": { + "type": "integer", + "description": "Inner property", + } + }, + "description": "Nested object", + }, + "array_prop": { + "type": "array", + "items": {"type": "string"}, + "description": "Array of strings", + }, + }, + } + + descriptions = builder._generate_detailed_description(complex_schema) + + assert isinstance(descriptions, list) + assert len(descriptions) > 0 + + description_text = "\n".join(descriptions) + assert "simple_string" in description_text + assert "nested_object" in description_text + assert "array_prop" in description_text diff --git a/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_tools.py b/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_tools.py new file mode 100644 index 000000000..b69b073ed --- /dev/null +++ b/lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_tools.py @@ -0,0 +1,115 @@ +import unittest +from unittest.mock import Mock, patch + +from crewai_tools.tools.crewai_platform_tools import CrewaiPlatformTools + + +class TestCrewaiPlatformTools(unittest.TestCase): + @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}) + @patch( + "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get" + ) + def test_crewai_platform_tools_basic(self, mock_get): + mock_response = Mock() + mock_response.raise_for_status.return_value = None + mock_response.json.return_value = {"actions": {"github": []}} + mock_get.return_value = mock_response + + tools = CrewaiPlatformTools(apps=["github"]) + assert tools is not None + assert isinstance(tools, list) + + @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}) + @patch( + "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get" + ) + def test_crewai_platform_tools_multiple_apps(self, mock_get): + mock_response = Mock() + mock_response.raise_for_status.return_value = None + mock_response.json.return_value = { + "actions": { + "github": [ + { + "name": "create_issue", + "description": "Create a GitHub issue", + "parameters": { + "type": "object", + "properties": { + "title": { + "type": "string", + "description": "Issue title", + }, + "body": {"type": "string", "description": "Issue body"}, + }, + "required": ["title"], + }, + } + ], + "slack": [ + { + "name": "send_message", + "description": "Send a Slack message", + "parameters": { + "type": "object", + "properties": { + "channel": { + "type": "string", + "description": "Channel to send to", + }, + "text": { + "type": "string", + "description": "Message text", + }, + }, + "required": ["channel", "text"], + }, + } + ], + } + } + mock_get.return_value = mock_response + + tools = CrewaiPlatformTools(apps=["github", "slack"]) + assert tools is not None + assert isinstance(tools, list) + assert len(tools) == 2 + + mock_get.assert_called_once() + args, kwargs = mock_get.call_args + assert ( + "apps=github,slack" in args[0] + or kwargs.get("params", {}).get("apps") == "github,slack" + ) + + @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}) + def test_crewai_platform_tools_empty_apps(self): + with patch( + "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get" + ) as mock_get: + mock_response = Mock() + mock_response.raise_for_status.return_value = None + mock_response.json.return_value = {"actions": {}} + mock_get.return_value = mock_response + + tools = CrewaiPlatformTools(apps=[]) + assert tools is not None + assert isinstance(tools, list) + assert len(tools) == 0 + + @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}) + @patch( + "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get" + ) + def test_crewai_platform_tools_api_error_handling(self, mock_get): + mock_get.side_effect = Exception("API Error") + + tools = CrewaiPlatformTools(apps=["github"]) + assert tools is not None + assert isinstance(tools, list) + assert len(tools) == 0 + + def test_crewai_platform_tools_no_token(self): + with patch.dict("os.environ", {}, clear=True): + with self.assertRaises(ValueError) as context: + CrewaiPlatformTools(apps=["github"]) + assert "No platform integration token found" in str(context.exception) diff --git a/lib/crewai-tools/tests/tools/exa_search_tool_test.py b/lib/crewai-tools/tests/tools/exa_search_tool_test.py new file mode 100644 index 000000000..0a4060503 --- /dev/null +++ b/lib/crewai-tools/tests/tools/exa_search_tool_test.py @@ -0,0 +1,86 @@ +import os +from unittest.mock import patch + +from crewai_tools import EXASearchTool +import pytest + + +@pytest.fixture +def exa_search_tool(): + return EXASearchTool(api_key="test_api_key") + + +@pytest.fixture(autouse=True) +def mock_exa_api_key(): + with patch.dict(os.environ, {"EXA_API_KEY": "test_key_from_env"}): + yield + + +def test_exa_search_tool_initialization(): + with patch.dict(os.environ, {}, clear=True): + with patch( + "crewai_tools.tools.exa_tools.exa_search_tool.Exa" + ) as mock_exa_class: + api_key = "test_api_key" + tool = EXASearchTool(api_key=api_key) + + assert tool.api_key == api_key + assert tool.content is False + assert tool.summary is False + assert tool.type == "auto" + mock_exa_class.assert_called_once_with(api_key=api_key) + + +def test_exa_search_tool_initialization_with_env(mock_exa_api_key): + with patch.dict(os.environ, {"EXA_API_KEY": "test_key_from_env"}, clear=True): + with patch( + "crewai_tools.tools.exa_tools.exa_search_tool.Exa" + ) as mock_exa_class: + EXASearchTool() + mock_exa_class.assert_called_once_with(api_key="test_key_from_env") + + +def test_exa_search_tool_initialization_with_base_url(): + with patch.dict(os.environ, {}, clear=True): + with patch( + "crewai_tools.tools.exa_tools.exa_search_tool.Exa" + ) as mock_exa_class: + api_key = "test_api_key" + base_url = "https://custom.exa.api.com" + tool = EXASearchTool(api_key=api_key, base_url=base_url) + + assert tool.api_key == api_key + assert tool.base_url == base_url + assert tool.content is False + assert tool.summary is False + assert tool.type == "auto" + mock_exa_class.assert_called_once_with(api_key=api_key, base_url=base_url) + + +@pytest.fixture +def mock_exa_base_url(): + with patch.dict(os.environ, {"EXA_BASE_URL": "https://env.exa.api.com"}): + yield + + +def test_exa_search_tool_initialization_with_env_base_url( + mock_exa_api_key, mock_exa_base_url +): + with patch("crewai_tools.tools.exa_tools.exa_search_tool.Exa") as mock_exa_class: + EXASearchTool() + mock_exa_class.assert_called_once_with( + api_key="test_key_from_env", base_url="https://env.exa.api.com" + ) + + +def test_exa_search_tool_initialization_without_base_url(): + with patch.dict(os.environ, {}, clear=True): + with patch( + "crewai_tools.tools.exa_tools.exa_search_tool.Exa" + ) as mock_exa_class: + api_key = "test_api_key" + tool = EXASearchTool(api_key=api_key) + + assert tool.api_key == api_key + assert tool.base_url is None + mock_exa_class.assert_called_once_with(api_key=api_key) diff --git a/lib/crewai-tools/tests/tools/files_compressor_tool_test.py b/lib/crewai-tools/tests/tools/files_compressor_tool_test.py new file mode 100644 index 000000000..4fb38a13a --- /dev/null +++ b/lib/crewai-tools/tests/tools/files_compressor_tool_test.py @@ -0,0 +1,131 @@ +from unittest.mock import patch + +from crewai_tools.tools.files_compressor_tool import FileCompressorTool +import pytest + + +@pytest.fixture +def tool(): + return FileCompressorTool() + + +@patch("os.path.exists", return_value=False) +def test_input_path_does_not_exist(mock_exists, tool): + result = tool._run("nonexistent_path") + assert "does not exist" in result + + +@patch("os.path.exists", return_value=True) +@patch("os.getcwd", return_value="/mocked/cwd") +@patch.object(FileCompressorTool, "_compress_zip") # Mock actual compression +@patch.object(FileCompressorTool, "_prepare_output", return_value=True) +def test_generate_output_path_default( + mock_prepare, mock_compress, mock_cwd, mock_exists, tool +): + result = tool._run(input_path="mydir", format="zip") + assert "Successfully compressed" in result + mock_compress.assert_called_once() + + +@patch("os.path.exists", return_value=True) +@patch.object(FileCompressorTool, "_compress_zip") +@patch.object(FileCompressorTool, "_prepare_output", return_value=True) +def test_zip_compression(mock_prepare, mock_compress, mock_exists, tool): + result = tool._run( + input_path="some/path", output_path="archive.zip", format="zip", overwrite=True + ) + assert "Successfully compressed" in result + mock_compress.assert_called_once() + + +@patch("os.path.exists", return_value=True) +@patch.object(FileCompressorTool, "_compress_tar") +@patch.object(FileCompressorTool, "_prepare_output", return_value=True) +def test_tar_gz_compression(mock_prepare, mock_compress, mock_exists, tool): + result = tool._run( + input_path="some/path", + output_path="archive.tar.gz", + format="tar.gz", + overwrite=True, + ) + assert "Successfully compressed" in result + mock_compress.assert_called_once() + + +@pytest.mark.parametrize("format", ["tar", "tar.bz2", "tar.xz"]) +@patch("os.path.exists", return_value=True) +@patch.object(FileCompressorTool, "_compress_tar") +@patch.object(FileCompressorTool, "_prepare_output", return_value=True) +def test_other_tar_formats(mock_prepare, mock_compress, mock_exists, format, tool): + result = tool._run( + input_path="path/to/input", + output_path=f"archive.{format}", + format=format, + overwrite=True, + ) + assert "Successfully compressed" in result + mock_compress.assert_called_once() + + +@pytest.mark.parametrize("format", ["rar", "7z"]) +@patch("os.path.exists", return_value=True) # Ensure input_path exists +def test_unsupported_format(_, tool, format): + result = tool._run( + input_path="some/path", output_path=f"archive.{format}", format=format + ) + assert "not supported" in result + + +@patch("os.path.exists", return_value=True) +def test_extension_mismatch(_, tool): + result = tool._run( + input_path="some/path", output_path="archive.zip", format="tar.gz" + ) + assert "must have a '.tar.gz' extension" in result + + +@patch("os.path.exists", return_value=True) +@patch("os.path.isfile", return_value=True) +@patch("os.path.exists", return_value=True) +def test_existing_output_no_overwrite(_, __, ___, tool): + result = tool._run( + input_path="some/path", output_path="archive.zip", format="zip", overwrite=False + ) + assert "overwrite is set to False" in result + + +@patch("os.path.exists", return_value=True) +@patch("zipfile.ZipFile", side_effect=PermissionError) +def test_permission_error(mock_zip, _, tool): + result = tool._run( + input_path="file.txt", output_path="file.zip", format="zip", overwrite=True + ) + assert "Permission denied" in result + + +@patch("os.path.exists", return_value=True) +@patch("zipfile.ZipFile", side_effect=FileNotFoundError) +def test_file_not_found_during_zip(mock_zip, _, tool): + result = tool._run( + input_path="file.txt", output_path="file.zip", format="zip", overwrite=True + ) + assert "File not found" in result + + +@patch("os.path.exists", return_value=True) +@patch("zipfile.ZipFile", side_effect=Exception("Unexpected")) +def test_general_exception_during_zip(mock_zip, _, tool): + result = tool._run( + input_path="file.txt", output_path="file.zip", format="zip", overwrite=True + ) + assert "unexpected error" in result + + +# Test: Output directory is created when missing +@patch("os.makedirs") +@patch("os.path.exists", return_value=False) +def test_prepare_output_makes_dir(mock_exists, mock_makedirs): + tool = FileCompressorTool() + result = tool._prepare_output("some/missing/path/file.zip", overwrite=True) + assert result is True + mock_makedirs.assert_called_once() diff --git a/lib/crewai-tools/tests/tools/generate_crewai_automation_tool_test.py b/lib/crewai-tools/tests/tools/generate_crewai_automation_tool_test.py new file mode 100644 index 000000000..c32f94eae --- /dev/null +++ b/lib/crewai-tools/tests/tools/generate_crewai_automation_tool_test.py @@ -0,0 +1,186 @@ +import os +from unittest.mock import MagicMock, patch + +from crewai_tools.tools.generate_crewai_automation_tool.generate_crewai_automation_tool import ( + GenerateCrewaiAutomationTool, + GenerateCrewaiAutomationToolSchema, +) +import pytest +import requests + + +@pytest.fixture(autouse=True) +def mock_env(): + with patch.dict(os.environ, {"CREWAI_PERSONAL_ACCESS_TOKEN": "test_token"}): + os.environ.pop("CREWAI_PLUS_URL", None) + yield + + +@pytest.fixture +def tool(): + return GenerateCrewaiAutomationTool() + + +@pytest.fixture +def custom_url_tool(): + with patch.dict(os.environ, {"CREWAI_PLUS_URL": "https://custom.crewai.com"}): + return GenerateCrewaiAutomationTool() + + +def test_default_initialization(tool): + assert tool.crewai_enterprise_url == "https://app.crewai.com" + assert tool.personal_access_token == "test_token" + assert tool.name == "Generate CrewAI Automation" + + +def test_custom_base_url_from_environment(custom_url_tool): + assert custom_url_tool.crewai_enterprise_url == "https://custom.crewai.com" + + +def test_personal_access_token_from_environment(tool): + assert tool.personal_access_token == "test_token" + + +def test_valid_prompt_only(): + schema = GenerateCrewaiAutomationToolSchema( + prompt="Create a web scraping automation" + ) + assert schema.prompt == "Create a web scraping automation" + assert schema.organization_id is None + + +def test_valid_prompt_with_organization_id(): + schema = GenerateCrewaiAutomationToolSchema( + prompt="Create automation", organization_id="org-123" + ) + assert schema.prompt == "Create automation" + assert schema.organization_id == "org-123" + + +def test_empty_prompt_validation(): + schema = GenerateCrewaiAutomationToolSchema(prompt="") + assert schema.prompt == "" + assert schema.organization_id is None + + +@patch("requests.post") +def test_successful_generation_without_org_id(mock_post, tool): + mock_response = MagicMock() + mock_response.json.return_value = { + "url": "https://app.crewai.com/studio/project-123" + } + mock_post.return_value = mock_response + + result = tool.run(prompt="Create automation") + + assert ( + result + == "Generated CrewAI Studio project URL: https://app.crewai.com/studio/project-123" + ) + mock_post.assert_called_once_with( + "https://app.crewai.com/crewai_plus/api/v1/studio", + headers={ + "Authorization": "Bearer test_token", + "Content-Type": "application/json", + "Accept": "application/json", + }, + json={"prompt": "Create automation"}, + ) + + +@patch("requests.post") +def test_successful_generation_with_org_id(mock_post, tool): + mock_response = MagicMock() + mock_response.json.return_value = { + "url": "https://app.crewai.com/studio/project-456" + } + mock_post.return_value = mock_response + + result = tool.run(prompt="Create automation", organization_id="org-456") + + assert ( + result + == "Generated CrewAI Studio project URL: https://app.crewai.com/studio/project-456" + ) + mock_post.assert_called_once_with( + "https://app.crewai.com/crewai_plus/api/v1/studio", + headers={ + "Authorization": "Bearer test_token", + "Content-Type": "application/json", + "Accept": "application/json", + "X-Crewai-Organization-Id": "org-456", + }, + json={"prompt": "Create automation"}, + ) + + +@patch("requests.post") +def test_custom_base_url_usage(mock_post, custom_url_tool): + mock_response = MagicMock() + mock_response.json.return_value = { + "url": "https://custom.crewai.com/studio/project-789" + } + mock_post.return_value = mock_response + + custom_url_tool.run(prompt="Create automation") + + mock_post.assert_called_once_with( + "https://custom.crewai.com/crewai_plus/api/v1/studio", + headers={ + "Authorization": "Bearer test_token", + "Content-Type": "application/json", + "Accept": "application/json", + }, + json={"prompt": "Create automation"}, + ) + + +@patch("requests.post") +def test_api_error_response_handling(mock_post, tool): + mock_post.return_value.raise_for_status.side_effect = requests.HTTPError( + "400 Bad Request" + ) + + with pytest.raises(requests.HTTPError): + tool.run(prompt="Create automation") + + +@patch("requests.post") +def test_network_error_handling(mock_post, tool): + mock_post.side_effect = requests.ConnectionError("Network unreachable") + + with pytest.raises(requests.ConnectionError): + tool.run(prompt="Create automation") + + +@patch("requests.post") +def test_api_response_missing_url(mock_post, tool): + mock_response = MagicMock() + mock_response.json.return_value = {"status": "success"} + mock_post.return_value = mock_response + + result = tool.run(prompt="Create automation") + + assert result == "Generated CrewAI Studio project URL: None" + + +def test_authorization_header_construction(tool): + headers = tool._get_headers() + + assert headers["Authorization"] == "Bearer test_token" + assert headers["Content-Type"] == "application/json" + assert headers["Accept"] == "application/json" + assert "X-Crewai-Organization-Id" not in headers + + +def test_authorization_header_with_org_id(tool): + headers = tool._get_headers(organization_id="org-123") + + assert headers["Authorization"] == "Bearer test_token" + assert headers["X-Crewai-Organization-Id"] == "org-123" + + +def test_missing_personal_access_token(): + with patch.dict(os.environ, {}, clear=True): + tool = GenerateCrewaiAutomationTool() + assert tool.personal_access_token is None diff --git a/lib/crewai-tools/tests/tools/parallel_search_tool_test.py b/lib/crewai-tools/tests/tools/parallel_search_tool_test.py new file mode 100644 index 000000000..453fc259b --- /dev/null +++ b/lib/crewai-tools/tests/tools/parallel_search_tool_test.py @@ -0,0 +1,44 @@ +import json +from unittest.mock import patch +from urllib.parse import urlparse + +from crewai_tools.tools.parallel_tools.parallel_search_tool import ( + ParallelSearchTool, +) + + +def test_requires_env_var(monkeypatch): + monkeypatch.delenv("PARALLEL_API_KEY", raising=False) + tool = ParallelSearchTool() + result = tool.run(objective="test") + assert "PARALLEL_API_KEY" in result + + +@patch("crewai_tools.tools.parallel_tools.parallel_search_tool.requests.post") +def test_happy_path(mock_post, monkeypatch): + monkeypatch.setenv("PARALLEL_API_KEY", "test") + + mock_post.return_value.status_code = 200 + mock_post.return_value.json.return_value = { + "search_id": "search_123", + "results": [ + { + "url": "https://www.un.org/en/about-us/history-of-the-un", + "title": "History of the United Nations", + "excerpts": [ + "Four months after the San Francisco Conference ended, the United Nations officially began, on 24 October 1945..." + ], + } + ], + } + + tool = ParallelSearchTool() + result = tool.run( + objective="When was the UN established?", search_queries=["Founding year UN"] + ) + data = json.loads(result) + assert "search_id" in data + urls = [r.get("url", "") for r in data.get("results", [])] + # Validate host against allowed set instead of substring matching + allowed_hosts = {"www.un.org", "un.org"} + assert any(urlparse(u).netloc in allowed_hosts for u in urls) diff --git a/lib/crewai-tools/tests/tools/rag/rag_tool_test.py b/lib/crewai-tools/tests/tools/rag/rag_tool_test.py new file mode 100644 index 000000000..5298ce1e2 --- /dev/null +++ b/lib/crewai-tools/tests/tools/rag/rag_tool_test.py @@ -0,0 +1,178 @@ +"""Tests for RAG tool with mocked embeddings and vector database.""" + +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import cast +from unittest.mock import MagicMock, Mock, patch + +from crewai_tools.adapters.crewai_rag_adapter import CrewAIRagAdapter +from crewai_tools.tools.rag.rag_tool import RagTool + + +@patch("crewai_tools.adapters.crewai_rag_adapter.get_rag_client") +@patch("crewai_tools.adapters.crewai_rag_adapter.create_client") +def test_rag_tool_initialization( + mock_create_client: Mock, mock_get_rag_client: Mock +) -> None: + """Test that RagTool initializes with CrewAI adapter by default.""" + mock_client = MagicMock() + mock_client.get_or_create_collection = MagicMock(return_value=None) + mock_get_rag_client.return_value = mock_client + mock_create_client.return_value = mock_client + + class MyTool(RagTool): + pass + + tool = MyTool() + assert tool.adapter is not None + assert isinstance(tool.adapter, CrewAIRagAdapter) + + adapter = cast(CrewAIRagAdapter, tool.adapter) + assert adapter.collection_name == "rag_tool_collection" + assert adapter._client is not None + + +@patch("crewai_tools.adapters.crewai_rag_adapter.get_rag_client") +@patch("crewai_tools.adapters.crewai_rag_adapter.create_client") +def test_rag_tool_add_and_query( + mock_create_client: Mock, mock_get_rag_client: Mock +) -> None: + """Test adding content and querying with RagTool.""" + mock_client = MagicMock() + mock_client.get_or_create_collection = MagicMock(return_value=None) + mock_client.add_documents = MagicMock(return_value=None) + mock_client.search = MagicMock( + return_value=[ + {"content": "The sky is blue on a clear day.", "metadata": {}, "score": 0.9} + ] + ) + mock_get_rag_client.return_value = mock_client + mock_create_client.return_value = mock_client + + class MyTool(RagTool): + pass + + tool = MyTool() + + tool.add("The sky is blue on a clear day.") + tool.add("Machine learning is a subset of artificial intelligence.") + + # Verify documents were added + assert mock_client.add_documents.call_count == 2 + + result = tool._run(query="What color is the sky?") + assert "Relevant Content:" in result + assert "The sky is blue" in result + + mock_client.search.return_value = [ + { + "content": "Machine learning is a subset of artificial intelligence.", + "metadata": {}, + "score": 0.85, + } + ] + + result = tool._run(query="Tell me about machine learning") + assert "Relevant Content:" in result + assert "Machine learning" in result + + +@patch("crewai_tools.adapters.crewai_rag_adapter.get_rag_client") +@patch("crewai_tools.adapters.crewai_rag_adapter.create_client") +def test_rag_tool_with_file( + mock_create_client: Mock, mock_get_rag_client: Mock +) -> None: + """Test RagTool with file content.""" + mock_client = MagicMock() + mock_client.get_or_create_collection = MagicMock(return_value=None) + mock_client.add_documents = MagicMock(return_value=None) + mock_client.search = MagicMock( + return_value=[ + { + "content": "Python is a programming language known for its simplicity.", + "metadata": {"file_path": "test.txt"}, + "score": 0.95, + } + ] + ) + mock_get_rag_client.return_value = mock_client + mock_create_client.return_value = mock_client + + with TemporaryDirectory() as tmpdir: + test_file = Path(tmpdir) / "test.txt" + test_file.write_text( + "Python is a programming language known for its simplicity." + ) + + class MyTool(RagTool): + pass + + tool = MyTool() + tool.add(str(test_file)) + + assert mock_client.add_documents.called + + result = tool._run(query="What is Python?") + assert "Relevant Content:" in result + assert "Python is a programming language" in result + + +@patch("crewai_tools.tools.rag.rag_tool.RagTool._create_embedding_function") +@patch("crewai_tools.adapters.crewai_rag_adapter.create_client") +def test_rag_tool_with_custom_embeddings( + mock_create_client: Mock, mock_create_embedding: Mock +) -> None: + """Test RagTool with custom embeddings configuration to ensure no API calls.""" + mock_embedding_func = MagicMock() + mock_embedding_func.return_value = [[0.2] * 1536] + mock_create_embedding.return_value = mock_embedding_func + + mock_client = MagicMock() + mock_client.get_or_create_collection = MagicMock(return_value=None) + mock_client.add_documents = MagicMock(return_value=None) + mock_client.search = MagicMock( + return_value=[{"content": "Test content", "metadata": {}, "score": 0.8}] + ) + mock_create_client.return_value = mock_client + + class MyTool(RagTool): + pass + + config = { + "vectordb": {"provider": "chromadb", "config": {}}, + "embedding_model": { + "provider": "openai", + "config": {"model": "text-embedding-3-small"}, + }, + } + + tool = MyTool(config=config) + tool.add("Test content") + + result = tool._run(query="Test query") + assert "Relevant Content:" in result + assert "Test content" in result + + mock_create_embedding.assert_called() + + +@patch("crewai_tools.adapters.crewai_rag_adapter.get_rag_client") +@patch("crewai_tools.adapters.crewai_rag_adapter.create_client") +def test_rag_tool_no_results( + mock_create_client: Mock, mock_get_rag_client: Mock +) -> None: + """Test RagTool when no relevant content is found.""" + mock_client = MagicMock() + mock_client.get_or_create_collection = MagicMock(return_value=None) + mock_client.search = MagicMock(return_value=[]) + mock_get_rag_client.return_value = mock_client + mock_create_client.return_value = mock_client + + class MyTool(RagTool): + pass + + tool = MyTool() + + result = tool._run(query="Non-existent content") + assert "Relevant Content:" in result + assert "No relevant content found" in result diff --git a/lib/crewai-tools/tests/tools/selenium_scraping_tool_test.py b/lib/crewai-tools/tests/tools/selenium_scraping_tool_test.py new file mode 100644 index 000000000..c60629453 --- /dev/null +++ b/lib/crewai-tools/tests/tools/selenium_scraping_tool_test.py @@ -0,0 +1,131 @@ +import os +import tempfile +from unittest.mock import MagicMock, patch + +from bs4 import BeautifulSoup +from crewai_tools.tools.selenium_scraping_tool.selenium_scraping_tool import ( + SeleniumScrapingTool, +) +from selenium.webdriver.chrome.options import Options + + +def mock_driver_with_html(html_content): + driver = MagicMock() + mock_element = MagicMock() + mock_element.get_attribute.return_value = html_content + bs = BeautifulSoup(html_content, "html.parser") + mock_element.text = bs.get_text() + + driver.find_elements.return_value = [mock_element] + driver.find_element.return_value = mock_element + + return driver + + +def initialize_tool_with(mock_driver): + tool = SeleniumScrapingTool(driver=mock_driver) + return tool + + +@patch("selenium.webdriver.Chrome") +def test_tool_initialization(mocked_chrome): + temp_dir = tempfile.mkdtemp() + mocked_chrome.return_value = MagicMock() + + tool = SeleniumScrapingTool() + + assert tool.website_url is None + assert tool.css_element is None + assert tool.cookie is None + assert tool.wait_time == 3 + assert tool.return_html is False + + try: + os.rmdir(temp_dir) + except: + pass + + +@patch("selenium.webdriver.Chrome") +def test_tool_initialization_with_options(mocked_chrome): + mocked_chrome.return_value = MagicMock() + + options = Options() + options.add_argument("--disable-gpu") + + SeleniumScrapingTool(options=options) + + mocked_chrome.assert_called_once_with(options=options) + + +@patch("selenium.webdriver.Chrome") +def test_scrape_without_css_selector(_mocked_chrome_driver): + html_content = "
test content
" + mock_driver = mock_driver_with_html(html_content) + tool = initialize_tool_with(mock_driver) + + result = tool._run(website_url="https://example.com") + + assert "test content" in result + mock_driver.get.assert_called_once_with("https://example.com") + mock_driver.find_element.assert_called_with("tag name", "body") + mock_driver.close.assert_called_once() + + +@patch("selenium.webdriver.Chrome") +def test_scrape_with_css_selector(_mocked_chrome_driver): + html_content = "
test content
test content in a specific div
" + mock_driver = mock_driver_with_html(html_content) + tool = initialize_tool_with(mock_driver) + + result = tool._run(website_url="https://example.com", css_element="div.test") + + assert "test content in a specific div" in result + mock_driver.get.assert_called_once_with("https://example.com") + mock_driver.find_elements.assert_called_with("css selector", "div.test") + mock_driver.close.assert_called_once() + + +@patch("selenium.webdriver.Chrome") +def test_scrape_with_return_html_true(_mocked_chrome_driver): + html_content = "
HTML content
" + mock_driver = mock_driver_with_html(html_content) + tool = initialize_tool_with(mock_driver) + + result = tool._run(website_url="https://example.com", return_html=True) + + assert html_content in result + mock_driver.get.assert_called_once_with("https://example.com") + mock_driver.find_element.assert_called_with("tag name", "body") + mock_driver.close.assert_called_once() + + +@patch("selenium.webdriver.Chrome") +def test_scrape_with_return_html_false(_mocked_chrome_driver): + html_content = "
HTML content
" + mock_driver = mock_driver_with_html(html_content) + tool = initialize_tool_with(mock_driver) + + result = tool._run(website_url="https://example.com", return_html=False) + + assert "HTML content" in result + mock_driver.get.assert_called_once_with("https://example.com") + mock_driver.find_element.assert_called_with("tag name", "body") + mock_driver.close.assert_called_once() + + +@patch("selenium.webdriver.Chrome") +def test_scrape_with_driver_error(_mocked_chrome_driver): + mock_driver = MagicMock() + mock_driver.find_element.side_effect = Exception("WebDriver error occurred") + tool = initialize_tool_with(mock_driver) + result = tool._run(website_url="https://example.com") + assert result == "Error scraping website: WebDriver error occurred" + mock_driver.close.assert_called_once() + + +@patch("selenium.webdriver.Chrome") +def test_initialization_with_driver(_mocked_chrome_driver): + mock_driver = MagicMock() + tool = initialize_tool_with(mock_driver) + assert tool.driver == mock_driver diff --git a/lib/crewai-tools/tests/tools/serper_dev_tool_test.py b/lib/crewai-tools/tests/tools/serper_dev_tool_test.py new file mode 100644 index 000000000..535b9538a --- /dev/null +++ b/lib/crewai-tools/tests/tools/serper_dev_tool_test.py @@ -0,0 +1,141 @@ +import os +from unittest.mock import patch + +from crewai_tools.tools.serper_dev_tool.serper_dev_tool import SerperDevTool +import pytest + + +@pytest.fixture(autouse=True) +def mock_serper_api_key(): + with patch.dict(os.environ, {"SERPER_API_KEY": "test_key"}): + yield + + +@pytest.fixture +def serper_tool(): + return SerperDevTool(n_results=2) + + +def test_serper_tool_initialization(): + tool = SerperDevTool() + assert tool.n_results == 10 + assert tool.save_file is False + assert tool.search_type == "search" + assert tool.country == "" + assert tool.location == "" + assert tool.locale == "" + + +def test_serper_tool_custom_initialization(): + tool = SerperDevTool( + n_results=5, + save_file=True, + search_type="news", + country="US", + location="New York", + locale="en", + ) + assert tool.n_results == 5 + assert tool.save_file is True + assert tool.search_type == "news" + assert tool.country == "US" + assert tool.location == "New York" + assert tool.locale == "en" + + +@patch("requests.post") +def test_serper_tool_search(mock_post): + tool = SerperDevTool(n_results=2) + mock_response = { + "searchParameters": {"q": "test query", "type": "search"}, + "organic": [ + { + "title": "Test Title 1", + "link": "http://test1.com", + "snippet": "Test Description 1", + "position": 1, + }, + { + "title": "Test Title 2", + "link": "http://test2.com", + "snippet": "Test Description 2", + "position": 2, + }, + ], + "peopleAlsoAsk": [ + { + "question": "Test Question", + "snippet": "Test Answer", + "title": "Test Source", + "link": "http://test.com", + } + ], + } + mock_post.return_value.json.return_value = mock_response + mock_post.return_value.status_code = 200 + + result = tool.run(search_query="test query") + + assert "searchParameters" in result + assert result["searchParameters"]["q"] == "test query" + assert len(result["organic"]) == 2 + assert result["organic"][0]["title"] == "Test Title 1" + + +@patch("requests.post") +def test_serper_tool_news_search(mock_post): + tool = SerperDevTool(n_results=2, search_type="news") + mock_response = { + "searchParameters": {"q": "test news", "type": "news"}, + "news": [ + { + "title": "News Title 1", + "link": "http://news1.com", + "snippet": "News Description 1", + "date": "2024-01-01", + "source": "News Source 1", + "imageUrl": "http://image1.com", + } + ], + } + mock_post.return_value.json.return_value = mock_response + mock_post.return_value.status_code = 200 + + result = tool.run(search_query="test news") + + assert "news" in result + assert len(result["news"]) == 1 + assert result["news"][0]["title"] == "News Title 1" + + +@patch("requests.post") +def test_serper_tool_with_location_params(mock_post): + tool = SerperDevTool(n_results=2, country="US", location="New York", locale="en") + + tool.run(search_query="test") + + called_payload = mock_post.call_args.kwargs["json"] + assert called_payload["gl"] == "US" + assert called_payload["location"] == "New York" + assert called_payload["hl"] == "en" + + +def test_invalid_search_type(): + tool = SerperDevTool() + with pytest.raises(ValueError) as exc_info: + tool.run(search_query="test", search_type="invalid") + assert "Invalid search type" in str(exc_info.value) + + +@patch("requests.post") +def test_api_error_handling(mock_post): + tool = SerperDevTool() + mock_post.side_effect = Exception("API Error") + + with pytest.raises(Exception) as exc_info: + tool.run(search_query="test") + assert "API Error" in str(exc_info.value) + + +if __name__ == "__main__": + pytest.main([__file__]) diff --git a/lib/crewai-tools/tests/tools/singlestore_search_tool_test.py b/lib/crewai-tools/tests/tools/singlestore_search_tool_test.py new file mode 100644 index 000000000..18b1584db --- /dev/null +++ b/lib/crewai-tools/tests/tools/singlestore_search_tool_test.py @@ -0,0 +1,335 @@ +from collections.abc import Generator +import os + +from crewai_tools import SingleStoreSearchTool +from crewai_tools.tools.singlestore_search_tool import SingleStoreSearchToolSchema +import pytest +from singlestoredb import connect +from singlestoredb.server import docker + + +@pytest.fixture(scope="session") +def docker_server_url() -> Generator[str, None, None]: + """Start a SingleStore Docker server for tests.""" + try: + sdb = docker.start(license="") + conn = sdb.connect() + curr = conn.cursor() + curr.execute("CREATE DATABASE test_crewai") + curr.close() + conn.close() + yield sdb.connection_url + sdb.stop() + except Exception as e: + pytest.skip(f"Could not start SingleStore Docker container: {e}") + + +@pytest.fixture(scope="function") +def clean_db_url(docker_server_url) -> Generator[str, None, None]: + """Provide a clean database URL and clean up tables after test.""" + yield docker_server_url + try: + conn = connect(host=docker_server_url, database="test_crewai") + curr = conn.cursor() + curr.execute("SHOW TABLES") + results = curr.fetchall() + for result in results: + curr.execute(f"DROP TABLE {result[0]}") + curr.close() + conn.close() + except Exception: + # Ignore cleanup errors + pass + + +@pytest.fixture +def sample_table_setup(clean_db_url): + """Set up sample tables for testing.""" + conn = connect(host=clean_db_url, database="test_crewai") + curr = conn.cursor() + + # Create sample tables + curr.execute( + """ + CREATE TABLE employees ( + id INT PRIMARY KEY, + name VARCHAR(100), + department VARCHAR(50), + salary DECIMAL(10,2) + ) + """ + ) + + curr.execute( + """ + CREATE TABLE departments ( + id INT PRIMARY KEY, + name VARCHAR(100), + budget DECIMAL(12,2) + ) + """ + ) + + # Insert sample data + curr.execute( + """ + INSERT INTO employees VALUES + (1, 'Alice Smith', 'Engineering', 75000.00), + (2, 'Bob Johnson', 'Marketing', 65000.00), + (3, 'Carol Davis', 'Engineering', 80000.00) + """ + ) + + curr.execute( + """ + INSERT INTO departments VALUES + (1, 'Engineering', 500000.00), + (2, 'Marketing', 300000.00) + """ + ) + + curr.close() + conn.close() + return clean_db_url + + +class TestSingleStoreSearchTool: + """Test suite for SingleStoreSearchTool.""" + + def test_tool_creation_with_connection_params(self, sample_table_setup): + """Test tool creation with individual connection parameters.""" + # Parse URL components for individual parameters + url_parts = sample_table_setup.split("@")[1].split(":") + host = url_parts[0] + port = int(url_parts[1].split("/")[0]) + user = "root" + password = sample_table_setup.split("@")[0].split(":")[2] + tool = SingleStoreSearchTool( + tables=[], + host=host, + port=port, + user=user, + password=password, + database="test_crewai", + ) + + assert tool.name == "Search a database's table(s) content" + assert "SingleStore" in tool.description + assert ( + "employees(id int(11), name varchar(100), department varchar(50), salary decimal(10,2))" + in tool.description.lower() + ) + assert ( + "departments(id int(11), name varchar(100), budget decimal(12,2))" + in tool.description.lower() + ) + assert tool.args_schema == SingleStoreSearchToolSchema + assert tool.connection_pool is not None + + def test_tool_creation_with_connection_url(self, sample_table_setup): + """Test tool creation with connection URL.""" + tool = SingleStoreSearchTool(host=f"{sample_table_setup}/test_crewai") + + assert tool.name == "Search a database's table(s) content" + assert tool.connection_pool is not None + + def test_tool_creation_with_specific_tables(self, sample_table_setup): + """Test tool creation with specific table list.""" + tool = SingleStoreSearchTool( + tables=["employees"], + host=sample_table_setup, + database="test_crewai", + ) + + # Check that description includes specific tables + assert "employees" in tool.description + assert "departments" not in tool.description + + def test_tool_creation_with_nonexistent_table(self, sample_table_setup): + """Test tool creation fails with non-existent table.""" + + with pytest.raises(ValueError, match="Table nonexistent does not exist"): + SingleStoreSearchTool( + tables=["employees", "nonexistent"], + host=sample_table_setup, + database="test_crewai", + ) + + def test_tool_creation_with_empty_database(self, clean_db_url): + """Test tool creation fails with empty database.""" + + with pytest.raises(ValueError, match="No tables found in the database"): + SingleStoreSearchTool(host=clean_db_url, database="test_crewai") + + def test_description_generation(self, sample_table_setup): + """Test that tool description is properly generated with table info.""" + + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + # Check description contains table definitions + assert "employees(" in tool.description + assert "departments(" in tool.description + assert "id int" in tool.description.lower() + assert "name varchar" in tool.description.lower() + + def test_query_validation_select_allowed(self, sample_table_setup): + """Test that SELECT queries are allowed.""" + os.environ["SINGLESTOREDB_URL"] = sample_table_setup + tool = SingleStoreSearchTool(database="test_crewai") + + valid, message = tool._validate_query("SELECT * FROM employees") + assert valid is True + assert message == "Valid query" + + def test_query_validation_show_allowed(self, sample_table_setup): + """Test that SHOW queries are allowed.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + valid, message = tool._validate_query("SHOW TABLES") + assert valid is True + assert message == "Valid query" + + def test_query_validation_case_insensitive(self, sample_table_setup): + """Test that query validation is case insensitive.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + valid, _ = tool._validate_query("select * from employees") + assert valid is True + + valid, _ = tool._validate_query("SHOW tables") + assert valid is True + + def test_query_validation_insert_denied(self, sample_table_setup): + """Test that INSERT queries are denied.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + valid, message = tool._validate_query( + "INSERT INTO employees VALUES (4, 'Test', 'Test', 1000)" + ) + assert valid is False + assert "Only SELECT and SHOW queries are supported" in message + + def test_query_validation_update_denied(self, sample_table_setup): + """Test that UPDATE queries are denied.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + valid, message = tool._validate_query("UPDATE employees SET salary = 90000") + assert valid is False + assert "Only SELECT and SHOW queries are supported" in message + + def test_query_validation_delete_denied(self, sample_table_setup): + """Test that DELETE queries are denied.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + valid, message = tool._validate_query("DELETE FROM employees WHERE id = 1") + assert valid is False + assert "Only SELECT and SHOW queries are supported" in message + + def test_query_validation_non_string(self, sample_table_setup): + """Test that non-string queries are rejected.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + valid, message = tool._validate_query(123) + assert valid is False + assert "Search query must be a string" in message + + def test_run_select_query(self, sample_table_setup): + """Test executing a SELECT query.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + result = tool._run("SELECT * FROM employees ORDER BY id") + + assert "Search Results:" in result + assert "Alice Smith" in result + assert "Bob Johnson" in result + assert "Carol Davis" in result + + def test_run_filtered_query(self, sample_table_setup): + """Test executing a filtered SELECT query.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + result = tool._run( + "SELECT name FROM employees WHERE department = 'Engineering'" + ) + + assert "Search Results:" in result + assert "Alice Smith" in result + assert "Carol Davis" in result + assert "Bob Johnson" not in result + + def test_run_show_query(self, sample_table_setup): + """Test executing a SHOW query.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + result = tool._run("SHOW TABLES") + + assert "Search Results:" in result + assert "employees" in result + assert "departments" in result + + def test_run_empty_result(self, sample_table_setup): + """Test executing a query that returns no results.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + result = tool._run("SELECT * FROM employees WHERE department = 'NonExistent'") + + assert result == "No results found." + + def test_run_invalid_query_syntax(self, sample_table_setup): + """Test executing a query with invalid syntax.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + result = tool._run("SELECT * FORM employees") # Intentional typo + + assert "Error executing search query:" in result + + def test_run_denied_query(self, sample_table_setup): + """Test that denied queries return appropriate error message.""" + tool = SingleStoreSearchTool(host=sample_table_setup, database="test_crewai") + + result = tool._run("DELETE FROM employees") + + assert "Invalid search query:" in result + assert "Only SELECT and SHOW queries are supported" in result + + def test_connection_pool_usage(self, sample_table_setup): + """Test that connection pooling works correctly.""" + tool = SingleStoreSearchTool( + host=sample_table_setup, + database="test_crewai", + pool_size=2, + ) + + # Execute multiple queries to test pool usage + results = [] + for _ in range(5): + result = tool._run("SELECT COUNT(*) FROM employees") + results.append(result) + + # All queries should succeed + for result in results: + assert "Search Results:" in result + assert "3" in result # Count of employees + + def test_tool_schema_validation(self): + """Test that the tool schema validation works correctly.""" + # Valid input + valid_input = SingleStoreSearchToolSchema(search_query="SELECT * FROM test") + assert valid_input.search_query == "SELECT * FROM test" + + # Test that description is present + schema_dict = SingleStoreSearchToolSchema.model_json_schema() + assert "search_query" in schema_dict["properties"] + assert "description" in schema_dict["properties"]["search_query"] + + def test_connection_error_handling(self): + """Test handling of connection errors.""" + with pytest.raises(Exception): + # This should fail due to invalid connection parameters + SingleStoreSearchTool( + host="invalid_host", + port=9999, + user="invalid_user", + password="invalid_password", + database="invalid_db", + ) diff --git a/lib/crewai-tools/tests/tools/snowflake_search_tool_test.py b/lib/crewai-tools/tests/tools/snowflake_search_tool_test.py new file mode 100644 index 000000000..fe827d5df --- /dev/null +++ b/lib/crewai-tools/tests/tools/snowflake_search_tool_test.py @@ -0,0 +1,102 @@ +import asyncio +from unittest.mock import MagicMock, patch + +from crewai_tools import SnowflakeConfig, SnowflakeSearchTool +import pytest + + +# Unit Test Fixtures +@pytest.fixture +def mock_snowflake_connection(): + mock_conn = MagicMock() + mock_cursor = MagicMock() + mock_cursor.description = [("col1",), ("col2",)] + mock_cursor.fetchall.return_value = [(1, "value1"), (2, "value2")] + mock_cursor.execute.return_value = None + mock_conn.cursor.return_value = mock_cursor + return mock_conn + + +@pytest.fixture +def mock_config(): + return SnowflakeConfig( + account="test_account", + user="test_user", + password="test_password", + warehouse="test_warehouse", + database="test_db", + snowflake_schema="test_schema", + ) + + +@pytest.fixture +def snowflake_tool(mock_config): + with patch("snowflake.connector.connect"): + tool = SnowflakeSearchTool(config=mock_config) + yield tool + + +# Unit Tests +@pytest.mark.asyncio +async def test_successful_query_execution(snowflake_tool, mock_snowflake_connection): + with patch.object(snowflake_tool, "_create_connection") as mock_create_conn: + mock_create_conn.return_value = mock_snowflake_connection + + results = await snowflake_tool._run( + query="SELECT * FROM test_table", timeout=300 + ) + + assert len(results) == 2 + assert results[0]["col1"] == 1 + assert results[0]["col2"] == "value1" + mock_snowflake_connection.cursor.assert_called_once() + + +@pytest.mark.asyncio +async def test_connection_pooling(snowflake_tool, mock_snowflake_connection): + with patch.object(snowflake_tool, "_create_connection") as mock_create_conn: + mock_create_conn.return_value = mock_snowflake_connection + + # Execute multiple queries + await asyncio.gather( + snowflake_tool._run("SELECT 1"), + snowflake_tool._run("SELECT 2"), + snowflake_tool._run("SELECT 3"), + ) + + # Should reuse connections from pool + assert mock_create_conn.call_count <= snowflake_tool.pool_size + + +@pytest.mark.asyncio +async def test_cleanup_on_deletion(snowflake_tool, mock_snowflake_connection): + with patch.object(snowflake_tool, "_create_connection") as mock_create_conn: + mock_create_conn.return_value = mock_snowflake_connection + + # Add connection to pool + await snowflake_tool._get_connection() + + # Return connection to pool + async with snowflake_tool._pool_lock: + snowflake_tool._connection_pool.append(mock_snowflake_connection) + + # Trigger cleanup + snowflake_tool.__del__() + + mock_snowflake_connection.close.assert_called_once() + + +def test_config_validation(): + # Test missing required fields + with pytest.raises(ValueError): + SnowflakeConfig() + + # Test invalid account format + with pytest.raises(ValueError): + SnowflakeConfig( + account="invalid//account", user="test_user", password="test_pass" + ) + + # Test missing authentication + with pytest.raises(ValueError): + SnowflakeConfig(account="test_account", user="test_user") diff --git a/lib/crewai-tools/tests/tools/stagehand_tool_test.py b/lib/crewai-tools/tests/tools/stagehand_tool_test.py new file mode 100644 index 000000000..bed0cd311 --- /dev/null +++ b/lib/crewai-tools/tests/tools/stagehand_tool_test.py @@ -0,0 +1,281 @@ +import sys +from unittest.mock import MagicMock, patch + +import pytest + + +# Create mock classes that will be used by our fixture +class MockStagehandModule: + def __init__(self): + self.Stagehand = MagicMock() + self.StagehandConfig = MagicMock() + self.StagehandPage = MagicMock() + + +class MockStagehandSchemas: + def __init__(self): + self.ActOptions = MagicMock() + self.ExtractOptions = MagicMock() + self.ObserveOptions = MagicMock() + self.AvailableModel = MagicMock() + + +class MockStagehandUtils: + def __init__(self): + self.configure_logging = MagicMock() + + +@pytest.fixture(scope="module", autouse=True) +def mock_stagehand_modules(): + """Mock stagehand modules at the start of this test module.""" + # Store original modules if they exist + original_modules = {} + for module_name in ["stagehand", "stagehand.schemas", "stagehand.utils"]: + if module_name in sys.modules: + original_modules[module_name] = sys.modules[module_name] + + # Create and inject mock modules + mock_stagehand = MockStagehandModule() + mock_stagehand_schemas = MockStagehandSchemas() + mock_stagehand_utils = MockStagehandUtils() + + sys.modules["stagehand"] = mock_stagehand + sys.modules["stagehand.schemas"] = mock_stagehand_schemas + sys.modules["stagehand.utils"] = mock_stagehand_utils + + # Import after mocking + from crewai_tools.tools.stagehand_tool.stagehand_tool import ( + StagehandResult, + StagehandTool, + ) + + # Make these available to tests in this module + sys.modules[__name__].StagehandResult = StagehandResult + sys.modules[__name__].StagehandTool = StagehandTool + + yield + + # Restore original modules + for module_name, module in original_modules.items(): + sys.modules[module_name] = module + + +class MockStagehandPage(MagicMock): + def act(self, options): + mock_result = MagicMock() + mock_result.model_dump.return_value = { + "message": "Action completed successfully" + } + return mock_result + + def goto(self, url): + return MagicMock() + + def extract(self, options): + mock_result = MagicMock() + mock_result.model_dump.return_value = { + "data": "Extracted content", + "metadata": {"source": "test"}, + } + return mock_result + + def observe(self, options): + result1 = MagicMock() + result1.description = "Button element" + result1.method = "click" + + result2 = MagicMock() + result2.description = "Input field" + result2.method = "type" + + return [result1, result2] + + +class MockStagehand(MagicMock): + def init(self): + self.session_id = "test-session-id" + self.page = MockStagehandPage() + + def close(self): + pass + + +@pytest.fixture +def mock_stagehand_instance(): + with patch( + "crewai_tools.tools.stagehand_tool.stagehand_tool.Stagehand", + return_value=MockStagehand(), + ) as mock: + yield mock + + +@pytest.fixture +def stagehand_tool(): + return StagehandTool( + api_key="test_api_key", + project_id="test_project_id", + model_api_key="test_model_api_key", + _testing=True, # Enable testing mode to bypass dependency check + ) + + +def test_stagehand_tool_initialization(): + """Test that the StagehandTool initializes with the correct default values.""" + tool = StagehandTool( + api_key="test_api_key", + project_id="test_project_id", + model_api_key="test_model_api_key", + _testing=True, # Enable testing mode + ) + + assert tool.api_key == "test_api_key" + assert tool.project_id == "test_project_id" + assert tool.model_api_key == "test_model_api_key" + assert tool.headless is False + assert tool.dom_settle_timeout_ms == 3000 + assert tool.self_heal is True + assert tool.wait_for_captcha_solves is True + + +@patch( + "crewai_tools.tools.stagehand_tool.stagehand_tool.StagehandTool._run", autospec=True +) +def test_act_command(mock_run, stagehand_tool): + """Test the 'act' command functionality.""" + # Setup mock + mock_run.return_value = "Action result: Action completed successfully" + + # Run the tool + result = stagehand_tool._run( + instruction="Click the submit button", command_type="act" + ) + + # Assertions + assert "Action result" in result + assert "Action completed successfully" in result + + +@patch( + "crewai_tools.tools.stagehand_tool.stagehand_tool.StagehandTool._run", autospec=True +) +def test_navigate_command(mock_run, stagehand_tool): + """Test the 'navigate' command functionality.""" + # Setup mock + mock_run.return_value = "Successfully navigated to https://example.com" + + # Run the tool + result = stagehand_tool._run( + instruction="Go to example.com", + url="https://example.com", + command_type="navigate", + ) + + # Assertions + assert "https://example.com" in result + + +@patch( + "crewai_tools.tools.stagehand_tool.stagehand_tool.StagehandTool._run", autospec=True +) +def test_extract_command(mock_run, stagehand_tool): + """Test the 'extract' command functionality.""" + # Setup mock + mock_run.return_value = ( + 'Extracted data: {"data": "Extracted content", "metadata": {"source": "test"}}' + ) + + # Run the tool + result = stagehand_tool._run( + instruction="Extract all product names and prices", command_type="extract" + ) + + # Assertions + assert "Extracted data" in result + assert "Extracted content" in result + + +@patch( + "crewai_tools.tools.stagehand_tool.stagehand_tool.StagehandTool._run", autospec=True +) +def test_observe_command(mock_run, stagehand_tool): + """Test the 'observe' command functionality.""" + # Setup mock + mock_run.return_value = "Element 1: Button element\nSuggested action: click\nElement 2: Input field\nSuggested action: type" + + # Run the tool + result = stagehand_tool._run( + instruction="Find all interactive elements", command_type="observe" + ) + + # Assertions + assert "Element 1: Button element" in result + assert "Element 2: Input field" in result + assert "Suggested action: click" in result + assert "Suggested action: type" in result + + +@patch( + "crewai_tools.tools.stagehand_tool.stagehand_tool.StagehandTool._run", autospec=True +) +def test_error_handling(mock_run, stagehand_tool): + """Test error handling in the tool.""" + # Setup mock + mock_run.return_value = "Error: Browser automation error" + + # Run the tool + result = stagehand_tool._run( + instruction="Click a non-existent button", command_type="act" + ) + + # Assertions + assert "Error:" in result + assert "Browser automation error" in result + + +def test_initialization_parameters(): + """Test that the StagehandTool initializes with the correct parameters.""" + # Create tool with custom parameters + tool = StagehandTool( + api_key="custom_api_key", + project_id="custom_project_id", + model_api_key="custom_model_api_key", + headless=True, + dom_settle_timeout_ms=5000, + self_heal=False, + wait_for_captcha_solves=False, + verbose=3, + _testing=True, # Enable testing mode + ) + + # Verify the tool was initialized with the correct parameters + assert tool.api_key == "custom_api_key" + assert tool.project_id == "custom_project_id" + assert tool.model_api_key == "custom_model_api_key" + assert tool.headless is True + assert tool.dom_settle_timeout_ms == 5000 + assert tool.self_heal is False + assert tool.wait_for_captcha_solves is False + assert tool.verbose == 3 + + +def test_close_method(): + """Test that the close method cleans up resources correctly.""" + # Create the tool with testing mode + tool = StagehandTool( + api_key="test_api_key", + project_id="test_project_id", + model_api_key="test_model_api_key", + _testing=True, + ) + + # Setup mock stagehand instance + tool._stagehand = MagicMock() + tool._stagehand.close = MagicMock() # Non-async mock + tool._page = MagicMock() + + # Call the close method + tool.close() + + # Verify resources were cleaned up + assert tool._stagehand is None + assert tool._page is None diff --git a/lib/crewai-tools/tests/tools/test_code_interpreter_tool.py b/lib/crewai-tools/tests/tools/test_code_interpreter_tool.py new file mode 100644 index 000000000..ca1f21a23 --- /dev/null +++ b/lib/crewai-tools/tests/tools/test_code_interpreter_tool.py @@ -0,0 +1,174 @@ +from unittest.mock import patch + +from crewai_tools.tools.code_interpreter_tool.code_interpreter_tool import ( + CodeInterpreterTool, + SandboxPython, +) +import pytest + + +@pytest.fixture +def printer_mock(): + with patch("crewai_tools.printer.Printer.print") as mock: + yield mock + + +@pytest.fixture +def docker_unavailable_mock(): + with patch( + "crewai_tools.tools.code_interpreter_tool.code_interpreter_tool.CodeInterpreterTool._check_docker_available", + return_value=False, + ) as mock: + yield mock + + +@patch("crewai_tools.tools.code_interpreter_tool.code_interpreter_tool.docker_from_env") +def test_run_code_in_docker(docker_mock, printer_mock): + tool = CodeInterpreterTool() + code = "print('Hello, World!')" + libraries_used = ["numpy", "pandas"] + expected_output = "Hello, World!\n" + + docker_mock().containers.run().exec_run().exit_code = 0 + docker_mock().containers.run().exec_run().output = expected_output.encode() + + result = tool.run_code_in_docker(code, libraries_used) + assert result == expected_output + printer_mock.assert_called_with( + "Running code in Docker environment", color="bold_blue" + ) + + +@patch("crewai_tools.tools.code_interpreter_tool.code_interpreter_tool.docker_from_env") +def test_run_code_in_docker_with_error(docker_mock, printer_mock): + tool = CodeInterpreterTool() + code = "print(1/0)" + libraries_used = ["numpy", "pandas"] + expected_output = "Something went wrong while running the code: \nZeroDivisionError: division by zero\n" + + docker_mock().containers.run().exec_run().exit_code = 1 + docker_mock().containers.run().exec_run().output = ( + b"ZeroDivisionError: division by zero\n" + ) + + result = tool.run_code_in_docker(code, libraries_used) + assert result == expected_output + printer_mock.assert_called_with( + "Running code in Docker environment", color="bold_blue" + ) + + +@patch("crewai_tools.tools.code_interpreter_tool.code_interpreter_tool.docker_from_env") +def test_run_code_in_docker_with_script(docker_mock, printer_mock): + tool = CodeInterpreterTool() + code = """print("This is line 1") +print("This is line 2")""" + libraries_used = [] + expected_output = "This is line 1\nThis is line 2\n" + + docker_mock().containers.run().exec_run().exit_code = 0 + docker_mock().containers.run().exec_run().output = expected_output.encode() + + result = tool.run_code_in_docker(code, libraries_used) + assert result == expected_output + printer_mock.assert_called_with( + "Running code in Docker environment", color="bold_blue" + ) + + +def test_restricted_sandbox_basic_code_execution(printer_mock, docker_unavailable_mock): + """Test basic code execution.""" + tool = CodeInterpreterTool() + code = """ +result = 2 + 2 +print(result) +""" + result = tool.run(code=code, libraries_used=[]) + printer_mock.assert_called_with( + "Running code in restricted sandbox", color="yellow" + ) + assert result == 4 + + +def test_restricted_sandbox_running_with_blocked_modules( + printer_mock, docker_unavailable_mock +): + """Test that restricted modules cannot be imported.""" + tool = CodeInterpreterTool() + restricted_modules = SandboxPython.BLOCKED_MODULES + + for module in restricted_modules: + code = f""" +import {module} +result = "Import succeeded" +""" + result = tool.run(code=code, libraries_used=[]) + printer_mock.assert_called_with( + "Running code in restricted sandbox", color="yellow" + ) + + assert f"An error occurred: Importing '{module}' is not allowed" in result + + +def test_restricted_sandbox_running_with_blocked_builtins( + printer_mock, docker_unavailable_mock +): + """Test that restricted builtins are not available.""" + tool = CodeInterpreterTool() + restricted_builtins = SandboxPython.UNSAFE_BUILTINS + + for builtin in restricted_builtins: + code = f""" +{builtin}("test") +result = "Builtin available" +""" + result = tool.run(code=code, libraries_used=[]) + printer_mock.assert_called_with( + "Running code in restricted sandbox", color="yellow" + ) + assert f"An error occurred: name '{builtin}' is not defined" in result + + +def test_restricted_sandbox_running_with_no_result_variable( + printer_mock, docker_unavailable_mock +): + """Test behavior when no result variable is set.""" + tool = CodeInterpreterTool() + code = """ +x = 10 +""" + result = tool.run(code=code, libraries_used=[]) + printer_mock.assert_called_with( + "Running code in restricted sandbox", color="yellow" + ) + assert result == "No result variable found." + + +def test_unsafe_mode_running_with_no_result_variable( + printer_mock, docker_unavailable_mock +): + """Test behavior when no result variable is set.""" + tool = CodeInterpreterTool(unsafe_mode=True) + code = """ +x = 10 +""" + result = tool.run(code=code, libraries_used=[]) + printer_mock.assert_called_with( + "WARNING: Running code in unsafe mode", color="bold_magenta" + ) + assert result == "No result variable found." + + +def test_unsafe_mode_running_unsafe_code(printer_mock, docker_unavailable_mock): + """Test behavior when no result variable is set.""" + tool = CodeInterpreterTool(unsafe_mode=True) + code = """ +import os +os.system("ls -la") +result = eval("5/1") +""" + result = tool.run(code=code, libraries_used=[]) + printer_mock.assert_called_with( + "WARNING: Running code in unsafe mode", color="bold_magenta" + ) + assert 5.0 == result diff --git a/lib/crewai-tools/tests/tools/test_file_writer_tool.py b/lib/crewai-tools/tests/tools/test_file_writer_tool.py new file mode 100644 index 000000000..53f80b950 --- /dev/null +++ b/lib/crewai-tools/tests/tools/test_file_writer_tool.py @@ -0,0 +1,137 @@ +import os +import shutil +import tempfile + +from crewai_tools.tools.file_writer_tool.file_writer_tool import FileWriterTool +import pytest + + +@pytest.fixture +def tool(): + return FileWriterTool() + + +@pytest.fixture +def temp_env(): + temp_dir = tempfile.mkdtemp() + test_file = "test.txt" + test_content = "Hello, World!" + + yield { + "temp_dir": temp_dir, + "test_file": test_file, + "test_content": test_content, + } + + shutil.rmtree(temp_dir, ignore_errors=True) + + +def get_test_path(filename, directory): + return os.path.join(directory, filename) + + +def read_file(path): + with open(path, "r") as f: + return f.read() + + +def test_basic_file_write(tool, temp_env): + result = tool._run( + filename=temp_env["test_file"], + directory=temp_env["temp_dir"], + content=temp_env["test_content"], + overwrite=True, + ) + + path = get_test_path(temp_env["test_file"], temp_env["temp_dir"]) + assert os.path.exists(path) + assert read_file(path) == temp_env["test_content"] + assert "successfully written" in result + + +def test_directory_creation(tool, temp_env): + new_dir = os.path.join(temp_env["temp_dir"], "nested_dir") + result = tool._run( + filename=temp_env["test_file"], + directory=new_dir, + content=temp_env["test_content"], + overwrite=True, + ) + + path = get_test_path(temp_env["test_file"], new_dir) + assert os.path.exists(new_dir) + assert os.path.exists(path) + assert "successfully written" in result + + +@pytest.mark.parametrize( + "overwrite", + ["y", "yes", "t", "true", "on", "1", True], +) +def test_overwrite_true(tool, temp_env, overwrite): + path = get_test_path(temp_env["test_file"], temp_env["temp_dir"]) + with open(path, "w") as f: + f.write("Original content") + + result = tool._run( + filename=temp_env["test_file"], + directory=temp_env["temp_dir"], + content="New content", + overwrite=overwrite, + ) + + assert read_file(path) == "New content" + assert "successfully written" in result + + +def test_invalid_overwrite_value(tool, temp_env): + result = tool._run( + filename=temp_env["test_file"], + directory=temp_env["temp_dir"], + content=temp_env["test_content"], + overwrite="invalid", + ) + assert "invalid value" in result + + +def test_missing_required_fields(tool, temp_env): + result = tool._run( + directory=temp_env["temp_dir"], + content=temp_env["test_content"], + overwrite=True, + ) + assert "An error occurred while accessing key: 'filename'" in result + + +def test_empty_content(tool, temp_env): + result = tool._run( + filename=temp_env["test_file"], + directory=temp_env["temp_dir"], + content="", + overwrite=True, + ) + + path = get_test_path(temp_env["test_file"], temp_env["temp_dir"]) + assert os.path.exists(path) + assert read_file(path) == "" + assert "successfully written" in result + + +@pytest.mark.parametrize( + "overwrite", + ["n", "no", "f", "false", "off", "0", False], +) +def test_file_exists_error_handling(tool, temp_env, overwrite): + path = get_test_path(temp_env["test_file"], temp_env["temp_dir"]) + with open(path, "w") as f: + f.write("Pre-existing content") + + result = tool._run( + filename=temp_env["test_file"], + directory=temp_env["temp_dir"], + content="Should not be written", + overwrite=overwrite, + ) + + assert "already exists and overwrite option was not passed" in result + assert read_file(path) == "Pre-existing content" diff --git a/lib/crewai-tools/tests/tools/test_import_without_warnings.py b/lib/crewai-tools/tests/tools/test_import_without_warnings.py new file mode 100644 index 000000000..fc977a129 --- /dev/null +++ b/lib/crewai-tools/tests/tools/test_import_without_warnings.py @@ -0,0 +1,10 @@ +from pydantic.warnings import PydanticDeprecatedSince20 +import pytest + + +@pytest.mark.filterwarnings("error", category=PydanticDeprecatedSince20) +def test_import_tools_without_pydantic_deprecation_warnings(): + # This test is to ensure that the import of crewai_tools does not raise any Pydantic deprecation warnings. + import crewai_tools + + assert crewai_tools diff --git a/lib/crewai-tools/tests/tools/test_mongodb_vector_search_tool.py b/lib/crewai-tools/tests/tools/test_mongodb_vector_search_tool.py new file mode 100644 index 000000000..d5f27249e --- /dev/null +++ b/lib/crewai-tools/tests/tools/test_mongodb_vector_search_tool.py @@ -0,0 +1,74 @@ +import json +from unittest.mock import patch + +from crewai_tools import MongoDBVectorSearchConfig, MongoDBVectorSearchTool +import pytest + + +# Unit Test Fixtures +@pytest.fixture +def mongodb_vector_search_tool(): + tool = MongoDBVectorSearchTool( + connection_string="foo", database_name="bar", collection_name="test" + ) + tool._embed_texts = lambda x: [[0.1]] + yield tool + + +# Unit Tests +def test_successful_query_execution(mongodb_vector_search_tool): + # Enable embedding + with patch.object(mongodb_vector_search_tool._coll, "aggregate") as mock_aggregate: + mock_aggregate.return_value = [dict(text="foo", score=0.1, _id=1)] + + results = json.loads(mongodb_vector_search_tool._run(query="sandwiches")) + + assert len(results) == 1 + assert results[0]["text"] == "foo" + assert results[0]["_id"] == 1 + + +def test_provide_config(): + query_config = MongoDBVectorSearchConfig(limit=10) + tool = MongoDBVectorSearchTool( + connection_string="foo", + database_name="bar", + collection_name="test", + query_config=query_config, + vector_index_name="foo", + embedding_model="bar", + ) + tool._embed_texts = lambda x: [[0.1]] + with patch.object(tool._coll, "aggregate") as mock_aggregate: + mock_aggregate.return_value = [dict(text="foo", score=0.1, _id=1)] + + tool._run(query="sandwiches") + assert mock_aggregate.mock_calls[-1].args[0][0]["$vectorSearch"]["limit"] == 10 + + mock_aggregate.return_value = [dict(text="foo", score=0.1, _id=1)] + + +def test_cleanup_on_deletion(mongodb_vector_search_tool): + with patch.object(mongodb_vector_search_tool, "_client") as mock_client: + # Trigger cleanup + mongodb_vector_search_tool.__del__() + + mock_client.close.assert_called_once() + + +def test_create_search_index(mongodb_vector_search_tool): + with patch( + "crewai_tools.tools.mongodb_vector_search_tool.vector_search.create_vector_search_index" + ) as mock_create_search_index: + mongodb_vector_search_tool.create_vector_search_index(dimensions=10) + kwargs = mock_create_search_index.mock_calls[0].kwargs + assert kwargs["dimensions"] == 10 + assert kwargs["similarity"] == "cosine" + + +def test_add_texts(mongodb_vector_search_tool): + with patch.object(mongodb_vector_search_tool._coll, "bulk_write") as bulk_write: + mongodb_vector_search_tool.add_texts(["foo"]) + args = bulk_write.mock_calls[0].args + assert "ReplaceOne" in str(args[0][0]) + assert "foo" in str(args[0][0]) diff --git a/lib/crewai-tools/tests/tools/test_oxylabs_tools.py b/lib/crewai-tools/tests/tools/test_oxylabs_tools.py new file mode 100644 index 000000000..2b0bef76f --- /dev/null +++ b/lib/crewai-tools/tests/tools/test_oxylabs_tools.py @@ -0,0 +1,161 @@ +import json +import os +from unittest.mock import MagicMock + +from crewai.tools.base_tool import BaseTool +from crewai_tools import ( + OxylabsAmazonProductScraperTool, + OxylabsAmazonSearchScraperTool, + OxylabsGoogleSearchScraperTool, + OxylabsUniversalScraperTool, +) +from crewai_tools.tools.oxylabs_amazon_product_scraper_tool.oxylabs_amazon_product_scraper_tool import ( + OxylabsAmazonProductScraperConfig, +) +from crewai_tools.tools.oxylabs_google_search_scraper_tool.oxylabs_google_search_scraper_tool import ( + OxylabsGoogleSearchScraperConfig, +) +from oxylabs import RealtimeClient +from oxylabs.sources.response import Response as OxylabsResponse +from pydantic import BaseModel +import pytest + + +@pytest.fixture +def oxylabs_api() -> RealtimeClient: + oxylabs_api_mock = MagicMock() + + html_content = """ + + + + + Scraping Sandbox + + +
+
+
+

Amazing product

+

Price $14.99

+
+
+

Good product

+

Price $9.99

+
+
+
+ + + """ + + json_content = { + "results": { + "products": [ + {"title": "Amazing product", "price": 14.99, "currency": "USD"}, + {"title": "Good product", "price": 9.99, "currency": "USD"}, + ], + }, + } + + html_response = OxylabsResponse({"results": [{"content": html_content}]}) + json_response = OxylabsResponse({"results": [{"content": json_content}]}) + + oxylabs_api_mock.universal.scrape_url.side_effect = [json_response, html_response] + oxylabs_api_mock.amazon.scrape_search.side_effect = [json_response, html_response] + oxylabs_api_mock.amazon.scrape_product.side_effect = [json_response, html_response] + oxylabs_api_mock.google.scrape_search.side_effect = [json_response, html_response] + + return oxylabs_api_mock + + +@pytest.mark.parametrize( + ("tool_class",), + [ + (OxylabsUniversalScraperTool,), + (OxylabsAmazonSearchScraperTool,), + (OxylabsGoogleSearchScraperTool,), + (OxylabsAmazonProductScraperTool,), + ], +) +def test_tool_initialization(tool_class: type[BaseTool]): + tool = tool_class(username="username", password="password") + assert isinstance(tool, tool_class) + + +@pytest.mark.parametrize( + ("tool_class",), + [ + (OxylabsUniversalScraperTool,), + (OxylabsAmazonSearchScraperTool,), + (OxylabsGoogleSearchScraperTool,), + (OxylabsAmazonProductScraperTool,), + ], +) +def test_tool_initialization_with_env_vars(tool_class: type[BaseTool]): + os.environ["OXYLABS_USERNAME"] = "username" + os.environ["OXYLABS_PASSWORD"] = "password" + + tool = tool_class() + assert isinstance(tool, tool_class) + + del os.environ["OXYLABS_USERNAME"] + del os.environ["OXYLABS_PASSWORD"] + + +@pytest.mark.parametrize( + ("tool_class",), + [ + (OxylabsUniversalScraperTool,), + (OxylabsAmazonSearchScraperTool,), + (OxylabsGoogleSearchScraperTool,), + (OxylabsAmazonProductScraperTool,), + ], +) +def test_tool_initialization_failure(tool_class: type[BaseTool]): + # making sure env vars are not set + for key in ["OXYLABS_USERNAME", "OXYLABS_PASSWORD"]: + if key in os.environ: + del os.environ[key] + + with pytest.raises(ValueError): + tool_class() + + +@pytest.mark.parametrize( + ("tool_class", "tool_config"), + [ + (OxylabsUniversalScraperTool, {"geo_location": "Paris, France"}), + ( + OxylabsAmazonSearchScraperTool, + {"domain": "co.uk"}, + ), + ( + OxylabsGoogleSearchScraperTool, + OxylabsGoogleSearchScraperConfig(render="html"), + ), + ( + OxylabsAmazonProductScraperTool, + OxylabsAmazonProductScraperConfig(parse=True), + ), + ], +) +def test_tool_invocation( + tool_class: type[BaseTool], + tool_config: BaseModel, + oxylabs_api: RealtimeClient, +): + tool = tool_class(username="username", password="password", config=tool_config) + + # setting via __dict__ to bypass pydantic validation + tool.__dict__["oxylabs_api"] = oxylabs_api + + # verifying parsed job returns json content + result = tool.run("Scraping Query 1") + assert isinstance(result, str) + assert isinstance(json.loads(result), dict) + + # verifying raw job returns str + result = tool.run("Scraping Query 2") + assert isinstance(result, str) + assert "" in result diff --git a/lib/crewai-tools/tests/tools/test_search_tools.py b/lib/crewai-tools/tests/tools/test_search_tools.py new file mode 100644 index 000000000..298ecf62f --- /dev/null +++ b/lib/crewai-tools/tests/tools/test_search_tools.py @@ -0,0 +1,352 @@ +import os +from pathlib import Path +import tempfile +from unittest.mock import MagicMock + +from crewai_tools.rag.data_types import DataType +from crewai_tools.tools import ( + CSVSearchTool, + CodeDocsSearchTool, + DOCXSearchTool, + DirectorySearchTool, + GithubSearchTool, + JSONSearchTool, + MDXSearchTool, + PDFSearchTool, + TXTSearchTool, + WebsiteSearchTool, + XMLSearchTool, + YoutubeChannelSearchTool, + YoutubeVideoSearchTool, +) +from crewai_tools.tools.rag.rag_tool import Adapter +import pytest + + +pytestmark = [pytest.mark.vcr(filter_headers=["authorization"])] + + +@pytest.fixture +def mock_adapter(): + mock_adapter = MagicMock(spec=Adapter) + return mock_adapter + + +def test_directory_search_tool(): + with tempfile.TemporaryDirectory() as temp_dir: + test_file = Path(temp_dir) / "test.txt" + test_file.write_text("This is a test file for directory search") + + tool = DirectorySearchTool(directory=temp_dir) + result = tool._run(search_query="test file") + assert "test file" in result.lower() + + +def test_pdf_search_tool(mock_adapter): + mock_adapter.query.return_value = "this is a test" + + tool = PDFSearchTool(pdf="test.pdf", adapter=mock_adapter) + result = tool._run(query="test content") + assert "this is a test" in result.lower() + mock_adapter.add.assert_called_once_with("test.pdf", data_type=DataType.PDF_FILE) + mock_adapter.query.assert_called_once_with( + "test content", similarity_threshold=0.6, limit=5 + ) + + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = PDFSearchTool(adapter=mock_adapter) + result = tool._run(pdf="test.pdf", query="test content") + assert "this is a test" in result.lower() + mock_adapter.add.assert_called_once_with("test.pdf", data_type=DataType.PDF_FILE) + mock_adapter.query.assert_called_once_with( + "test content", similarity_threshold=0.6, limit=5 + ) + + +def test_txt_search_tool(): + with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as temp_file: + temp_file.write(b"This is a test file for txt search") + temp_file_path = temp_file.name + + try: + tool = TXTSearchTool() + tool.add(temp_file_path) + result = tool._run(search_query="test file") + assert "test file" in result.lower() + finally: + os.unlink(temp_file_path) + + +def test_docx_search_tool(mock_adapter): + mock_adapter.query.return_value = "this is a test" + + tool = DOCXSearchTool(docx="test.docx", adapter=mock_adapter) + result = tool._run(search_query="test content") + assert "this is a test" in result.lower() + mock_adapter.add.assert_called_once_with("test.docx", data_type=DataType.DOCX) + mock_adapter.query.assert_called_once_with( + "test content", similarity_threshold=0.6, limit=5 + ) + + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = DOCXSearchTool(adapter=mock_adapter) + result = tool._run(docx="test.docx", search_query="test content") + assert "this is a test" in result.lower() + mock_adapter.add.assert_called_once_with("test.docx", data_type=DataType.DOCX) + mock_adapter.query.assert_called_once_with( + "test content", similarity_threshold=0.6, limit=5 + ) + + +def test_json_search_tool(): + with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as temp_file: + temp_file.write(b'{"test": "This is a test JSON file"}') + temp_file_path = temp_file.name + + try: + tool = JSONSearchTool() + result = tool._run(search_query="test JSON", json_path=temp_file_path) + assert "test json" in result.lower() + finally: + os.unlink(temp_file_path) + + +def test_xml_search_tool(mock_adapter): + mock_adapter.query.return_value = "this is a test" + + tool = XMLSearchTool(adapter=mock_adapter) + result = tool._run(search_query="test XML", xml="test.xml") + assert "this is a test" in result.lower() + mock_adapter.add.assert_called_once_with("test.xml") + mock_adapter.query.assert_called_once_with( + "test XML", similarity_threshold=0.6, limit=5 + ) + + +def test_csv_search_tool(): + with tempfile.NamedTemporaryFile(suffix=".csv", delete=False) as temp_file: + temp_file.write(b"name,description\ntest,This is a test CSV file") + temp_file_path = temp_file.name + + try: + tool = CSVSearchTool() + tool.add(temp_file_path) + result = tool._run(search_query="test CSV") + assert "test csv" in result.lower() + finally: + os.unlink(temp_file_path) + + +def test_mdx_search_tool(): + with tempfile.NamedTemporaryFile(suffix=".mdx", delete=False) as temp_file: + temp_file.write(b"# Test MDX\nThis is a test MDX file") + temp_file_path = temp_file.name + + try: + tool = MDXSearchTool() + tool.add(temp_file_path) + result = tool._run(search_query="test MDX") + assert "test mdx" in result.lower() + finally: + os.unlink(temp_file_path) + + +def test_website_search_tool(mock_adapter): + mock_adapter.query.return_value = "this is a test" + + website = "https://crewai.com" + search_query = "what is crewai?" + tool = WebsiteSearchTool(website=website, adapter=mock_adapter) + result = tool._run(search_query=search_query) + + mock_adapter.query.assert_called_once_with( + "what is crewai?", similarity_threshold=0.6, limit=5 + ) + mock_adapter.add.assert_called_once_with(website, data_type=DataType.WEBSITE) + + assert "this is a test" in result.lower() + + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = WebsiteSearchTool(adapter=mock_adapter) + result = tool._run(website=website, search_query=search_query) + + mock_adapter.query.assert_called_once_with( + "what is crewai?", similarity_threshold=0.6, limit=5 + ) + mock_adapter.add.assert_called_once_with(website, data_type=DataType.WEBSITE) + + assert "this is a test" in result.lower() + + +def test_youtube_video_search_tool(mock_adapter): + mock_adapter.query.return_value = "some video description" + + youtube_video_url = "https://www.youtube.com/watch?v=sample-video-id" + search_query = "what is the video about?" + tool = YoutubeVideoSearchTool( + youtube_video_url=youtube_video_url, + adapter=mock_adapter, + ) + result = tool._run(search_query=search_query) + assert "some video description" in result + + mock_adapter.add.assert_called_once_with( + youtube_video_url, data_type=DataType.YOUTUBE_VIDEO + ) + mock_adapter.query.assert_called_once_with( + search_query, similarity_threshold=0.6, limit=5 + ) + + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = YoutubeVideoSearchTool(adapter=mock_adapter) + result = tool._run(youtube_video_url=youtube_video_url, search_query=search_query) + assert "some video description" in result + + mock_adapter.add.assert_called_once_with( + youtube_video_url, data_type=DataType.YOUTUBE_VIDEO + ) + mock_adapter.query.assert_called_once_with( + search_query, similarity_threshold=0.6, limit=5 + ) + + +def test_youtube_channel_search_tool(mock_adapter): + mock_adapter.query.return_value = "channel description" + + youtube_channel_handle = "@crewai" + search_query = "what is the channel about?" + tool = YoutubeChannelSearchTool( + youtube_channel_handle=youtube_channel_handle, adapter=mock_adapter + ) + result = tool._run(search_query=search_query) + assert "channel description" in result + mock_adapter.add.assert_called_once_with( + youtube_channel_handle, data_type=DataType.YOUTUBE_CHANNEL + ) + mock_adapter.query.assert_called_once_with( + search_query, similarity_threshold=0.6, limit=5 + ) + + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = YoutubeChannelSearchTool(adapter=mock_adapter) + result = tool._run( + youtube_channel_handle=youtube_channel_handle, search_query=search_query + ) + assert "channel description" in result + + mock_adapter.add.assert_called_once_with( + youtube_channel_handle, data_type=DataType.YOUTUBE_CHANNEL + ) + mock_adapter.query.assert_called_once_with( + search_query, similarity_threshold=0.6, limit=5 + ) + + +def test_code_docs_search_tool(mock_adapter): + mock_adapter.query.return_value = "test documentation" + + docs_url = "https://crewai.com/any-docs-url" + search_query = "test documentation" + tool = CodeDocsSearchTool(docs_url=docs_url, adapter=mock_adapter) + result = tool._run(search_query=search_query) + assert "test documentation" in result + mock_adapter.add.assert_called_once_with(docs_url, data_type=DataType.DOCS_SITE) + mock_adapter.query.assert_called_once_with( + search_query, similarity_threshold=0.6, limit=5 + ) + + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = CodeDocsSearchTool(adapter=mock_adapter) + result = tool._run(docs_url=docs_url, search_query=search_query) + assert "test documentation" in result + mock_adapter.add.assert_called_once_with(docs_url, data_type=DataType.DOCS_SITE) + mock_adapter.query.assert_called_once_with( + search_query, similarity_threshold=0.6, limit=5 + ) + + +def test_github_search_tool(mock_adapter): + mock_adapter.query.return_value = "repo description" + + # ensure the provided repo and content types are used after initialization + tool = GithubSearchTool( + gh_token="test_token", + github_repo="crewai/crewai", + content_types=["code"], + adapter=mock_adapter, + ) + result = tool._run(search_query="tell me about crewai repo") + assert "repo description" in result + mock_adapter.add.assert_called_once_with( + "https://github.com/crewai/crewai", + data_type=DataType.GITHUB, + metadata={"content_types": ["code"], "gh_token": "test_token"}, + ) + mock_adapter.query.assert_called_once_with( + "tell me about crewai repo", similarity_threshold=0.6, limit=5 + ) + + # ensure content types provided by run call is used + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = GithubSearchTool(gh_token="test_token", adapter=mock_adapter) + result = tool._run( + github_repo="crewai/crewai", + content_types=["code", "issue"], + search_query="tell me about crewai repo", + ) + assert "repo description" in result + mock_adapter.add.assert_called_once_with( + "https://github.com/crewai/crewai", + data_type=DataType.GITHUB, + metadata={"content_types": ["code", "issue"], "gh_token": "test_token"}, + ) + mock_adapter.query.assert_called_once_with( + "tell me about crewai repo", similarity_threshold=0.6, limit=5 + ) + + # ensure default content types are used if not provided + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = GithubSearchTool(gh_token="test_token", adapter=mock_adapter) + result = tool._run( + github_repo="crewai/crewai", + search_query="tell me about crewai repo", + ) + assert "repo description" in result + mock_adapter.add.assert_called_once_with( + "https://github.com/crewai/crewai", + data_type=DataType.GITHUB, + metadata={ + "content_types": ["code", "repo", "pr", "issue"], + "gh_token": "test_token", + }, + ) + mock_adapter.query.assert_called_once_with( + "tell me about crewai repo", similarity_threshold=0.6, limit=5 + ) + + # ensure nothing is added if no repo is provided + mock_adapter.query.reset_mock() + mock_adapter.add.reset_mock() + + tool = GithubSearchTool(gh_token="test_token", adapter=mock_adapter) + result = tool._run(search_query="tell me about crewai repo") + mock_adapter.add.assert_not_called() + mock_adapter.query.assert_called_once_with( + "tell me about crewai repo", similarity_threshold=0.6, limit=5 + ) diff --git a/lib/crewai-tools/tests/tools/tool_collection_test.py b/lib/crewai-tools/tests/tools/tool_collection_test.py new file mode 100644 index 000000000..b2d9471ce --- /dev/null +++ b/lib/crewai-tools/tests/tools/tool_collection_test.py @@ -0,0 +1,231 @@ +import unittest +from unittest.mock import MagicMock + +from crewai.tools import BaseTool +from crewai_tools.adapters.tool_collection import ToolCollection + + +class TestToolCollection(unittest.TestCase): + def setUp(self): + self.search_tool = self._create_mock_tool( + "SearcH", "Search Tool" + ) # Tool name is case sensitive + self.calculator_tool = self._create_mock_tool("calculator", "Calculator Tool") + self.translator_tool = self._create_mock_tool("translator", "Translator Tool") + + self.tools = ToolCollection( + [self.search_tool, self.calculator_tool, self.translator_tool] + ) + + def _create_mock_tool(self, name, description): + mock_tool = MagicMock(spec=BaseTool) + mock_tool.name = name + mock_tool.description = description + return mock_tool + + def test_initialization(self): + self.assertEqual(len(self.tools), 3) + self.assertEqual(self.tools[0].name, "SearcH") + self.assertEqual(self.tools[1].name, "calculator") + self.assertEqual(self.tools[2].name, "translator") + + def test_empty_initialization(self): + empty_collection = ToolCollection() + self.assertEqual(len(empty_collection), 0) + self.assertEqual(empty_collection._name_cache, {}) + + def test_initialization_with_none(self): + collection = ToolCollection(None) + self.assertEqual(len(collection), 0) + self.assertEqual(collection._name_cache, {}) + + def test_access_by_index(self): + self.assertEqual(self.tools[0], self.search_tool) + self.assertEqual(self.tools[1], self.calculator_tool) + self.assertEqual(self.tools[2], self.translator_tool) + + def test_access_by_name(self): + self.assertEqual(self.tools["search"], self.search_tool) + self.assertEqual(self.tools["calculator"], self.calculator_tool) + self.assertEqual(self.tools["translator"], self.translator_tool) + + def test_key_error_for_invalid_name(self): + with self.assertRaises(KeyError): + _ = self.tools["nonexistent"] + + def test_index_error_for_invalid_index(self): + with self.assertRaises(IndexError): + _ = self.tools[10] + + def test_negative_index(self): + self.assertEqual(self.tools[-1], self.translator_tool) + self.assertEqual(self.tools[-2], self.calculator_tool) + self.assertEqual(self.tools[-3], self.search_tool) + + def test_append(self): + new_tool = self._create_mock_tool("new", "New Tool") + self.tools.append(new_tool) + + self.assertEqual(len(self.tools), 4) + self.assertEqual(self.tools[3], new_tool) + self.assertEqual(self.tools["new"], new_tool) + self.assertIn("new", self.tools._name_cache) + + def test_append_duplicate_name(self): + duplicate_tool = self._create_mock_tool("search", "Duplicate Search Tool") + self.tools.append(duplicate_tool) + + self.assertEqual(len(self.tools), 4) + self.assertEqual(self.tools["search"], duplicate_tool) + + def test_extend(self): + new_tools = [ + self._create_mock_tool("tool4", "Tool 4"), + self._create_mock_tool("tool5", "Tool 5"), + ] + self.tools.extend(new_tools) + + self.assertEqual(len(self.tools), 5) + self.assertEqual(self.tools["tool4"], new_tools[0]) + self.assertEqual(self.tools["tool5"], new_tools[1]) + self.assertIn("tool4", self.tools._name_cache) + self.assertIn("tool5", self.tools._name_cache) + + def test_insert(self): + new_tool = self._create_mock_tool("inserted", "Inserted Tool") + self.tools.insert(1, new_tool) + + self.assertEqual(len(self.tools), 4) + self.assertEqual(self.tools[1], new_tool) + self.assertEqual(self.tools["inserted"], new_tool) + self.assertIn("inserted", self.tools._name_cache) + + def test_remove(self): + self.tools.remove(self.calculator_tool) + + self.assertEqual(len(self.tools), 2) + with self.assertRaises(KeyError): + _ = self.tools["calculator"] + self.assertNotIn("calculator", self.tools._name_cache) + + def test_remove_nonexistent_tool(self): + nonexistent_tool = self._create_mock_tool("nonexistent", "Nonexistent Tool") + + with self.assertRaises(ValueError): + self.tools.remove(nonexistent_tool) + + def test_pop(self): + popped = self.tools.pop(1) + + self.assertEqual(popped, self.calculator_tool) + self.assertEqual(len(self.tools), 2) + with self.assertRaises(KeyError): + _ = self.tools["calculator"] + self.assertNotIn("calculator", self.tools._name_cache) + + def test_pop_last(self): + popped = self.tools.pop() + + self.assertEqual(popped, self.translator_tool) + self.assertEqual(len(self.tools), 2) + with self.assertRaises(KeyError): + _ = self.tools["translator"] + self.assertNotIn("translator", self.tools._name_cache) + + def test_clear(self): + self.tools.clear() + + self.assertEqual(len(self.tools), 0) + self.assertEqual(self.tools._name_cache, {}) + with self.assertRaises(KeyError): + _ = self.tools["search"] + + def test_iteration(self): + tools_list = list(self.tools) + self.assertEqual( + tools_list, [self.search_tool, self.calculator_tool, self.translator_tool] + ) + + def test_contains(self): + self.assertIn(self.search_tool, self.tools) + self.assertIn(self.calculator_tool, self.tools) + self.assertIn(self.translator_tool, self.tools) + + nonexistent_tool = self._create_mock_tool("nonexistent", "Nonexistent Tool") + self.assertNotIn(nonexistent_tool, self.tools) + + def test_slicing(self): + slice_result = self.tools[1:3] + self.assertEqual(len(slice_result), 2) + self.assertEqual(slice_result[0], self.calculator_tool) + self.assertEqual(slice_result[1], self.translator_tool) + + self.assertIsInstance(slice_result, list) + self.assertNotIsInstance(slice_result, ToolCollection) + + def test_getitem_with_tool_name_as_int(self): + numeric_name_tool = self._create_mock_tool("123", "Numeric Name Tool") + self.tools.append(numeric_name_tool) + + self.assertEqual(self.tools["123"], numeric_name_tool) + + with self.assertRaises(IndexError): + _ = self.tools[123] + + def test_filter_by_names(self): + filtered = self.tools.filter_by_names(None) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 3) + + filtered = self.tools.filter_by_names(["search", "translator"]) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 2) + self.assertEqual(filtered[0], self.search_tool) + self.assertEqual(filtered[1], self.translator_tool) + self.assertEqual(filtered["search"], self.search_tool) + self.assertEqual(filtered["translator"], self.translator_tool) + + filtered = self.tools.filter_by_names(["search", "nonexistent"]) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 1) + self.assertEqual(filtered[0], self.search_tool) + + filtered = self.tools.filter_by_names(["nonexistent1", "nonexistent2"]) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 0) + + filtered = self.tools.filter_by_names([]) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 0) + + def test_filter_where(self): + filtered = self.tools.filter_where(lambda tool: tool.name.startswith("S")) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 1) + self.assertEqual(filtered[0], self.search_tool) + self.assertEqual(filtered["search"], self.search_tool) + + filtered = self.tools.filter_where(lambda tool: True) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 3) + self.assertEqual(filtered[0], self.search_tool) + self.assertEqual(filtered[1], self.calculator_tool) + self.assertEqual(filtered[2], self.translator_tool) + + filtered = self.tools.filter_where(lambda tool: False) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 0) + filtered = self.tools.filter_where(lambda tool: len(tool.name) > 8) + + self.assertIsInstance(filtered, ToolCollection) + self.assertEqual(len(filtered), 2) + self.assertEqual(filtered[0], self.calculator_tool) + self.assertEqual(filtered[1], self.translator_tool) diff --git a/lib/crewai-tools/tool.specs.json b/lib/crewai-tools/tool.specs.json new file mode 100644 index 000000000..c16abee40 --- /dev/null +++ b/lib/crewai-tools/tool.specs.json @@ -0,0 +1,9612 @@ +{ + "tools": [ + { + "description": "A wrapper around [AI-Minds](https://mindsdb.com/minds). Useful for when you need answers to questions from your data, stored in data sources including PostgreSQL, MySQL, MariaDB, ClickHouse, Snowflake and Google BigQuery. Input should be a question in natural language.", + "env_vars": [ + { + "default": null, + "description": "API key for AI-Minds", + "name": "MINDS_API_KEY", + "required": true + } + ], + "humanized_name": "AIMind Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "datasources": { + "anyOf": [ + { + "items": { + "additionalProperties": true, + "type": "object" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Datasources" + }, + "mind_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Mind Name" + } + }, + "title": "AIMindTool", + "type": "object" + }, + "name": "AIMindTool", + "package_dependencies": [ + "minds-sdk" + ], + "run_params_schema": { + "description": "Input for AIMind Tool.", + "properties": { + "query": { + "description": "Question in natural language to ask the AI-Mind", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "AIMindToolInputSchema", + "type": "object" + } + }, + { + "description": "Fetches metadata from Arxiv based on a search query and optionally downloads PDFs.", + "env_vars": [], + "humanized_name": "Arxiv Paper Fetcher and Downloader", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "additionalProperties": true, + "properties": {}, + "title": "ArxivPaperTool", + "type": "object" + }, + "name": "ArxivPaperTool", + "package_dependencies": [ + "pydantic" + ], + "run_params_schema": { + "properties": { + "max_results": { + "default": 5, + "description": "Max results to fetch; must be between 1 and 100", + "maximum": 100, + "minimum": 1, + "title": "Max Results", + "type": "integer" + }, + "search_query": { + "description": "Search query for Arxiv, e.g., 'transformer neural network'", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "ArxivToolInput", + "type": "object" + } + }, + { + "description": "A tool that can be used to search the internet with a search_query.", + "env_vars": [ + { + "default": null, + "description": "API key for Brave Search", + "name": "BRAVE_API_KEY", + "required": true + } + ], + "humanized_name": "Brave Web Search the internet", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "BraveSearchTool - A tool for performing web searches using the Brave Search API.\n\nThis module provides functionality to search the internet using Brave's Search API,\nsupporting customizable result counts and country-specific searches.\n\nDependencies:\n - requests\n - pydantic\n - python-dotenv (for API key management)", + "properties": { + "country": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "", + "title": "Country" + }, + "n_results": { + "default": 10, + "title": "N Results", + "type": "integer" + }, + "save_file": { + "default": false, + "title": "Save File", + "type": "boolean" + }, + "search_url": { + "default": "https://api.search.brave.com/res/v1/web/search", + "title": "Search Url", + "type": "string" + } + }, + "title": "BraveSearchTool", + "type": "object" + }, + "name": "BraveSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for BraveSearchTool.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to search the internet", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "BraveSearchToolSchema", + "type": "object" + } + }, + { + "description": "Scrapes structured data using Bright Data Dataset API from a URL and optional input parameters", + "env_vars": [], + "humanized_name": "Bright Data Dataset Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "CrewAI-compatible tool for scraping structured data using Bright Data Datasets.\n\nAttributes:\n name (str): Tool name displayed in the CrewAI environment.\n description (str): Tool description shown to agents or users.\n args_schema (Type[BaseModel]): Pydantic schema for validating input arguments.", + "properties": { + "additional_params": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Additional Params" + }, + "dataset_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Dataset Type" + }, + "format": { + "default": "json", + "title": "Format", + "type": "string" + }, + "url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Url" + }, + "zipcode": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Zipcode" + } + }, + "title": "BrightDataDatasetTool", + "type": "object" + }, + "name": "BrightDataDatasetTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Schema for validating input parameters for the BrightDataDatasetTool.\n\nAttributes:\n dataset_type (str): Required Bright Data Dataset Type used to specify which dataset to access.\n format (str): Response format (json by default). Multiple formats exist - json, ndjson, jsonl, csv\n url (str): The URL from which structured data needs to be extracted.\n zipcode (Optional[str]): An optional ZIP code to narrow down the data geographically.\n additional_params (Optional[Dict]): Extra parameters for the Bright Data API call.", + "properties": { + "additional_params": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Additional params if any", + "title": "Additional Params" + }, + "dataset_type": { + "description": "The Bright Data Dataset Type", + "title": "Dataset Type", + "type": "string" + }, + "format": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "json", + "description": "Response format (json by default)", + "title": "Format" + }, + "url": { + "description": "The URL to extract data from", + "title": "Url", + "type": "string" + }, + "zipcode": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional zipcode", + "title": "Zipcode" + } + }, + "required": [ + "dataset_type", + "url" + ], + "title": "BrightDataDatasetToolSchema", + "type": "object" + } + }, + { + "description": "Tool to perform web search using Bright Data SERP API.", + "env_vars": [], + "humanized_name": "Bright Data SERP Search", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A web search tool that utilizes Bright Data's SERP API to perform queries and return either structured results\nor raw page content from search engines like Google or Bing.\n\nAttributes:\n name (str): Tool name used by the agent.\n description (str): A brief explanation of what the tool does.\n args_schema (Type[BaseModel]): Schema class for validating tool arguments.\n base_url (str): The Bright Data API endpoint used for making the POST request.\n api_key (str): Bright Data API key loaded from the environment variable 'BRIGHT_DATA_API_KEY'.\n zone (str): Zone identifier from Bright Data, loaded from the environment variable 'BRIGHT_DATA_ZONE'.\n\nRaises:\n ValueError: If API key or zone environment variables are not set.", + "properties": { + "api_key": { + "default": "", + "title": "Api Key", + "type": "string" + }, + "base_url": { + "default": "", + "title": "Base Url", + "type": "string" + }, + "country": { + "default": "us", + "title": "Country", + "type": "string" + }, + "device_type": { + "default": "desktop", + "title": "Device Type", + "type": "string" + }, + "language": { + "default": "en", + "title": "Language", + "type": "string" + }, + "parse_results": { + "default": true, + "title": "Parse Results", + "type": "boolean" + }, + "query": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Query" + }, + "search_engine": { + "default": "google", + "title": "Search Engine", + "type": "string" + }, + "search_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Search Type" + }, + "zone": { + "default": "", + "title": "Zone", + "type": "string" + } + }, + "title": "BrightDataSearchTool", + "type": "object" + }, + "name": "BrightDataSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Schema that defines the input arguments for the BrightDataSearchToolSchema.\n\nAttributes:\n query (str): The search query to be executed (e.g., \"latest AI news\").\n search_engine (Optional[str]): The search engine to use (\"google\", \"bing\", \"yandex\"). Default is \"google\".\n country (Optional[str]): Two-letter country code for geo-targeting (e.g., \"us\", \"in\"). Default is \"us\".\n language (Optional[str]): Language code for search results (e.g., \"en\", \"es\"). Default is \"en\".\n search_type (Optional[str]): Type of search, such as \"isch\" (images), \"nws\" (news), \"jobs\", etc.\n device_type (Optional[str]): Device type to simulate (\"desktop\", \"mobile\", \"ios\", \"android\"). Default is \"desktop\".\n parse_results (Optional[bool]): If True, results will be returned in structured JSON. If False, raw HTML. Default is True.", + "properties": { + "country": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "us", + "description": "Two-letter country code for geo-targeting (e.g., 'us', 'gb')", + "title": "Country" + }, + "device_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "desktop", + "description": "Device type to simulate (e.g., 'mobile', 'desktop', 'ios')", + "title": "Device Type" + }, + "language": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "en", + "description": "Language code (e.g., 'en', 'es') used in the query URL", + "title": "Language" + }, + "parse_results": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": true, + "description": "Whether to parse and return JSON (True) or raw HTML/text (False)", + "title": "Parse Results" + }, + "query": { + "description": "Search query to perform", + "title": "Query", + "type": "string" + }, + "search_engine": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "google", + "description": "Search engine domain (e.g., 'google', 'bing', 'yandex')", + "title": "Search Engine" + }, + "search_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Type of search (e.g., 'isch' for images, 'nws' for news)", + "title": "Search Type" + } + }, + "required": [ + "query" + ], + "title": "BrightDataSearchToolSchema", + "type": "object" + } + }, + { + "description": "Tool to perform web scraping using Bright Data Web Unlocker", + "env_vars": [], + "humanized_name": "Bright Data Web Unlocker Scraping", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A tool for performing web scraping using the Bright Data Web Unlocker API.\n\nThis tool allows automated and programmatic access to web pages by routing requests\nthrough Bright Data's unlocking and proxy infrastructure, which can bypass bot\nprotection mechanisms like CAPTCHA, geo-restrictions, and anti-bot detection.\n\nAttributes:\n name (str): Name of the tool.\n description (str): Description of what the tool does.\n args_schema (Type[BaseModel]): Pydantic model schema for expected input arguments.\n base_url (str): Base URL of the Bright Data Web Unlocker API.\n api_key (str): Bright Data API key (must be set in the BRIGHT_DATA_API_KEY environment variable).\n zone (str): Bright Data zone identifier (must be set in the BRIGHT_DATA_ZONE environment variable).\n\nMethods:\n _run(**kwargs: Any) -> Any:\n Sends a scraping request to Bright Data's Web Unlocker API and returns the result.", + "properties": { + "api_key": { + "default": "", + "title": "Api Key", + "type": "string" + }, + "base_url": { + "default": "", + "title": "Base Url", + "type": "string" + }, + "data_format": { + "default": "markdown", + "title": "Data Format", + "type": "string" + }, + "format": { + "default": "raw", + "title": "Format", + "type": "string" + }, + "url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Url" + }, + "zone": { + "default": "", + "title": "Zone", + "type": "string" + } + }, + "title": "BrightDataWebUnlockerTool", + "type": "object" + }, + "name": "BrightDataWebUnlockerTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Pydantic schema for input parameters used by the BrightDataWebUnlockerTool.\n\nThis schema defines the structure and validation for parameters passed when performing\na web scraping request using Bright Data's Web Unlocker.\n\nAttributes:\n url (str): The target URL to scrape.\n format (Optional[str]): Format of the response returned by Bright Data. Default 'raw' format.\n data_format (Optional[str]): Response data format (html by default). markdown is one more option.", + "properties": { + "data_format": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "markdown", + "description": "Response data format (html by default)", + "title": "Data Format" + }, + "format": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "raw", + "description": "Response format (raw is standard)", + "title": "Format" + }, + "url": { + "description": "URL to perform the web scraping", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url" + ], + "title": "BrightDataUnlockerToolSchema", + "type": "object" + } + }, + { + "description": "Load webpages url in a headless browser using Browserbase and return the contents", + "env_vars": [ + { + "default": null, + "description": "API key for Browserbase services", + "name": "BROWSERBASE_API_KEY", + "required": false + }, + { + "default": null, + "description": "Project ID for Browserbase services", + "name": "BROWSERBASE_PROJECT_ID", + "required": false + } + ], + "humanized_name": "Browserbase web load tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "browserbase": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Browserbase" + }, + "project_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Project Id" + }, + "proxy": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Proxy" + }, + "session_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Session Id" + }, + "text_content": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": false, + "title": "Text Content" + } + }, + "title": "BrowserbaseLoadTool", + "type": "object" + }, + "name": "BrowserbaseLoadTool", + "package_dependencies": [ + "browserbase" + ], + "run_params_schema": { + "properties": { + "url": { + "description": "Website URL", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url" + ], + "title": "BrowserbaseLoadToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a CSV's content.", + "env_vars": [], + "humanized_name": "Search a CSV's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "CSVSearchTool", + "type": "object" + }, + "name": "CSVSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for CSVSearchTool.", + "properties": { + "csv": { + "description": "Mandatory csv path you want to search", + "title": "Csv", + "type": "string" + }, + "search_query": { + "description": "Mandatory search query you want to use to search the CSV's content", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query", + "csv" + ], + "title": "CSVSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a Code Docs content.", + "env_vars": [], + "humanized_name": "Search a Code Docs content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "CodeDocsSearchTool", + "type": "object" + }, + "name": "CodeDocsSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for CodeDocsSearchTool.", + "properties": { + "docs_url": { + "description": "Mandatory docs_url path you want to search", + "title": "Docs Url", + "type": "string" + }, + "search_query": { + "description": "Mandatory search query you want to use to search the Code Docs content", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query", + "docs_url" + ], + "title": "CodeDocsSearchToolSchema", + "type": "object" + } + }, + { + "description": "Interprets Python3 code strings with a final print statement.", + "env_vars": [], + "humanized_name": "Code Interpreter", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A tool for executing Python code in isolated environments.\n\nThis tool provides functionality to run Python code either in a Docker container\nfor safe isolation or directly in a restricted sandbox. It can handle installing\nPython packages and executing arbitrary Python code.", + "properties": { + "code": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Code" + }, + "default_image_tag": { + "default": "code-interpreter:latest", + "title": "Default Image Tag", + "type": "string" + }, + "unsafe_mode": { + "default": false, + "title": "Unsafe Mode", + "type": "boolean" + }, + "user_docker_base_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "User Docker Base Url" + }, + "user_dockerfile_path": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "User Dockerfile Path" + } + }, + "title": "CodeInterpreterTool", + "type": "object" + }, + "name": "CodeInterpreterTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Schema for defining inputs to the CodeInterpreterTool.\n\nThis schema defines the required parameters for code execution,\nincluding the code to run and any libraries that need to be installed.", + "properties": { + "code": { + "description": "Python3 code used to be interpreted in the Docker container. ALWAYS PRINT the final result and the output of the code", + "title": "Code", + "type": "string" + }, + "libraries_used": { + "description": "List of libraries used in the code with proper installing names separated by commas. Example: numpy,pandas,beautifulsoup4", + "items": { + "type": "string" + }, + "title": "Libraries Used", + "type": "array" + } + }, + "required": [ + "code", + "libraries_used" + ], + "title": "CodeInterpreterSchema", + "type": "object" + } + }, + { + "description": "", + "env_vars": [ + { + "default": null, + "description": "API key for Composio services", + "name": "COMPOSIO_API_KEY", + "required": true + } + ], + "humanized_name": "ComposioTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Wrapper for composio tools.", + "properties": {}, + "required": [ + "name", + "description" + ], + "title": "ComposioTool", + "type": "object" + }, + "name": "ComposioTool", + "package_dependencies": [], + "run_params_schema": {} + }, + { + "description": "Create a new Contextual AI RAG agent with documents and datastore", + "env_vars": [], + "humanized_name": "Contextual AI Create Agent Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to create Contextual AI RAG agents with documents.", + "properties": { + "api_key": { + "title": "Api Key", + "type": "string" + }, + "contextual_client": { + "default": null, + "title": "Contextual Client" + } + }, + "required": [ + "api_key" + ], + "title": "ContextualAICreateAgentTool", + "type": "object" + }, + "name": "ContextualAICreateAgentTool", + "package_dependencies": [ + "contextual-client" + ], + "run_params_schema": { + "description": "Schema for contextual create agent tool.", + "properties": { + "agent_description": { + "description": "Description for the new agent", + "title": "Agent Description", + "type": "string" + }, + "agent_name": { + "description": "Name for the new agent", + "title": "Agent Name", + "type": "string" + }, + "datastore_name": { + "description": "Name for the new datastore", + "title": "Datastore Name", + "type": "string" + }, + "document_paths": { + "description": "List of file paths to upload", + "items": { + "type": "string" + }, + "title": "Document Paths", + "type": "array" + } + }, + "required": [ + "agent_name", + "agent_description", + "datastore_name", + "document_paths" + ], + "title": "ContextualAICreateAgentSchema", + "type": "object" + } + }, + { + "description": "Parse documents using Contextual AI's advanced document parser", + "env_vars": [], + "humanized_name": "Contextual AI Document Parser", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to parse documents using Contextual AI's parser.", + "properties": { + "api_key": { + "title": "Api Key", + "type": "string" + } + }, + "required": [ + "api_key" + ], + "title": "ContextualAIParseTool", + "type": "object" + }, + "name": "ContextualAIParseTool", + "package_dependencies": [ + "contextual-client" + ], + "run_params_schema": { + "description": "Schema for contextual parse tool.", + "properties": { + "enable_document_hierarchy": { + "default": true, + "description": "Enable document hierarchy", + "title": "Enable Document Hierarchy", + "type": "boolean" + }, + "figure_caption_mode": { + "default": "concise", + "description": "Figure caption mode", + "title": "Figure Caption Mode", + "type": "string" + }, + "file_path": { + "description": "Path to the document to parse", + "title": "File Path", + "type": "string" + }, + "output_types": { + "default": [ + "markdown-per-page" + ], + "description": "List of output types", + "items": { + "type": "string" + }, + "title": "Output Types", + "type": "array" + }, + "page_range": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Page range to parse (e.g., '0-5')", + "title": "Page Range" + }, + "parse_mode": { + "default": "standard", + "description": "Parsing mode", + "title": "Parse Mode", + "type": "string" + } + }, + "required": [ + "file_path" + ], + "title": "ContextualAIParseSchema", + "type": "object" + } + }, + { + "description": "Use this tool to query a Contextual AI RAG agent with access to your documents", + "env_vars": [], + "humanized_name": "Contextual AI Query Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to query Contextual AI RAG agents.", + "properties": { + "api_key": { + "title": "Api Key", + "type": "string" + }, + "contextual_client": { + "default": null, + "title": "Contextual Client" + } + }, + "required": [ + "api_key" + ], + "title": "ContextualAIQueryTool", + "type": "object" + }, + "name": "ContextualAIQueryTool", + "package_dependencies": [ + "contextual-client" + ], + "run_params_schema": { + "description": "Schema for contextual query tool.", + "properties": { + "agent_id": { + "description": "ID of the Contextual AI agent to query", + "title": "Agent Id", + "type": "string" + }, + "datastore_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional datastore ID for document readiness verification", + "title": "Datastore Id" + }, + "query": { + "description": "Query to send to the Contextual AI agent.", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query", + "agent_id" + ], + "title": "ContextualAIQuerySchema", + "type": "object" + } + }, + { + "description": "Rerank documents using Contextual AI's instruction-following reranker", + "env_vars": [], + "humanized_name": "Contextual AI Document Reranker", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to rerank documents using Contextual AI's instruction-following reranker.", + "properties": { + "api_key": { + "title": "Api Key", + "type": "string" + } + }, + "required": [ + "api_key" + ], + "title": "ContextualAIRerankTool", + "type": "object" + }, + "name": "ContextualAIRerankTool", + "package_dependencies": [ + "contextual-client" + ], + "run_params_schema": { + "description": "Schema for contextual rerank tool.", + "properties": { + "documents": { + "description": "List of document texts to rerank", + "items": { + "type": "string" + }, + "title": "Documents", + "type": "array" + }, + "instruction": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional instruction for reranking behavior", + "title": "Instruction" + }, + "metadata": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional metadata for each document", + "title": "Metadata" + }, + "model": { + "default": "ctxl-rerank-en-v1-instruct", + "description": "Reranker model to use", + "title": "Model", + "type": "string" + }, + "query": { + "description": "The search query to rerank documents against", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query", + "documents" + ], + "title": "ContextualAIRerankSchema", + "type": "object" + } + }, + { + "description": "A tool to search the Couchbase database for relevant information on internal documents.", + "env_vars": [], + "humanized_name": "CouchbaseFTSVectorSearchTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to search the Couchbase database", + "properties": { + "bucket_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": [ + null + ], + "title": "Bucket Name" + }, + "cluster": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Cluster" + }, + "collection_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": [ + null + ], + "title": "Collection Name" + }, + "embedding_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "embedding", + "description": "Name of the field in the search index that stores the vector", + "title": "Embedding Key" + }, + "index_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": [ + null + ], + "title": "Index Name" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 3, + "title": "Limit" + }, + "scope_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": [ + null + ], + "title": "Scope Name" + }, + "scoped_index": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "title": "Scoped Index" + } + }, + "title": "CouchbaseFTSVectorSearchTool", + "type": "object" + }, + "name": "CouchbaseFTSVectorSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for CouchbaseTool.", + "properties": { + "query": { + "description": "The query to search retrieve relevant information from the Couchbase database. Pass only the query, not the question.", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "CouchbaseToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a DOCX's content.", + "env_vars": [], + "humanized_name": "Search a DOCX's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "DOCXSearchTool", + "type": "object" + }, + "name": "DOCXSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for DOCXSearchTool.", + "properties": { + "docx": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Mandatory docx path you want to search", + "title": "Docx" + }, + "search_query": { + "description": "Mandatory search query you want to use to search the DOCX's content", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "docx", + "search_query" + ], + "title": "DOCXSearchToolSchema", + "type": "object" + } + }, + { + "description": "Generates images using OpenAI's Dall-E model.", + "env_vars": [ + { + "default": null, + "description": "API key for OpenAI services", + "name": "OPENAI_API_KEY", + "required": true + } + ], + "humanized_name": "Dall-E Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "model": { + "default": "dall-e-3", + "title": "Model", + "type": "string" + }, + "n": { + "default": 1, + "title": "N", + "type": "integer" + }, + "quality": { + "default": "standard", + "title": "Quality", + "type": "string" + }, + "size": { + "default": "1024x1024", + "title": "Size", + "type": "string" + } + }, + "title": "DallETool", + "type": "object" + }, + "name": "DallETool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for Dall-E Tool.", + "properties": { + "image_description": { + "description": "Description of the image to be generated by Dall-E.", + "title": "Image Description", + "type": "string" + } + }, + "required": [ + "image_description" + ], + "title": "ImagePromptSchema", + "type": "object" + } + }, + { + "description": "Execute SQL queries against Databricks workspace tables and return the results. Provide a 'query' parameter with the SQL query to execute.", + "env_vars": [], + "humanized_name": "Databricks SQL Query", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A tool for querying Databricks workspace tables using SQL.\n\nThis tool executes SQL queries against Databricks tables and returns the results.\nIt requires Databricks authentication credentials to be set as environment variables.\n\nAuthentication can be provided via:\n- Databricks CLI profile: Set DATABRICKS_CONFIG_PROFILE environment variable\n- Direct credentials: Set DATABRICKS_HOST and DATABRICKS_TOKEN environment variables\n\nExample:\n >>> tool = DatabricksQueryTool()\n >>> results = tool.run(query=\"SELECT * FROM my_table LIMIT 10\")", + "properties": { + "default_catalog": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default Catalog" + }, + "default_schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default Schema" + }, + "default_warehouse_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default Warehouse Id" + } + }, + "title": "DatabricksQueryTool", + "type": "object" + }, + "name": "DatabricksQueryTool", + "package_dependencies": [ + "databricks-sdk" + ], + "run_params_schema": { + "description": "Input schema for DatabricksQueryTool.", + "properties": { + "catalog": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Databricks catalog name (optional, defaults to configured catalog)", + "title": "Catalog" + }, + "db_schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Databricks schema name (optional, defaults to configured schema)", + "title": "Db Schema" + }, + "query": { + "description": "SQL query to execute against the Databricks workspace table", + "title": "Query", + "type": "string" + }, + "row_limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 1000, + "description": "Maximum number of rows to return (default: 1000)", + "title": "Row Limit" + }, + "warehouse_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Databricks SQL warehouse ID (optional, defaults to configured warehouse)", + "title": "Warehouse Id" + } + }, + "required": [ + "query" + ], + "title": "DatabricksQueryToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to recursively list a directory's content.", + "env_vars": [], + "humanized_name": "List files in directory", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "directory": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Directory" + } + }, + "title": "DirectoryReadTool", + "type": "object" + }, + "name": "DirectoryReadTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for DirectoryReadTool.", + "properties": { + "directory": { + "description": "Mandatory directory to list content", + "title": "Directory", + "type": "string" + } + }, + "required": [ + "directory" + ], + "title": "DirectoryReadToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a directory's content.", + "env_vars": [], + "humanized_name": "Search a directory's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "DirectorySearchTool", + "type": "object" + }, + "name": "DirectorySearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for DirectorySearchTool.", + "properties": { + "directory": { + "description": "Mandatory directory you want to search", + "title": "Directory", + "type": "string" + }, + "search_query": { + "description": "Mandatory search query you want to use to search the directory's content", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query", + "directory" + ], + "title": "DirectorySearchToolSchema", + "type": "object" + } + }, + { + "description": "Search the internet using Exa", + "env_vars": [ + { + "default": null, + "description": "API key for Exa services", + "name": "EXA_API_KEY", + "required": false + } + ], + "humanized_name": "EXASearchTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "API key for Exa services", + "required": false, + "title": "Api Key" + }, + "client": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Client" + }, + "content": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": false, + "title": "Content" + }, + "summary": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": false, + "title": "Summary" + }, + "type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "auto", + "title": "Type" + } + }, + "title": "EXASearchTool", + "type": "object" + }, + "name": "EXASearchTool", + "package_dependencies": [ + "exa_py" + ], + "run_params_schema": { + "properties": { + "end_published_date": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "End date for the search", + "title": "End Published Date" + }, + "include_domains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "List of domains to include in the search", + "title": "Include Domains" + }, + "search_query": { + "description": "Mandatory search query you want to use to search the internet", + "title": "Search Query", + "type": "string" + }, + "start_published_date": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Start date for the search", + "title": "Start Published Date" + } + }, + "required": [ + "search_query" + ], + "title": "EXABaseToolSchema", + "type": "object" + } + }, + { + "description": "Compresses a file or directory into an archive (.zip currently supported). Useful for archiving logs, documents, or backups.", + "env_vars": [], + "humanized_name": "File Compressor Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": {}, + "title": "FileCompressorTool", + "type": "object" + }, + "name": "FileCompressorTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input schema for FileCompressorTool.", + "properties": { + "format": { + "default": "zip", + "description": "Compression format ('zip', 'tar', 'tar.gz', 'tar.bz2', 'tar.xz').", + "title": "Format", + "type": "string" + }, + "input_path": { + "description": "Path to the file or directory to compress.", + "title": "Input Path", + "type": "string" + }, + "output_path": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional output archive filename.", + "title": "Output Path" + }, + "overwrite": { + "default": false, + "description": "Whether to overwrite the archive if it already exists.", + "title": "Overwrite", + "type": "boolean" + } + }, + "required": [ + "input_path" + ], + "title": "FileCompressorToolInput", + "type": "object" + } + }, + { + "description": "A tool that reads the content of a file. To use this tool, provide a 'file_path' parameter with the path to the file you want to read. Optionally, provide 'start_line' to start reading from a specific line and 'line_count' to limit the number of lines read.", + "env_vars": [], + "humanized_name": "Read a file's content", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A tool for reading file contents.\n\nThis tool inherits its schema handling from BaseTool to avoid recursive schema\ndefinition issues. The args_schema is set to FileReadToolSchema which defines\nthe required file_path parameter. The schema should not be overridden in the\nconstructor as it would break the inheritance chain and cause infinite loops.\n\nThe tool supports two ways of specifying the file path:\n1. At construction time via the file_path parameter\n2. At runtime via the file_path parameter in the tool's input\n\nArgs:\n file_path (Optional[str]): Path to the file to be read. If provided,\n this becomes the default file path for the tool.\n **kwargs: Additional keyword arguments passed to BaseTool.\n\nExample:\n >>> tool = FileReadTool(file_path=\"/path/to/file.txt\")\n >>> content = tool.run() # Reads /path/to/file.txt\n >>> content = tool.run(file_path=\"/path/to/other.txt\") # Reads other.txt\n >>> content = tool.run(file_path=\"/path/to/file.txt\", start_line=100, line_count=50) # Reads lines 100-149", + "properties": { + "file_path": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "File Path" + } + }, + "title": "FileReadTool", + "type": "object" + }, + "name": "FileReadTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for FileReadTool.", + "properties": { + "file_path": { + "description": "Mandatory file full path to read the file", + "title": "File Path", + "type": "string" + }, + "line_count": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Number of lines to read. If None, reads the entire file", + "title": "Line Count" + }, + "start_line": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 1, + "description": "Line number to start reading from (1-indexed)", + "title": "Start Line" + } + }, + "required": [ + "file_path" + ], + "title": "FileReadToolSchema", + "type": "object" + } + }, + { + "description": "A tool to write content to a specified file. Accepts filename, content, and optionally a directory path and overwrite flag as input.", + "env_vars": [], + "humanized_name": "File Writer Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": {}, + "title": "FileWriterTool", + "type": "object" + }, + "name": "FileWriterTool", + "package_dependencies": [], + "run_params_schema": { + "properties": { + "content": { + "title": "Content", + "type": "string" + }, + "directory": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "./", + "title": "Directory" + }, + "filename": { + "title": "Filename", + "type": "string" + }, + "overwrite": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "boolean" + } + ], + "default": false, + "title": "Overwrite" + } + }, + "required": [ + "filename", + "content" + ], + "title": "FileWriterToolInput", + "type": "object" + } + }, + { + "description": "Crawl webpages using Firecrawl and return the contents", + "env_vars": [ + { + "default": null, + "description": "API key for Firecrawl services", + "name": "FIRECRAWL_API_KEY", + "required": true + } + ], + "humanized_name": "Firecrawl web crawl tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool for crawling websites using Firecrawl. To run this tool, you need to have a Firecrawl API key.\n\nArgs:\n api_key (str): Your Firecrawl API key.\n config (dict): Optional. It contains Firecrawl API parameters.\n\nDefault configuration options:\n max_depth (int): Maximum depth to crawl. Default: 2\n ignore_sitemap (bool): Whether to ignore sitemap. Default: True\n limit (int): Maximum number of pages to crawl. Default: 100\n allow_backward_links (bool): Allow crawling backward links. Default: False\n allow_external_links (bool): Allow crawling external links. Default: False\n scrape_options (ScrapeOptions): Options for scraping content\n - formats (list[str]): Content formats to return. Default: [\"markdown\", \"screenshot\", \"links\"]\n - only_main_content (bool): Only return main content. Default: True\n - timeout (int): Timeout in milliseconds. Default: 30000", + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Config" + } + }, + "title": "FirecrawlCrawlWebsiteTool", + "type": "object" + }, + "name": "FirecrawlCrawlWebsiteTool", + "package_dependencies": [ + "firecrawl-py" + ], + "run_params_schema": { + "properties": { + "url": { + "description": "Website URL", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url" + ], + "title": "FirecrawlCrawlWebsiteToolSchema", + "type": "object" + } + }, + { + "description": "Scrape webpages using Firecrawl and return the contents", + "env_vars": [ + { + "default": null, + "description": "API key for Firecrawl services", + "name": "FIRECRAWL_API_KEY", + "required": true + } + ], + "humanized_name": "Firecrawl web scrape tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool for scraping webpages using Firecrawl. To run this tool, you need to have a Firecrawl API key.\n\nArgs:\n api_key (str): Your Firecrawl API key.\n config (dict): Optional. It contains Firecrawl API parameters.\n\nDefault configuration options:\n formats (list[str]): Content formats to return. Default: [\"markdown\"]\n onlyMainContent (bool): Only return main content. Default: True\n includeTags (list[str]): Tags to include. Default: []\n excludeTags (list[str]): Tags to exclude. Default: []\n headers (dict): Headers to include. Default: {}\n waitFor (int): Time to wait for page to load in ms. Default: 0\n json_options (dict): Options for JSON extraction. Default: None", + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "config": { + "additionalProperties": true, + "title": "Config", + "type": "object" + } + }, + "title": "FirecrawlScrapeWebsiteTool", + "type": "object" + }, + "name": "FirecrawlScrapeWebsiteTool", + "package_dependencies": [ + "firecrawl-py" + ], + "run_params_schema": { + "properties": { + "url": { + "description": "Website URL", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url" + ], + "title": "FirecrawlScrapeWebsiteToolSchema", + "type": "object" + } + }, + { + "description": "Search webpages using Firecrawl and return the results", + "env_vars": [ + { + "default": null, + "description": "API key for Firecrawl services", + "name": "FIRECRAWL_API_KEY", + "required": true + } + ], + "humanized_name": "Firecrawl web search tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool for searching webpages using Firecrawl. To run this tool, you need to have a Firecrawl API key.\n\nArgs:\n api_key (str): Your Firecrawl API key.\n config (dict): Optional. It contains Firecrawl API parameters.\n\nDefault configuration options:\n limit (int): Maximum number of pages to crawl. Default: 5\n tbs (str): Time before search. Default: None\n lang (str): Language. Default: \"en\"\n country (str): Country. Default: \"us\"\n location (str): Location. Default: None\n timeout (int): Timeout in milliseconds. Default: 60000", + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Config" + } + }, + "title": "FirecrawlSearchTool", + "type": "object" + }, + "name": "FirecrawlSearchTool", + "package_dependencies": [ + "firecrawl-py" + ], + "run_params_schema": { + "properties": { + "query": { + "description": "Search query", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "FirecrawlSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool that leverages CrewAI Studio's capabilities to automatically generate complete CrewAI automations based on natural language descriptions. It translates high-level requirements into functional CrewAI implementations.", + "env_vars": [ + { + "default": null, + "description": "Personal Access Token for CrewAI AMP API", + "name": "CREWAI_PERSONAL_ACCESS_TOKEN", + "required": true + }, + { + "default": null, + "description": "Base URL for CrewAI AMP API", + "name": "CREWAI_PLUS_URL", + "required": false + } + ], + "humanized_name": "Generate CrewAI Automation", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "crewai_enterprise_url": { + "description": "The base URL of CrewAI AMP. If not provided, it will be loaded from the environment variable CREWAI_PLUS_URL with default https://app.crewai.com.", + "title": "Crewai Enterprise Url", + "type": "string" + }, + "personal_access_token": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The user's Personal Access Token to access CrewAI AMP API. If not provided, it will be loaded from the environment variable CREWAI_PERSONAL_ACCESS_TOKEN.", + "title": "Personal Access Token" + } + }, + "title": "GenerateCrewaiAutomationTool", + "type": "object" + }, + "name": "GenerateCrewaiAutomationTool", + "package_dependencies": [], + "run_params_schema": { + "properties": { + "organization_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The identifier for the CrewAI AMP organization. If not specified, a default organization will be used.", + "title": "Organization Id" + }, + "prompt": { + "description": "The prompt to generate the CrewAI automation, e.g. 'Generate a CrewAI automation that will scrape the website and store the data in a database.'", + "title": "Prompt", + "type": "string" + } + }, + "required": [ + "prompt" + ], + "title": "GenerateCrewaiAutomationToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a github repo's content. This is not the GitHub API, but instead a tool that can provide semantic search capabilities.", + "env_vars": [], + "humanized_name": "Search a github repo's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "content_types": { + "description": "Content types you want to be included search, options: [code, repo, pr, issue]", + "items": { + "type": "string" + }, + "title": "Content Types", + "type": "array" + }, + "gh_token": { + "title": "Gh Token", + "type": "string" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "required": [ + "gh_token" + ], + "title": "GithubSearchTool", + "type": "object" + }, + "name": "GithubSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for GithubSearchTool.", + "properties": { + "content_types": { + "description": "Mandatory content types you want to be included search, options: [code, repo, pr, issue]", + "items": { + "type": "string" + }, + "title": "Content Types", + "type": "array" + }, + "github_repo": { + "description": "Mandatory github you want to search", + "title": "Github Repo", + "type": "string" + }, + "search_query": { + "description": "Mandatory search query you want to use to search the github repo's content", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query", + "github_repo", + "content_types" + ], + "title": "GithubSearchToolSchema", + "type": "object" + } + }, + { + "description": "Scrape or crawl a website using Hyperbrowser and return the contents in properly formatted markdown or html", + "env_vars": [ + { + "default": null, + "description": "API key for Hyperbrowser services", + "name": "HYPERBROWSER_API_KEY", + "required": false + } + ], + "humanized_name": "Hyperbrowser web load tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "HyperbrowserLoadTool.\n\nScrape or crawl web pages and load the contents with optional parameters for configuring content extraction.\nRequires the `hyperbrowser` package.\nGet your API Key from https://app.hyperbrowser.ai/\n\nArgs:\n api_key: The Hyperbrowser API key, can be set as an environment variable `HYPERBROWSER_API_KEY` or passed directly", + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "hyperbrowser": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Hyperbrowser" + } + }, + "title": "HyperbrowserLoadTool", + "type": "object" + }, + "name": "HyperbrowserLoadTool", + "package_dependencies": [ + "hyperbrowser" + ], + "run_params_schema": { + "properties": { + "operation": { + "description": "Operation to perform on the website. Either 'scrape' or 'crawl'", + "enum": [ + "scrape", + "crawl" + ], + "title": "Operation", + "type": "string" + }, + "params": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "description": "Optional params for scrape or crawl. For more information on the supported params, visit https://docs.hyperbrowser.ai/reference/sdks/python/scrape#start-scrape-job-and-wait or https://docs.hyperbrowser.ai/reference/sdks/python/crawl#start-crawl-job-and-wait", + "title": "Params" + }, + "url": { + "description": "Website URL", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url", + "operation", + "params" + ], + "title": "HyperbrowserLoadToolSchema", + "type": "object" + } + }, + { + "description": "Invokes an CrewAI Platform Automation using API", + "env_vars": [], + "humanized_name": "invoke_amp_automation", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A CrewAI tool for invoking external crew/flows APIs.\n\nThis tool provides CrewAI Platform API integration with external crew services, supporting:\n- Dynamic input schema configuration\n- Automatic polling for task completion\n- Bearer token authentication\n- Comprehensive error handling\n\nExample:\n Basic usage:\n >>> tool = InvokeCrewAIAutomationTool(\n ... crew_api_url=\"https://api.example.com\",\n ... crew_bearer_token=\"your_token\",\n ... crew_name=\"My Crew\",\n ... crew_description=\"Description of what the crew does\"\n ... )\n \n With custom inputs:\n >>> custom_inputs = {\n ... \"param1\": Field(..., description=\"Description of param1\"),\n ... \"param2\": Field(default=\"default_value\", description=\"Description of param2\")\n ... }\n >>> tool = InvokeCrewAIAutomationTool(\n ... crew_api_url=\"https://api.example.com\",\n ... crew_bearer_token=\"your_token\",\n ... crew_name=\"My Crew\",\n ... crew_description=\"Description of what the crew does\",\n ... crew_inputs=custom_inputs\n ... )\n \n Example:\n >>> tools=[\n ... InvokeCrewAIAutomationTool(\n ... crew_api_url=\"https://canary-crew-[...].crewai.com\",\n ... crew_bearer_token=\"[Your token: abcdef012345]\",\n ... crew_name=\"State of AI Report\",\n ... crew_description=\"Retrieves a report on state of AI for a given year.\",\n ... crew_inputs={\n ... \"year\": Field(..., description=\"Year to retrieve the report for (integer)\")\n ... }\n ... )\n ... ]", + "properties": { + "crew_api_url": { + "title": "Crew Api Url", + "type": "string" + }, + "crew_bearer_token": { + "title": "Crew Bearer Token", + "type": "string" + }, + "max_polling_time": { + "default": 600, + "title": "Max Polling Time", + "type": "integer" + } + }, + "required": [ + "crew_api_url", + "crew_bearer_token" + ], + "title": "InvokeCrewAIAutomationTool", + "type": "object" + }, + "name": "InvokeCrewAIAutomationTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input schema for InvokeCrewAIAutomationTool.", + "properties": { + "prompt": { + "description": "The prompt or query to send to the crew", + "title": "Prompt", + "type": "string" + } + }, + "required": [ + "prompt" + ], + "title": "InvokeCrewAIAutomationInput", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a JSON's content.", + "env_vars": [], + "humanized_name": "Search a JSON's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "JSONSearchTool", + "type": "object" + }, + "name": "JSONSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for JSONSearchTool.", + "properties": { + "json_path": { + "description": "Mandatory json path you want to search", + "title": "Json Path", + "type": "string" + }, + "search_query": { + "description": "Mandatory search query you want to use to search the JSON's content", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query", + "json_path" + ], + "title": "JSONSearchToolSchema", + "type": "object" + } + }, + { + "description": "Performs an API call to Linkup to retrieve contextual information.", + "env_vars": [ + { + "default": null, + "description": "API key for Linkup", + "name": "LINKUP_API_KEY", + "required": true + } + ], + "humanized_name": "Linkup Search Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": {}, + "title": "LinkupSearchTool", + "type": "object" + }, + "name": "LinkupSearchTool", + "package_dependencies": [ + "linkup-sdk" + ], + "run_params_schema": {} + }, + { + "description": "", + "env_vars": [], + "humanized_name": "LlamaIndexTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to wrap LlamaIndex tools/query engines.", + "properties": { + "llama_index_tool": { + "title": "Llama Index Tool" + } + }, + "required": [ + "name", + "description", + "llama_index_tool" + ], + "title": "LlamaIndexTool", + "type": "object" + }, + "name": "LlamaIndexTool", + "package_dependencies": [], + "run_params_schema": {} + }, + { + "description": "A tool that can be used to semantic search a query from a MDX's content.", + "env_vars": [], + "humanized_name": "Search a MDX's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "MDXSearchTool", + "type": "object" + }, + "name": "MDXSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for MDXSearchTool.", + "properties": { + "mdx": { + "description": "Mandatory mdx path you want to search", + "title": "Mdx", + "type": "string" + }, + "search_query": { + "description": "Mandatory search query you want to use to search the MDX's content", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query", + "mdx" + ], + "title": "MDXSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool to perfrom a vector search on a MongoDB database for relevant information on internal documents.", + "env_vars": [ + { + "default": null, + "description": "API key for Browserbase services", + "name": "BROWSERBASE_API_KEY", + "required": false + }, + { + "default": null, + "description": "Project ID for Browserbase services", + "name": "BROWSERBASE_PROJECT_ID", + "required": false + } + ], + "humanized_name": "MongoDBVectorSearchTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + }, + "MongoDBVectorSearchConfig": { + "description": "Configuration for MongoDB vector search queries.", + "properties": { + "include_embeddings": { + "default": false, + "description": "Whether to include the embedding vector of each result in metadata.", + "title": "Include Embeddings", + "type": "boolean" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 4, + "description": "number of documents to return.", + "title": "Limit" + }, + "oversampling_factor": { + "default": 10, + "description": "Multiple of limit used when generating number of candidates at each step in the HNSW Vector Search", + "title": "Oversampling Factor", + "type": "integer" + }, + "post_filter_pipeline": { + "anyOf": [ + { + "items": { + "additionalProperties": true, + "type": "object" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Pipeline of MongoDB aggregation stages to filter/process results after $vectorSearch.", + "title": "Post Filter Pipeline" + }, + "pre_filter": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "List of MQL match expressions comparing an indexed field", + "title": "Pre Filter" + } + }, + "title": "MongoDBVectorSearchConfig", + "type": "object" + } + }, + "description": "Tool to perfrom a vector search the MongoDB database", + "properties": { + "collection_name": { + "description": "The name of the MongoDB collection", + "title": "Collection Name", + "type": "string" + }, + "connection_string": { + "description": "The connection string of the MongoDB cluster", + "title": "Connection String", + "type": "string" + }, + "database_name": { + "description": "The name of the MongoDB database", + "title": "Database Name", + "type": "string" + }, + "dimensions": { + "default": 1536, + "description": "Number of dimensions in the embedding vector", + "title": "Dimensions", + "type": "integer" + }, + "embedding_key": { + "default": "embedding", + "description": "Field that will contain the embedding for each document", + "title": "Embedding Key", + "type": "string" + }, + "embedding_model": { + "default": "text-embedding-3-large", + "description": "Text OpenAI embedding model to use", + "title": "Embedding Model", + "type": "string" + }, + "query_config": { + "anyOf": [ + { + "$ref": "#/$defs/MongoDBVectorSearchConfig" + }, + { + "type": "null" + } + ], + "default": null, + "description": "MongoDB Vector Search query configuration" + }, + "text_key": { + "default": "text", + "description": "MongoDB field that will contain the text for each document", + "title": "Text Key", + "type": "string" + }, + "vector_index_name": { + "default": "vector_index", + "description": "Name of the Atlas Search vector index", + "title": "Vector Index Name", + "type": "string" + } + }, + "required": [ + "database_name", + "collection_name", + "connection_string" + ], + "title": "MongoDBVectorSearchTool", + "type": "object" + }, + "name": "MongoDBVectorSearchTool", + "package_dependencies": [ + "mongdb" + ], + "run_params_schema": { + "description": "Input for MongoDBTool.", + "properties": { + "query": { + "description": "The query to search retrieve relevant information from the MongoDB database. Pass only the query, not the question.", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "MongoDBToolSchema", + "type": "object" + } + }, + { + "description": "Multion gives the ability for LLMs to control web browsers using natural language instructions.\n If the status is 'CONTINUE', reissue the same instruction to continue execution", + "env_vars": [ + { + "default": null, + "description": "API key for Multion", + "name": "MULTION_API_KEY", + "required": true + } + ], + "humanized_name": "Multion Browse Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to wrap MultiOn Browse Capabilities.", + "properties": { + "local": { + "default": false, + "title": "Local", + "type": "boolean" + }, + "max_steps": { + "default": 3, + "title": "Max Steps", + "type": "integer" + }, + "multion": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Multion" + }, + "session_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Session Id" + } + }, + "title": "MultiOnTool", + "type": "object" + }, + "name": "MultiOnTool", + "package_dependencies": [ + "multion" + ], + "run_params_schema": {} + }, + { + "description": "A tool that can be used to semantic search a query from a database table's content.", + "env_vars": [], + "humanized_name": "Search a database's table content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "db_uri": { + "description": "Mandatory database URI", + "title": "Db Uri", + "type": "string" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "required": [ + "db_uri" + ], + "title": "MySQLSearchTool", + "type": "object" + }, + "name": "MySQLSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for MySQLSearchTool.", + "properties": { + "search_query": { + "description": "Mandatory semantic search query you want to use to search the database's content", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "MySQLSearchToolSchema", + "type": "object" + } + }, + { + "description": "Converts natural language to SQL queries and executes them.", + "env_vars": [], + "humanized_name": "NL2SQLTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "columns": { + "additionalProperties": true, + "default": {}, + "title": "Columns", + "type": "object" + }, + "db_uri": { + "description": "The URI of the database to connect to.", + "title": "Database URI", + "type": "string" + }, + "tables": { + "default": [], + "items": {}, + "title": "Tables", + "type": "array" + } + }, + "required": [ + "db_uri" + ], + "title": "NL2SQLTool", + "type": "object" + }, + "name": "NL2SQLTool", + "package_dependencies": [], + "run_params_schema": { + "properties": { + "sql_query": { + "description": "The SQL query to execute.", + "title": "SQL Query", + "type": "string" + } + }, + "required": [ + "sql_query" + ], + "title": "NL2SQLToolInput", + "type": "object" + } + }, + { + "description": "This tool uses an LLM's API to extract text from an image file.", + "env_vars": [], + "humanized_name": "Optical Character Recognition Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A tool for performing Optical Character Recognition on images.\n\nThis tool leverages LLMs to extract text from images. It can process\nboth local image files and images available via URLs.\n\nAttributes:\n name (str): Name of the tool.\n description (str): Description of the tool's functionality.\n args_schema (Type[BaseModel]): Pydantic schema for input validation.\n\nPrivate Attributes:\n _llm (Optional[LLM]): Language model instance for making API calls.", + "properties": {}, + "title": "OCRTool", + "type": "object" + }, + "name": "OCRTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input schema for Optical Character Recognition Tool.\n\nAttributes:\n image_path_url (str): Path to a local image file or URL of an image.\n For local files, provide the absolute or relative path.\n For remote images, provide the complete URL starting with 'http' or 'https'.", + "properties": { + "image_path_url": { + "default": "The image path or URL.", + "title": "Image Path Url", + "type": "string" + } + }, + "title": "OCRToolSchema", + "type": "object" + } + }, + { + "description": "Scrape Amazon product pages with Oxylabs Amazon Product Scraper", + "env_vars": [ + { + "default": null, + "description": "Username for Oxylabs", + "name": "OXYLABS_USERNAME", + "required": true + }, + { + "default": null, + "description": "Password for Oxylabs", + "name": "OXYLABS_PASSWORD", + "required": true + } + ], + "humanized_name": "Oxylabs Amazon Product Scraper tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + }, + "OxylabsAmazonProductScraperConfig": { + "description": "Amazon Product Scraper configuration options:\nhttps://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/amazon/product", + "properties": { + "callback_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "URL to your callback endpoint.", + "title": "Callback Url" + }, + "context": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Additional advanced settings and controls for specialized requirements.", + "title": "Context" + }, + "domain": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The domain to limit the search results to.", + "title": "Domain" + }, + "geo_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The Deliver to location.", + "title": "Geo Location" + }, + "parse": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": null, + "description": "True will return structured data.", + "title": "Parse" + }, + "parsing_instructions": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Instructions for parsing the results.", + "title": "Parsing Instructions" + }, + "render": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Enables JavaScript rendering.", + "title": "Render" + }, + "user_agent_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Device type and browser.", + "title": "User Agent Type" + } + }, + "title": "OxylabsAmazonProductScraperConfig", + "type": "object" + } + }, + "description": "Scrape Amazon product pages with OxylabsAmazonProductScraperTool.\n\nGet Oxylabs account:\nhttps://dashboard.oxylabs.io/en\n\nArgs:\n username (str): Oxylabs username.\n password (str): Oxylabs password.\n config: Configuration options. See ``OxylabsAmazonProductScraperConfig``", + "properties": { + "config": { + "$ref": "#/$defs/OxylabsAmazonProductScraperConfig" + }, + "oxylabs_api": { + "title": "Oxylabs Api" + } + }, + "required": [ + "oxylabs_api", + "config" + ], + "title": "OxylabsAmazonProductScraperTool", + "type": "object" + }, + "name": "OxylabsAmazonProductScraperTool", + "package_dependencies": [ + "oxylabs" + ], + "run_params_schema": { + "properties": { + "query": { + "description": "Amazon product ASIN", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "OxylabsAmazonProductScraperArgs", + "type": "object" + } + }, + { + "description": "Scrape Amazon search results with Oxylabs Amazon Search Scraper", + "env_vars": [ + { + "default": null, + "description": "Username for Oxylabs", + "name": "OXYLABS_USERNAME", + "required": true + }, + { + "default": null, + "description": "Password for Oxylabs", + "name": "OXYLABS_PASSWORD", + "required": true + } + ], + "humanized_name": "Oxylabs Amazon Search Scraper tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + }, + "OxylabsAmazonSearchScraperConfig": { + "description": "Amazon Search Scraper configuration options:\nhttps://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/amazon/search", + "properties": { + "callback_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "URL to your callback endpoint.", + "title": "Callback Url" + }, + "context": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Additional advanced settings and controls for specialized requirements.", + "title": "Context" + }, + "domain": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The domain to limit the search results to.", + "title": "Domain" + }, + "geo_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The Deliver to location.", + "title": "Geo Location" + }, + "pages": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The number of pages to scrape.", + "title": "Pages" + }, + "parse": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": null, + "description": "True will return structured data.", + "title": "Parse" + }, + "parsing_instructions": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Instructions for parsing the results.", + "title": "Parsing Instructions" + }, + "render": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Enables JavaScript rendering.", + "title": "Render" + }, + "start_page": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The starting page number.", + "title": "Start Page" + }, + "user_agent_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Device type and browser.", + "title": "User Agent Type" + } + }, + "title": "OxylabsAmazonSearchScraperConfig", + "type": "object" + } + }, + "description": "Scrape Amazon search results with OxylabsAmazonSearchScraperTool.\n\nGet Oxylabs account:\nhttps://dashboard.oxylabs.io/en\n\nArgs:\n username (str): Oxylabs username.\n password (str): Oxylabs password.\n config: Configuration options. See ``OxylabsAmazonSearchScraperConfig``", + "properties": { + "config": { + "$ref": "#/$defs/OxylabsAmazonSearchScraperConfig" + }, + "oxylabs_api": { + "title": "Oxylabs Api" + } + }, + "required": [ + "oxylabs_api", + "config" + ], + "title": "OxylabsAmazonSearchScraperTool", + "type": "object" + }, + "name": "OxylabsAmazonSearchScraperTool", + "package_dependencies": [ + "oxylabs" + ], + "run_params_schema": { + "properties": { + "query": { + "description": "Amazon search term", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "OxylabsAmazonSearchScraperArgs", + "type": "object" + } + }, + { + "description": "Scrape Google Search results with Oxylabs Google Search Scraper", + "env_vars": [ + { + "default": null, + "description": "Username for Oxylabs", + "name": "OXYLABS_USERNAME", + "required": true + }, + { + "default": null, + "description": "Password for Oxylabs", + "name": "OXYLABS_PASSWORD", + "required": true + } + ], + "humanized_name": "Oxylabs Google Search Scraper tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + }, + "OxylabsGoogleSearchScraperConfig": { + "description": "Google Search Scraper configuration options:\nhttps://developers.oxylabs.io/scraper-apis/web-scraper-api/targets/google/search/search", + "properties": { + "callback_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "URL to your callback endpoint.", + "title": "Callback Url" + }, + "context": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Additional advanced settings and controls for specialized requirements.", + "title": "Context" + }, + "domain": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The domain to limit the search results to.", + "title": "Domain" + }, + "geo_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The Deliver to location.", + "title": "Geo Location" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Number of results to retrieve in each page.", + "title": "Limit" + }, + "pages": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The number of pages to scrape.", + "title": "Pages" + }, + "parse": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": null, + "description": "True will return structured data.", + "title": "Parse" + }, + "parsing_instructions": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Instructions for parsing the results.", + "title": "Parsing Instructions" + }, + "render": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Enables JavaScript rendering.", + "title": "Render" + }, + "start_page": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The starting page number.", + "title": "Start Page" + }, + "user_agent_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Device type and browser.", + "title": "User Agent Type" + } + }, + "title": "OxylabsGoogleSearchScraperConfig", + "type": "object" + } + }, + "description": "Scrape Google Search results with OxylabsGoogleSearchScraperTool.\n\nGet Oxylabs account:\nhttps://dashboard.oxylabs.io/en\n\nArgs:\n username (str): Oxylabs username.\n password (str): Oxylabs password.\n config: Configuration options. See ``OxylabsGoogleSearchScraperConfig``", + "properties": { + "config": { + "$ref": "#/$defs/OxylabsGoogleSearchScraperConfig" + }, + "oxylabs_api": { + "title": "Oxylabs Api" + } + }, + "required": [ + "oxylabs_api", + "config" + ], + "title": "OxylabsGoogleSearchScraperTool", + "type": "object" + }, + "name": "OxylabsGoogleSearchScraperTool", + "package_dependencies": [ + "oxylabs" + ], + "run_params_schema": { + "properties": { + "query": { + "description": "Search query", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "OxylabsGoogleSearchScraperArgs", + "type": "object" + } + }, + { + "description": "Scrape any url with Oxylabs Universal Scraper", + "env_vars": [ + { + "default": null, + "description": "Username for Oxylabs", + "name": "OXYLABS_USERNAME", + "required": true + }, + { + "default": null, + "description": "Password for Oxylabs", + "name": "OXYLABS_PASSWORD", + "required": true + } + ], + "humanized_name": "Oxylabs Universal Scraper tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + }, + "OxylabsUniversalScraperConfig": { + "description": "Universal Scraper configuration options:\nhttps://developers.oxylabs.io/scraper-apis/web-scraper-api/other-websites", + "properties": { + "callback_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "URL to your callback endpoint.", + "title": "Callback Url" + }, + "context": { + "anyOf": [ + { + "items": {}, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Additional advanced settings and controls for specialized requirements.", + "title": "Context" + }, + "geo_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The Deliver to location.", + "title": "Geo Location" + }, + "parse": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": null, + "description": "True will return structured data.", + "title": "Parse" + }, + "parsing_instructions": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Instructions for parsing the results.", + "title": "Parsing Instructions" + }, + "render": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Enables JavaScript rendering.", + "title": "Render" + }, + "user_agent_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Device type and browser.", + "title": "User Agent Type" + } + }, + "title": "OxylabsUniversalScraperConfig", + "type": "object" + } + }, + "description": "Scrape any website with OxylabsUniversalScraperTool.\n\nGet Oxylabs account:\nhttps://dashboard.oxylabs.io/en\n\nArgs:\n username (str): Oxylabs username.\n password (str): Oxylabs password.\n config: Configuration options. See ``OxylabsUniversalScraperConfig``", + "properties": { + "config": { + "$ref": "#/$defs/OxylabsUniversalScraperConfig" + }, + "oxylabs_api": { + "title": "Oxylabs Api" + } + }, + "required": [ + "oxylabs_api", + "config" + ], + "title": "OxylabsUniversalScraperTool", + "type": "object" + }, + "name": "OxylabsUniversalScraperTool", + "package_dependencies": [ + "oxylabs" + ], + "run_params_schema": { + "properties": { + "url": { + "description": "Website URL", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url" + ], + "title": "OxylabsUniversalScraperArgs", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a PDF's content.", + "env_vars": [], + "humanized_name": "Search a PDF's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "PDFSearchTool", + "type": "object" + }, + "name": "PDFSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for PDFSearchTool.", + "properties": { + "pdf": { + "description": "Mandatory pdf path you want to search", + "title": "Pdf", + "type": "string" + }, + "query": { + "description": "Mandatory query you want to use to search the PDF's content", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query", + "pdf" + ], + "title": "PDFSearchToolSchema", + "type": "object" + } + }, + { + "description": "Search the web using Parallel's Search API (v1beta). Returns ranked results with compressed excerpts optimized for LLMs.", + "env_vars": [ + { + "default": null, + "description": "API key for Parallel", + "name": "PARALLEL_API_KEY", + "required": true + } + ], + "humanized_name": "Parallel Web Search Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "search_url": { + "default": "https://api.parallel.ai/v1beta/search", + "title": "Search Url", + "type": "string" + } + }, + "title": "ParallelSearchTool", + "type": "object" + }, + "name": "ParallelSearchTool", + "package_dependencies": [ + "requests" + ], + "run_params_schema": { + "description": "Input schema for ParallelSearchTool using the Search API (v1beta).\n\nAt least one of objective or search_queries is required.", + "properties": { + "max_chars_per_result": { + "default": 6000, + "description": "Maximum characters per result excerpt (values >30000 not guaranteed)", + "minimum": 100, + "title": "Max Chars Per Result", + "type": "integer" + }, + "max_results": { + "default": 10, + "description": "Maximum number of search results to return (processor limits apply)", + "maximum": 40, + "minimum": 1, + "title": "Max Results", + "type": "integer" + }, + "objective": { + "anyOf": [ + { + "maxLength": 5000, + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Natural-language goal for the web research (<=5000 chars)", + "title": "Objective" + }, + "processor": { + "default": "base", + "description": "Search processor: 'base' (fast/low cost) or 'pro' (higher quality/freshness)", + "pattern": "^(base|pro)$", + "title": "Processor", + "type": "string" + }, + "search_queries": { + "anyOf": [ + { + "items": { + "maxLength": 200, + "type": "string" + }, + "maxItems": 5, + "minItems": 1, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional list of keyword queries (<=5 items, each <=200 chars)", + "title": "Search Queries" + }, + "source_policy": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional source policy configuration", + "title": "Source Policy" + } + }, + "title": "ParallelSearchInput", + "type": "object" + } + }, + { + "description": "", + "env_vars": [ + { + "default": null, + "description": "API key for Patronus evaluation services", + "name": "PATRONUS_API_KEY", + "required": true + } + ], + "humanized_name": "Patronus Evaluation Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "criteria": { + "default": [], + "items": { + "additionalProperties": { + "type": "string" + }, + "type": "object" + }, + "title": "Criteria", + "type": "array" + }, + "evaluate_url": { + "default": "https://api.patronus.ai/v1/evaluate", + "title": "Evaluate Url", + "type": "string" + }, + "evaluators": { + "default": [], + "items": { + "additionalProperties": { + "type": "string" + }, + "type": "object" + }, + "title": "Evaluators", + "type": "array" + } + }, + "title": "PatronusEvalTool", + "type": "object" + }, + "name": "PatronusEvalTool", + "package_dependencies": [], + "run_params_schema": {} + }, + { + "description": "This tool calls the Patronus Evaluation API that takes the following arguments:", + "env_vars": [], + "humanized_name": "Call Patronus API tool for evaluation of model inputs and outputs", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "PatronusEvalTool is a tool to automatically evaluate and score agent interactions.\n\nResults are logged to the Patronus platform at app.patronus.ai", + "properties": { + "evaluate_url": { + "default": "https://api.patronus.ai/v1/evaluate", + "title": "Evaluate Url", + "type": "string" + }, + "evaluators": { + "default": [], + "items": { + "additionalProperties": { + "type": "string" + }, + "type": "object" + }, + "title": "Evaluators", + "type": "array" + } + }, + "title": "PatronusPredefinedCriteriaEvalTool", + "type": "object" + }, + "name": "PatronusPredefinedCriteriaEvalTool", + "package_dependencies": [], + "run_params_schema": { + "properties": { + "evaluated_model_gold_answer": { + "additionalProperties": true, + "description": "The agent's gold answer only if available", + "title": "Evaluated Model Gold Answer", + "type": "object" + }, + "evaluated_model_input": { + "additionalProperties": true, + "description": "The agent's task description in simple text", + "title": "Evaluated Model Input", + "type": "object" + }, + "evaluated_model_output": { + "additionalProperties": true, + "description": "The agent's output of the task", + "title": "Evaluated Model Output", + "type": "object" + }, + "evaluated_model_retrieved_context": { + "additionalProperties": true, + "description": "The agent's context", + "title": "Evaluated Model Retrieved Context", + "type": "object" + }, + "evaluators": { + "description": "List of dictionaries containing the evaluator and criteria to evaluate the model input and output. An example input for this field: [{'evaluator': '[evaluator-from-user]', 'criteria': '[criteria-from-user]'}]", + "items": { + "additionalProperties": { + "type": "string" + }, + "type": "object" + }, + "title": "Evaluators", + "type": "array" + } + }, + "required": [ + "evaluated_model_input", + "evaluated_model_output", + "evaluated_model_retrieved_context", + "evaluated_model_gold_answer", + "evaluators" + ], + "title": "FixedBaseToolSchema", + "type": "object" + } + }, + { + "description": "A tool to search the Qdrant database for relevant information on internal documents.", + "env_vars": [], + "humanized_name": "QdrantVectorSearchTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to query and filter results from a Qdrant database.\n\nThis tool enables vector similarity search on internal documents stored in Qdrant,\nwith optional filtering capabilities.\n\nAttributes:\n client: Configured QdrantClient instance\n collection_name: Name of the Qdrant collection to search\n limit: Maximum number of results to return\n score_threshold: Minimum similarity score threshold\n qdrant_url: Qdrant server URL\n qdrant_api_key: Authentication key for Qdrant", + "properties": { + "collection_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Collection Name" + }, + "filter_by": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Filter By" + }, + "filter_value": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Filter Value" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 3, + "title": "Limit" + }, + "qdrant_api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The API key for the Qdrant server", + "title": "Qdrant Api Key" + }, + "qdrant_url": { + "description": "The URL of the Qdrant server", + "title": "Qdrant Url", + "type": "string" + }, + "query": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Query" + }, + "score_threshold": { + "default": 0.35, + "title": "Score Threshold", + "type": "number" + } + }, + "required": [ + "qdrant_url" + ], + "title": "QdrantVectorSearchTool", + "type": "object" + }, + "name": "QdrantVectorSearchTool", + "package_dependencies": [ + "qdrant-client" + ], + "run_params_schema": { + "description": "Input for QdrantTool.", + "properties": { + "filter_by": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Filter by properties. Pass only the properties, not the question.", + "title": "Filter By" + }, + "filter_value": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Filter by value. Pass only the value, not the question.", + "title": "Filter Value" + }, + "query": { + "description": "The query to search retrieve relevant information from the Qdrant database. Pass only the query, not the question.", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "QdrantToolSchema", + "type": "object" + } + }, + { + "description": "A knowledge base that can be used to answer questions.", + "env_vars": [], + "humanized_name": "Knowledge base", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "RagTool", + "type": "object" + }, + "name": "RagTool", + "package_dependencies": [], + "run_params_schema": {} + }, + { + "description": "A tool that can be used to read a website content.", + "env_vars": [], + "humanized_name": "Read a website content", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "cookies": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Cookies" + }, + "css_element": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Css Element" + }, + "headers": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": { + "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", + "Accept-Encoding": "gzip, deflate, br", + "Accept-Language": "en-US,en;q=0.9", + "Connection": "keep-alive", + "Referer": "https://www.google.com/", + "Upgrade-Insecure-Requests": "1", + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36" + }, + "title": "Headers" + }, + "website_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Website Url" + } + }, + "title": "ScrapeElementFromWebsiteTool", + "type": "object" + }, + "name": "ScrapeElementFromWebsiteTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for ScrapeElementFromWebsiteTool.", + "properties": { + "css_element": { + "description": "Mandatory css reference for element to scrape from the website", + "title": "Css Element", + "type": "string" + }, + "website_url": { + "description": "Mandatory website url to read the file", + "title": "Website Url", + "type": "string" + } + }, + "required": [ + "website_url", + "css_element" + ], + "title": "ScrapeElementFromWebsiteToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to read a website content.", + "env_vars": [], + "humanized_name": "Read website content", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "cookies": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Cookies" + }, + "headers": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": { + "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", + "Accept-Language": "en-US,en;q=0.9", + "Connection": "keep-alive", + "Referer": "https://www.google.com/", + "Upgrade-Insecure-Requests": "1", + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36" + }, + "title": "Headers" + }, + "website_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Website Url" + } + }, + "title": "ScrapeWebsiteTool", + "type": "object" + }, + "name": "ScrapeWebsiteTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for ScrapeWebsiteTool.", + "properties": { + "website_url": { + "description": "Mandatory website url to read the file", + "title": "Website Url", + "type": "string" + } + }, + "required": [ + "website_url" + ], + "title": "ScrapeWebsiteToolSchema", + "type": "object" + } + }, + { + "description": "A tool that uses Scrapegraph AI to intelligently scrape website content.", + "env_vars": [ + { + "default": null, + "description": "API key for Scrapegraph AI services", + "name": "SCRAPEGRAPH_API_KEY", + "required": false + } + ], + "humanized_name": "Scrapegraph website scraper", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A tool that uses Scrapegraph AI to intelligently scrape website content.\n\nRaises:\n ValueError: If API key is missing or URL format is invalid\n RateLimitError: If API rate limits are exceeded\n RuntimeError: If scraping operation fails", + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "enable_logging": { + "default": false, + "title": "Enable Logging", + "type": "boolean" + }, + "user_prompt": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "User Prompt" + }, + "website_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Website Url" + } + }, + "title": "ScrapegraphScrapeTool", + "type": "object" + }, + "name": "ScrapegraphScrapeTool", + "package_dependencies": [ + "scrapegraph-py" + ], + "run_params_schema": { + "description": "Input for ScrapegraphScrapeTool.", + "properties": { + "user_prompt": { + "default": "Extract the main content of the webpage", + "description": "Prompt to guide the extraction of content", + "title": "User Prompt", + "type": "string" + }, + "website_url": { + "description": "Mandatory website url to scrape", + "title": "Website Url", + "type": "string" + } + }, + "required": [ + "website_url" + ], + "title": "ScrapegraphScrapeToolSchema", + "type": "object" + } + }, + { + "description": "Scrape a webpage url using Scrapfly and return its content as markdown or text", + "env_vars": [ + { + "default": null, + "description": "API key for Scrapfly", + "name": "SCRAPFLY_API_KEY", + "required": true + } + ], + "humanized_name": "Scrapfly web scraping API tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "api_key": { + "default": null, + "title": "Api Key", + "type": "string" + }, + "scrapfly": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Scrapfly" + } + }, + "title": "ScrapflyScrapeWebsiteTool", + "type": "object" + }, + "name": "ScrapflyScrapeWebsiteTool", + "package_dependencies": [ + "scrapfly-sdk" + ], + "run_params_schema": { + "properties": { + "ignore_scrape_failures": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": null, + "description": "whether to ignore failures", + "title": "Ignore Scrape Failures" + }, + "scrape_config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Scrapfly request scrape config", + "title": "Scrape Config" + }, + "scrape_format": { + "anyOf": [ + { + "enum": [ + "raw", + "markdown", + "text" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "default": "markdown", + "description": "Webpage extraction format", + "title": "Scrape Format" + }, + "url": { + "description": "Webpage URL", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url" + ], + "title": "ScrapflyScrapeWebsiteToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to read a website content.", + "env_vars": [], + "humanized_name": "Read a website content", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "cookie": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Cookie" + }, + "css_element": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Css Element" + }, + "driver": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Driver" + }, + "return_html": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": false, + "title": "Return Html" + }, + "wait_time": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 3, + "title": "Wait Time" + }, + "website_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Website Url" + } + }, + "title": "SeleniumScrapingTool", + "type": "object" + }, + "name": "SeleniumScrapingTool", + "package_dependencies": [ + "selenium", + "webdriver-manager" + ], + "run_params_schema": { + "description": "Input for SeleniumScrapingTool.", + "properties": { + "css_element": { + "description": "Mandatory css reference for element to scrape from the website", + "title": "Css Element", + "type": "string" + }, + "website_url": { + "description": "Mandatory website url to read the file. Must start with http:// or https://", + "title": "Website Url", + "type": "string" + } + }, + "required": [ + "website_url", + "css_element" + ], + "title": "SeleniumScrapingToolSchema", + "type": "object" + } + }, + { + "description": "A tool to perform to perform a Google search with a search_query.", + "env_vars": [ + { + "default": null, + "description": "API key for SerpApi searches", + "name": "SERPAPI_API_KEY", + "required": true + } + ], + "humanized_name": "Google Search", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "client": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Client" + } + }, + "title": "SerpApiGoogleSearchTool", + "type": "object" + }, + "name": "SerpApiGoogleSearchTool", + "package_dependencies": [ + "serpapi" + ], + "run_params_schema": { + "description": "Input for Google Search.", + "properties": { + "location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Location you want the search to be performed in.", + "title": "Location" + }, + "search_query": { + "description": "Mandatory search query you want to use to Google search.", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "SerpApiGoogleSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool to perform search on Google shopping with a search_query.", + "env_vars": [ + { + "default": null, + "description": "API key for SerpApi searches", + "name": "SERPAPI_API_KEY", + "required": true + } + ], + "humanized_name": "Google Shopping", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "client": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Client" + } + }, + "title": "SerpApiGoogleShoppingTool", + "type": "object" + }, + "name": "SerpApiGoogleShoppingTool", + "package_dependencies": [ + "serpapi" + ], + "run_params_schema": { + "description": "Input for Google Shopping.", + "properties": { + "location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Location you want the search to be performed in.", + "title": "Location" + }, + "search_query": { + "description": "Mandatory search query you want to use to Google shopping.", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "SerpApiGoogleShoppingToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to search the internet with a search_query. Supports different search types: 'search' (default), 'news'", + "env_vars": [ + { + "default": null, + "description": "API key for Serper", + "name": "SERPER_API_KEY", + "required": true + } + ], + "humanized_name": "Search the internet with Serper", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "base_url": { + "default": "https://google.serper.dev", + "title": "Base Url", + "type": "string" + }, + "country": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "", + "title": "Country" + }, + "locale": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "", + "title": "Locale" + }, + "location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "", + "title": "Location" + }, + "n_results": { + "default": 10, + "title": "N Results", + "type": "integer" + }, + "save_file": { + "default": false, + "title": "Save File", + "type": "boolean" + }, + "search_type": { + "default": "search", + "title": "Search Type", + "type": "string" + } + }, + "title": "SerperDevTool", + "type": "object" + }, + "name": "SerperDevTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for SerperDevTool.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to search the internet", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "SerperDevToolSchema", + "type": "object" + } + }, + { + "description": "Scrapes website content using Serper's scraping API. This tool can extract clean, readable content from any website URL, optionally including markdown formatting for better structure.", + "env_vars": [ + { + "default": null, + "description": "API key for Serper", + "name": "SERPER_API_KEY", + "required": true + } + ], + "humanized_name": "serper_scrape_website", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": {}, + "title": "SerperScrapeWebsiteTool", + "type": "object" + }, + "name": "SerperScrapeWebsiteTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input schema for SerperScrapeWebsite.", + "properties": { + "include_markdown": { + "default": true, + "description": "Whether to include markdown formatting in the scraped content", + "title": "Include Markdown", + "type": "boolean" + }, + "url": { + "description": "The URL of the website to scrape", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url" + ], + "title": "SerperScrapeWebsiteInput", + "type": "object" + } + }, + { + "description": "A tool to perform to perform a job search in the US with a search_query.", + "env_vars": [ + { + "default": null, + "description": "API key for Serply services", + "name": "SERPLY_API_KEY", + "required": true + } + ], + "humanized_name": "Job Search", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "headers": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": {}, + "title": "Headers" + }, + "proxy_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "US", + "title": "Proxy Location" + }, + "request_url": { + "default": "https://api.serply.io/v1/job/search/", + "title": "Request Url", + "type": "string" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "SerplyJobSearchTool", + "type": "object" + }, + "name": "SerplyJobSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for Job Search.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to fetch jobs postings.", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "SerplyJobSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool to perform News article search with a search_query.", + "env_vars": [ + { + "default": null, + "description": "API key for Serply services", + "name": "SERPLY_API_KEY", + "required": true + } + ], + "humanized_name": "News Search", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "headers": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": {}, + "title": "Headers" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 10, + "title": "Limit" + }, + "proxy_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "US", + "title": "Proxy Location" + }, + "search_url": { + "default": "https://api.serply.io/v1/news/", + "title": "Search Url", + "type": "string" + } + }, + "title": "SerplyNewsSearchTool", + "type": "object" + }, + "name": "SerplyNewsSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for Serply News Search.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to fetch news articles", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "SerplyNewsSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool to perform scholarly literature search with a search_query.", + "env_vars": [ + { + "default": null, + "description": "API key for Serply services", + "name": "SERPLY_API_KEY", + "required": true + } + ], + "humanized_name": "Scholar Search", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "headers": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": {}, + "title": "Headers" + }, + "hl": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "us", + "title": "Hl" + }, + "proxy_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "US", + "title": "Proxy Location" + }, + "search_url": { + "default": "https://api.serply.io/v1/scholar/", + "title": "Search Url", + "type": "string" + } + }, + "title": "SerplyScholarSearchTool", + "type": "object" + }, + "name": "SerplyScholarSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for Serply Scholar Search.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to fetch scholarly literature", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "SerplyScholarSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool to perform Google search with a search_query.", + "env_vars": [ + { + "default": null, + "description": "API key for Serply services", + "name": "SERPLY_API_KEY", + "required": true + } + ], + "humanized_name": "Google Search", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "device_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "desktop", + "title": "Device Type" + }, + "headers": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": {}, + "title": "Headers" + }, + "hl": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "us", + "title": "Hl" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 10, + "title": "Limit" + }, + "proxy_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "US", + "title": "Proxy Location" + }, + "query_payload": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": {}, + "title": "Query Payload" + }, + "search_url": { + "default": "https://api.serply.io/v1/search/", + "title": "Search Url", + "type": "string" + } + }, + "title": "SerplyWebSearchTool", + "type": "object" + }, + "name": "SerplyWebSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for Serply Web Search.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to Google search", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "SerplyWebSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool to perform convert a webpage to markdown to make it easier for LLMs to understand", + "env_vars": [ + { + "default": null, + "description": "API key for Serply services", + "name": "SERPLY_API_KEY", + "required": true + } + ], + "humanized_name": "Webpage to Markdown", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "headers": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": {}, + "title": "Headers" + }, + "proxy_location": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "US", + "title": "Proxy Location" + }, + "request_url": { + "default": "https://api.serply.io/v1/request", + "title": "Request Url", + "type": "string" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "SerplyWebpageToMarkdownTool", + "type": "object" + }, + "name": "SerplyWebpageToMarkdownTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for Serply Search.", + "properties": { + "url": { + "description": "Mandatory url you want to use to fetch and convert to markdown", + "title": "Url", + "type": "string" + } + }, + "required": [ + "url" + ], + "title": "SerplyWebpageToMarkdownToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a database.", + "env_vars": [ + { + "default": null, + "description": "A comprehensive URL string that can encapsulate host, port, username, password, and database information, often used in environments like SingleStore notebooks or specific frameworks. For example: 'me:p455w0rd@s2-host.com/my_db'", + "name": "SINGLESTOREDB_URL", + "required": false + }, + { + "default": null, + "description": "Specifies the hostname, IP address, or URL of the SingleStoreDB workspace or cluster", + "name": "SINGLESTOREDB_HOST", + "required": false + }, + { + "default": null, + "description": "Defines the port number on which the SingleStoreDB server is listening", + "name": "SINGLESTOREDB_PORT", + "required": false + }, + { + "default": null, + "description": "Specifies the database user name", + "name": "SINGLESTOREDB_USER", + "required": false + }, + { + "default": null, + "description": "Specifies the database user password", + "name": "SINGLESTOREDB_PASSWORD", + "required": false + }, + { + "default": null, + "description": "Name of the database to connect to", + "name": "SINGLESTOREDB_DATABASE", + "required": false + }, + { + "default": null, + "description": "File containing SSL key", + "name": "SINGLESTOREDB_SSL_KEY", + "required": false + }, + { + "default": null, + "description": "File containing SSL certificate", + "name": "SINGLESTOREDB_SSL_CERT", + "required": false + }, + { + "default": null, + "description": "File containing SSL certificate authority", + "name": "SINGLESTOREDB_SSL_CA", + "required": false + }, + { + "default": null, + "description": "The timeout for connecting to the database in seconds", + "name": "SINGLESTOREDB_CONNECT_TIMEOUT", + "required": false + } + ], + "humanized_name": "Search a database's table(s) content", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A tool for performing semantic searches on SingleStore database tables.\n\nThis tool provides a safe interface for executing SELECT and SHOW queries\nagainst a SingleStore database with connection pooling for optimal performance.", + "properties": { + "connection_args": { + "additionalProperties": true, + "default": {}, + "title": "Connection Args", + "type": "object" + }, + "connection_pool": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Connection Pool" + } + }, + "title": "SingleStoreSearchTool", + "type": "object" + }, + "name": "SingleStoreSearchTool", + "package_dependencies": [ + "singlestoredb", + "SQLAlchemy" + ], + "run_params_schema": { + "description": "Input schema for SingleStoreSearchTool.\n\nThis schema defines the expected input format for the search tool,\nensuring that only valid SELECT and SHOW queries are accepted.", + "properties": { + "search_query": { + "description": "Mandatory semantic search query you want to use to search the database's content. Only SELECT and SHOW queries are supported.", + "title": "Search Query", + "type": "string" + } + }, + "required": [ + "search_query" + ], + "title": "SingleStoreSearchToolSchema", + "type": "object" + } + }, + { + "description": "Execute SQL queries or semantic search on Snowflake data warehouse. Supports both raw SQL and natural language queries.", + "env_vars": [], + "humanized_name": "Snowflake Database Search", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + }, + "SnowflakeConfig": { + "description": "Configuration for Snowflake connection.", + "properties": { + "account": { + "description": "Snowflake account identifier", + "pattern": "^[a-zA-Z0-9\\-_]+$", + "title": "Account", + "type": "string" + }, + "database": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Default database", + "title": "Database" + }, + "password": { + "anyOf": [ + { + "format": "password", + "type": "string", + "writeOnly": true + }, + { + "type": "null" + } + ], + "default": null, + "description": "Snowflake password", + "title": "Password" + }, + "private_key_path": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Path to private key file", + "title": "Private Key Path" + }, + "role": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Snowflake role", + "title": "Role" + }, + "session_parameters": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "description": "Session parameters", + "title": "Session Parameters" + }, + "snowflake_schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Default schema", + "title": "Snowflake Schema" + }, + "user": { + "description": "Snowflake username", + "title": "User", + "type": "string" + }, + "warehouse": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Snowflake warehouse", + "title": "Warehouse" + } + }, + "required": [ + "account", + "user" + ], + "title": "SnowflakeConfig", + "type": "object" + } + }, + "description": "Tool for executing queries and semantic search on Snowflake.", + "properties": { + "config": { + "$ref": "#/$defs/SnowflakeConfig", + "description": "Snowflake connection configuration" + }, + "enable_caching": { + "default": true, + "description": "Enable query result caching", + "title": "Enable Caching", + "type": "boolean" + }, + "max_retries": { + "default": 3, + "description": "Maximum retry attempts", + "title": "Max Retries", + "type": "integer" + }, + "pool_size": { + "default": 5, + "description": "Size of connection pool", + "title": "Pool Size", + "type": "integer" + }, + "retry_delay": { + "default": 1.0, + "description": "Delay between retries in seconds", + "title": "Retry Delay", + "type": "number" + } + }, + "required": [ + "config" + ], + "title": "SnowflakeSearchTool", + "type": "object" + }, + "name": "SnowflakeSearchTool", + "package_dependencies": [ + "snowflake-connector-python", + "snowflake-sqlalchemy", + "cryptography" + ], + "run_params_schema": { + "description": "Input schema for SnowflakeSearchTool.", + "properties": { + "database": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Override default database", + "title": "Database" + }, + "query": { + "description": "SQL query or semantic search query to execute", + "title": "Query", + "type": "string" + }, + "snowflake_schema": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Override default schema", + "title": "Snowflake Schema" + }, + "timeout": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 300, + "description": "Query timeout in seconds", + "title": "Timeout" + } + }, + "required": [ + "query" + ], + "title": "SnowflakeSearchToolInput", + "type": "object" + } + }, + { + "description": "A tool to scrape or crawl a website and return LLM-ready content.", + "env_vars": [ + { + "default": null, + "description": "API key for Spider.cloud", + "name": "SPIDER_API_KEY", + "required": true + } + ], + "humanized_name": "SpiderTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + }, + "SpiderToolConfig": { + "description": "Configuration settings for SpiderTool.\n\nContains all default values and constants used by SpiderTool.\nCentralizes configuration management for easier maintenance.", + "properties": { + "DEFAULT_CRAWL_LIMIT": { + "default": 5, + "title": "Default Crawl Limit", + "type": "integer" + }, + "DEFAULT_REQUEST_MODE": { + "default": "smart", + "title": "Default Request Mode", + "type": "string" + }, + "DEFAULT_RETURN_FORMAT": { + "default": "markdown", + "title": "Default Return Format", + "type": "string" + }, + "FILTER_SVG": { + "default": true, + "title": "Filter Svg", + "type": "boolean" + } + }, + "title": "SpiderToolConfig", + "type": "object" + } + }, + "description": "Tool for scraping and crawling websites.\nThis tool provides functionality to either scrape a single webpage or crawl multiple\npages, returning content in a format suitable for LLM processing.", + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "config": { + "$ref": "#/$defs/SpiderToolConfig", + "default": { + "DEFAULT_CRAWL_LIMIT": 5, + "DEFAULT_REQUEST_MODE": "smart", + "DEFAULT_RETURN_FORMAT": "markdown", + "FILTER_SVG": true + } + }, + "custom_params": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Custom Params" + }, + "log_failures": { + "default": true, + "title": "Log Failures", + "type": "boolean" + }, + "spider": { + "default": null, + "title": "Spider" + }, + "website_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Website Url" + } + }, + "title": "SpiderTool", + "type": "object" + }, + "name": "SpiderTool", + "package_dependencies": [ + "spider-client" + ], + "run_params_schema": { + "description": "Input schema for SpiderTool.", + "properties": { + "mode": { + "default": "scrape", + "description": "The mode of the SpiderTool. The only two allowed modes are `scrape` or `crawl`. Crawl mode will follow up to 5 links and return their content in markdown format.", + "enum": [ + "scrape", + "crawl" + ], + "title": "Mode", + "type": "string" + }, + "website_url": { + "description": "Mandatory website URL to scrape or crawl", + "title": "Website Url", + "type": "string" + } + }, + "required": [ + "website_url" + ], + "title": "SpiderToolSchema", + "type": "object" + } + }, + { + "description": "Use this tool to control a web browser and interact with websites using natural language.\n\n Capabilities:\n - Navigate to websites and follow links\n - Click buttons, links, and other elements\n - Fill in forms and input fields\n - Search within websites\n - Extract information from web pages\n - Identify and analyze elements on a page\n\n To use this tool, provide a natural language instruction describing what you want to do.\n For reliability on complex pages, use specific, atomic instructions with location hints:\n - Good: \"Click the search box in the header\"\n - Good: \"Type 'Italy' in the focused field\"\n - Bad: \"Search for Italy and click the first result\"\n\n For different types of tasks, specify the command_type:\n - 'act': For performing one atomic action (default)\n - 'navigate': For navigating to a URL\n - 'extract': For getting data from a specific page section\n - 'observe': For finding elements in a specific area", + "env_vars": [], + "humanized_name": "Web Automation Tool", + "init_params_schema": { + "$defs": { + "AvailableModel": { + "enum": [ + "gpt-4o", + "gpt-4o-mini", + "claude-3-5-sonnet-latest", + "claude-3-7-sonnet-latest", + "computer-use-preview", + "gemini-2.0-flash" + ], + "title": "AvailableModel", + "type": "string" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "A tool that uses Stagehand to automate web browser interactions using natural language with atomic action handling.\n\nStagehand allows AI agents to interact with websites through a browser,\nperforming actions like clicking buttons, filling forms, and extracting data.\n\nThe tool supports four main command types:\n1. act - Perform actions like clicking, typing, scrolling, or navigating\n2. navigate - Specifically navigate to a URL (shorthand for act with navigation)\n3. extract - Extract structured data from web pages\n4. observe - Identify and analyze elements on a page\n\nUsage examples:\n- Navigate to a website: instruction=\"Go to the homepage\", url=\"https://example.com\"\n- Click a button: instruction=\"Click the login button\"\n- Fill a form: instruction=\"Fill the login form with username 'user' and password 'pass'\"\n- Extract data: instruction=\"Extract all product prices and names\", command_type=\"extract\"\n- Observe elements: instruction=\"Find all navigation menu items\", command_type=\"observe\"\n- Complex tasks: instruction=\"Step 1: Navigate to https://example.com; Step 2: Scroll down to the 'Features' section; Step 3: Click 'Learn More'\", command_type=\"act\"\n\nExample of breaking down \"Search for OpenAI\" into multiple steps:\n1. First navigation: instruction=\"Go to Google\", url=\"https://google.com\", command_type=\"navigate\"\n2. Enter search term: instruction=\"Type 'OpenAI' in the search box\", command_type=\"act\"\n3. Submit search: instruction=\"Press the Enter key or click the search button\", command_type=\"act\"\n4. Click on result: instruction=\"Click on the OpenAI website link in the search results\", command_type=\"act\"", + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Api Key" + }, + "dom_settle_timeout_ms": { + "default": 3000, + "title": "Dom Settle Timeout Ms", + "type": "integer" + }, + "headless": { + "default": false, + "title": "Headless", + "type": "boolean" + }, + "max_retries_on_token_limit": { + "default": 3, + "title": "Max Retries On Token Limit", + "type": "integer" + }, + "model_api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Model Api Key" + }, + "model_name": { + "anyOf": [ + { + "$ref": "#/$defs/AvailableModel" + }, + { + "type": "null" + } + ], + "default": "claude-3-7-sonnet-latest" + }, + "project_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Project Id" + }, + "self_heal": { + "default": true, + "title": "Self Heal", + "type": "boolean" + }, + "server_url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "https://api.stagehand.browserbase.com/v1", + "title": "Server Url" + }, + "use_simplified_dom": { + "default": true, + "title": "Use Simplified Dom", + "type": "boolean" + }, + "verbose": { + "default": 1, + "title": "Verbose", + "type": "integer" + }, + "wait_for_captcha_solves": { + "default": true, + "title": "Wait For Captcha Solves", + "type": "boolean" + } + }, + "title": "StagehandTool", + "type": "object" + }, + "name": "StagehandTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for StagehandTool.", + "properties": { + "command_type": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": "act", + "description": "The type of command to execute (choose one):\n - 'act': Perform an action like clicking buttons, filling forms, etc. (default)\n - 'navigate': Specifically navigate to a URL\n - 'extract': Extract structured data from the page\n - 'observe': Identify and analyze elements on the page\n ", + "title": "Command Type" + }, + "instruction": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Single atomic action with location context. For reliability on complex pages, use ONE specific action with location hints. Good examples: 'Click the search input field in the header', 'Type Italy in the focused field', 'Press Enter', 'Click the first link in the results area'. Avoid combining multiple actions. For 'navigate' command type, this can be omitted if only URL is provided.", + "title": "Instruction" + }, + "url": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The URL to navigate to before executing the instruction. MUST be used with 'navigate' command. ", + "title": "Url" + } + }, + "title": "StagehandToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a txt's content.", + "env_vars": [], + "humanized_name": "Search a txt's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "TXTSearchTool", + "type": "object" + }, + "name": "TXTSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for TXTSearchTool.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to search the txt's content", + "title": "Search Query", + "type": "string" + }, + "txt": { + "description": "Mandatory txt path you want to search", + "title": "Txt", + "type": "string" + } + }, + "required": [ + "search_query", + "txt" + ], + "title": "TXTSearchToolSchema", + "type": "object" + } + }, + { + "description": "Extracts content from one or more web pages using the Tavily API. Returns structured data.", + "env_vars": [ + { + "default": null, + "description": "API key for Tavily extraction service", + "name": "TAVILY_API_KEY", + "required": true + } + ], + "humanized_name": "TavilyExtractorTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The Tavily API key. If not provided, it will be loaded from the environment variable TAVILY_API_KEY.", + "title": "Api Key" + }, + "async_client": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Async Client" + }, + "client": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Client" + }, + "extract_depth": { + "default": "basic", + "description": "The depth of extraction. 'basic' for basic extraction, 'advanced' for advanced extraction.", + "enum": [ + "basic", + "advanced" + ], + "title": "Extract Depth", + "type": "string" + }, + "include_images": { + "default": false, + "description": "Whether to include images in the extraction.", + "title": "Include Images", + "type": "boolean" + }, + "proxies": { + "anyOf": [ + { + "additionalProperties": { + "type": "string" + }, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional proxies to use for the Tavily API requests.", + "title": "Proxies" + }, + "timeout": { + "default": 60, + "description": "The timeout for the extraction request in seconds.", + "title": "Timeout", + "type": "integer" + } + }, + "title": "TavilyExtractorTool", + "type": "object" + }, + "name": "TavilyExtractorTool", + "package_dependencies": [ + "tavily-python" + ], + "run_params_schema": { + "description": "Input schema for TavilyExtractorTool.", + "properties": { + "urls": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "string" + } + ], + "description": "The URL(s) to extract data from. Can be a single URL or a list of URLs.", + "title": "Urls" + } + }, + "required": [ + "urls" + ], + "title": "TavilyExtractorToolSchema", + "type": "object" + } + }, + { + "description": "A tool that performs web searches using the Tavily Search API. It returns a JSON object containing the search results.", + "env_vars": [ + { + "default": null, + "description": "API key for Tavily search service", + "name": "TAVILY_API_KEY", + "required": true + } + ], + "humanized_name": "Tavily Search", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool that uses the Tavily Search API to perform web searches.\n\nAttributes:\n client: An instance of TavilyClient.\n async_client: An instance of AsyncTavilyClient.\n name: The name of the tool.\n description: A description of the tool's purpose.\n args_schema: The schema for the tool's arguments.\n api_key: The Tavily API key.\n proxies: Optional proxies for the API requests.\n search_depth: The depth of the search.\n topic: The topic to focus the search on.\n time_range: The time range for the search.\n days: The number of days to search back.\n max_results: The maximum number of results to return.\n include_domains: A list of domains to include in the search.\n exclude_domains: A list of domains to exclude from the search.\n include_answer: Whether to include a direct answer to the query.\n include_raw_content: Whether to include the raw content of the search results.\n include_images: Whether to include images in the search results.\n timeout: The timeout for the search request in seconds.\n max_content_length_per_result: Maximum length for the 'content' of each search result.", + "properties": { + "api_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The Tavily API key. If not provided, it will be loaded from the environment variable TAVILY_API_KEY.", + "title": "Api Key" + }, + "async_client": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Async Client" + }, + "client": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Client" + }, + "days": { + "default": 7, + "description": "The number of days to search back.", + "title": "Days", + "type": "integer" + }, + "exclude_domains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A list of domains to exclude from the search.", + "title": "Exclude Domains" + }, + "include_answer": { + "anyOf": [ + { + "type": "boolean" + }, + { + "enum": [ + "basic", + "advanced" + ], + "type": "string" + } + ], + "default": false, + "description": "Whether to include a direct answer to the query.", + "title": "Include Answer" + }, + "include_domains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "A list of domains to include in the search.", + "title": "Include Domains" + }, + "include_images": { + "default": false, + "description": "Whether to include images in the search results.", + "title": "Include Images", + "type": "boolean" + }, + "include_raw_content": { + "default": false, + "description": "Whether to include the raw content of the search results.", + "title": "Include Raw Content", + "type": "boolean" + }, + "max_content_length_per_result": { + "default": 1000, + "description": "Maximum length for the 'content' of each search result to avoid context window issues.", + "title": "Max Content Length Per Result", + "type": "integer" + }, + "max_results": { + "default": 5, + "description": "The maximum number of results to return.", + "title": "Max Results", + "type": "integer" + }, + "proxies": { + "anyOf": [ + { + "additionalProperties": { + "type": "string" + }, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional proxies to use for the Tavily API requests.", + "title": "Proxies" + }, + "search_depth": { + "default": "basic", + "description": "The depth of the search.", + "enum": [ + "basic", + "advanced" + ], + "title": "Search Depth", + "type": "string" + }, + "time_range": { + "anyOf": [ + { + "enum": [ + "day", + "week", + "month", + "year" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "The time range for the search.", + "title": "Time Range" + }, + "timeout": { + "default": 60, + "description": "The timeout for the search request in seconds.", + "title": "Timeout", + "type": "integer" + }, + "topic": { + "default": "general", + "description": "The topic to focus the search on.", + "enum": [ + "general", + "news", + "finance" + ], + "title": "Topic", + "type": "string" + } + }, + "title": "TavilySearchTool", + "type": "object" + }, + "name": "TavilySearchTool", + "package_dependencies": [ + "tavily-python" + ], + "run_params_schema": { + "description": "Input schema for TavilySearchTool.", + "properties": { + "query": { + "description": "The search query string.", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "TavilySearchToolSchema", + "type": "object" + } + }, + { + "description": "This tool uses OpenAI's Vision API to describe the contents of an image.", + "env_vars": [ + { + "default": null, + "description": "API key for OpenAI services", + "name": "OPENAI_API_KEY", + "required": true + } + ], + "humanized_name": "Vision Tool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool for analyzing images using vision models.\n\nArgs:\n llm: Optional LLM instance to use\n model: Model identifier to use if no LLM is provided", + "properties": {}, + "title": "VisionTool", + "type": "object" + }, + "name": "VisionTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for Vision Tool.", + "properties": { + "image_path_url": { + "default": "The image path or URL.", + "title": "Image Path Url", + "type": "string" + } + }, + "title": "ImagePromptSchema", + "type": "object" + } + }, + { + "description": "A tool to search the Weaviate database for relevant information on internal documents.", + "env_vars": [ + { + "default": null, + "description": "OpenAI API key for embedding generation and retrieval", + "name": "OPENAI_API_KEY", + "required": true + } + ], + "humanized_name": "WeaviateVectorSearchTool", + "init_params_schema": { + "$defs": { + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "description": "Tool to search the Weaviate database", + "properties": { + "alpha": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 0.75, + "title": "Alpha" + }, + "collection_name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Collection Name" + }, + "generative_model": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Generative Model" + }, + "headers": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Headers" + }, + "limit": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "default": 3, + "title": "Limit" + }, + "query": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Query" + }, + "vectorizer": { + "anyOf": [ + {}, + { + "type": "null" + } + ], + "default": null, + "title": "Vectorizer" + }, + "weaviate_api_key": { + "description": "The API key for the Weaviate cluster", + "title": "Weaviate Api Key", + "type": "string" + }, + "weaviate_cluster_url": { + "description": "The URL of the Weaviate cluster", + "title": "Weaviate Cluster Url", + "type": "string" + } + }, + "required": [ + "weaviate_cluster_url", + "weaviate_api_key" + ], + "title": "WeaviateVectorSearchTool", + "type": "object" + }, + "name": "WeaviateVectorSearchTool", + "package_dependencies": [ + "weaviate-client" + ], + "run_params_schema": { + "description": "Input for WeaviateTool.", + "properties": { + "query": { + "description": "The query to search retrieve relevant information from the Weaviate database. Pass only the query, not the question.", + "title": "Query", + "type": "string" + } + }, + "required": [ + "query" + ], + "title": "WeaviateToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a specific URL content.", + "env_vars": [], + "humanized_name": "Search in a specific website", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "WebsiteSearchTool", + "type": "object" + }, + "name": "WebsiteSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for WebsiteSearchTool.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to search a specific website", + "title": "Search Query", + "type": "string" + }, + "website": { + "description": "Mandatory valid website URL you want to search on", + "title": "Website", + "type": "string" + } + }, + "required": [ + "search_query", + "website" + ], + "title": "WebsiteSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a XML's content.", + "env_vars": [], + "humanized_name": "Search a XML's content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "XMLSearchTool", + "type": "object" + }, + "name": "XMLSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for XMLSearchTool.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to search the XML's content", + "title": "Search Query", + "type": "string" + }, + "xml": { + "description": "Mandatory xml path you want to search", + "title": "Xml", + "type": "string" + } + }, + "required": [ + "search_query", + "xml" + ], + "title": "XMLSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a Youtube Channels content.", + "env_vars": [], + "humanized_name": "Search a Youtube Channels content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "YoutubeChannelSearchTool", + "type": "object" + }, + "name": "YoutubeChannelSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for YoutubeChannelSearchTool.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to search the Youtube Channels content", + "title": "Search Query", + "type": "string" + }, + "youtube_channel_handle": { + "description": "Mandatory youtube_channel_handle path you want to search", + "title": "Youtube Channel Handle", + "type": "string" + } + }, + "required": [ + "search_query", + "youtube_channel_handle" + ], + "title": "YoutubeChannelSearchToolSchema", + "type": "object" + } + }, + { + "description": "A tool that can be used to semantic search a query from a Youtube Video content.", + "env_vars": [], + "humanized_name": "Search a Youtube Video content", + "init_params_schema": { + "$defs": { + "Adapter": { + "properties": {}, + "title": "Adapter", + "type": "object" + }, + "EnvVar": { + "properties": { + "default": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Default" + }, + "description": { + "title": "Description", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "required": { + "default": true, + "title": "Required", + "type": "boolean" + } + }, + "required": [ + "name", + "description" + ], + "title": "EnvVar", + "type": "object" + } + }, + "properties": { + "adapter": { + "$ref": "#/$defs/Adapter" + }, + "config": { + "anyOf": [ + { + "additionalProperties": true, + "type": "object" + }, + { + "type": "null" + } + ], + "default": null, + "title": "Config" + }, + "summarize": { + "default": false, + "title": "Summarize", + "type": "boolean" + } + }, + "title": "YoutubeVideoSearchTool", + "type": "object" + }, + "name": "YoutubeVideoSearchTool", + "package_dependencies": [], + "run_params_schema": { + "description": "Input for YoutubeVideoSearchTool.", + "properties": { + "search_query": { + "description": "Mandatory search query you want to use to search the Youtube Video content", + "title": "Search Query", + "type": "string" + }, + "youtube_video_url": { + "description": "Mandatory youtube_video_url path you want to search", + "title": "Youtube Video Url", + "type": "string" + } + }, + "required": [ + "search_query", + "youtube_video_url" + ], + "title": "YoutubeVideoSearchToolSchema", + "type": "object" + } + } + ] +} \ No newline at end of file diff --git a/lib/crewai/README.md b/lib/crewai/README.md new file mode 100644 index 000000000..f821ac6aa --- /dev/null +++ b/lib/crewai/README.md @@ -0,0 +1,777 @@ +

+ + Open source Multi-AI Agent orchestration framework + +

+

+ + crewAIInc%2FcrewAI | Trendshift + +

+ +

+ Homepage + · + Docs + · + Start Cloud Trial + · + Blog + · + Forum +

+ +

+ + GitHub Repo stars + + + GitHub forks + + + GitHub issues + + + GitHub pull requests + + + License: MIT + +

+ +

+ + PyPI version + + + PyPI downloads + + + Twitter Follow + +

+ +### Fast and Flexible Multi-Agent Automation Framework + +> CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely **independent of LangChain or other agent frameworks**. +> It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario. + +- **CrewAI Crews**: Optimize for autonomy and collaborative intelligence. +- **CrewAI Flows**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively + +With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the +standard for enterprise-ready AI automation. + +# CrewAI AMP Suite + +CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation. + +You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com) + +## Crew Control Plane Key Features: + +- **Tracing & Observability**: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces. +- **Unified Control Plane**: A centralized platform for managing, monitoring, and scaling your AI agents and workflows. +- **Seamless Integrations**: Easily connect with existing enterprise systems, data sources, and cloud infrastructure. +- **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management. +- **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making. +- **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues. +- **On-premise and Cloud Deployment Options**: Deploy CrewAI AMP on-premise or in the cloud, depending on your security and compliance requirements. + +CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient, +intelligent automations. + +## Table of contents + +- [Why CrewAI?](#why-crewai) +- [Getting Started](#getting-started) +- [Key Features](#key-features) +- [Understanding Flows and Crews](#understanding-flows-and-crews) +- [CrewAI vs LangGraph](#how-crewai-compares) +- [Examples](#examples) + - [Quick Tutorial](#quick-tutorial) + - [Write Job Descriptions](#write-job-descriptions) + - [Trip Planner](#trip-planner) + - [Stock Analysis](#stock-analysis) + - [Using Crews and Flows Together](#using-crews-and-flows-together) +- [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model) +- [How CrewAI Compares](#how-crewai-compares) +- [Frequently Asked Questions (FAQ)](#frequently-asked-questions-faq) +- [Contribution](#contribution) +- [Telemetry](#telemetry) +- [License](#license) + +## Why CrewAI? + +
+ CrewAI Logo +
+ +CrewAI unlocks the true potential of multi-agent automation, delivering the best-in-class combination of speed, flexibility, and control with either Crews of AI Agents or Flows of Events: + +- **Standalone Framework**: Built from scratch, independent of LangChain or any other agent framework. +- **High Performance**: Optimized for speed and minimal resource usage, enabling faster execution. +- **Flexible Low Level Customization**: Complete freedom to customize at both high and low levels - from overall workflows and system architecture to granular agent behaviors, internal prompts, and execution logic. +- **Ideal for Every Use Case**: Proven effective for both simple tasks and highly complex, real-world, enterprise-grade scenarios. +- **Robust Community**: Backed by a rapidly growing community of over **100,000 certified** developers offering comprehensive support and resources. + +CrewAI empowers developers and enterprises to confidently build intelligent automations, bridging the gap between simplicity, flexibility, and performance. + +## Getting Started + +Setup and run your first CrewAI agents by following this tutorial. + +[![CrewAI Getting Started Tutorial](https://img.youtube.com/vi/-kSOTtYzgEw/hqdefault.jpg)](https://www.youtube.com/watch?v=-kSOTtYzgEw "CrewAI Getting Started Tutorial") + +### + Learning Resources + +Learn CrewAI through our comprehensive courses: + +- [Multi AI Agent Systems with CrewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) - Master the fundamentals of multi-agent systems +- [Practical Multi AI Agents and Advanced Use Cases](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/) - Deep dive into advanced implementations + +### Understanding Flows and Crews + +CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications: + +1. **Crews**: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable: + + - Natural, autonomous decision-making between agents + - Dynamic task delegation and collaboration + - Specialized roles with defined goals and expertise + - Flexible problem-solving approaches +2. **Flows**: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide: + + - Fine-grained control over execution paths for real-world scenarios + - Secure, consistent state management between tasks + - Clean integration of AI agents with production Python code + - Conditional branching for complex business logic + +The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to: + +- Build complex, production-grade applications +- Balance autonomy with precise control +- Handle sophisticated real-world scenarios +- Maintain clean, maintainable code structure + +### Getting Started with Installation + +To get started with CrewAI, follow these simple steps: + +### 1. Installation + +Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. + +First, install CrewAI: + +```shell +pip install crewai +``` + +If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command: + +```shell +pip install 'crewai[tools]' +``` + +The command above installs the basic package and also adds extra components which require more dependencies to function. + +### Troubleshooting Dependencies + +If you encounter issues during installation or usage, here are some common solutions: + +#### Common Issues + +1. **ModuleNotFoundError: No module named 'tiktoken'** + + - Install tiktoken explicitly: `pip install 'crewai[embeddings]'` + - If using embedchain or other tools: `pip install 'crewai[tools]'` +2. **Failed building wheel for tiktoken** + + - Ensure Rust compiler is installed (see installation steps above) + - For Windows: Verify Visual C++ Build Tools are installed + - Try upgrading pip: `pip install --upgrade pip` + - If issues persist, use a pre-built wheel: `pip install tiktoken --prefer-binary` + +### 2. Setting Up Your Crew with the YAML Configuration + +To create a new CrewAI project, run the following CLI (Command Line Interface) command: + +```shell +crewai create crew +``` + +This command creates a new project folder with the following structure: + +``` +my_project/ +├── .gitignore +├── pyproject.toml +├── README.md +├── .env +└── src/ + └── my_project/ + ├── __init__.py + ├── main.py + ├── crew.py + ├── tools/ + │ ├── custom_tool.py + │ └── __init__.py + └── config/ + ├── agents.yaml + └── tasks.yaml +``` + +You can now start developing your crew by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of the project, the `crew.py` file is where you define your crew, the `agents.yaml` file is where you define your agents, and the `tasks.yaml` file is where you define your tasks. + +#### To customize your project, you can: + +- Modify `src/my_project/config/agents.yaml` to define your agents. +- Modify `src/my_project/config/tasks.yaml` to define your tasks. +- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments. +- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks. +- Add your environment variables into the `.env` file. + +#### Example of a simple crew with a sequential process: + +Instantiate your crew: + +```shell +crewai create crew latest-ai-development +``` + +Modify the files as needed to fit your use case: + +**agents.yaml** + +```yaml +# src/my_project/config/agents.yaml +researcher: + role: > + {topic} Senior Data Researcher + goal: > + Uncover cutting-edge developments in {topic} + backstory: > + You're a seasoned researcher with a knack for uncovering the latest + developments in {topic}. Known for your ability to find the most relevant + information and present it in a clear and concise manner. + +reporting_analyst: + role: > + {topic} Reporting Analyst + goal: > + Create detailed reports based on {topic} data analysis and research findings + backstory: > + You're a meticulous analyst with a keen eye for detail. You're known for + your ability to turn complex data into clear and concise reports, making + it easy for others to understand and act on the information you provide. +``` + +**tasks.yaml** + +```yaml +# src/my_project/config/tasks.yaml +research_task: + description: > + Conduct a thorough research about {topic} + Make sure you find any interesting and relevant information given + the current year is 2025. + expected_output: > + A list with 10 bullet points of the most relevant information about {topic} + agent: researcher + +reporting_task: + description: > + Review the context you got and expand each topic into a full section for a report. + Make sure the report is detailed and contains any and all relevant information. + expected_output: > + A fully fledge reports with the mains topics, each with a full section of information. + Formatted as markdown without '```' + agent: reporting_analyst + output_file: report.md +``` + +**crew.py** + +```python +# src/my_project/crew.py +from crewai import Agent, Crew, Process, Task +from crewai.project import CrewBase, agent, crew, task +from crewai_tools import SerperDevTool +from crewai.agents.agent_builder.base_agent import BaseAgent +from typing import List + +@CrewBase +class LatestAiDevelopmentCrew(): + """LatestAiDevelopment crew""" + agents: List[BaseAgent] + tasks: List[Task] + + @agent + def researcher(self) -> Agent: + return Agent( + config=self.agents_config['researcher'], + verbose=True, + tools=[SerperDevTool()] + ) + + @agent + def reporting_analyst(self) -> Agent: + return Agent( + config=self.agents_config['reporting_analyst'], + verbose=True + ) + + @task + def research_task(self) -> Task: + return Task( + config=self.tasks_config['research_task'], + ) + + @task + def reporting_task(self) -> Task: + return Task( + config=self.tasks_config['reporting_task'], + output_file='report.md' + ) + + @crew + def crew(self) -> Crew: + """Creates the LatestAiDevelopment crew""" + return Crew( + agents=self.agents, # Automatically created by the @agent decorator + tasks=self.tasks, # Automatically created by the @task decorator + process=Process.sequential, + verbose=True, + ) +``` + +**main.py** + +```python +#!/usr/bin/env python +# src/my_project/main.py +import sys +from latest_ai_development.crew import LatestAiDevelopmentCrew + +def run(): + """ + Run the crew. + """ + inputs = { + 'topic': 'AI Agents' + } + LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs) +``` + +### 3. Running Your Crew + +Before running your crew, make sure you have the following keys set as environment variables in your `.env` file: + +- An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...` +- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE` + +Lock the dependencies and install them by using the CLI command but first, navigate to your project directory: + +```shell +cd my_project +crewai install (Optional) +``` + +To run your crew, execute the following command in the root of your project: + +```bash +crewai run +``` + +or + +```bash +python src/my_project/main.py +``` + +If an error happens due to the usage of poetry, please run the following command to update your crewai package: + +```bash +crewai update +``` + +You should see the output in the console and the `report.md` file should be created in the root of your project with the full final report. + +In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. [See more about the processes here](https://docs.crewai.com/core-concepts/Processes/). + +## Key Features + +CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks. + +- **Standalone & Lean**: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands. +- **Flexible & Precise**: Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect balance for your needs. +- **Seamless Integration**: Effortlessly combine Crews (autonomy) and Flows (precision) to create complex, real-world automations. +- **Deep Customization**: Tailor every aspect—from high-level workflows down to low-level internal prompts and agent behaviors. +- **Reliable Performance**: Consistent results across simple tasks and complex, enterprise-level automations. +- **Thriving Community**: Backed by robust documentation and over 100,000 certified developers, providing exceptional support and guidance. + +Choose CrewAI to easily build powerful, adaptable, and production-ready AI automations. + +## Examples + +You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file): + +- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator) +- [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution) +- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) +- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) + +### Quick Tutorial + +[![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/maxresdefault.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial") + +### Write Job Descriptions + +[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below: + +[![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings") + +### Trip Planner + +[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below: + +[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner") + +### Stock Analysis + +[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below: + +[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis") + +### Using Crews and Flows Together + +CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. +CrewAI flows support logical operators like `or_` and `and_` to combine multiple conditions. This can be used with `@start`, `@listen`, or `@router` decorators to create complex triggering conditions. + +- `or_`: Triggers when any of the specified conditions are met. +- `and_`Triggers when all of the specified conditions are met. + +Here's how you can orchestrate multiple Crews within a Flow: + +```python +from crewai.flow.flow import Flow, listen, start, router, or_ +from crewai import Crew, Agent, Task, Process +from pydantic import BaseModel + +# Define structured state for precise control +class MarketState(BaseModel): + sentiment: str = "neutral" + confidence: float = 0.0 + recommendations: list = [] + +class AdvancedAnalysisFlow(Flow[MarketState]): + @start() + def fetch_market_data(self): + # Demonstrate low-level control with structured state + self.state.sentiment = "analyzing" + return {"sector": "tech", "timeframe": "1W"} # These parameters match the task description template + + @listen(fetch_market_data) + def analyze_with_crew(self, market_data): + # Show crew agency through specialized roles + analyst = Agent( + role="Senior Market Analyst", + goal="Conduct deep market analysis with expert insight", + backstory="You're a veteran analyst known for identifying subtle market patterns" + ) + researcher = Agent( + role="Data Researcher", + goal="Gather and validate supporting market data", + backstory="You excel at finding and correlating multiple data sources" + ) + + analysis_task = Task( + description="Analyze {sector} sector data for the past {timeframe}", + expected_output="Detailed market analysis with confidence score", + agent=analyst + ) + research_task = Task( + description="Find supporting data to validate the analysis", + expected_output="Corroborating evidence and potential contradictions", + agent=researcher + ) + + # Demonstrate crew autonomy + analysis_crew = Crew( + agents=[analyst, researcher], + tasks=[analysis_task, research_task], + process=Process.sequential, + verbose=True + ) + return analysis_crew.kickoff(inputs=market_data) # Pass market_data as named inputs + + @router(analyze_with_crew) + def determine_next_steps(self): + # Show flow control with conditional routing + if self.state.confidence > 0.8: + return "high_confidence" + elif self.state.confidence > 0.5: + return "medium_confidence" + return "low_confidence" + + @listen("high_confidence") + def execute_strategy(self): + # Demonstrate complex decision making + strategy_crew = Crew( + agents=[ + Agent(role="Strategy Expert", + goal="Develop optimal market strategy") + ], + tasks=[ + Task(description="Create detailed strategy based on analysis", + expected_output="Step-by-step action plan") + ] + ) + return strategy_crew.kickoff() + + @listen(or_("medium_confidence", "low_confidence")) + def request_additional_analysis(self): + self.state.recommendations.append("Gather more data") + return "Additional analysis required" +``` + +This example demonstrates how to: + +1. Use Python code for basic data operations +2. Create and execute Crews as steps in your workflow +3. Use Flow decorators to manage the sequence of operations +4. Implement conditional branching based on Crew results + +## Connecting Your Crew to a Model + +CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool. + +Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models. + +## How CrewAI Compares + +**CrewAI's Advantage**: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control. + +- **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant boilerplate code and complex state management patterns. The framework's tight coupling with LangChain can limit flexibility when implementing custom agent behaviors or integrating with external systems. + +*P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb)).* + +- **Autogen**: While Autogen excels at creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows. +- **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications. + +## Contribution + +CrewAI is open-source and we welcome contributions. If you're looking to contribute, please: + +- Fork the repository. +- Create a new branch for your feature. +- Add your feature or improvement. +- Send a pull request. +- We appreciate your input! + +### Installing Dependencies + +```bash +uv lock +uv sync +``` + +### Virtual Env + +```bash +uv venv +``` + +### Pre-commit hooks + +```bash +pre-commit install +``` + +### Running Tests + +```bash +uv run pytest . +``` + +### Running static type checks + +```bash +uvx mypy src +``` + +### Packaging + +```bash +uv build +``` + +### Installing Locally + +```bash +pip install dist/*.tar.gz +``` + +## Telemetry + +CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools. + +It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true. + +Data collected includes: + +- Version of CrewAI + - So we can understand how many users are using the latest version +- Version of Python + - So we can decide on what versions to better support +- General OS (e.g. number of CPUs, macOS/Windows/Linux) + - So we know what OS we should focus on and if we could build specific OS related features +- Number of agents and tasks in a crew + - So we make sure we are testing internally with similar use cases and educate people on the best practices +- Crew Process being used + - Understand where we should focus our efforts +- If Agents are using memory or allowing delegation + - Understand if we improved the features or maybe even drop them +- If Tasks are being executed in parallel or sequentially + - Understand if we should focus more on parallel execution +- Language model being used + - Improved support on most used languages +- Roles of agents in a crew + - Understand high level use cases so we can build better tools, integrations and examples about it +- Tools names available + - Understand out of the publicly available tools, which ones are being used the most so we can improve them + +Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share. + +## License + +CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE). + +## Frequently Asked Questions (FAQ) + +### General + +- [What exactly is CrewAI?](#q-what-exactly-is-crewai) +- [How do I install CrewAI?](#q-how-do-i-install-crewai) +- [Does CrewAI depend on LangChain?](#q-does-crewai-depend-on-langchain) +- [Is CrewAI open-source?](#q-is-crewai-open-source) +- [Does CrewAI collect data from users?](#q-does-crewai-collect-data-from-users) + +### Features and Capabilities + +- [Can CrewAI handle complex use cases?](#q-can-crewai-handle-complex-use-cases) +- [Can I use CrewAI with local AI models?](#q-can-i-use-crewai-with-local-ai-models) +- [What makes Crews different from Flows?](#q-what-makes-crews-different-from-flows) +- [How is CrewAI better than LangChain?](#q-how-is-crewai-better-than-langchain) +- [Does CrewAI support fine-tuning or training custom models?](#q-does-crewai-support-fine-tuning-or-training-custom-models) + +### Resources and Community + +- [Where can I find real-world CrewAI examples?](#q-where-can-i-find-real-world-crewai-examples) +- [How can I contribute to CrewAI?](#q-how-can-i-contribute-to-crewai) + +### Enterprise Features + +- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-enterprise-offer) +- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments) +- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-enterprise-for-free) + +### Q: What exactly is CrewAI? + +A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler. + +### Q: How do I install CrewAI? + +A: Install CrewAI using pip: + +```shell +pip install crewai +``` + +For additional tools, use: + +```shell +pip install 'crewai[tools]' +``` + +### Q: Does CrewAI depend on LangChain? + +A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience. + +### Q: Can CrewAI handle complex use cases? + +A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration. + +### Q: Can I use CrewAI with local AI models? + +A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details. + +### Q: What makes Crews different from Flows? + +A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness. + +### Q: How is CrewAI better than LangChain? + +A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain. + +### Q: Is CrewAI open-source? + +A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration. + +### Q: Does CrewAI collect data from users? + +A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user. + +### Q: Where can I find real-world CrewAI examples? + +A: Check out practical examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), covering use cases like trip planners, stock analysis, and job postings. + +### Q: How can I contribute to CrewAI? + +A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines. + +### Q: What additional features does CrewAI AMP offer? + +A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support. + +### Q: Is CrewAI AMP available for cloud and on-premise deployments? + +A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements. + +### Q: Can I try CrewAI AMP for free? + +A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free. + +### Q: Does CrewAI support fine-tuning or training custom models? + +A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy. + +### Q: Can CrewAI agents interact with external tools and APIs? + +A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources. + +### Q: Is CrewAI suitable for production environments? + +A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments. + +### Q: How scalable is CrewAI? + +A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously. + +### Q: Does CrewAI offer debugging and monitoring tools? + +A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations. + +### Q: What programming languages does CrewAI support? + +A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities. + +### Q: Does CrewAI offer educational resources for beginners? + +A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels. + +### Q: Can CrewAI automate human-in-the-loop workflows? + +A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making. diff --git a/lib/crewai/pyproject.toml b/lib/crewai/pyproject.toml new file mode 100644 index 000000000..05c9b6d90 --- /dev/null +++ b/lib/crewai/pyproject.toml @@ -0,0 +1,149 @@ +[project] +name = "crewai" +dynamic = ["version"] +description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks." +readme = "README.md" +authors = [ + { name = "Joao Moura", email = "joao@crewai.com" } +] +requires-python = ">=3.10, <3.14" +dependencies = [ + # Core Dependencies + "pydantic>=2.11.9", + "openai>=1.13.3", + "instructor>=1.3.3", + # Text Processing + "pdfplumber>=0.11.4", + "regex>=2024.9.11", + # Telemetry and Monitoring + "opentelemetry-api>=1.30.0", + "opentelemetry-sdk>=1.30.0", + "opentelemetry-exporter-otlp-proto-http>=1.30.0", + # Data Handling + "chromadb~=1.1.0", + "tokenizers>=0.20.3", + "openpyxl>=3.1.5", + "pyvis>=0.3.2", + # Authentication and Security + "python-dotenv>=1.1.1", + "pyjwt>=2.9.0", + # Configuration and Utils + "click>=8.1.7", + "appdirs>=1.4.4", + "jsonref>=1.1.0", + "json-repair==0.25.2", + "uv>=0.4.25", + "tomli-w>=1.1.0", + "tomli>=2.0.2", + "json5>=0.10.0", + "portalocker==2.7.0", + "pydantic-settings>=2.10.1", + "mcp>=1.16.0", +] + +[project.urls] +Homepage = "https://crewai.com" +Documentation = "https://docs.crewai.com" +Repository = "https://github.com/crewAIInc/crewAI" + + +[project.optional-dependencies] +tools = [ + "crewai-tools==1.0.0b3", +] +embeddings = [ + "tiktoken~=0.8.0" +] +pdfplumber = [ + "pdfplumber>=0.11.4", +] +pandas = [ + "pandas>=2.2.3", +] +openpyxl = [ + "openpyxl>=3.1.5", +] +mem0 = ["mem0ai>=0.1.94"] +docling = [ + "docling>=2.12.0", +] +aisuite = [ + "aisuite>=0.1.11", + + +] +qdrant = [ + "qdrant-client[fastembed]>=1.14.3", +] +aws = [ + "boto3>=1.40.38", +] +watson = [ + "ibm-watsonx-ai>=1.3.39", +] +voyageai = [ + "voyageai>=0.3.5", +] +litellm = [ + "litellm>=1.74.9", +] +bedrock = [ + "boto3>=1.40.45", +] +google-genai = [ + "google-genai>=1.2.0", +] +azure-ai-inference = [ + "azure-ai-inference>=1.0.0b9", +] +anthropic = [ + "anthropic>=0.69.0", +] +# a2a = [ +# "a2a-sdk~=0.3.9", +# "httpx-sse>=0.4.0", +# ] + + +[project.scripts] +crewai = "crewai.cli.cli:crewai" + + +# PyTorch index configuration, since torch 2.5.0 is not compatible with python 3.13 +[[tool.uv.index]] +name = "pytorch-nightly" +url = "https://download.pytorch.org/whl/nightly/cpu" +explicit = true + +[[tool.uv.index]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch-nightly", marker = "python_version >= '3.13'" }, + { index = "pytorch", marker = "python_version < '3.13'" }, +] +torchvision = [ + { index = "pytorch-nightly", marker = "python_version >= '3.13'" }, + { index = "pytorch", marker = "python_version < '3.13'" }, +] + + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.version] +path = "src/crewai/__init__.py" + +# Declare mutually exclusive extras due to conflicting httpx requirements +# a2a requires httpx>=0.28.1, while aisuite requires httpx>=0.27.0,<0.28.0 +# [tool.uv] +# conflicts = [ +# [ +# { extra = "a2a" }, +# { extra = "aisuite" }, +# ], +# ] diff --git a/src/crewai/__init__.py b/lib/crewai/src/crewai/__init__.py similarity index 98% rename from src/crewai/__init__.py rename to lib/crewai/src/crewai/__init__.py index 9f44bc054..0822347bf 100644 --- a/src/crewai/__init__.py +++ b/lib/crewai/src/crewai/__init__.py @@ -1,7 +1,7 @@ import threading +from typing import Any import urllib.request import warnings -from typing import Any from crewai.agent import Agent from crewai.crew import Crew @@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None: _suppress_pydantic_deprecation_warnings() -__version__ = "0.203.1" +__version__ = "1.0.0b3" _telemetry_submitted = False diff --git a/src/crewai/agent.py b/lib/crewai/src/crewai/agent.py similarity index 65% rename from src/crewai/agent.py rename to lib/crewai/src/crewai/agent.py index 80f995de8..1b1383169 100644 --- a/src/crewai/agent.py +++ b/lib/crewai/src/crewai/agent.py @@ -1,16 +1,21 @@ +from __future__ import annotations + +import asyncio +from collections.abc import Sequence import shutil import subprocess import time -from collections.abc import Callable, Sequence from typing import ( + TYPE_CHECKING, Any, Literal, ) -from pydantic import Field, InstanceOf, PrivateAttr, model_validator +from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator +from typing_extensions import Self -from crewai.agents import CacheHandler from crewai.agents.agent_builder.base_agent import BaseAgent +from crewai.agents.cache.cache_handler import CacheHandler from crewai.agents.crew_agent_executor import CrewAgentExecutor from crewai.events.event_bus import crewai_event_bus from crewai.events.types.agent_events import ( @@ -33,15 +38,12 @@ from crewai.events.types.memory_events import ( from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context -from crewai.lite_agent import LiteAgent, LiteAgentOutput -from crewai.llm import BaseLLM +from crewai.lite_agent import LiteAgent +from crewai.llms.base_llm import BaseLLM from crewai.memory.contextual.contextual_memory import ContextualMemory from crewai.rag.embeddings.types import EmbedderConfig -from crewai.security import Fingerprint -from crewai.task import Task -from crewai.tools import BaseTool +from crewai.security.fingerprint import Fingerprint from crewai.tools.agent_tools.agent_tools import AgentTools -from crewai.utilities import Converter, Prompts from crewai.utilities.agent_utils import ( get_tool_names, load_agent_from_repository, @@ -49,11 +51,33 @@ from crewai.utilities.agent_utils import ( render_text_description_and_args, ) from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE -from crewai.utilities.converter import generate_model_description +from crewai.utilities.converter import Converter, generate_model_description +from crewai.utilities.guardrail_types import GuardrailType from crewai.utilities.llm_utils import create_llm +from crewai.utilities.prompts import Prompts from crewai.utilities.token_counter_callback import TokenCalcHandler from crewai.utilities.training_handler import CrewTrainingHandler -from crewai.utilities.types import LLMMessage + + +if TYPE_CHECKING: + from crewai_tools import CodeInterpreterTool + + from crewai.agents.agent_builder.base_agent import PlatformAppOrAction + from crewai.lite_agent_output import LiteAgentOutput + from crewai.task import Task + from crewai.tools.base_tool import BaseTool + from crewai.utilities.types import LLMMessage + + +# MCP Connection timeout constants (in seconds) +MCP_CONNECTION_TIMEOUT = 10 +MCP_TOOL_EXECUTION_TIMEOUT = 30 +MCP_DISCOVERY_TIMEOUT = 15 +MCP_MAX_RETRIES = 3 + +# Simple in-memory cache for MCP tool schemas (duration: 5 minutes) +_mcp_schema_cache = {} +_cache_ttl = 300 # 5 minutes class Agent(BaseAgent): @@ -79,6 +103,8 @@ class Agent(BaseAgent): step_callback: Callback to be executed after each step of the agent execution. knowledge_sources: Knowledge sources for the agent. embedder: Embedder configuration for the agent. + apps: List of applications that the agent can access through CrewAI Platform. + mcps: List of MCP server references for tool integration. """ _times_executed: int = PrivateAttr(default=0) @@ -86,8 +112,6 @@ class Agent(BaseAgent): default=None, description="Maximum execution time for an agent to execute a task", ) - agent_ops_agent_name: str = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str") - agent_ops_agent_id: str = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str") step_callback: Any | None = Field( default=None, description="Callback to be executed after each step of the agent execution.", @@ -166,7 +190,7 @@ class Agent(BaseAgent): default=None, description="The Agent's role to be used from your repository.", ) - guardrail: Callable[[Any], tuple[bool, Any]] | str | None = Field( + guardrail: GuardrailType | None = Field( default=None, description="Function or string description of a guardrail to validate agent output", ) @@ -175,15 +199,13 @@ class Agent(BaseAgent): ) @model_validator(mode="before") - def validate_from_repository(cls, v): # noqa: N805 + def validate_from_repository(cls, v: Any) -> dict[str, Any] | None | Any: # noqa: N805 if v is not None and (from_repository := v.get("from_repository")): return load_agent_from_repository(from_repository) | v return v @model_validator(mode="after") - def post_init_setup(self): - self.agent_ops_agent_name = self.role - + def post_init_setup(self) -> Self: self.llm = create_llm(self.llm) if self.function_calling_llm and not isinstance( self.function_calling_llm, BaseLLM @@ -198,12 +220,12 @@ class Agent(BaseAgent): return self - def _setup_agent_executor(self): + def _setup_agent_executor(self) -> None: if not self.cache_handler: self.cache_handler = CacheHandler() self.set_cache_handler(self.cache_handler) - def set_knowledge(self, crew_embedder: EmbedderConfig | None = None): + def set_knowledge(self, crew_embedder: EmbedderConfig | None = None) -> None: try: if self.embedder is None and crew_embedder: self.embedder = crew_embedder @@ -241,7 +263,7 @@ class Agent(BaseAgent): task: Task, context: str | None = None, tools: list[BaseTool] | None = None, - ) -> str: + ) -> Any: """Execute a task with the agent. Args: @@ -272,11 +294,7 @@ class Agent(BaseAgent): # Add the reasoning plan to the task description task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}" except Exception as e: - if hasattr(self, "_logger"): - self._logger.log("error", f"Error during reasoning process: {e!s}") - else: - print(f"Error during reasoning process: {e!s}") - + self._logger.log("error", f"Error during reasoning process: {e!s}") self._inject_date_to_task(task) if self.tools_handler: @@ -328,7 +346,7 @@ class Agent(BaseAgent): agent=self, task=task, ) - memory = contextual_memory.build_context_for_task(task, context) # type: ignore[arg-type] + memory = contextual_memory.build_context_for_task(task, context or "") if memory.strip() != "": task_prompt += self.i18n.slice("memory").format(memory=memory) @@ -348,17 +366,17 @@ class Agent(BaseAgent): ) if self.knowledge or (self.crew and self.crew.knowledge): + crewai_event_bus.emit( + self, + event=KnowledgeRetrievalStartedEvent( + from_task=task, + from_agent=self, + ), + ) try: self.knowledge_search_query = self._get_knowledge_search_query( task_prompt, task ) - crewai_event_bus.emit( - self, - event=KnowledgeRetrievalStartedEvent( - from_task=task, - from_agent=self, - ), - ) if self.knowledge_search_query: # Quering agent specific knowledge if self.knowledge: @@ -488,7 +506,7 @@ class Agent(BaseAgent): # If there was any tool in self.tools_results that had result_as_answer # set to True, return the results of the last tool that had # result_as_answer set to True - for tool_result in self.tools_results: # type: ignore # Item "None" of "list[Any] | None" has no attribute "__iter__" (not iterable) + for tool_result in self.tools_results: if tool_result.get("result_as_answer", False): result = tool_result["result"] crewai_event_bus.emit( @@ -497,7 +515,7 @@ class Agent(BaseAgent): ) return result - def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> str: + def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> Any: """Execute a task with a timeout. Args: @@ -530,7 +548,7 @@ class Agent(BaseAgent): future.cancel() raise RuntimeError(f"Task execution failed: {e!s}") from e - def _execute_without_timeout(self, task_prompt: str, task: Task) -> str: + def _execute_without_timeout(self, task_prompt: str, task: Task) -> Any: """Execute a task without a timeout. Args: @@ -540,6 +558,9 @@ class Agent(BaseAgent): Returns: The output of the agent. """ + if not self.agent_executor: + raise RuntimeError("Agent executor is not initialized.") + return self.agent_executor.invoke( { "input": task_prompt, @@ -550,7 +571,7 @@ class Agent(BaseAgent): )["output"] def create_agent_executor( - self, tools: list[BaseTool] | None = None, task=None + self, tools: list[BaseTool] | None = None, task: Task | None = None ) -> None: """Create an agent executor for the agent. @@ -579,11 +600,11 @@ class Agent(BaseAgent): self.agent_executor = CrewAgentExecutor( llm=self.llm, - task=task, + task=task, # type: ignore[arg-type] agent=self, crew=self.crew, tools=parsed_tools, - prompt=prompt, # type: ignore[arg-type] + prompt=prompt, original_tools=raw_tools, stop_words=stop_words, max_iter=self.max_iter, @@ -599,18 +620,367 @@ class Agent(BaseAgent): callbacks=[TokenCalcHandler(self._token_process)], ) - def get_delegation_tools(self, agents: list[BaseAgent]): + def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]: agent_tools = AgentTools(agents=agents) return agent_tools.tools() + def get_platform_tools(self, apps: list[PlatformAppOrAction]) -> list[BaseTool]: + try: + from crewai_tools import ( + CrewaiPlatformTools, + ) + + return CrewaiPlatformTools(apps=apps) + except Exception as e: + self._logger.log("error", f"Error getting platform tools: {e!s}") + return [] + + def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]: + """Convert MCP server references to CrewAI tools.""" + all_tools = [] + + for mcp_ref in mcps: + try: + if mcp_ref.startswith("crewai-amp:"): + tools = self._get_amp_mcp_tools(mcp_ref) + elif mcp_ref.startswith("https://"): + tools = self._get_external_mcp_tools(mcp_ref) + else: + continue + + all_tools.extend(tools) + self._logger.log( + "info", f"Successfully loaded {len(tools)} tools from {mcp_ref}" + ) + + except Exception as e: + self._logger.log("warning", f"Skipping MCP {mcp_ref} due to error: {e}") + continue + + return all_tools + + def _get_external_mcp_tools(self, mcp_ref: str) -> list[BaseTool]: + """Get tools from external HTTPS MCP server with graceful error handling.""" + from crewai.tools.mcp_tool_wrapper import MCPToolWrapper + + # Parse server URL and optional tool name + if "#" in mcp_ref: + server_url, specific_tool = mcp_ref.split("#", 1) + else: + server_url, specific_tool = mcp_ref, None + + server_params = {"url": server_url} + server_name = self._extract_server_name(server_url) + + try: + # Get tool schemas with timeout and error handling + tool_schemas = self._get_mcp_tool_schemas(server_params) + + if not tool_schemas: + self._logger.log( + "warning", f"No tools discovered from MCP server: {server_url}" + ) + return [] + + tools = [] + for tool_name, schema in tool_schemas.items(): + # Skip if specific tool requested and this isn't it + if specific_tool and tool_name != specific_tool: + continue + + try: + wrapper = MCPToolWrapper( + mcp_server_params=server_params, + tool_name=tool_name, + tool_schema=schema, + server_name=server_name, + ) + tools.append(wrapper) + except Exception as e: + self._logger.log( + "warning", + f"Failed to create MCP tool wrapper for {tool_name}: {e}", + ) + continue + + if specific_tool and not tools: + self._logger.log( + "warning", + f"Specific tool '{specific_tool}' not found on MCP server: {server_url}", + ) + + return tools + + except Exception as e: + self._logger.log( + "warning", f"Failed to connect to MCP server {server_url}: {e}" + ) + return [] + + def _get_amp_mcp_tools(self, amp_ref: str) -> list[BaseTool]: + """Get tools from CrewAI AMP MCP marketplace.""" + # Parse: "crewai-amp:mcp-name" or "crewai-amp:mcp-name#tool_name" + amp_part = amp_ref.replace("crewai-amp:", "") + if "#" in amp_part: + mcp_name, specific_tool = amp_part.split("#", 1) + else: + mcp_name, specific_tool = amp_part, None + + # Call AMP API to get MCP server URLs + mcp_servers = self._fetch_amp_mcp_servers(mcp_name) + + tools = [] + for server_config in mcp_servers: + server_ref = server_config["url"] + if specific_tool: + server_ref += f"#{specific_tool}" + server_tools = self._get_external_mcp_tools(server_ref) + tools.extend(server_tools) + + return tools + + def _extract_server_name(self, server_url: str) -> str: + """Extract clean server name from URL for tool prefixing.""" + from urllib.parse import urlparse + + parsed = urlparse(server_url) + domain = parsed.netloc.replace(".", "_") + path = parsed.path.replace("/", "_").strip("_") + return f"{domain}_{path}" if path else domain + + def _get_mcp_tool_schemas(self, server_params: dict) -> dict[str, dict]: + """Get tool schemas from MCP server for wrapper creation with caching.""" + server_url = server_params["url"] + + # Check cache first + cache_key = server_url + current_time = time.time() + + if cache_key in _mcp_schema_cache: + cached_data, cache_time = _mcp_schema_cache[cache_key] + if current_time - cache_time < _cache_ttl: + self._logger.log( + "debug", f"Using cached MCP tool schemas for {server_url}" + ) + return cached_data + + try: + schemas = asyncio.run(self._get_mcp_tool_schemas_async(server_params)) + + # Cache successful results + _mcp_schema_cache[cache_key] = (schemas, current_time) + + return schemas + except Exception as e: + # Log warning but don't raise - this allows graceful degradation + self._logger.log( + "warning", f"Failed to get MCP tool schemas from {server_url}: {e}" + ) + return {} + + async def _get_mcp_tool_schemas_async(self, server_params: dict) -> dict[str, dict]: + """Async implementation of MCP tool schema retrieval with timeouts and retries.""" + server_url = server_params["url"] + return await self._retry_mcp_discovery( + self._discover_mcp_tools_with_timeout, server_url + ) + + async def _retry_mcp_discovery( + self, operation_func, server_url: str + ) -> dict[str, dict]: + """Retry MCP discovery operation with exponential backoff, avoiding try-except in loop.""" + last_error = None + + for attempt in range(MCP_MAX_RETRIES): + # Execute single attempt outside try-except loop structure + result, error, should_retry = await self._attempt_mcp_discovery( + operation_func, server_url + ) + + # Success case - return immediately + if result is not None: + return result + + # Non-retryable error - raise immediately + if not should_retry: + raise RuntimeError(error) + + # Retryable error - continue with backoff + last_error = error + if attempt < MCP_MAX_RETRIES - 1: + wait_time = 2**attempt # Exponential backoff + await asyncio.sleep(wait_time) + + raise RuntimeError( + f"Failed to discover MCP tools after {MCP_MAX_RETRIES} attempts: {last_error}" + ) + + async def _attempt_mcp_discovery( + self, operation_func, server_url: str + ) -> tuple[dict[str, dict] | None, str, bool]: + """Attempt single MCP discovery operation and return (result, error_message, should_retry).""" + try: + result = await operation_func(server_url) + return result, "", False + + except ImportError: + return ( + None, + "MCP library not available. Please install with: pip install mcp", + False, + ) + + except asyncio.TimeoutError: + return ( + None, + f"MCP discovery timed out after {MCP_DISCOVERY_TIMEOUT} seconds", + True, + ) + + except Exception as e: + error_str = str(e).lower() + + # Classify errors as retryable or non-retryable + if "authentication" in error_str or "unauthorized" in error_str: + return None, f"Authentication failed for MCP server: {e!s}", False + if "connection" in error_str or "network" in error_str: + return None, f"Network connection failed: {e!s}", True + if "json" in error_str or "parsing" in error_str: + return None, f"Server response parsing error: {e!s}", True + return None, f"MCP discovery error: {e!s}", False + + async def _discover_mcp_tools_with_timeout( + self, server_url: str + ) -> dict[str, dict]: + """Discover MCP tools with timeout wrapper.""" + return await asyncio.wait_for( + self._discover_mcp_tools(server_url), timeout=MCP_DISCOVERY_TIMEOUT + ) + + async def _discover_mcp_tools(self, server_url: str) -> dict[str, dict]: + """Discover tools from MCP server with proper timeout handling.""" + from mcp import ClientSession + from mcp.client.streamable_http import streamablehttp_client + + async with streamablehttp_client(server_url) as (read, write, _): + async with ClientSession(read, write) as session: + # Initialize the connection with timeout + await asyncio.wait_for( + session.initialize(), timeout=MCP_CONNECTION_TIMEOUT + ) + + # List available tools with timeout + tools_result = await asyncio.wait_for( + session.list_tools(), + timeout=MCP_DISCOVERY_TIMEOUT - MCP_CONNECTION_TIMEOUT, + ) + + schemas = {} + for tool in tools_result.tools: + args_schema = None + if hasattr(tool, "inputSchema") and tool.inputSchema: + args_schema = self._json_schema_to_pydantic( + tool.name, tool.inputSchema + ) + + schemas[tool.name] = { + "description": getattr(tool, "description", ""), + "args_schema": args_schema, + } + return schemas + + def _json_schema_to_pydantic(self, tool_name: str, json_schema: dict) -> type: + """Convert JSON Schema to Pydantic model for tool arguments. + + Args: + tool_name: Name of the tool (used for model naming) + json_schema: JSON Schema dict with 'properties', 'required', etc. + + Returns: + Pydantic BaseModel class + """ + from pydantic import Field, create_model + + properties = json_schema.get("properties", {}) + required_fields = json_schema.get("required", []) + + field_definitions = {} + + for field_name, field_schema in properties.items(): + field_type = self._json_type_to_python(field_schema) + field_description = field_schema.get("description", "") + + is_required = field_name in required_fields + + if is_required: + field_definitions[field_name] = ( + field_type, + Field(..., description=field_description), + ) + else: + field_definitions[field_name] = ( + field_type | None, + Field(default=None, description=field_description), + ) + + model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema" + return create_model(model_name, **field_definitions) + + def _json_type_to_python(self, field_schema: dict) -> type: + """Convert JSON Schema type to Python type. + + Args: + field_schema: JSON Schema field definition + + Returns: + Python type + """ + from typing import Any + + json_type = field_schema.get("type") + + if "anyOf" in field_schema: + types = [] + for option in field_schema["anyOf"]: + if "const" in option: + types.append(str) + else: + types.append(self._json_type_to_python(option)) + unique_types = list(set(types)) + if len(unique_types) > 1: + result = unique_types[0] + for t in unique_types[1:]: + result = result | t + return result + return unique_types[0] + + type_mapping = { + "string": str, + "number": float, + "integer": int, + "boolean": bool, + "array": list, + "object": dict, + } + + return type_mapping.get(json_type, Any) + + def _fetch_amp_mcp_servers(self, mcp_name: str) -> list[dict]: + """Fetch MCP server configurations from CrewAI AMP API.""" + # TODO: Implement AMP API call to "integrations/mcps" endpoint + # Should return list of server configs with URLs + return [] + def get_multimodal_tools(self) -> Sequence[BaseTool]: from crewai.tools.agent_tools.add_image_tool import AddImageTool return [AddImageTool()] - def get_code_execution_tools(self): + def get_code_execution_tools(self) -> list[CodeInterpreterTool]: try: - from crewai_tools import CodeInterpreterTool # type: ignore + from crewai_tools import ( + CodeInterpreterTool, + ) # Set the unsafe_mode based on the code_execution_mode attribute unsafe_mode = self.code_execution_mode == "unsafe" @@ -619,8 +989,11 @@ class Agent(BaseAgent): self._logger.log( "info", "Coding tools not available. Install crewai_tools. " ) + return [] - def get_output_converter(self, llm, text, model, instructions): + def get_output_converter( + self, llm: BaseLLM, text: str, model: type[BaseModel], instructions: str + ) -> Converter: return Converter(llm=llm, text=text, model=model, instructions=instructions) def _training_handler(self, task_prompt: str) -> str: @@ -666,7 +1039,7 @@ class Agent(BaseAgent): ] ) - def _inject_date_to_task(self, task): + def _inject_date_to_task(self, task: Task) -> None: """Inject the current date into the task description if inject_date is enabled.""" if self.inject_date: from datetime import datetime @@ -692,21 +1065,19 @@ class Agent(BaseAgent): current_date = datetime.now().strftime(self.date_format) task.description += f"\n\nCurrent Date: {current_date}" except Exception as e: - if hasattr(self, "_logger"): - self._logger.log("warning", f"Failed to inject date: {e!s}") - else: - print(f"Warning: Failed to inject date: {e!s}") + self._logger.log("warning", f"Failed to inject date: {e!s}") def _validate_docker_installation(self) -> None: """Check if Docker is installed and running.""" - if not shutil.which("docker"): + docker_path = shutil.which("docker") + if not docker_path: raise RuntimeError( f"Docker is not installed. Please install Docker to use code execution with agent: {self.role}" ) try: - subprocess.run( - ["docker", "info"], # noqa: S607 + subprocess.run( # noqa: S603 + [docker_path, "info"], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, @@ -715,8 +1086,12 @@ class Agent(BaseAgent): raise RuntimeError( f"Docker is not running. Please start Docker to use code execution with agent: {self.role}" ) from e + except subprocess.TimeoutExpired as e: + raise RuntimeError( + f"Docker command timed out. Please check your Docker installation for agent: {self.role}" + ) from e - def __repr__(self): + def __repr__(self) -> str: return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})" @property @@ -729,7 +1104,7 @@ class Agent(BaseAgent): """ return self.security_config.fingerprint - def set_fingerprint(self, fingerprint: Fingerprint): + def set_fingerprint(self, fingerprint: Fingerprint) -> None: self.security_config.fingerprint = fingerprint def _get_knowledge_search_query(self, task_prompt: str, task: Task) -> str | None: @@ -864,6 +1239,7 @@ class Agent(BaseAgent): i18n=self.i18n, original_agent=self, guardrail=self.guardrail, + guardrail_max_retries=self.guardrail_max_retries, ) return await lite_agent.kickoff_async(messages) diff --git a/src/crewai/agents/__init__.py b/lib/crewai/src/crewai/agents/__init__.py similarity index 99% rename from src/crewai/agents/__init__.py rename to lib/crewai/src/crewai/agents/__init__.py index 541d4ebaf..ae7c33797 100644 --- a/src/crewai/agents/__init__.py +++ b/lib/crewai/src/crewai/agents/__init__.py @@ -2,6 +2,7 @@ from crewai.agents.cache.cache_handler import CacheHandler from crewai.agents.parser import AgentAction, AgentFinish, OutputParserError, parse from crewai.agents.tools_handler import ToolsHandler + __all__ = [ "AgentAction", "AgentFinish", diff --git a/lib/crewai/src/crewai/agents/agent_adapters/__init__.py b/lib/crewai/src/crewai/agents/agent_adapters/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/agents/agent_adapters/base_agent_adapter.py b/lib/crewai/src/crewai/agents/agent_adapters/base_agent_adapter.py similarity index 86% rename from src/crewai/agents/agent_adapters/base_agent_adapter.py rename to lib/crewai/src/crewai/agents/agent_adapters/base_agent_adapter.py index 4bfcf2ab7..8001628ed 100644 --- a/src/crewai/agents/agent_adapters/base_agent_adapter.py +++ b/lib/crewai/src/crewai/agents/agent_adapters/base_agent_adapter.py @@ -1,10 +1,15 @@ +from __future__ import annotations + from abc import ABC, abstractmethod -from typing import Any +from typing import TYPE_CHECKING, Any from pydantic import ConfigDict, PrivateAttr -from crewai.agent import BaseAgent -from crewai.tools import BaseTool +from crewai.agents.agent_builder.base_agent import BaseAgent + + +if TYPE_CHECKING: + from crewai.tools.base_tool import BaseTool class BaseAgentAdapter(BaseAgent, ABC): diff --git a/lib/crewai/src/crewai/agents/agent_adapters/base_converter_adapter.py b/lib/crewai/src/crewai/agents/agent_adapters/base_converter_adapter.py new file mode 100644 index 000000000..fc8e010f9 --- /dev/null +++ b/lib/crewai/src/crewai/agents/agent_adapters/base_converter_adapter.py @@ -0,0 +1,154 @@ +"""Base converter adapter for structured output conversion.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +import json +import re +from typing import TYPE_CHECKING, Final, Literal + +from crewai.utilities.converter import generate_model_description + + + +if TYPE_CHECKING: + from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter + from crewai.task import Task + + +_CODE_BLOCK_PATTERN: Final[re.Pattern[str]] = re.compile( + r"```(?:json)?\s*([\s\S]*?)```" +) +_JSON_OBJECT_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{[\s\S]*}") + + +class BaseConverterAdapter(ABC): + """Abstract base class for converter adapters in CrewAI. + + Defines the common interface for converting agent outputs to structured formats. + All converter adapters must implement the methods defined here. + + Attributes: + agent_adapter: The agent adapter instance. + _output_format: The expected output format (json, pydantic, or None). + _schema: The schema description for the expected output. + """ + + def __init__(self, agent_adapter: BaseAgentAdapter) -> None: + """Initialize the converter adapter. + + Args: + agent_adapter: The agent adapter to configure for structured output. + """ + self.agent_adapter = agent_adapter + self._output_format: Literal["json", "pydantic"] | None = None + self._schema: str | None = None + + @abstractmethod + def configure_structured_output(self, task: Task) -> None: + """Configure agents to return structured output. + + Must support both JSON and Pydantic output formats. + + Args: + task: The task requiring structured output. + """ + + @abstractmethod + def enhance_system_prompt(self, base_prompt: str) -> str: + """Enhance the system prompt with structured output instructions. + + Args: + base_prompt: The original system prompt. + + Returns: + Enhanced prompt with structured output guidance. + """ + + def post_process_result(self, result: str) -> str: + """Post-process the result to ensure proper string format. + + Extracts valid JSON from text that may contain markdown or other formatting. + + Args: + result: The raw result from agent execution. + + Returns: + Processed result as a string. + """ + if not self._output_format: + return result + + return self._extract_json_from_text(result) + + @staticmethod + def _validate_json(text: str) -> str | None: + """Validate if text is valid JSON and return it, or None if invalid. + + Args: + text: The text to validate as JSON. + + Returns: + The text if it's valid JSON, None otherwise. + """ + try: + json.loads(text) + return text + except json.JSONDecodeError: + return None + + @staticmethod + def _extract_json_from_text(result: str) -> str: + """Extract valid JSON from text that may contain markdown or other formatting. + + This method provides a comprehensive approach to extracting JSON from LLM responses, + handling cases where JSON may be wrapped in Markdown code blocks or embedded in text. + + Args: + result: The text potentially containing JSON. + + Returns: + Extracted JSON string if found and valid, otherwise the original result. + """ + if not isinstance(result, str): + return str(result) + + if valid := BaseConverterAdapter._validate_json(result): + return valid + + for match in _CODE_BLOCK_PATTERN.finditer(result): + if valid := BaseConverterAdapter._validate_json(match.group(1).strip()): + return valid + + for match in _JSON_OBJECT_PATTERN.finditer(result): + if valid := BaseConverterAdapter._validate_json(match.group()): + return valid + + return result + + @staticmethod + def _configure_format_from_task( + task: Task, + ) -> tuple[Literal["json", "pydantic"] | None, str | None]: + """Determine output format and schema from task requirements. + + This is a helper method that examines the task's output requirements + and returns the appropriate format type and schema description. + + Args: + task: The task containing output format requirements. + + Returns: + A tuple of (output_format, schema) where both may be None if no + structured output is required. + """ + + if not (task.output_json or task.output_pydantic): + return None, None + + if task.output_json: + return "json", generate_model_description(task.output_json) + if task.output_pydantic: + return "pydantic", generate_model_description(task.output_pydantic) + + return None, None diff --git a/src/crewai/agents/agent_adapters/base_tool_adapter.py b/lib/crewai/src/crewai/agents/agent_adapters/base_tool_adapter.py similarity index 77% rename from src/crewai/agents/agent_adapters/base_tool_adapter.py rename to lib/crewai/src/crewai/agents/agent_adapters/base_tool_adapter.py index 513090d64..d44c9b764 100644 --- a/src/crewai/agents/agent_adapters/base_tool_adapter.py +++ b/lib/crewai/src/crewai/agents/agent_adapters/base_tool_adapter.py @@ -1,7 +1,11 @@ -from abc import ABC, abstractmethod -from typing import Any +from __future__ import annotations -from crewai.tools.base_tool import BaseTool +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Any + + +if TYPE_CHECKING: + from crewai.tools.base_tool import BaseTool class BaseToolAdapter(ABC): @@ -12,12 +16,9 @@ class BaseToolAdapter(ABC): different frameworks and platforms. """ - original_tools: list[BaseTool] - converted_tools: list[Any] - def __init__(self, tools: list[BaseTool] | None = None): self.original_tools = tools or [] - self.converted_tools = [] + self.converted_tools: list[Any] = [] @abstractmethod def configure_tools(self, tools: list[BaseTool]) -> None: @@ -31,6 +32,7 @@ class BaseToolAdapter(ABC): """Return all converted tools.""" return self.converted_tools - def sanitize_tool_name(self, tool_name: str) -> str: + @staticmethod + def sanitize_tool_name(tool_name: str) -> str: """Sanitize tool name for API compatibility.""" return tool_name.replace(" ", "_") diff --git a/src/crewai/agents/agent_adapters/langgraph/__init__.py b/lib/crewai/src/crewai/agents/agent_adapters/langgraph/__init__.py similarity index 100% rename from src/crewai/agents/agent_adapters/langgraph/__init__.py rename to lib/crewai/src/crewai/agents/agent_adapters/langgraph/__init__.py diff --git a/src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py b/lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py similarity index 100% rename from src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py rename to lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py diff --git a/src/crewai/agents/agent_adapters/langgraph/langgraph_tool_adapter.py b/lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_tool_adapter.py similarity index 98% rename from src/crewai/agents/agent_adapters/langgraph/langgraph_tool_adapter.py rename to lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_tool_adapter.py index 9dc055e37..9679c90af 100644 --- a/src/crewai/agents/agent_adapters/langgraph/langgraph_tool_adapter.py +++ b/lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_tool_adapter.py @@ -4,8 +4,8 @@ This module contains the LangGraphToolAdapter class that converts CrewAI tools to LangGraph-compatible format using langchain_core.tools. """ -import inspect from collections.abc import Awaitable +import inspect from typing import Any from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter @@ -38,8 +38,7 @@ class LangGraphToolAdapter(BaseToolAdapter): Args: tools: List of CrewAI tools to convert. """ - from langchain_core.tools import BaseTool as LangChainBaseTool - from langchain_core.tools import StructuredTool + from langchain_core.tools import BaseTool as LangChainBaseTool, StructuredTool converted_tools: list[Any] = [] if self.original_tools: diff --git a/src/crewai/agents/agent_adapters/langgraph/protocols.py b/lib/crewai/src/crewai/agents/agent_adapters/langgraph/protocols.py similarity index 100% rename from src/crewai/agents/agent_adapters/langgraph/protocols.py rename to lib/crewai/src/crewai/agents/agent_adapters/langgraph/protocols.py diff --git a/src/crewai/agents/agent_adapters/langgraph/structured_output_converter.py b/lib/crewai/src/crewai/agents/agent_adapters/langgraph/structured_output_converter.py similarity index 55% rename from src/crewai/agents/agent_adapters/langgraph/structured_output_converter.py rename to lib/crewai/src/crewai/agents/agent_adapters/langgraph/structured_output_converter.py index ce615f4ff..4bcfac873 100644 --- a/src/crewai/agents/agent_adapters/langgraph/structured_output_converter.py +++ b/lib/crewai/src/crewai/agents/agent_adapters/langgraph/structured_output_converter.py @@ -4,12 +4,9 @@ This module contains the LangGraphConverterAdapter class that handles structured output conversion for LangGraph agents, supporting JSON and Pydantic model formats. """ -import json -import re -from typing import Any, Literal +from typing import Any from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter -from crewai.utilities.converter import generate_model_description class LangGraphConverterAdapter(BaseConverterAdapter): @@ -17,6 +14,9 @@ class LangGraphConverterAdapter(BaseConverterAdapter): Converts task output requirements into system prompt modifications and post-processing logic to ensure agents return properly structured outputs. + + Attributes: + _system_prompt_appendix: Cached system prompt instructions for structured output. """ def __init__(self, agent_adapter: Any) -> None: @@ -27,8 +27,6 @@ class LangGraphConverterAdapter(BaseConverterAdapter): """ super().__init__(agent_adapter=agent_adapter) self.agent_adapter: Any = agent_adapter - self._output_format: Literal["json", "pydantic"] | None = None - self._schema: str | None = None self._system_prompt_appendix: str | None = None def configure_structured_output(self, task: Any) -> None: @@ -40,19 +38,7 @@ class LangGraphConverterAdapter(BaseConverterAdapter): Args: task: The task object containing output format specifications. """ - if not (task.output_json or task.output_pydantic): - self._output_format = None - self._schema = None - self._system_prompt_appendix = None - return - - if task.output_json: - self._output_format = "json" - self._schema = generate_model_description(task.output_json) - elif task.output_pydantic: - self._output_format = "pydantic" - self._schema = generate_model_description(task.output_pydantic) - + self._output_format, self._schema = self._configure_format_from_task(task) self._system_prompt_appendix = self._generate_system_prompt_appendix() def _generate_system_prompt_appendix(self) -> str: @@ -89,40 +75,3 @@ The output should be raw JSON that exactly matches the specified schema. return original_prompt return f"{original_prompt}\n{self._system_prompt_appendix}" - - def post_process_result(self, result: str) -> str: - """Post-process the result to ensure it matches the expected format. - - Attempts to extract and validate JSON content from agent responses, - handling cases where JSON may be wrapped in markdown or other formatting. - - Args: - result: The raw result string from the agent. - - Returns: - Processed result string, ideally in valid JSON format. - """ - if not self._output_format: - return result - - # Try to extract valid JSON if it's wrapped in code blocks or other text - if self._output_format in ["json", "pydantic"]: - try: - # First, try to parse as is - json.loads(result) - return result - except json.JSONDecodeError: - # Try to extract JSON from the text - json_match: re.Match[str] | None = re.search( - r"(\{.*})", result, re.DOTALL - ) - if json_match: - try: - extracted: str = json_match.group(1) - # Validate it's proper JSON - json.loads(extracted) - return extracted - except json.JSONDecodeError: - pass - - return result diff --git a/src/crewai/agents/agent_adapters/openai_agents/__init__.py b/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/__init__.py similarity index 100% rename from src/crewai/agents/agent_adapters/openai_agents/__init__.py rename to lib/crewai/src/crewai/agents/agent_adapters/openai_agents/__init__.py diff --git a/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py b/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py similarity index 98% rename from src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py rename to lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py index da0f185f1..58687276a 100644 --- a/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py +++ b/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py @@ -15,10 +15,8 @@ from crewai.agents.agent_adapters.openai_agents.openai_agent_tool_adapter import ) from crewai.agents.agent_adapters.openai_agents.protocols import ( AgentKwargs, - OpenAIAgentsModule, -) -from crewai.agents.agent_adapters.openai_agents.protocols import ( OpenAIAgent as OpenAIAgentProtocol, + OpenAIAgentsModule, ) from crewai.agents.agent_adapters.openai_agents.structured_output_converter import ( OpenAIConverterAdapter, @@ -35,6 +33,7 @@ from crewai.tools.agent_tools.agent_tools import AgentTools from crewai.utilities import Logger from crewai.utilities.import_utils import require + openai_agents_module = cast( OpenAIAgentsModule, require( @@ -145,6 +144,9 @@ class OpenAIAgentAdapter(BaseAgentAdapter): task=task, ), ) + if not self.agent_executor or not isinstance(self.agent_executor, Runner): + raise ValueError("Agent executor is not configured.") + result: Any = self.agent_executor.run_sync(self._openai_agent, task_prompt) final_answer: str = self.handle_execution_result(result) crewai_event_bus.emit( diff --git a/src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py b/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py similarity index 99% rename from src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py rename to lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py index 6c8323a88..a3848fb4f 100644 --- a/src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py +++ b/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py @@ -4,10 +4,10 @@ This module contains the OpenAIAgentToolAdapter class that converts CrewAI tools to OpenAI Assistant-compatible format using the agents library. """ +from collections.abc import Awaitable import inspect import json import re -from collections.abc import Awaitable from typing import Any, cast from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter @@ -18,6 +18,7 @@ from crewai.agents.agent_adapters.openai_agents.protocols import ( from crewai.tools import BaseTool from crewai.utilities.import_utils import require + agents_module = cast( Any, require( diff --git a/src/crewai/agents/agent_adapters/openai_agents/protocols.py b/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/protocols.py similarity index 100% rename from src/crewai/agents/agent_adapters/openai_agents/protocols.py rename to lib/crewai/src/crewai/agents/agent_adapters/openai_agents/protocols.py diff --git a/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py b/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py new file mode 100644 index 000000000..c476fe6ff --- /dev/null +++ b/lib/crewai/src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py @@ -0,0 +1,67 @@ +"""OpenAI structured output converter for CrewAI task integration. + +This module contains the OpenAIConverterAdapter class that handles structured +output conversion for OpenAI agents, supporting JSON and Pydantic model formats. +""" + +from typing import Any + +from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter +from crewai.utilities.i18n import I18N + + +class OpenAIConverterAdapter(BaseConverterAdapter): + """Adapter for handling structured output conversion in OpenAI agents. + + This adapter enhances the OpenAI agent to handle structured output formats + and post-processes the results when needed. + + Attributes: + _output_model: The Pydantic model for the output (OpenAI-specific). + """ + + def __init__(self, agent_adapter: Any) -> None: + """Initialize the converter adapter with a reference to the agent adapter. + + Args: + agent_adapter: The OpenAI agent adapter instance. + """ + super().__init__(agent_adapter=agent_adapter) + self.agent_adapter: Any = agent_adapter + self._output_model: Any = None + + def configure_structured_output(self, task: Any) -> None: + """Configure the structured output for OpenAI agent based on task requirements. + + Args: + task: The task containing output format requirements. + """ + self._output_format, self._schema = self._configure_format_from_task(task) + self._output_model = None + + if task.output_json: + self.agent_adapter._openai_agent.output_type = task.output_json + self._output_model = task.output_json + elif task.output_pydantic: + self.agent_adapter._openai_agent.output_type = task.output_pydantic + self._output_model = task.output_pydantic + + def enhance_system_prompt(self, base_prompt: str) -> str: + """Enhance the base system prompt with structured output requirements if needed. + + Args: + base_prompt: The original system prompt. + + Returns: + Enhanced system prompt with output format instructions if needed. + """ + if not self._output_format: + return base_prompt + + output_schema: str = ( + I18N() + .slice("formatted_task_instructions") + .format(output_format=self._schema) + ) + + return f"{base_prompt}\n\n{output_schema}" diff --git a/lib/crewai/src/crewai/agents/agent_builder/__init__.py b/lib/crewai/src/crewai/agents/agent_builder/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/agents/agent_builder/base_agent.py b/lib/crewai/src/crewai/agents/agent_builder/base_agent.py similarity index 76% rename from src/crewai/agents/agent_builder/base_agent.py rename to lib/crewai/src/crewai/agents/agent_builder/base_agent.py index 27a2840c5..dd7d7d7f7 100644 --- a/src/crewai/agents/agent_builder/base_agent.py +++ b/lib/crewai/src/crewai/agents/agent_builder/base_agent.py @@ -1,20 +1,22 @@ -import uuid +from __future__ import annotations + from abc import ABC, abstractmethod from collections.abc import Callable from copy import copy as shallow_copy from hashlib import md5 -from typing import Any, TypeVar +from typing import Any, Literal +import uuid from pydantic import ( UUID4, BaseModel, Field, - InstanceOf, PrivateAttr, field_validator, model_validator, ) from pydantic_core import PydanticCustomError +from typing_extensions import Self from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess from crewai.agents.cache.cache_handler import CacheHandler @@ -25,14 +27,36 @@ from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.rag.embeddings.types import EmbedderConfig from crewai.security.security_config import SecurityConfig from crewai.tools.base_tool import BaseTool, Tool -from crewai.utilities import I18N, Logger, RPMController from crewai.utilities.config import process_config +from crewai.utilities.i18n import I18N +from crewai.utilities.logger import Logger +from crewai.utilities.rpm_controller import RPMController from crewai.utilities.string_utils import interpolate_only -T = TypeVar("T", bound="BaseAgent") + +PlatformApp = Literal[ + "asana", + "box", + "clickup", + "github", + "gmail", + "google_calendar", + "google_sheets", + "hubspot", + "jira", + "linear", + "notion", + "salesforce", + "shopify", + "slack", + "stripe", + "zendesk", +] + +PlatformAppOrAction = PlatformApp | str -class BaseAgent(ABC, BaseModel): +class BaseAgent(BaseModel, ABC): """Abstract Base Class for all third party agents compatible with CrewAI. Attributes: @@ -41,34 +65,36 @@ class BaseAgent(ABC, BaseModel): goal (str): Objective of the agent. backstory (str): Backstory of the agent. cache (bool): Whether the agent should use a cache for tool usage. - config (Optional[Dict[str, Any]]): Configuration for the agent. + config (dict[str, Any] | None): Configuration for the agent. verbose (bool): Verbose mode for the Agent Execution. - max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution. + max_rpm (int | None): Maximum number of requests per minute for the agent execution. allow_delegation (bool): Allow delegation of tasks to agents. - tools (Optional[List[Any]]): Tools at the agent's disposal. + tools (list[Any] | None): Tools at the agent's disposal. max_iter (int): Maximum iterations for an agent to execute a task. - agent_executor (InstanceOf): An instance of the CrewAgentExecutor class. + agent_executor: An instance of the CrewAgentExecutor class. llm (Any): Language model that will run the agent. crew (Any): Crew to which the agent belongs. i18n (I18N): Internationalization settings. - cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class. - tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class. + cache_handler ([CacheHandler]): An instance of the CacheHandler class. + tools_handler ([ToolsHandler]): An instance of the ToolsHandler class. max_tokens: Maximum number of tokens for the agent to generate in a response. knowledge_sources: Knowledge sources for the agent. knowledge_storage: Custom knowledge storage for the agent. security_config: Security configuration for the agent, including fingerprinting. - + apps: List of enterprise applications that the agent can access through CrewAI AMP Tools. Methods: - execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[BaseTool]] = None) -> str: + execute_task(task: Any, context: str | None = None, tools: list[BaseTool] | None = None) -> str: Abstract method to execute a task. create_agent_executor(tools=None) -> None: Abstract method to create an agent executor. - get_delegation_tools(agents: List["BaseAgent"]): + get_delegation_tools(agents: list["BaseAgent"]): Abstract method to set the agents task tools for handling delegation and question asking to other agents in crew. + get_platform_tools(apps: list[PlatformAppOrAction]): + Abstract method to get platform tools for the specified list of applications and/or application/action combinations. get_output_converter(llm, model, instructions): Abstract method to get the converter class for the agent to create json/pydantic outputs. - interpolate_inputs(inputs: Dict[str, Any]) -> None: + interpolate_inputs(inputs: dict[str, Any]) -> None: Interpolate inputs into the agent description and backstory. set_cache_handler(cache_handler: CacheHandler) -> None: Set the cache handler for the agent. @@ -115,18 +141,20 @@ class BaseAgent(ABC, BaseModel): max_iter: int = Field( default=25, description="Maximum iterations for an agent to execute a task" ) - agent_executor: InstanceOf = Field( + agent_executor: Any = Field( default=None, description="An instance of the CrewAgentExecutor class." ) llm: Any = Field( default=None, description="Language model that will run the agent." ) crew: Any = Field(default=None, description="Crew to which the agent belongs.") - i18n: I18N = Field(default=I18N(), description="Internationalization settings.") - cache_handler: InstanceOf[CacheHandler] | None = Field( + i18n: I18N = Field( + default_factory=I18N, description="Internationalization settings." + ) + cache_handler: CacheHandler | None = Field( default=None, description="An instance of the CacheHandler class." ) - tools_handler: InstanceOf[ToolsHandler] = Field( + tools_handler: ToolsHandler = Field( default_factory=ToolsHandler, description="An instance of the ToolsHandler class.", ) @@ -161,6 +189,14 @@ class BaseAgent(ABC, BaseModel): default=None, description="Knowledge configuration for the agent such as limits and threshold", ) + apps: list[PlatformAppOrAction] | None = Field( + default=None, + description="List of applications or application/action combinations that the agent can access through CrewAI Platform. Can contain app names (e.g., 'gmail') or specific actions (e.g., 'gmail/send_email')", + ) + mcps: list[str] | None = Field( + default=None, + description="List of MCP server references. Supports 'https://server.com/path' for external servers and 'crewai-amp:mcp-name' for AMP marketplace. Use '#tool_name' suffix for specific tools.", + ) @model_validator(mode="before") @classmethod @@ -196,6 +232,41 @@ class BaseAgent(ABC, BaseModel): ) return processed_tools + @field_validator("apps") + @classmethod + def validate_apps( + cls, apps: list[PlatformAppOrAction] | None + ) -> list[PlatformAppOrAction] | None: + if not apps: + return apps + + validated_apps = [] + for app in apps: + if app.count("/") > 1: + raise ValueError( + f"Invalid app format '{app}'. Apps can only have one '/' for app/action format (e.g., 'gmail/send_email')" + ) + validated_apps.append(app) + + return list(set(validated_apps)) + + @field_validator("mcps") + @classmethod + def validate_mcps(cls, mcps: list[str] | None) -> list[str] | None: + if not mcps: + return mcps + + validated_mcps = [] + for mcp in mcps: + if mcp.startswith(("https://", "crewai-amp:")): + validated_mcps.append(mcp) + else: + raise ValueError( + f"Invalid MCP reference: {mcp}. Must start with 'https://' or 'crewai-amp:'" + ) + + return list(set(validated_mcps)) + @model_validator(mode="after") def validate_and_set_attributes(self): # Validate required fields @@ -263,10 +334,18 @@ class BaseAgent(ABC, BaseModel): pass @abstractmethod - def get_delegation_tools(self, agents: list["BaseAgent"]) -> list[BaseTool]: + def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]: """Set the task tools that init BaseAgenTools class.""" - def copy(self: T) -> T: # type: ignore # Signature of "copy" incompatible with supertype "BaseModel" + @abstractmethod + def get_platform_tools(self, apps: list[PlatformAppOrAction]) -> list[BaseTool]: + """Get platform tools for the specified list of applications and/or application/action combinations.""" + + @abstractmethod + def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]: + """Get MCP tools for the specified list of MCP server references.""" + + def copy(self) -> Self: # type: ignore # Signature of "copy" incompatible with supertype "BaseModel" """Create a deep copy of the Agent.""" exclude = { "id", @@ -282,6 +361,9 @@ class BaseAgent(ABC, BaseModel): "knowledge_sources", "knowledge_storage", "knowledge", + "apps", + "mcps", + "actions", } # Copy llm diff --git a/src/crewai/agents/agent_builder/base_agent_executor_mixin.py b/lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py similarity index 88% rename from src/crewai/agents/agent_builder/base_agent_executor_mixin.py rename to lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py index 60de79dcc..5864a4995 100644 --- a/src/crewai/agents/agent_builder/base_agent_executor_mixin.py +++ b/lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py @@ -1,27 +1,31 @@ +from __future__ import annotations + import time from typing import TYPE_CHECKING from crewai.events.event_listener import event_listener from crewai.memory.entity.entity_memory_item import EntityMemoryItem from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem -from crewai.utilities import I18N from crewai.utilities.converter import ConverterError from crewai.utilities.evaluators.task_evaluator import TaskEvaluator from crewai.utilities.printer import Printer + if TYPE_CHECKING: - from crewai.agents.agent_builder.base_agent import BaseAgent + from crewai.agent import Agent from crewai.crew import Crew from crewai.task import Task + from crewai.utilities.i18n import I18N + from crewai.utilities.types import LLMMessage class CrewAgentExecutorMixin: - crew: "Crew" - agent: "BaseAgent" - task: "Task" + crew: Crew + agent: Agent + task: Task iterations: int max_iter: int - messages: list[dict[str, str]] + messages: list[LLMMessage] _i18n: I18N _printer: Printer = Printer() @@ -45,7 +49,9 @@ class CrewAgentExecutorMixin: }, ) except Exception as e: - print(f"Failed to add to short term memory: {e}") + self.agent._logger.log( + "error", f"Failed to add to short term memory: {e}" + ) def _create_external_memory(self, output) -> None: """Create and save a external-term memory item if conditions are met.""" @@ -65,7 +71,9 @@ class CrewAgentExecutorMixin: }, ) except Exception as e: - print(f"Failed to add to external memory: {e}") + self.agent._logger.log( + "error", f"Failed to add to external memory: {e}" + ) def _create_long_term_memory(self, output) -> None: """Create and save long-term and entity memory items based on evaluation.""" @@ -110,9 +118,13 @@ class CrewAgentExecutorMixin: if entity_memories: self.crew._entity_memory.save(entity_memories) except AttributeError as e: - print(f"Missing attributes for long term memory: {e}") + self.agent._logger.log( + "error", f"Missing attributes for long term memory: {e}" + ) except Exception as e: - print(f"Failed to add to long term memory: {e}") + self.agent._logger.log( + "error", f"Failed to add to long term memory: {e}" + ) elif ( self.crew and self.crew._long_term_memory diff --git a/lib/crewai/src/crewai/agents/agent_builder/utilities/__init__.py b/lib/crewai/src/crewai/agents/agent_builder/utilities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/agents/agent_builder/utilities/base_output_converter.py b/lib/crewai/src/crewai/agents/agent_builder/utilities/base_output_converter.py similarity index 100% rename from src/crewai/agents/agent_builder/utilities/base_output_converter.py rename to lib/crewai/src/crewai/agents/agent_builder/utilities/base_output_converter.py diff --git a/src/crewai/agents/agent_builder/utilities/base_token_process.py b/lib/crewai/src/crewai/agents/agent_builder/utilities/base_token_process.py similarity index 100% rename from src/crewai/agents/agent_builder/utilities/base_token_process.py rename to lib/crewai/src/crewai/agents/agent_builder/utilities/base_token_process.py diff --git a/lib/crewai/src/crewai/agents/cache/__init__.py b/lib/crewai/src/crewai/agents/cache/__init__.py new file mode 100644 index 000000000..d18771ca3 --- /dev/null +++ b/lib/crewai/src/crewai/agents/cache/__init__.py @@ -0,0 +1,5 @@ +from crewai.agents.cache.cache_handler import CacheHandler + + + +__all__ = ["CacheHandler"] diff --git a/src/crewai/agents/cache/cache_handler.py b/lib/crewai/src/crewai/agents/cache/cache_handler.py similarity index 65% rename from src/crewai/agents/cache/cache_handler.py rename to lib/crewai/src/crewai/agents/cache/cache_handler.py index 5f9154087..368bcfa20 100644 --- a/src/crewai/agents/cache/cache_handler.py +++ b/lib/crewai/src/crewai/agents/cache/cache_handler.py @@ -4,17 +4,21 @@ from typing import Any from pydantic import BaseModel, PrivateAttr +from crewai.utilities.rw_lock import RWLock + class CacheHandler(BaseModel): """Handles caching of tool execution results. - Provides in-memory caching for tool outputs based on tool name and input. + Provides thread-safe in-memory caching for tool outputs based on tool name and input. + Uses a read-write lock to allow concurrent reads while ensuring exclusive write access. Notes: - - TODO: Make thread-safe. + - TODO: Rename 'input' parameter to avoid shadowing builtin. """ _cache: dict[str, Any] = PrivateAttr(default_factory=dict) + _lock: RWLock = PrivateAttr(default_factory=RWLock) def add(self, tool: str, input: str, output: Any) -> None: """Add a tool result to the cache. @@ -27,7 +31,8 @@ class CacheHandler(BaseModel): Notes: - TODO: Rename 'input' parameter to avoid shadowing builtin. """ - self._cache[f"{tool}-{input}"] = output + with self._lock.w_locked(): + self._cache[f"{tool}-{input}"] = output def read(self, tool: str, input: str) -> Any | None: """Retrieve a cached tool result. @@ -42,4 +47,5 @@ class CacheHandler(BaseModel): Notes: - TODO: Rename 'input' parameter to avoid shadowing builtin. """ - return self._cache.get(f"{tool}-{input}") + with self._lock.r_locked(): + return self._cache.get(f"{tool}-{input}") diff --git a/src/crewai/agents/constants.py b/lib/crewai/src/crewai/agents/constants.py similarity index 99% rename from src/crewai/agents/constants.py rename to lib/crewai/src/crewai/agents/constants.py index 2019b1cf1..326d53d02 100644 --- a/src/crewai/agents/constants.py +++ b/lib/crewai/src/crewai/agents/constants.py @@ -3,6 +3,7 @@ import re from typing import Final + # crewai.agents.parser constants FINAL_ANSWER_ACTION: Final[str] = "Final Answer:" diff --git a/src/crewai/agents/crew_agent_executor.py b/lib/crewai/src/crewai/agents/crew_agent_executor.py similarity index 89% rename from src/crewai/agents/crew_agent_executor.py rename to lib/crewai/src/crewai/agents/crew_agent_executor.py index d912bdf3c..07db1de0a 100644 --- a/src/crewai/agents/crew_agent_executor.py +++ b/lib/crewai/src/crewai/agents/crew_agent_executor.py @@ -4,26 +4,25 @@ Handles agent execution flow including LLM interactions, tool execution, and memory management. """ -from collections.abc import Callable -from typing import Any +from __future__ import annotations + +from collections.abc import Callable +from typing import TYPE_CHECKING, Any, Literal, cast + +from pydantic import GetCoreSchemaHandler +from pydantic_core import CoreSchema, core_schema -from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin from crewai.agents.parser import ( AgentAction, AgentFinish, OutputParserError, ) -from crewai.agents.tools_handler import ToolsHandler from crewai.events.event_bus import crewai_event_bus from crewai.events.types.logging_events import ( AgentLogsExecutionEvent, AgentLogsStartedEvent, ) -from crewai.llms.base_llm import BaseLLM -from crewai.tools.structured_tool import CrewStructuredTool -from crewai.tools.tool_types import ToolResult -from crewai.utilities import I18N, Printer from crewai.utilities.agent_utils import ( enforce_rpm_limit, format_message_for_llm, @@ -38,10 +37,25 @@ from crewai.utilities.agent_utils import ( process_llm_response, ) from crewai.utilities.constants import TRAINING_DATA_FILE +from crewai.utilities.i18n import I18N +from crewai.utilities.printer import Printer from crewai.utilities.tool_utils import execute_tool_and_check_finality from crewai.utilities.training_handler import CrewTrainingHandler +if TYPE_CHECKING: + from crewai.agent import Agent + from crewai.agents.tools_handler import ToolsHandler + from crewai.crew import Crew + from crewai.llms.base_llm import BaseLLM + from crewai.task import Task + from crewai.tools.base_tool import BaseTool + from crewai.tools.structured_tool import CrewStructuredTool + from crewai.tools.tool_types import ToolResult + from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult + from crewai.utilities.types import LLMMessage + + class CrewAgentExecutor(CrewAgentExecutorMixin): """Executor for crew agents. @@ -51,11 +65,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): def __init__( self, - llm: Any, - task: Any, - crew: Any, - agent: BaseAgent, - prompt: dict[str, str], + llm: BaseLLM | Any, + task: Task, + crew: Crew, + agent: Agent, + prompt: SystemPromptResult | StandardPromptResult, max_iter: int, tools: list[CrewStructuredTool], tools_names: str, @@ -63,8 +77,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): tools_description: str, tools_handler: ToolsHandler, step_callback: Any = None, - original_tools: list[Any] | None = None, - function_calling_llm: Any = None, + original_tools: list[BaseTool] | None = None, + function_calling_llm: BaseLLM | Any | None = None, respect_context_window: bool = False, request_within_rpm_limit: Callable[[], bool] | None = None, callbacks: list[Any] | None = None, @@ -91,7 +105,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): callbacks: Optional callbacks list. """ self._i18n: I18N = I18N() - self.llm: BaseLLM = llm + self.llm = llm self.task = task self.agent = agent self.crew = crew @@ -111,10 +125,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): self.respect_context_window = respect_context_window self.request_within_rpm_limit = request_within_rpm_limit self.ask_for_human_input = False - self.messages: list[dict[str, str]] = [] + self.messages: list[LLMMessage] = [] self.iterations = 0 self.log_error_after = 3 - existing_stop = self.llm.stop or [] + existing_stop = getattr(self.llm, "stop", []) self.llm.stop = list( set( existing_stop + self.stop @@ -123,7 +137,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): ) ) - def invoke(self, inputs: dict[str, str]) -> dict[str, Any]: + def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]: """Execute the agent with given inputs. Args: @@ -133,8 +147,12 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): Dictionary with agent output. """ if "system" in self.prompt: - system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs) - user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs) + system_prompt = self._format_prompt( + cast(str, self.prompt.get("system", "")), inputs + ) + user_prompt = self._format_prompt( + cast(str, self.prompt.get("user", "")), inputs + ) self.messages.append(format_message_for_llm(system_prompt, role="system")) self.messages.append(format_message_for_llm(user_prompt)) else: @@ -192,6 +210,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): callbacks=self.callbacks, printer=self._printer, from_task=self.task, + from_agent=self.agent, ) formatted_answer = process_llm_response(answer, self.use_stop_words) @@ -309,7 +328,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): if self.step_callback: self.step_callback(formatted_answer) - def _append_message(self, text: str, role: str = "assistant") -> None: + def _append_message( + self, text: str, role: Literal["user", "assistant", "system"] = "assistant" + ) -> None: """Add message to conversation history. Args: @@ -505,3 +526,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): ) ) return self._invoke_loop() + + @classmethod + def __get_pydantic_core_schema__( + cls, _source_type: Any, _handler: GetCoreSchemaHandler + ) -> CoreSchema: + """Generate Pydantic core schema for BaseClient Protocol. + + This allows the Protocol to be used in Pydantic models without + requiring arbitrary_types_allowed=True. + """ + return core_schema.any_schema() diff --git a/src/crewai/agents/parser.py b/lib/crewai/src/crewai/agents/parser.py similarity index 99% rename from src/crewai/agents/parser.py rename to lib/crewai/src/crewai/agents/parser.py index 2e4bba53d..d38fe73ae 100644 --- a/src/crewai/agents/parser.py +++ b/lib/crewai/src/crewai/agents/parser.py @@ -20,6 +20,7 @@ from crewai.agents.constants import ( ) from crewai.utilities.i18n import I18N + _I18N = I18N() diff --git a/src/crewai/agents/tools_handler.py b/lib/crewai/src/crewai/agents/tools_handler.py similarity index 70% rename from src/crewai/agents/tools_handler.py rename to lib/crewai/src/crewai/agents/tools_handler.py index ac7e0799b..8b39196e5 100644 --- a/src/crewai/agents/tools_handler.py +++ b/lib/crewai/src/crewai/agents/tools_handler.py @@ -1,10 +1,19 @@ """Tools handler for managing tool execution and caching.""" -import json +from __future__ import annotations + +import json +from typing import TYPE_CHECKING, Any + +from pydantic import GetCoreSchemaHandler +from pydantic_core import CoreSchema, core_schema -from crewai.agents.cache.cache_handler import CacheHandler from crewai.tools.cache_tools.cache_tools import CacheTools -from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling + + +if TYPE_CHECKING: + from crewai.agents.cache.cache_handler import CacheHandler + from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling class ToolsHandler: @@ -52,3 +61,14 @@ class ToolsHandler: input=input_str, output=output, ) + + @classmethod + def __get_pydantic_core_schema__( + cls, _source_type: Any, _handler: GetCoreSchemaHandler + ) -> CoreSchema: + """Generate Pydantic core schema for BaseClient Protocol. + + This allows the Protocol to be used in Pydantic models without + requiring arbitrary_types_allowed=True. + """ + return core_schema.any_schema() diff --git a/lib/crewai/src/crewai/cli/__init__.py b/lib/crewai/src/crewai/cli/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/add_crew_to_flow.py b/lib/crewai/src/crewai/cli/add_crew_to_flow.py similarity index 90% rename from src/crewai/cli/add_crew_to_flow.py rename to lib/crewai/src/crewai/cli/add_crew_to_flow.py index ef693a22b..a3e0f5209 100644 --- a/src/crewai/cli/add_crew_to_flow.py +++ b/lib/crewai/src/crewai/cli/add_crew_to_flow.py @@ -3,13 +3,19 @@ from pathlib import Path import click from crewai.cli.utils import copy_template +from crewai.utilities.printer import Printer + + +_printer = Printer() def add_crew_to_flow(crew_name: str) -> None: """Add a new crew to the current flow.""" # Check if pyproject.toml exists in the current directory if not Path("pyproject.toml").exists(): - print("This command must be run from the root of a flow project.") + _printer.print( + "This command must be run from the root of a flow project.", color="red" + ) raise click.ClickException( "This command must be run from the root of a flow project." ) @@ -19,7 +25,7 @@ def add_crew_to_flow(crew_name: str) -> None: crews_folder = flow_folder / "src" / flow_folder.name / "crews" if not crews_folder.exists(): - print("Crews folder does not exist in the current flow.") + _printer.print("Crews folder does not exist in the current flow.", color="red") raise click.ClickException("Crews folder does not exist in the current flow.") # Create the crew within the flow's crews directory diff --git a/lib/crewai/src/crewai/cli/authentication/__init__.py b/lib/crewai/src/crewai/cli/authentication/__init__.py new file mode 100644 index 000000000..687ccdfa9 --- /dev/null +++ b/lib/crewai/src/crewai/cli/authentication/__init__.py @@ -0,0 +1,5 @@ +from crewai.cli.authentication.main import AuthenticationCommand + + + +__all__ = ["AuthenticationCommand"] diff --git a/src/crewai/cli/authentication/constants.py b/lib/crewai/src/crewai/cli/authentication/constants.py similarity index 100% rename from src/crewai/cli/authentication/constants.py rename to lib/crewai/src/crewai/cli/authentication/constants.py diff --git a/src/crewai/cli/authentication/main.py b/lib/crewai/src/crewai/cli/authentication/main.py similarity index 90% rename from src/crewai/cli/authentication/main.py rename to lib/crewai/src/crewai/cli/authentication/main.py index 09bc1fa1d..b23fe9114 100644 --- a/src/crewai/cli/authentication/main.py +++ b/lib/crewai/src/crewai/cli/authentication/main.py @@ -1,15 +1,15 @@ import time +from typing import Any import webbrowser -from typing import Any, Dict, Optional +from pydantic import BaseModel, Field import requests from rich.console import Console -from pydantic import BaseModel, Field - -from .utils import validate_jwt_token -from crewai.cli.shared.token_manager import TokenManager +from crewai.cli.authentication.utils import validate_jwt_token from crewai.cli.config import Settings +from crewai.cli.shared.token_manager import TokenManager + console = Console() @@ -24,7 +24,7 @@ class Oauth2Settings(BaseModel): domain: str = Field( description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens." ) - audience: Optional[str] = Field( + audience: str | None = Field( description="OAuth2 audience value, typically used to identify the target API or resource.", default=None, ) @@ -43,7 +43,7 @@ class Oauth2Settings(BaseModel): class ProviderFactory: @classmethod - def from_settings(cls, settings: Optional[Oauth2Settings] = None): + def from_settings(cls, settings: Oauth2Settings | None = None): settings = settings or Oauth2Settings.from_settings() import importlib @@ -63,14 +63,14 @@ class AuthenticationCommand: def login(self) -> None: """Sign up to CrewAI+""" - console.print("Signing in to CrewAI Enterprise...\n", style="bold blue") + console.print("Signing in to CrewAI AMP...\n", style="bold blue") device_code_data = self._get_device_code() self._display_auth_instructions(device_code_data) return self._poll_for_token(device_code_data) - def _get_device_code(self) -> Dict[str, Any]: + def _get_device_code(self) -> dict[str, Any]: """Get the device code to authenticate the user.""" device_code_payload = { @@ -86,13 +86,13 @@ class AuthenticationCommand: response.raise_for_status() return response.json() - def _display_auth_instructions(self, device_code_data: Dict[str, str]) -> None: + def _display_auth_instructions(self, device_code_data: dict[str, str]) -> None: """Display the authentication instructions to the user.""" console.print("1. Navigate to: ", device_code_data["verification_uri_complete"]) console.print("2. Enter the following code: ", device_code_data["user_code"]) webbrowser.open(device_code_data["verification_uri_complete"]) - def _poll_for_token(self, device_code_data: Dict[str, Any]) -> None: + def _poll_for_token(self, device_code_data: dict[str, Any]) -> None: """Polls the server for the token until it is received, or max attempts are reached.""" token_payload = { @@ -120,9 +120,7 @@ class AuthenticationCommand: self._login_to_tool_repository() - console.print( - "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n" - ) + console.print("\n[bold green]Welcome to CrewAI AMP![/bold green]\n") return if token_data["error"] not in ("authorization_pending", "slow_down"): @@ -135,7 +133,7 @@ class AuthenticationCommand: "Timeout: Failed to get the token. Please try again.", style="bold red" ) - def _validate_and_save_token(self, token_data: Dict[str, Any]) -> None: + def _validate_and_save_token(self, token_data: dict[str, Any]) -> None: """Validates the JWT token and saves the token to the token manager.""" jwt_token = token_data["access_token"] diff --git a/lib/crewai/src/crewai/cli/authentication/providers/__init__.py b/lib/crewai/src/crewai/cli/authentication/providers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/authentication/providers/auth0.py b/lib/crewai/src/crewai/cli/authentication/providers/auth0.py similarity index 100% rename from src/crewai/cli/authentication/providers/auth0.py rename to lib/crewai/src/crewai/cli/authentication/providers/auth0.py diff --git a/src/crewai/cli/authentication/providers/base_provider.py b/lib/crewai/src/crewai/cli/authentication/providers/base_provider.py similarity index 100% rename from src/crewai/cli/authentication/providers/base_provider.py rename to lib/crewai/src/crewai/cli/authentication/providers/base_provider.py diff --git a/src/crewai/cli/authentication/providers/okta.py b/lib/crewai/src/crewai/cli/authentication/providers/okta.py similarity index 100% rename from src/crewai/cli/authentication/providers/okta.py rename to lib/crewai/src/crewai/cli/authentication/providers/okta.py diff --git a/src/crewai/cli/authentication/providers/workos.py b/lib/crewai/src/crewai/cli/authentication/providers/workos.py similarity index 100% rename from src/crewai/cli/authentication/providers/workos.py rename to lib/crewai/src/crewai/cli/authentication/providers/workos.py diff --git a/src/crewai/cli/authentication/token.py b/lib/crewai/src/crewai/cli/authentication/token.py similarity index 100% rename from src/crewai/cli/authentication/token.py rename to lib/crewai/src/crewai/cli/authentication/token.py diff --git a/src/crewai/cli/authentication/utils.py b/lib/crewai/src/crewai/cli/authentication/utils.py similarity index 100% rename from src/crewai/cli/authentication/utils.py rename to lib/crewai/src/crewai/cli/authentication/utils.py diff --git a/src/crewai/cli/cli.py b/lib/crewai/src/crewai/cli/cli.py similarity index 88% rename from src/crewai/cli/cli.py rename to lib/crewai/src/crewai/cli/cli.py index 991082de0..2e6f5eaa9 100644 --- a/src/crewai/cli/cli.py +++ b/lib/crewai/src/crewai/cli/cli.py @@ -1,35 +1,35 @@ +from importlib.metadata import version as get_version import os import subprocess -from importlib.metadata import version as get_version import click from crewai.cli.add_crew_to_flow import add_crew_to_flow +from crewai.cli.authentication.main import AuthenticationCommand from crewai.cli.config import Settings from crewai.cli.create_crew import create_crew from crewai.cli.create_flow import create_flow from crewai.cli.crew_chat import run_chat +from crewai.cli.deploy.main import DeployCommand +from crewai.cli.enterprise.main import EnterpriseConfigureCommand +from crewai.cli.evaluate_crew import evaluate_crew +from crewai.cli.install_crew import install_crew +from crewai.cli.kickoff_flow import kickoff_flow +from crewai.cli.organization.main import OrganizationCommand +from crewai.cli.plot_flow import plot_flow +from crewai.cli.replay_from_task import replay_task_command +from crewai.cli.reset_memories_command import reset_memories_command +from crewai.cli.run_crew import run_crew from crewai.cli.settings.main import SettingsCommand +from crewai.cli.tools.main import ToolCommand +from crewai.cli.train_crew import train_crew +from crewai.cli.triggers.main import TriggersCommand +from crewai.cli.update_crew import update_crew from crewai.cli.utils import build_env_with_tool_repository_credentials, read_toml from crewai.memory.storage.kickoff_task_outputs_storage import ( KickoffTaskOutputsSQLiteStorage, ) -from .authentication.main import AuthenticationCommand -from .deploy.main import DeployCommand -from .enterprise.main import EnterpriseConfigureCommand -from .evaluate_crew import evaluate_crew -from .install_crew import install_crew -from .kickoff_flow import kickoff_flow -from .organization.main import OrganizationCommand -from .plot_flow import plot_flow -from .replay_from_task import replay_task_command -from .reset_memories_command import reset_memories_command -from .run_crew import run_crew -from .tools.main import ToolCommand -from .train_crew import train_crew -from .update_crew import update_crew - @click.group() @click.version_option(get_version("crewai")) @@ -271,7 +271,7 @@ def update(): @crewai.command() def login(): - """Sign Up/Login to CrewAI Enterprise.""" + """Sign Up/Login to CrewAI AMP.""" Settings().clear_user_settings() AuthenticationCommand().login() @@ -392,6 +392,26 @@ def flow_add_crew(crew_name): add_crew_to_flow(crew_name) +@crewai.group() +def triggers(): + """Trigger related commands. Use 'crewai triggers list' to see available triggers, or 'crewai triggers run app_slug/trigger_slug' to execute.""" + + +@triggers.command(name="list") +def triggers_list(): + """List all available triggers from integrations.""" + triggers_cmd = TriggersCommand() + triggers_cmd.list_triggers() + + +@triggers.command(name="run") +@click.argument("trigger_path") +def triggers_run(trigger_path: str): + """Execute crew with trigger payload. Format: app_slug/trigger_slug""" + triggers_cmd = TriggersCommand() + triggers_cmd.execute_with_trigger(trigger_path) + + @crewai.command() def chat(): """ @@ -440,7 +460,7 @@ def enterprise(): @enterprise.command("configure") @click.argument("enterprise_url") def enterprise_configure(enterprise_url: str): - """Configure CrewAI Enterprise OAuth2 settings from the provided Enterprise URL.""" + """Configure CrewAI AMP OAuth2 settings from the provided Enterprise URL.""" enterprise_command = EnterpriseConfigureCommand() enterprise_command.configure(enterprise_url) diff --git a/src/crewai/cli/command.py b/lib/crewai/src/crewai/cli/command.py similarity index 89% rename from src/crewai/cli/command.py rename to lib/crewai/src/crewai/cli/command.py index 7ddddeafd..e889b7125 100644 --- a/src/crewai/cli/command.py +++ b/lib/crewai/src/crewai/cli/command.py @@ -6,6 +6,7 @@ from crewai.cli.authentication.token import get_auth_token from crewai.cli.plus_api import PlusAPI from crewai.telemetry.telemetry import Telemetry + console = Console() @@ -27,7 +28,7 @@ class PlusAPIMixin: style="bold red", ) console.print("Run 'crewai login' to sign up/login.", style="bold green") - raise SystemExit + raise SystemExit from None def _validate_response(self, response: requests.Response) -> None: """ @@ -44,8 +45,10 @@ class PlusAPIMixin: style="bold red", ) console.print(f"Status Code: {response.status_code}") - console.print(f"Response:\n{response.content}") - raise SystemExit + console.print( + f"Response:\n{response.content.decode('utf-8', errors='replace')}" + ) + raise SystemExit from None if response.status_code == 422: console.print( @@ -66,7 +69,7 @@ class PlusAPIMixin: details = ( json_response.get("error") or json_response.get("message") - or response.content + or response.content.decode("utf-8", errors="replace") ) console.print(f"{details}") raise SystemExit diff --git a/src/crewai/cli/config.py b/lib/crewai/src/crewai/cli/config.py similarity index 99% rename from src/crewai/cli/config.py rename to lib/crewai/src/crewai/cli/config.py index e4ed1fad5..dea3691ae 100644 --- a/src/crewai/cli/config.py +++ b/lib/crewai/src/crewai/cli/config.py @@ -1,7 +1,7 @@ import json -import tempfile from logging import getLogger from pathlib import Path +import tempfile from pydantic import BaseModel, Field @@ -14,6 +14,7 @@ from crewai.cli.constants import ( ) from crewai.cli.shared.token_manager import TokenManager + logger = getLogger(__name__) DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json" @@ -99,7 +100,7 @@ HIDDEN_SETTINGS_KEYS = [ class Settings(BaseModel): enterprise_base_url: str | None = Field( default=DEFAULT_CLI_SETTINGS["enterprise_base_url"], - description="Base URL of the CrewAI Enterprise instance", + description="Base URL of the CrewAI AMP instance", ) tool_repository_username: str | None = Field( None, description="Username for interacting with the Tool Repository" diff --git a/src/crewai/cli/constants.py b/lib/crewai/src/crewai/cli/constants.py similarity index 100% rename from src/crewai/cli/constants.py rename to lib/crewai/src/crewai/cli/constants.py diff --git a/src/crewai/cli/create_crew.py b/lib/crewai/src/crewai/cli/create_crew.py similarity index 100% rename from src/crewai/cli/create_crew.py rename to lib/crewai/src/crewai/cli/create_crew.py index 3c0408637..e4d84e8bc 100644 --- a/src/crewai/cli/create_crew.py +++ b/lib/crewai/src/crewai/cli/create_crew.py @@ -1,6 +1,6 @@ +from pathlib import Path import shutil import sys -from pathlib import Path import click diff --git a/src/crewai/cli/create_flow.py b/lib/crewai/src/crewai/cli/create_flow.py similarity index 100% rename from src/crewai/cli/create_flow.py rename to lib/crewai/src/crewai/cli/create_flow.py diff --git a/src/crewai/cli/crew_chat.py b/lib/crewai/src/crewai/cli/crew_chat.py similarity index 98% rename from src/crewai/cli/crew_chat.py rename to lib/crewai/src/crewai/cli/crew_chat.py index 6fe9d87c8..feca9e4ca 100644 --- a/src/crewai/cli/crew_chat.py +++ b/lib/crewai/src/crewai/cli/crew_chat.py @@ -1,15 +1,15 @@ import json +from pathlib import Path import platform import re import sys import threading import time -from pathlib import Path -from typing import Any +from typing import Any, Final, Literal import click -import tomli from packaging import version +import tomli from crewai.cli.utils import read_toml from crewai.cli.version import get_crewai_version @@ -17,8 +17,13 @@ from crewai.crew import Crew from crewai.llm import LLM, BaseLLM from crewai.types.crew_chat import ChatInputField, ChatInputs from crewai.utilities.llm_utils import create_llm +from crewai.utilities.printer import Printer +from crewai.utilities.types import LLMMessage -MIN_REQUIRED_VERSION = "0.98.0" + +_printer = Printer() + +MIN_REQUIRED_VERSION: Final[Literal["0.98.0"]] = "0.98.0" def check_conversational_crews_version( @@ -111,9 +116,9 @@ def run_chat(): def show_loading(event: threading.Event): """Display animated loading dots while processing.""" while not event.is_set(): - print(".", end="", flush=True) + _printer.print(".", end="") time.sleep(1) - print() + _printer.print("") def initialize_chat_llm(crew: Crew) -> LLM | BaseLLM | None: @@ -221,7 +226,7 @@ def get_user_input() -> str: def handle_user_input( user_input: str, chat_llm: LLM, - messages: list[dict[str, str]], + messages: list[LLMMessage], crew_tool_schema: dict[str, Any], available_functions: dict[str, Any], ) -> None: @@ -397,8 +402,7 @@ def generate_crew_chat_inputs(crew: Crew, crew_name: str, chat_llm) -> ChatInput def fetch_required_inputs(crew: Crew) -> set[str]: - """ - Extracts placeholders from the crew's tasks and agents. + """Extracts placeholders from the crew's tasks and agents. Args: crew (Crew): The crew object. diff --git a/lib/crewai/src/crewai/cli/deploy/__init__.py b/lib/crewai/src/crewai/cli/deploy/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/deploy/main.py b/lib/crewai/src/crewai/cli/deploy/main.py similarity index 91% rename from src/crewai/cli/deploy/main.py rename to lib/crewai/src/crewai/cli/deploy/main.py index 486959201..87cf2777c 100644 --- a/src/crewai/cli/deploy/main.py +++ b/lib/crewai/src/crewai/cli/deploy/main.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional +from typing import Any from rich.console import Console @@ -6,6 +6,7 @@ from crewai.cli import git from crewai.cli.command import BaseCommand, PlusAPIMixin from crewai.cli.utils import fetch_and_json_env_file, get_project_name + console = Console() @@ -32,7 +33,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): style="bold red", ) - def _display_deployment_info(self, json_response: Dict[str, Any]) -> None: + def _display_deployment_info(self, json_response: dict[str, Any]) -> None: """ Display deployment information. @@ -45,9 +46,9 @@ class DeployCommand(BaseCommand, PlusAPIMixin): console.print("\nTo check the status of the deployment, run:") console.print("crewai deploy status") console.print(" or") - console.print(f"crewai deploy status --uuid \"{json_response['uuid']}\"") + console.print(f'crewai deploy status --uuid "{json_response["uuid"]}"') - def _display_logs(self, log_messages: List[Dict[str, Any]]) -> None: + def _display_logs(self, log_messages: list[dict[str, Any]]) -> None: """ Display log messages. @@ -59,7 +60,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): f"{log_message['timestamp']} - {log_message['level']}: {log_message['message']}" ) - def deploy(self, uuid: Optional[str] = None) -> None: + def deploy(self, uuid: str | None = None) -> None: """ Deploy a crew using either UUID or project name. @@ -110,7 +111,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): self._display_creation_success(response.json()) def _confirm_input( - self, env_vars: Dict[str, str], remote_repo_url: str, confirm: bool + self, env_vars: dict[str, str], remote_repo_url: str, confirm: bool ) -> None: """ Confirm input parameters with the user. @@ -128,9 +129,9 @@ class DeployCommand(BaseCommand, PlusAPIMixin): def _create_payload( self, - env_vars: Dict[str, str], + env_vars: dict[str, str], remote_repo_url: str, - ) -> Dict[str, Any]: + ) -> dict[str, Any]: """ Create the payload for crew creation. @@ -149,7 +150,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): } } - def _display_creation_success(self, json_response: Dict[str, Any]) -> None: + def _display_creation_success(self, json_response: dict[str, Any]) -> None: """ Display success message after crew creation. @@ -179,7 +180,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): else: self._display_no_crews_message() - def _display_crews(self, crews_data: List[Dict[str, Any]]) -> None: + def _display_crews(self, crews_data: list[dict[str, Any]]) -> None: """ Display the list of crews. @@ -198,7 +199,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): console.print("You don't have any Crews yet. Let's create one!", style="yellow") console.print(" crewai create crew ", style="green") - def get_crew_status(self, uuid: Optional[str] = None) -> None: + def get_crew_status(self, uuid: str | None = None) -> None: """ Get the status of a crew. @@ -217,7 +218,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): self._validate_response(response) self._display_crew_status(response.json()) - def _display_crew_status(self, status_data: Dict[str, str]) -> None: + def _display_crew_status(self, status_data: dict[str, str]) -> None: """ Display the status of a crew. @@ -227,7 +228,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): console.print(f"Name:\t {status_data['name']}") console.print(f"Status:\t {status_data['status']}") - def get_crew_logs(self, uuid: Optional[str], log_type: str = "deployment") -> None: + def get_crew_logs(self, uuid: str | None, log_type: str = "deployment") -> None: """ Get logs for a crew. @@ -249,7 +250,7 @@ class DeployCommand(BaseCommand, PlusAPIMixin): self._validate_response(response) self._display_logs(response.json()) - def remove_crew(self, uuid: Optional[str]) -> None: + def remove_crew(self, uuid: str | None) -> None: """ Remove a crew deployment. diff --git a/lib/crewai/src/crewai/cli/enterprise/__init__.py b/lib/crewai/src/crewai/cli/enterprise/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/enterprise/main.py b/lib/crewai/src/crewai/cli/enterprise/main.py similarity index 54% rename from src/crewai/cli/enterprise/main.py rename to lib/crewai/src/crewai/cli/enterprise/main.py index d0770ef01..62002608e 100644 --- a/src/crewai/cli/enterprise/main.py +++ b/lib/crewai/src/crewai/cli/enterprise/main.py @@ -1,12 +1,14 @@ +from typing import Any + import requests -from typing import Dict, Any +from requests.exceptions import JSONDecodeError, RequestException from rich.console import Console -from requests.exceptions import RequestException, JSONDecodeError from crewai.cli.command import BaseCommand from crewai.cli.settings.main import SettingsCommand from crewai.cli.version import get_crewai_version + console = Console() @@ -17,22 +19,24 @@ class EnterpriseConfigureCommand(BaseCommand): def configure(self, enterprise_url: str) -> None: try: - enterprise_url = enterprise_url.rstrip('/') + enterprise_url = enterprise_url.rstrip("/") oauth_config = self._fetch_oauth_config(enterprise_url) self._update_oauth_settings(enterprise_url, oauth_config) console.print( - f"✅ Successfully configured CrewAI Enterprise with OAuth2 settings from {enterprise_url}", - style="bold green" + f"✅ Successfully configured CrewAI AMP with OAuth2 settings from {enterprise_url}", + style="bold green", ) except Exception as e: - console.print(f"❌ Failed to configure Enterprise settings: {str(e)}", style="bold red") - raise SystemExit(1) + console.print( + f"❌ Failed to configure Enterprise settings: {e!s}", style="bold red" + ) + raise SystemExit(1) from e - def _fetch_oauth_config(self, enterprise_url: str) -> Dict[str, Any]: + def _fetch_oauth_config(self, enterprise_url: str) -> dict[str, Any]: oauth_endpoint = f"{enterprise_url}/auth/parameters" try: @@ -47,31 +51,44 @@ class EnterpriseConfigureCommand(BaseCommand): try: oauth_config = response.json() - except JSONDecodeError: - raise ValueError(f"Invalid JSON response from {oauth_endpoint}") + except JSONDecodeError as e: + raise ValueError(f"Invalid JSON response from {oauth_endpoint}") from e - required_fields = ['audience', 'domain', 'device_authorization_client_id', 'provider'] - missing_fields = [field for field in required_fields if field not in oauth_config] + required_fields = [ + "audience", + "domain", + "device_authorization_client_id", + "provider", + ] + missing_fields = [ + field for field in required_fields if field not in oauth_config + ] if missing_fields: - raise ValueError(f"Missing required fields in OAuth2 configuration: {', '.join(missing_fields)}") + raise ValueError( + f"Missing required fields in OAuth2 configuration: {', '.join(missing_fields)}" + ) - console.print("✅ Successfully retrieved OAuth2 configuration", style="green") + console.print( + "✅ Successfully retrieved OAuth2 configuration", style="green" + ) return oauth_config except RequestException as e: - raise ValueError(f"Failed to connect to enterprise URL: {str(e)}") + raise ValueError(f"Failed to connect to enterprise URL: {e!s}") from e except Exception as e: - raise ValueError(f"Error fetching OAuth2 configuration: {str(e)}") + raise ValueError(f"Error fetching OAuth2 configuration: {e!s}") from e - def _update_oauth_settings(self, enterprise_url: str, oauth_config: Dict[str, Any]) -> None: + def _update_oauth_settings( + self, enterprise_url: str, oauth_config: dict[str, Any] + ) -> None: try: config_mapping = { - 'enterprise_base_url': enterprise_url, - 'oauth2_provider': oauth_config['provider'], - 'oauth2_audience': oauth_config['audience'], - 'oauth2_client_id': oauth_config['device_authorization_client_id'], - 'oauth2_domain': oauth_config['domain'] + "enterprise_base_url": enterprise_url, + "oauth2_provider": oauth_config["provider"], + "oauth2_audience": oauth_config["audience"], + "oauth2_client_id": oauth_config["device_authorization_client_id"], + "oauth2_domain": oauth_config["domain"], } console.print("🔄 Updating local OAuth2 configuration...") @@ -81,4 +98,4 @@ class EnterpriseConfigureCommand(BaseCommand): console.print(f" ✓ Set {key}: {value}", style="dim") except Exception as e: - raise ValueError(f"Failed to update OAuth2 settings: {str(e)}") + raise ValueError(f"Failed to update OAuth2 settings: {e!s}") from e diff --git a/src/crewai/cli/evaluate_crew.py b/lib/crewai/src/crewai/cli/evaluate_crew.py similarity index 96% rename from src/crewai/cli/evaluate_crew.py rename to lib/crewai/src/crewai/cli/evaluate_crew.py index 374f9f27d..a158eeaa7 100644 --- a/src/crewai/cli/evaluate_crew.py +++ b/lib/crewai/src/crewai/cli/evaluate_crew.py @@ -17,7 +17,7 @@ def evaluate_crew(n_iterations: int, model: str) -> None: if n_iterations <= 0: raise ValueError("The number of iterations must be a positive integer.") - result = subprocess.run(command, capture_output=False, text=True, check=True) + result = subprocess.run(command, capture_output=False, text=True, check=True) # noqa: S603 if result.stderr: click.echo(result.stderr, err=True) diff --git a/src/crewai/cli/git.py b/lib/crewai/src/crewai/cli/git.py similarity index 100% rename from src/crewai/cli/git.py rename to lib/crewai/src/crewai/cli/git.py index 5828a717c..b493e88c0 100644 --- a/src/crewai/cli/git.py +++ b/lib/crewai/src/crewai/cli/git.py @@ -1,5 +1,5 @@ -import subprocess from functools import lru_cache +import subprocess class Repository: diff --git a/src/crewai/cli/install_crew.py b/lib/crewai/src/crewai/cli/install_crew.py similarity index 100% rename from src/crewai/cli/install_crew.py rename to lib/crewai/src/crewai/cli/install_crew.py diff --git a/src/crewai/cli/kickoff_flow.py b/lib/crewai/src/crewai/cli/kickoff_flow.py similarity index 94% rename from src/crewai/cli/kickoff_flow.py rename to lib/crewai/src/crewai/cli/kickoff_flow.py index 2123a6c15..b5bc0d81e 100644 --- a/src/crewai/cli/kickoff_flow.py +++ b/lib/crewai/src/crewai/cli/kickoff_flow.py @@ -10,7 +10,7 @@ def kickoff_flow() -> None: command = ["uv", "run", "kickoff"] try: - result = subprocess.run(command, capture_output=False, text=True, check=True) + result = subprocess.run(command, capture_output=False, text=True, check=True) # noqa: S603 if result.stderr: click.echo(result.stderr, err=True) diff --git a/src/crewai/cli/organization/__init__.py b/lib/crewai/src/crewai/cli/organization/__init__.py similarity index 100% rename from src/crewai/cli/organization/__init__.py rename to lib/crewai/src/crewai/cli/organization/__init__.py diff --git a/lib/crewai/src/crewai/cli/organization/main.py b/lib/crewai/src/crewai/cli/organization/main.py new file mode 100644 index 000000000..4ee954698 --- /dev/null +++ b/lib/crewai/src/crewai/cli/organization/main.py @@ -0,0 +1,107 @@ +from requests import HTTPError +from rich.console import Console +from rich.table import Table + +from crewai.cli.command import BaseCommand, PlusAPIMixin +from crewai.cli.config import Settings + + +console = Console() + + +class OrganizationCommand(BaseCommand, PlusAPIMixin): + def __init__(self): + BaseCommand.__init__(self) + PlusAPIMixin.__init__(self, telemetry=self._telemetry) + + def list(self): + try: + response = self.plus_api_client.get_organizations() + response.raise_for_status() + orgs = response.json() + + if not orgs: + console.print( + "You don't belong to any organizations yet.", style="yellow" + ) + return + + table = Table(title="Your Organizations") + table.add_column("Name", style="cyan") + table.add_column("ID", style="green") + for org in orgs: + table.add_row(org["name"], org["uuid"]) + + console.print(table) + except HTTPError as e: + if e.response.status_code == 401: + console.print( + "You are not logged in to any organization. Use 'crewai login' to login.", + style="bold red", + ) + return + console.print( + f"Failed to retrieve organization list: {e!s}", style="bold red" + ) + raise SystemExit(1) from e + except Exception as e: + console.print( + f"Failed to retrieve organization list: {e!s}", style="bold red" + ) + raise SystemExit(1) from e + + def switch(self, org_id): + try: + response = self.plus_api_client.get_organizations() + response.raise_for_status() + orgs = response.json() + + org = next((o for o in orgs if o["uuid"] == org_id), None) + if not org: + console.print( + f"Organization with id '{org_id}' not found.", style="bold red" + ) + return + + settings = Settings() + settings.org_name = org["name"] + settings.org_uuid = org["uuid"] + settings.dump() + + console.print( + f"Successfully switched to {org['name']} ({org['uuid']})", + style="bold green", + ) + except HTTPError as e: + if e.response.status_code == 401: + console.print( + "You are not logged in to any organization. Use 'crewai login' to login.", + style="bold red", + ) + return + console.print( + f"Failed to retrieve organization list: {e!s}", style="bold red" + ) + raise SystemExit(1) from e + except Exception as e: + console.print(f"Failed to switch organization: {e!s}", style="bold red") + raise SystemExit(1) from e + + def current(self): + settings = Settings() + if settings.org_uuid: + console.print( + f"Currently logged in to organization {settings.org_name} ({settings.org_uuid})", + style="bold green", + ) + else: + console.print( + "You're not currently logged in to any organization.", style="yellow" + ) + console.print( + "Use 'crewai org list' to see available organizations.", style="yellow" + ) + console.print( + "Use 'crewai org switch ' to switch to an organization.", + style="yellow", + ) diff --git a/src/crewai/cli/plot_flow.py b/lib/crewai/src/crewai/cli/plot_flow.py similarity index 94% rename from src/crewai/cli/plot_flow.py rename to lib/crewai/src/crewai/cli/plot_flow.py index 848c55d69..d97ccba77 100644 --- a/src/crewai/cli/plot_flow.py +++ b/lib/crewai/src/crewai/cli/plot_flow.py @@ -10,7 +10,7 @@ def plot_flow() -> None: command = ["uv", "run", "plot"] try: - result = subprocess.run(command, capture_output=False, text=True, check=True) + result = subprocess.run(command, capture_output=False, text=True, check=True) # noqa: S603 if result.stderr: click.echo(result.stderr, err=True) diff --git a/src/crewai/cli/plus_api.py b/lib/crewai/src/crewai/cli/plus_api.py similarity index 91% rename from src/crewai/cli/plus_api.py rename to lib/crewai/src/crewai/cli/plus_api.py index 77b7fe5fd..6121dd718 100644 --- a/src/crewai/cli/plus_api.py +++ b/lib/crewai/src/crewai/cli/plus_api.py @@ -18,6 +18,7 @@ class PlusAPI: AGENTS_RESOURCE = "/crewai_plus/api/v1/agents" TRACING_RESOURCE = "/crewai_plus/api/v1/tracing" EPHEMERAL_TRACING_RESOURCE = "/crewai_plus/api/v1/tracing/ephemeral" + INTEGRATIONS_RESOURCE = "/crewai_plus/api/v1/integrations" def __init__(self, api_key: str) -> None: self.api_key = api_key @@ -176,3 +177,15 @@ class PlusAPI: json={"status": "failed", "failure_reason": error_message}, timeout=30, ) + + def get_triggers(self) -> requests.Response: + """Get all available triggers from integrations.""" + return self._make_request("GET", f"{self.INTEGRATIONS_RESOURCE}/apps") + + def get_trigger_payload( + self, app_slug: str, trigger_slug: str + ) -> requests.Response: + """Get sample payload for a specific trigger.""" + return self._make_request( + "GET", f"{self.INTEGRATIONS_RESOURCE}/{app_slug}/{trigger_slug}/payload" + ) diff --git a/src/crewai/cli/provider.py b/lib/crewai/src/crewai/cli/provider.py similarity index 100% rename from src/crewai/cli/provider.py rename to lib/crewai/src/crewai/cli/provider.py index 3374fef00..ec6edc0cb 100644 --- a/src/crewai/cli/provider.py +++ b/lib/crewai/src/crewai/cli/provider.py @@ -1,8 +1,8 @@ +from collections import defaultdict import json import os -import time -from collections import defaultdict from pathlib import Path +import time import certifi import click diff --git a/src/crewai/cli/replay_from_task.py b/lib/crewai/src/crewai/cli/replay_from_task.py similarity index 94% rename from src/crewai/cli/replay_from_task.py rename to lib/crewai/src/crewai/cli/replay_from_task.py index 7e34c3394..f3c8ae557 100644 --- a/src/crewai/cli/replay_from_task.py +++ b/lib/crewai/src/crewai/cli/replay_from_task.py @@ -13,7 +13,7 @@ def replay_task_command(task_id: str) -> None: command = ["uv", "run", "replay", task_id] try: - result = subprocess.run(command, capture_output=False, text=True, check=True) + result = subprocess.run(command, capture_output=False, text=True, check=True) # noqa: S603 if result.stderr: click.echo(result.stderr, err=True) diff --git a/src/crewai/cli/reset_memories_command.py b/lib/crewai/src/crewai/cli/reset_memories_command.py similarity index 96% rename from src/crewai/cli/reset_memories_command.py rename to lib/crewai/src/crewai/cli/reset_memories_command.py index d8910f735..494744731 100644 --- a/src/crewai/cli/reset_memories_command.py +++ b/lib/crewai/src/crewai/cli/reset_memories_command.py @@ -28,7 +28,9 @@ def reset_memories_command( """ try: - if not any([long, short, entity, kickoff_outputs, knowledge, agent_knowledge, all]): + if not any( + [long, short, entity, kickoff_outputs, knowledge, agent_knowledge, all] + ): click.echo( "No memory type specified. Please specify at least one type to reset." ) diff --git a/src/crewai/cli/run_crew.py b/lib/crewai/src/crewai/cli/run_crew.py similarity index 100% rename from src/crewai/cli/run_crew.py rename to lib/crewai/src/crewai/cli/run_crew.py index 0358e58ee..e2b942512 100644 --- a/src/crewai/cli/run_crew.py +++ b/lib/crewai/src/crewai/cli/run_crew.py @@ -1,6 +1,6 @@ +from enum import Enum import os import subprocess -from enum import Enum import click from packaging import version diff --git a/lib/crewai/src/crewai/cli/settings/__init__.py b/lib/crewai/src/crewai/cli/settings/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/settings/main.py b/lib/crewai/src/crewai/cli/settings/main.py similarity index 96% rename from src/crewai/cli/settings/main.py rename to lib/crewai/src/crewai/cli/settings/main.py index b54aa3b0c..3fa4f2af0 100644 --- a/src/crewai/cli/settings/main.py +++ b/lib/crewai/src/crewai/cli/settings/main.py @@ -1,8 +1,11 @@ +from typing import Any + from rich.console import Console from rich.table import Table + from crewai.cli.command import BaseCommand -from crewai.cli.config import Settings, READONLY_SETTINGS_KEYS, HIDDEN_SETTINGS_KEYS -from typing import Any +from crewai.cli.config import HIDDEN_SETTINGS_KEYS, READONLY_SETTINGS_KEYS, Settings + console = Console() diff --git a/lib/crewai/src/crewai/cli/shared/__init__.py b/lib/crewai/src/crewai/cli/shared/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/shared/token_manager.py b/lib/crewai/src/crewai/cli/shared/token_manager.py similarity index 100% rename from src/crewai/cli/shared/token_manager.py rename to lib/crewai/src/crewai/cli/shared/token_manager.py index 89d44c573..5641943d0 100644 --- a/src/crewai/cli/shared/token_manager.py +++ b/lib/crewai/src/crewai/cli/shared/token_manager.py @@ -1,8 +1,8 @@ +from datetime import datetime import json import os -import sys -from datetime import datetime from pathlib import Path +import sys from cryptography.fernet import Fernet diff --git a/lib/crewai/src/crewai/cli/templates/__init__.py b/lib/crewai/src/crewai/cli/templates/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/templates/crew/.gitignore b/lib/crewai/src/crewai/cli/templates/crew/.gitignore similarity index 100% rename from src/crewai/cli/templates/crew/.gitignore rename to lib/crewai/src/crewai/cli/templates/crew/.gitignore diff --git a/src/crewai/cli/templates/crew/README.md b/lib/crewai/src/crewai/cli/templates/crew/README.md similarity index 100% rename from src/crewai/cli/templates/crew/README.md rename to lib/crewai/src/crewai/cli/templates/crew/README.md diff --git a/lib/crewai/src/crewai/cli/templates/crew/__init__.py b/lib/crewai/src/crewai/cli/templates/crew/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/templates/crew/config/agents.yaml b/lib/crewai/src/crewai/cli/templates/crew/config/agents.yaml similarity index 100% rename from src/crewai/cli/templates/crew/config/agents.yaml rename to lib/crewai/src/crewai/cli/templates/crew/config/agents.yaml diff --git a/src/crewai/cli/templates/crew/config/tasks.yaml b/lib/crewai/src/crewai/cli/templates/crew/config/tasks.yaml similarity index 100% rename from src/crewai/cli/templates/crew/config/tasks.yaml rename to lib/crewai/src/crewai/cli/templates/crew/config/tasks.yaml diff --git a/src/crewai/cli/templates/crew/crew.py b/lib/crewai/src/crewai/cli/templates/crew/crew.py similarity index 100% rename from src/crewai/cli/templates/crew/crew.py rename to lib/crewai/src/crewai/cli/templates/crew/crew.py diff --git a/src/crewai/cli/templates/crew/knowledge/user_preference.txt b/lib/crewai/src/crewai/cli/templates/crew/knowledge/user_preference.txt similarity index 100% rename from src/crewai/cli/templates/crew/knowledge/user_preference.txt rename to lib/crewai/src/crewai/cli/templates/crew/knowledge/user_preference.txt diff --git a/src/crewai/cli/templates/crew/main.py b/lib/crewai/src/crewai/cli/templates/crew/main.py similarity index 71% rename from src/crewai/cli/templates/crew/main.py rename to lib/crewai/src/crewai/cli/templates/crew/main.py index b604d8ceb..bb36963cc 100644 --- a/src/crewai/cli/templates/crew/main.py +++ b/lib/crewai/src/crewai/cli/templates/crew/main.py @@ -21,7 +21,7 @@ def run(): 'topic': 'AI LLMs', 'current_year': str(datetime.now().year) } - + try: {{crew_name}}().crew().kickoff(inputs=inputs) except Exception as e: @@ -60,9 +60,35 @@ def test(): "topic": "AI LLMs", "current_year": str(datetime.now().year) } - + try: {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), eval_llm=sys.argv[2], inputs=inputs) except Exception as e: raise Exception(f"An error occurred while testing the crew: {e}") + +def run_with_trigger(): + """ + Run the crew with trigger payload. + """ + import json + + if len(sys.argv) < 2: + raise Exception("No trigger payload provided. Please provide JSON payload as argument.") + + try: + trigger_payload = json.loads(sys.argv[1]) + except json.JSONDecodeError: + raise Exception("Invalid JSON payload provided as argument") + + inputs = { + "crewai_trigger_payload": trigger_payload, + "topic": "", + "current_year": "" + } + + try: + result = {{crew_name}}().crew().kickoff(inputs=inputs) + return result + except Exception as e: + raise Exception(f"An error occurred while running the crew with trigger: {e}") diff --git a/src/crewai/cli/templates/crew/pyproject.toml b/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml similarity index 90% rename from src/crewai/cli/templates/crew/pyproject.toml rename to lib/crewai/src/crewai/cli/templates/crew/pyproject.toml index b34088f16..f09d72949 100644 --- a/src/crewai/cli/templates/crew/pyproject.toml +++ b/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml @@ -14,6 +14,7 @@ run_crew = "{{folder_name}}.main:run" train = "{{folder_name}}.main:train" replay = "{{folder_name}}.main:replay" test = "{{folder_name}}.main:test" +run_with_trigger = "{{folder_name}}.main:run_with_trigger" [build-system] requires = ["hatchling"] diff --git a/lib/crewai/src/crewai/cli/templates/crew/tools/__init__.py b/lib/crewai/src/crewai/cli/templates/crew/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/templates/crew/tools/custom_tool.py b/lib/crewai/src/crewai/cli/templates/crew/tools/custom_tool.py similarity index 100% rename from src/crewai/cli/templates/crew/tools/custom_tool.py rename to lib/crewai/src/crewai/cli/templates/crew/tools/custom_tool.py diff --git a/src/crewai/cli/templates/flow/.gitignore b/lib/crewai/src/crewai/cli/templates/flow/.gitignore similarity index 100% rename from src/crewai/cli/templates/flow/.gitignore rename to lib/crewai/src/crewai/cli/templates/flow/.gitignore diff --git a/src/crewai/cli/templates/flow/README.md b/lib/crewai/src/crewai/cli/templates/flow/README.md similarity index 100% rename from src/crewai/cli/templates/flow/README.md rename to lib/crewai/src/crewai/cli/templates/flow/README.md diff --git a/lib/crewai/src/crewai/cli/templates/flow/__init__.py b/lib/crewai/src/crewai/cli/templates/flow/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/templates/flow/crews/poem_crew/__init__.py b/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/__init__.py similarity index 100% rename from src/crewai/cli/templates/flow/crews/poem_crew/__init__.py rename to lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/__init__.py diff --git a/src/crewai/cli/templates/flow/crews/poem_crew/config/agents.yaml b/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/config/agents.yaml similarity index 100% rename from src/crewai/cli/templates/flow/crews/poem_crew/config/agents.yaml rename to lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/config/agents.yaml diff --git a/src/crewai/cli/templates/flow/crews/poem_crew/config/tasks.yaml b/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/config/tasks.yaml similarity index 100% rename from src/crewai/cli/templates/flow/crews/poem_crew/config/tasks.yaml rename to lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/config/tasks.yaml diff --git a/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py b/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py similarity index 99% rename from src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py rename to lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py index 1f2a81466..8c3358097 100644 --- a/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py +++ b/lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py @@ -1,8 +1,9 @@ -from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task -from crewai.agents.agent_builder.base_agent import BaseAgent from typing import List +from crewai import Agent, Crew, Process, Task +from crewai.agents.agent_builder.base_agent import BaseAgent +from crewai.project import CrewBase, agent, crew, task + # If you want to run a snippet of code before or after the crew starts, # you can use the @before_kickoff and @after_kickoff decorators # https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators diff --git a/lib/crewai/src/crewai/cli/templates/flow/main.py b/lib/crewai/src/crewai/cli/templates/flow/main.py new file mode 100644 index 000000000..795ee78c3 --- /dev/null +++ b/lib/crewai/src/crewai/cli/templates/flow/main.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python +from random import randint + +from pydantic import BaseModel + +from crewai.flow import Flow, listen, start + +from {{folder_name}}.crews.poem_crew.poem_crew import PoemCrew + + +class PoemState(BaseModel): + sentence_count: int = 1 + poem: str = "" + + +class PoemFlow(Flow[PoemState]): + + @start() + def generate_sentence_count(self, crewai_trigger_payload: dict = None): + print("Generating sentence count") + + # Use trigger payload if available + if crewai_trigger_payload: + # Example: use trigger data to influence sentence count + self.state.sentence_count = crewai_trigger_payload.get('sentence_count', randint(1, 5)) + print(f"Using trigger payload: {crewai_trigger_payload}") + else: + self.state.sentence_count = randint(1, 5) + + @listen(generate_sentence_count) + def generate_poem(self): + print("Generating poem") + result = ( + PoemCrew() + .crew() + .kickoff(inputs={"sentence_count": self.state.sentence_count}) + ) + + print("Poem generated", result.raw) + self.state.poem = result.raw + + @listen(generate_poem) + def save_poem(self): + print("Saving poem") + with open("poem.txt", "w") as f: + f.write(self.state.poem) + + +def kickoff(): + poem_flow = PoemFlow() + poem_flow.kickoff() + + +def plot(): + poem_flow = PoemFlow() + poem_flow.plot() + + +def run_with_trigger(): + """ + Run the flow with trigger payload. + """ + import json + import sys + + # Get trigger payload from command line argument + if len(sys.argv) < 2: + raise Exception("No trigger payload provided. Please provide JSON payload as argument.") + + try: + trigger_payload = json.loads(sys.argv[1]) + except json.JSONDecodeError: + raise Exception("Invalid JSON payload provided as argument") + + # Create flow and kickoff with trigger payload + # The @start() methods will automatically receive crewai_trigger_payload parameter + poem_flow = PoemFlow() + + try: + result = poem_flow.kickoff({"crewai_trigger_payload": trigger_payload}) + return result + except Exception as e: + raise Exception(f"An error occurred while running the flow with trigger: {e}") + + +if __name__ == "__main__": + kickoff() diff --git a/src/crewai/cli/templates/flow/pyproject.toml b/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml similarity index 89% rename from src/crewai/cli/templates/flow/pyproject.toml rename to lib/crewai/src/crewai/cli/templates/flow/pyproject.toml index e3c0c816e..7105029c6 100644 --- a/src/crewai/cli/templates/flow/pyproject.toml +++ b/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml @@ -12,6 +12,7 @@ dependencies = [ kickoff = "{{folder_name}}.main:kickoff" run_crew = "{{folder_name}}.main:kickoff" plot = "{{folder_name}}.main:plot" +run_with_trigger = "{{folder_name}}.main:run_with_trigger" [build-system] requires = ["hatchling"] diff --git a/lib/crewai/src/crewai/cli/templates/flow/tools/__init__.py b/lib/crewai/src/crewai/cli/templates/flow/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/templates/flow/tools/custom_tool.py b/lib/crewai/src/crewai/cli/templates/flow/tools/custom_tool.py similarity index 78% rename from src/crewai/cli/templates/flow/tools/custom_tool.py rename to lib/crewai/src/crewai/cli/templates/flow/tools/custom_tool.py index 718d2be1b..f57d56740 100644 --- a/src/crewai/cli/templates/flow/tools/custom_tool.py +++ b/lib/crewai/src/crewai/cli/templates/flow/tools/custom_tool.py @@ -1,8 +1,9 @@ from typing import Type -from crewai.tools import BaseTool from pydantic import BaseModel, Field +from crewai.tools import BaseTool + class MyCustomToolInput(BaseModel): """Input schema for MyCustomTool.""" @@ -12,9 +13,7 @@ class MyCustomToolInput(BaseModel): class MyCustomTool(BaseTool): name: str = "Name of my tool" - description: str = ( - "Clear description for what this tool is useful for, your agent will need this information to use it." - ) + description: str = "Clear description for what this tool is useful for, your agent will need this information to use it." args_schema: Type[BaseModel] = MyCustomToolInput def _run(self, argument: str) -> str: diff --git a/src/crewai/cli/templates/tool/.gitignore b/lib/crewai/src/crewai/cli/templates/tool/.gitignore similarity index 100% rename from src/crewai/cli/templates/tool/.gitignore rename to lib/crewai/src/crewai/cli/templates/tool/.gitignore diff --git a/src/crewai/cli/templates/tool/README.md b/lib/crewai/src/crewai/cli/templates/tool/README.md similarity index 100% rename from src/crewai/cli/templates/tool/README.md rename to lib/crewai/src/crewai/cli/templates/tool/README.md diff --git a/src/crewai/cli/templates/tool/pyproject.toml b/lib/crewai/src/crewai/cli/templates/tool/pyproject.toml similarity index 100% rename from src/crewai/cli/templates/tool/pyproject.toml rename to lib/crewai/src/crewai/cli/templates/tool/pyproject.toml diff --git a/src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py b/lib/crewai/src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py similarity index 100% rename from src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py rename to lib/crewai/src/crewai/cli/templates/tool/src/{{folder_name}}/__init__.py diff --git a/src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py b/lib/crewai/src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py similarity index 100% rename from src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py rename to lib/crewai/src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py diff --git a/lib/crewai/src/crewai/cli/tools/__init__.py b/lib/crewai/src/crewai/cli/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/cli/tools/main.py b/lib/crewai/src/crewai/cli/tools/main.py similarity index 99% rename from src/crewai/cli/tools/main.py rename to lib/crewai/src/crewai/cli/tools/main.py index a7fc718c7..09bc927d3 100644 --- a/src/crewai/cli/tools/main.py +++ b/lib/crewai/src/crewai/cli/tools/main.py @@ -1,8 +1,8 @@ import base64 import os +from pathlib import Path import subprocess import tempfile -from pathlib import Path from typing import Any import click @@ -21,6 +21,7 @@ from crewai.cli.utils import ( tree_find_and_replace, ) + console = Console() diff --git a/src/crewai/cli/train_crew.py b/lib/crewai/src/crewai/cli/train_crew.py similarity index 96% rename from src/crewai/cli/train_crew.py rename to lib/crewai/src/crewai/cli/train_crew.py index 14a5e1a06..bab568ea3 100644 --- a/src/crewai/cli/train_crew.py +++ b/lib/crewai/src/crewai/cli/train_crew.py @@ -19,7 +19,7 @@ def train_crew(n_iterations: int, filename: str) -> None: if not filename.endswith(".pkl"): raise ValueError("The filename must not end with .pkl") - result = subprocess.run(command, capture_output=False, text=True, check=True) + result = subprocess.run(command, capture_output=False, text=True, check=True) # noqa: S603 if result.stderr: click.echo(result.stderr, err=True) diff --git a/lib/crewai/src/crewai/cli/triggers/__init__.py b/lib/crewai/src/crewai/cli/triggers/__init__.py new file mode 100644 index 000000000..9158b063d --- /dev/null +++ b/lib/crewai/src/crewai/cli/triggers/__init__.py @@ -0,0 +1,6 @@ +"""Triggers command module for CrewAI CLI.""" + +from crewai.cli.triggers.main import TriggersCommand + + +__all__ = ["TriggersCommand"] diff --git a/lib/crewai/src/crewai/cli/triggers/main.py b/lib/crewai/src/crewai/cli/triggers/main.py new file mode 100644 index 000000000..569c99ace --- /dev/null +++ b/lib/crewai/src/crewai/cli/triggers/main.py @@ -0,0 +1,137 @@ +import json +import subprocess +from typing import Any + +from rich.console import Console +from rich.table import Table + +from crewai.cli.command import BaseCommand, PlusAPIMixin + + +console = Console() + + +class TriggersCommand(BaseCommand, PlusAPIMixin): + """ + A class to handle trigger-related operations for CrewAI projects. + """ + + def __init__(self): + BaseCommand.__init__(self) + PlusAPIMixin.__init__(self, telemetry=self._telemetry) + + def list_triggers(self) -> None: + """List all available triggers from integrations.""" + try: + console.print("[bold blue]Fetching available triggers...[/bold blue]") + response = self.plus_api_client.get_triggers() + self._validate_response(response) + + triggers_data = response.json() + self._display_triggers(triggers_data) + + except Exception as e: + console.print(f"[bold red]Error fetching triggers: {e}[/bold red]") + raise SystemExit(1) from e + + def execute_with_trigger(self, trigger_path: str) -> None: + """Execute crew with trigger payload.""" + try: + # Parse app_slug/trigger_slug + if "/" not in trigger_path: + console.print( + "[bold red]Error: Trigger must be in format 'app_slug/trigger_slug'[/bold red]" + ) + raise SystemExit(1) + + app_slug, trigger_slug = trigger_path.split("/", 1) + + console.print( + f"[bold blue]Fetching trigger payload for {app_slug}/{trigger_slug}...[/bold blue]" + ) + response = self.plus_api_client.get_trigger_payload(app_slug, trigger_slug) + + if response.status_code == 404: + error_data = response.json() + console.print( + f"[bold red]Error: {error_data.get('error', 'Trigger not found')}[/bold red]" + ) + raise SystemExit(1) + + self._validate_response(response) + + trigger_data = response.json() + self._display_trigger_info(trigger_data) + + # Run crew with trigger payload + self._run_crew_with_payload(trigger_data.get("sample_payload", {})) + + except Exception as e: + console.print( + f"[bold red]Error executing crew with trigger: {e}[/bold red]" + ) + raise SystemExit(1) from e + + def _display_triggers(self, triggers_data: dict[str, Any]) -> None: + """Display triggers in a formatted table.""" + apps = triggers_data.get("apps", []) + + if not apps: + console.print("[yellow]No triggers found.[/yellow]") + return + + for app in apps: + app_name = app.get("name", "Unknown App") + app_slug = app.get("slug", "unknown") + is_connected = app.get("is_connected", False) + connection_status = ( + "[green]✓ Connected[/green]" + if is_connected + else "[red]✗ Not Connected[/red]" + ) + + console.print( + f"\n[bold cyan]{app_name}[/bold cyan] ({app_slug}) - {connection_status}" + ) + console.print( + f"[dim]{app.get('description', 'No description available')}[/dim]" + ) + + triggers = app.get("triggers", []) + if triggers: + table = Table(show_header=True, header_style="bold magenta") + table.add_column("Trigger", style="cyan") + table.add_column("Name", style="green") + table.add_column("Description", style="dim") + + for trigger in triggers: + trigger_path = f"{app_slug}/{trigger.get('slug', 'unknown')}" + table.add_row( + trigger_path, + trigger.get("name", "Unknown"), + trigger.get("description", "No description"), + ) + + console.print(table) + else: + console.print("[dim] No triggers available[/dim]") + + def _display_trigger_info(self, trigger_data: dict[str, Any]) -> None: + """Display trigger information before execution.""" + sample_payload = trigger_data.get("sample_payload", {}) + if sample_payload: + console.print("\n[bold yellow]Sample Payload:[/bold yellow]") + console.print(json.dumps(sample_payload, indent=2)) + + def _run_crew_with_payload(self, payload: dict[str, Any]) -> None: + """Run the crew with the trigger payload using the run_with_trigger method.""" + try: + subprocess.run( # noqa: S603 + ["uv", "run", "run_with_trigger", json.dumps(payload)], # noqa: S607 + capture_output=False, + text=True, + check=True, + ) + + except Exception as e: + raise SystemExit(1) from e diff --git a/src/crewai/cli/update_crew.py b/lib/crewai/src/crewai/cli/update_crew.py similarity index 94% rename from src/crewai/cli/update_crew.py rename to lib/crewai/src/crewai/cli/update_crew.py index 979fb950e..55161797f 100644 --- a/src/crewai/cli/update_crew.py +++ b/lib/crewai/src/crewai/cli/update_crew.py @@ -94,23 +94,19 @@ def migrate_pyproject(input_file, output_file): # Backup the old pyproject.toml backup_file = "pyproject-old.toml" shutil.copy2(input_file, backup_file) - print(f"Original pyproject.toml backed up as {backup_file}") # Rename the poetry.lock file lock_file = "poetry.lock" lock_backup = "poetry-old.lock" if os.path.exists(lock_file): os.rename(lock_file, lock_backup) - print(f"Original poetry.lock renamed to {lock_backup}") else: - print("No poetry.lock file found to rename.") + pass # Write the new pyproject.toml with open(output_file, "wb") as f: tomli_w.dump(new_pyproject, f) - print(f"Migration complete. New pyproject.toml written to {output_file}") - def parse_version(version: str) -> str: """Parse and convert version specifiers.""" diff --git a/src/crewai/cli/utils.py b/lib/crewai/src/crewai/cli/utils.py similarity index 99% rename from src/crewai/cli/utils.py rename to lib/crewai/src/crewai/cli/utils.py index fc0ad7ab3..041bc4e9d 100644 --- a/src/crewai/cli/utils.py +++ b/lib/crewai/src/crewai/cli/utils.py @@ -1,21 +1,22 @@ +from functools import reduce import importlib.util +from inspect import getmro, isclass, isfunction, ismethod import os +from pathlib import Path import shutil import sys -from functools import reduce -from inspect import getmro, isclass, isfunction, ismethod -from pathlib import Path from typing import Any, get_type_hints import click -import tomli from rich.console import Console +import tomli from crewai.cli.config import Settings from crewai.cli.constants import ENV_VARS from crewai.crew import Crew from crewai.flow import Flow + if sys.version_info >= (3, 11): import tomllib diff --git a/src/crewai/cli/version.py b/lib/crewai/src/crewai/cli/version.py similarity index 100% rename from src/crewai/cli/version.py rename to lib/crewai/src/crewai/cli/version.py diff --git a/lib/crewai/src/crewai/context.py b/lib/crewai/src/crewai/context.py new file mode 100644 index 000000000..8edc4fdfb --- /dev/null +++ b/lib/crewai/src/crewai/context.py @@ -0,0 +1,45 @@ +from collections.abc import Generator +from contextlib import contextmanager +import contextvars +import os +from typing import Any + + +_platform_integration_token: contextvars.ContextVar[str | None] = ( + contextvars.ContextVar("platform_integration_token", default=None) +) + + +def set_platform_integration_token(integration_token: str) -> None: + """Set the platform integration token in the current context. + + Args: + integration_token: The integration token to set. + """ + _platform_integration_token.set(integration_token) + + +def get_platform_integration_token() -> str | None: + """Get the platform integration token from the current context or environment. + + Returns: + The integration token if set, otherwise None. + """ + token = _platform_integration_token.get() + if token is None: + token = os.getenv("CREWAI_PLATFORM_INTEGRATION_TOKEN") + return token + + +@contextmanager +def platform_context(integration_token: str) -> Generator[None, Any, None]: + """Context manager to temporarily set the platform integration token. + + Args: + integration_token: The integration token to set within the context. + """ + token = _platform_integration_token.set(integration_token) + try: + yield + finally: + _platform_integration_token.reset(token) diff --git a/src/crewai/crew.py b/lib/crewai/src/crewai/crew.py similarity index 94% rename from src/crewai/crew.py rename to lib/crewai/src/crewai/crew.py index ed9479bdc..a8e88ce55 100644 --- a/src/crewai/crew.py +++ b/lib/crewai/src/crewai/crew.py @@ -1,16 +1,18 @@ +from __future__ import annotations + import asyncio -import json -import re -import uuid -import warnings from collections.abc import Callable from concurrent.futures import Future from copy import copy as shallow_copy from hashlib import md5 +import json +import re from typing import ( Any, cast, ) +import uuid +import warnings from opentelemetry import baggage from opentelemetry.context import attach, detach @@ -28,7 +30,7 @@ from pydantic_core import PydanticCustomError from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent -from crewai.agents.cache import CacheHandler +from crewai.agents.cache.cache_handler import CacheHandler from crewai.crews.crew_output import CrewOutput from crewai.events.event_bus import crewai_event_bus from crewai.events.event_listener import EventListener @@ -53,7 +55,8 @@ from crewai.events.types.crew_events import ( from crewai.flow.flow_trackable import FlowTrackable from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource -from crewai.llm import LLM, BaseLLM +from crewai.llm import LLM +from crewai.llms.base_llm import BaseLLM from crewai.memory.entity.entity_memory import EntityMemory from crewai.memory.external.external_memory import ExternalMemory from crewai.memory.long_term.long_term_memory import LongTermMemory @@ -61,27 +64,33 @@ from crewai.memory.short_term.short_term_memory import ShortTermMemory from crewai.process import Process from crewai.rag.embeddings.types import EmbedderConfig from crewai.rag.types import SearchResult -from crewai.security import Fingerprint, SecurityConfig +from crewai.security.fingerprint import Fingerprint +from crewai.security.security_config import SecurityConfig from crewai.task import Task from crewai.tasks.conditional_task import ConditionalTask from crewai.tasks.task_output import TaskOutput from crewai.tools.agent_tools.agent_tools import AgentTools from crewai.tools.base_tool import BaseTool, Tool from crewai.types.usage_metrics import UsageMetrics -from crewai.utilities import I18N, FileHandler, Logger, RPMController from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE from crewai.utilities.crew.models import CrewContext from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator from crewai.utilities.evaluators.task_evaluator import TaskEvaluator +from crewai.utilities.file_handler import FileHandler from crewai.utilities.formatter import ( aggregate_raw_outputs_from_task_outputs, aggregate_raw_outputs_from_tasks, ) +from crewai.utilities.i18n import I18N from crewai.utilities.llm_utils import create_llm +from crewai.utilities.logger import Logger from crewai.utilities.planning_handler import CrewPlanner +from crewai.utilities.printer import PrinterColor +from crewai.utilities.rpm_controller import RPMController from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler from crewai.utilities.training_handler import CrewTrainingHandler + warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd") @@ -123,12 +132,12 @@ class Crew(FlowTrackable, BaseModel): fingerprinting. """ - __hash__ = object.__hash__ # type: ignore + __hash__ = object.__hash__ _execution_span: Any = PrivateAttr() _rpm_controller: RPMController = PrivateAttr() _logger: Logger = PrivateAttr() _file_handler: FileHandler = PrivateAttr() - _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default=CacheHandler()) + _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default_factory=CacheHandler) _short_term_memory: InstanceOf[ShortTermMemory] | None = PrivateAttr() _long_term_memory: InstanceOf[LongTermMemory] | None = PrivateAttr() _entity_memory: InstanceOf[EntityMemory] | None = PrivateAttr() @@ -136,7 +145,7 @@ class Crew(FlowTrackable, BaseModel): _train: bool | None = PrivateAttr(default=False) _train_iteration: int | None = PrivateAttr() _inputs: dict[str, Any] | None = PrivateAttr(default=None) - _logging_color: str = PrivateAttr( + _logging_color: PrinterColor = PrivateAttr( default="bold_purple", ) _task_output_handler: TaskOutputStorageHandler = PrivateAttr( @@ -297,7 +306,7 @@ class Crew(FlowTrackable, BaseModel): return json.loads(v) if isinstance(v, Json) else v # type: ignore @model_validator(mode="after") - def set_private_attrs(self) -> "Crew": + def set_private_attrs(self) -> Crew: """set private attributes.""" self._cache_handler = CacheHandler() @@ -332,7 +341,7 @@ class Crew(FlowTrackable, BaseModel): ) @model_validator(mode="after") - def create_crew_memory(self) -> "Crew": + def create_crew_memory(self) -> Crew: """Initialize private memory attributes.""" self._external_memory = ( # External memory does not support a default value since it was @@ -350,7 +359,7 @@ class Crew(FlowTrackable, BaseModel): return self @model_validator(mode="after") - def create_crew_knowledge(self) -> "Crew": + def create_crew_knowledge(self) -> Crew: """Create the knowledge for the crew.""" if self.knowledge_sources: try: @@ -454,7 +463,7 @@ class Crew(FlowTrackable, BaseModel): return self @model_validator(mode="after") - def validate_must_have_non_conditional_task(self) -> "Crew": + def validate_must_have_non_conditional_task(self) -> Crew: """Ensure that a crew has at least one non-conditional task.""" if not self.tasks: return self @@ -470,7 +479,7 @@ class Crew(FlowTrackable, BaseModel): return self @model_validator(mode="after") - def validate_first_task(self) -> "Crew": + def validate_first_task(self) -> Crew: """Ensure the first task is not a ConditionalTask.""" if self.tasks and isinstance(self.tasks[0], ConditionalTask): raise PydanticCustomError( @@ -481,7 +490,7 @@ class Crew(FlowTrackable, BaseModel): return self @model_validator(mode="after") - def validate_async_tasks_not_async(self) -> "Crew": + def validate_async_tasks_not_async(self) -> Crew: """Ensure that ConditionalTask is not async.""" for task in self.tasks: if task.async_execution and isinstance(task, ConditionalTask): @@ -986,7 +995,13 @@ class Crew(FlowTrackable, BaseModel): ): tools = self._add_multimodal_tools(agent, tools) - # Return a List[BaseTool] compatible with Task.execute_sync and execute_async + if agent and (hasattr(agent, "apps") and getattr(agent, "apps", None)): + tools = self._add_platform_tools(task, tools) + + if agent and (hasattr(agent, "mcps") and getattr(agent, "mcps", None)): + tools = self._add_mcp_tools(task, tools) + + # Return a list[BaseTool] compatible with Task.execute_sync and execute_async return cast(list[BaseTool], tools) def _get_agent_to_use(self, task: Task) -> BaseAgent | None: @@ -1026,6 +1041,29 @@ class Crew(FlowTrackable, BaseModel): return self._merge_tools(tools, cast(list[BaseTool], delegation_tools)) return cast(list[BaseTool], tools) + def _inject_platform_tools( + self, + tools: list[Tool] | list[BaseTool], + task_agent: BaseAgent, + ) -> list[BaseTool]: + apps = getattr(task_agent, "apps", None) or [] + + if hasattr(task_agent, "get_platform_tools") and apps: + platform_tools = task_agent.get_platform_tools(apps=apps) + return self._merge_tools(tools, cast(list[BaseTool], platform_tools)) + return cast(list[BaseTool], tools) + + def _inject_mcp_tools( + self, + tools: list[Tool] | list[BaseTool], + task_agent: BaseAgent, + ) -> list[BaseTool]: + mcps = getattr(task_agent, "mcps", None) or [] + if hasattr(task_agent, "get_mcp_tools") and mcps: + mcp_tools = task_agent.get_mcp_tools(mcps=mcps) + return self._merge_tools(tools, cast(list[BaseTool], mcp_tools)) + return cast(list[BaseTool], tools) + def _add_multimodal_tools( self, agent: BaseAgent, tools: list[Tool] | list[BaseTool] ) -> list[BaseTool]: @@ -1056,6 +1094,22 @@ class Crew(FlowTrackable, BaseModel): ) return cast(list[BaseTool], tools) + def _add_platform_tools( + self, task: Task, tools: list[Tool] | list[BaseTool] + ) -> list[BaseTool]: + if task.agent: + tools = self._inject_platform_tools(tools, task.agent) + + return cast(list[BaseTool], tools or []) + + def _add_mcp_tools( + self, task: Task, tools: list[Tool] | list[BaseTool] + ) -> list[BaseTool]: + if task.agent: + tools = self._inject_mcp_tools(tools, task.agent) + + return cast(list[BaseTool], tools or []) + def _log_task_start(self, task: Task, role: str = "None"): if self.output_log_file: self._file_handler.log( @@ -1330,13 +1384,34 @@ class Crew(FlowTrackable, BaseModel): def calculate_usage_metrics(self) -> UsageMetrics: """Calculates and returns the usage metrics.""" total_usage_metrics = UsageMetrics() + for agent in self.agents: - if hasattr(agent, "_token_process"): - token_sum = agent._token_process.get_summary() - total_usage_metrics.add_usage_metrics(token_sum) + if isinstance(agent.llm, BaseLLM): + llm_usage = agent.llm.get_token_usage_summary() + + total_usage_metrics.add_usage_metrics(llm_usage) + else: + # fallback litellm + if hasattr(agent, "_token_process"): + token_sum = agent._token_process.get_summary() + total_usage_metrics.add_usage_metrics(token_sum) + if self.manager_agent and hasattr(self.manager_agent, "_token_process"): token_sum = self.manager_agent._token_process.get_summary() total_usage_metrics.add_usage_metrics(token_sum) + + if ( + self.manager_agent + and hasattr(self.manager_agent, "llm") + and hasattr(self.manager_agent.llm, "get_token_usage_summary") + ): + if isinstance(self.manager_agent.llm, BaseLLM): + llm_usage = self.manager_agent.llm.get_token_usage_summary() + else: + llm_usage = self.manager_agent.llm._token_process.get_summary() + + total_usage_metrics.add_usage_metrics(llm_usage) + self.usage_metrics = total_usage_metrics return total_usage_metrics diff --git a/lib/crewai/src/crewai/crews/__init__.py b/lib/crewai/src/crewai/crews/__init__.py new file mode 100644 index 000000000..8b46d5c2b --- /dev/null +++ b/lib/crewai/src/crewai/crews/__init__.py @@ -0,0 +1,5 @@ +from crewai.crews.crew_output import CrewOutput + + + +__all__ = ["CrewOutput"] diff --git a/src/crewai/crews/crew_output.py b/lib/crewai/src/crewai/crews/crew_output.py similarity index 98% rename from src/crewai/crews/crew_output.py rename to lib/crewai/src/crewai/crews/crew_output.py index f54398b59..9f2f03185 100644 --- a/src/crewai/crews/crew_output.py +++ b/lib/crewai/src/crewai/crews/crew_output.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import json from typing import Any diff --git a/src/crewai/events/__init__.py b/lib/crewai/src/crewai/events/__init__.py similarity index 95% rename from src/crewai/events/__init__.py rename to lib/crewai/src/crewai/events/__init__.py index 5bb7a85f3..e3eb4920f 100644 --- a/src/crewai/events/__init__.py +++ b/lib/crewai/src/crewai/events/__init__.py @@ -5,10 +5,13 @@ This module provides the event infrastructure that allows users to: - Track memory operations and performance - Build custom logging and analytics - Extend CrewAI with custom event handlers +- Declare handler dependencies for ordered execution """ from crewai.events.base_event_listener import BaseEventListener +from crewai.events.depends import Depends from crewai.events.event_bus import crewai_event_bus +from crewai.events.handler_graph import CircularDependencyError from crewai.events.types.agent_events import ( AgentEvaluationCompletedEvent, AgentEvaluationFailedEvent, @@ -96,6 +99,7 @@ from crewai.events.types.tool_usage_events import ( ToolValidateInputErrorEvent, ) + __all__ = [ "AgentEvaluationCompletedEvent", "AgentEvaluationFailedEvent", @@ -109,6 +113,7 @@ __all__ = [ "AgentReasoningFailedEvent", "AgentReasoningStartedEvent", "BaseEventListener", + "CircularDependencyError", "CrewKickoffCompletedEvent", "CrewKickoffFailedEvent", "CrewKickoffStartedEvent", @@ -119,6 +124,7 @@ __all__ = [ "CrewTrainCompletedEvent", "CrewTrainFailedEvent", "CrewTrainStartedEvent", + "Depends", "FlowCreatedEvent", "FlowEvent", "FlowFinishedEvent", diff --git a/src/crewai/events/base_event_listener.py b/lib/crewai/src/crewai/events/base_event_listener.py similarity index 88% rename from src/crewai/events/base_event_listener.py rename to lib/crewai/src/crewai/events/base_event_listener.py index cb4bb81b7..8cc49af84 100644 --- a/src/crewai/events/base_event_listener.py +++ b/lib/crewai/src/crewai/events/base_event_listener.py @@ -9,6 +9,7 @@ class BaseEventListener(ABC): def __init__(self): super().__init__() self.setup_listeners(crewai_event_bus) + crewai_event_bus.validate_dependencies() @abstractmethod def setup_listeners(self, crewai_event_bus: CrewAIEventsBus): diff --git a/src/crewai/events/base_events.py b/lib/crewai/src/crewai/events/base_events.py similarity index 87% rename from src/crewai/events/base_events.py rename to lib/crewai/src/crewai/events/base_events.py index 6287f42bd..4f4e80434 100644 --- a/src/crewai/events/base_events.py +++ b/lib/crewai/src/crewai/events/base_events.py @@ -17,6 +17,11 @@ class BaseEvent(BaseModel): ) fingerprint_metadata: dict[str, Any] | None = None # Any relevant metadata + task_id: str | None = None + task_name: str | None = None + agent_id: str | None = None + agent_role: str | None = None + def to_json(self, exclude: set[str] | None = None): """ Converts the event to a JSON-serializable dictionary. @@ -31,7 +36,7 @@ class BaseEvent(BaseModel): def _set_task_params(self, data: dict[str, Any]): if "from_task" in data and (task := data["from_task"]): - self.task_id = task.id + self.task_id = str(task.id) self.task_name = task.name or task.description self.from_task = None @@ -42,6 +47,6 @@ class BaseEvent(BaseModel): if not agent: return - self.agent_id = agent.id + self.agent_id = str(agent.id) self.agent_role = agent.role self.from_agent = None diff --git a/lib/crewai/src/crewai/events/depends.py b/lib/crewai/src/crewai/events/depends.py new file mode 100644 index 000000000..4b2e1952c --- /dev/null +++ b/lib/crewai/src/crewai/events/depends.py @@ -0,0 +1,105 @@ +"""Dependency injection system for event handlers. + +This module provides a FastAPI-style dependency system that allows event handlers +to declare dependencies on other handlers, ensuring proper execution order while +maintaining parallelism where possible. +""" + +from collections.abc import Coroutine +from typing import Any, Generic, Protocol, TypeVar + +from crewai.events.base_events import BaseEvent + + +EventT_co = TypeVar("EventT_co", bound=BaseEvent, contravariant=True) + + +class EventHandler(Protocol[EventT_co]): + """Protocol for event handler functions. + + Generic protocol that accepts any subclass of BaseEvent. + Handlers can be either synchronous (returning None) or asynchronous + (returning a coroutine). + """ + + def __call__( + self, source: Any, event: EventT_co, / + ) -> None | Coroutine[Any, Any, None]: + """Event handler signature. + + Args: + source: The object that emitted the event + event: The event instance (any BaseEvent subclass) + + Returns: + None for sync handlers, Coroutine for async handlers + """ + ... + + +T = TypeVar("T", bound=EventHandler[Any]) + + +class Depends(Generic[T]): + """Declares a dependency on another event handler. + + Similar to FastAPI's Depends, this allows handlers to specify that they + depend on other handlers completing first. Handlers with dependencies will + execute after their dependencies, while independent handlers can run in parallel. + + Args: + handler: The handler function that this handler depends on + + Example: + >>> from crewai.events import Depends, crewai_event_bus + >>> from crewai.events import LLMCallStartedEvent + >>> @crewai_event_bus.on(LLMCallStartedEvent) + >>> def setup_context(source, event): + ... return {"initialized": True} + >>> + >>> @crewai_event_bus.on(LLMCallStartedEvent, depends_on=Depends(setup_context)) + >>> def process(source, event): + ... # Runs after setup_context completes + ... pass + """ + + def __init__(self, handler: T) -> None: + """Initialize a dependency on a handler. + + Args: + handler: The handler function this depends on + """ + self.handler = handler + + def __repr__(self) -> str: + """Return a string representation of the dependency. + + Returns: + A string showing the dependent handler name + """ + handler_name = getattr(self.handler, "__name__", repr(self.handler)) + return f"Depends({handler_name})" + + def __eq__(self, other: object) -> bool: + """Check equality based on the handler reference. + + Args: + other: Another Depends instance to compare + + Returns: + True if both depend on the same handler, False otherwise + """ + if not isinstance(other, Depends): + return False + return self.handler is other.handler + + def __hash__(self) -> int: + """Return hash based on handler identity. + + Since equality is based on identity (is), we hash the handler + object directly rather than its id for consistency. + + Returns: + Hash of the handler object + """ + return id(self.handler) diff --git a/lib/crewai/src/crewai/events/event_bus.py b/lib/crewai/src/crewai/events/event_bus.py new file mode 100644 index 000000000..e7d6e279e --- /dev/null +++ b/lib/crewai/src/crewai/events/event_bus.py @@ -0,0 +1,509 @@ +"""Event bus for managing and dispatching events in CrewAI. + +This module provides a singleton event bus that allows registration and handling +of events throughout the CrewAI system, supporting both synchronous and asynchronous +event handlers with optional dependency management. +""" + +import asyncio +import atexit +from collections.abc import Callable, Generator +from concurrent.futures import Future, ThreadPoolExecutor +from contextlib import contextmanager +import threading +from typing import Any, Final, ParamSpec, TypeVar + +from typing_extensions import Self + +from crewai.events.base_events import BaseEvent +from crewai.events.depends import Depends +from crewai.events.handler_graph import build_execution_plan +from crewai.events.types.event_bus_types import ( + AsyncHandler, + AsyncHandlerSet, + ExecutionPlan, + Handler, + SyncHandler, + SyncHandlerSet, +) +from crewai.events.types.llm_events import LLMStreamChunkEvent +from crewai.events.utils.console_formatter import ConsoleFormatter +from crewai.events.utils.handlers import is_async_handler, is_call_handler_safe +from crewai.utilities.rw_lock import RWLock + + +P = ParamSpec("P") +R = TypeVar("R") + + +class CrewAIEventsBus: + """Singleton event bus for handling events in CrewAI. + + This class manages event registration and emission for both synchronous + and asynchronous event handlers, automatically scheduling async handlers + in a dedicated background event loop. + + Synchronous handlers execute in a thread pool executor to ensure completion + before program exit. Asynchronous handlers execute in a dedicated event loop + running in a daemon thread, with graceful shutdown waiting for completion. + + Attributes: + _instance: Singleton instance of the event bus + _instance_lock: Reentrant lock for singleton initialization (class-level) + _rwlock: Read-write lock for handler registration and access (instance-level) + _sync_handlers: Mapping of event types to registered synchronous handlers + _async_handlers: Mapping of event types to registered asynchronous handlers + _sync_executor: Thread pool executor for running synchronous handlers + _loop: Dedicated asyncio event loop for async handler execution + _loop_thread: Background daemon thread running the event loop + _console: Console formatter for error output + """ + + _instance: Self | None = None + _instance_lock: threading.RLock = threading.RLock() + _rwlock: RWLock + _sync_handlers: dict[type[BaseEvent], SyncHandlerSet] + _async_handlers: dict[type[BaseEvent], AsyncHandlerSet] + _handler_dependencies: dict[type[BaseEvent], dict[Handler, list[Depends[Any]]]] + _execution_plan_cache: dict[type[BaseEvent], ExecutionPlan] + _console: ConsoleFormatter + _shutting_down: bool + + def __new__(cls) -> Self: + """Create or return the singleton instance. + + Returns: + The singleton CrewAIEventsBus instance + """ + if cls._instance is None: + with cls._instance_lock: + if cls._instance is None: + cls._instance = super().__new__(cls) + cls._instance._initialize() + return cls._instance + + def _initialize(self) -> None: + """Initialize the event bus internal state. + + Creates handler dictionaries and starts a dedicated background + event loop for async handler execution. + """ + self._shutting_down = False + self._rwlock = RWLock() + self._sync_handlers: dict[type[BaseEvent], SyncHandlerSet] = {} + self._async_handlers: dict[type[BaseEvent], AsyncHandlerSet] = {} + self._handler_dependencies: dict[ + type[BaseEvent], dict[Handler, list[Depends[Any]]] + ] = {} + self._execution_plan_cache: dict[type[BaseEvent], ExecutionPlan] = {} + self._sync_executor = ThreadPoolExecutor( + max_workers=10, + thread_name_prefix="CrewAISyncHandler", + ) + self._console = ConsoleFormatter() + + self._loop = asyncio.new_event_loop() + self._loop_thread = threading.Thread( + target=self._run_loop, + name="CrewAIEventsLoop", + daemon=True, + ) + self._loop_thread.start() + + def _run_loop(self) -> None: + """Run the background async event loop.""" + asyncio.set_event_loop(self._loop) + self._loop.run_forever() + + def _register_handler( + self, + event_type: type[BaseEvent], + handler: Callable[..., Any], + dependencies: list[Depends[Any]] | None = None, + ) -> None: + """Register a handler for the given event type. + + Args: + event_type: The event class to listen for + handler: The handler function to register + dependencies: Optional list of dependencies + """ + with self._rwlock.w_locked(): + if is_async_handler(handler): + existing_async = self._async_handlers.get(event_type, frozenset()) + self._async_handlers[event_type] = existing_async | {handler} + else: + existing_sync = self._sync_handlers.get(event_type, frozenset()) + self._sync_handlers[event_type] = existing_sync | {handler} + + if dependencies: + if event_type not in self._handler_dependencies: + self._handler_dependencies[event_type] = {} + self._handler_dependencies[event_type][handler] = dependencies + + self._execution_plan_cache.pop(event_type, None) + + def on( + self, + event_type: type[BaseEvent], + depends_on: Depends[Any] | list[Depends[Any]] | None = None, + ) -> Callable[[Callable[P, R]], Callable[P, R]]: + """Decorator to register an event handler for a specific event type. + + Args: + event_type: The event class to listen for + depends_on: Optional dependency or list of dependencies. Handlers with + dependencies will execute after their dependencies complete. + + Returns: + Decorator function that registers the handler + + Example: + >>> from crewai.events import crewai_event_bus, Depends + >>> from crewai.events.types.llm_events import LLMCallStartedEvent + >>> + >>> @crewai_event_bus.on(LLMCallStartedEvent) + >>> def setup_context(source, event): + ... print("Setting up context") + >>> + >>> @crewai_event_bus.on(LLMCallStartedEvent, depends_on=Depends(setup_context)) + >>> def process(source, event): + ... print("Processing (runs after setup_context)") + """ + + def decorator(handler: Callable[P, R]) -> Callable[P, R]: + """Register the handler and return it unchanged. + + Args: + handler: Event handler function to register + + Returns: + The same handler function unchanged + """ + deps = None + if depends_on is not None: + deps = [depends_on] if isinstance(depends_on, Depends) else depends_on + + self._register_handler(event_type, handler, dependencies=deps) + return handler + + return decorator + + def _call_handlers( + self, + source: Any, + event: BaseEvent, + handlers: SyncHandlerSet, + ) -> None: + """Call provided synchronous handlers. + + Args: + source: The emitting object + event: The event instance + handlers: Frozenset of sync handlers to call + """ + errors: list[tuple[SyncHandler, Exception]] = [ + (handler, error) + for handler in handlers + if (error := is_call_handler_safe(handler, source, event)) is not None + ] + + if errors: + for handler, error in errors: + self._console.print( + f"[CrewAIEventsBus] Sync handler error in {handler.__name__}: {error}" + ) + + async def _acall_handlers( + self, + source: Any, + event: BaseEvent, + handlers: AsyncHandlerSet, + ) -> None: + """Asynchronously call provided async handlers. + + Args: + source: The object that emitted the event + event: The event instance + handlers: Frozenset of async handlers to call + """ + coros = [handler(source, event) for handler in handlers] + results = await asyncio.gather(*coros, return_exceptions=True) + for handler, result in zip(handlers, results, strict=False): + if isinstance(result, Exception): + self._console.print( + f"[CrewAIEventsBus] Async handler error in {getattr(handler, '__name__', handler)}: {result}" + ) + + async def _emit_with_dependencies(self, source: Any, event: BaseEvent) -> None: + """Emit an event with dependency-aware handler execution. + + Handlers are grouped into execution levels based on their dependencies. + Within each level, async handlers run concurrently while sync handlers + run sequentially (or in thread pool). Each level completes before the + next level starts. + + Uses a cached execution plan for performance. The plan is built once + per event type and cached until handlers are modified. + + Args: + source: The emitting object + event: The event instance to emit + """ + event_type = type(event) + + with self._rwlock.r_locked(): + if self._shutting_down: + return + cached_plan = self._execution_plan_cache.get(event_type) + if cached_plan is not None: + sync_handlers = self._sync_handlers.get(event_type, frozenset()) + async_handlers = self._async_handlers.get(event_type, frozenset()) + + if cached_plan is None: + with self._rwlock.w_locked(): + if self._shutting_down: + return + cached_plan = self._execution_plan_cache.get(event_type) + if cached_plan is None: + sync_handlers = self._sync_handlers.get(event_type, frozenset()) + async_handlers = self._async_handlers.get(event_type, frozenset()) + dependencies = dict(self._handler_dependencies.get(event_type, {})) + all_handlers = list(sync_handlers | async_handlers) + + if not all_handlers: + return + + cached_plan = build_execution_plan(all_handlers, dependencies) + self._execution_plan_cache[event_type] = cached_plan + else: + sync_handlers = self._sync_handlers.get(event_type, frozenset()) + async_handlers = self._async_handlers.get(event_type, frozenset()) + + for level in cached_plan: + level_sync = frozenset(h for h in level if h in sync_handlers) + level_async = frozenset(h for h in level if h in async_handlers) + + if level_sync: + if event_type is LLMStreamChunkEvent: + self._call_handlers(source, event, level_sync) + else: + future = self._sync_executor.submit( + self._call_handlers, source, event, level_sync + ) + await asyncio.get_running_loop().run_in_executor( + None, future.result + ) + + if level_async: + await self._acall_handlers(source, event, level_async) + + def emit(self, source: Any, event: BaseEvent) -> Future[None] | None: + """Emit an event to all registered handlers. + + If handlers have dependencies (registered with depends_on), they execute + in dependency order. Otherwise, handlers execute as before (sync in thread + pool, async fire-and-forget). + + Stream chunk events always execute synchronously to preserve ordering. + + Args: + source: The emitting object + event: The event instance to emit + + Returns: + Future that completes when handlers finish. Returns: + - Future for sync-only handlers (ThreadPoolExecutor future) + - Future for async handlers or mixed handlers (asyncio future) + - Future for dependency-managed handlers (asyncio future) + - None if no handlers or sync stream chunk events + + Example: + >>> future = crewai_event_bus.emit(source, event) + >>> if future: + ... await asyncio.wrap_future(future) # In async test + ... # or future.result(timeout=5.0) in sync code + """ + event_type = type(event) + + with self._rwlock.r_locked(): + if self._shutting_down: + self._console.print( + "[CrewAIEventsBus] Warning: Attempted to emit event during shutdown. Ignoring." + ) + return None + has_dependencies = event_type in self._handler_dependencies + sync_handlers = self._sync_handlers.get(event_type, frozenset()) + async_handlers = self._async_handlers.get(event_type, frozenset()) + + if has_dependencies: + return asyncio.run_coroutine_threadsafe( + self._emit_with_dependencies(source, event), + self._loop, + ) + + if sync_handlers: + if event_type is LLMStreamChunkEvent: + self._call_handlers(source, event, sync_handlers) + else: + sync_future = self._sync_executor.submit( + self._call_handlers, source, event, sync_handlers + ) + if not async_handlers: + return sync_future + + if async_handlers: + return asyncio.run_coroutine_threadsafe( + self._acall_handlers(source, event, async_handlers), + self._loop, + ) + + return None + + async def aemit(self, source: Any, event: BaseEvent) -> None: + """Asynchronously emit an event to registered async handlers. + + Only processes async handlers. Use in async contexts. + + Args: + source: The object emitting the event + event: The event instance to emit + """ + event_type = type(event) + + with self._rwlock.r_locked(): + if self._shutting_down: + self._console.print( + "[CrewAIEventsBus] Warning: Attempted to emit event during shutdown. Ignoring." + ) + return + async_handlers = self._async_handlers.get(event_type, frozenset()) + + if async_handlers: + await self._acall_handlers(source, event, async_handlers) + + def register_handler( + self, + event_type: type[BaseEvent], + handler: SyncHandler | AsyncHandler, + ) -> None: + """Register an event handler for a specific event type. + + Args: + event_type: The event class to listen for + handler: The handler function to register + """ + self._register_handler(event_type, handler) + + def validate_dependencies(self) -> None: + """Validate all registered handler dependencies. + + Attempts to build execution plans for all event types with dependencies. + This detects circular dependencies and cross-event-type dependencies + before events are emitted. + + Raises: + CircularDependencyError: If circular dependencies or unresolved + dependencies (e.g., cross-event-type) are detected + """ + with self._rwlock.r_locked(): + for event_type in self._handler_dependencies: + sync_handlers = self._sync_handlers.get(event_type, frozenset()) + async_handlers = self._async_handlers.get(event_type, frozenset()) + dependencies = dict(self._handler_dependencies.get(event_type, {})) + all_handlers = list(sync_handlers | async_handlers) + + if all_handlers and dependencies: + build_execution_plan(all_handlers, dependencies) + + @contextmanager + def scoped_handlers(self) -> Generator[None, Any, None]: + """Context manager for temporary event handling scope. + + Useful for testing or temporary event handling. All handlers registered + within this context are cleared when the context exits. + + Example: + >>> from crewai.events.event_bus import crewai_event_bus + >>> from crewai.events.event_types import CrewKickoffStartedEvent + >>> with crewai_event_bus.scoped_handlers(): + ... + ... @crewai_event_bus.on(CrewKickoffStartedEvent) + ... def temp_handler(source, event): + ... print("Temporary handler") + ... + ... # Do stuff... + ... # Handlers are cleared after the context + """ + with self._rwlock.w_locked(): + prev_sync = self._sync_handlers + prev_async = self._async_handlers + prev_deps = self._handler_dependencies + prev_cache = self._execution_plan_cache + self._sync_handlers = {} + self._async_handlers = {} + self._handler_dependencies = {} + self._execution_plan_cache = {} + + try: + yield + finally: + with self._rwlock.w_locked(): + self._sync_handlers = prev_sync + self._async_handlers = prev_async + self._handler_dependencies = prev_deps + self._execution_plan_cache = prev_cache + + def shutdown(self, wait: bool = True) -> None: + """Gracefully shutdown the event loop and wait for all tasks to finish. + + Args: + wait: If True, wait for all pending tasks to complete before stopping. + If False, cancel all pending tasks immediately. + """ + with self._rwlock.w_locked(): + self._shutting_down = True + loop = getattr(self, "_loop", None) + + if loop is None or loop.is_closed(): + return + + if wait: + + async def _wait_for_all_tasks() -> None: + tasks = { + t + for t in asyncio.all_tasks(loop) + if t is not asyncio.current_task() + } + if tasks: + await asyncio.gather(*tasks, return_exceptions=True) + + future = asyncio.run_coroutine_threadsafe(_wait_for_all_tasks(), loop) + try: + future.result() + except Exception as e: + self._console.print(f"[CrewAIEventsBus] Error waiting for tasks: {e}") + else: + + def _cancel_tasks() -> None: + for task in asyncio.all_tasks(loop): + if task is not asyncio.current_task(): + task.cancel() + + loop.call_soon_threadsafe(_cancel_tasks) + + loop.call_soon_threadsafe(loop.stop) + self._loop_thread.join() + loop.close() + self._sync_executor.shutdown(wait=wait) + + with self._rwlock.w_locked(): + self._sync_handlers.clear() + self._async_handlers.clear() + self._execution_plan_cache.clear() + + +crewai_event_bus: Final[CrewAIEventsBus] = CrewAIEventsBus() + +atexit.register(crewai_event_bus.shutdown) diff --git a/src/crewai/events/event_listener.py b/lib/crewai/src/crewai/events/event_listener.py similarity index 98% rename from src/crewai/events/event_listener.py rename to lib/crewai/src/crewai/events/event_listener.py index a0b113f35..8140ccc2b 100644 --- a/src/crewai/events/event_listener.py +++ b/lib/crewai/src/crewai/events/event_listener.py @@ -6,6 +6,7 @@ from typing import Any from pydantic import Field, PrivateAttr from crewai.events.base_event_listener import BaseEventListener +from crewai.events.listeners.memory_listener import MemoryListener from crewai.events.types.agent_events import ( AgentExecutionCompletedEvent, AgentExecutionStartedEvent, @@ -25,6 +26,14 @@ from crewai.events.types.crew_events import ( CrewTrainFailedEvent, CrewTrainStartedEvent, ) +from crewai.events.types.flow_events import ( + FlowCreatedEvent, + FlowFinishedEvent, + FlowStartedEvent, + MethodExecutionFailedEvent, + MethodExecutionFinishedEvent, + MethodExecutionStartedEvent, +) from crewai.events.types.knowledge_events import ( KnowledgeQueryCompletedEvent, KnowledgeQueryFailedEvent, @@ -47,6 +56,21 @@ from crewai.events.types.logging_events import ( AgentLogsExecutionEvent, AgentLogsStartedEvent, ) +from crewai.events.types.reasoning_events import ( + AgentReasoningCompletedEvent, + AgentReasoningFailedEvent, + AgentReasoningStartedEvent, +) +from crewai.events.types.task_events import ( + TaskCompletedEvent, + TaskFailedEvent, + TaskStartedEvent, +) +from crewai.events.types.tool_usage_events import ( + ToolUsageErrorEvent, + ToolUsageFinishedEvent, + ToolUsageStartedEvent, +) from crewai.events.utils.console_formatter import ConsoleFormatter from crewai.llm import LLM from crewai.task import Task @@ -54,27 +78,6 @@ from crewai.telemetry.telemetry import Telemetry from crewai.utilities import Logger from crewai.utilities.constants import EMITTER_COLOR -from .listeners.memory_listener import MemoryListener -from .types.flow_events import ( - FlowCreatedEvent, - FlowFinishedEvent, - FlowStartedEvent, - MethodExecutionFailedEvent, - MethodExecutionFinishedEvent, - MethodExecutionStartedEvent, -) -from .types.reasoning_events import ( - AgentReasoningCompletedEvent, - AgentReasoningFailedEvent, - AgentReasoningStartedEvent, -) -from .types.task_events import TaskCompletedEvent, TaskFailedEvent, TaskStartedEvent -from .types.tool_usage_events import ( - ToolUsageErrorEvent, - ToolUsageFinishedEvent, - ToolUsageStartedEvent, -) - class EventListener(BaseEventListener): _instance = None @@ -378,12 +381,8 @@ class EventListener(BaseEventListener): @crewai_event_bus.on(LLMStreamChunkEvent) def on_llm_stream_chunk(source, event: LLMStreamChunkEvent): self.text_stream.write(event.chunk) - self.text_stream.seek(self.next_chunk) - - # Read from the in-memory stream - content = self.text_stream.read() - print(content, end="", flush=True) + self.text_stream.read() self.next_chunk = self.text_stream.tell() # ----------- LLM GUARDRAIL EVENTS ----------- diff --git a/src/crewai/events/event_types.py b/lib/crewai/src/crewai/events/event_types.py similarity index 86% rename from src/crewai/events/event_types.py rename to lib/crewai/src/crewai/events/event_types.py index e84494813..f7a4d1f72 100644 --- a/src/crewai/events/event_types.py +++ b/lib/crewai/src/crewai/events/event_types.py @@ -4,8 +4,7 @@ from crewai.events.types.agent_events import ( AgentExecutionStartedEvent, LiteAgentExecutionCompletedEvent, ) - -from .types.crew_events import ( +from crewai.events.types.crew_events import ( CrewKickoffCompletedEvent, CrewKickoffFailedEvent, CrewKickoffStartedEvent, @@ -16,14 +15,14 @@ from .types.crew_events import ( CrewTrainFailedEvent, CrewTrainStartedEvent, ) -from .types.flow_events import ( +from crewai.events.types.flow_events import ( FlowFinishedEvent, FlowStartedEvent, MethodExecutionFailedEvent, MethodExecutionFinishedEvent, MethodExecutionStartedEvent, ) -from .types.knowledge_events import ( +from crewai.events.types.knowledge_events import ( KnowledgeQueryCompletedEvent, KnowledgeQueryFailedEvent, KnowledgeQueryStartedEvent, @@ -31,17 +30,17 @@ from .types.knowledge_events import ( KnowledgeRetrievalStartedEvent, KnowledgeSearchQueryFailedEvent, ) -from .types.llm_events import ( +from crewai.events.types.llm_events import ( LLMCallCompletedEvent, LLMCallFailedEvent, LLMCallStartedEvent, LLMStreamChunkEvent, ) -from .types.llm_guardrail_events import ( +from crewai.events.types.llm_guardrail_events import ( LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent, ) -from .types.memory_events import ( +from crewai.events.types.memory_events import ( MemoryQueryCompletedEvent, MemoryQueryFailedEvent, MemoryQueryStartedEvent, @@ -51,22 +50,23 @@ from .types.memory_events import ( MemorySaveFailedEvent, MemorySaveStartedEvent, ) -from .types.reasoning_events import ( +from crewai.events.types.reasoning_events import ( AgentReasoningCompletedEvent, AgentReasoningFailedEvent, AgentReasoningStartedEvent, ) -from .types.task_events import ( +from crewai.events.types.task_events import ( TaskCompletedEvent, TaskFailedEvent, TaskStartedEvent, ) -from .types.tool_usage_events import ( +from crewai.events.types.tool_usage_events import ( ToolUsageErrorEvent, ToolUsageFinishedEvent, ToolUsageStartedEvent, ) + EventTypes = ( CrewKickoffStartedEvent | CrewKickoffCompletedEvent diff --git a/lib/crewai/src/crewai/events/handler_graph.py b/lib/crewai/src/crewai/events/handler_graph.py new file mode 100644 index 000000000..8648299c2 --- /dev/null +++ b/lib/crewai/src/crewai/events/handler_graph.py @@ -0,0 +1,126 @@ +"""Dependency graph resolution for event handlers. + +This module resolves handler dependencies into execution levels, ensuring +handlers execute in correct order while maximizing parallelism. +""" + +from collections import defaultdict, deque +from collections.abc import Sequence + +from crewai.events.depends import Depends +from crewai.events.types.event_bus_types import ExecutionPlan, Handler + + +class CircularDependencyError(Exception): + """Exception raised when circular dependencies are detected in event handlers. + + Attributes: + handlers: The handlers involved in the circular dependency + """ + + def __init__(self, handlers: list[Handler]) -> None: + """Initialize the circular dependency error. + + Args: + handlers: The handlers involved in the circular dependency + """ + handler_names = ", ".join(getattr(h, "__name__", repr(h)) for h in handlers[:5]) + message = f"Circular dependency detected in event handlers: {handler_names}" + super().__init__(message) + self.handlers = handlers + + +class HandlerGraph: + """Resolves handler dependencies into parallel execution levels. + + Handlers are organized into levels where: + - Level 0: Handlers with no dependencies (can run first) + - Level N: Handlers that depend on handlers in levels 0...N-1 + + Handlers within the same level can execute in parallel. + + Attributes: + levels: List of handler sets, where each level can execute in parallel + """ + + def __init__( + self, + handlers: dict[Handler, list[Depends]], + ) -> None: + """Initialize the dependency graph. + + Args: + handlers: Mapping of handler -> list of `crewai.events.depends.Depends` objects + """ + self.handlers = handlers + self.levels: ExecutionPlan = [] + self._resolve() + + def _resolve(self) -> None: + """Resolve dependencies into execution levels using topological sort.""" + dependents: dict[Handler, set[Handler]] = defaultdict(set) + in_degree: dict[Handler, int] = {} + + for handler in self.handlers: + in_degree[handler] = 0 + + for handler, deps in self.handlers.items(): + in_degree[handler] = len(deps) + for dep in deps: + dependents[dep.handler].add(handler) + + queue: deque[Handler] = deque([h for h, deg in in_degree.items() if deg == 0]) + + while queue: + current_level: set[Handler] = set() + + for _ in range(len(queue)): + handler = queue.popleft() + current_level.add(handler) + + for dependent in dependents[handler]: + in_degree[dependent] -= 1 + if in_degree[dependent] == 0: + queue.append(dependent) + + if current_level: + self.levels.append(current_level) + + remaining = [h for h, deg in in_degree.items() if deg > 0] + if remaining: + raise CircularDependencyError(remaining) + + def get_execution_plan(self) -> ExecutionPlan: + """Get the ordered execution plan. + + Returns: + List of handler sets, where each set represents handlers that can + execute in parallel. Sets are ordered such that dependencies are + satisfied. + """ + return self.levels + + +def build_execution_plan( + handlers: Sequence[Handler], + dependencies: dict[Handler, list[Depends]], +) -> ExecutionPlan: + """Build an execution plan from handlers and their dependencies. + + Args: + handlers: All handlers for an event type + dependencies: Mapping of handler -> list of dependencies + + Returns: + Execution plan as list of levels, where each level is a set of + handlers that can execute in parallel + + Raises: + CircularDependencyError: If circular dependencies are detected + """ + handler_dict: dict[Handler, list[Depends]] = { + h: dependencies.get(h, []) for h in handlers + } + + graph = HandlerGraph(handler_dict) + return graph.get_execution_plan() diff --git a/src/crewai/events/listeners/__init__.py b/lib/crewai/src/crewai/events/listeners/__init__.py similarity index 100% rename from src/crewai/events/listeners/__init__.py rename to lib/crewai/src/crewai/events/listeners/__init__.py diff --git a/src/crewai/events/listeners/memory_listener.py b/lib/crewai/src/crewai/events/listeners/memory_listener.py similarity index 100% rename from src/crewai/events/listeners/memory_listener.py rename to lib/crewai/src/crewai/events/listeners/memory_listener.py diff --git a/lib/crewai/src/crewai/events/listeners/tracing/__init__.py b/lib/crewai/src/crewai/events/listeners/tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/events/listeners/tracing/first_time_trace_handler.py b/lib/crewai/src/crewai/events/listeners/tracing/first_time_trace_handler.py similarity index 97% rename from src/crewai/events/listeners/tracing/first_time_trace_handler.py rename to lib/crewai/src/crewai/events/listeners/tracing/first_time_trace_handler.py index 4de56dc26..5a81a44f1 100644 --- a/src/crewai/events/listeners/tracing/first_time_trace_handler.py +++ b/lib/crewai/src/crewai/events/listeners/tracing/first_time_trace_handler.py @@ -1,7 +1,7 @@ import logging +from pathlib import Path import uuid import webbrowser -from pathlib import Path from rich.console import Console from rich.panel import Panel @@ -13,6 +13,7 @@ from crewai.events.listeners.tracing.utils import ( should_auto_collect_first_time_traces, ) + logger = logging.getLogger(__name__) @@ -60,9 +61,9 @@ def _update_or_create_env_file(): class FirstTimeTraceHandler: """Handles the first-time user trace collection and display flow.""" - def __init__(self): - self.is_first_time: bool = False - self.collected_events: bool = False + def __init__(self) -> None: + self.is_first_time = False + self.collected_events = False self.trace_batch_id: str | None = None self.ephemeral_url: str | None = None self.batch_manager: TraceBatchManager | None = None @@ -86,7 +87,7 @@ class FirstTimeTraceHandler: return try: - user_wants_traces = prompt_user_for_trace_viewing(timeout_seconds=20) + user_wants_traces = prompt_user_for_trace_viewing() if user_wants_traces: self._initialize_backend_and_send_events() diff --git a/src/crewai/events/listeners/tracing/trace_batch_manager.py b/lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py similarity index 74% rename from src/crewai/events/listeners/tracing/trace_batch_manager.py rename to lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py index 386cab91c..dac8207ec 100644 --- a/src/crewai/events/listeners/tracing/trace_batch_manager.py +++ b/lib/crewai/src/crewai/events/listeners/tracing/trace_batch_manager.py @@ -1,8 +1,9 @@ -import uuid from dataclasses import dataclass, field from datetime import datetime, timezone from logging import getLogger +from threading import Condition, Lock from typing import Any +import uuid from rich.console import Console from rich.panel import Panel @@ -14,6 +15,7 @@ from crewai.events.listeners.tracing.types import TraceEvent from crewai.events.listeners.tracing.utils import should_auto_collect_first_time_traces from crewai.utilities.constants import CREWAI_BASE_URL + logger = getLogger(__name__) @@ -40,8 +42,13 @@ class TraceBatch: class TraceBatchManager: """Single responsibility: Manage batches and event buffering""" - def __init__(self): - self.is_current_batch_ephemeral: bool = False + def __init__(self) -> None: + self._init_lock = Lock() + self._pending_events_lock = Lock() + self._pending_events_cv = Condition(self._pending_events_lock) + self._pending_events_count = 0 + + self.is_current_batch_ephemeral = False self.trace_batch_id: str | None = None self.current_batch: TraceBatch | None = None self.event_buffer: list[TraceEvent] = [] @@ -64,24 +71,30 @@ class TraceBatchManager: execution_metadata: dict[str, Any], use_ephemeral: bool = False, ) -> TraceBatch: - """Initialize a new trace batch""" - self.current_batch = TraceBatch( - user_context=user_context, execution_metadata=execution_metadata - ) - self.event_buffer.clear() - self.is_current_batch_ephemeral = use_ephemeral + """Initialize a new trace batch (thread-safe)""" + with self._init_lock: + if self.current_batch is not None: + logger.debug( + "Batch already initialized, skipping duplicate initialization" + ) + return self.current_batch - self.record_start_time("execution") - - if should_auto_collect_first_time_traces(): - self.trace_batch_id = self.current_batch.batch_id - else: - self._initialize_backend_batch( - user_context, execution_metadata, use_ephemeral + self.current_batch = TraceBatch( + user_context=user_context, execution_metadata=execution_metadata ) - self.backend_initialized = True + self.is_current_batch_ephemeral = use_ephemeral - return self.current_batch + self.record_start_time("execution") + + if should_auto_collect_first_time_traces(): + self.trace_batch_id = self.current_batch.batch_id + else: + self._initialize_backend_batch( + user_context, execution_metadata, use_ephemeral + ) + self.backend_initialized = True + + return self.current_batch def _initialize_backend_batch( self, @@ -148,6 +161,40 @@ class TraceBatchManager: f"Error initializing trace batch: {e}. Continuing without tracing." ) + def begin_event_processing(self): + """Mark that an event handler started processing (for synchronization)""" + with self._pending_events_lock: + self._pending_events_count += 1 + + def end_event_processing(self): + """Mark that an event handler finished processing (for synchronization)""" + with self._pending_events_cv: + self._pending_events_count -= 1 + if self._pending_events_count == 0: + self._pending_events_cv.notify_all() + + def wait_for_pending_events(self, timeout: float = 2.0) -> bool: + """Wait for all pending event handlers to finish processing + + Args: + timeout: Maximum time to wait in seconds (default: 2.0) + + Returns: + True if all handlers completed, False if timeout occurred + """ + with self._pending_events_cv: + if self._pending_events_count > 0: + logger.debug( + f"Waiting for {self._pending_events_count} pending event handlers..." + ) + self._pending_events_cv.wait(timeout) + if self._pending_events_count > 0: + logger.error( + f"Timeout waiting for event handlers. {self._pending_events_count} still pending. Events may be incomplete!" + ) + return False + return True + def add_event(self, trace_event: TraceEvent): """Add event to buffer""" self.event_buffer.append(trace_event) @@ -180,8 +227,8 @@ class TraceBatchManager: self.event_buffer.clear() return 200 - logger.warning( - f"Failed to send events: {response.status_code}. Events will be lost." + logger.error( + f"Failed to send events: {response.status_code}. Response: {response.text}. Events will be lost." ) return 500 @@ -196,15 +243,38 @@ class TraceBatchManager: if not self.current_batch: return None - self.current_batch.events = self.event_buffer.copy() - if self.event_buffer: + all_handlers_completed = self.wait_for_pending_events() + + if not all_handlers_completed and self.trace_batch_id: + logger.error( + "Event handler timeout - marking batch as failed due to incomplete events" + ) + self.plus_api.mark_trace_batch_as_failed( + self.trace_batch_id, + "Timeout waiting for event handlers - events incomplete", + ) + return None + + sorted_events = sorted( + self.event_buffer, + key=lambda e: e.timestamp + if hasattr(e, "timestamp") and e.timestamp + else "", + ) + + self.current_batch.events = sorted_events + events_sent_count = len(sorted_events) + if sorted_events: + original_buffer = self.event_buffer + self.event_buffer = sorted_events events_sent_to_backend_status = self._send_events_to_backend() - if events_sent_to_backend_status == 500: + self.event_buffer = original_buffer + if events_sent_to_backend_status == 500 and self.trace_batch_id: self.plus_api.mark_trace_batch_as_failed( self.trace_batch_id, "Error sending events to backend" ) return None - self._finalize_backend_batch() + self._finalize_backend_batch(events_sent_count) finalized_batch = self.current_batch @@ -220,18 +290,20 @@ class TraceBatchManager: return finalized_batch - def _finalize_backend_batch(self): - """Send batch finalization to backend""" + def _finalize_backend_batch(self, events_count: int = 0): + """Send batch finalization to backend + + Args: + events_count: Number of events that were successfully sent + """ if not self.plus_api or not self.trace_batch_id: return try: - total_events = len(self.current_batch.events) if self.current_batch else 0 - payload = { "status": "completed", "duration_ms": self.calculate_duration("execution"), - "final_event_count": total_events, + "final_event_count": events_count, } response = ( diff --git a/src/crewai/events/listeners/tracing/trace_listener.py b/lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py similarity index 95% rename from src/crewai/events/listeners/tracing/trace_listener.py rename to lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py index cef3bb6ee..00818bc3a 100644 --- a/src/crewai/events/listeners/tracing/trace_listener.py +++ b/lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py @@ -1,6 +1,6 @@ import os -import uuid from typing import Any, ClassVar +import uuid from crewai.cli.authentication.token import AuthError, get_auth_token from crewai.cli.version import get_crewai_version @@ -8,6 +8,7 @@ from crewai.events.base_event_listener import BaseEventListener from crewai.events.listeners.tracing.first_time_trace_handler import ( FirstTimeTraceHandler, ) +from crewai.events.listeners.tracing.trace_batch_manager import TraceBatchManager from crewai.events.listeners.tracing.types import TraceEvent from crewai.events.listeners.tracing.utils import safe_serialize_to_dict from crewai.events.types.agent_events import ( @@ -72,8 +73,6 @@ from crewai.events.types.tool_usage_events import ( ToolUsageStartedEvent, ) -from .trace_batch_manager import TraceBatchManager - class TraceCollectionListener(BaseEventListener): """ @@ -170,14 +169,6 @@ class TraceCollectionListener(BaseEventListener): def on_flow_finished(source, event): self._handle_trace_event("flow_finished", source, event) - if self.batch_manager.batch_owner_type == "flow": - if self.first_time_handler.is_first_time: - self.first_time_handler.mark_events_collected() - self.first_time_handler.handle_execution_completion() - else: - # Normal flow finalization - self.batch_manager.finalize_batch() - @event_bus.on(FlowPlotEvent) def on_flow_plot(source, event): self._handle_action_event("flow_plot", source, event) @@ -383,10 +374,12 @@ class TraceCollectionListener(BaseEventListener): def _handle_trace_event(self, event_type: str, source: Any, event: Any): """Generic handler for context end events""" - - trace_event = self._create_trace_event(event_type, source, event) - - self.batch_manager.add_event(trace_event) + self.batch_manager.begin_event_processing() + try: + trace_event = self._create_trace_event(event_type, source, event) + self.batch_manager.add_event(trace_event) + finally: + self.batch_manager.end_event_processing() def _handle_action_event(self, event_type: str, source: Any, event: Any): """Generic handler for action events (LLM calls, tool usage)""" @@ -399,18 +392,29 @@ class TraceCollectionListener(BaseEventListener): } self.batch_manager.initialize_batch(user_context, execution_metadata) - trace_event = self._create_trace_event(event_type, source, event) - self.batch_manager.add_event(trace_event) + self.batch_manager.begin_event_processing() + try: + trace_event = self._create_trace_event(event_type, source, event) + self.batch_manager.add_event(trace_event) + finally: + self.batch_manager.end_event_processing() def _create_trace_event( self, event_type: str, source: Any, event: Any ) -> TraceEvent: """Create a trace event""" - trace_event = TraceEvent( - type=event_type, - ) + if hasattr(event, "timestamp") and event.timestamp: + trace_event = TraceEvent( + type=event_type, + timestamp=event.timestamp.isoformat(), + ) + else: + trace_event = TraceEvent( + type=event_type, + ) trace_event.event_data = self._build_event_data(event_type, event, source) + return trace_event def _build_event_data( diff --git a/src/crewai/events/listeners/tracing/types.py b/lib/crewai/src/crewai/events/listeners/tracing/types.py similarity index 100% rename from src/crewai/events/listeners/tracing/types.py rename to lib/crewai/src/crewai/events/listeners/tracing/types.py index 8603949a0..cdc2b6c26 100644 --- a/src/crewai/events/listeners/tracing/types.py +++ b/lib/crewai/src/crewai/events/listeners/tracing/types.py @@ -1,7 +1,7 @@ -import uuid from dataclasses import asdict, dataclass, field from datetime import datetime, timezone from typing import Any +import uuid @dataclass diff --git a/src/crewai/events/listeners/tracing/utils.py b/lib/crewai/src/crewai/events/listeners/tracing/utils.py similarity index 99% rename from src/crewai/events/listeners/tracing/utils.py rename to lib/crewai/src/crewai/events/listeners/tracing/utils.py index 03089994c..9c5a30a05 100644 --- a/src/crewai/events/listeners/tracing/utils.py +++ b/lib/crewai/src/crewai/events/listeners/tracing/utils.py @@ -1,15 +1,15 @@ +from datetime import datetime import getpass import hashlib import json import logging import os +from pathlib import Path import platform import re import subprocess -import uuid -from datetime import datetime -from pathlib import Path from typing import Any +import uuid import click from rich.console import Console @@ -19,6 +19,7 @@ from rich.text import Text from crewai.utilities.paths import db_storage_path from crewai.utilities.serialization import to_serializable + logger = logging.getLogger(__name__) diff --git a/src/crewai/events/types/__init__.py b/lib/crewai/src/crewai/events/types/__init__.py similarity index 100% rename from src/crewai/events/types/__init__.py rename to lib/crewai/src/crewai/events/types/__init__.py diff --git a/src/crewai/events/types/agent_events.py b/lib/crewai/src/crewai/events/types/agent_events.py similarity index 100% rename from src/crewai/events/types/agent_events.py rename to lib/crewai/src/crewai/events/types/agent_events.py diff --git a/src/crewai/events/types/crew_events.py b/lib/crewai/src/crewai/events/types/crew_events.py similarity index 99% rename from src/crewai/events/types/crew_events.py rename to lib/crewai/src/crewai/events/types/crew_events.py index 10f98847a..8fdcbfd2b 100644 --- a/src/crewai/events/types/crew_events.py +++ b/lib/crewai/src/crewai/events/types/crew_events.py @@ -2,6 +2,7 @@ from typing import TYPE_CHECKING, Any from crewai.events.base_events import BaseEvent + if TYPE_CHECKING: from crewai.crew import Crew else: diff --git a/lib/crewai/src/crewai/events/types/event_bus_types.py b/lib/crewai/src/crewai/events/types/event_bus_types.py new file mode 100644 index 000000000..8a650a731 --- /dev/null +++ b/lib/crewai/src/crewai/events/types/event_bus_types.py @@ -0,0 +1,15 @@ +"""Type definitions for event handlers.""" + +from collections.abc import Callable, Coroutine +from typing import Any, TypeAlias + +from crewai.events.base_events import BaseEvent + + +SyncHandler: TypeAlias = Callable[[Any, BaseEvent], None] +AsyncHandler: TypeAlias = Callable[[Any, BaseEvent], Coroutine[Any, Any, None]] +SyncHandlerSet: TypeAlias = frozenset[SyncHandler] +AsyncHandlerSet: TypeAlias = frozenset[AsyncHandler] + +Handler: TypeAlias = Callable[[Any, BaseEvent], Any] +ExecutionPlan: TypeAlias = list[set[Handler]] diff --git a/src/crewai/events/types/flow_events.py b/lib/crewai/src/crewai/events/types/flow_events.py similarity index 100% rename from src/crewai/events/types/flow_events.py rename to lib/crewai/src/crewai/events/types/flow_events.py diff --git a/src/crewai/events/types/knowledge_events.py b/lib/crewai/src/crewai/events/types/knowledge_events.py similarity index 100% rename from src/crewai/events/types/knowledge_events.py rename to lib/crewai/src/crewai/events/types/knowledge_events.py diff --git a/src/crewai/events/types/llm_events.py b/lib/crewai/src/crewai/events/types/llm_events.py similarity index 80% rename from src/crewai/events/types/llm_events.py rename to lib/crewai/src/crewai/events/types/llm_events.py index 32314ad4e..c6db9405d 100644 --- a/src/crewai/events/types/llm_events.py +++ b/lib/crewai/src/crewai/events/types/llm_events.py @@ -7,19 +7,23 @@ from crewai.events.base_events import BaseEvent class LLMEventBase(BaseEvent): - task_name: str | None = None - task_id: str | None = None - - agent_id: str | None = None - agent_role: str | None = None - from_task: Any | None = None from_agent: Any | None = None def __init__(self, **data): + if data.get("from_task"): + task = data["from_task"] + data["task_id"] = str(task.id) + data["task_name"] = task.name or task.description + data["from_task"] = None + + if data.get("from_agent"): + agent = data["from_agent"] + data["agent_id"] = str(agent.id) + data["agent_role"] = agent.role + data["from_agent"] = None + super().__init__(**data) - self._set_agent_params(data) - self._set_task_params(data) class LLMCallType(Enum): diff --git a/src/crewai/events/types/llm_guardrail_events.py b/lib/crewai/src/crewai/events/types/llm_guardrail_events.py similarity index 100% rename from src/crewai/events/types/llm_guardrail_events.py rename to lib/crewai/src/crewai/events/types/llm_guardrail_events.py diff --git a/src/crewai/events/types/logging_events.py b/lib/crewai/src/crewai/events/types/logging_events.py similarity index 100% rename from src/crewai/events/types/logging_events.py rename to lib/crewai/src/crewai/events/types/logging_events.py diff --git a/src/crewai/events/types/memory_events.py b/lib/crewai/src/crewai/events/types/memory_events.py similarity index 100% rename from src/crewai/events/types/memory_events.py rename to lib/crewai/src/crewai/events/types/memory_events.py diff --git a/src/crewai/events/types/reasoning_events.py b/lib/crewai/src/crewai/events/types/reasoning_events.py similarity index 100% rename from src/crewai/events/types/reasoning_events.py rename to lib/crewai/src/crewai/events/types/reasoning_events.py diff --git a/src/crewai/events/types/task_events.py b/lib/crewai/src/crewai/events/types/task_events.py similarity index 100% rename from src/crewai/events/types/task_events.py rename to lib/crewai/src/crewai/events/types/task_events.py diff --git a/src/crewai/events/types/tool_usage_events.py b/lib/crewai/src/crewai/events/types/tool_usage_events.py similarity index 87% rename from src/crewai/events/types/tool_usage_events.py rename to lib/crewai/src/crewai/events/types/tool_usage_events.py index 22fc488ab..7fe9b897f 100644 --- a/src/crewai/events/types/tool_usage_events.py +++ b/lib/crewai/src/crewai/events/types/tool_usage_events.py @@ -27,9 +27,20 @@ class ToolUsageEvent(BaseEvent): model_config = ConfigDict(arbitrary_types_allowed=True) def __init__(self, **data): + if data.get("from_task"): + task = data["from_task"] + data["task_id"] = str(task.id) + data["task_name"] = task.name or task.description + data["from_task"] = None + + if data.get("from_agent"): + agent = data["from_agent"] + data["agent_id"] = str(agent.id) + data["agent_role"] = agent.role + data["from_agent"] = None + super().__init__(**data) - self._set_agent_params(data) - self._set_task_params(data) + # Set fingerprint data from the agent if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint: self.source_fingerprint = self.agent.fingerprint.uuid_str diff --git a/src/crewai/events/utils/__init__.py b/lib/crewai/src/crewai/events/utils/__init__.py similarity index 100% rename from src/crewai/events/utils/__init__.py rename to lib/crewai/src/crewai/events/utils/__init__.py diff --git a/src/crewai/events/utils/console_formatter.py b/lib/crewai/src/crewai/events/utils/console_formatter.py similarity index 100% rename from src/crewai/events/utils/console_formatter.py rename to lib/crewai/src/crewai/events/utils/console_formatter.py diff --git a/lib/crewai/src/crewai/events/utils/handlers.py b/lib/crewai/src/crewai/events/utils/handlers.py new file mode 100644 index 000000000..bc3e76eee --- /dev/null +++ b/lib/crewai/src/crewai/events/utils/handlers.py @@ -0,0 +1,59 @@ +"""Handler utility functions for event processing.""" + +import functools +import inspect +from typing import Any + +from typing_extensions import TypeIs + +from crewai.events.base_events import BaseEvent +from crewai.events.types.event_bus_types import AsyncHandler, SyncHandler + + +def is_async_handler( + handler: Any, +) -> TypeIs[AsyncHandler]: + """Type guard to check if handler is an async handler. + + Args: + handler: The handler to check + + Returns: + True if handler is an async coroutine function + """ + try: + if inspect.iscoroutinefunction(handler) or ( + callable(handler) and inspect.iscoroutinefunction(handler.__call__) + ): + return True + except AttributeError: + return False + + if isinstance(handler, functools.partial) and inspect.iscoroutinefunction( + handler.func + ): + return True + + return False + + +def is_call_handler_safe( + handler: SyncHandler, + source: Any, + event: BaseEvent, +) -> Exception | None: + """Safely call a single handler and return any exception. + + Args: + handler: The handler function to call + source: The object that emitted the event + event: The event instance + + Returns: + Exception if handler raised one, None otherwise + """ + try: + handler(source, event) + return None + except Exception as e: + return e diff --git a/src/crewai/experimental/__init__.py b/lib/crewai/src/crewai/experimental/__init__.py similarity index 99% rename from src/crewai/experimental/__init__.py rename to lib/crewai/src/crewai/experimental/__init__.py index 8e8554dd3..09e34820d 100644 --- a/src/crewai/experimental/__init__.py +++ b/lib/crewai/src/crewai/experimental/__init__.py @@ -18,6 +18,7 @@ from crewai.experimental.evaluation import ( create_evaluation_callbacks, ) + __all__ = [ "AgentEvaluationResult", "AgentEvaluator", diff --git a/lib/crewai/src/crewai/experimental/a2a/__init__.py b/lib/crewai/src/crewai/experimental/a2a/__init__.py new file mode 100644 index 000000000..060feabef --- /dev/null +++ b/lib/crewai/src/crewai/experimental/a2a/__init__.py @@ -0,0 +1,65 @@ +"""A2A (Agent-to-Agent) Protocol adapter for CrewAI. + +This module provides integration with A2A protocol-compliant agents, +enabling CrewAI to orchestrate external agents like ServiceNow, Bedrock Agents, +Glean, and other A2A-compliant systems. + +Example: + ```python + from crewai.experimental.a2a import A2AAgentAdapter + + # Create A2A agent + servicenow_agent = A2AAgentAdapter( + agent_card_url="https://servicenow.example.com/.well-known/agent-card.json", + auth_token="your-token", + role="ServiceNow Incident Manager", + goal="Create and manage IT incidents", + backstory="Expert at incident management", + ) + + # Use in crew + crew = Crew(agents=[servicenow_agent], tasks=[task]) + ``` +""" + +from crewai.experimental.a2a.a2a_adapter import A2AAgentAdapter +from crewai.experimental.a2a.auth import ( + APIKeyAuth, + AuthScheme, + BearerTokenAuth, + HTTPBasicAuth, + HTTPDigestAuth, + OAuth2AuthorizationCode, + OAuth2ClientCredentials, + create_auth_from_agent_card, +) +from crewai.experimental.a2a.exceptions import ( + A2AAuthenticationError, + A2AConfigurationError, + A2AConnectionError, + A2AError, + A2AInputRequiredError, + A2ATaskCanceledError, + A2ATaskFailedError, +) + + +__all__ = [ + "A2AAgentAdapter", + "A2AAuthenticationError", + "A2AConfigurationError", + "A2AConnectionError", + "A2AError", + "A2AInputRequiredError", + "A2ATaskCanceledError", + "A2ATaskFailedError", + "APIKeyAuth", + # Authentication + "AuthScheme", + "BearerTokenAuth", + "HTTPBasicAuth", + "HTTPDigestAuth", + "OAuth2AuthorizationCode", + "OAuth2ClientCredentials", + "create_auth_from_agent_card", +] diff --git a/lib/crewai/src/crewai/experimental/a2a/a2a_adapter.py b/lib/crewai/src/crewai/experimental/a2a/a2a_adapter.py new file mode 100644 index 000000000..cdea7c236 --- /dev/null +++ b/lib/crewai/src/crewai/experimental/a2a/a2a_adapter.py @@ -0,0 +1,1375 @@ +"""A2A Agent Adapter implementation for CrewAI. + +This module provides the main adapter class for integrating A2A protocol-compliant +agents into CrewAI workflows. +""" + +from __future__ import annotations + +import asyncio +import json +from typing import TYPE_CHECKING, Any, Literal +import uuid + +from a2a.client import A2ACardResolver, ClientConfig, ClientFactory +from a2a.types import ( + GetTaskPushNotificationConfigParams, + Message, + Part, + PushNotificationAuthenticationInfo, + PushNotificationConfig, + Role, + TaskIdParams, + TaskPushNotificationConfig, + TaskQueryParams, + TaskState, + TextPart, + TransportProtocol, +) +import httpx +from pydantic import Field, PrivateAttr + +from crewai.agents.agent_builder.base_agent import BaseAgent +from crewai.experimental.a2a.auth import AuthScheme, BearerTokenAuth +from crewai.experimental.a2a.exceptions import ( + A2AAuthenticationError, + A2AConfigurationError, + A2AConnectionError, + A2AInputRequiredError, + A2ATaskCanceledError, + A2ATaskFailedError, +) +from crewai.tools.base_tool import BaseTool + + +if TYPE_CHECKING: + from a2a.types import AgentCard + + +class A2AAgentAdapter(BaseAgent): + """Adapter for A2A protocol-compliant agents. + + Integrates external A2A agents (ServiceNow, Bedrock, Glean, etc.) into CrewAI workflows. + Uses the official a2a-sdk for protocol compliance and multi-transport support. + + The adapter handles: + - AgentCard discovery and validation + - Message formatting and translation + - Task lifecycle management (creation, cancellation, retrieval) + - Streaming and polling execution modes with automatic selection + - Async/sync bridging + - Authentication via Bearer tokens + + Attributes: + agent_card_url: URL to the A2A AgentCard (supports .well-known/agent-card.json). + auth_token: Optional Bearer token for authentication. + timeout: Request timeout in seconds (default: 120). + preferred_transport: Preferred transport protocol (default: "JSONRPC"). + Supported: "JSONRPC", "GRPC", "HTTP+JSON". + enable_streaming: Whether to enable streaming responses (default: True). + + Example: + ```python + from crewai import Agent, Task, Crew + from crewai.experimental.a2a import A2AAgentAdapter + + servicenow_agent = A2AAgentAdapter( + agent_card_url="https://servicenow.example.com/.well-known/agent-card.json", + auth_token="your-token-here", + role="ServiceNow Incident Manager", + goal="Create and manage IT incidents", + backstory="Expert at incident management with 10 years experience", + ) + + task = Task( + description="Create a P1 incident for database outage", + expected_output="Incident ticket number and details", + agent=servicenow_agent, + ) + + crew = Crew(agents=[servicenow_agent], tasks=[task]) + result = crew.kickoff() + ``` + + Note: + Requires a2a-sdk to be installed: + ```bash + uv add 'crewai[a2a]' + ``` + """ + + agent_card_url: str = Field( + description="URL to the A2A AgentCard (supports .well-known/agent-card.json)" + ) + auth_token: str | None = Field( + default=None, + description="Bearer token for authentication (deprecated: use auth_scheme)", + ) + auth_scheme: AuthScheme | None = Field( + default=None, + description="Authentication scheme (Bearer, OAuth2, API Key, HTTP Basic/Digest)", + ) + timeout: int = Field(default=120, description="Request timeout in seconds") + preferred_transport: Literal["JSONRPC", "GRPC", "HTTP+JSON", "HTTP_JSON"] = Field( + default="JSONRPC", + description="Preferred transport protocol (JSONRPC, GRPC, HTTP+JSON)", + ) + enable_streaming: bool = Field( + default=True, description="Whether to enable streaming responses" + ) + adapted_agent: bool = Field(default=True, init=False) + function_calling_llm: Any = Field( + default=None, description="Not used for A2A agents" + ) + step_callback: Any = Field(default=None, description="Not used for A2A agents") + + _agent_card: AgentCard | None = PrivateAttr(default=None) + _a2a_sdk_available: bool = PrivateAttr(default=False) + _headers: dict[str, str] = PrivateAttr(default_factory=dict) + _transport_protocol: TransportProtocol | None = PrivateAttr(default=None) + _base_url: str = PrivateAttr(default="") + _current_task_id: str | None = PrivateAttr(default=None) + + def __init__(self, **data): + """Initialize A2A adapter. + + Raises: + ImportError: If a2a-sdk is not installed. + """ + super().__init__(**data) + + try: + import a2a # noqa: F401 + + self._a2a_sdk_available = True + except ImportError as e: + msg = ( + "A2A SDK not installed. Install with: uv add 'crewai[a2a]' " + "or uv add 'a2a-sdk>=0.1.0'" + ) + raise ImportError(msg) from e + + def create_agent_executor(self, tools: list[BaseTool] | None = None) -> None: + """Initialize the A2A agent configuration and fetch AgentCard. + + This method: + 1. Sets up authentication headers + 2. Maps transport protocol + 3. Discovers the AgentCard from the provided URL + 4. Stores configuration for later use + + Args: + tools: Optional list of tools (not used for A2A agents as they define their own skills). + + Raises: + A2AConfigurationError: If a2a-sdk is not installed. + A2AConnectionError: If AgentCard discovery or client initialization fails. + """ + if not self._a2a_sdk_available: + msg = "A2A SDK not available. Install with: pip install 'crewai[a2a]'" + raise ImportError(msg) + + # Handle backward compatibility: auth_token -> auth_scheme + if self.auth_token and not self.auth_scheme: + self.auth_scheme = BearerTokenAuth(token=self.auth_token) + + transport_map = { + "JSONRPC": TransportProtocol.jsonrpc, + "GRPC": TransportProtocol.grpc, + "HTTP+JSON": TransportProtocol.http_json, + "HTTP_JSON": TransportProtocol.http_json, + } + self._transport_protocol = transport_map.get( + self.preferred_transport.upper(), TransportProtocol.http_json + ) + + agent_card_url = self.agent_card_url + + if "/.well-known/" in agent_card_url: + base_url, path_part = agent_card_url.rsplit("/.well-known/", 1) + agent_card_path = f"/.well-known/{path_part}" + else: + base_url = agent_card_url + agent_card_path = "/.well-known/agent-card.json" + + self._base_url = base_url + + async def _fetch_agent_card(): + async with httpx.AsyncClient( + timeout=self.timeout, headers=self._headers + ) as httpx_client: + # Configure authentication on the client + if self.auth_scheme: + self.auth_scheme.configure_client(httpx_client) + # Apply auth to headers + self._headers = await self.auth_scheme.apply_auth( + httpx_client, self._headers + ) + + resolver = A2ACardResolver( + httpx_client=httpx_client, + base_url=base_url, + agent_card_path=agent_card_path, + ) + return await resolver.get_agent_card() + + self._agent_card = asyncio.run(_fetch_agent_card()) + + self._logger.log( + "info", + f"A2A agent initialized: {self._agent_card.name} v{self._agent_card.version}", + ) + self._logger.log( + "info", + f"Skills available: {len(self._agent_card.skills)} | " + f"Streaming: {self._agent_card.capabilities.streaming}", + ) + + self._check_io_mode_compatibility() + self._check_state_transition_history() + + def execute_task( + self, + task: Any, + context: str | None = None, + tools: list[BaseTool] | None = None, + ) -> str: + """Execute a CrewAI task via A2A protocol. + + Converts the CrewAI task to an A2A message, sends it to the agent, + and aggregates the response(s) into a string result. + + The execution flow: + 1. Build A2A message from task description and context + 2. Send message to A2A agent (streaming or blocking) + 3. Process responses/events + 4. Extract final result from task history or artifacts + 5. Handle error states (input_required, failed, etc.) + + Args: + task: CrewAI Task object containing description and expected output. + context: Optional context string from previous tasks. + tools: Optional tools (not used - A2A agents define their own skills). + + Returns: + String result from the A2A agent execution. + + Raises: + A2ATaskFailedError: If the A2A agent task fails or is rejected. + A2AInputRequiredError: If the A2A agent requires additional input. + A2AAuthenticationError: If the A2A agent requires authentication. + A2ATaskCanceledError: If the A2A task is canceled. + A2AConnectionError: If connection to the A2A agent fails. + """ + if not self._agent_card: + self.create_agent_executor(tools) + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + return loop.run_until_complete(self._execute_async(task, context, tools)) + finally: + loop.close() + + async def _execute_async( + self, + task: Any, + context: str | None, + tools: list[BaseTool] | None, + ) -> str: + """Async implementation of task execution via A2A protocol. + + Automatically selects between streaming and polling modes based on: + - Agent capabilities (AgentCard.capabilities.streaming) + - Configuration (enable_streaming) + - Fallback on streaming errors + + Args: + task: CrewAI Task object. + context: Optional context from previous tasks. + tools: Optional tools (not used). + + Returns: + String result from A2A agent. + + Raises: + A2ATaskFailedError: If the A2A agent task fails or is rejected. + A2AInputRequiredError: If the A2A agent requires additional input. + A2AAuthenticationError: If the A2A agent requires authentication. + A2ATaskCanceledError: If the A2A task is canceled. + A2AConnectionError: If connection to the A2A agent fails. + """ + streaming_supported = ( + self._agent_card.capabilities.streaming + if self._agent_card + and self._agent_card.capabilities + and self._agent_card.capabilities.streaming is not None + else True + ) + + use_streaming = self.enable_streaming and streaming_supported + + if not streaming_supported: + self._logger.log( + "info", "Agent does not support streaming, using polling mode" + ) + + if use_streaming: + try: + return await self._execute_streaming(task, context) + except Exception as e: + self._logger.log( + "warning", f"Streaming failed ({e}), falling back to polling mode" + ) + return await self._execute_polling(task, context) + + return await self._execute_polling(task, context) + + def _extract_artifacts_with_metadata(self, artifacts: list[Any]) -> str: + """Extract artifacts with full metadata preservation. + + Args: + artifacts: List of A2A Artifact objects. + + Returns: + JSON-formatted string containing artifact data with metadata. + """ + artifacts_data = [] + + for artifact in artifacts: + artifact_content: dict[str, Any] = { + "id": artifact.artifact_id, + "name": artifact.name, + "description": artifact.description, + "parts": [], + } + + if artifact.metadata: + artifact_content["metadata"] = artifact.metadata + + for part in artifact.parts: + if part.root.kind == "text": + artifact_content["parts"].append( + { + "type": "text", + "content": part.root.text, + } + ) + elif part.root.kind == "file": + part_data: dict[str, str] = { + "type": "file", + "uri": part.root.file.uri, + } + if part.root.file.mime_type: + part_data["media_type"] = part.root.file.mime_type + artifact_content["parts"].append(part_data) + elif part.root.kind == "data": + artifact_content["parts"].append( + { + "type": "data", + "data": part.root.data, + } + ) + + artifacts_data.append(artifact_content) + + return f"\n\nArtifacts:\n{json.dumps(artifacts_data, indent=2)}" + + async def _execute_streaming( + self, + task: Any, + context: str | None, + ) -> str: + """Execute task using streaming mode with automatic reconnection. + + This method implements automatic reconnection on network failures using + the A2A protocol's resubscribe functionality. If the connection drops + mid-stream, it will attempt to reconnect up to 3 times with exponential backoff. + + Args: + task: CrewAI Task object. + context: Optional context from previous tasks. + + Returns: + String result from A2A agent. + + Raises: + A2ATaskFailedError: If the A2A agent task fails or is rejected. + A2AInputRequiredError: If the A2A agent requires additional input. + A2AAuthenticationError: If the A2A agent requires authentication. + A2ATaskCanceledError: If the A2A task is canceled. + A2AConnectionError: If connection to the A2A agent fails after all retries. + """ + max_retries = 3 + saved_task_id: str | None = None + + for attempt in range(max_retries): + try: + return await self._execute_streaming_attempt( + task, context, saved_task_id + ) + except ( + httpx.TimeoutException, + httpx.NetworkError, + httpx.RemoteProtocolError, + ) as e: + if saved_task_id is None: + saved_task_id = self._current_task_id + + if attempt < max_retries - 1 and saved_task_id: + backoff_time = 2**attempt + self._logger.log( + "warning", + f"Connection lost ({type(e).__name__}), retrying in {backoff_time}s " + f"(attempt {attempt + 1}/{max_retries})...", + ) + await asyncio.sleep(backoff_time) + continue + + error_msg = f"Connection failed after {max_retries} attempts: {e}" + self._logger.log("error", error_msg) + raise A2AConnectionError(error_msg) from e + except ( + A2ATaskFailedError, + A2AInputRequiredError, + A2AAuthenticationError, + A2ATaskCanceledError, + ): + raise + + msg = f"Streaming execution failed after {max_retries} attempts" + raise A2AConnectionError(msg) + + async def _execute_streaming_attempt( + self, + task: Any, + context: str | None, + resubscribe_task_id: str | None = None, + ) -> str: + """Single attempt at streaming execution, with optional resubscription. + + Args: + task: CrewAI Task object. + context: Optional context from previous tasks. + resubscribe_task_id: Optional task ID to resubscribe to (for reconnection). + + Returns: + String result from A2A agent. + + Raises: + A2ATaskFailedError: If the A2A agent task fails or is rejected. + A2AInputRequiredError: If the A2A agent requires additional input. + A2AAuthenticationError: If the A2A agent requires authentication. + A2ATaskCanceledError: If the A2A task is canceled. + httpx.TimeoutException, httpx.NetworkError: If connection fails. + """ + content_parts = [] + + if context: + content_parts.append(f"Context:\n{context}\n\n") + + content_parts.append(f"Task: {task.description}\n") + content_parts.append(f"Expected Output: {task.expected_output}") + + message_text = "".join(content_parts) + + if not self._agent_card: + msg = "Agent card not initialized" + raise A2AConfigurationError(msg) + + message = Message( + role=Role.user, + message_id=str(uuid.uuid4()), + parts=[Part(root=TextPart(text=message_text))], + ) + + self._logger.log( + "info", f"Sending task to A2A agent (streaming): {self._agent_card.name}" + ) + + if not self._transport_protocol: + msg = "Transport protocol not configured" + raise A2AConfigurationError(msg) + + async with httpx.AsyncClient( + timeout=self.timeout, headers=self._headers + ) as httpx_client: + config = ClientConfig( + httpx_client=httpx_client, + supported_transports=[str(self._transport_protocol.value)], + streaming=self.enable_streaming, + ) + + factory = ClientFactory(config) + client = factory.create(self._agent_card) + + result_parts = [] + + if resubscribe_task_id: + self._logger.log( + "info", f"Resubscribing to task: {resubscribe_task_id}" + ) + params = TaskIdParams(id=resubscribe_task_id) + event_stream = client.resubscribe(params) + else: + # send_message returns Message | tuple, so we can't type-narrow event_stream + event_stream = client.send_message(message) # type: ignore[assignment] + + async for event in event_stream: + if isinstance(event, Message): + self._logger.log("debug", "Received direct message response") + for part in event.parts: + if part.root.kind == "text": + result_parts.append(part.root.text) + + elif isinstance(event, tuple): + a2a_task, update = event + + if a2a_task.id and not self._current_task_id: + self._current_task_id = a2a_task.id + self._logger.log( + "debug", f"Tracking task ID: {self._current_task_id}" + ) + + self._logger.log( + "debug", + f"Task state: {a2a_task.status.state} | " + f"Update: {type(update).__name__ if update else 'None'}", + ) + + if a2a_task.status.state == TaskState.completed: + if a2a_task.history: + for history_msg in reversed(a2a_task.history): + if history_msg.role == Role.agent: + for part in history_msg.parts: + if part.root.kind == "text": + result_parts.append(part.root.text) + break + + if a2a_task.artifacts: + artifacts_text = self._extract_artifacts_with_metadata( + a2a_task.artifacts + ) + result_parts.append(artifacts_text) + + self._logger.log("info", "Task completed successfully") + break + + if a2a_task.status.state in [ + TaskState.failed, + TaskState.rejected, + ]: + error_msg = "Task failed without error message" + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + error_msg = first_part.root.text + self._logger.log("error", f"Task failed: {error_msg}") + raise A2ATaskFailedError(error_msg) + + if a2a_task.status.state == TaskState.input_required: + error_msg = "Additional input required" + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + error_msg = first_part.root.text + self._logger.log("warning", f"Task requires input: {error_msg}") + raise A2AInputRequiredError(error_msg) + + if a2a_task.status.state == TaskState.auth_required: + error_msg = "Authentication required to continue" + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + error_msg = first_part.root.text + self._logger.log( + "error", f"Task requires authentication: {error_msg}" + ) + raise A2AAuthenticationError(error_msg) + + if a2a_task.status.state == TaskState.canceled: + error_msg = "Task was canceled" + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + error_msg = first_part.root.text + self._logger.log("warning", f"Task canceled: {error_msg}") + raise A2ATaskCanceledError(error_msg) + + if a2a_task.status.state == TaskState.unknown: + self._logger.log( + "warning", + "Task in unknown state, continuing to wait for state change...", + ) + + result = ( + "\n".join(result_parts) + if result_parts + else "No response from A2A agent" + ) + self._logger.log( + "info", f"A2A execution complete. Result length: {len(result)} chars" + ) + + self._current_task_id = None + + return result + + async def _execute_polling( + self, + task: Any, + context: str | None, + ) -> str: + """Execute task using polling mode. + + This method sends the initial message to create a task, then polls + the task status using get_task() until completion. + + Args: + task: CrewAI Task object. + context: Optional context from previous tasks. + + Returns: + String result from A2A agent. + + Raises: + A2ATaskFailedError: If the A2A agent task fails or is rejected. + A2AInputRequiredError: If the A2A agent requires additional input. + A2AAuthenticationError: If the A2A agent requires authentication. + A2ATaskCanceledError: If the A2A task is canceled. + A2AConnectionError: If connection to the A2A agent fails. + """ + content_parts = [] + + if context: + content_parts.append(f"Context:\n{context}\n\n") + + content_parts.append(f"Task: {task.description}\n") + content_parts.append(f"Expected Output: {task.expected_output}") + + message_text = "".join(content_parts) + + message = Message( + role=Role.user, + message_id=str(uuid.uuid4()), + parts=[Part(root=TextPart(text=message_text))], + ) + + if not self._agent_card: + msg = "Agent card not initialized" + raise A2AConfigurationError(msg) + + if not self._transport_protocol: + msg = "Transport protocol not configured" + raise A2AConfigurationError(msg) + + self._logger.log( + "info", f"Sending task to A2A agent (polling): {self._agent_card.name}" + ) + + async with httpx.AsyncClient( + timeout=self.timeout, headers=self._headers + ) as httpx_client: + config = ClientConfig( + httpx_client=httpx_client, + supported_transports=[str(self._transport_protocol.value)], + streaming=False, + ) + + factory = ClientFactory(config) + client = factory.create(self._agent_card) + + task_id = None + + async for event in client.send_message(message): + if isinstance(event, tuple): + a2a_task, _ = event + if a2a_task.id: + task_id = a2a_task.id + self._current_task_id = task_id + self._logger.log("info", f"Task created with ID: {task_id}") + break + + if not task_id: + msg = "Failed to obtain task ID from A2A agent" + self._logger.log("error", msg) + raise A2AConnectionError(msg) + + self._logger.log("debug", "Starting polling loop") + poll_interval = 2 + max_polls = self.timeout // poll_interval + poll_count = 0 + + while poll_count < max_polls: + poll_count += 1 + await asyncio.sleep(poll_interval) + + params = TaskQueryParams(id=task_id) + a2a_task = await client.get_task(params) + + self._logger.log( + "debug", f"Poll {poll_count}: Task state = {a2a_task.status.state}" + ) + + if a2a_task.status.state == TaskState.completed: + self._logger.log("info", "Task completed successfully") + + result_parts = [] + if a2a_task.history: + for history_msg in reversed(a2a_task.history): + if history_msg.role == Role.agent: + for part in history_msg.parts: + if part.root.kind == "text": + result_parts.append(part.root.text) + break + + if a2a_task.artifacts: + artifacts_text = self._extract_artifacts_with_metadata( + a2a_task.artifacts + ) + result_parts.append(artifacts_text) + + result = ( + "\n".join(result_parts) + if result_parts + else "No response from A2A agent" + ) + self._logger.log( + "info", + f"A2A execution complete. Result length: {len(result)} chars", + ) + + self._current_task_id = None + + return result + + if a2a_task.status.state in [TaskState.failed, TaskState.rejected]: + error_msg = "Task failed without error message" + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + error_msg = first_part.root.text + self._logger.log("error", f"Task failed: {error_msg}") + self._current_task_id = None + raise A2ATaskFailedError(error_msg) + + if a2a_task.status.state == TaskState.input_required: + error_msg = "Additional input required" + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + error_msg = first_part.root.text + self._logger.log("warning", f"Task requires input: {error_msg}") + self._current_task_id = None + raise A2AInputRequiredError(error_msg) + + if a2a_task.status.state == TaskState.auth_required: + error_msg = "Authentication required to continue" + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + error_msg = first_part.root.text + self._logger.log( + "error", f"Task requires authentication: {error_msg}" + ) + self._current_task_id = None + raise A2AAuthenticationError(error_msg) + + if a2a_task.status.state == TaskState.canceled: + error_msg = "Task was canceled" + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + error_msg = first_part.root.text + self._logger.log("warning", f"Task canceled: {error_msg}") + self._current_task_id = None + raise A2ATaskCanceledError(error_msg) + + if a2a_task.status.state == TaskState.unknown: + self._logger.log( + "warning", + f"Task in unknown state, continuing to poll (attempt {poll_count}/{max_polls})...", + ) + + msg = f"Task polling timeout after {self.timeout} seconds" + self._logger.log("error", msg) + self._current_task_id = None + raise A2AConnectionError(msg) + + def cancel_task(self, task_id: str | None = None) -> bool: + """Cancel a running A2A task. + + Args: + task_id: The ID of the task to cancel. If None, cancels the currently + executing task (if any). + + Returns: + True if task was successfully canceled, False otherwise. + + Raises: + A2AConnectionError: If connection to agent fails. + A2AConfigurationError: If agent card is not initialized. + + Example: + ```python + adapter.cancel_task() + adapter.cancel_task("task-123") + ``` + """ + if not self._agent_card: + msg = "Agent card not initialized. Call create_agent_executor() first." + raise A2AConfigurationError(msg) + + cancel_id = task_id or self._current_task_id + + if not cancel_id: + self._logger.log("warning", "No task ID to cancel") + return False + + self._logger.log("info", f"Canceling task: {cancel_id}") + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + return loop.run_until_complete(self._cancel_task_async(cancel_id)) + finally: + loop.close() + + async def _cancel_task_async(self, task_id: str) -> bool: + """Async implementation of task cancellation. + + Args: + task_id: The ID of the task to cancel. + + Returns: + True if cancellation was successful, False otherwise. + + Raises: + A2AConnectionError: If connection to agent fails. + """ + try: + async with httpx.AsyncClient( + timeout=self.timeout, headers=self._headers + ) as httpx_client: + if not self._agent_card: + msg = "Agent card not initialized" + raise A2AConfigurationError(msg) + + if not self._transport_protocol: + msg = "Transport protocol not configured" + raise A2AConfigurationError(msg) + + config = ClientConfig( + httpx_client=httpx_client, + supported_transports=[str(self._transport_protocol.value)], + streaming=self.enable_streaming, + ) + + factory = ClientFactory(config) + client = factory.create(self._agent_card) + + params = TaskIdParams(id=task_id) + await client.cancel_task(params) + + self._logger.log("info", f"Task {task_id} canceled successfully") + + if self._current_task_id == task_id: + self._current_task_id = None + + return True + + except Exception as e: + self._logger.log("error", f"Failed to cancel task {task_id}: {e}") + raise A2AConnectionError(f"Failed to cancel task: {e}") from e + + def get_task(self, task_id: str) -> dict[str, Any]: + """Retrieve the current status and details of an A2A task. + + This method allows polling for task status, which is useful for: + - Checking task progress after disconnection + - Retrieving final results without streaming + - Monitoring long-running tasks + + Args: + task_id: The ID of the task to retrieve. + + Returns: + Dictionary containing task information with keys: + - task_id: The task identifier + - state: Current task state (e.g., "completed", "working", "failed") + - result: Task result (if completed) + - error: Error message (if failed) + - history: Message history + - artifacts: Task artifacts + + Raises: + A2AConnectionError: If connection to agent fails. + A2AConfigurationError: If agent card is not initialized. + + Example: + ```python + task_info = adapter.get_task("task-123") + print(f"State: {task_info['state']}") + if task_info["state"] == "completed": + print(f"Result: {task_info['result']}") + ``` + """ + if not self._agent_card: + msg = "Agent card not initialized. Call create_agent_executor() first." + raise A2AConfigurationError(msg) + + self._logger.log("info", f"Retrieving task: {task_id}") + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + return loop.run_until_complete(self._get_task_async(task_id)) + finally: + loop.close() + + async def _get_task_async(self, task_id: str) -> dict[str, Any]: + """Async implementation of task retrieval. + + Args: + task_id: The ID of the task to retrieve. + + Returns: + Dictionary with task information. + + Raises: + A2AConnectionError: If connection to agent fails. + """ + try: + async with httpx.AsyncClient( + timeout=self.timeout, headers=self._headers + ) as httpx_client: + if not self._agent_card: + msg = "Agent card not initialized" + raise A2AConfigurationError(msg) + + if not self._transport_protocol: + msg = "Transport protocol not configured" + raise A2AConfigurationError(msg) + + config = ClientConfig( + httpx_client=httpx_client, + supported_transports=[str(self._transport_protocol.value)], + streaming=self.enable_streaming, + ) + + factory = ClientFactory(config) + client = factory.create(self._agent_card) + + params = TaskQueryParams(id=task_id) + a2a_task = await client.get_task(params) + + task_info: dict[str, Any] = { + "task_id": a2a_task.id, + "state": str(a2a_task.status.state), + "result": None, + "error": None, + "history": [], + "artifacts": [], + } + + if a2a_task.history: + for history_msg in reversed(a2a_task.history): + if history_msg.role == Role.agent: + text_parts = [] + for part in history_msg.parts: + if part.root.kind == "text": + text_parts.append(part.root.text) + if text_parts: + task_info["result"] = "\n".join(text_parts) + break + + task_info["history"] = [ + { + "role": str(history_msg.role), + "content": [ + part.root.text + for part in history_msg.parts + if part.root.kind == "text" + ], + } + for history_msg in a2a_task.history + ] + + if a2a_task.artifacts: + artifact_list: list[dict[str, Any]] = [] + for artifact in a2a_task.artifacts: + artifact_data = { + "id": artifact.artifact_id, + "name": artifact.name, + "description": artifact.description, + "parts": [ + part.root.text + for part in artifact.parts + if part.root.kind == "text" + ], + } + artifact_list.append(artifact_data) + task_info["artifacts"] = artifact_list + + if a2a_task.status.message and a2a_task.status.message.parts: + first_part = a2a_task.status.message.parts[0] + if first_part.root.kind == "text": + task_info["error"] = first_part.root.text + + self._logger.log( + "info", f"Retrieved task {task_id}: state={task_info['state']}" + ) + return task_info + + except Exception as e: + self._logger.log("error", f"Failed to retrieve task {task_id}: {e}") + raise A2AConnectionError(f"Failed to retrieve task: {e}") from e + + def set_task_callback( + self, + task_id: str, + webhook_url: str, + auth_token: str | None = None, + ) -> dict[str, Any]: + """Configure push notification webhook for a task. + + This method allows you to set up a webhook that the A2A agent will call + when the task state changes, instead of requiring streaming or polling. + + Note: You must provide your own webhook server to receive notifications. + This method only configures the A2A agent to send notifications to your URL. + + Args: + task_id: The ID of the task to configure notifications for. + webhook_url: The URL where the A2A agent should send notifications. + auth_token: Optional Bearer token for authenticating webhook requests. + + Returns: + Dictionary containing the push notification configuration with keys: + - config_id: The configuration identifier + - url: The webhook URL + - task_id: The associated task ID + + Raises: + A2AConnectionError: If connection to agent fails. + A2AConfigurationError: If agent card is not initialized. + + Example: + ```python + config = adapter.set_task_callback( + task_id="task-123", + webhook_url="https://myapp.com/webhooks/a2a", + auth_token="my-webhook-secret", + ) + print(f"Webhook configured: {config['config_id']}") + ``` + """ + if not self._agent_card: + msg = "Agent card not initialized. Call create_agent_executor() first." + raise A2AConfigurationError(msg) + + self._logger.log("info", f"Configuring webhook for task: {task_id}") + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + return loop.run_until_complete( + self._set_task_callback_async(task_id, webhook_url, auth_token) + ) + finally: + loop.close() + + async def _set_task_callback_async( + self, + task_id: str, + webhook_url: str, + auth_token: str | None, + ) -> dict[str, Any]: + """Async implementation of webhook configuration. + + Args: + task_id: The ID of the task. + webhook_url: The webhook URL. + auth_token: Optional auth token for webhook. + + Returns: + Dictionary with configuration information. + + Raises: + A2AConnectionError: If connection to agent fails. + """ + try: + async with httpx.AsyncClient( + timeout=self.timeout, headers=self._headers + ) as httpx_client: + if not self._agent_card: + msg = "Agent card not initialized" + raise A2AConfigurationError(msg) + + if not self._transport_protocol: + msg = "Transport protocol not configured" + raise A2AConfigurationError(msg) + + config = ClientConfig( + httpx_client=httpx_client, + supported_transports=[str(self._transport_protocol.value)], + streaming=self.enable_streaming, + ) + + factory = ClientFactory(config) + client = factory.create(self._agent_card) + + push_config = PushNotificationConfig( + url=webhook_url, + token=auth_token, + authentication=( + PushNotificationAuthenticationInfo( + schemes=["bearer"], + credentials={"token": auth_token} if auth_token else {}, + ) + if auth_token + else None + ), + ) + + callback_config = TaskPushNotificationConfig( + task_id=task_id, + push_notification_config=push_config, + ) + response = await client.set_task_callback(callback_config) + + result = { + "config_id": response.task_id, + "url": response.push_notification_config.url + if response.push_notification_config + else None, + "task_id": task_id, + } + + self._logger.log( + "info", f"Webhook configured successfully for task {task_id}" + ) + return result + + except Exception as e: + self._logger.log( + "error", f"Failed to configure webhook for task {task_id}: {e}" + ) + raise A2AConnectionError(f"Failed to configure webhook: {e}") from e + + def get_task_callback(self, task_id: str) -> dict[str, Any] | None: + """Retrieve push notification webhook configuration for a task. + + Args: + task_id: The ID of the task to retrieve webhook config for. + + Returns: + Dictionary containing the webhook configuration, or None if not configured. + Dictionary keys: + - config_id: The configuration identifier + - url: The webhook URL + - task_id: The associated task ID + + Raises: + A2AConnectionError: If connection to agent fails. + A2AConfigurationError: If agent card is not initialized. + + Example: + ```python + config = adapter.get_task_callback("task-123") + if config: + print(f"Webhook URL: {config['url']}") + else: + print("No webhook configured") + ``` + """ + if not self._agent_card: + msg = "Agent card not initialized. Call create_agent_executor() first." + raise A2AConfigurationError(msg) + + self._logger.log("info", f"Retrieving webhook config for task: {task_id}") + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + return loop.run_until_complete(self._get_task_callback_async(task_id)) + finally: + loop.close() + + async def _get_task_callback_async(self, task_id: str) -> dict[str, Any] | None: + """Async implementation of webhook configuration retrieval. + + Args: + task_id: The ID of the task. + + Returns: + Dictionary with configuration or None. + + Raises: + A2AConnectionError: If connection to agent fails. + """ + try: + async with httpx.AsyncClient( + timeout=self.timeout, headers=self._headers + ) as httpx_client: + if not self._agent_card: + msg = "Agent card not initialized" + raise A2AConfigurationError(msg) + + if not self._transport_protocol: + msg = "Transport protocol not configured" + raise A2AConfigurationError(msg) + + config = ClientConfig( + httpx_client=httpx_client, + supported_transports=[str(self._transport_protocol.value)], + streaming=self.enable_streaming, + ) + + factory = ClientFactory(config) + client = factory.create(self._agent_card) + + params = GetTaskPushNotificationConfigParams(id=task_id) + response = await client.get_task_callback(params) + + if not response.push_notification_config: + self._logger.log( + "info", f"No webhook configured for task {task_id}" + ) + return None + + result = { + "config_id": response.task_id, + "url": response.push_notification_config.url, + "task_id": task_id, + } + + self._logger.log("info", f"Retrieved webhook config for task {task_id}") + return result + + except Exception as e: + self._logger.log( + "error", f"Failed to retrieve webhook config for task {task_id}: {e}" + ) + raise A2AConnectionError(f"Failed to retrieve webhook config: {e}") from e + + def delete_task_callback(self, task_id: str, config_id: str | None = None) -> bool: + """Delete push notification webhook configuration for a task. + + Args: + task_id: The ID of the task to delete webhook config for. + config_id: Optional configuration ID. If not provided, deletes all + webhook configurations for the task. + + Returns: + True if deletion was successful, False otherwise. + + Raises: + A2AConnectionError: If connection to agent fails. + A2AConfigurationError: If agent card is not initialized. + + Example: + ```python + success = adapter.delete_task_callback("task-123") + if success: + print("Webhook configuration deleted") + ``` + """ + if not self._agent_card: + msg = "Agent card not initialized. Call create_agent_executor() first." + raise A2AConfigurationError(msg) + + self._logger.log("info", f"Deleting webhook config for task: {task_id}") + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + return loop.run_until_complete( + self._delete_task_callback_async(task_id, config_id) + ) + finally: + loop.close() + + async def _delete_task_callback_async( + self, + task_id: str, + config_id: str | None, + ) -> bool: + """Async implementation of webhook configuration deletion. + + Args: + task_id: The ID of the task. + config_id: Optional configuration ID. + + Returns: + True if successful, False otherwise. + + Raises: + A2AConnectionError: If connection to agent fails. + """ + # Note: delete_task_callback is not yet available in current a2a-sdk versions + # This method is provided for future compatibility + msg = "delete_task_callback is not yet supported in current a2a-sdk version" + self._logger.log("warning", msg) + raise NotImplementedError(msg) + + def _check_io_mode_compatibility(self) -> None: + """Check input/output mode compatibility and log warnings. + + Validates that the A2A agent supports text-based input/output, + which is the only mode currently supported by this adapter. + """ + if not self._agent_card: + return + + if "text" not in self._agent_card.default_input_modes: + self._logger.log( + "warning", + f"Agent prefers {self._agent_card.default_input_modes} input modes, " + "but CrewAI only supports 'text'. Communication may be limited or fail.", + ) + + if "text" not in self._agent_card.default_output_modes: + self._logger.log( + "warning", + f"Agent prefers {self._agent_card.default_output_modes} output modes, " + "but CrewAI only supports 'text'. Response parsing may be limited.", + ) + + def _check_state_transition_history(self) -> None: + """Check if agent supports state transition history. + + Logs whether the agent tracks full history of task state transitions, + which can be useful for debugging, monitoring, and auditing. + """ + if not self._agent_card: + return + + if self._agent_card.capabilities.state_transition_history: + self._logger.log( + "debug", "Agent supports state transition history tracking" + ) + + def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]: + """Get delegation tools for A2A agents. + + A2A agents don't support CrewAI-style delegation as they manage + their own agent interactions via the A2A protocol. + + Args: + agents: List of agents in the crew. + + Returns: + Empty list (A2A agents handle their own delegation). + """ + return [] + + def get_platform_tools(self) -> list[BaseTool]: + """Get platform-specific tools for A2A agents. + + Currently, no platform-specific tools are provided for A2A agents. + + Returns: + Empty list (no platform-specific tools for A2A agents). + """ + return [] diff --git a/lib/crewai/src/crewai/experimental/a2a/auth.py b/lib/crewai/src/crewai/experimental/a2a/auth.py new file mode 100644 index 000000000..7e710c324 --- /dev/null +++ b/lib/crewai/src/crewai/experimental/a2a/auth.py @@ -0,0 +1,424 @@ +"""Authentication schemes for A2A protocol agents. + +This module provides support for various authentication methods: +- Bearer tokens (existing) +- OAuth2 (Client Credentials, Authorization Code) +- API Keys (header, query, cookie) +- HTTP Basic authentication +- HTTP Digest authentication +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +import base64 +from collections.abc import Awaitable, Callable +from typing import TYPE_CHECKING, Any, Literal + +import httpx +from pydantic import BaseModel, Field + + +if TYPE_CHECKING: + from a2a.types import AgentCard + + +class AuthScheme(ABC, BaseModel): + """Base class for authentication schemes.""" + + @abstractmethod + async def apply_auth( + self, client: httpx.AsyncClient, headers: dict[str, str] + ) -> dict[str, str]: + """Apply authentication to request headers. + + Args: + client: HTTP client for making auth requests. + headers: Current request headers. + + Returns: + Updated headers with authentication applied. + """ + ... + + @abstractmethod + def configure_client(self, client: httpx.AsyncClient) -> None: + """Configure the HTTP client for this auth scheme. + + Args: + client: HTTP client to configure. + """ + ... + + +class BearerTokenAuth(AuthScheme): + """Bearer token authentication (Authorization: Bearer ).""" + + token: str = Field(description="Bearer token") + + async def apply_auth( + self, client: httpx.AsyncClient, headers: dict[str, str] + ) -> dict[str, str]: + """Apply Bearer token to Authorization header.""" + headers["Authorization"] = f"Bearer {self.token}" + return headers + + def configure_client(self, client: httpx.AsyncClient) -> None: + """No client configuration needed for Bearer tokens.""" + + +class HTTPBasicAuth(AuthScheme): + """HTTP Basic authentication.""" + + username: str = Field(description="Username") + password: str = Field(description="Password") + + async def apply_auth( + self, client: httpx.AsyncClient, headers: dict[str, str] + ) -> dict[str, str]: + """Apply HTTP Basic authentication.""" + credentials = f"{self.username}:{self.password}" + encoded = base64.b64encode(credentials.encode()).decode() + headers["Authorization"] = f"Basic {encoded}" + return headers + + def configure_client(self, client: httpx.AsyncClient) -> None: + """No client configuration needed for Basic auth.""" + + +class HTTPDigestAuth(AuthScheme): + """HTTP Digest authentication. + + Note: Uses httpx-auth library for proper digest implementation. + """ + + username: str = Field(description="Username") + password: str = Field(description="Password") + + async def apply_auth( + self, client: httpx.AsyncClient, headers: dict[str, str] + ) -> dict[str, str]: + """Digest auth is handled by httpx auth flow, not headers.""" + return headers + + def configure_client(self, client: httpx.AsyncClient) -> None: + """Configure client with Digest auth.""" + try: + from httpx_auth import DigestAuth # type: ignore[import-not-found] + + client.auth = DigestAuth(self.username, self.password) # type: ignore[import-not-found] + except ImportError as e: + msg = "httpx-auth required for Digest authentication. Install with: pip install httpx-auth" + raise ImportError(msg) from e + + +class APIKeyAuth(AuthScheme): + """API Key authentication (header, query, or cookie).""" + + api_key: str = Field(description="API key value") + location: Literal["header", "query", "cookie"] = Field( + default="header", description="Where to send the API key" + ) + name: str = Field(default="X-API-Key", description="Parameter name for the API key") + + async def apply_auth( + self, client: httpx.AsyncClient, headers: dict[str, str] + ) -> dict[str, str]: + """Apply API key authentication.""" + if self.location == "header": + headers[self.name] = self.api_key + elif self.location == "cookie": + headers["Cookie"] = f"{self.name}={self.api_key}" + # Query params are handled in configure_client via event hooks + return headers + + def configure_client(self, client: httpx.AsyncClient) -> None: + """Configure client for query param API keys.""" + if self.location == "query": + # Add API key to all requests via event hook + async def add_api_key_param(request: httpx.Request) -> None: + url = httpx.URL(request.url) + request.url = url.copy_add_param(self.name, self.api_key) + + client.event_hooks["request"].append(add_api_key_param) + + +class OAuth2ClientCredentials(AuthScheme): + """OAuth2 Client Credentials flow authentication.""" + + token_url: str = Field(description="OAuth2 token endpoint") + client_id: str = Field(description="OAuth2 client ID") + client_secret: str = Field(description="OAuth2 client secret") + scopes: list[str] = Field( + default_factory=list, description="Required OAuth2 scopes" + ) + + _access_token: str | None = None + _token_expires_at: float | None = None + + async def apply_auth( + self, client: httpx.AsyncClient, headers: dict[str, str] + ) -> dict[str, str]: + """Apply OAuth2 access token to Authorization header.""" + # Get or refresh token if needed + import time + + if ( + self._access_token is None + or self._token_expires_at is None + or time.time() >= self._token_expires_at + ): + await self._fetch_token(client) + + if self._access_token: + headers["Authorization"] = f"Bearer {self._access_token}" + + return headers + + async def _fetch_token(self, client: httpx.AsyncClient) -> None: + """Fetch OAuth2 access token using client credentials flow.""" + import time + + data = { + "grant_type": "client_credentials", + "client_id": self.client_id, + "client_secret": self.client_secret, + } + + if self.scopes: + data["scope"] = " ".join(self.scopes) + + response = await client.post(self.token_url, data=data) + response.raise_for_status() + + token_data = response.json() + self._access_token = token_data["access_token"] + + # Calculate expiration time (default to 3600 seconds if not provided) + expires_in = token_data.get("expires_in", 3600) + self._token_expires_at = time.time() + expires_in - 60 # 60s buffer + + def configure_client(self, client: httpx.AsyncClient) -> None: + """No client configuration needed for OAuth2.""" + + +class OAuth2AuthorizationCode(AuthScheme): + """OAuth2 Authorization Code flow authentication. + + Note: This requires interactive authorization and is typically used + for user-facing applications. For server-to-server, use ClientCredentials. + """ + + authorization_url: str = Field(description="OAuth2 authorization endpoint") + token_url: str = Field(description="OAuth2 token endpoint") + client_id: str = Field(description="OAuth2 client ID") + client_secret: str = Field(description="OAuth2 client secret") + redirect_uri: str = Field(description="OAuth2 redirect URI") + scopes: list[str] = Field( + default_factory=list, description="Required OAuth2 scopes" + ) + + _access_token: str | None = None + _refresh_token: str | None = None + _token_expires_at: float | None = None + _authorization_callback: Callable[[str], Awaitable[str]] | None = None + + def set_authorization_callback( + self, callback: Callable[[str], Awaitable[str]] | None + ) -> None: + """Set callback to handle authorization URL. + + The callback receives the authorization URL and should return + the authorization code after user completes the flow. + """ + self._authorization_callback = callback + + async def apply_auth( + self, client: httpx.AsyncClient, headers: dict[str, str] + ) -> dict[str, str]: + """Apply OAuth2 access token to Authorization header.""" + import time + + # Get or refresh token if needed + if self._access_token is None: + if self._authorization_callback is None: + msg = "Authorization callback not set. Use set_authorization_callback()" + raise ValueError(msg) + await self._fetch_initial_token(client) + elif self._token_expires_at and time.time() >= self._token_expires_at: + await self._refresh_access_token(client) + + if self._access_token: + headers["Authorization"] = f"Bearer {self._access_token}" + + return headers + + async def _fetch_initial_token(self, client: httpx.AsyncClient) -> None: + """Fetch initial access token using authorization code flow.""" + import time + import urllib.parse + + # Build authorization URL + params = { + "response_type": "code", + "client_id": self.client_id, + "redirect_uri": self.redirect_uri, + "scope": " ".join(self.scopes), + } + auth_url = f"{self.authorization_url}?{urllib.parse.urlencode(params)}" + + # Get authorization code from callback + if self._authorization_callback is None: + msg = "Authorization callback not set" + raise ValueError(msg) + auth_code = await self._authorization_callback(auth_url) + + # Exchange code for token + data = { + "grant_type": "authorization_code", + "code": auth_code, + "client_id": self.client_id, + "client_secret": self.client_secret, + "redirect_uri": self.redirect_uri, + } + + response = await client.post(self.token_url, data=data) + response.raise_for_status() + + token_data = response.json() + self._access_token = token_data["access_token"] + self._refresh_token = token_data.get("refresh_token") + + expires_in = token_data.get("expires_in", 3600) + self._token_expires_at = time.time() + expires_in - 60 + + async def _refresh_access_token(self, client: httpx.AsyncClient) -> None: + """Refresh the access token using refresh token.""" + import time + + if not self._refresh_token: + # Re-authorize if no refresh token + await self._fetch_initial_token(client) + return + + data = { + "grant_type": "refresh_token", + "refresh_token": self._refresh_token, + "client_id": self.client_id, + "client_secret": self.client_secret, + } + + response = await client.post(self.token_url, data=data) + response.raise_for_status() + + token_data = response.json() + self._access_token = token_data["access_token"] + if "refresh_token" in token_data: + self._refresh_token = token_data["refresh_token"] + + expires_in = token_data.get("expires_in", 3600) + self._token_expires_at = time.time() + expires_in - 60 + + def configure_client(self, client: httpx.AsyncClient) -> None: + """No client configuration needed for OAuth2.""" + + +def create_auth_from_agent_card( + agent_card: AgentCard, credentials: dict[str, Any] +) -> AuthScheme | None: + """Create an appropriate authentication scheme from AgentCard security config. + + Args: + agent_card: The A2A AgentCard containing security requirements. + credentials: User-provided credentials (passwords, tokens, keys, etc.). + + Returns: + Configured AuthScheme, or None if no authentication required. + + Example: + ```python + # For OAuth2 + credentials = { + "client_id": "my-app", + "client_secret": "secret123", + } + auth = create_auth_from_agent_card(agent_card, credentials) + + # For API Key + credentials = {"api_key": "key-12345"} + auth = create_auth_from_agent_card(agent_card, credentials) + + # For HTTP Basic + credentials = {"username": "user", "password": "pass"} + auth = create_auth_from_agent_card(agent_card, credentials) + ``` + """ + if not agent_card.security or not agent_card.security_schemes: + return None + + # Get the first required security scheme + first_security_req = agent_card.security[0] if agent_card.security else {} + + for scheme_name, _scopes in first_security_req.items(): + security_scheme_obj = agent_card.security_schemes.get(scheme_name) + if not security_scheme_obj: + continue + + # SecurityScheme is a dict-like object + security_scheme = dict(security_scheme_obj) # type: ignore[arg-type] + scheme_type = str(security_scheme.get("type", "")).lower() + + # OAuth2 + if scheme_type == "oauth2": + flows = security_scheme.get("flows", {}) + + if "clientCredentials" in flows: + flow = flows["clientCredentials"] + return OAuth2ClientCredentials( + token_url=str(flow["tokenUrl"]), + client_id=str(credentials.get("client_id", "")), + client_secret=str(credentials.get("client_secret", "")), + scopes=list(flow.get("scopes", {}).keys()), + ) + + if "authorizationCode" in flows: + flow = flows["authorizationCode"] + return OAuth2AuthorizationCode( + authorization_url=str(flow["authorizationUrl"]), + token_url=str(flow["tokenUrl"]), + client_id=str(credentials.get("client_id", "")), + client_secret=str(credentials.get("client_secret", "")), + redirect_uri=str(credentials.get("redirect_uri", "")), + scopes=list(flow.get("scopes", {}).keys()), + ) + + # API Key + elif scheme_type == "apikey": + location = str(security_scheme.get("in", "header")) + name = str(security_scheme.get("name", "X-API-Key")) + return APIKeyAuth( + api_key=str(credentials.get("api_key", "")), + location=location, # type: ignore[arg-type] + name=name, + ) + + # HTTP Auth + elif scheme_type == "http": + http_scheme = str(security_scheme.get("scheme", "")).lower() + + if http_scheme == "basic": + return HTTPBasicAuth( + username=str(credentials.get("username", "")), + password=str(credentials.get("password", "")), + ) + + if http_scheme == "digest": + return HTTPDigestAuth( + username=str(credentials.get("username", "")), + password=str(credentials.get("password", "")), + ) + + if http_scheme == "bearer": + return BearerTokenAuth(token=str(credentials.get("token", ""))) + + return None diff --git a/lib/crewai/src/crewai/experimental/a2a/exceptions.py b/lib/crewai/src/crewai/experimental/a2a/exceptions.py new file mode 100644 index 000000000..b1ef23b6e --- /dev/null +++ b/lib/crewai/src/crewai/experimental/a2a/exceptions.py @@ -0,0 +1,56 @@ +"""Custom exceptions for A2A Agent Adapter.""" + + +class A2AError(Exception): + """Base exception for A2A adapter errors.""" + + +class A2ATaskFailedError(A2AError): + """Raised when A2A agent task fails or is rejected. + + This exception is raised when the A2A agent reports a task + in the 'failed' or 'rejected' state. + """ + + +class A2AInputRequiredError(A2AError): + """Raised when A2A agent requires additional input. + + This exception is raised when the A2A agent reports a task + in the 'input_required' state, indicating that it needs more + information to complete the task. + """ + + +class A2AConfigurationError(A2AError): + """Raised when A2A adapter configuration is invalid. + + This exception is raised during initialization or setup when + the adapter configuration is invalid or incompatible. + """ + + +class A2AConnectionError(A2AError): + """Raised when connection to A2A agent fails. + + This exception is raised when the adapter cannot establish + a connection to the A2A agent or when network errors occur. + """ + + +class A2AAuthenticationError(A2AError): + """Raised when A2A agent requires authentication. + + This exception is raised when the A2A agent reports a task + in the 'auth_required' state, indicating that authentication + is needed before the task can continue. + """ + + +class A2ATaskCanceledError(A2AError): + """Raised when A2A task is canceled. + + This exception is raised when the A2A agent reports a task + in the 'canceled' state, indicating the task was canceled + either by the user or the system. + """ diff --git a/lib/crewai/src/crewai/experimental/a2a/protocols.py b/lib/crewai/src/crewai/experimental/a2a/protocols.py new file mode 100644 index 000000000..3486413d1 --- /dev/null +++ b/lib/crewai/src/crewai/experimental/a2a/protocols.py @@ -0,0 +1,56 @@ +"""Type protocols for A2A SDK components. + +These protocols define the expected interfaces for A2A SDK types, +allowing for type checking without requiring the SDK to be installed. +""" + +from collections.abc import AsyncIterator +from typing import Any, Protocol, runtime_checkable + + +@runtime_checkable +class AgentCardProtocol(Protocol): + """Protocol for A2A AgentCard.""" + + name: str + version: str + description: str + skills: list[Any] + capabilities: Any + + +@runtime_checkable +class ClientProtocol(Protocol): + """Protocol for A2A Client.""" + + async def send_message(self, message: Any) -> AsyncIterator[Any]: + """Send message to A2A agent.""" + ... + + async def get_card(self) -> AgentCardProtocol: + """Get agent card.""" + ... + + async def close(self) -> None: + """Close client connection.""" + ... + + +@runtime_checkable +class MessageProtocol(Protocol): + """Protocol for A2A Message.""" + + role: Any + message_id: str + parts: list[Any] + + +@runtime_checkable +class TaskProtocol(Protocol): + """Protocol for A2A Task.""" + + id: str + context_id: str + status: Any + history: list[Any] | None + artifacts: list[Any] | None diff --git a/src/crewai/experimental/evaluation/__init__.py b/lib/crewai/src/crewai/experimental/evaluation/__init__.py similarity index 99% rename from src/crewai/experimental/evaluation/__init__.py rename to lib/crewai/src/crewai/experimental/evaluation/__init__.py index 6b2f7bb88..97e4752f6 100644 --- a/src/crewai/experimental/evaluation/__init__.py +++ b/lib/crewai/src/crewai/experimental/evaluation/__init__.py @@ -26,6 +26,7 @@ from crewai.experimental.evaluation.metrics import ( ToolSelectionEvaluator, ) + __all__ = [ "AgentEvaluationResult", "AgentEvaluator", diff --git a/src/crewai/experimental/evaluation/agent_evaluator.py b/lib/crewai/src/crewai/experimental/evaluation/agent_evaluator.py similarity index 88% rename from src/crewai/experimental/evaluation/agent_evaluator.py rename to lib/crewai/src/crewai/experimental/evaluation/agent_evaluator.py index 8dfdfc2c6..4f3abe9f8 100644 --- a/src/crewai/experimental/evaluation/agent_evaluator.py +++ b/lib/crewai/src/crewai/experimental/evaluation/agent_evaluator.py @@ -1,5 +1,5 @@ -import threading from collections.abc import Sequence +import threading from typing import Any from crewai.agent import Agent @@ -52,19 +52,14 @@ class AgentEvaluator: self.console_formatter = ConsoleFormatter() self.display_formatter = EvaluationDisplayFormatter() - self._thread_local: threading.local = threading.local() + self._execution_state = ExecutionState() + self._state_lock = threading.Lock() for agent in self.agents: self._execution_state.agent_evaluators[str(agent.id)] = self.evaluators self._subscribe_to_events() - @property - def _execution_state(self) -> ExecutionState: - if not hasattr(self._thread_local, "execution_state"): - self._thread_local.execution_state = ExecutionState() - return self._thread_local.execution_state - def _subscribe_to_events(self) -> None: from typing import cast @@ -102,7 +97,7 @@ class AgentEvaluator: ) if not trace: - return + trace = {} result = self.evaluate( agent=agent, @@ -112,21 +107,22 @@ class AgentEvaluator: state=state, ) - current_iteration = self._execution_state.iteration - if current_iteration not in self._execution_state.iterations_results: - self._execution_state.iterations_results[current_iteration] = {} + with self._state_lock: + current_iteration = self._execution_state.iteration + if current_iteration not in self._execution_state.iterations_results: + self._execution_state.iterations_results[current_iteration] = {} + + if ( + agent.role + not in self._execution_state.iterations_results[current_iteration] + ): + self._execution_state.iterations_results[current_iteration][ + agent.role + ] = [] - if ( - agent.role - not in self._execution_state.iterations_results[current_iteration] - ): self._execution_state.iterations_results[current_iteration][ agent.role - ] = [] - - self._execution_state.iterations_results[current_iteration][ - agent.role - ].append(result) + ].append(result) def _handle_lite_agent_completed( self, source: object, event: LiteAgentExecutionCompletedEvent @@ -155,7 +151,7 @@ class AgentEvaluator: ) if not trace: - return + trace = {} result = self.evaluate( agent=target_agent, @@ -164,22 +160,23 @@ class AgentEvaluator: state=state, ) - current_iteration = self._execution_state.iteration - if current_iteration not in self._execution_state.iterations_results: - self._execution_state.iterations_results[current_iteration] = {} + with self._state_lock: + current_iteration = self._execution_state.iteration + if current_iteration not in self._execution_state.iterations_results: + self._execution_state.iterations_results[current_iteration] = {} + + agent_role = target_agent.role + if ( + agent_role + not in self._execution_state.iterations_results[current_iteration] + ): + self._execution_state.iterations_results[current_iteration][ + agent_role + ] = [] - agent_role = target_agent.role - if ( - agent_role - not in self._execution_state.iterations_results[current_iteration] - ): self._execution_state.iterations_results[current_iteration][ agent_role - ] = [] - - self._execution_state.iterations_results[current_iteration][ - agent_role - ].append(result) + ].append(result) def set_iteration(self, iteration: int) -> None: self._execution_state.iteration = iteration diff --git a/src/crewai/experimental/evaluation/base_evaluator.py b/lib/crewai/src/crewai/experimental/evaluation/base_evaluator.py similarity index 100% rename from src/crewai/experimental/evaluation/base_evaluator.py rename to lib/crewai/src/crewai/experimental/evaluation/base_evaluator.py diff --git a/src/crewai/experimental/evaluation/evaluation_display.py b/lib/crewai/src/crewai/experimental/evaluation/evaluation_display.py similarity index 99% rename from src/crewai/experimental/evaluation/evaluation_display.py rename to lib/crewai/src/crewai/experimental/evaluation/evaluation_display.py index ca66f17c0..7791715a1 100644 --- a/src/crewai/experimental/evaluation/evaluation_display.py +++ b/lib/crewai/src/crewai/experimental/evaluation/evaluation_display.py @@ -14,6 +14,7 @@ from crewai.experimental.evaluation.base_evaluator import ( MetricCategory, ) from crewai.utilities.llm_utils import create_llm +from crewai.utilities.types import LLMMessage class EvaluationDisplayFormatter: @@ -339,7 +340,7 @@ class EvaluationDisplayFormatter: else: strategy_guidance = "Provide a balanced analysis of strengths and weaknesses across all tasks." - prompt = [ + prompt: list[LLMMessage] = [ { "role": "system", "content": f"""You are an expert evaluator creating a comprehensive summary of agent performance feedback. diff --git a/src/crewai/experimental/evaluation/evaluation_listener.py b/lib/crewai/src/crewai/experimental/evaluation/evaluation_listener.py similarity index 100% rename from src/crewai/experimental/evaluation/evaluation_listener.py rename to lib/crewai/src/crewai/experimental/evaluation/evaluation_listener.py diff --git a/src/crewai/experimental/evaluation/experiment/__init__.py b/lib/crewai/src/crewai/experimental/evaluation/experiment/__init__.py similarity index 99% rename from src/crewai/experimental/evaluation/experiment/__init__.py rename to lib/crewai/src/crewai/experimental/evaluation/experiment/__init__.py index 4466de01f..461f31ead 100644 --- a/src/crewai/experimental/evaluation/experiment/__init__.py +++ b/lib/crewai/src/crewai/experimental/evaluation/experiment/__init__.py @@ -4,4 +4,5 @@ from crewai.experimental.evaluation.experiment.result import ( ) from crewai.experimental.evaluation.experiment.runner import ExperimentRunner + __all__ = ["ExperimentResult", "ExperimentResults", "ExperimentRunner"] diff --git a/src/crewai/experimental/evaluation/experiment/result.py b/lib/crewai/src/crewai/experimental/evaluation/experiment/result.py similarity index 100% rename from src/crewai/experimental/evaluation/experiment/result.py rename to lib/crewai/src/crewai/experimental/evaluation/experiment/result.py index 0868b00f1..1b6513333 100644 --- a/src/crewai/experimental/evaluation/experiment/result.py +++ b/lib/crewai/src/crewai/experimental/evaluation/experiment/result.py @@ -1,6 +1,6 @@ +from datetime import datetime, timezone import json import os -from datetime import datetime, timezone from typing import Any from pydantic import BaseModel diff --git a/src/crewai/experimental/evaluation/experiment/result_display.py b/lib/crewai/src/crewai/experimental/evaluation/experiment/result_display.py similarity index 100% rename from src/crewai/experimental/evaluation/experiment/result_display.py rename to lib/crewai/src/crewai/experimental/evaluation/experiment/result_display.py diff --git a/src/crewai/experimental/evaluation/experiment/runner.py b/lib/crewai/src/crewai/experimental/evaluation/experiment/runner.py similarity index 100% rename from src/crewai/experimental/evaluation/experiment/runner.py rename to lib/crewai/src/crewai/experimental/evaluation/experiment/runner.py diff --git a/src/crewai/experimental/evaluation/json_parser.py b/lib/crewai/src/crewai/experimental/evaluation/json_parser.py similarity index 100% rename from src/crewai/experimental/evaluation/json_parser.py rename to lib/crewai/src/crewai/experimental/evaluation/json_parser.py diff --git a/src/crewai/experimental/evaluation/metrics/__init__.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/__init__.py similarity index 99% rename from src/crewai/experimental/evaluation/metrics/__init__.py rename to lib/crewai/src/crewai/experimental/evaluation/metrics/__init__.py index 0a249ccc6..9aaa3358d 100644 --- a/src/crewai/experimental/evaluation/metrics/__init__.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/__init__.py @@ -11,6 +11,7 @@ from crewai.experimental.evaluation.metrics.tools_metrics import ( ToolSelectionEvaluator, ) + __all__ = [ "GoalAlignmentEvaluator", "ParameterExtractionEvaluator", diff --git a/src/crewai/experimental/evaluation/metrics/goal_metrics.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/goal_metrics.py similarity index 94% rename from src/crewai/experimental/evaluation/metrics/goal_metrics.py rename to lib/crewai/src/crewai/experimental/evaluation/metrics/goal_metrics.py index 52fedb716..9758a7fe4 100644 --- a/src/crewai/experimental/evaluation/metrics/goal_metrics.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/goal_metrics.py @@ -9,6 +9,7 @@ from crewai.experimental.evaluation.base_evaluator import ( ) from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response from crewai.task import Task +from crewai.utilities.types import LLMMessage class GoalAlignmentEvaluator(BaseEvaluator): @@ -27,7 +28,7 @@ class GoalAlignmentEvaluator(BaseEvaluator): if task is not None: task_context = f"Task description: {task.description}\nExpected output: {task.expected_output}\n" - prompt = [ + prompt: list[LLMMessage] = [ { "role": "system", "content": """You are an expert evaluator assessing how well an AI agent's output aligns with its assigned task goal. @@ -62,7 +63,7 @@ Evaluate how well the agent's output aligns with the assigned task goal. ] if self.llm is None: raise ValueError("LLM must be initialized") - response = self.llm.call(prompt) + response = self.llm.call(prompt) # type: ignore[arg-type] try: evaluation_data: dict[str, Any] = extract_json_from_llm_response(response) diff --git a/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py similarity index 99% rename from src/crewai/experimental/evaluation/metrics/reasoning_metrics.py rename to lib/crewai/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py index 51e92504b..4531103f5 100644 --- a/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py @@ -6,10 +6,10 @@ This module provides evaluator implementations for: - Thinking-to-action ratio """ -import logging -import re from collections.abc import Sequence from enum import Enum +import logging +import re from typing import Any import numpy as np @@ -24,6 +24,7 @@ from crewai.experimental.evaluation.base_evaluator import ( from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response from crewai.task import Task from crewai.tasks.task_output import TaskOutput +from crewai.utilities.types import LLMMessage class ReasoningPatternType(Enum): @@ -105,7 +106,7 @@ class ReasoningEfficiencyEvaluator(BaseEvaluator): final_output.raw if isinstance(final_output, TaskOutput) else final_output ) - prompt = [ + prompt: list[LLMMessage] = [ { "role": "system", "content": """You are an expert evaluator assessing the reasoning efficiency of an AI agent's thought process. diff --git a/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py similarity index 97% rename from src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py rename to lib/crewai/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py index 8f638b91c..7a0bd488a 100644 --- a/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py @@ -9,6 +9,7 @@ from crewai.experimental.evaluation.base_evaluator import ( ) from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response from crewai.task import Task +from crewai.utilities.types import LLMMessage class SemanticQualityEvaluator(BaseEvaluator): @@ -26,7 +27,7 @@ class SemanticQualityEvaluator(BaseEvaluator): task_context = "" if task is not None: task_context = f"Task description: {task.description}" - prompt = [ + prompt: list[LLMMessage] = [ { "role": "system", "content": """You are an expert evaluator assessing the semantic quality of an AI agent's output. diff --git a/src/crewai/experimental/evaluation/metrics/tools_metrics.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/tools_metrics.py similarity index 99% rename from src/crewai/experimental/evaluation/metrics/tools_metrics.py rename to lib/crewai/src/crewai/experimental/evaluation/metrics/tools_metrics.py index 59ed8936f..baf7c69c5 100644 --- a/src/crewai/experimental/evaluation/metrics/tools_metrics.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/tools_metrics.py @@ -10,6 +10,7 @@ from crewai.experimental.evaluation.base_evaluator import ( ) from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response from crewai.task import Task +from crewai.utilities.types import LLMMessage class ToolSelectionEvaluator(BaseEvaluator): @@ -54,7 +55,7 @@ class ToolSelectionEvaluator(BaseEvaluator): for tool_type in sorted(unique_tool_types): tool_types_summary += f"- {tool_type}\n" - prompt = [ + prompt: list[LLMMessage] = [ { "role": "system", "content": """You are an expert evaluator assessing if an AI agent selected the most appropriate tools for a given task. @@ -211,7 +212,7 @@ class ParameterExtractionEvaluator(BaseEvaluator): f"\n...and {len(validation_errors) - 3} more validation errors." ) param_samples_text = "\n\n".join(param_samples) - prompt = [ + prompt: list[LLMMessage] = [ { "role": "system", "content": """You are an expert evaluator assessing how well an AI agent extracts and formats PARAMETER VALUES for tool calls. @@ -362,7 +363,7 @@ class ToolInvocationEvaluator(BaseEvaluator): error_type_summary += f"- {error_type}: {count} occurrences ({(count / tool_count):.1%})\n" invocation_samples_text = "\n\n".join(invocation_samples) - prompt = [ + prompt: list[LLMMessage] = [ { "role": "system", "content": """You are an expert evaluator assessing how correctly an AI agent's tool invocations are STRUCTURED. diff --git a/src/crewai/experimental/evaluation/testing.py b/lib/crewai/src/crewai/experimental/evaluation/testing.py similarity index 100% rename from src/crewai/experimental/evaluation/testing.py rename to lib/crewai/src/crewai/experimental/evaluation/testing.py diff --git a/src/crewai/flow/__init__.py b/lib/crewai/src/crewai/flow/__init__.py similarity index 99% rename from src/crewai/flow/__init__.py rename to lib/crewai/src/crewai/flow/__init__.py index b15c0a720..8e055d939 100644 --- a/src/crewai/flow/__init__.py +++ b/lib/crewai/src/crewai/flow/__init__.py @@ -1,4 +1,5 @@ from crewai.flow.flow import Flow, and_, listen, or_, router, start from crewai.flow.persistence import persist + __all__ = ["Flow", "and_", "listen", "or_", "persist", "router", "start"] diff --git a/src/crewai/flow/assets/crewai_flow_visual_template.html b/lib/crewai/src/crewai/flow/assets/crewai_flow_visual_template.html similarity index 100% rename from src/crewai/flow/assets/crewai_flow_visual_template.html rename to lib/crewai/src/crewai/flow/assets/crewai_flow_visual_template.html diff --git a/src/crewai/flow/assets/crewai_logo.svg b/lib/crewai/src/crewai/flow/assets/crewai_logo.svg similarity index 100% rename from src/crewai/flow/assets/crewai_logo.svg rename to lib/crewai/src/crewai/flow/assets/crewai_logo.svg diff --git a/lib/crewai/src/crewai/flow/config.py b/lib/crewai/src/crewai/flow/config.py new file mode 100644 index 000000000..021cb65bb --- /dev/null +++ b/lib/crewai/src/crewai/flow/config.py @@ -0,0 +1,133 @@ +from typing import Any, Literal, TypedDict + +from typing_extensions import NotRequired + + +DarkGray = Literal["#333333"] +CrewAIOrange = Literal["#FF5A50"] +Gray = Literal["#666666"] +White = Literal["#FFFFFF"] +Black = Literal["#000000"] + + +DARK_GRAY: Literal["#333333"] = "#333333" +CREWAI_ORANGE: Literal["#FF5A50"] = "#FF5A50" +GRAY: Literal["#666666"] = "#666666" +WHITE: Literal["#FFFFFF"] = "#FFFFFF" +BLACK: Literal["#000000"] = "#000000" + + +class FlowColors(TypedDict): + bg: White + start: CrewAIOrange + method: DarkGray + router: DarkGray + router_border: CrewAIOrange + edge: Gray + router_edge: CrewAIOrange + text: White + + +class FontStyles(TypedDict, total=False): + color: DarkGray | CrewAIOrange | Gray | White | Black + multi: Literal["html"] + + +class StartNodeStyle(TypedDict): + color: CrewAIOrange + shape: Literal["box"] + font: FontStyles + label: NotRequired[str] + margin: dict[str, int] + + +class MethodNodeStyle(TypedDict): + color: DarkGray + shape: Literal["box"] + font: FontStyles + label: NotRequired[str] + margin: dict[str, int] + + +class RouterNodeStyle(TypedDict): + color: dict[str, Any] + shape: Literal["box"] + font: FontStyles + label: NotRequired[str] + borderWidth: int + borderWidthSelected: int + shapeProperties: dict[str, list[int] | bool] + margin: dict[str, int] + + +class CrewNodeStyle(TypedDict): + color: dict[str, CrewAIOrange | White] + shape: Literal["box"] + font: FontStyles + label: NotRequired[str] + borderWidth: int + borderWidthSelected: int + shapeProperties: dict[str, bool] + margin: dict[str, int] + + +class NodeStyles(TypedDict): + start: StartNodeStyle + method: MethodNodeStyle + router: RouterNodeStyle + crew: CrewNodeStyle + + +COLORS: FlowColors = { + "bg": WHITE, + "start": CREWAI_ORANGE, + "method": DARK_GRAY, + "router": DARK_GRAY, + "router_border": CREWAI_ORANGE, + "edge": GRAY, + "router_edge": CREWAI_ORANGE, + "text": WHITE, +} + +NODE_STYLES: NodeStyles = { + "start": { + "color": CREWAI_ORANGE, + "shape": "box", + "font": {"color": WHITE}, + "margin": {"top": 10, "bottom": 8, "left": 10, "right": 10}, + }, + "method": { + "color": DARK_GRAY, + "shape": "box", + "font": {"color": WHITE}, + "margin": {"top": 10, "bottom": 8, "left": 10, "right": 10}, + }, + "router": { + "color": { + "background": DARK_GRAY, + "border": CREWAI_ORANGE, + "highlight": { + "border": CREWAI_ORANGE, + "background": DARK_GRAY, + }, + }, + "shape": "box", + "font": {"color": WHITE}, + "borderWidth": 3, + "borderWidthSelected": 4, + "shapeProperties": {"borderDashes": [5, 5]}, + "margin": {"top": 10, "bottom": 8, "left": 10, "right": 10}, + }, + "crew": { + "color": { + "background": WHITE, + "border": CREWAI_ORANGE, + }, + "shape": "box", + "font": {"color": BLACK}, + "borderWidth": 3, + "borderWidthSelected": 4, + "shapeProperties": {"borderDashes": False}, + "margin": {"top": 10, "bottom": 8, "left": 10, "right": 10}, + }, +} diff --git a/src/crewai/flow/flow.py b/lib/crewai/src/crewai/flow/flow.py similarity index 77% rename from src/crewai/flow/flow.py rename to lib/crewai/src/crewai/flow/flow.py index 84783b081..44dcd0ec9 100644 --- a/src/crewai/flow/flow.py +++ b/lib/crewai/src/crewai/flow/flow.py @@ -1,9 +1,20 @@ +from __future__ import annotations + import asyncio +from collections.abc import Callable +from concurrent.futures import Future import copy import inspect import logging -from collections.abc import Callable -from typing import Any, ClassVar, Generic, TypeVar, cast +from typing import ( + Any, + ClassVar, + Generic, + Literal, + ParamSpec, + TypeVar, + cast, +) from uuid import uuid4 from opentelemetry import baggage @@ -28,11 +39,29 @@ from crewai.events.types.flow_events import ( MethodExecutionStartedEvent, ) from crewai.flow.flow_visualizer import plot_flow +from crewai.flow.flow_wrappers import ( + FlowCondition, + FlowConditions, + FlowMethod, + ListenMethod, + RouterMethod, + SimpleFlowCondition, + StartMethod, +) from crewai.flow.persistence.base import FlowPersistence -from crewai.flow.types import FlowExecutionData -from crewai.flow.utils import get_possible_return_constants +from crewai.flow.types import FlowExecutionData, FlowMethodName, PendingListenerKey +from crewai.flow.utils import ( + get_possible_return_constants, + is_flow_condition_dict, + is_flow_condition_list, + is_flow_method, + is_flow_method_callable, + is_flow_method_name, + is_simple_flow_condition, +) from crewai.utilities.printer import Printer, PrinterColor + logger = logging.getLogger(__name__) @@ -50,24 +79,14 @@ T = TypeVar("T", bound=dict[str, Any] | BaseModel) # Generic flow state type pa StateT = TypeVar( "StateT", bound=dict[str, Any] | BaseModel ) # State validation type parameter +P = ParamSpec("P") # ParamSpec for preserving function signatures in decorators +R = TypeVar("R") # Generic return type for decorated methods +F = TypeVar("F", bound=Callable[..., Any]) # Function type for decorator preservation def ensure_state_type(state: Any, expected_type: type[StateT]) -> StateT: """Ensure state matches expected type with proper validation. - Args: - state: State instance to validate - expected_type: Expected type for the state - - Returns: - Validated state instance - - Raises: - TypeError: If state doesn't match expected type - ValueError: If state validation fails - """ - """Ensure state matches expected type with proper validation. - Args: state: State instance to validate expected_type: Expected type for the state @@ -88,11 +107,13 @@ def ensure_state_type(state: Any, expected_type: type[StateT]) -> StateT: raise TypeError( f"Expected {expected_type.__name__}, got {type(state).__name__}" ) - return cast(StateT, state) + return state raise TypeError(f"Invalid expected_type: {expected_type}") -def start(condition: str | dict | Callable | None = None) -> Callable: +def start( + condition: str | FlowCondition | Callable[..., Any] | None = None, +) -> Callable[[Callable[P, R]], StartMethod[P, R]]: """ Marks a method as a flow's starting point. @@ -102,17 +123,18 @@ def start(condition: str | dict | Callable | None = None) -> Callable: Parameters ---------- - condition : Optional[Union[str, dict, Callable]], optional + condition : Optional[Union[str, FlowCondition, Callable[..., Any]]], optional Defines when the start method should execute. Can be: - str: Name of a method that triggers this start - - dict: Result from or_() or and_(), including nested conditions - - Callable: A method reference that triggers this start + - FlowCondition: Result from or_() or and_(), including nested conditions + - Callable[..., Any]: A method reference that triggers this start Default is None, meaning unconditional start. Returns ------- - Callable - A decorator function that marks the method as a flow start point. + Callable[[Callable[P, R]], StartMethod[P, R]] + A decorator function that wraps the method as a flow start point + and preserves its signature. Raises ------ @@ -134,37 +156,40 @@ def start(condition: str | dict | Callable | None = None) -> Callable: ... pass """ - def decorator(func): - func.__is_start_method__ = True + def decorator(func: Callable[P, R]) -> StartMethod[P, R]: + wrapper = StartMethod(func) + if condition is not None: - if isinstance(condition, str): - func.__trigger_methods__ = [condition] - func.__condition_type__ = "OR" - elif isinstance(condition, dict) and "type" in condition: + if is_flow_method_name(condition): + wrapper.__trigger_methods__ = [condition] + wrapper.__condition_type__ = "OR" + elif is_flow_condition_dict(condition): if "conditions" in condition: - func.__trigger_condition__ = condition - func.__trigger_methods__ = _extract_all_methods(condition) - func.__condition_type__ = condition["type"] + wrapper.__trigger_condition__ = condition + wrapper.__trigger_methods__ = _extract_all_methods(condition) + wrapper.__condition_type__ = condition["type"] elif "methods" in condition: - func.__trigger_methods__ = condition["methods"] - func.__condition_type__ = condition["type"] + wrapper.__trigger_methods__ = condition["methods"] + wrapper.__condition_type__ = condition["type"] else: raise ValueError( "Condition dict must contain 'conditions' or 'methods'" ) - elif callable(condition) and hasattr(condition, "__name__"): - func.__trigger_methods__ = [condition.__name__] - func.__condition_type__ = "OR" + elif is_flow_method_callable(condition): + wrapper.__trigger_methods__ = [condition.__name__] + wrapper.__condition_type__ = "OR" else: raise ValueError( "Condition must be a method, string, or a result of or_() or and_()" ) - return func + return wrapper return decorator -def listen(condition: str | dict | Callable) -> Callable: +def listen( + condition: str | FlowCondition | Callable[..., Any], +) -> Callable[[Callable[P, R]], ListenMethod[P, R]]: """ Creates a listener that executes when specified conditions are met. @@ -174,16 +199,17 @@ def listen(condition: str | dict | Callable) -> Callable: Parameters ---------- - condition : Union[str, dict, Callable] + condition : Union[str, FlowCondition, Callable[..., Any]] Specifies when the listener should execute. Can be: - str: Name of a method that triggers this listener - - dict: Result from or_() or and_(), including nested conditions - - Callable: A method reference that triggers this listener + - FlowCondition: Result from or_() or and_(), including nested conditions + - Callable[..., Any]: A method reference that triggers this listener Returns ------- - Callable - A decorator function that sets up the method as a listener. + Callable[[Callable[P, R]], ListenMethod[P, R]] + A decorator function that wraps the method as a listener + and preserves its signature. Raises ------ @@ -201,35 +227,39 @@ def listen(condition: str | dict | Callable) -> Callable: ... pass """ - def decorator(func): - if isinstance(condition, str): - func.__trigger_methods__ = [condition] - func.__condition_type__ = "OR" - elif isinstance(condition, dict) and "type" in condition: + def decorator(func: Callable[P, R]) -> ListenMethod[P, R]: + wrapper = ListenMethod(func) + + if is_flow_method_name(condition): + wrapper.__trigger_methods__ = [condition] + wrapper.__condition_type__ = "OR" + elif is_flow_condition_dict(condition): if "conditions" in condition: - func.__trigger_condition__ = condition - func.__trigger_methods__ = _extract_all_methods(condition) - func.__condition_type__ = condition["type"] + wrapper.__trigger_condition__ = condition + wrapper.__trigger_methods__ = _extract_all_methods(condition) + wrapper.__condition_type__ = condition["type"] elif "methods" in condition: - func.__trigger_methods__ = condition["methods"] - func.__condition_type__ = condition["type"] + wrapper.__trigger_methods__ = condition["methods"] + wrapper.__condition_type__ = condition["type"] else: raise ValueError( "Condition dict must contain 'conditions' or 'methods'" ) - elif callable(condition) and hasattr(condition, "__name__"): - func.__trigger_methods__ = [condition.__name__] - func.__condition_type__ = "OR" + elif is_flow_method_callable(condition): + wrapper.__trigger_methods__ = [condition.__name__] + wrapper.__condition_type__ = "OR" else: raise ValueError( "Condition must be a method, string, or a result of or_() or and_()" ) - return func + return wrapper return decorator -def router(condition: str | dict | Callable) -> Callable: +def router( + condition: str | FlowCondition | Callable[..., Any], +) -> Callable[[Callable[P, R]], RouterMethod[P, R]]: """ Creates a routing method that directs flow execution based on conditions. @@ -240,16 +270,17 @@ def router(condition: str | dict | Callable) -> Callable: Parameters ---------- - condition : Union[str, dict, Callable] + condition : Union[str, FlowCondition, Callable[..., Any]] Specifies when the router should execute. Can be: - str: Name of a method that triggers this router - - dict: Result from or_() or and_(), including nested conditions - - Callable: A method reference that triggers this router + - FlowCondition: Result from or_() or and_(), including nested conditions + - Callable[..., Any]: A method reference that triggers this router Returns ------- - Callable - A decorator function that sets up the method as a router. + Callable[[Callable[P, R]], RouterMethod[P, R]] + A decorator function that wraps the method as a router + and preserves its signature. Raises ------ @@ -271,36 +302,37 @@ def router(condition: str | dict | Callable) -> Callable: ... return STOP """ - def decorator(func): - func.__is_router__ = True - if isinstance(condition, str): - func.__trigger_methods__ = [condition] - func.__condition_type__ = "OR" - elif isinstance(condition, dict) and "type" in condition: + def decorator(func: Callable[P, R]) -> RouterMethod[P, R]: + wrapper = RouterMethod(func) + + if is_flow_method_name(condition): + wrapper.__trigger_methods__ = [condition] + wrapper.__condition_type__ = "OR" + elif is_flow_condition_dict(condition): if "conditions" in condition: - func.__trigger_condition__ = condition - func.__trigger_methods__ = _extract_all_methods(condition) - func.__condition_type__ = condition["type"] + wrapper.__trigger_condition__ = condition + wrapper.__trigger_methods__ = _extract_all_methods(condition) + wrapper.__condition_type__ = condition["type"] elif "methods" in condition: - func.__trigger_methods__ = condition["methods"] - func.__condition_type__ = condition["type"] + wrapper.__trigger_methods__ = condition["methods"] + wrapper.__condition_type__ = condition["type"] else: raise ValueError( "Condition dict must contain 'conditions' or 'methods'" ) - elif callable(condition) and hasattr(condition, "__name__"): - func.__trigger_methods__ = [condition.__name__] - func.__condition_type__ = "OR" + elif is_flow_method_callable(condition): + wrapper.__trigger_methods__ = [condition.__name__] + wrapper.__condition_type__ = "OR" else: raise ValueError( "Condition must be a method, string, or a result of or_() or and_()" ) - return func + return wrapper return decorator -def or_(*conditions: str | dict | Callable) -> dict: +def or_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition: """ Combines multiple conditions with OR logic for flow control. @@ -310,15 +342,15 @@ def or_(*conditions: str | dict | Callable) -> dict: Parameters ---------- - *conditions : Union[str, dict, Callable] + *conditions : Union[str, dict[str, Any], Callable[..., Any]] Variable number of conditions that can be: - str: Method names - - dict: Existing condition dictionaries (nested conditions) - - Callable: Method references + - dict[str, Any]: Existing condition dictionaries (nested conditions) + - Callable[..., Any]: Method references Returns ------- - dict + dict[str, Any] A condition dictionary with format: {"type": "OR", "conditions": list_of_conditions} where each condition can be a string (method name) or a nested dict @@ -338,20 +370,18 @@ def or_(*conditions: str | dict | Callable) -> dict: >>> def handle_nested(self): ... pass """ - processed_conditions: list[str | dict[str, Any]] = [] + processed_conditions: FlowConditions = [] for condition in conditions: - if isinstance(condition, dict): + if is_flow_condition_dict(condition) or is_flow_method_name(condition): processed_conditions.append(condition) - elif isinstance(condition, str): - processed_conditions.append(condition) - elif callable(condition): - processed_conditions.append(getattr(condition, "__name__", repr(condition))) + elif is_flow_method_callable(condition): + processed_conditions.append(condition.__name__) else: raise ValueError("Invalid condition in or_()") return {"type": "OR", "conditions": processed_conditions} -def and_(*conditions: str | dict | Callable) -> dict: +def and_(*conditions: str | FlowCondition | Callable[..., Any]) -> FlowCondition: """ Combines multiple conditions with AND logic for flow control. @@ -361,15 +391,15 @@ def and_(*conditions: str | dict | Callable) -> dict: Parameters ---------- - *conditions : Union[str, dict, Callable] + *conditions : Union[str, dict[str, Any], Callable[..., Any]] Variable number of conditions that can be: - str: Method names - - dict: Existing condition dictionaries (nested conditions) - - Callable: Method references + - dict[str, Any]: Existing condition dictionaries (nested conditions) + - Callable[..., Any]: Method references Returns ------- - dict + dict[str, Any] A condition dictionary with format: {"type": "AND", "conditions": list_of_conditions} where each condition can be a string (method name) or a nested dict @@ -389,20 +419,20 @@ def and_(*conditions: str | dict | Callable) -> dict: >>> def handle_nested(self): ... pass """ - processed_conditions: list[str | dict[str, Any]] = [] + processed_conditions: FlowConditions = [] for condition in conditions: - if isinstance(condition, dict): + if is_flow_condition_dict(condition) or is_flow_method_name(condition): processed_conditions.append(condition) - elif isinstance(condition, str): - processed_conditions.append(condition) - elif callable(condition): - processed_conditions.append(getattr(condition, "__name__", repr(condition))) + elif is_flow_method_callable(condition): + processed_conditions.append(condition.__name__) else: raise ValueError("Invalid condition in and_()") return {"type": "AND", "conditions": processed_conditions} -def _normalize_condition(condition: str | dict | list) -> dict: +def _normalize_condition( + condition: FlowConditions | FlowCondition | FlowMethodName, +) -> FlowCondition: """Normalize a condition to standard format with 'conditions' key. Args: @@ -411,20 +441,23 @@ def _normalize_condition(condition: str | dict | list) -> dict: Returns: Normalized dict with 'type' and 'conditions' keys """ - if isinstance(condition, str): + if is_flow_method_name(condition): return {"type": "OR", "conditions": [condition]} - if isinstance(condition, dict): + if is_flow_condition_dict(condition): if "conditions" in condition: return condition if "methods" in condition: return {"type": condition["type"], "conditions": condition["methods"]} return condition - if isinstance(condition, list): + if is_flow_condition_list(condition): return {"type": "OR", "conditions": condition} - return {"type": "OR", "conditions": [condition]} + + raise ValueError(f"Cannot normalize condition: {condition}") -def _extract_all_methods(condition: str | dict | list) -> list[str]: +def _extract_all_methods( + condition: str | FlowCondition | dict[str, Any] | list[Any], +) -> list[FlowMethodName]: """Extract all method names from a condition (including nested). Args: @@ -433,9 +466,9 @@ def _extract_all_methods(condition: str | dict | list) -> list[str]: Returns: List of all method names in the condition tree """ - if isinstance(condition, str): + if is_flow_method_name(condition): return [condition] - if isinstance(condition, dict): + if is_flow_condition_dict(condition): normalized = _normalize_condition(condition) methods = [] for sub_cond in normalized.get("conditions", []): @@ -450,15 +483,21 @@ def _extract_all_methods(condition: str | dict | list) -> list[str]: class FlowMeta(type): - def __new__(mcs, name, bases, dct): - cls = super().__new__(mcs, name, bases, dct) + def __new__( + mcs, + name: str, + bases: tuple[type, ...], + namespace: dict[str, Any], + **kwargs: Any, + ) -> type: + cls = super().__new__(mcs, name, bases, namespace) start_methods = [] listeners = {} router_paths = {} routers = set() - for attr_name, attr_value in dct.items(): + for attr_name, attr_value in namespace.items(): # Check for any flow-related attributes if ( hasattr(attr_value, "__is_flow_method__") @@ -471,10 +510,16 @@ class FlowMeta(type): start_methods.append(attr_name) # Register listeners and routers - if hasattr(attr_value, "__trigger_methods__"): + if ( + hasattr(attr_value, "__trigger_methods__") + and attr_value.__trigger_methods__ is not None + ): methods = attr_value.__trigger_methods__ condition_type = getattr(attr_value, "__condition_type__", "OR") - if hasattr(attr_value, "__trigger_condition__"): + if ( + hasattr(attr_value, "__trigger_condition__") + and attr_value.__trigger_condition__ is not None + ): listeners[attr_name] = attr_value.__trigger_condition__ else: listeners[attr_name] = (condition_type, methods) @@ -488,10 +533,10 @@ class FlowMeta(type): if possible_returns: router_paths[attr_name] = possible_returns - cls._start_methods = start_methods - cls._listeners = listeners - cls._routers = routers - cls._router_paths = router_paths + cls._start_methods = start_methods # type: ignore[attr-defined] + cls._listeners = listeners # type: ignore[attr-defined] + cls._routers = routers # type: ignore[attr-defined] + cls._router_paths = router_paths # type: ignore[attr-defined] return cls @@ -501,19 +546,19 @@ class Flow(Generic[T], metaclass=FlowMeta): type parameter T must be either dict[str, Any] or a subclass of BaseModel.""" - _printer = Printer() + _printer: ClassVar[Printer] = Printer() - _start_methods: ClassVar[list[str]] = [] - _listeners: ClassVar[dict[str, tuple[str, list[str]]]] = {} - _routers: ClassVar[set[str]] = set() - _router_paths: ClassVar[dict[str, list[str]]] = {} + _start_methods: ClassVar[list[FlowMethodName]] = [] + _listeners: ClassVar[dict[FlowMethodName, SimpleFlowCondition | FlowCondition]] = {} + _routers: ClassVar[set[FlowMethodName]] = set() + _router_paths: ClassVar[dict[FlowMethodName, list[FlowMethodName]]] = {} initial_state: type[T] | T | None = None name: str | None = None tracing: bool | None = False - def __class_getitem__(cls: type["Flow"], item: type[T]) -> type["Flow"]: + def __class_getitem__(cls: type[Flow[StateT]], item: type[T]) -> type[Flow[StateT]]: class _FlowGeneric(cls): # type: ignore - _initial_state_t = item # type: ignore + _initial_state_t = item _FlowGeneric.__name__ = f"{cls.__name__}[{item.__name__}]" return _FlowGeneric @@ -531,13 +576,16 @@ class Flow(Generic[T], metaclass=FlowMeta): **kwargs: Additional state values to initialize or override """ # Initialize basic instance attributes - self._methods: dict[str, Callable] = {} - self._method_execution_counts: dict[str, int] = {} - self._pending_and_listeners: dict[str, set[str]] = {} + self._methods: dict[FlowMethodName, FlowMethod[Any, Any]] = {} + self._method_execution_counts: dict[FlowMethodName, int] = {} + self._pending_and_listeners: dict[PendingListenerKey, set[FlowMethodName]] = {} self._method_outputs: list[Any] = [] # list to store all method outputs - self._completed_methods: set[str] = set() # Track completed methods for reload + self._completed_methods: set[FlowMethodName] = ( + set() + ) # Track completed methods for reload self._persistence: FlowPersistence | None = persistence self._is_execution_resuming: bool = False + self._event_futures: list[Future[None]] = [] # Initialize state with initial values self._state = self._create_initial_state() @@ -548,7 +596,7 @@ class Flow(Generic[T], metaclass=FlowMeta): or should_auto_collect_first_time_traces() ): trace_listener = TraceCollectionListener() - trace_listener.setup_listeners(crewai_event_bus) + trace_listener.setup_listeners(crewai_event_bus) # type: ignore[no-untyped-call] # Apply any additional kwargs if kwargs: self._initialize_state(kwargs) @@ -565,17 +613,11 @@ class Flow(Generic[T], metaclass=FlowMeta): for method_name in dir(self): if not method_name.startswith("_"): method = getattr(self, method_name) - # Check for any flow-related attributes - if ( - hasattr(method, "__is_flow_method__") - or hasattr(method, "__is_start_method__") - or hasattr(method, "__trigger_methods__") - or hasattr(method, "__is_router__") - ): + if is_flow_method(method): # Ensure method is bound to this instance if not hasattr(method, "__self__"): method = method.__get__(self, self.__class__) - self._methods[method_name] = method + self._methods[method.__name__] = method def _create_initial_state(self) -> T: """Create and initialize flow state with UUID and default values. @@ -616,13 +658,13 @@ class Flow(Generic[T], metaclass=FlowMeta): # Handle case where initial_state is a type (class) if isinstance(self.initial_state, type): if issubclass(self.initial_state, FlowState): - return cast(T, self.initial_state()) # Uses model defaults + return self.initial_state() # Uses model defaults if issubclass(self.initial_state, BaseModel): # Validate that the model has an id field model_fields = getattr(self.initial_state, "model_fields", None) if not model_fields or "id" not in model_fields: raise ValueError("Flow state model must have an 'id' field") - return cast(T, self.initial_state()) # Uses model defaults + return self.initial_state() # Uses model defaults if self.initial_state is dict: return cast(T, {"id": str(uuid4())}) @@ -810,7 +852,7 @@ class Flow(Generic[T], metaclass=FlowMeta): self._update_state_field("id", flow_id) self._completed_methods = { - name + cast(FlowMethodName, name) for method_data in execution_data.get("completed_methods", []) if (name := method_data.get("flow_method", {}).get("name")) is not None } @@ -839,7 +881,9 @@ class Flow(Generic[T], metaclass=FlowMeta): self._apply_state_updates(state_to_apply) for method in sorted_methods[:-1]: - method_name = method.get("flow_method", {}).get("name") + method_name = cast( + FlowMethodName | None, method.get("flow_method", {}).get("name") + ) if method_name: self._completed_methods.add(method_name) @@ -867,10 +911,10 @@ class Flow(Generic[T], metaclass=FlowMeta): emission is handled in the asynchronous method. """ - async def run_flow(): + async def _run_flow() -> Any: return await self.kickoff_async(inputs) - return asyncio.run(run_flow()) + return asyncio.run(_run_flow()) async def kickoff_async(self, inputs: dict[str, Any] | None = None) -> Any: """ @@ -916,8 +960,7 @@ class Flow(Generic[T], metaclass=FlowMeta): stored_state = self._persistence.load_state(restore_uuid) if stored_state: self._log_flow_event( - f"Loading flow state from memory for UUID: {restore_uuid}", - color="yellow", + f"Loading flow state from memory for UUID: {restore_uuid}" ) self._restore_state(stored_state) else: @@ -931,7 +974,7 @@ class Flow(Generic[T], metaclass=FlowMeta): self._initialize_state(filtered_inputs) # Emit FlowStartedEvent and log the start of the flow. - crewai_event_bus.emit( + future = crewai_event_bus.emit( self, FlowStartedEvent( type="flow_started", @@ -939,6 +982,8 @@ class Flow(Generic[T], metaclass=FlowMeta): inputs=inputs, ), ) + if future: + self._event_futures.append(future) self._log_flow_event( f"Flow started with ID: {self.flow_id}", color="bold_magenta" ) @@ -957,7 +1002,7 @@ class Flow(Generic[T], metaclass=FlowMeta): final_output = self._method_outputs[-1] if self._method_outputs else None - crewai_event_bus.emit( + future = crewai_event_bus.emit( self, FlowFinishedEvent( type="flow_finished", @@ -965,12 +1010,33 @@ class Flow(Generic[T], metaclass=FlowMeta): result=final_output, ), ) + if future: + self._event_futures.append(future) + + if self._event_futures: + await asyncio.gather( + *[asyncio.wrap_future(f) for f in self._event_futures] + ) + self._event_futures.clear() + + if ( + is_tracing_enabled() + or self.tracing + or should_auto_collect_first_time_traces() + ): + trace_listener = TraceCollectionListener() + if trace_listener.batch_manager.batch_owner_type == "flow": + if trace_listener.first_time_handler.is_first_time: + trace_listener.first_time_handler.mark_events_collected() # type: ignore[no-untyped-call] + trace_listener.first_time_handler.handle_execution_completion() # type: ignore[no-untyped-call] + else: + trace_listener.batch_manager.finalize_batch() return final_output finally: detach(flow_token) - async def _execute_start_method(self, start_method_name: str) -> None: + async def _execute_start_method(self, start_method_name: FlowMethodName) -> None: """ Executes a flow's start method and its triggered listeners. @@ -1006,10 +1072,12 @@ class Flow(Generic[T], metaclass=FlowMeta): await self._execute_listeners(start_method_name, result) def _inject_trigger_payload_for_start_method( - self, original_method: Callable - ) -> Callable: - def prepare_kwargs(*args, **kwargs): - inputs = baggage.get_baggage("flow_inputs") or {} + self, original_method: Callable[..., Any] + ) -> Callable[..., Any]: + def prepare_kwargs( + *args: Any, **kwargs: Any + ) -> tuple[tuple[Any, ...], dict[str, Any]]: + inputs = cast(dict[str, Any], baggage.get_baggage("flow_inputs") or {}) trigger_payload = inputs.get("crewai_trigger_payload") sig = inspect.signature(original_method) @@ -1019,19 +1087,18 @@ class Flow(Generic[T], metaclass=FlowMeta): kwargs["crewai_trigger_payload"] = trigger_payload elif trigger_payload is not None: self._log_flow_event( - f"Trigger payload available but {original_method.__name__} doesn't accept crewai_trigger_payload parameter", - color="yellow", + f"Trigger payload available but {original_method.__name__} doesn't accept crewai_trigger_payload parameter" ) return args, kwargs if asyncio.iscoroutinefunction(original_method): - async def enhanced_method(*args, **kwargs): + async def enhanced_method(*args: Any, **kwargs: Any) -> Any: args, kwargs = prepare_kwargs(*args, **kwargs) return await original_method(*args, **kwargs) else: - def enhanced_method(*args, **kwargs): + def enhanced_method(*args: Any, **kwargs: Any) -> Any: # type: ignore[misc] args, kwargs = prepare_kwargs(*args, **kwargs) return original_method(*args, **kwargs) @@ -1041,13 +1108,17 @@ class Flow(Generic[T], metaclass=FlowMeta): return enhanced_method async def _execute_method( - self, method_name: str, method: Callable, *args: Any, **kwargs: Any + self, + method_name: FlowMethodName, + method: Callable[..., Any], + *args: Any, + **kwargs: Any, ) -> Any: try: dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | ( kwargs or {} ) - crewai_event_bus.emit( + future = crewai_event_bus.emit( self, MethodExecutionStartedEvent( type="method_execution_started", @@ -1057,6 +1128,8 @@ class Flow(Generic[T], metaclass=FlowMeta): state=self._copy_state(), ), ) + if future: + self._event_futures.append(future) result = ( await method(*args, **kwargs) @@ -1070,7 +1143,7 @@ class Flow(Generic[T], metaclass=FlowMeta): ) self._completed_methods.add(method_name) - crewai_event_bus.emit( + future = crewai_event_bus.emit( self, MethodExecutionFinishedEvent( type="method_execution_finished", @@ -1080,10 +1153,12 @@ class Flow(Generic[T], metaclass=FlowMeta): result=result, ), ) + if future: + self._event_futures.append(future) return result except Exception as e: - crewai_event_bus.emit( + future = crewai_event_bus.emit( self, MethodExecutionFailedEvent( type="method_execution_failed", @@ -1092,9 +1167,13 @@ class Flow(Generic[T], metaclass=FlowMeta): error=e, ), ) + if future: + self._event_futures.append(future) raise e - async def _execute_listeners(self, trigger_method: str, result: Any) -> None: + async def _execute_listeners( + self, trigger_method: FlowMethodName, result: Any + ) -> None: """ Executes all listeners and routers triggered by a method completion. @@ -1137,9 +1216,9 @@ class Flow(Generic[T], metaclass=FlowMeta): if router_result: # Only add non-None results router_results.append(router_result) current_trigger = ( - str(router_result) + FlowMethodName(str(router_result)) if router_result is not None - else "" # Update for next iteration of router chain + else FlowMethodName("") # Update for next iteration of router chain ) # Now execute normal listeners for all router results and the original trigger @@ -1164,7 +1243,7 @@ class Flow(Generic[T], metaclass=FlowMeta): if method_name in self._listeners: condition_data = self._listeners[method_name] should_trigger = False - if isinstance(condition_data, tuple): + if is_simple_flow_condition(condition_data): _, trigger_methods = condition_data should_trigger = current_trigger in trigger_methods elif isinstance(condition_data, dict): @@ -1182,7 +1261,10 @@ class Flow(Generic[T], metaclass=FlowMeta): self._is_execution_resuming = was_resuming def _evaluate_condition( - self, condition: str | dict, trigger_method: str, listener_name: str + self, + condition: FlowMethodName | FlowCondition, + trigger_method: FlowMethodName, + listener_name: FlowMethodName, ) -> bool: """Recursively evaluate a condition (simple or nested). @@ -1194,10 +1276,10 @@ class Flow(Generic[T], metaclass=FlowMeta): Returns: True if the condition is satisfied, False otherwise """ - if isinstance(condition, str): + if is_flow_method_name(condition): return condition == trigger_method - if isinstance(condition, dict): + if is_flow_condition_dict(condition): normalized = _normalize_condition(condition) cond_type = normalized.get("type", "OR") sub_conditions = normalized.get("conditions", []) @@ -1209,7 +1291,7 @@ class Flow(Generic[T], metaclass=FlowMeta): ) if cond_type == "AND": - pending_key = f"{listener_name}:{id(condition)}" + pending_key = PendingListenerKey(f"{listener_name}:{id(condition)}") if pending_key not in self._pending_and_listeners: all_methods = set(_extract_all_methods(condition)) @@ -1227,8 +1309,8 @@ class Flow(Generic[T], metaclass=FlowMeta): return False def _find_triggered_methods( - self, trigger_method: str, router_only: bool - ) -> list[str]: + self, trigger_method: FlowMethodName, router_only: bool + ) -> list[FlowMethodName]: """ Finds all methods that should be triggered based on conditions. @@ -1254,7 +1336,7 @@ class Flow(Generic[T], metaclass=FlowMeta): - Maintains state for AND conditions using _pending_and_listeners - Separates router and normal listener evaluation """ - triggered = [] + triggered: list[FlowMethodName] = [] for listener_name, condition_data in self._listeners.items(): is_router = listener_name in self._routers @@ -1265,25 +1347,24 @@ class Flow(Generic[T], metaclass=FlowMeta): if not router_only and listener_name in self._start_methods: continue - if isinstance(condition_data, tuple): + if is_simple_flow_condition(condition_data): condition_type, methods = condition_data if condition_type == "OR": if trigger_method in methods: triggered.append(listener_name) elif condition_type == "AND": - if listener_name not in self._pending_and_listeners: - self._pending_and_listeners[listener_name] = set(methods) - if trigger_method in self._pending_and_listeners[listener_name]: - self._pending_and_listeners[listener_name].discard( - trigger_method - ) + pending_key = PendingListenerKey(listener_name) + if pending_key not in self._pending_and_listeners: + self._pending_and_listeners[pending_key] = set(methods) + if trigger_method in self._pending_and_listeners[pending_key]: + self._pending_and_listeners[pending_key].discard(trigger_method) - if not self._pending_and_listeners[listener_name]: + if not self._pending_and_listeners[pending_key]: triggered.append(listener_name) - self._pending_and_listeners.pop(listener_name, None) + self._pending_and_listeners.pop(pending_key, None) - elif isinstance(condition_data, dict): + elif is_flow_condition_dict(condition_data): if self._evaluate_condition( condition_data, trigger_method, listener_name ): @@ -1291,7 +1372,9 @@ class Flow(Generic[T], metaclass=FlowMeta): return triggered - async def _execute_single_listener(self, listener_name: str, result: Any) -> None: + async def _execute_single_listener( + self, listener_name: FlowMethodName, result: Any + ) -> None: """ Executes a single listener method with proper event handling. @@ -1350,7 +1433,10 @@ class Flow(Generic[T], metaclass=FlowMeta): raise def _log_flow_event( - self, message: str, color: PrinterColor | None = "yellow", level: str = "info" + self, + message: str, + color: PrinterColor = "yellow", + level: Literal["info", "warning"] = "info", ) -> None: """Centralized logging method for flow events. @@ -1372,8 +1458,7 @@ class Flow(Generic[T], metaclass=FlowMeta): self._printer.print(message, color=color) if level == "info": logger.info(message) - elif level == "warning": - logger.warning(message) + logger.warning(message) def plot(self, filename: str = "crewai_flow") -> None: crewai_event_bus.emit( diff --git a/src/crewai/flow/flow_trackable.py b/lib/crewai/src/crewai/flow/flow_trackable.py similarity index 91% rename from src/crewai/flow/flow_trackable.py rename to lib/crewai/src/crewai/flow/flow_trackable.py index 30303c272..eee558523 100644 --- a/src/crewai/flow/flow_trackable.py +++ b/lib/crewai/src/crewai/flow/flow_trackable.py @@ -1,8 +1,9 @@ import inspect from pydantic import BaseModel, Field, InstanceOf, model_validator +from typing_extensions import Self -from crewai.flow import Flow +from crewai.flow.flow import Flow class FlowTrackable(BaseModel): @@ -19,7 +20,7 @@ class FlowTrackable(BaseModel): ) @model_validator(mode="after") - def _set_parent_flow(self) -> "FlowTrackable": + def _set_parent_flow(self) -> Self: max_depth = 5 frame = inspect.currentframe() diff --git a/src/crewai/flow/flow_visualizer.py b/lib/crewai/src/crewai/flow/flow_visualizer.py similarity index 84% rename from src/crewai/flow/flow_visualizer.py rename to lib/crewai/src/crewai/flow/flow_visualizer.py index 5b50c3844..d928377e2 100644 --- a/src/crewai/flow/flow_visualizer.py +++ b/lib/crewai/src/crewai/flow/flow_visualizer.py @@ -1,10 +1,12 @@ # flow_visualizer.py +from __future__ import annotations import os +from typing import TYPE_CHECKING from pyvis.network import Network # type: ignore[import-untyped] -from crewai.flow.config import COLORS, NODE_STYLES +from crewai.flow.config import COLORS, NODE_STYLES, NodeStyles from crewai.flow.html_template_handler import HTMLTemplateHandler from crewai.flow.legend_generator import generate_legend_items_html, get_legend_items from crewai.flow.path_utils import safe_path_join @@ -14,12 +16,20 @@ from crewai.flow.visualization_utils import ( add_nodes_to_network, compute_positions, ) +from crewai.utilities.printer import Printer + + +if TYPE_CHECKING: + from crewai.flow.flow import Flow + + +_printer = Printer() class FlowPlot: """Handles the creation and rendering of flow visualization diagrams.""" - def __init__(self, flow): + def __init__(self, flow: Flow) -> None: """ Initialize FlowPlot with a flow object. @@ -33,18 +43,11 @@ class FlowPlot: ValueError If flow object is invalid or missing required attributes. """ - if not hasattr(flow, "_methods"): - raise ValueError("Invalid flow object: missing '_methods' attribute") - if not hasattr(flow, "_listeners"): - raise ValueError("Invalid flow object: missing '_listeners' attribute") - if not hasattr(flow, "_start_methods"): - raise ValueError("Invalid flow object: missing '_start_methods' attribute") - self.flow = flow self.colors = COLORS - self.node_styles = NODE_STYLES + self.node_styles: NodeStyles = NODE_STYLES - def plot(self, filename): + def plot(self, filename: str) -> None: """ Generate and save an HTML visualization of the flow. @@ -62,18 +65,10 @@ class FlowPlot: RuntimeError If network visualization generation fails. """ - if not filename or not isinstance(filename, str): - raise ValueError("Filename must be a non-empty string") try: # Initialize network - net = Network( - directed=True, - height="750px", - width="100%", - bgcolor=self.colors["bg"], - layout=None, - ) + net = Network(directed=True, height="750px", bgcolor=self.colors["bg"]) # Set options to disable physics net.set_options( @@ -128,7 +123,7 @@ class FlowPlot: try: with open(f"{filename}.html", "w", encoding="utf-8") as f: f.write(final_html_content) - print(f"Plot saved as {filename}.html") + _printer.print(f"Plot saved as {filename}.html", color="green") except IOError as e: raise IOError( f"Failed to save flow visualization to {filename}.html: {e!s}" @@ -143,7 +138,7 @@ class FlowPlot: finally: self._cleanup_pyvis_lib() - def _generate_final_html(self, network_html): + def _generate_final_html(self, network_html: str) -> str: """ Generate the final HTML content with network visualization and legend. @@ -190,7 +185,8 @@ class FlowPlot: except Exception as e: raise IOError(f"Failed to generate visualization HTML: {e!s}") from e - def _cleanup_pyvis_lib(self): + @staticmethod + def _cleanup_pyvis_lib() -> None: """ Clean up the generated lib folder from pyvis. @@ -204,12 +200,12 @@ class FlowPlot: shutil.rmtree(lib_folder) except ValueError as e: - print(f"Error validating lib folder path: {e}") + _printer.print(f"Error validating lib folder path: {e}", color="red") except Exception as e: - print(f"Error cleaning up lib folder: {e}") + _printer.print(f"Error cleaning up lib folder: {e}", color="red") -def plot_flow(flow, filename="flow_plot"): +def plot_flow(flow: Flow, filename: str = "flow_plot") -> None: """ Convenience function to create and save a flow visualization. diff --git a/lib/crewai/src/crewai/flow/flow_wrappers.py b/lib/crewai/src/crewai/flow/flow_wrappers.py new file mode 100644 index 000000000..0f3b1a5fe --- /dev/null +++ b/lib/crewai/src/crewai/flow/flow_wrappers.py @@ -0,0 +1,156 @@ +"""Wrapper classes for flow decorated methods with type-safe metadata.""" + +from __future__ import annotations + +from collections.abc import Callable, Sequence +import functools +import inspect +import types +from typing import Any, Generic, Literal, ParamSpec, TypeAlias, TypeVar, TypedDict + +from typing_extensions import Required, Self + +from crewai.flow.types import FlowMethodName + + +P = ParamSpec("P") +R = TypeVar("R") + +FlowConditionType: TypeAlias = Literal["OR", "AND"] + +# Simple flow condition stored as tuple (condition_type, method_list) +SimpleFlowCondition: TypeAlias = tuple[FlowConditionType, list[FlowMethodName]] + + +class FlowCondition(TypedDict, total=False): + """Type definition for flow trigger conditions. + + This is a recursive structure where conditions can contain nested FlowConditions. + """ + + type: Required[FlowConditionType] + conditions: Sequence[FlowMethodName | FlowCondition] + methods: list[FlowMethodName] + + +FlowConditions: TypeAlias = list[FlowMethodName | FlowCondition] + + +class FlowMethod(Generic[P, R]): + """Base wrapper for flow methods with decorator metadata. + + This class provides a type-safe way to add metadata to methods + while preserving their callable signature and attributes. It handles + both bound (instance) and unbound (class) method states. + """ + + def __init__(self, meth: Callable[P, R], instance: Any = None) -> None: + """Initialize the flow method wrapper. + + Args: + meth: The method to wrap. + instance: The instance to bind to (None for unbound). + """ + self._meth = meth + self._instance = instance + functools.update_wrapper(self, meth, updated=[]) + self.__name__: FlowMethodName = FlowMethodName(self.__name__) + self.__signature__ = inspect.signature(meth) + + if instance is not None: + self.__self__ = instance + + if inspect.iscoroutinefunction(meth): + try: + inspect.markcoroutinefunction(self) + except AttributeError: + import asyncio.coroutines + + self._is_coroutine = asyncio.coroutines._is_coroutine # type: ignore[attr-defined] + + def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R: + """Call the wrapped method. + + Args: + *args: Positional arguments. + **kwargs: Keyword arguments. + + Returns: + The result of calling the wrapped method. + """ + if self._instance is not None: + bound = types.MethodType(self._meth, self._instance) + return bound(*args, **kwargs) + return self._meth(*args, **kwargs) + + def unwrap(self) -> Callable[P, R]: + """Get the original unwrapped method. + + Returns: + The original method before decoration. + """ + return self._meth + + def __get__(self, instance: Any, owner: type | None = None) -> Self: + """Support the descriptor protocol for method binding. + + This allows the wrapped method to be properly bound to an instance + when accessed as an attribute. + + Args: + instance: The instance the method is being accessed from. + owner: The class that owns the method. + + Returns: + A new wrapper bound to the instance, or self if accessed from the class. + """ + if instance is None: + return self + + bound = type(self)(self._meth, instance) + + skip = { + "_meth", + "_instance", + "__name__", + "__doc__", + "__signature__", + "__self__", + "_is_coroutine", + "__module__", + "__qualname__", + "__annotations__", + "__type_params__", + "__wrapped__", + } + for attr, value in self.__dict__.items(): + if attr not in skip: + setattr(bound, attr, value) + + return bound + + +class StartMethod(FlowMethod[P, R]): + """Wrapper for methods marked as flow start points.""" + + __is_start_method__: bool = True + __trigger_methods__: list[FlowMethodName] | None = None + __condition_type__: FlowConditionType | None = None + __trigger_condition__: FlowCondition | None = None + + +class ListenMethod(FlowMethod[P, R]): + """Wrapper for methods marked as flow listeners.""" + + __trigger_methods__: list[FlowMethodName] | None = None + __condition_type__: FlowConditionType | None = None + __trigger_condition__: FlowCondition | None = None + + +class RouterMethod(FlowMethod[P, R]): + """Wrapper for methods marked as flow routers.""" + + __is_router__: bool = True + __trigger_methods__: list[FlowMethodName] | None = None + __condition_type__: FlowConditionType | None = None + __trigger_condition__: FlowCondition | None = None diff --git a/src/crewai/flow/html_template_handler.py b/lib/crewai/src/crewai/flow/html_template_handler.py similarity index 100% rename from src/crewai/flow/html_template_handler.py rename to lib/crewai/src/crewai/flow/html_template_handler.py diff --git a/src/crewai/flow/legend_generator.py b/lib/crewai/src/crewai/flow/legend_generator.py similarity index 85% rename from src/crewai/flow/legend_generator.py rename to lib/crewai/src/crewai/flow/legend_generator.py index f250dec20..760b013fa 100644 --- a/src/crewai/flow/legend_generator.py +++ b/lib/crewai/src/crewai/flow/legend_generator.py @@ -1,4 +1,3 @@ - def get_legend_items(colors): return [ {"label": "Start Method", "color": colors["start"]}, @@ -32,23 +31,23 @@ def generate_legend_items_html(legend_items): style = "dashed" if item["dashed"] else "solid" legend_items_html += f"""
-
-
{item['label']}
+
+
{item["label"]}
""" elif item.get("dashed") is not None: style = "dashed" if item["dashed"] else "solid" legend_items_html += f"""
-
-
{item['label']}
+
+
{item["label"]}
""" else: legend_items_html += f"""
-
-
{item['label']}
+
+
{item["label"]}
""" return legend_items_html diff --git a/src/crewai/flow/path_utils.py b/lib/crewai/src/crewai/flow/path_utils.py similarity index 100% rename from src/crewai/flow/path_utils.py rename to lib/crewai/src/crewai/flow/path_utils.py diff --git a/src/crewai/flow/persistence/__init__.py b/lib/crewai/src/crewai/flow/persistence/__init__.py similarity index 99% rename from src/crewai/flow/persistence/__init__.py rename to lib/crewai/src/crewai/flow/persistence/__init__.py index 3a542f52c..50de9abcc 100644 --- a/src/crewai/flow/persistence/__init__.py +++ b/lib/crewai/src/crewai/flow/persistence/__init__.py @@ -12,6 +12,7 @@ from crewai.flow.persistence.base import FlowPersistence from crewai.flow.persistence.decorators import persist from crewai.flow.persistence.sqlite import SQLiteFlowPersistence + __all__ = ["FlowPersistence", "SQLiteFlowPersistence", "persist"] StateType = TypeVar("StateType", bound=dict[str, Any] | BaseModel) diff --git a/src/crewai/flow/persistence/base.py b/lib/crewai/src/crewai/flow/persistence/base.py similarity index 91% rename from src/crewai/flow/persistence/base.py rename to lib/crewai/src/crewai/flow/persistence/base.py index df7f00add..fd7b27566 100644 --- a/src/crewai/flow/persistence/base.py +++ b/lib/crewai/src/crewai/flow/persistence/base.py @@ -1,19 +1,19 @@ """Base class for flow state persistence.""" -import abc +from abc import ABC, abstractmethod from typing import Any from pydantic import BaseModel -class FlowPersistence(abc.ABC): +class FlowPersistence(ABC): """Abstract base class for flow state persistence. This class defines the interface that all persistence implementations must follow. It supports both structured (Pydantic BaseModel) and unstructured (dict) states. """ - @abc.abstractmethod + @abstractmethod def init_db(self) -> None: """Initialize the persistence backend. @@ -23,7 +23,7 @@ class FlowPersistence(abc.ABC): - Setting up indexes """ - @abc.abstractmethod + @abstractmethod def save_state( self, flow_uuid: str, method_name: str, state_data: dict[str, Any] | BaseModel ) -> None: @@ -35,7 +35,7 @@ class FlowPersistence(abc.ABC): state_data: Current state data (either dict or Pydantic model) """ - @abc.abstractmethod + @abstractmethod def load_state(self, flow_uuid: str) -> dict[str, Any] | None: """Load the most recent state for a given flow UUID. diff --git a/src/crewai/flow/persistence/decorators.py b/lib/crewai/src/crewai/flow/persistence/decorators.py similarity index 97% rename from src/crewai/flow/persistence/decorators.py rename to lib/crewai/src/crewai/flow/persistence/decorators.py index fc7ed6bc0..3f5be17db 100644 --- a/src/crewai/flow/persistence/decorators.py +++ b/lib/crewai/src/crewai/flow/persistence/decorators.py @@ -6,6 +6,7 @@ Example: from crewai.flow.flow import Flow, start from crewai.flow.persistence import persist, SQLiteFlowPersistence + class MyFlow(Flow): @start() @persist(SQLiteFlowPersistence()) @@ -21,15 +22,13 @@ Example: ``` """ +from __future__ import annotations + import asyncio +from collections.abc import Callable import functools import logging -from collections.abc import Callable -from typing import ( - Any, - TypeVar, - cast, -) +from typing import TYPE_CHECKING, Any, ClassVar, Final, TypeVar, cast from pydantic import BaseModel @@ -37,11 +36,16 @@ from crewai.flow.persistence.base import FlowPersistence from crewai.flow.persistence.sqlite import SQLiteFlowPersistence from crewai.utilities.printer import Printer + +if TYPE_CHECKING: + from crewai.flow.flow import Flow + + logger = logging.getLogger(__name__) T = TypeVar("T") # Constants for log messages -LOG_MESSAGES = { +LOG_MESSAGES: Final[dict[str, str]] = { "save_state": "Saving flow state to memory for ID: {}", "save_error": "Failed to persist state for method {}: {}", "state_missing": "Flow instance has no state", @@ -52,12 +56,12 @@ LOG_MESSAGES = { class PersistenceDecorator: """Class to handle flow state persistence with consistent logging.""" - _printer = Printer() # Class-level printer instance + _printer: ClassVar[Printer] = Printer() @classmethod def persist_state( cls, - flow_instance: Any, + flow_instance: Flow, method_name: str, persistence_instance: FlowPersistence, verbose: bool = False, diff --git a/src/crewai/flow/persistence/sqlite.py b/lib/crewai/src/crewai/flow/persistence/sqlite.py similarity index 96% rename from src/crewai/flow/persistence/sqlite.py rename to lib/crewai/src/crewai/flow/persistence/sqlite.py index 1163d86c5..a8016c606 100644 --- a/src/crewai/flow/persistence/sqlite.py +++ b/lib/crewai/src/crewai/flow/persistence/sqlite.py @@ -2,15 +2,16 @@ SQLite-based implementation of flow state persistence. """ -import json -import sqlite3 from datetime import datetime, timezone +import json from pathlib import Path +import sqlite3 from typing import Any from pydantic import BaseModel from crewai.flow.persistence.base import FlowPersistence +from crewai.utilities.paths import db_storage_path class SQLiteFlowPersistence(FlowPersistence): @@ -21,9 +22,7 @@ class SQLiteFlowPersistence(FlowPersistence): moderate performance requirements. """ - db_path: str - - def __init__(self, db_path: str | None = None): + def __init__(self, db_path: str | None = None) -> None: """Initialize SQLite persistence. Args: @@ -33,7 +32,6 @@ class SQLiteFlowPersistence(FlowPersistence): Raises: ValueError: If db_path is invalid """ - from crewai.utilities.paths import db_storage_path # Get path from argument or default location path = db_path or str(Path(db_storage_path()) / "flow_states.db") diff --git a/src/crewai/flow/types.py b/lib/crewai/src/crewai/flow/types.py similarity index 82% rename from src/crewai/flow/types.py rename to lib/crewai/src/crewai/flow/types.py index 38e3b7376..819f9b09a 100644 --- a/src/crewai/flow/types.py +++ b/lib/crewai/src/crewai/flow/types.py @@ -4,11 +4,37 @@ This module contains TypedDict definitions and type aliases used throughout the Flow system. """ -from typing import Any, TypedDict +from typing import ( + Annotated, + Any, + NewType, + ParamSpec, + Protocol, + TypeVar, + TypedDict, +) from typing_extensions import NotRequired, Required +P = ParamSpec("P") +R = TypeVar("R", covariant=True) + +FlowMethodName = NewType("FlowMethodName", str) +PendingListenerKey = NewType( + "PendingListenerKey", + Annotated[str, "nested flow conditions use 'listener_name:object_id'"], +) + + +class FlowMethodCallable(Protocol[P, R]): + """A callable that can be used as a flow method reference.""" + + __name__: FlowMethodName + + def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R: ... + + class FlowMethodData(TypedDict): """Flow method information. diff --git a/src/crewai/flow/utils.py b/lib/crewai/src/crewai/flow/utils.py similarity index 74% rename from src/crewai/flow/utils.py rename to lib/crewai/src/crewai/flow/utils.py index 74e617bee..753eb280a 100644 --- a/src/crewai/flow/utils.py +++ b/lib/crewai/src/crewai/flow/utils.py @@ -14,11 +14,25 @@ Example """ import ast +from collections import defaultdict, deque import inspect import textwrap -from collections import defaultdict, deque from typing import Any +from typing_extensions import TypeIs + +from crewai.flow.flow_wrappers import ( + FlowCondition, + FlowConditions, + FlowMethod, + SimpleFlowCondition, +) +from crewai.flow.types import FlowMethodCallable, FlowMethodName +from crewai.utilities.printer import Printer + + +_printer = Printer() + def get_possible_return_constants(function: Any) -> list[str] | None: try: @@ -27,7 +41,10 @@ def get_possible_return_constants(function: Any) -> list[str] | None: # Can't get source code return None except Exception as e: - print(f"Error retrieving source code for function {function.__name__}: {e}") + _printer.print( + f"Error retrieving source code for function {function.__name__}: {e}", + color="red", + ) return None try: @@ -36,16 +53,25 @@ def get_possible_return_constants(function: Any) -> list[str] | None: # Parse the source code into an AST code_ast = ast.parse(source) except IndentationError as e: - print(f"IndentationError while parsing source code of {function.__name__}: {e}") - print(f"Source code:\n{source}") + _printer.print( + f"IndentationError while parsing source code of {function.__name__}: {e}", + color="red", + ) + _printer.print(f"Source code:\n{source}", color="yellow") return None except SyntaxError as e: - print(f"SyntaxError while parsing source code of {function.__name__}: {e}") - print(f"Source code:\n{source}") + _printer.print( + f"SyntaxError while parsing source code of {function.__name__}: {e}", + color="red", + ) + _printer.print(f"Source code:\n{source}", color="yellow") return None except Exception as e: - print(f"Unexpected error while parsing source code of {function.__name__}: {e}") - print(f"Source code:\n{source}") + _printer.print( + f"Unexpected error while parsing source code of {function.__name__}: {e}", + color="red", + ) + _printer.print(f"Source code:\n{source}", color="yellow") return None return_values = set() @@ -374,3 +400,120 @@ def process_router_paths(flow, current, current_level, levels, queue): ): levels[listener_name] = current_level + 1 queue.append(listener_name) + + +def is_flow_method_name(obj: Any) -> TypeIs[FlowMethodName]: + """Check if the object is a valid flow method name. + + Args: + obj: The object to check. + Returns: + True if the object is a valid flow method name, False otherwise. + """ + return isinstance(obj, str) + + +def is_flow_method_callable(obj: Any) -> TypeIs[FlowMethodCallable]: + """Check if the object is a callable flow method. + + Args: + obj: The object to check. + + Returns: + True if the object is a callable, False otherwise. + """ + return callable(obj) and hasattr(obj, "__name__") + + +def is_flow_condition_list(obj: Any) -> TypeIs[FlowConditions]: + """Check if the object is a list of FlowCondition dictionaries. + + Args: + obj: The object to check. + + Returns: + True if the object is a list of FlowCondition dictionaries, False otherwise. + """ + if not isinstance(obj, list): + return False + + for item in obj: + if not (is_flow_method_name(item) or is_flow_condition_dict(item)): + return False + + return True + + +def is_simple_flow_condition(obj: Any) -> TypeIs[SimpleFlowCondition]: + """Check if the object is a simple flow condition tuple. + + Args: + obj: The object to check. + + Returns: + True if the object is a (condition_type, methods) tuple, False otherwise. + """ + return ( + isinstance(obj, tuple) + and len(obj) == 2 + and isinstance(obj[0], str) + and isinstance(obj[1], list) + ) + + +def is_flow_method(obj: Any) -> TypeIs[FlowMethod[Any, Any]]: + """Check if the object is a flow method wrapper. + + Checks for attributes added by @start, @listen, or @router decorators. + + Args: + obj: The object to check. + + Returns: + True if the object is a FlowMethod subclass (StartMethod, ListenMethod, or RouterMethod). + """ + return ( + hasattr(obj, "__is_flow_method__") + or hasattr(obj, "__is_start_method__") + or hasattr(obj, "__trigger_methods__") + or hasattr(obj, "__is_router__") + ) + + +def is_flow_condition_dict(obj: Any) -> TypeIs[FlowCondition]: + """Check if the object matches the FlowCondition structure. + + Args: + obj: The object to check. + + Returns: + True if the object is a valid FlowCondition dictionary, False otherwise. + """ + if not isinstance(obj, dict): + return False + + type_value = obj.get("type") + if type_value not in ("AND", "OR"): + return False + + if "conditions" in obj: + conditions = obj["conditions"] + if not isinstance(conditions, list): + return False + for cond in conditions: + if not ( + isinstance(cond, str) + or (isinstance(cond, dict) and is_flow_condition_dict(cond)) + ): + return False + + if "methods" in obj: + methods = obj["methods"] + if not (isinstance(methods, list) and all(isinstance(m, str) for m in methods)): + return False + + allowed_keys = {"type", "conditions", "methods"} + if not set(obj).issubset(allowed_keys): + return False + + return True diff --git a/src/crewai/flow/visualization_utils.py b/lib/crewai/src/crewai/flow/visualization_utils.py similarity index 93% rename from src/crewai/flow/visualization_utils.py rename to lib/crewai/src/crewai/flow/visualization_utils.py index 721aef23b..89be3afcd 100644 --- a/src/crewai/flow/visualization_utils.py +++ b/lib/crewai/src/crewai/flow/visualization_utils.py @@ -19,12 +19,25 @@ import ast import inspect from typing import Any -from .utils import ( +from crewai.flow.config import ( + CrewNodeStyle, + FlowColors, + MethodNodeStyle, + NodeStyles, + RouterNodeStyle, + StartNodeStyle, +) +from crewai.flow.utils import ( build_ancestor_dict, build_parent_children_dict, get_child_index, is_ancestor, ) +from crewai.utilities.printer import Printer + + + +_printer = Printer() def method_calls_crew(method: Any) -> bool: @@ -51,7 +64,7 @@ def method_calls_crew(method: Any) -> bool: source = inspect.cleandoc(source) tree = ast.parse(source) except Exception as e: - print(f"Could not parse method {method.__name__}: {e}") + _printer.print(f"Could not parse method {method.__name__}: {e}", color="red") return False class CrewCallVisitor(ast.NodeVisitor): @@ -75,7 +88,7 @@ def add_nodes_to_network( net: Any, flow: Any, node_positions: dict[str, tuple[float, float]], - node_styles: dict[str, dict[str, Any]], + node_styles: NodeStyles, ) -> None: """ Add nodes to the network visualization with appropriate styling. @@ -103,6 +116,9 @@ def add_nodes_to_network( def human_friendly_label(method_name): return method_name.replace("_", " ").title() + node_style: ( + StartNodeStyle | RouterNodeStyle | CrewNodeStyle | MethodNodeStyle | None + ) for method_name, (x, y) in node_positions.items(): method = flow._methods.get(method_name) if hasattr(method, "__is_start_method__"): @@ -183,7 +199,7 @@ def add_edges( net: Any, flow: Any, node_positions: dict[str, tuple[float, float]], - colors: dict[str, str], + colors: FlowColors, ) -> None: edge_smooth: dict[str, str | float] = {"type": "continuous"} # Default value """ @@ -263,8 +279,9 @@ def add_edges( # If it's a known router edge and the method is known, don't warn. # This means the path is legitimate, just not reflected as nodes here. if not (is_router_edge and method_known): - print( - f"Warning: No node found for '{trigger}' or '{method_name}'. Skipping edge." + _printer.print( + f"Warning: No node found for '{trigger}' or '{method_name}'. Skipping edge.", + color="yellow", ) # Edges for router return paths @@ -318,6 +335,7 @@ def add_edges( # Same check here: known router edge and known method? method_known = listener_name in flow._methods if not method_known: - print( - f"Warning: No node found for '{router_method_name}' or '{listener_name}'. Skipping edge." + _printer.print( + f"Warning: No node found for '{router_method_name}' or '{listener_name}'. Skipping edge.", + color="yellow", ) diff --git a/lib/crewai/src/crewai/knowledge/__init__.py b/lib/crewai/src/crewai/knowledge/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/knowledge/knowledge.py b/lib/crewai/src/crewai/knowledge/knowledge.py similarity index 98% rename from src/crewai/knowledge/knowledge.py rename to lib/crewai/src/crewai/knowledge/knowledge.py index c1f326c9d..cb53ab3d6 100644 --- a/src/crewai/knowledge/knowledge.py +++ b/lib/crewai/src/crewai/knowledge/knowledge.py @@ -7,6 +7,7 @@ from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage from crewai.rag.embeddings.types import EmbedderConfig from crewai.rag.types import SearchResult + os.environ["TOKENIZERS_PARALLELISM"] = "false" # removes logging from fastembed @@ -61,7 +62,7 @@ class Knowledge(BaseModel): score_threshold=score_threshold, ) - def add_sources(self): + def add_sources(self) -> None: try: for source in self.sources: source.storage = self.storage diff --git a/src/crewai/knowledge/knowledge_config.py b/lib/crewai/src/crewai/knowledge/knowledge_config.py similarity index 100% rename from src/crewai/knowledge/knowledge_config.py rename to lib/crewai/src/crewai/knowledge/knowledge_config.py diff --git a/lib/crewai/src/crewai/knowledge/source/__init__.py b/lib/crewai/src/crewai/knowledge/source/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/knowledge/source/base_file_knowledge_source.py b/lib/crewai/src/crewai/knowledge/source/base_file_knowledge_source.py similarity index 100% rename from src/crewai/knowledge/source/base_file_knowledge_source.py rename to lib/crewai/src/crewai/knowledge/source/base_file_knowledge_source.py diff --git a/src/crewai/knowledge/source/base_knowledge_source.py b/lib/crewai/src/crewai/knowledge/source/base_knowledge_source.py similarity index 100% rename from src/crewai/knowledge/source/base_knowledge_source.py rename to lib/crewai/src/crewai/knowledge/source/base_knowledge_source.py diff --git a/src/crewai/knowledge/source/crew_docling_source.py b/lib/crewai/src/crewai/knowledge/source/crew_docling_source.py similarity index 93% rename from src/crewai/knowledge/source/crew_docling_source.py rename to lib/crewai/src/crewai/knowledge/source/crew_docling_source.py index 9a371866c..9061fe3fd 100644 --- a/src/crewai/knowledge/source/crew_docling_source.py +++ b/lib/crewai/src/crewai/knowledge/source/crew_docling_source.py @@ -1,7 +1,10 @@ +from __future__ import annotations + from collections.abc import Iterator from pathlib import Path from urllib.parse import urlparse + try: from docling.datamodel.base_models import ( # type: ignore[import-not-found] InputFormat, @@ -47,8 +50,8 @@ class CrewDoclingSource(BaseKnowledgeSource): file_paths: list[Path | str] = Field(default_factory=list) chunks: list[str] = Field(default_factory=list) safe_file_paths: list[Path | str] = Field(default_factory=list) - content: list["DoclingDocument"] = Field(default_factory=list) - document_converter: "DocumentConverter" = Field( + content: list[DoclingDocument] = Field(default_factory=list) + document_converter: DocumentConverter = Field( default_factory=lambda: DocumentConverter( allowed_formats=[ InputFormat.MD, @@ -74,7 +77,7 @@ class CrewDoclingSource(BaseKnowledgeSource): self.safe_file_paths = self.validate_content() self.content = self._load_content() - def _load_content(self) -> list["DoclingDocument"]: + def _load_content(self) -> list[DoclingDocument]: try: return self._convert_source_to_docling_documents() except ConversionError as e: @@ -96,11 +99,11 @@ class CrewDoclingSource(BaseKnowledgeSource): self.chunks.extend(list(new_chunks_iterable)) self._save_documents() - def _convert_source_to_docling_documents(self) -> list["DoclingDocument"]: + def _convert_source_to_docling_documents(self) -> list[DoclingDocument]: conv_results_iter = self.document_converter.convert_all(self.safe_file_paths) return [result.document for result in conv_results_iter] - def _chunk_doc(self, doc: "DoclingDocument") -> Iterator[str]: + def _chunk_doc(self, doc: DoclingDocument) -> Iterator[str]: chunker = HierarchicalChunker() for chunk in chunker.chunk(doc): yield chunk.text diff --git a/src/crewai/knowledge/source/csv_knowledge_source.py b/lib/crewai/src/crewai/knowledge/source/csv_knowledge_source.py similarity index 100% rename from src/crewai/knowledge/source/csv_knowledge_source.py rename to lib/crewai/src/crewai/knowledge/source/csv_knowledge_source.py diff --git a/src/crewai/knowledge/source/excel_knowledge_source.py b/lib/crewai/src/crewai/knowledge/source/excel_knowledge_source.py similarity index 100% rename from src/crewai/knowledge/source/excel_knowledge_source.py rename to lib/crewai/src/crewai/knowledge/source/excel_knowledge_source.py diff --git a/src/crewai/knowledge/source/json_knowledge_source.py b/lib/crewai/src/crewai/knowledge/source/json_knowledge_source.py similarity index 100% rename from src/crewai/knowledge/source/json_knowledge_source.py rename to lib/crewai/src/crewai/knowledge/source/json_knowledge_source.py diff --git a/src/crewai/knowledge/source/pdf_knowledge_source.py b/lib/crewai/src/crewai/knowledge/source/pdf_knowledge_source.py similarity index 100% rename from src/crewai/knowledge/source/pdf_knowledge_source.py rename to lib/crewai/src/crewai/knowledge/source/pdf_knowledge_source.py diff --git a/src/crewai/knowledge/source/string_knowledge_source.py b/lib/crewai/src/crewai/knowledge/source/string_knowledge_source.py similarity index 100% rename from src/crewai/knowledge/source/string_knowledge_source.py rename to lib/crewai/src/crewai/knowledge/source/string_knowledge_source.py diff --git a/src/crewai/knowledge/source/text_file_knowledge_source.py b/lib/crewai/src/crewai/knowledge/source/text_file_knowledge_source.py similarity index 100% rename from src/crewai/knowledge/source/text_file_knowledge_source.py rename to lib/crewai/src/crewai/knowledge/source/text_file_knowledge_source.py diff --git a/lib/crewai/src/crewai/knowledge/storage/__init__.py b/lib/crewai/src/crewai/knowledge/storage/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/knowledge/storage/base_knowledge_storage.py b/lib/crewai/src/crewai/knowledge/storage/base_knowledge_storage.py similarity index 82% rename from src/crewai/knowledge/storage/base_knowledge_storage.py rename to lib/crewai/src/crewai/knowledge/storage/base_knowledge_storage.py index 2bc63fb30..044837a07 100644 --- a/src/crewai/knowledge/storage/base_knowledge_storage.py +++ b/lib/crewai/src/crewai/knowledge/storage/base_knowledge_storage.py @@ -1,7 +1,11 @@ -from abc import ABC, abstractmethod -from typing import Any +from __future__ import annotations -from crewai.rag.types import SearchResult +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Any + + +if TYPE_CHECKING: + from crewai.rag.types import SearchResult class BaseKnowledgeStorage(ABC): diff --git a/src/crewai/knowledge/storage/knowledge_storage.py b/lib/crewai/src/crewai/knowledge/storage/knowledge_storage.py similarity index 100% rename from src/crewai/knowledge/storage/knowledge_storage.py rename to lib/crewai/src/crewai/knowledge/storage/knowledge_storage.py index 8019d4d11..7eed0e0de 100644 --- a/src/crewai/knowledge/storage/knowledge_storage.py +++ b/lib/crewai/src/crewai/knowledge/storage/knowledge_storage.py @@ -1,7 +1,7 @@ import logging import traceback -import warnings from typing import Any, cast +import warnings from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage from crewai.rag.chromadb.config import ChromaDBConfig diff --git a/src/crewai/knowledge/utils/__init__.py b/lib/crewai/src/crewai/knowledge/utils/__init__.py similarity index 100% rename from src/crewai/knowledge/utils/__init__.py rename to lib/crewai/src/crewai/knowledge/utils/__init__.py diff --git a/src/crewai/knowledge/utils/knowledge_utils.py b/lib/crewai/src/crewai/knowledge/utils/knowledge_utils.py similarity index 100% rename from src/crewai/knowledge/utils/knowledge_utils.py rename to lib/crewai/src/crewai/knowledge/utils/knowledge_utils.py diff --git a/src/crewai/lite_agent.py b/lib/crewai/src/crewai/lite_agent.py similarity index 92% rename from src/crewai/lite_agent.py rename to lib/crewai/src/crewai/lite_agent.py index b80f499b4..d613681e2 100644 --- a/src/crewai/lite_agent.py +++ b/lib/crewai/src/crewai/lite_agent.py @@ -1,7 +1,6 @@ import asyncio -import inspect -import uuid from collections.abc import Callable +import inspect from typing import ( Any, Literal, @@ -9,6 +8,7 @@ from typing import ( get_args, get_origin, ) +import uuid from pydantic import ( UUID4, @@ -23,7 +23,7 @@ from typing_extensions import Self from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess -from crewai.agents.cache import CacheHandler +from crewai.agents.cache.cache_handler import CacheHandler from crewai.agents.parser import ( AgentAction, AgentFinish, @@ -37,10 +37,11 @@ from crewai.events.types.agent_events import ( ) from crewai.events.types.logging_events import AgentLogsExecutionEvent from crewai.flow.flow_trackable import FlowTrackable -from crewai.llm import LLM, BaseLLM +from crewai.lite_agent_output import LiteAgentOutput +from crewai.llm import LLM +from crewai.llms.base_llm import BaseLLM from crewai.tools.base_tool import BaseTool from crewai.tools.structured_tool import CrewStructuredTool -from crewai.utilities import I18N from crewai.utilities.agent_utils import ( enforce_rpm_limit, format_message_for_llm, @@ -59,6 +60,8 @@ from crewai.utilities.agent_utils import ( ) from crewai.utilities.converter import generate_model_description from crewai.utilities.guardrail import process_guardrail +from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType +from crewai.utilities.i18n import I18N from crewai.utilities.llm_utils import create_llm from crewai.utilities.printer import Printer from crewai.utilities.token_counter_callback import TokenCalcHandler @@ -66,33 +69,6 @@ from crewai.utilities.tool_utils import execute_tool_and_check_finality from crewai.utilities.types import LLMMessage -class LiteAgentOutput(BaseModel): - """Class that represents the result of a LiteAgent execution.""" - - model_config = {"arbitrary_types_allowed": True} - - raw: str = Field(description="Raw output of the agent", default="") - pydantic: BaseModel | None = Field( - description="Pydantic output of the agent", default=None - ) - agent_role: str = Field(description="Role of the agent that produced this output") - usage_metrics: dict[str, Any] | None = Field( - description="Token usage metrics for this execution", default=None - ) - - def to_dict(self) -> dict[str, Any]: - """Convert pydantic_output to a dictionary.""" - if self.pydantic: - return self.pydantic.model_dump() - return {} - - def __str__(self) -> str: - """String representation of the output.""" - if self.pydantic: - return str(self.pydantic) - return self.raw - - class LiteAgent(FlowTrackable, BaseModel): """ A lightweight agent that can process messages and use tools. @@ -146,7 +122,9 @@ class LiteAgent(FlowTrackable, BaseModel): default=None, description="Callback to check if the request is within the RPM limit", ) - i18n: I18N = Field(default=I18N(), description="Internationalization settings.") + i18n: I18N = Field( + default_factory=I18N, description="Internationalization settings." + ) # Output and Formatting Properties response_format: type[BaseModel] | None = Field( @@ -156,11 +134,11 @@ class LiteAgent(FlowTrackable, BaseModel): default=False, description="Whether to print execution details" ) callbacks: list[Callable] = Field( - default=[], description="Callbacks to be used for the agent" + default_factory=list, description="Callbacks to be used for the agent" ) # Guardrail Properties - guardrail: Callable[[LiteAgentOutput], tuple[bool, Any]] | str | None = Field( + guardrail: GuardrailType | None = Field( default=None, description="Function or string description of a guardrail to validate agent output", ) @@ -170,7 +148,7 @@ class LiteAgent(FlowTrackable, BaseModel): # State and Results tools_results: list[dict[str, Any]] = Field( - default=[], description="Results of the tools used by the agent." + default_factory=list, description="Results of the tools used by the agent." ) # Reference of Agent @@ -185,11 +163,11 @@ class LiteAgent(FlowTrackable, BaseModel): _messages: list[LLMMessage] = PrivateAttr(default_factory=list) _iterations: int = PrivateAttr(default=0) _printer: Printer = PrivateAttr(default_factory=Printer) - _guardrail: Callable | None = PrivateAttr(default=None) + _guardrail: GuardrailCallable | None = PrivateAttr(default=None) _guardrail_retry_count: int = PrivateAttr(default=0) @model_validator(mode="after") - def setup_llm(self): + def setup_llm(self) -> Self: """Set up the LLM and other components after initialization.""" self.llm = create_llm(self.llm) if not isinstance(self.llm, BaseLLM): @@ -221,7 +199,10 @@ class LiteAgent(FlowTrackable, BaseModel): raise TypeError( f"Guardrail requires LLM instance of type BaseLLM, got {type(self.llm).__name__}" ) - self._guardrail = LLMGuardrail(description=self.guardrail, llm=self.llm) + self._guardrail = cast( + GuardrailCallable, + LLMGuardrail(description=self.guardrail, llm=self.llm), + ) return self @@ -352,7 +333,10 @@ class LiteAgent(FlowTrackable, BaseModel): ) # Calculate token usage metrics - usage_metrics = self._token_process.get_summary() + if isinstance(self.llm, BaseLLM): + usage_metrics = self.llm.get_token_usage_summary() + else: + usage_metrics = self._token_process.get_summary() # Create output output = LiteAgentOutput( @@ -402,7 +386,10 @@ class LiteAgent(FlowTrackable, BaseModel): elif isinstance(guardrail_result.result, BaseModel): output.pydantic = guardrail_result.result - usage_metrics = self._token_process.get_summary() + if isinstance(self.llm, BaseLLM): + usage_metrics = self.llm.get_token_usage_summary() + else: + usage_metrics = self._token_process.get_summary() output.usage_metrics = usage_metrics.model_dump() if usage_metrics else None # Emit completion event diff --git a/lib/crewai/src/crewai/lite_agent_output.py b/lib/crewai/src/crewai/lite_agent_output.py new file mode 100644 index 000000000..582f52cdd --- /dev/null +++ b/lib/crewai/src/crewai/lite_agent_output.py @@ -0,0 +1,32 @@ +"""Output class for LiteAgent execution results.""" + +from __future__ import annotations + +from typing import Any + +from pydantic import BaseModel, Field + + +class LiteAgentOutput(BaseModel): + """Class that represents the result of a LiteAgent execution.""" + + model_config = {"arbitrary_types_allowed": True} + + raw: str = Field(description="Raw output of the agent", default="") + pydantic: BaseModel | None = Field( + description="Pydantic output of the agent", default=None + ) + agent_role: str = Field(description="Role of the agent that produced this output") + usage_metrics: dict[str, Any] | None = Field( + description="Token usage metrics for this execution", default=None + ) + + def to_dict(self) -> dict[str, Any]: + """Convert pydantic_output to a dictionary.""" + if self.pydantic: + return self.pydantic.model_dump() + return {} + + def __str__(self) -> str: + """Return the raw output as a string.""" + return self.raw diff --git a/src/crewai/llm.py b/lib/crewai/src/crewai/llm.py similarity index 84% rename from src/crewai/llm.py rename to lib/crewai/src/crewai/llm.py index 733b46c79..66eebec6f 100644 --- a/src/crewai/llm.py +++ b/lib/crewai/src/crewai/llm.py @@ -1,13 +1,16 @@ +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Callable +from datetime import datetime import io import json import logging import os import sys import threading -from collections import defaultdict -from collections.abc import Callable -from datetime import datetime from typing import ( + TYPE_CHECKING, Any, Final, Literal, @@ -17,7 +20,6 @@ from typing import ( ) from dotenv import load_dotenv -from litellm.types.utils import ChatCompletionDeltaToolCall from pydantic import BaseModel, Field from crewai.events.event_bus import crewai_event_bus @@ -38,20 +40,44 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import ( LLMContextLengthExceededError, ) from crewai.utilities.logger_utils import suppress_warnings +from crewai.utilities.types import LLMMessage -with suppress_warnings(): + +if TYPE_CHECKING: + from litellm import Choices + from litellm.exceptions import ContextWindowExceededError + from litellm.litellm_core_utils.get_supported_openai_params import ( + get_supported_openai_params, + ) + from litellm.types.utils import ChatCompletionDeltaToolCall, ModelResponse + from litellm.utils import supports_response_schema + +try: import litellm from litellm import Choices, CustomLogger from litellm.exceptions import ContextWindowExceededError from litellm.litellm_core_utils.get_supported_openai_params import ( get_supported_openai_params, ) - from litellm.types.utils import ModelResponse + from litellm.types.utils import ChatCompletionDeltaToolCall, ModelResponse from litellm.utils import supports_response_schema -load_dotenv() + LITELLM_AVAILABLE = True +except ImportError: + LITELLM_AVAILABLE = False + litellm = None # type: ignore + Choices = None # type: ignore + ContextWindowExceededError = Exception # type: ignore + get_supported_openai_params = None # type: ignore + ChatCompletionDeltaToolCall = None # type: ignore + ModelResponse = None # type: ignore + supports_response_schema = None # type: ignore -litellm.suppress_debug_info = True + +load_dotenv() +logger = logging.getLogger(__name__) +if LITELLM_AVAILABLE: + litellm.suppress_debug_info = True class FilteredStream(io.TextIOBase): @@ -250,6 +276,17 @@ LLM_CONTEXT_WINDOW_SIZES: Final[dict[str, int]] = { DEFAULT_CONTEXT_WINDOW_SIZE: Final[int] = 8192 CONTEXT_WINDOW_USAGE_RATIO: Final[float] = 0.85 +SUPPORTED_NATIVE_PROVIDERS: Final[list[str]] = [ + "openai", + "anthropic", + "claude", + "azure", + "azure_openai", + "google", + "gemini", + "bedrock", + "aws", +] class Delta(TypedDict): @@ -275,6 +312,63 @@ class AccumulatedToolArgs(BaseModel): class LLM(BaseLLM): completion_cost: float | None = None + def __new__(cls, model: str, is_litellm: bool = False, **kwargs) -> LLM: + """Factory method that routes to native SDK or falls back to LiteLLM.""" + if not model or not isinstance(model, str): + raise ValueError("Model must be a non-empty string") + + provider = model.partition("/")[0] if "/" in model else "openai" + + native_class = cls._get_native_provider(provider) + if native_class and not is_litellm and provider in SUPPORTED_NATIVE_PROVIDERS: + try: + model_string = model.partition("/")[2] if "/" in model else model + return native_class(model=model_string, provider=provider, **kwargs) + except Exception as e: + raise ImportError(f"Error importing native provider: {e}") from e + + # FALLBACK to LiteLLM + if not LITELLM_AVAILABLE: + logger.error("LiteLLM is not available, falling back to LiteLLM") + raise ImportError("Fallback to LiteLLM is not available") from None + + instance = object.__new__(cls) + super(LLM, instance).__init__(model=model, is_litellm=True, **kwargs) + instance.is_litellm = True + return instance + + @classmethod + def _get_native_provider(cls, provider: str) -> type | None: + """Get native provider class if available.""" + if provider == "openai": + from crewai.llms.providers.openai.completion import OpenAICompletion + + return OpenAICompletion + + if provider == "anthropic" or provider == "claude": + from crewai.llms.providers.anthropic.completion import ( + AnthropicCompletion, + ) + + return AnthropicCompletion + + if provider == "azure" or provider == "azure_openai": + from crewai.llms.providers.azure.completion import AzureCompletion + + return AzureCompletion + + if provider == "google" or provider == "gemini": + from crewai.llms.providers.gemini.completion import GeminiCompletion + + return GeminiCompletion + + if provider == "bedrock": + from crewai.llms.providers.bedrock.completion import BedrockCompletion + + return BedrockCompletion + + return None + def __init__( self, model: str, @@ -284,7 +378,7 @@ class LLM(BaseLLM): n: int | None = None, stop: str | list[str] | None = None, max_completion_tokens: int | None = None, - max_tokens: int | None = None, + max_tokens: int | float | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, logit_bias: dict[int, float] | None = None, @@ -301,6 +395,11 @@ class LLM(BaseLLM): stream: bool = False, **kwargs, ): + """Initialize LLM instance. + + Note: This __init__ method is only called for fallback instances. + Native provider instances handle their own initialization in their respective classes. + """ self.model = model self.timeout = timeout self.temperature = temperature @@ -328,7 +427,7 @@ class LLM(BaseLLM): litellm.drop_params = True - # Normalize self.stop to always be a List[str] + # Normalize self.stop to always be a list[str] if stop is None: self.stop: list[str] = [] elif isinstance(stop, str): @@ -349,11 +448,12 @@ class LLM(BaseLLM): Returns: bool: True if the model is from Anthropic, False otherwise. """ - return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES) + anthropic_prefixes = ("anthropic/", "claude-", "claude/") + return any(prefix in model.lower() for prefix in anthropic_prefixes) def _prepare_completion_params( self, - messages: str | list[dict[str, str]], + messages: str | list[LLMMessage], tools: list[dict] | None = None, ) -> dict[str, Any]: """Prepare parameters for the completion call. @@ -514,10 +614,6 @@ class LLM(BaseLLM): # Add the chunk content to the full response full_response += chunk_content - # Emit the chunk event - if not hasattr(crewai_event_bus, "emit"): - raise Exception("crewai_event_bus must have an `emit` method") - crewai_event_bus.emit( self, event=LLMStreamChunkEvent( @@ -623,7 +719,9 @@ class LLM(BaseLLM): # --- 8) If no tool calls or no available functions, return the text response directly if not tool_calls or not available_functions: - # Log token usage if available in streaming mode + # Track token usage and log callbacks if available in streaming mode + if usage_info: + self._track_token_usage_internal(usage_info) self._handle_streaming_callbacks(callbacks, usage_info, last_chunk) # Emit completion event and return response self._handle_emit_call_events( @@ -640,7 +738,9 @@ class LLM(BaseLLM): if tool_result is not None: return tool_result - # --- 10) Log token usage if available in streaming mode + # --- 10) Track token usage and log callbacks if available in streaming mode + if usage_info: + self._track_token_usage_internal(usage_info) self._handle_streaming_callbacks(callbacks, usage_info, last_chunk) # --- 11) Emit completion event and return response @@ -671,11 +771,6 @@ class LLM(BaseLLM): ) return full_response - # Emit failed event and re-raise the exception - if not hasattr(crewai_event_bus, "emit"): - raise AttributeError( - "crewai_event_bus must have an 'emit' method" - ) from e crewai_event_bus.emit( self, event=LLMCallFailedEvent( @@ -702,8 +797,7 @@ class LLM(BaseLLM): current_tool_accumulator.function.arguments += ( tool_call.function.arguments ) - if not hasattr(crewai_event_bus, "emit"): - raise AttributeError("crewai_event_bus must have an 'emit' method") + crewai_event_bus.emit( self, event=LLMStreamChunkEvent( @@ -832,6 +926,7 @@ class LLM(BaseLLM): messages=params["messages"], ) return text_response + # --- 6) If there is no text response, no available functions, but there are tool calls, return the tool calls if tool_calls and not available_functions and not text_response: return tool_calls @@ -886,9 +981,6 @@ class LLM(BaseLLM): function_args = json.loads(tool_call.function.arguments) fn = available_functions[function_name] - # --- 3.2) Execute function - if not hasattr(crewai_event_bus, "emit"): - raise AttributeError("crewai_event_bus must have an 'emit' method") started_at = datetime.now() crewai_event_bus.emit( self, @@ -928,10 +1020,6 @@ class LLM(BaseLLM): function_name, lambda: None ) # Ensure fn is always a callable logging.error(f"Error executing function '{function_name}': {e}") - if not hasattr(crewai_event_bus, "emit"): - raise AttributeError( - "crewai_event_bus must have an 'emit' method" - ) from e crewai_event_bus.emit( self, event=LLMCallFailedEvent(error=f"Tool execution error: {e!s}"), @@ -950,7 +1038,7 @@ class LLM(BaseLLM): def call( self, - messages: str | list[dict[str, str]], + messages: str | list[LLMMessage], tools: list[dict] | None = None, callbacks: list[Any] | None = None, available_functions: dict[str, Any] | None = None, @@ -982,9 +1070,6 @@ class LLM(BaseLLM): ValueError: If response format is not supported LLMContextLengthExceededError: If input exceeds model's context limit """ - # --- 1) Emit call started event - if not hasattr(crewai_event_bus, "emit"): - raise AttributeError("crewai_event_bus must have an 'emit' method") crewai_event_bus.emit( self, event=LLMCallStartedEvent( @@ -1008,7 +1093,8 @@ class LLM(BaseLLM): if "o1" in self.model.lower(): for message in messages: if message.get("role") == "system": - message["role"] = "assistant" + msg_role: Literal["assistant"] = "assistant" + message["role"] = msg_role # --- 5) Set up callbacks if provided with suppress_warnings(): if callbacks and len(callbacks) > 0: @@ -1021,10 +1107,10 @@ class LLM(BaseLLM): return self._handle_streaming_response( params, callbacks, available_functions, from_task, from_agent ) + return self._handle_non_streaming_response( params, callbacks, available_functions, from_task, from_agent ) - except LLMContextLengthExceededError: # Re-raise LLMContextLengthExceededError as it should be handled # by the CrewAgentExecutor._invoke_loop method, which can then decide @@ -1057,10 +1143,6 @@ class LLM(BaseLLM): from_agent=from_agent, ) - if not hasattr(crewai_event_bus, "emit"): - raise AttributeError( - "crewai_event_bus must have an 'emit' method" - ) from e crewai_event_bus.emit( self, event=LLMCallFailedEvent( @@ -1086,8 +1168,6 @@ class LLM(BaseLLM): from_agent: Optional agent object messages: Optional messages object """ - if not hasattr(crewai_event_bus, "emit"): - raise AttributeError("crewai_event_bus must have an 'emit' method") crewai_event_bus.emit( self, event=LLMCallCompletedEvent( @@ -1101,7 +1181,7 @@ class LLM(BaseLLM): ) def _format_messages_for_provider( - self, messages: list[dict[str, str]] + self, messages: list[LLMMessage] ) -> list[dict[str, str]]: """Format messages according to provider requirements. @@ -1136,15 +1216,15 @@ class LLM(BaseLLM): {"role": "assistant", "content": msg["content"]} ) else: - formatted_messages.append(msg) - return formatted_messages + formatted_messages.append(msg) # type: ignore[arg-type] + return formatted_messages # type: ignore[return-value] # Handle Mistral models - they require the last message to have a role of 'user' or 'tool' if "mistral" in self.model.lower(): # Check if the last message has a role of 'assistant' if messages and messages[-1]["role"] == "assistant": - return [*messages, {"role": "user", "content": "Please continue."}] - return messages + return [*messages, {"role": "user", "content": "Please continue."}] # type: ignore[list-item] + return messages # type: ignore[return-value] # TODO: Remove this code after merging PR https://github.com/BerriAI/litellm/pull/10917 # Ollama doesn't supports last message to be 'assistant' @@ -1153,18 +1233,18 @@ class LLM(BaseLLM): and messages and messages[-1]["role"] == "assistant" ): - return [*messages, {"role": "user", "content": ""}] + return [*messages, {"role": "user", "content": ""}] # type: ignore[list-item] # Handle Anthropic models if not self.is_anthropic: - return messages + return messages # type: ignore[return-value] # Anthropic requires messages to start with 'user' role if not messages or messages[0]["role"] == "system": # If first message is system or empty, add a placeholder user message - return [{"role": "user", "content": "."}, *messages] + return [{"role": "user", "content": "."}, *messages] # type: ignore[list-item] - return messages + return messages # type: ignore[return-value] def _get_custom_llm_provider(self) -> str | None: """ @@ -1225,11 +1305,14 @@ class LLM(BaseLLM): if self.context_window_size != 0: return self.context_window_size + min_context = 1024 + max_context = 2097152 # Current max from gemini-1.5-pro + # Validate all context window sizes for key, value in LLM_CONTEXT_WINDOW_SIZES.items(): - if value < MIN_CONTEXT or value > MAX_CONTEXT: + if value < min_context or value > max_context: raise ValueError( - f"Context window for {key} must be between {MIN_CONTEXT} and {MAX_CONTEXT}" + f"Context window for {key} must be between {min_context} and {max_context}" ) self.context_window_size = int( @@ -1293,3 +1376,129 @@ class LLM(BaseLLM): litellm.success_callback = success_callbacks litellm.failure_callback = failure_callbacks + + def __copy__(self): + """Create a shallow copy of the LLM instance.""" + # Filter out parameters that are already explicitly passed to avoid conflicts + filtered_params = { + k: v + for k, v in self.additional_params.items() + if k + not in [ + "model", + "is_litellm", + "temperature", + "top_p", + "n", + "max_completion_tokens", + "max_tokens", + "presence_penalty", + "frequency_penalty", + "logit_bias", + "response_format", + "seed", + "logprobs", + "top_logprobs", + "base_url", + "api_base", + "api_version", + "api_key", + "callbacks", + "reasoning_effort", + "stream", + "stop", + ] + } + + # Create a new instance with the same parameters + return LLM( + model=self.model, + is_litellm=self.is_litellm, + temperature=self.temperature, + top_p=self.top_p, + n=self.n, + max_completion_tokens=self.max_completion_tokens, + max_tokens=self.max_tokens, + presence_penalty=self.presence_penalty, + frequency_penalty=self.frequency_penalty, + logit_bias=self.logit_bias, + response_format=self.response_format, + seed=self.seed, + logprobs=self.logprobs, + top_logprobs=self.top_logprobs, + base_url=self.base_url, + api_base=self.api_base, + api_version=self.api_version, + api_key=self.api_key, + callbacks=self.callbacks, + reasoning_effort=self.reasoning_effort, + stream=self.stream, + stop=self.stop, + **filtered_params, + ) + + def __deepcopy__(self, memo): + """Create a deep copy of the LLM instance.""" + import copy + + # Filter out parameters that are already explicitly passed to avoid conflicts + filtered_params = { + k: copy.deepcopy(v, memo) + for k, v in self.additional_params.items() + if k + not in [ + "model", + "is_litellm", + "temperature", + "top_p", + "n", + "max_completion_tokens", + "max_tokens", + "presence_penalty", + "frequency_penalty", + "logit_bias", + "response_format", + "seed", + "logprobs", + "top_logprobs", + "base_url", + "api_base", + "api_version", + "api_key", + "callbacks", + "reasoning_effort", + "stream", + "stop", + ] + } + + # Create a new instance with the same parameters + return LLM( + model=self.model, + is_litellm=self.is_litellm, + temperature=self.temperature, + top_p=self.top_p, + n=self.n, + max_completion_tokens=self.max_completion_tokens, + max_tokens=self.max_tokens, + presence_penalty=self.presence_penalty, + frequency_penalty=self.frequency_penalty, + logit_bias=copy.deepcopy(self.logit_bias, memo) + if self.logit_bias + else None, + response_format=copy.deepcopy(self.response_format, memo) + if self.response_format + else None, + seed=self.seed, + logprobs=self.logprobs, + top_logprobs=self.top_logprobs, + base_url=self.base_url, + api_base=self.api_base, + api_version=self.api_version, + api_key=self.api_key, + callbacks=copy.deepcopy(self.callbacks, memo) if self.callbacks else None, + reasoning_effort=self.reasoning_effort, + stream=self.stream, + stop=copy.deepcopy(self.stop, memo) if self.stop else None, + **filtered_params, + ) diff --git a/src/crewai/llms/__init__.py b/lib/crewai/src/crewai/llms/__init__.py similarity index 100% rename from src/crewai/llms/__init__.py rename to lib/crewai/src/crewai/llms/__init__.py diff --git a/lib/crewai/src/crewai/llms/base_llm.py b/lib/crewai/src/crewai/llms/base_llm.py new file mode 100644 index 000000000..ae6865d8b --- /dev/null +++ b/lib/crewai/src/crewai/llms/base_llm.py @@ -0,0 +1,542 @@ +"""Base LLM abstract class for CrewAI. + +This module provides the abstract base class for all LLM implementations +in CrewAI, including common functionality for native SDK implementations. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from datetime import datetime +import json +import logging +from typing import TYPE_CHECKING, Any, Final + +from pydantic import BaseModel + +from crewai.events.event_bus import crewai_event_bus +from crewai.events.types.llm_events import ( + LLMCallCompletedEvent, + LLMCallFailedEvent, + LLMCallStartedEvent, + LLMCallType, + LLMStreamChunkEvent, +) +from crewai.events.types.tool_usage_events import ( + ToolUsageErrorEvent, + ToolUsageFinishedEvent, + ToolUsageStartedEvent, +) +from crewai.types.usage_metrics import UsageMetrics + + +if TYPE_CHECKING: + from crewai.utilities.types import LLMMessage + + +DEFAULT_CONTEXT_WINDOW_SIZE: Final[int] = 4096 +DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True + + +class BaseLLM(ABC): + """Abstract base class for LLM implementations. + + This class defines the interface that all LLM implementations must follow. + Users can extend this class to create custom LLM implementations that don't + rely on litellm's authentication mechanism. + + Custom LLM implementations should handle error cases gracefully, including + timeouts, authentication failures, and malformed responses. They should also + implement proper validation for input parameters and provide clear error + messages when things go wrong. + + Attributes: + model: The model identifier/name. + temperature: Optional temperature setting for response generation. + stop: A list of stop sequences that the LLM should use to stop generation. + additional_params: Additional provider-specific parameters. + """ + + is_litellm: bool = False + + def __init__( + self, + model: str, + temperature: float | None = None, + api_key: str | None = None, + base_url: str | None = None, + timeout: float | None = None, + provider: str | None = None, + **kwargs, + ) -> None: + """Initialize the BaseLLM with default attributes. + + Args: + model: The model identifier/name. + temperature: Optional temperature setting for response generation. + stop: Optional list of stop sequences for generation. + **kwargs: Additional provider-specific parameters. + """ + if not model: + raise ValueError("Model name is required and cannot be empty") + + self.model = model + self.temperature = temperature + self.api_key = api_key + self.base_url = base_url + # Store additional parameters for provider-specific use + self.additional_params = kwargs + self._provider = provider or "openai" + + stop = kwargs.pop("stop", None) + if stop is None: + self.stop: list[str] = [] + elif isinstance(stop, str): + self.stop = [stop] + else: + self.stop = stop + + self._token_usage = { + "total_tokens": 0, + "prompt_tokens": 0, + "completion_tokens": 0, + "successful_requests": 0, + "cached_prompt_tokens": 0, + } + + @property + def provider(self) -> str: + """Get the provider of the LLM.""" + return self._provider + + @provider.setter + def provider(self, value: str) -> None: + """Set the provider of the LLM.""" + self._provider = value + + @abstractmethod + def call( + self, + messages: str | list[LLMMessage], + tools: list[dict] | None = None, + callbacks: list[Any] | None = None, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Call the LLM with the given messages. + + Args: + messages: Input messages for the LLM. + Can be a string or list of message dictionaries. + If string, it will be converted to a single user message. + If list, each dict must have 'role' and 'content' keys. + tools: Optional list of tool schemas for function calling. + Each tool should define its name, description, and parameters. + callbacks: Optional list of callback functions to be executed + during and after the LLM call. + available_functions: Optional dict mapping function names to callables + that can be invoked by the LLM. + from_task: Optional task caller to be used for the LLM call. + from_agent: Optional agent caller to be used for the LLM call. + + Returns: + Either a text response from the LLM (str) or + the result of a tool function call (Any). + + Raises: + ValueError: If the messages format is invalid. + TimeoutError: If the LLM request times out. + RuntimeError: If the LLM request fails for other reasons. + """ + + def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]: + """Convert tools to a format that can be used for interference. + + Args: + tools: List of tools to convert. + + Returns: + List of converted tools (default implementation returns as-is) + """ + return tools + + def supports_stop_words(self) -> bool: + """Check if the LLM supports stop words. + + Returns: + True if the LLM supports stop words, False otherwise. + """ + return DEFAULT_SUPPORTS_STOP_WORDS + + def _supports_stop_words_implementation(self) -> bool: + """Check if stop words are configured for this LLM instance. + + Native providers can override supports_stop_words() to return this value + to ensure consistent behavior based on whether stop words are actually configured. + + Returns: + True if stop words are configured and can be applied + """ + return bool(self.stop) + + def _apply_stop_words(self, content: str) -> str: + """Apply stop words to truncate response content. + + This method provides consistent stop word behavior across all native SDK providers. + Native providers should call this method to post-process their responses. + + Args: + content: The raw response content from the LLM + + Returns: + Content truncated at the first occurrence of any stop word + + Example: + >>> llm = MyNativeLLM(stop=["Observation:", "Final Answer:"]) + >>> response = ( + ... "I need to search.\\n\\nAction: search\\nObservation: Found results" + ... ) + >>> llm._apply_stop_words(response) + "I need to search.\\n\\nAction: search" + """ + if not self.stop or not content: + return content + + # Find the earliest occurrence of any stop word + earliest_stop_pos = len(content) + found_stop_word = None + + for stop_word in self.stop: + stop_pos = content.find(stop_word) + if stop_pos != -1 and stop_pos < earliest_stop_pos: + earliest_stop_pos = stop_pos + found_stop_word = stop_word + + # Truncate at the stop word if found + if found_stop_word is not None: + truncated = content[:earliest_stop_pos].strip() + logging.debug( + f"Applied stop word '{found_stop_word}' at position {earliest_stop_pos}" + ) + return truncated + + return content + + def get_context_window_size(self) -> int: + """Get the context window size for the LLM. + + Returns: + The number of tokens/characters the model can handle. + """ + # Default implementation - subclasses should override with model-specific values + return DEFAULT_CONTEXT_WINDOW_SIZE + + # Common helper methods for native SDK implementations + + def _emit_call_started_event( + self, + messages: str | list[LLMMessage], + tools: list[dict] | None = None, + callbacks: list[Any] | None = None, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> None: + """Emit LLM call started event.""" + if not hasattr(crewai_event_bus, "emit"): + raise ValueError("crewai_event_bus does not have an emit method") from None + + crewai_event_bus.emit( + self, + event=LLMCallStartedEvent( + messages=messages, + tools=tools, + callbacks=callbacks, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + model=self.model, + ), + ) + + def _emit_call_completed_event( + self, + response: Any, + call_type: LLMCallType, + from_task: Any | None = None, + from_agent: Any | None = None, + messages: str | list[dict[str, Any]] | None = None, + ) -> None: + """Emit LLM call completed event.""" + crewai_event_bus.emit( + self, + event=LLMCallCompletedEvent( + messages=messages, + response=response, + call_type=call_type, + from_task=from_task, + from_agent=from_agent, + model=self.model, + ), + ) + + def _emit_call_failed_event( + self, + error: str, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> None: + """Emit LLM call failed event.""" + if not hasattr(crewai_event_bus, "emit"): + raise ValueError("crewai_event_bus does not have an emit method") from None + + crewai_event_bus.emit( + self, + event=LLMCallFailedEvent( + error=error, + from_task=from_task, + from_agent=from_agent, + ), + ) + + def _emit_stream_chunk_event( + self, + chunk: str, + from_task: Any | None = None, + from_agent: Any | None = None, + tool_call: dict[str, Any] | None = None, + ) -> None: + """Emit stream chunk event.""" + if not hasattr(crewai_event_bus, "emit"): + raise ValueError("crewai_event_bus does not have an emit method") from None + + crewai_event_bus.emit( + self, + event=LLMStreamChunkEvent( + chunk=chunk, + tool_call=tool_call, + from_task=from_task, + from_agent=from_agent, + ), + ) + + def _handle_tool_execution( + self, + function_name: str, + function_args: dict[str, Any], + available_functions: dict[str, Any], + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | None: + """Handle tool execution with proper event emission. + + Args: + function_name: Name of the function to execute + function_args: Arguments to pass to the function + available_functions: Dict of available functions + from_task: Optional task object + from_agent: Optional agent object + + Returns: + Result of function execution or None if function not found + """ + if function_name not in available_functions: + logging.warning( + f"Function '{function_name}' not found in available functions" + ) + return None + + try: + # Emit tool usage started event + started_at = datetime.now() + + crewai_event_bus.emit( + self, + event=ToolUsageStartedEvent( + tool_name=function_name, + tool_args=function_args, + from_agent=from_agent, + from_task=from_task, + ), + ) + + # Execute the function + fn = available_functions[function_name] + result = fn(**function_args) + + # Emit tool usage finished event + crewai_event_bus.emit( + self, + event=ToolUsageFinishedEvent( + output=result, + tool_name=function_name, + tool_args=function_args, + started_at=started_at, + finished_at=datetime.now(), + from_task=from_task, + from_agent=from_agent, + ), + ) + + # Emit LLM call completed event for tool call + self._emit_call_completed_event( + response=result, + call_type=LLMCallType.TOOL_CALL, + from_task=from_task, + from_agent=from_agent, + ) + + return str(result) + + except Exception as e: + error_msg = f"Error executing function '{function_name}': {e!s}" + logging.error(error_msg) + + # Emit tool usage error event + if not hasattr(crewai_event_bus, "emit"): + raise ValueError( + "crewai_event_bus does not have an emit method" + ) from None + + crewai_event_bus.emit( + self, + event=ToolUsageErrorEvent( + tool_name=function_name, + tool_args=function_args, + error=error_msg, + from_task=from_task, + from_agent=from_agent, + ), + ) + + # Emit LLM call failed event + self._emit_call_failed_event( + error=error_msg, + from_task=from_task, + from_agent=from_agent, + ) + + return None + + def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]: + """Convert messages to standard format. + + Args: + messages: Input messages (string or list of message dicts) + + Returns: + List of message dictionaries with 'role' and 'content' keys + + Raises: + ValueError: If message format is invalid + """ + if isinstance(messages, str): + return [{"role": "user", "content": messages}] + + # Validate message format + for i, msg in enumerate(messages): + if not isinstance(msg, dict): + raise ValueError(f"Message at index {i} must be a dictionary") + if "role" not in msg or "content" not in msg: + raise ValueError( + f"Message at index {i} must have 'role' and 'content' keys" + ) + + return messages # type: ignore[return-value] + + def _validate_structured_output( + self, + response: str, + response_format: type[BaseModel] | None, + ) -> str | BaseModel: + """Validate and parse structured output. + + Args: + response: Raw response string + response_format: Optional Pydantic model for structured output + + Returns: + Parsed response (BaseModel instance if response_format provided, otherwise string) + + Raises: + ValueError: If structured output validation fails + """ + if response_format is None: + return response + + try: + # Try to parse as JSON first + if response.strip().startswith("{") or response.strip().startswith("["): + data = json.loads(response) + return response_format.model_validate(data) + + # Try to extract JSON from response + import re + + json_match = re.search(r"\{.*\}", response, re.DOTALL) + if json_match: + data = json.loads(json_match.group()) + return response_format.model_validate(data) + + raise ValueError("No JSON found in response") + + except (json.JSONDecodeError, ValueError) as e: + logging.warning(f"Failed to parse structured output: {e}") + raise ValueError( + f"Failed to parse response into {response_format.__name__}: {e}" + ) from e + + def _extract_provider(self, model: str) -> str: + """Extract provider from model string. + + Args: + model: Model string (e.g., 'openai/gpt-4' or 'gpt-4') + + Returns: + Provider name (e.g., 'openai') + """ + if "/" in model: + return model.partition("/")[0] + return "openai" # Default provider + + def _track_token_usage_internal(self, usage_data: dict[str, Any]) -> None: + """Track token usage internally in the LLM instance. + + Args: + usage_data: Token usage data from the API response + """ + # Extract tokens in a provider-agnostic way + prompt_tokens = ( + usage_data.get("prompt_tokens") + or usage_data.get("prompt_token_count") + or usage_data.get("input_tokens") + or 0 + ) + + completion_tokens = ( + usage_data.get("completion_tokens") + or usage_data.get("candidates_token_count") + or usage_data.get("output_tokens") + or 0 + ) + + cached_tokens = ( + usage_data.get("cached_tokens") + or usage_data.get("cached_prompt_tokens") + or 0 + ) + + self._token_usage["prompt_tokens"] += prompt_tokens + self._token_usage["completion_tokens"] += completion_tokens + self._token_usage["total_tokens"] += prompt_tokens + completion_tokens + self._token_usage["successful_requests"] += 1 + self._token_usage["cached_prompt_tokens"] += cached_tokens + + def get_token_usage_summary(self) -> UsageMetrics: + """Get summary of token usage for this LLM instance. + + Returns: + Dictionary with token usage totals + """ + return UsageMetrics(**self._token_usage) diff --git a/lib/crewai/src/crewai/llms/providers/__init__.py b/lib/crewai/src/crewai/llms/providers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/src/crewai/llms/providers/anthropic/__init__.py b/lib/crewai/src/crewai/llms/providers/anthropic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/src/crewai/llms/providers/anthropic/completion.py b/lib/crewai/src/crewai/llms/providers/anthropic/completion.py new file mode 100644 index 000000000..fad6f1904 --- /dev/null +++ b/lib/crewai/src/crewai/llms/providers/anthropic/completion.py @@ -0,0 +1,560 @@ +import logging +import os +from typing import Any, cast + +from crewai.events.types.llm_events import LLMCallType +from crewai.llms.base_llm import BaseLLM +from crewai.utilities.agent_utils import is_context_length_exceeded +from crewai.utilities.exceptions.context_window_exceeding_exception import ( + LLMContextLengthExceededError, +) +from crewai.utilities.types import LLMMessage + + +try: + from anthropic import Anthropic + from anthropic.types import Message + from anthropic.types.tool_use_block import ToolUseBlock +except ImportError: + raise ImportError( + 'Anthropic native provider not available, to install: uv add "crewai[anthropic]"' + ) from None + + +class AnthropicCompletion(BaseLLM): + """Anthropic native completion implementation. + + This class provides direct integration with the Anthropic Python SDK, + offering native tool use, streaming support, and proper message formatting. + """ + + def __init__( + self, + model: str = "claude-3-5-sonnet-20241022", + api_key: str | None = None, + base_url: str | None = None, + timeout: float | None = None, + max_retries: int = 2, + temperature: float | None = None, + max_tokens: int = 4096, # Required for Anthropic + top_p: float | None = None, + stop_sequences: list[str] | None = None, + stream: bool = False, + client_params: dict[str, Any] | None = None, + **kwargs, + ): + """Initialize Anthropic chat completion client. + + Args: + model: Anthropic model name (e.g., 'claude-3-5-sonnet-20241022') + api_key: Anthropic API key (defaults to ANTHROPIC_API_KEY env var) + base_url: Custom base URL for Anthropic API + timeout: Request timeout in seconds + max_retries: Maximum number of retries + temperature: Sampling temperature (0-1) + max_tokens: Maximum tokens in response (required for Anthropic) + top_p: Nucleus sampling parameter + stop_sequences: Stop sequences (Anthropic uses stop_sequences, not stop) + stream: Enable streaming responses + client_params: Additional parameters for the Anthropic client + **kwargs: Additional parameters + """ + super().__init__( + model=model, temperature=temperature, stop=stop_sequences or [], **kwargs + ) + + # Client params + self.client_params = client_params + self.base_url = base_url + self.timeout = timeout + self.max_retries = max_retries + + self.client = Anthropic(**self._get_client_params()) + + # Store completion parameters + self.max_tokens = max_tokens + self.top_p = top_p + self.stream = stream + self.stop_sequences = stop_sequences or [] + + # Model-specific settings + self.is_claude_3 = "claude-3" in model.lower() + self.supports_tools = self.is_claude_3 # Claude 3+ supports tool use + + def _get_client_params(self) -> dict[str, Any]: + """Get client parameters.""" + + if self.api_key is None: + self.api_key = os.getenv("ANTHROPIC_API_KEY") + if self.api_key is None: + raise ValueError("ANTHROPIC_API_KEY is required") + + client_params = { + "api_key": self.api_key, + "base_url": self.base_url, + "timeout": self.timeout, + "max_retries": self.max_retries, + } + + if self.client_params: + client_params.update(self.client_params) + + return client_params + + def call( + self, + messages: str | list[LLMMessage], + tools: list[dict] | None = None, + callbacks: list[Any] | None = None, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Call Anthropic messages API. + + Args: + messages: Input messages for the chat completion + tools: List of tool/function definitions + callbacks: Callback functions (not used in native implementation) + available_functions: Available functions for tool calling + from_task: Task that initiated the call + from_agent: Agent that initiated the call + + Returns: + Chat completion response or tool call result + """ + try: + # Emit call started event + self._emit_call_started_event( + messages=messages, # type: ignore[arg-type] + tools=tools, + callbacks=callbacks, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + # Format messages for Anthropic + formatted_messages, system_message = self._format_messages_for_anthropic( + messages # type: ignore[arg-type] + ) + + # Prepare completion parameters + completion_params = self._prepare_completion_params( + formatted_messages, system_message, tools + ) + + # Handle streaming vs non-streaming + if self.stream: + return self._handle_streaming_completion( + completion_params, available_functions, from_task, from_agent + ) + + return self._handle_completion( + completion_params, available_functions, from_task, from_agent + ) + + except Exception as e: + error_msg = f"Anthropic API call failed: {e!s}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise + + def _prepare_completion_params( + self, + messages: list[LLMMessage], + system_message: str | None = None, + tools: list[dict] | None = None, + ) -> dict[str, Any]: + """Prepare parameters for Anthropic messages API. + + Args: + messages: Formatted messages for Anthropic + system_message: Extracted system message + tools: Tool definitions + + Returns: + Parameters dictionary for Anthropic API + """ + params = { + "model": self.model, + "messages": messages, + "max_tokens": self.max_tokens, + "stream": self.stream, + } + + # Add system message if present + if system_message: + params["system"] = system_message + + # Add optional parameters if set + if self.temperature is not None: + params["temperature"] = self.temperature + if self.top_p is not None: + params["top_p"] = self.top_p + if self.stop_sequences: + params["stop_sequences"] = self.stop_sequences + + # Handle tools for Claude 3+ + if tools and self.supports_tools: + params["tools"] = self._convert_tools_for_interference(tools) + + return params + + def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]: + """Convert CrewAI tool format to Anthropic tool use format.""" + anthropic_tools = [] + + for tool in tools: + if "input_schema" in tool and "name" in tool and "description" in tool: + anthropic_tools.append(tool) + continue + + try: + from crewai.llms.providers.utils.common import safe_tool_conversion + + name, description, parameters = safe_tool_conversion(tool, "Anthropic") + except (ImportError, KeyError, ValueError) as e: + logging.error(f"Error converting tool to Anthropic format: {e}") + raise e + + anthropic_tool = { + "name": name, + "description": description, + } + + if parameters and isinstance(parameters, dict): + anthropic_tool["input_schema"] = parameters # type: ignore[assignment] + else: + anthropic_tool["input_schema"] = { # type: ignore[assignment] + "type": "object", + "properties": {}, + "required": [], + } + + anthropic_tools.append(anthropic_tool) + + return anthropic_tools + + def _format_messages_for_anthropic( + self, messages: str | list[LLMMessage] + ) -> tuple[list[LLMMessage], str | None]: + """Format messages for Anthropic API. + + Anthropic has specific requirements: + - System messages are separate from conversation messages + - Messages must alternate between user and assistant + - First message must be from user + + Args: + messages: Input messages + + Returns: + Tuple of (formatted_messages, system_message) + """ + # Use base class formatting first + base_formatted = super()._format_messages(messages) + + formatted_messages: list[LLMMessage] = [] + system_message: str | None = None + + for message in base_formatted: + role = message.get("role") + content = message.get("content", "") + + if role == "system": + if system_message: + system_message += f"\n\n{content}" + else: + system_message = cast(str, content) + else: + role_str = role if role is not None else "user" + content_str = content if content is not None else "" + formatted_messages.append({"role": role_str, "content": content_str}) + + # Ensure first message is from user (Anthropic requirement) + if not formatted_messages: + # If no messages, add a default user message + formatted_messages.append({"role": "user", "content": "Hello"}) + elif formatted_messages[0]["role"] != "user": + # If first message is not from user, insert a user message at the beginning + formatted_messages.insert(0, {"role": "user", "content": "Hello"}) + + return formatted_messages, system_message + + def _handle_completion( + self, + params: dict[str, Any], + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Handle non-streaming message completion.""" + try: + response: Message = self.client.messages.create(**params) + + except Exception as e: + if is_context_length_exceeded(e): + logging.error(f"Context window exceeded: {e}") + raise LLMContextLengthExceededError(str(e)) from e + raise e from e + + usage = self._extract_anthropic_token_usage(response) + self._track_token_usage_internal(usage) + + # Check if Claude wants to use tools + if response.content and available_functions: + tool_uses = [ + block for block in response.content if isinstance(block, ToolUseBlock) + ] + + if tool_uses: + # Handle tool use conversation flow + return self._handle_tool_use_conversation( + response, + tool_uses, + params, + available_functions, + from_task, + from_agent, + ) + + # Extract text content + content = "" + if response.content: + for content_block in response.content: + if hasattr(content_block, "text"): + content += content_block.text + + content = self._apply_stop_words(content) + + self._emit_call_completed_event( + response=content, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=params["messages"], + ) + + if usage.get("total_tokens", 0) > 0: + logging.info(f"Anthropic API usage: {usage}") + + return content + + def _handle_streaming_completion( + self, + params: dict[str, Any], + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str: + """Handle streaming message completion.""" + full_response = "" + + # Remove 'stream' parameter as messages.stream() doesn't accept it + # (the SDK sets it internally) + stream_params = {k: v for k, v in params.items() if k != "stream"} + + # Make streaming API call + with self.client.messages.stream(**stream_params) as stream: + for event in stream: + if hasattr(event, "delta") and hasattr(event.delta, "text"): + text_delta = event.delta.text + full_response += text_delta + self._emit_stream_chunk_event( + chunk=text_delta, + from_task=from_task, + from_agent=from_agent, + ) + + final_message: Message = stream.get_final_message() + + usage = self._extract_anthropic_token_usage(final_message) + self._track_token_usage_internal(usage) + + if final_message.content and available_functions: + tool_uses = [ + block + for block in final_message.content + if isinstance(block, ToolUseBlock) + ] + + if tool_uses: + # Handle tool use conversation flow + return self._handle_tool_use_conversation( + final_message, + tool_uses, + params, + available_functions, + from_task, + from_agent, + ) + + # Apply stop words to full response + full_response = self._apply_stop_words(full_response) + + # Emit completion event and return full response + self._emit_call_completed_event( + response=full_response, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=params["messages"], + ) + + return full_response + + def _handle_tool_use_conversation( + self, + initial_response: Message, + tool_uses: list[ToolUseBlock], + params: dict[str, Any], + available_functions: dict[str, Any], + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str: + """Handle the complete tool use conversation flow. + + This implements the proper Anthropic tool use pattern: + 1. Claude requests tool use + 2. We execute the tools + 3. We send tool results back to Claude + 4. Claude processes results and generates final response + """ + # Execute all requested tools and collect results + tool_results = [] + + for tool_use in tool_uses: + function_name = tool_use.name + function_args = tool_use.input + + # Execute the tool + result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, # type: ignore + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + # Create tool result in Anthropic format + tool_result = { + "type": "tool_result", + "tool_use_id": tool_use.id, + "content": str(result) + if result is not None + else "Tool execution completed", + } + tool_results.append(tool_result) + + # Prepare follow-up conversation with tool results + follow_up_params = params.copy() + + # Add Claude's tool use response to conversation + assistant_message = {"role": "assistant", "content": initial_response.content} + + # Add user message with tool results + user_message = {"role": "user", "content": tool_results} + + # Update messages for follow-up call + follow_up_params["messages"] = params["messages"] + [ + assistant_message, + user_message, + ] + + try: + # Send tool results back to Claude for final response + final_response: Message = self.client.messages.create(**follow_up_params) + + # Track token usage for follow-up call + follow_up_usage = self._extract_anthropic_token_usage(final_response) + self._track_token_usage_internal(follow_up_usage) + + # Extract final text content + final_content = "" + if final_response.content: + for content_block in final_response.content: + if hasattr(content_block, "text"): + final_content += content_block.text + + final_content = self._apply_stop_words(final_content) + + # Emit completion event for the final response + self._emit_call_completed_event( + response=final_content, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=follow_up_params["messages"], + ) + + # Log combined token usage + total_usage = { + "input_tokens": follow_up_usage.get("input_tokens", 0), + "output_tokens": follow_up_usage.get("output_tokens", 0), + "total_tokens": follow_up_usage.get("total_tokens", 0), + } + + if total_usage.get("total_tokens", 0) > 0: + logging.info(f"Anthropic API tool conversation usage: {total_usage}") + + return final_content + + except Exception as e: + if is_context_length_exceeded(e): + logging.error(f"Context window exceeded in tool follow-up: {e}") + raise LLMContextLengthExceededError(str(e)) from e + + logging.error(f"Tool follow-up conversation failed: {e}") + # Fallback: return the first tool result if follow-up fails + if tool_results: + return tool_results[0]["content"] + raise e + + def supports_function_calling(self) -> bool: + """Check if the model supports function calling.""" + return self.supports_tools + + def supports_stop_words(self) -> bool: + """Check if the model supports stop words.""" + return True # All Claude models support stop sequences + + def get_context_window_size(self) -> int: + """Get the context window size for the model.""" + from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO + + # Context window sizes for Anthropic models + context_windows = { + "claude-3-5-sonnet": 200000, + "claude-3-5-haiku": 200000, + "claude-3-opus": 200000, + "claude-3-sonnet": 200000, + "claude-3-haiku": 200000, + "claude-3-7-sonnet": 200000, + "claude-2.1": 200000, + "claude-2": 100000, + "claude-instant": 100000, + } + + # Find the best match for the model name + for model_prefix, size in context_windows.items(): + if self.model.startswith(model_prefix): + return int(size * CONTEXT_WINDOW_USAGE_RATIO) + + # Default context window size for Claude models + return int(200000 * CONTEXT_WINDOW_USAGE_RATIO) + + def _extract_anthropic_token_usage(self, response: Message) -> dict[str, Any]: + """Extract token usage from Anthropic response.""" + if hasattr(response, "usage") and response.usage: + usage = response.usage + input_tokens = getattr(usage, "input_tokens", 0) + output_tokens = getattr(usage, "output_tokens", 0) + return { + "input_tokens": input_tokens, + "output_tokens": output_tokens, + "total_tokens": input_tokens + output_tokens, + } + return {"total_tokens": 0} diff --git a/lib/crewai/src/crewai/llms/providers/azure/__init__.py b/lib/crewai/src/crewai/llms/providers/azure/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/src/crewai/llms/providers/azure/completion.py b/lib/crewai/src/crewai/llms/providers/azure/completion.py new file mode 100644 index 000000000..0d52143de --- /dev/null +++ b/lib/crewai/src/crewai/llms/providers/azure/completion.py @@ -0,0 +1,537 @@ +import json +import logging +import os +from typing import Any + +from crewai.utilities.agent_utils import is_context_length_exceeded +from crewai.utilities.exceptions.context_window_exceeding_exception import ( + LLMContextLengthExceededError, +) +from crewai.utilities.types import LLMMessage + + +try: + from azure.ai.inference import ( # type: ignore[import-not-found] + ChatCompletionsClient, + ) + from azure.ai.inference.models import ( # type: ignore[import-not-found] + ChatCompletions, + ChatCompletionsToolCall, + StreamingChatCompletionsUpdate, + ) + from azure.core.credentials import ( # type: ignore[import-not-found] + AzureKeyCredential, + ) + from azure.core.exceptions import ( # type: ignore[import-not-found] + HttpResponseError, + ) + + from crewai.events.types.llm_events import LLMCallType + from crewai.llms.base_llm import BaseLLM + +except ImportError: + raise ImportError( + 'Azure AI Inference native provider not available, to install: uv add "crewai[azure-ai-inference]"' + ) from None + + +class AzureCompletion(BaseLLM): + """Azure AI Inference native completion implementation. + + This class provides direct integration with the Azure AI Inference Python SDK, + offering native function calling, streaming support, and proper Azure authentication. + """ + + def __init__( + self, + model: str, + api_key: str | None = None, + endpoint: str | None = None, + api_version: str | None = None, + timeout: float | None = None, + max_retries: int = 2, + temperature: float | None = None, + top_p: float | None = None, + frequency_penalty: float | None = None, + presence_penalty: float | None = None, + max_tokens: int | None = None, + stop: list[str] | None = None, + stream: bool = False, + **kwargs, + ): + """Initialize Azure AI Inference chat completion client. + + Args: + model: Azure deployment name or model name + api_key: Azure API key (defaults to AZURE_API_KEY env var) + endpoint: Azure endpoint URL (defaults to AZURE_ENDPOINT env var) + api_version: Azure API version (defaults to AZURE_API_VERSION env var) + timeout: Request timeout in seconds + max_retries: Maximum number of retries + temperature: Sampling temperature (0-2) + top_p: Nucleus sampling parameter + frequency_penalty: Frequency penalty (-2 to 2) + presence_penalty: Presence penalty (-2 to 2) + max_tokens: Maximum tokens in response + stop: Stop sequences + stream: Enable streaming responses + **kwargs: Additional parameters + """ + super().__init__( + model=model, temperature=temperature, stop=stop or [], **kwargs + ) + + self.api_key = api_key or os.getenv("AZURE_API_KEY") + self.endpoint = ( + endpoint + or os.getenv("AZURE_ENDPOINT") + or os.getenv("AZURE_OPENAI_ENDPOINT") + or os.getenv("AZURE_API_BASE") + ) + self.api_version = api_version or os.getenv("AZURE_API_VERSION") or "2024-06-01" + self.timeout = timeout + self.max_retries = max_retries + + if not self.api_key: + raise ValueError( + "Azure API key is required. Set AZURE_API_KEY environment variable or pass api_key parameter." + ) + if not self.endpoint: + raise ValueError( + "Azure endpoint is required. Set AZURE_ENDPOINT environment variable or pass endpoint parameter." + ) + + # Validate and potentially fix Azure OpenAI endpoint URL + self.endpoint = self._validate_and_fix_endpoint(self.endpoint, model) + + # Build client kwargs + client_kwargs = { + "endpoint": self.endpoint, + "credential": AzureKeyCredential(self.api_key), + } + + # Add api_version if specified (primarily for Azure OpenAI endpoints) + if self.api_version: + client_kwargs["api_version"] = self.api_version + + self.client = ChatCompletionsClient(**client_kwargs) + + self.top_p = top_p + self.frequency_penalty = frequency_penalty + self.presence_penalty = presence_penalty + self.max_tokens = max_tokens + self.stream = stream + + self.is_openai_model = any( + prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"] + ) + + self.is_azure_openai_endpoint = ( + "openai.azure.com" in self.endpoint + and "/openai/deployments/" in self.endpoint + ) + + def _validate_and_fix_endpoint(self, endpoint: str, model: str) -> str: + """Validate and fix Azure endpoint URL format. + + Azure OpenAI endpoints should be in the format: + https://.openai.azure.com/openai/deployments/ + + Args: + endpoint: The endpoint URL + model: The model/deployment name + + Returns: + Validated and potentially corrected endpoint URL + """ + if "openai.azure.com" in endpoint and "/openai/deployments/" not in endpoint: + endpoint = endpoint.rstrip("/") + + if not endpoint.endswith("/openai/deployments"): + deployment_name = model.replace("azure/", "") + endpoint = f"{endpoint}/openai/deployments/{deployment_name}" + logging.info(f"Constructed Azure OpenAI endpoint URL: {endpoint}") + + return endpoint + + def call( + self, + messages: str | list[LLMMessage], + tools: list[dict] | None = None, + callbacks: list[Any] | None = None, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Call Azure AI Inference chat completions API. + + Args: + messages: Input messages for the chat completion + tools: List of tool/function definitions + callbacks: Callback functions (not used in native implementation) + available_functions: Available functions for tool calling + from_task: Task that initiated the call + from_agent: Agent that initiated the call + + Returns: + Chat completion response or tool call result + """ + try: + # Emit call started event + self._emit_call_started_event( + messages=messages, + tools=tools, + callbacks=callbacks, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + # Format messages for Azure + formatted_messages = self._format_messages_for_azure(messages) + + # Prepare completion parameters + completion_params = self._prepare_completion_params( + formatted_messages, tools + ) + + # Handle streaming vs non-streaming + if self.stream: + return self._handle_streaming_completion( + completion_params, available_functions, from_task, from_agent + ) + + return self._handle_completion( + completion_params, available_functions, from_task, from_agent + ) + + except HttpResponseError as e: + if e.status_code == 401: + error_msg = "Azure authentication failed. Check your API key." + elif e.status_code == 404: + error_msg = ( + f"Azure endpoint not found. Check endpoint URL: {self.endpoint}" + ) + elif e.status_code == 429: + error_msg = "Azure API rate limit exceeded. Please retry later." + else: + error_msg = f"Azure API HTTP error: {e.status_code} - {e.message}" + + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise + except Exception as e: + error_msg = f"Azure API call failed: {e!s}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise + + def _prepare_completion_params( + self, + messages: list[LLMMessage], + tools: list[dict] | None = None, + ) -> dict[str, Any]: + """Prepare parameters for Azure AI Inference chat completion. + + Args: + messages: Formatted messages for Azure + tools: Tool definitions + + Returns: + Parameters dictionary for Azure API + """ + params = { + "messages": messages, + "stream": self.stream, + } + + # Only include model parameter for non-Azure OpenAI endpoints + # Azure OpenAI endpoints have the deployment name in the URL + if not self.is_azure_openai_endpoint: + params["model"] = self.model + + # Add optional parameters if set + if self.temperature is not None: + params["temperature"] = self.temperature + if self.top_p is not None: + params["top_p"] = self.top_p + if self.frequency_penalty is not None: + params["frequency_penalty"] = self.frequency_penalty + if self.presence_penalty is not None: + params["presence_penalty"] = self.presence_penalty + if self.max_tokens is not None: + params["max_tokens"] = self.max_tokens + if self.stop: + params["stop"] = self.stop + + # Handle tools/functions for Azure OpenAI models + if tools and self.is_openai_model: + params["tools"] = self._convert_tools_for_interference(tools) + params["tool_choice"] = "auto" + + return params + + def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]: + """Convert CrewAI tool format to Azure OpenAI function calling format.""" + + from crewai.llms.providers.utils.common import safe_tool_conversion + + azure_tools = [] + + for tool in tools: + name, description, parameters = safe_tool_conversion(tool, "Azure") + + azure_tool = { + "type": "function", + "function": { + "name": name, + "description": description, + }, + } + + if parameters: + if isinstance(parameters, dict): + azure_tool["function"]["parameters"] = parameters # type: ignore + else: + azure_tool["function"]["parameters"] = dict(parameters) + + azure_tools.append(azure_tool) + + return azure_tools + + def _format_messages_for_azure( + self, messages: str | list[LLMMessage] + ) -> list[LLMMessage]: + """Format messages for Azure AI Inference API. + + Args: + messages: Input messages + + Returns: + List of dict objects with 'role' and 'content' keys + """ + # Use base class formatting first + base_formatted = super()._format_messages(messages) + + azure_messages: list[LLMMessage] = [] + + for message in base_formatted: + role = message.get("role", "user") # Default to user if no role + content = message.get("content", "") + + # Azure AI Inference requires both 'role' and 'content' + azure_messages.append({"role": role, "content": content}) + + return azure_messages + + def _handle_completion( + self, + params: dict[str, Any], + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Handle non-streaming chat completion.""" + # Make API call + try: + response: ChatCompletions = self.client.complete(**params) + + if not response.choices: + raise ValueError("No choices returned from Azure API") + + choice = response.choices[0] + message = choice.message + + # Extract and track token usage + usage = self._extract_azure_token_usage(response) + self._track_token_usage_internal(usage) + + # Handle tool calls + if message.tool_calls and available_functions: + tool_call = message.tool_calls[0] # Handle first tool call + if isinstance(tool_call, ChatCompletionsToolCall): + function_name = tool_call.function.name + + try: + function_args = json.loads(tool_call.function.arguments) + except json.JSONDecodeError as e: + logging.error(f"Failed to parse tool arguments: {e}") + function_args = {} + + # Execute tool + result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + if result is not None: + return result + + # Extract content + content = message.content or "" + + # Apply stop words + content = self._apply_stop_words(content) + + # Emit completion event and return content + self._emit_call_completed_event( + response=content, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=params["messages"], + ) + + except Exception as e: + if is_context_length_exceeded(e): + logging.error(f"Context window exceeded: {e}") + raise LLMContextLengthExceededError(str(e)) from e + + error_msg = f"Azure API call failed: {e!s}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise e + + return content + + def _handle_streaming_completion( + self, + params: dict[str, Any], + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str: + """Handle streaming chat completion.""" + full_response = "" + tool_calls = {} + + # Make streaming API call + for update in self.client.complete(**params): + if isinstance(update, StreamingChatCompletionsUpdate): + if update.choices: + choice = update.choices[0] + if choice.delta and choice.delta.content: + content_delta = choice.delta.content + full_response += content_delta + self._emit_stream_chunk_event( + chunk=content_delta, + from_task=from_task, + from_agent=from_agent, + ) + + # Handle tool call streaming + if choice.delta and choice.delta.tool_calls: + for tool_call in choice.delta.tool_calls: + call_id = tool_call.id or "default" + if call_id not in tool_calls: + tool_calls[call_id] = { + "name": "", + "arguments": "", + } + + if tool_call.function and tool_call.function.name: + tool_calls[call_id]["name"] = tool_call.function.name + if tool_call.function and tool_call.function.arguments: + tool_calls[call_id]["arguments"] += ( + tool_call.function.arguments + ) + + # Handle completed tool calls + if tool_calls and available_functions: + for call_data in tool_calls.values(): + function_name = call_data["name"] + + try: + function_args = json.loads(call_data["arguments"]) + except json.JSONDecodeError as e: + logging.error(f"Failed to parse streamed tool arguments: {e}") + continue + + # Execute tool + result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + if result is not None: + return result + + # Apply stop words to full response + full_response = self._apply_stop_words(full_response) + + # Emit completion event and return full response + self._emit_call_completed_event( + response=full_response, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=params["messages"], + ) + + return full_response + + def supports_function_calling(self) -> bool: + """Check if the model supports function calling.""" + # Azure OpenAI models support function calling + return self.is_openai_model + + def supports_stop_words(self) -> bool: + """Check if the model supports stop words.""" + return True # Most Azure models support stop sequences + + def get_context_window_size(self) -> int: + """Get the context window size for the model.""" + from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES + + min_context = 1024 + max_context = 2097152 + + for key, value in LLM_CONTEXT_WINDOW_SIZES.items(): + if value < min_context or value > max_context: + raise ValueError( + f"Context window for {key} must be between {min_context} and {max_context}" + ) + + # Context window sizes for common Azure models + context_windows = { + "gpt-4": 8192, + "gpt-4o": 128000, + "gpt-4o-mini": 200000, + "gpt-4-turbo": 128000, + "gpt-35-turbo": 16385, + "gpt-3.5-turbo": 16385, + "text-embedding": 8191, + } + + # Find the best match for the model name + for model_prefix, size in sorted( + context_windows.items(), key=lambda x: len(x[0]), reverse=True + ): + if self.model.startswith(model_prefix): + return int(size * CONTEXT_WINDOW_USAGE_RATIO) + + # Default context window size + return int(8192 * CONTEXT_WINDOW_USAGE_RATIO) + + def _extract_azure_token_usage(self, response: ChatCompletions) -> dict[str, Any]: + """Extract token usage from Azure response.""" + if hasattr(response, "usage") and response.usage: + usage = response.usage + return { + "prompt_tokens": getattr(usage, "prompt_tokens", 0), + "completion_tokens": getattr(usage, "completion_tokens", 0), + "total_tokens": getattr(usage, "total_tokens", 0), + } + return {"total_tokens": 0} diff --git a/lib/crewai/src/crewai/llms/providers/bedrock/__init__.py b/lib/crewai/src/crewai/llms/providers/bedrock/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/src/crewai/llms/providers/bedrock/completion.py b/lib/crewai/src/crewai/llms/providers/bedrock/completion.py new file mode 100644 index 000000000..4607316c5 --- /dev/null +++ b/lib/crewai/src/crewai/llms/providers/bedrock/completion.py @@ -0,0 +1,862 @@ +from __future__ import annotations + +from collections.abc import Mapping, Sequence +import logging +import os +from typing import TYPE_CHECKING, Any, TypedDict, cast + +from typing_extensions import Required + +from crewai.events.types.llm_events import LLMCallType +from crewai.llms.base_llm import BaseLLM +from crewai.utilities.agent_utils import is_context_length_exceeded +from crewai.utilities.exceptions.context_window_exceeding_exception import ( + LLMContextLengthExceededError, +) +from crewai.utilities.types import LLMMessage + + +if TYPE_CHECKING: + from mypy_boto3_bedrock_runtime.type_defs import ( + GuardrailConfigurationTypeDef, + GuardrailStreamConfigurationTypeDef, + InferenceConfigurationTypeDef, + MessageOutputTypeDef, + MessageTypeDef, + SystemContentBlockTypeDef, + TokenUsageTypeDef, + ToolConfigurationTypeDef, + ToolTypeDef, + ) + + +try: + from boto3.session import Session + from botocore.config import Config + from botocore.exceptions import BotoCoreError, ClientError +except ImportError: + raise ImportError( + 'AWS Bedrock native provider not available, to install: uv add "crewai[bedrock]"' + ) from None + + +if TYPE_CHECKING: + + class EnhancedInferenceConfigurationTypeDef( + InferenceConfigurationTypeDef, total=False + ): + """Extended InferenceConfigurationTypeDef with topK support. + + AWS Bedrock supports topK for Claude models, but it's not in the boto3 type stubs. + This extends the base type to include topK while maintaining all other fields. + """ + + topK: int # noqa: N815 - AWS API uses topK naming + +else: + + class EnhancedInferenceConfigurationTypeDef(TypedDict, total=False): + """Extended InferenceConfigurationTypeDef with topK support. + + AWS Bedrock supports topK for Claude models, but it's not in the boto3 type stubs. + This extends the base type to include topK while maintaining all other fields. + """ + + maxTokens: int + temperature: float + topP: float + stopSequences: list[str] + topK: int + + +class ToolInputSchema(TypedDict): + """Type definition for tool input schema in Converse API.""" + + json: dict[str, Any] + + +class ToolSpec(TypedDict, total=False): + """Type definition for tool specification in Converse API.""" + + name: Required[str] + description: Required[str] + inputSchema: ToolInputSchema + + +class ConverseToolTypeDef(TypedDict): + """Type definition for a Converse API tool.""" + + toolSpec: ToolSpec + + +class BedrockConverseRequestBody(TypedDict, total=False): + """Type definition for AWS Bedrock Converse API request body. + + Based on AWS Bedrock Converse API specification. + """ + + inferenceConfig: Required[EnhancedInferenceConfigurationTypeDef] + system: list[SystemContentBlockTypeDef] + toolConfig: ToolConfigurationTypeDef + guardrailConfig: GuardrailConfigurationTypeDef + additionalModelRequestFields: dict[str, Any] + additionalModelResponseFieldPaths: list[str] + + +class BedrockConverseStreamRequestBody(TypedDict, total=False): + """Type definition for AWS Bedrock Converse Stream API request body. + + Based on AWS Bedrock Converse Stream API specification. + """ + + inferenceConfig: Required[EnhancedInferenceConfigurationTypeDef] + system: list[SystemContentBlockTypeDef] + toolConfig: ToolConfigurationTypeDef + guardrailConfig: GuardrailStreamConfigurationTypeDef + additionalModelRequestFields: dict[str, Any] + additionalModelResponseFieldPaths: list[str] + + +class BedrockCompletion(BaseLLM): + """AWS Bedrock native completion implementation using the Converse API. + + This class provides direct integration with AWS Bedrock using the modern + Converse API, which provides a unified interface across all Bedrock models. + + Features: + - Full tool calling support with proper conversation continuation + - Streaming and non-streaming responses with comprehensive event handling + - Guardrail configuration for content filtering + - Model-specific parameters via additionalModelRequestFields + - Custom response field extraction + - Proper error handling for all AWS exception types + - Token usage tracking and stop reason logging + - Support for both text and tool use content blocks + + The implementation follows AWS Bedrock Converse API best practices including: + - Proper tool use ID tracking for multi-turn tool conversations + - Complete streaming event handling (messageStart, contentBlockStart, etc.) + - Response metadata and trace information capture + - Model-specific conversation format handling (e.g., Cohere requirements) + """ + + def __init__( + self, + model: str = "anthropic.claude-3-5-sonnet-20241022-v2:0", + aws_access_key_id: str | None = None, + aws_secret_access_key: str | None = None, + aws_session_token: str | None = None, + region_name: str = "us-east-1", + temperature: float | None = None, + max_tokens: int | None = None, + top_p: float | None = None, + top_k: int | None = None, + stop_sequences: Sequence[str] | None = None, + stream: bool = False, + guardrail_config: dict[str, Any] | None = None, + additional_model_request_fields: dict[str, Any] | None = None, + additional_model_response_field_paths: list[str] | None = None, + **kwargs, + ): + """Initialize AWS Bedrock completion client. + + Args: + model: The Bedrock model ID to use + aws_access_key_id: AWS access key (defaults to environment variable) + aws_secret_access_key: AWS secret key (defaults to environment variable) + aws_session_token: AWS session token for temporary credentials + region_name: AWS region name + temperature: Sampling temperature for response generation + max_tokens: Maximum tokens to generate + top_p: Nucleus sampling parameter + top_k: Top-k sampling parameter (Claude models only) + stop_sequences: List of sequences that stop generation + stream: Whether to use streaming responses + guardrail_config: Guardrail configuration for content filtering + additional_model_request_fields: Model-specific request parameters + additional_model_response_field_paths: Custom response field paths + **kwargs: Additional parameters + """ + # Extract provider from kwargs to avoid duplicate argument + kwargs.pop("provider", None) + + super().__init__( + model=model, + temperature=temperature, + stop=stop_sequences or [], + provider="bedrock", + **kwargs, + ) + + # Initialize Bedrock client with proper configuration + session = Session( + aws_access_key_id=aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID"), + aws_secret_access_key=aws_secret_access_key + or os.getenv("AWS_SECRET_ACCESS_KEY"), + aws_session_token=aws_session_token or os.getenv("AWS_SESSION_TOKEN"), + region_name=region_name, + ) + + # Configure client with timeouts and retries following AWS best practices + config = Config( + read_timeout=300, + retries={ + "max_attempts": 3, + "mode": "adaptive", + }, + tcp_keepalive=True, + ) + + self.client = session.client("bedrock-runtime", config=config) + self.region_name = region_name + + # Store completion parameters + self.max_tokens = max_tokens + self.top_p = top_p + self.top_k = top_k + self.stream = stream + self.stop_sequences = stop_sequences or [] + + # Store advanced features (optional) + self.guardrail_config = guardrail_config + self.additional_model_request_fields = additional_model_request_fields + self.additional_model_response_field_paths = ( + additional_model_response_field_paths + ) + + # Model-specific settings + self.is_claude_model = "claude" in model.lower() + self.supports_tools = True # Converse API supports tools for most models + self.supports_streaming = True + + # Handle inference profiles for newer models + self.model_id = model + + def call( + self, + messages: str | list[LLMMessage], + tools: list[dict[Any, Any]] | None = None, + callbacks: list[Any] | None = None, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Call AWS Bedrock Converse API.""" + try: + # Emit call started event + self._emit_call_started_event( + messages=messages, # type: ignore[arg-type] + tools=tools, + callbacks=callbacks, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + # Format messages for Converse API + formatted_messages, system_message = self._format_messages_for_converse( + messages # type: ignore[arg-type] + ) + + # Prepare request body + body: BedrockConverseRequestBody = { + "inferenceConfig": self._get_inference_config(), + } + + # Add system message if present + if system_message: + body["system"] = cast( + "list[SystemContentBlockTypeDef]", + cast(object, [{"text": system_message}]), + ) + + # Add tool config if present + if tools: + tool_config: ToolConfigurationTypeDef = { + "tools": cast( + "Sequence[ToolTypeDef]", + cast(object, self._format_tools_for_converse(tools)), + ) + } + body["toolConfig"] = tool_config + + # Add optional advanced features if configured + if self.guardrail_config: + guardrail_config: GuardrailConfigurationTypeDef = cast( + "GuardrailConfigurationTypeDef", cast(object, self.guardrail_config) + ) + body["guardrailConfig"] = guardrail_config + + if self.additional_model_request_fields: + body["additionalModelRequestFields"] = ( + self.additional_model_request_fields + ) + + if self.additional_model_response_field_paths: + body["additionalModelResponseFieldPaths"] = ( + self.additional_model_response_field_paths + ) + + if self.stream: + return self._handle_streaming_converse( + formatted_messages, body, available_functions, from_task, from_agent + ) + + return self._handle_converse( + formatted_messages, body, available_functions, from_task, from_agent + ) + + except Exception as e: + if is_context_length_exceeded(e): + logging.error(f"Context window exceeded: {e}") + raise LLMContextLengthExceededError(str(e)) from e + + error_msg = f"AWS Bedrock API call failed: {e!s}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise + + def _handle_converse( + self, + messages: list[dict[str, Any]], + body: BedrockConverseRequestBody, + available_functions: Mapping[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str: + """Handle non-streaming converse API call following AWS best practices.""" + try: + # Validate messages format before API call + if not messages: + raise ValueError("Messages cannot be empty") + + # Ensure we have valid message structure + for i, msg in enumerate(messages): + if ( + not isinstance(msg, dict) + or "role" not in msg + or "content" not in msg + ): + raise ValueError(f"Invalid message format at index {i}") + + # Call Bedrock Converse API with proper error handling + response = self.client.converse( + modelId=self.model_id, + messages=cast( + "Sequence[MessageTypeDef | MessageOutputTypeDef]", + cast(object, messages), + ), + **body, + ) + + # Track token usage according to AWS response format + if "usage" in response: + self._track_token_usage_internal(response["usage"]) + + stop_reason = response.get("stopReason") + if stop_reason: + logging.debug(f"Response stop reason: {stop_reason}") + if stop_reason == "max_tokens": + logging.warning("Response truncated due to max_tokens limit") + elif stop_reason == "content_filtered": + logging.warning("Response was filtered due to content policy") + + # Extract content following AWS response structure + output = response.get("output", {}) + message = output.get("message", {}) + content = message.get("content", []) + + if not content: + logging.warning("No content in Bedrock response") + return ( + "I apologize, but I received an empty response. Please try again." + ) + + # Process content blocks and handle tool use correctly + text_content = "" + + for content_block in content: + # Handle text content + if "text" in content_block: + text_content += content_block["text"] + + # Handle tool use - corrected structure according to AWS API docs + elif "toolUse" in content_block and available_functions: + tool_use_block = content_block["toolUse"] + tool_use_id = tool_use_block.get("toolUseId") + function_name = tool_use_block["name"] + function_args = tool_use_block.get("input", {}) + + logging.debug( + f"Tool use requested: {function_name} with ID {tool_use_id}" + ) + + # Execute the tool + tool_result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, + available_functions=dict(available_functions), + from_task=from_task, + from_agent=from_agent, + ) + + if tool_result is not None: + messages.append( + { + "role": "assistant", + "content": [{"toolUse": tool_use_block}], + } + ) + + messages.append( + { + "role": "user", + "content": [ + { + "toolResult": { + "toolUseId": tool_use_id, + "content": [{"text": str(tool_result)}], + } + } + ], + } + ) + + return self._handle_converse( + messages, body, available_functions, from_task, from_agent + ) + + # Apply stop sequences if configured + text_content = self._apply_stop_words(text_content) + + # Validate final response + if not text_content or text_content.strip() == "": + logging.warning("Extracted empty text content from Bedrock response") + text_content = "I apologize, but I couldn't generate a proper response. Please try again." + + self._emit_call_completed_event( + response=text_content, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=messages, + ) + + return text_content + + except ClientError as e: + # Handle all AWS ClientError exceptions as per documentation + error_code = e.response.get("Error", {}).get("Code", "Unknown") + error_msg = e.response.get("Error", {}).get("Message", str(e)) + + # Log the specific error for debugging + logging.error(f"AWS Bedrock ClientError ({error_code}): {error_msg}") + + # Handle specific error codes as documented + if error_code == "ValidationException": + # This is the error we're seeing with Cohere + if "last turn" in error_msg and "user message" in error_msg: + raise ValueError( + f"Conversation format error: {error_msg}. Check message alternation." + ) from e + raise ValueError(f"Request validation failed: {error_msg}") from e + if error_code == "AccessDeniedException": + raise PermissionError( + f"Access denied to model {self.model_id}: {error_msg}" + ) from e + if error_code == "ResourceNotFoundException": + raise ValueError(f"Model {self.model_id} not found: {error_msg}") from e + if error_code == "ThrottlingException": + raise RuntimeError( + f"API throttled, please retry later: {error_msg}" + ) from e + if error_code == "ModelTimeoutException": + raise TimeoutError(f"Model request timed out: {error_msg}") from e + if error_code == "ServiceQuotaExceededException": + raise RuntimeError(f"Service quota exceeded: {error_msg}") from e + if error_code == "ModelNotReadyException": + raise RuntimeError( + f"Model {self.model_id} not ready: {error_msg}" + ) from e + if error_code == "ModelErrorException": + raise RuntimeError(f"Model error: {error_msg}") from e + if error_code == "InternalServerException": + raise RuntimeError(f"Internal server error: {error_msg}") from e + if error_code == "ServiceUnavailableException": + raise RuntimeError(f"Service unavailable: {error_msg}") from e + + raise RuntimeError(f"Bedrock API error ({error_code}): {error_msg}") from e + + except BotoCoreError as e: + error_msg = f"Bedrock connection error: {e}" + logging.error(error_msg) + raise ConnectionError(error_msg) from e + except Exception as e: + # Catch any other unexpected errors + error_msg = f"Unexpected error in Bedrock converse call: {e}" + logging.error(error_msg) + raise RuntimeError(error_msg) from e + + def _handle_streaming_converse( + self, + messages: list[dict[str, Any]], + body: BedrockConverseRequestBody, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str: + """Handle streaming converse API call with comprehensive event handling.""" + full_response = "" + current_tool_use = None + tool_use_id = None + + try: + response = self.client.converse_stream( + modelId=self.model_id, + messages=cast( + "Sequence[MessageTypeDef | MessageOutputTypeDef]", + cast(object, messages), + ), + **body, # type: ignore[arg-type] + ) + + stream = response.get("stream") + if stream: + for event in stream: + if "messageStart" in event: + role = event["messageStart"].get("role") + logging.debug(f"Streaming message started with role: {role}") + + elif "contentBlockStart" in event: + start = event["contentBlockStart"].get("start", {}) + if "toolUse" in start: + current_tool_use = start["toolUse"] + tool_use_id = current_tool_use.get("toolUseId") + logging.debug( + f"Tool use started in stream: {current_tool_use.get('name')} (ID: {tool_use_id})" + ) + + elif "contentBlockDelta" in event: + delta = event["contentBlockDelta"]["delta"] + if "text" in delta: + text_chunk = delta["text"] + logging.debug(f"Streaming text chunk: {text_chunk[:50]}...") + full_response += text_chunk + self._emit_stream_chunk_event( + chunk=text_chunk, + from_task=from_task, + from_agent=from_agent, + ) + elif "toolUse" in delta and current_tool_use: + tool_input = delta["toolUse"].get("input", "") + if tool_input: + logging.debug(f"Tool input delta: {tool_input}") + + # Content block stop - end of a content block + elif "contentBlockStop" in event: + logging.debug("Content block stopped in stream") + # If we were accumulating a tool use, it's now complete + if current_tool_use and available_functions: + function_name = current_tool_use["name"] + function_args = cast( + dict[str, Any], current_tool_use.get("input", {}) + ) + + # Execute tool + tool_result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + if tool_result is not None and tool_use_id: + # Continue conversation with tool result + messages.append( + { + "role": "assistant", + "content": [{"toolUse": current_tool_use}], + } + ) + + messages.append( + { + "role": "user", + "content": [ + { + "toolResult": { + "toolUseId": tool_use_id, + "content": [ + {"text": str(tool_result)} + ], + } + } + ], + } + ) + + # Recursive call - note this switches to non-streaming + return self._handle_converse( + messages, + body, + available_functions, + from_task, + from_agent, + ) + + current_tool_use = None + tool_use_id = None + + # Message stop - end of entire message + elif "messageStop" in event: + stop_reason = event["messageStop"].get("stopReason") + logging.debug(f"Streaming message stopped: {stop_reason}") + if stop_reason == "max_tokens": + logging.warning( + "Streaming response truncated due to max_tokens" + ) + elif stop_reason == "content_filtered": + logging.warning( + "Streaming response filtered due to content policy" + ) + break + + # Metadata - contains usage information and trace details + elif "metadata" in event: + metadata = event["metadata"] + if "usage" in metadata: + usage_metrics = metadata["usage"] + self._track_token_usage_internal(usage_metrics) + logging.debug(f"Token usage: {usage_metrics}") + if "trace" in metadata: + logging.debug( + f"Trace information available: {metadata['trace']}" + ) + + except ClientError as e: + error_msg = self._handle_client_error(e) + raise RuntimeError(error_msg) from e + except BotoCoreError as e: + error_msg = f"Bedrock streaming connection error: {e}" + logging.error(error_msg) + raise ConnectionError(error_msg) from e + + # Apply stop words to full response + full_response = self._apply_stop_words(full_response) + + # Ensure we don't return empty content + if not full_response or full_response.strip() == "": + logging.warning("Bedrock streaming returned empty content, using fallback") + full_response = ( + "I apologize, but I couldn't generate a response. Please try again." + ) + + # Emit completion event + self._emit_call_completed_event( + response=full_response, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=messages, + ) + + return full_response + + def _format_messages_for_converse( + self, messages: str | list[dict[str, str]] + ) -> tuple[list[dict[str, Any]], str | None]: + """Format messages for Converse API following AWS documentation.""" + # Use base class formatting first + formatted_messages = self._format_messages(messages) # type: ignore[arg-type] + + converse_messages = [] + system_message: str | None = None + + for message in formatted_messages: + role = message.get("role") + content = message.get("content", "") + + if role == "system": + # Extract system message - Converse API handles it separately + if system_message: + system_message += f"\n\n{content}" + else: + system_message = cast(str, content) + else: + # Convert to Converse API format with proper content structure + converse_messages.append({"role": role, "content": [{"text": content}]}) + + # CRITICAL: Handle model-specific conversation requirements + # Cohere and some other models require conversation to end with user message + if converse_messages: + last_message = converse_messages[-1] + if last_message["role"] == "assistant": + # For Cohere models, add a continuation user message + if "cohere" in self.model.lower(): + converse_messages.append( + { + "role": "user", + "content": [ + { + "text": "Please continue and provide your final answer." + } + ], + } + ) + # For other models that might have similar requirements + elif any( + model_family in self.model.lower() + for model_family in ["command", "coral"] + ): + converse_messages.append( + { + "role": "user", + "content": [{"text": "Continue your response."}], + } + ) + + # Ensure first message is from user (required by Converse API) + if not converse_messages: + converse_messages.append( + { + "role": "user", + "content": [{"text": "Hello, please help me with my request."}], + } + ) + elif converse_messages[0]["role"] != "user": + converse_messages.insert( + 0, + { + "role": "user", + "content": [{"text": "Hello, please help me with my request."}], + }, + ) + + return converse_messages, system_message + + @staticmethod + def _format_tools_for_converse(tools: list[dict]) -> list[ConverseToolTypeDef]: + """Convert CrewAI tools to Converse API format following AWS specification.""" + from crewai.llms.providers.utils.common import safe_tool_conversion + + converse_tools: list[ConverseToolTypeDef] = [] + + for tool in tools: + try: + name, description, parameters = safe_tool_conversion(tool, "Bedrock") + + tool_spec: ToolSpec = { + "name": name, + "description": description, + } + + if parameters and isinstance(parameters, dict): + input_schema: ToolInputSchema = {"json": parameters} + tool_spec["inputSchema"] = input_schema + + converse_tool: ConverseToolTypeDef = {"toolSpec": tool_spec} + + converse_tools.append(converse_tool) + + except Exception as e: # noqa: PERF203 + logging.warning( + f"Failed to convert tool {tool.get('name', 'unknown')}: {e}" + ) + continue + + return converse_tools + + def _get_inference_config(self) -> EnhancedInferenceConfigurationTypeDef: + """Get inference configuration following AWS Converse API specification.""" + config: EnhancedInferenceConfigurationTypeDef = {} + + if self.max_tokens: + config["maxTokens"] = self.max_tokens + + if self.temperature is not None: + config["temperature"] = float(self.temperature) + if self.top_p is not None: + config["topP"] = float(self.top_p) + if self.stop_sequences: + config["stopSequences"] = self.stop_sequences + + if self.is_claude_model and self.top_k is not None: + # top_k is supported by Claude models + config["topK"] = int(self.top_k) + + return config + + def _handle_client_error(self, e: ClientError) -> str: + """Handle AWS ClientError with specific error codes and return error message.""" + error_code = e.response.get("Error", {}).get("Code", "Unknown") + error_msg = e.response.get("Error", {}).get("Message", str(e)) + + error_mapping = { + "AccessDeniedException": f"Access denied to model {self.model_id}: {error_msg}", + "ResourceNotFoundException": f"Model {self.model_id} not found: {error_msg}", + "ThrottlingException": f"API throttled, please retry later: {error_msg}", + "ValidationException": f"Invalid request: {error_msg}", + "ModelTimeoutException": f"Model request timed out: {error_msg}", + "ServiceQuotaExceededException": f"Service quota exceeded: {error_msg}", + "ModelNotReadyException": f"Model {self.model_id} not ready: {error_msg}", + "ModelErrorException": f"Model error: {error_msg}", + } + + full_error_msg = error_mapping.get( + error_code, f"Bedrock API error: {error_msg}" + ) + logging.error(f"Bedrock client error ({error_code}): {full_error_msg}") + + return full_error_msg + + def _track_token_usage_internal(self, usage: TokenUsageTypeDef) -> None: # type: ignore[override] + """Track token usage from Bedrock response.""" + input_tokens = usage.get("inputTokens", 0) + output_tokens = usage.get("outputTokens", 0) + total_tokens = usage.get("totalTokens", input_tokens + output_tokens) + + self._token_usage["prompt_tokens"] += input_tokens + self._token_usage["completion_tokens"] += output_tokens + self._token_usage["total_tokens"] += total_tokens + self._token_usage["successful_requests"] += 1 + + def supports_function_calling(self) -> bool: + """Check if the model supports function calling.""" + return self.supports_tools + + def supports_stop_words(self) -> bool: + """Check if the model supports stop words.""" + return True + + def get_context_window_size(self) -> int: + """Get the context window size for the model.""" + from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO + + # Context window sizes for common Bedrock models + context_windows = { + "anthropic.claude-3-5-sonnet": 200000, + "anthropic.claude-3-5-haiku": 200000, + "anthropic.claude-3-opus": 200000, + "anthropic.claude-3-sonnet": 200000, + "anthropic.claude-3-haiku": 200000, + "anthropic.claude-3-7-sonnet": 200000, + "anthropic.claude-v2": 100000, + "amazon.titan-text-express": 8000, + "ai21.j2-ultra": 8192, + "cohere.command-text": 4096, + "meta.llama2-13b-chat": 4096, + "meta.llama2-70b-chat": 4096, + "meta.llama3-70b-instruct": 128000, + "deepseek.r1": 32768, + } + + # Find the best match for the model name + for model_prefix, size in context_windows.items(): + if self.model.startswith(model_prefix): + return int(size * CONTEXT_WINDOW_USAGE_RATIO) + + # Default context window size + return int(8192 * CONTEXT_WINDOW_USAGE_RATIO) diff --git a/lib/crewai/src/crewai/llms/providers/gemini/__init__.py b/lib/crewai/src/crewai/llms/providers/gemini/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/src/crewai/llms/providers/gemini/completion.py b/lib/crewai/src/crewai/llms/providers/gemini/completion.py new file mode 100644 index 000000000..5b692db89 --- /dev/null +++ b/lib/crewai/src/crewai/llms/providers/gemini/completion.py @@ -0,0 +1,563 @@ +import logging +import os +from typing import Any, cast + +from crewai.events.types.llm_events import LLMCallType +from crewai.llms.base_llm import BaseLLM +from crewai.utilities.agent_utils import is_context_length_exceeded +from crewai.utilities.exceptions.context_window_exceeding_exception import ( + LLMContextLengthExceededError, +) +from crewai.utilities.types import LLMMessage + + +try: + from google import genai # type: ignore[import-untyped] + from google.genai import types # type: ignore[import-untyped] + from google.genai.errors import APIError # type: ignore[import-untyped] +except ImportError: + raise ImportError( + 'Google Gen AI native provider not available, to install: uv add "crewai[google-genai]"' + ) from None + + +class GeminiCompletion(BaseLLM): + """Google Gemini native completion implementation. + + This class provides direct integration with the Google Gen AI Python SDK, + offering native function calling, streaming support, and proper Gemini formatting. + """ + + def __init__( + self, + model: str = "gemini-2.0-flash-001", + api_key: str | None = None, + project: str | None = None, + location: str | None = None, + temperature: float | None = None, + top_p: float | None = None, + top_k: int | None = None, + max_output_tokens: int | None = None, + stop_sequences: list[str] | None = None, + stream: bool = False, + safety_settings: dict[str, Any] | None = None, + client_params: dict[str, Any] | None = None, + **kwargs, + ): + """Initialize Google Gemini chat completion client. + + Args: + model: Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro') + api_key: Google API key (defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var) + project: Google Cloud project ID (for Vertex AI) + location: Google Cloud location (for Vertex AI, defaults to 'us-central1') + temperature: Sampling temperature (0-2) + top_p: Nucleus sampling parameter + top_k: Top-k sampling parameter + max_output_tokens: Maximum tokens in response + stop_sequences: Stop sequences + stream: Enable streaming responses + safety_settings: Safety filter settings + client_params: Additional parameters to pass to the Google Gen AI Client constructor. + Supports parameters like http_options, credentials, debug_config, etc. + **kwargs: Additional parameters + """ + super().__init__( + model=model, temperature=temperature, stop=stop_sequences or [], **kwargs + ) + + # Store client params for later use + self.client_params = client_params or {} + + # Get API configuration with environment variable fallbacks + self.api_key = ( + api_key or os.getenv("GOOGLE_API_KEY") or os.getenv("GEMINI_API_KEY") + ) + self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT") + self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1" + + use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true" + + self.client = self._initialize_client(use_vertexai) + + # Store completion parameters + self.top_p = top_p + self.top_k = top_k + self.max_output_tokens = max_output_tokens + self.stream = stream + self.safety_settings = safety_settings or {} + self.stop_sequences = stop_sequences or [] + + # Model-specific settings + self.is_gemini_2 = "gemini-2" in model.lower() + self.is_gemini_1_5 = "gemini-1.5" in model.lower() + self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2 + + def _initialize_client(self, use_vertexai: bool = False) -> genai.Client: + """Initialize the Google Gen AI client with proper parameter handling. + + Args: + use_vertexai: Whether to use Vertex AI (from environment variable) + + Returns: + Initialized Google Gen AI Client + """ + client_params = {} + + if self.client_params: + client_params.update(self.client_params) + + if use_vertexai or self.project: + client_params.update( + { + "vertexai": True, + "project": self.project, + "location": self.location, + } + ) + + client_params.pop("api_key", None) + + elif self.api_key: + client_params["api_key"] = self.api_key + + client_params.pop("vertexai", None) + client_params.pop("project", None) + client_params.pop("location", None) + + else: + try: + return genai.Client(**client_params) + except Exception as e: + raise ValueError( + "Either GOOGLE_API_KEY/GEMINI_API_KEY (for Gemini API) or " + "GOOGLE_CLOUD_PROJECT (for Vertex AI) must be set" + ) from e + + return genai.Client(**client_params) + + def _get_client_params(self) -> dict[str, Any]: + """Get client parameters for compatibility with base class. + + Note: This method is kept for compatibility but the Google Gen AI SDK + uses a different initialization pattern via the Client constructor. + """ + params = {} + + if ( + hasattr(self, "client") + and hasattr(self.client, "vertexai") + and self.client.vertexai + ): + # Vertex AI configuration + params.update( + { + "vertexai": True, + "project": self.project, + "location": self.location, + } + ) + elif self.api_key: + params["api_key"] = self.api_key + + if self.client_params: + params.update(self.client_params) + + return params + + def call( + self, + messages: str | list[LLMMessage], + tools: list[dict] | None = None, + callbacks: list[Any] | None = None, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Call Google Gemini generate content API. + + Args: + messages: Input messages for the chat completion + tools: List of tool/function definitions + callbacks: Callback functions (not used as token counts are handled by the reponse) + available_functions: Available functions for tool calling + from_task: Task that initiated the call + from_agent: Agent that initiated the call + + Returns: + Chat completion response or tool call result + """ + try: + self._emit_call_started_event( + messages=messages, # type: ignore[arg-type] + tools=tools, + callbacks=callbacks, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + self.tools = tools + + formatted_content, system_instruction = self._format_messages_for_gemini( + messages # type: ignore[arg-type] + ) + + config = self._prepare_generation_config(system_instruction, tools) + + if self.stream: + return self._handle_streaming_completion( + formatted_content, + config, + available_functions, + from_task, + from_agent, + ) + + return self._handle_completion( + formatted_content, + system_instruction, + config, + available_functions, + from_task, + from_agent, + ) + + except APIError as e: + error_msg = f"Google Gemini API error: {e.code} - {e.message}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise + except Exception as e: + error_msg = f"Google Gemini API call failed: {e!s}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise + + def _prepare_generation_config( + self, + system_instruction: str | None = None, + tools: list[dict] | None = None, + ) -> types.GenerateContentConfig: + """Prepare generation config for Google Gemini API. + + Args: + system_instruction: System instruction for the model + tools: Tool definitions + + Returns: + GenerateContentConfig object for Gemini API + """ + self.tools = tools + config_params = {} + + # Add system instruction if present + if system_instruction: + # Convert system instruction to Content format + system_content = types.Content( + role="user", parts=[types.Part.from_text(text=system_instruction)] + ) + config_params["system_instruction"] = system_content + + # Add generation config parameters + if self.temperature is not None: + config_params["temperature"] = self.temperature + if self.top_p is not None: + config_params["top_p"] = self.top_p + if self.top_k is not None: + config_params["top_k"] = self.top_k + if self.max_output_tokens is not None: + config_params["max_output_tokens"] = self.max_output_tokens + if self.stop_sequences: + config_params["stop_sequences"] = self.stop_sequences + + # Handle tools for supported models + if tools and self.supports_tools: + config_params["tools"] = self._convert_tools_for_interference(tools) + + if self.safety_settings: + config_params["safety_settings"] = self.safety_settings + + return types.GenerateContentConfig(**config_params) + + def _convert_tools_for_interference(self, tools: list[dict]) -> list[types.Tool]: + """Convert CrewAI tool format to Gemini function declaration format.""" + gemini_tools = [] + + from crewai.llms.providers.utils.common import safe_tool_conversion + + for tool in tools: + name, description, parameters = safe_tool_conversion(tool, "Gemini") + + function_declaration = types.FunctionDeclaration( + name=name, + description=description, + ) + + # Add parameters if present - ensure parameters is a dict + if parameters and isinstance(parameters, dict): + function_declaration.parameters = parameters + + gemini_tool = types.Tool(function_declarations=[function_declaration]) + gemini_tools.append(gemini_tool) + + return gemini_tools + + def _format_messages_for_gemini( + self, messages: str | list[LLMMessage] + ) -> tuple[list[types.Content], str | None]: + """Format messages for Gemini API. + + Gemini has specific requirements: + - System messages are separate system_instruction + - Content is organized as Content objects with Parts + - Roles are 'user' and 'model' (not 'assistant') + + Args: + messages: Input messages + + Returns: + Tuple of (formatted_contents, system_instruction) + """ + # Use base class formatting first + base_formatted = super()._format_messages(messages) + + contents = [] + system_instruction: str | None = None + + for message in base_formatted: + role = message.get("role") + content = message.get("content", "") + + if role == "system": + # Extract system instruction - Gemini handles it separately + if system_instruction: + system_instruction += f"\n\n{content}" + else: + system_instruction = cast(str, content) + else: + # Convert role for Gemini (assistant -> model) + gemini_role = "model" if role == "assistant" else "user" + + # Create Content object + gemini_content = types.Content( + role=gemini_role, parts=[types.Part.from_text(text=content)] + ) + contents.append(gemini_content) + + return contents, system_instruction + + def _handle_completion( + self, + contents: list[types.Content], + system_instruction: str | None, + config: types.GenerateContentConfig, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Handle non-streaming content generation.""" + api_params = { + "model": self.model, + "contents": contents, + "config": config, + } + + try: + response = self.client.models.generate_content(**api_params) + + usage = self._extract_token_usage(response) + except Exception as e: + if is_context_length_exceeded(e): + logging.error(f"Context window exceeded: {e}") + raise LLMContextLengthExceededError(str(e)) from e + raise e from e + + self._track_token_usage_internal(usage) + + if response.candidates and (self.tools or available_functions): + candidate = response.candidates[0] + if candidate.content and candidate.content.parts: + for part in candidate.content.parts: + if hasattr(part, "function_call") and part.function_call: + function_name = part.function_call.name + function_args = ( + dict(part.function_call.args) + if part.function_call.args + else {} + ) + + result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, + available_functions=available_functions, # type: ignore + from_task=from_task, + from_agent=from_agent, + ) + + if result is not None: + return result + + content = response.text if hasattr(response, "text") else "" + content = self._apply_stop_words(content) + + messages_for_event = self._convert_contents_to_dict(contents) + + self._emit_call_completed_event( + response=content, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=messages_for_event, + ) + + return content + + def _handle_streaming_completion( + self, + contents: list[types.Content], + config: types.GenerateContentConfig, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str: + """Handle streaming content generation.""" + full_response = "" + function_calls = {} + + api_params = { + "model": self.model, + "contents": contents, + "config": config, + } + + for chunk in self.client.models.generate_content_stream(**api_params): + if hasattr(chunk, "text") and chunk.text: + full_response += chunk.text + self._emit_stream_chunk_event( + chunk=chunk.text, + from_task=from_task, + from_agent=from_agent, + ) + + if hasattr(chunk, "candidates") and chunk.candidates: + candidate = chunk.candidates[0] + if candidate.content and candidate.content.parts: + for part in candidate.content.parts: + if hasattr(part, "function_call") and part.function_call: + call_id = part.function_call.name or "default" + if call_id not in function_calls: + function_calls[call_id] = { + "name": part.function_call.name, + "args": dict(part.function_call.args) + if part.function_call.args + else {}, + } + + # Handle completed function calls + if function_calls and available_functions: + for call_data in function_calls.values(): + function_name = call_data["name"] + function_args = call_data["args"] + + # Execute tool + result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + if result is not None: + return result + + messages_for_event = self._convert_contents_to_dict(contents) + + self._emit_call_completed_event( + response=full_response, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=messages_for_event, + ) + + return full_response + + def supports_function_calling(self) -> bool: + """Check if the model supports function calling.""" + return self.supports_tools + + def supports_stop_words(self) -> bool: + """Check if the model supports stop words.""" + return True + + def get_context_window_size(self) -> int: + """Get the context window size for the model.""" + from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES + + min_context = 1024 + max_context = 2097152 + + for key, value in LLM_CONTEXT_WINDOW_SIZES.items(): + if value < min_context or value > max_context: + raise ValueError( + f"Context window for {key} must be between {min_context} and {max_context}" + ) + + context_windows = { + "gemini-2.0-flash": 1048576, # 1M tokens + "gemini-2.0-flash-thinking": 32768, + "gemini-2.0-flash-lite": 1048576, + "gemini-2.5-flash": 1048576, + "gemini-2.5-pro": 1048576, + "gemini-1.5-pro": 2097152, # 2M tokens + "gemini-1.5-flash": 1048576, + "gemini-1.5-flash-8b": 1048576, + "gemini-1.0-pro": 32768, + "gemma-3-1b": 32000, + "gemma-3-4b": 128000, + "gemma-3-12b": 128000, + "gemma-3-27b": 128000, + } + + # Find the best match for the model name + for model_prefix, size in context_windows.items(): + if self.model.startswith(model_prefix): + return int(size * CONTEXT_WINDOW_USAGE_RATIO) + + # Default context window size for Gemini models + return int(1048576 * CONTEXT_WINDOW_USAGE_RATIO) # 1M tokens + + def _extract_token_usage(self, response: dict[str, Any]) -> dict[str, Any]: + """Extract token usage from Gemini response.""" + if hasattr(response, "usage_metadata"): + usage = response.usage_metadata + return { + "prompt_token_count": getattr(usage, "prompt_token_count", 0), + "candidates_token_count": getattr(usage, "candidates_token_count", 0), + "total_token_count": getattr(usage, "total_token_count", 0), + "total_tokens": getattr(usage, "total_token_count", 0), + } + return {"total_tokens": 0} + + def _convert_contents_to_dict( + self, contents: list[types.Content] + ) -> list[dict[str, str]]: + """Convert contents to dict format.""" + return [ + { + "role": "assistant" + if content_obj.role == "model" + else content_obj.role, + "content": " ".join( + part.text + for part in content_obj.parts + if hasattr(part, "text") and part.text + ), + } + for content_obj in contents + ] diff --git a/lib/crewai/src/crewai/llms/providers/openai/__init__.py b/lib/crewai/src/crewai/llms/providers/openai/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/src/crewai/llms/providers/openai/completion.py b/lib/crewai/src/crewai/llms/providers/openai/completion.py new file mode 100644 index 000000000..5fbde3eab --- /dev/null +++ b/lib/crewai/src/crewai/llms/providers/openai/completion.py @@ -0,0 +1,544 @@ +from collections.abc import Iterator +import json +import logging +import os +from typing import Any + +from openai import APIConnectionError, NotFoundError, OpenAI +from openai.types.chat import ChatCompletion, ChatCompletionChunk +from openai.types.chat.chat_completion import Choice +from openai.types.chat.chat_completion_chunk import ChoiceDelta +from pydantic import BaseModel + +from crewai.events.types.llm_events import LLMCallType +from crewai.llms.base_llm import BaseLLM +from crewai.utilities.agent_utils import is_context_length_exceeded +from crewai.utilities.exceptions.context_window_exceeding_exception import ( + LLMContextLengthExceededError, +) +from crewai.utilities.types import LLMMessage + + +class OpenAICompletion(BaseLLM): + """OpenAI native completion implementation. + + This class provides direct integration with the OpenAI Python SDK, + offering native structured outputs, function calling, and streaming support. + """ + + def __init__( + self, + model: str = "gpt-4o", + api_key: str | None = None, + base_url: str | None = None, + organization: str | None = None, + project: str | None = None, + timeout: float | None = None, + max_retries: int = 2, + default_headers: dict[str, str] | None = None, + default_query: dict[str, Any] | None = None, + client_params: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + frequency_penalty: float | None = None, + presence_penalty: float | None = None, + max_tokens: int | None = None, + max_completion_tokens: int | None = None, + seed: int | None = None, + stream: bool = False, + response_format: dict[str, Any] | type[BaseModel] | None = None, + logprobs: bool | None = None, + top_logprobs: int | None = None, + reasoning_effort: str | None = None, + provider: str | None = None, + **kwargs, + ): + """Initialize OpenAI chat completion client.""" + + if provider is None: + provider = kwargs.pop("provider", "openai") + + # Client configuration attributes + self.organization = organization + self.project = project + self.max_retries = max_retries + self.default_headers = default_headers + self.default_query = default_query + self.client_params = client_params + self.timeout = timeout + self.base_url = base_url + self.api_base = kwargs.pop("api_base", None) + + super().__init__( + model=model, + temperature=temperature, + api_key=api_key or os.getenv("OPENAI_API_KEY"), + base_url=base_url, + timeout=timeout, + provider=provider, + **kwargs, + ) + + client_config = self._get_client_params() + self.client = OpenAI(**client_config) + + # Completion parameters + self.top_p = top_p + self.frequency_penalty = frequency_penalty + self.presence_penalty = presence_penalty + self.max_tokens = max_tokens + self.max_completion_tokens = max_completion_tokens + self.seed = seed + self.stream = stream + self.response_format = response_format + self.logprobs = logprobs + self.top_logprobs = top_logprobs + self.reasoning_effort = reasoning_effort + self.is_o1_model = "o1" in model.lower() + self.is_gpt4_model = "gpt-4" in model.lower() + + def _get_client_params(self) -> dict[str, Any]: + """Get OpenAI client parameters.""" + + if self.api_key is None: + self.api_key = os.getenv("OPENAI_API_KEY") + if self.api_key is None: + raise ValueError("OPENAI_API_KEY is required") + + base_params = { + "api_key": self.api_key, + "organization": self.organization, + "project": self.project, + "base_url": self.base_url + or self.api_base + or os.getenv("OPENAI_BASE_URL") + or None, + "timeout": self.timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, + } + + client_params = {k: v for k, v in base_params.items() if v is not None} + + if self.client_params: + client_params.update(self.client_params) + + return client_params + + def call( + self, + messages: str | list[LLMMessage], + tools: list[dict] | None = None, + callbacks: list[Any] | None = None, + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Call OpenAI chat completion API. + + Args: + messages: Input messages for the chat completion + tools: list of tool/function definitions + callbacks: Callback functions (not used in native implementation) + available_functions: Available functions for tool calling + from_task: Task that initiated the call + from_agent: Agent that initiated the call + + Returns: + Chat completion response or tool call result + """ + try: + self._emit_call_started_event( + messages=messages, # type: ignore[arg-type] + tools=tools, + callbacks=callbacks, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + formatted_messages = self._format_messages(messages) # type: ignore[arg-type] + + completion_params = self._prepare_completion_params( + formatted_messages, tools + ) + + if self.stream: + return self._handle_streaming_completion( + completion_params, available_functions, from_task, from_agent + ) + + return self._handle_completion( + completion_params, available_functions, from_task, from_agent + ) + + except Exception as e: + error_msg = f"OpenAI API call failed: {e!s}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise + + def _prepare_completion_params( + self, messages: list[LLMMessage], tools: list[dict] | None = None + ) -> dict[str, Any]: + """Prepare parameters for OpenAI chat completion.""" + params = { + "model": self.model, + "messages": messages, + "stream": self.stream, + } + + params.update(self.additional_params) + + if self.temperature is not None: + params["temperature"] = self.temperature + if self.top_p is not None: + params["top_p"] = self.top_p + if self.frequency_penalty is not None: + params["frequency_penalty"] = self.frequency_penalty + if self.presence_penalty is not None: + params["presence_penalty"] = self.presence_penalty + if self.max_completion_tokens is not None: + params["max_completion_tokens"] = self.max_completion_tokens + elif self.max_tokens is not None: + params["max_tokens"] = self.max_tokens + if self.seed is not None: + params["seed"] = self.seed + if self.logprobs is not None: + params["logprobs"] = self.logprobs + if self.top_logprobs is not None: + params["top_logprobs"] = self.top_logprobs + + # Handle o1 model specific parameters + if self.is_o1_model and self.reasoning_effort: + params["reasoning_effort"] = self.reasoning_effort + + # Handle response format for structured outputs + if self.response_format: + if isinstance(self.response_format, type) and issubclass( + self.response_format, BaseModel + ): + # Convert Pydantic model to OpenAI response format + params["response_format"] = { + "type": "json_schema", + "json_schema": { + "name": self.response_format.__name__, + "schema": self.response_format.model_json_schema(), + }, + } + else: + params["response_format"] = self.response_format + + if tools: + params["tools"] = self._convert_tools_for_interference(tools) + params["tool_choice"] = "auto" + + # Filter out CrewAI-specific parameters that shouldn't go to the API + crewai_specific_params = { + "callbacks", + "available_functions", + "from_task", + "from_agent", + "provider", + "api_key", + "base_url", + "api_base", + "timeout", + } + + return {k: v for k, v in params.items() if k not in crewai_specific_params} + + def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]: + """Convert CrewAI tool format to OpenAI function calling format.""" + from crewai.llms.providers.utils.common import safe_tool_conversion + + openai_tools = [] + + for tool in tools: + name, description, parameters = safe_tool_conversion(tool, "OpenAI") + + openai_tool = { + "type": "function", + "function": { + "name": name, + "description": description, + }, + } + + if parameters: + if isinstance(parameters, dict): + openai_tool["function"]["parameters"] = parameters # type: ignore + else: + openai_tool["function"]["parameters"] = dict(parameters) + + openai_tools.append(openai_tool) + return openai_tools + + def _handle_completion( + self, + params: dict[str, Any], + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str | Any: + """Handle non-streaming chat completion.""" + try: + response: ChatCompletion = self.client.chat.completions.create(**params) + + usage = self._extract_openai_token_usage(response) + + self._track_token_usage_internal(usage) + + choice: Choice = response.choices[0] + message = choice.message + + if message.tool_calls and available_functions: + tool_call = message.tool_calls[0] + function_name = tool_call.function.name # type: ignore[union-attr] + + try: + function_args = json.loads(tool_call.function.arguments) # type: ignore[union-attr] + except json.JSONDecodeError as e: + logging.error(f"Failed to parse tool arguments: {e}") + function_args = {} + + result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + if result is not None: + return result + + content = message.content or "" + content = self._apply_stop_words(content) + + if self.response_format and isinstance(self.response_format, type): + try: + structured_result = self._validate_structured_output( + content, self.response_format + ) + self._emit_call_completed_event( + response=structured_result, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=params["messages"], + ) + return structured_result + except ValueError as e: + logging.warning(f"Structured output validation failed: {e}") + + self._emit_call_completed_event( + response=content, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=params["messages"], + ) + + if usage.get("total_tokens", 0) > 0: + logging.info(f"OpenAI API usage: {usage}") + except NotFoundError as e: + error_msg = f"Model {self.model} not found: {e}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise ValueError(error_msg) from e + except APIConnectionError as e: + error_msg = f"Failed to connect to OpenAI API: {e}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise ConnectionError(error_msg) from e + except Exception as e: + # Handle context length exceeded and other errors + if is_context_length_exceeded(e): + logging.error(f"Context window exceeded: {e}") + raise LLMContextLengthExceededError(str(e)) from e + + error_msg = f"OpenAI API call failed: {e!s}" + logging.error(error_msg) + self._emit_call_failed_event( + error=error_msg, from_task=from_task, from_agent=from_agent + ) + raise e from e + + return content + + def _handle_streaming_completion( + self, + params: dict[str, Any], + available_functions: dict[str, Any] | None = None, + from_task: Any | None = None, + from_agent: Any | None = None, + ) -> str: + """Handle streaming chat completion.""" + full_response = "" + tool_calls = {} + + # Make streaming API call + stream: Iterator[ChatCompletionChunk] = self.client.chat.completions.create( + **params + ) + + for chunk in stream: + if not chunk.choices: + continue + + choice = chunk.choices[0] + delta: ChoiceDelta = choice.delta + + # Handle content streaming + if delta.content: + full_response += delta.content + self._emit_stream_chunk_event( + chunk=delta.content, + from_task=from_task, + from_agent=from_agent, + ) + + # Handle tool call streaming + if delta.tool_calls: + for tool_call in delta.tool_calls: + call_id = tool_call.id or "default" + if call_id not in tool_calls: + tool_calls[call_id] = { + "name": "", + "arguments": "", + } + + if tool_call.function and tool_call.function.name: + tool_calls[call_id]["name"] = tool_call.function.name + if tool_call.function and tool_call.function.arguments: + tool_calls[call_id]["arguments"] += tool_call.function.arguments + + if tool_calls and available_functions: + for call_data in tool_calls.values(): + function_name = call_data["name"] + arguments = call_data["arguments"] + + # Skip if function name is empty or arguments are empty + if not function_name or not arguments: + continue + + # Check if function exists in available functions + if function_name not in available_functions: + logging.warning( + f"Function '{function_name}' not found in available functions" + ) + continue + + try: + function_args = json.loads(arguments) + except json.JSONDecodeError as e: + logging.error(f"Failed to parse streamed tool arguments: {e}") + continue + + result = self._handle_tool_execution( + function_name=function_name, + function_args=function_args, + available_functions=available_functions, + from_task=from_task, + from_agent=from_agent, + ) + + if result is not None: + return result + + # Apply stop words to full response + full_response = self._apply_stop_words(full_response) + + # Emit completion event and return full response + self._emit_call_completed_event( + response=full_response, + call_type=LLMCallType.LLM_CALL, + from_task=from_task, + from_agent=from_agent, + messages=params["messages"], + ) + + return full_response + + def supports_function_calling(self) -> bool: + """Check if the model supports function calling.""" + return not self.is_o1_model + + def supports_stop_words(self) -> bool: + """Check if the model supports stop words.""" + return not self.is_o1_model + + def get_context_window_size(self) -> int: + """Get the context window size for the model.""" + from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES + + min_context = 1024 + max_context = 2097152 + + for key, value in LLM_CONTEXT_WINDOW_SIZES.items(): + if value < min_context or value > max_context: + raise ValueError( + f"Context window for {key} must be between {min_context} and {max_context}" + ) + + # Context window sizes for OpenAI models + context_windows = { + "gpt-4": 8192, + "gpt-4o": 128000, + "gpt-4o-mini": 200000, + "gpt-4-turbo": 128000, + "gpt-4.1": 1047576, + "gpt-4.1-mini-2025-04-14": 1047576, + "gpt-4.1-nano-2025-04-14": 1047576, + "o1-preview": 128000, + "o1-mini": 128000, + "o3-mini": 200000, + "o4-mini": 200000, + } + + # Find the best match for the model name + for model_prefix, size in context_windows.items(): + if self.model.startswith(model_prefix): + return int(size * CONTEXT_WINDOW_USAGE_RATIO) + + # Default context window size + return int(8192 * CONTEXT_WINDOW_USAGE_RATIO) + + def _extract_openai_token_usage(self, response: ChatCompletion) -> dict[str, Any]: + """Extract token usage from OpenAI ChatCompletion response.""" + if hasattr(response, "usage") and response.usage: + usage = response.usage + return { + "prompt_tokens": getattr(usage, "prompt_tokens", 0), + "completion_tokens": getattr(usage, "completion_tokens", 0), + "total_tokens": getattr(usage, "total_tokens", 0), + } + return {"total_tokens": 0} + + def _format_messages( # type: ignore[override] + self, messages: str | list[LLMMessage] + ) -> list[LLMMessage]: + """Format messages for OpenAI API.""" + # Use base class formatting first + base_formatted = super()._format_messages(messages) # type: ignore[arg-type] + + # Apply OpenAI-specific formatting + formatted_messages: list[LLMMessage] = [] + + for message in base_formatted: + if self.is_o1_model and message.get("role") == "system": + formatted_messages.append( + {"role": "user", "content": f"System: {message['content']}"} + ) + else: + formatted_messages.append(message) + + return formatted_messages diff --git a/lib/crewai/src/crewai/llms/providers/utils/__init__.py b/lib/crewai/src/crewai/llms/providers/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/src/crewai/llms/providers/utils/common.py b/lib/crewai/src/crewai/llms/providers/utils/common.py new file mode 100644 index 000000000..f240a0808 --- /dev/null +++ b/lib/crewai/src/crewai/llms/providers/utils/common.py @@ -0,0 +1,136 @@ +import logging +import re +from typing import Any + + +def validate_function_name(name: str, provider: str = "LLM") -> str: + """Validate function name according to common LLM provider requirements. + + Most LLM providers (OpenAI, Gemini, Anthropic) have similar requirements: + - Must start with letter or underscore + - Only alphanumeric, underscore, dot, colon, dash allowed + - Maximum length of 64 characters + - Cannot be empty + + Args: + name: The function name to validate + provider: The provider name for error messages + + Returns: + The validated function name (unchanged if valid) + + Raises: + ValueError: If the function name is invalid + """ + if not name or not isinstance(name, str): + raise ValueError(f"{provider} function name cannot be empty") + + if not (name[0].isalpha() or name[0] == "_"): + raise ValueError( + f"{provider} function name '{name}' must start with a letter or underscore" + ) + + if len(name) > 64: + raise ValueError( + f"{provider} function name '{name}' exceeds 64 character limit" + ) + + # Check for invalid characters (most providers support these) + if not re.match(r"^[a-zA-Z_][a-zA-Z0-9_.\-:]*$", name): + raise ValueError( + f"{provider} function name '{name}' contains invalid characters. " + f"Only letters, numbers, underscore, dot, colon, dash allowed" + ) + + return name + + +def extract_tool_info(tool: dict[str, Any]) -> tuple[str, str, dict[str, Any]]: + """Extract tool information from various schema formats. + + Handles both OpenAI/standard format and direct format: + - OpenAI format: {"type": "function", "function": {"name": "...", ...}} + - Direct format: {"name": "...", "description": "...", ...} + + Args: + tool: Tool dictionary in any supported format + + Returns: + Tuple of (name, description, parameters) + + Raises: + ValueError: If tool format is invalid + """ + if not isinstance(tool, dict): + raise ValueError("Tool must be a dictionary") + + # Handle nested function schema format (OpenAI/standard) + if "function" in tool: + function_info = tool["function"] + if not isinstance(function_info, dict): + raise ValueError("Tool function must be a dictionary") + + name = function_info.get("name", "") + description = function_info.get("description", "") + parameters = function_info.get("parameters", {}) + else: + # Direct format + name = tool.get("name", "") + description = tool.get("description", "") + parameters = tool.get("parameters", {}) + + # Also check for args_schema (Pydantic format) + if not parameters and "args_schema" in tool: + if hasattr(tool["args_schema"], "model_json_schema"): + parameters = tool["args_schema"].model_json_schema() + + return name, description, parameters + + +def log_tool_conversion(tool: dict[str, Any], provider: str) -> None: + """Log tool conversion for debugging. + + Args: + tool: The tool being converted + provider: The provider name + """ + try: + name, description, parameters = extract_tool_info(tool) + logging.debug( + f"{provider}: Converting tool '{name}' (desc: {description[:50]}...)" + ) + logging.debug(f"{provider}: Tool parameters: {parameters}") + except Exception as e: + logging.error(f"{provider}: Error extracting tool info: {e}") + logging.error(f"{provider}: Tool structure: {tool}") + + +def safe_tool_conversion( + tool: dict[str, Any], provider: str +) -> tuple[str, str, dict[str, Any]]: + """Safely extract and validate tool information. + + Combines extraction, validation, and logging for robust tool conversion. + + Args: + tool: Tool dictionary to convert + provider: Provider name for error messages and logging + + Returns: + Tuple of (validated_name, description, parameters) + + Raises: + ValueError: If tool is invalid or name validation fails + """ + try: + log_tool_conversion(tool, provider) + + name, description, parameters = extract_tool_info(tool) + + validated_name = validate_function_name(name, provider) + + logging.info(f"{provider}: Successfully validated tool '{validated_name}'") + return validated_name, description, parameters + except Exception as e: + logging.error(f"{provider}: Error converting tool: {e}") + raise diff --git a/src/crewai/llms/third_party/__init__.py b/lib/crewai/src/crewai/llms/third_party/__init__.py similarity index 100% rename from src/crewai/llms/third_party/__init__.py rename to lib/crewai/src/crewai/llms/third_party/__init__.py diff --git a/src/crewai/llms/third_party/ai_suite.py b/lib/crewai/src/crewai/llms/third_party/ai_suite.py similarity index 94% rename from src/crewai/llms/third_party/ai_suite.py rename to lib/crewai/src/crewai/llms/third_party/ai_suite.py index 75eb2e18d..7f764bcf7 100644 --- a/src/crewai/llms/third_party/ai_suite.py +++ b/lib/crewai/src/crewai/llms/third_party/ai_suite.py @@ -31,11 +31,11 @@ class AISuiteLLM(BaseLLM): stop: Optional list of stop sequences for generation. **kwargs: Additional keyword arguments passed to the AI Suite client. """ - super().__init__(model, temperature, stop) + super().__init__(model=model, temperature=temperature, stop=stop) self.client = ai.Client() self.kwargs = kwargs - def call( + def call( # type: ignore[override] self, messages: str | list[dict[str, str]], tools: list[dict] | None = None, @@ -89,7 +89,8 @@ class AISuiteLLM(BaseLLM): return params - def supports_function_calling(self) -> bool: + @staticmethod + def supports_function_calling() -> bool: """Check if the LLM supports function calling. Returns: diff --git a/lib/crewai/src/crewai/memory/__init__.py b/lib/crewai/src/crewai/memory/__init__.py new file mode 100644 index 000000000..1109aef0a --- /dev/null +++ b/lib/crewai/src/crewai/memory/__init__.py @@ -0,0 +1,13 @@ +from crewai.memory.entity.entity_memory import EntityMemory +from crewai.memory.external.external_memory import ExternalMemory +from crewai.memory.long_term.long_term_memory import LongTermMemory +from crewai.memory.short_term.short_term_memory import ShortTermMemory + + + +__all__ = [ + "EntityMemory", + "ExternalMemory", + "LongTermMemory", + "ShortTermMemory", +] diff --git a/lib/crewai/src/crewai/memory/contextual/__init__.py b/lib/crewai/src/crewai/memory/contextual/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/memory/contextual/contextual_memory.py b/lib/crewai/src/crewai/memory/contextual/contextual_memory.py similarity index 99% rename from src/crewai/memory/contextual/contextual_memory.py rename to lib/crewai/src/crewai/memory/contextual/contextual_memory.py index ba7906ae1..b65850c3c 100644 --- a/src/crewai/memory/contextual/contextual_memory.py +++ b/lib/crewai/src/crewai/memory/contextual/contextual_memory.py @@ -9,6 +9,7 @@ from crewai.memory import ( ShortTermMemory, ) + if TYPE_CHECKING: from crewai.agent import Agent from crewai.task import Task diff --git a/lib/crewai/src/crewai/memory/entity/__init__.py b/lib/crewai/src/crewai/memory/entity/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/memory/entity/entity_memory.py b/lib/crewai/src/crewai/memory/entity/entity_memory.py similarity index 100% rename from src/crewai/memory/entity/entity_memory.py rename to lib/crewai/src/crewai/memory/entity/entity_memory.py diff --git a/src/crewai/memory/entity/entity_memory_item.py b/lib/crewai/src/crewai/memory/entity/entity_memory_item.py similarity index 100% rename from src/crewai/memory/entity/entity_memory_item.py rename to lib/crewai/src/crewai/memory/entity/entity_memory_item.py diff --git a/lib/crewai/src/crewai/memory/external/__init__.py b/lib/crewai/src/crewai/memory/external/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/memory/external/external_memory.py b/lib/crewai/src/crewai/memory/external/external_memory.py similarity index 96% rename from src/crewai/memory/external/external_memory.py rename to lib/crewai/src/crewai/memory/external/external_memory.py index f1f4af41a..c48ffd1e3 100644 --- a/src/crewai/memory/external/external_memory.py +++ b/lib/crewai/src/crewai/memory/external/external_memory.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import time from typing import TYPE_CHECKING, Any @@ -15,6 +17,7 @@ from crewai.memory.memory import Memory from crewai.memory.storage.interface import Storage from crewai.rag.embeddings.types import ProviderSpec + if TYPE_CHECKING: from crewai.memory.storage.mem0_storage import Mem0Storage @@ -24,7 +27,7 @@ class ExternalMemory(Memory): super().__init__(storage=storage, **data) @staticmethod - def _configure_mem0(crew: Any, config: dict[str, Any]) -> "Mem0Storage": + def _configure_mem0(crew: Any, config: dict[str, Any]) -> Mem0Storage: from crewai.memory.storage.mem0_storage import Mem0Storage return Mem0Storage(type="external", crew=crew, config=config) @@ -158,7 +161,7 @@ class ExternalMemory(Memory): def reset(self) -> None: self.storage.reset() - def set_crew(self, crew: Any) -> "ExternalMemory": + def set_crew(self, crew: Any) -> ExternalMemory: super().set_crew(crew) if not self.storage: diff --git a/src/crewai/memory/external/external_memory_item.py b/lib/crewai/src/crewai/memory/external/external_memory_item.py similarity index 100% rename from src/crewai/memory/external/external_memory_item.py rename to lib/crewai/src/crewai/memory/external/external_memory_item.py diff --git a/lib/crewai/src/crewai/memory/long_term/__init__.py b/lib/crewai/src/crewai/memory/long_term/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/memory/long_term/long_term_memory.py b/lib/crewai/src/crewai/memory/long_term/long_term_memory.py similarity index 100% rename from src/crewai/memory/long_term/long_term_memory.py rename to lib/crewai/src/crewai/memory/long_term/long_term_memory.py diff --git a/src/crewai/memory/long_term/long_term_memory_item.py b/lib/crewai/src/crewai/memory/long_term/long_term_memory_item.py similarity index 100% rename from src/crewai/memory/long_term/long_term_memory_item.py rename to lib/crewai/src/crewai/memory/long_term/long_term_memory_item.py diff --git a/src/crewai/memory/memory.py b/lib/crewai/src/crewai/memory/memory.py similarity index 80% rename from src/crewai/memory/memory.py rename to lib/crewai/src/crewai/memory/memory.py index dbb2469b5..74297f9e4 100644 --- a/src/crewai/memory/memory.py +++ b/lib/crewai/src/crewai/memory/memory.py @@ -1,9 +1,12 @@ -from typing import TYPE_CHECKING, Any, Optional +from __future__ import annotations + +from typing import TYPE_CHECKING, Any from pydantic import BaseModel from crewai.rag.embeddings.types import EmbedderConfig + if TYPE_CHECKING: from crewai.agent import Agent from crewai.task import Task @@ -18,29 +21,29 @@ class Memory(BaseModel): crew: Any | None = None storage: Any - _agent: Optional["Agent"] = None - _task: Optional["Task"] = None + _agent: Agent | None = None + _task: Task | None = None def __init__(self, storage: Any, **data: Any): super().__init__(storage=storage, **data) @property - def task(self) -> Optional["Task"]: + def task(self) -> Task | None: """Get the current task associated with this memory.""" return self._task @task.setter - def task(self, task: Optional["Task"]) -> None: + def task(self, task: Task | None) -> None: """Set the current task associated with this memory.""" self._task = task @property - def agent(self) -> Optional["Agent"]: + def agent(self) -> Agent | None: """Get the current agent associated with this memory.""" return self._agent @agent.setter - def agent(self, agent: Optional["Agent"]) -> None: + def agent(self, agent: Agent | None) -> None: """Set the current agent associated with this memory.""" self._agent = agent @@ -63,6 +66,6 @@ class Memory(BaseModel): query=query, limit=limit, score_threshold=score_threshold ) - def set_crew(self, crew: Any) -> "Memory": + def set_crew(self, crew: Any) -> Memory: self.crew = crew return self diff --git a/lib/crewai/src/crewai/memory/short_term/__init__.py b/lib/crewai/src/crewai/memory/short_term/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/memory/short_term/short_term_memory.py b/lib/crewai/src/crewai/memory/short_term/short_term_memory.py similarity index 99% rename from src/crewai/memory/short_term/short_term_memory.py rename to lib/crewai/src/crewai/memory/short_term/short_term_memory.py index b7ccfef26..5bc9ec604 100644 --- a/src/crewai/memory/short_term/short_term_memory.py +++ b/lib/crewai/src/crewai/memory/short_term/short_term_memory.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import time from typing import Any diff --git a/src/crewai/memory/short_term/short_term_memory_item.py b/lib/crewai/src/crewai/memory/short_term/short_term_memory_item.py similarity index 100% rename from src/crewai/memory/short_term/short_term_memory_item.py rename to lib/crewai/src/crewai/memory/short_term/short_term_memory_item.py diff --git a/src/crewai/memory/storage/__init__.py b/lib/crewai/src/crewai/memory/storage/__init__.py similarity index 100% rename from src/crewai/memory/storage/__init__.py rename to lib/crewai/src/crewai/memory/storage/__init__.py diff --git a/src/crewai/memory/storage/interface.py b/lib/crewai/src/crewai/memory/storage/interface.py similarity index 100% rename from src/crewai/memory/storage/interface.py rename to lib/crewai/src/crewai/memory/storage/interface.py diff --git a/src/crewai/memory/storage/kickoff_task_outputs_storage.py b/lib/crewai/src/crewai/memory/storage/kickoff_task_outputs_storage.py similarity index 99% rename from src/crewai/memory/storage/kickoff_task_outputs_storage.py rename to lib/crewai/src/crewai/memory/storage/kickoff_task_outputs_storage.py index c8643a153..5a9c57bac 100644 --- a/src/crewai/memory/storage/kickoff_task_outputs_storage.py +++ b/lib/crewai/src/crewai/memory/storage/kickoff_task_outputs_storage.py @@ -1,7 +1,7 @@ import json import logging -import sqlite3 from pathlib import Path +import sqlite3 from typing import Any from crewai.task import Task @@ -10,6 +10,7 @@ from crewai.utilities.crew_json_encoder import CrewJSONEncoder from crewai.utilities.errors import DatabaseError, DatabaseOperationError from crewai.utilities.paths import db_storage_path + logger = logging.getLogger(__name__) diff --git a/src/crewai/memory/storage/ltm_sqlite_storage.py b/lib/crewai/src/crewai/memory/storage/ltm_sqlite_storage.py similarity index 100% rename from src/crewai/memory/storage/ltm_sqlite_storage.py rename to lib/crewai/src/crewai/memory/storage/ltm_sqlite_storage.py index abf117c63..99895db38 100644 --- a/src/crewai/memory/storage/ltm_sqlite_storage.py +++ b/lib/crewai/src/crewai/memory/storage/ltm_sqlite_storage.py @@ -1,6 +1,6 @@ import json -import sqlite3 from pathlib import Path +import sqlite3 from typing import Any from crewai.utilities import Printer diff --git a/src/crewai/memory/storage/mem0_storage.py b/lib/crewai/src/crewai/memory/storage/mem0_storage.py similarity index 99% rename from src/crewai/memory/storage/mem0_storage.py rename to lib/crewai/src/crewai/memory/storage/mem0_storage.py index 036b9d2a4..73820ab11 100644 --- a/src/crewai/memory/storage/mem0_storage.py +++ b/lib/crewai/src/crewai/memory/storage/mem0_storage.py @@ -1,7 +1,7 @@ -import os -import re from collections import defaultdict from collections.abc import Iterable +import os +import re from typing import Any from mem0 import Memory, MemoryClient # type: ignore[import-untyped,import-not-found] @@ -9,6 +9,7 @@ from mem0 import Memory, MemoryClient # type: ignore[import-untyped,import-not- from crewai.memory.storage.interface import Storage from crewai.rag.chromadb.utils import _sanitize_collection_name + MAX_AGENT_ID_LENGTH_MEM0 = 255 diff --git a/src/crewai/memory/storage/rag_storage.py b/lib/crewai/src/crewai/memory/storage/rag_storage.py similarity index 94% rename from src/crewai/memory/storage/rag_storage.py rename to lib/crewai/src/crewai/memory/storage/rag_storage.py index f3c49d229..1060350ad 100644 --- a/src/crewai/memory/storage/rag_storage.py +++ b/lib/crewai/src/crewai/memory/storage/rag_storage.py @@ -1,22 +1,27 @@ +from __future__ import annotations + import logging import traceback +from typing import TYPE_CHECKING, Any, cast import warnings -from typing import Any, cast from crewai.rag.chromadb.config import ChromaDBConfig from crewai.rag.chromadb.types import ChromaEmbeddingFunctionWrapper from crewai.rag.config.utils import get_rag_client -from crewai.rag.core.base_client import BaseClient -from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider from crewai.rag.embeddings.factory import build_embedder -from crewai.rag.embeddings.types import ProviderSpec from crewai.rag.factory import create_client from crewai.rag.storage.base_rag_storage import BaseRAGStorage -from crewai.rag.types import BaseRecord from crewai.utilities.constants import MAX_FILE_NAME_LENGTH from crewai.utilities.paths import db_storage_path +if TYPE_CHECKING: + from crewai.rag.core.base_client import BaseClient + from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider + from crewai.rag.embeddings.types import ProviderSpec + from crewai.rag.types import BaseRecord + + class RAGStorage(BaseRAGStorage): """ Extends Storage to handle embeddings for memory entries, improving @@ -103,7 +108,8 @@ class RAGStorage(BaseRAGStorage): """ return role.replace("\n", "").replace(" ", "_").replace("/", "_") - def _build_storage_file_name(self, type: str, file_name: str) -> str: + @staticmethod + def _build_storage_file_name(type: str, file_name: str) -> str: """ Ensures file name does not exceed max allowed by OS """ diff --git a/src/crewai/process.py b/lib/crewai/src/crewai/process.py similarity index 100% rename from src/crewai/process.py rename to lib/crewai/src/crewai/process.py diff --git a/src/crewai/project/__init__.py b/lib/crewai/src/crewai/project/__init__.py similarity index 75% rename from src/crewai/project/__init__.py rename to lib/crewai/src/crewai/project/__init__.py index d60212153..b712138cc 100644 --- a/src/crewai/project/__init__.py +++ b/lib/crewai/src/crewai/project/__init__.py @@ -1,4 +1,6 @@ -from .annotations import ( +"""Project package for CrewAI.""" + +from crewai.project.annotations import ( after_kickoff, agent, before_kickoff, @@ -11,19 +13,20 @@ from .annotations import ( task, tool, ) -from .crew_base import CrewBase +from crewai.project.crew_base import CrewBase + __all__ = [ + "CrewBase", + "after_kickoff", "agent", + "before_kickoff", + "cache_handler", + "callback", "crew", - "task", + "llm", "output_json", "output_pydantic", + "task", "tool", - "callback", - "CrewBase", - "llm", - "cache_handler", - "before_kickoff", - "after_kickoff", ] diff --git a/lib/crewai/src/crewai/project/annotations.py b/lib/crewai/src/crewai/project/annotations.py new file mode 100644 index 000000000..17a07ddad --- /dev/null +++ b/lib/crewai/src/crewai/project/annotations.py @@ -0,0 +1,249 @@ +"""Decorators for defining crew components and their behaviors.""" + +from __future__ import annotations + +from collections.abc import Callable +from functools import wraps +from typing import TYPE_CHECKING, Concatenate, ParamSpec, TypeVar + +from crewai.project.utils import memoize + + +if TYPE_CHECKING: + from crewai import Agent, Crew, Task + +from crewai.project.wrappers import ( + AfterKickoffMethod, + AgentMethod, + BeforeKickoffMethod, + CacheHandlerMethod, + CallbackMethod, + CrewInstance, + LLMMethod, + OutputJsonClass, + OutputPydanticClass, + TaskMethod, + TaskResultT, + ToolMethod, +) + + +P = ParamSpec("P") +P2 = ParamSpec("P2") +R = TypeVar("R") +R2 = TypeVar("R2") +T = TypeVar("T") + + +def before_kickoff(meth: Callable[P, R]) -> BeforeKickoffMethod[P, R]: + """Marks a method to execute before crew kickoff. + + Args: + meth: The method to mark. + + Returns: + A wrapped method marked for before kickoff execution. + """ + return BeforeKickoffMethod(meth) + + +def after_kickoff(meth: Callable[P, R]) -> AfterKickoffMethod[P, R]: + """Marks a method to execute after crew kickoff. + + Args: + meth: The method to mark. + + Returns: + A wrapped method marked for after kickoff execution. + """ + return AfterKickoffMethod(meth) + + +def task(meth: Callable[P, TaskResultT]) -> TaskMethod[P, TaskResultT]: + """Marks a method as a crew task. + + Args: + meth: The method to mark. + + Returns: + A wrapped method marked as a task with memoization. + """ + return TaskMethod(memoize(meth)) + + +def agent(meth: Callable[P, R]) -> AgentMethod[P, R]: + """Marks a method as a crew agent. + + Args: + meth: The method to mark. + + Returns: + A wrapped method marked as an agent with memoization. + """ + return AgentMethod(memoize(meth)) + + +def llm(meth: Callable[P, R]) -> LLMMethod[P, R]: + """Marks a method as an LLM provider. + + Args: + meth: The method to mark. + + Returns: + A wrapped method marked as an LLM provider with memoization. + """ + return LLMMethod(memoize(meth)) + + +def output_json(cls: type[T]) -> OutputJsonClass[T]: + """Marks a class as JSON output format. + + Args: + cls: The class to mark. + + Returns: + A wrapped class marked as JSON output format. + """ + return OutputJsonClass(cls) + + +def output_pydantic(cls: type[T]) -> OutputPydanticClass[T]: + """Marks a class as Pydantic output format. + + Args: + cls: The class to mark. + + Returns: + A wrapped class marked as Pydantic output format. + """ + return OutputPydanticClass(cls) + + +def tool(meth: Callable[P, R]) -> ToolMethod[P, R]: + """Marks a method as a crew tool. + + Args: + meth: The method to mark. + + Returns: + A wrapped method marked as a tool with memoization. + """ + return ToolMethod(memoize(meth)) + + +def callback(meth: Callable[P, R]) -> CallbackMethod[P, R]: + """Marks a method as a crew callback. + + Args: + meth: The method to mark. + + Returns: + A wrapped method marked as a callback with memoization. + """ + return CallbackMethod(memoize(meth)) + + +def cache_handler(meth: Callable[P, R]) -> CacheHandlerMethod[P, R]: + """Marks a method as a cache handler. + + Args: + meth: The method to mark. + + Returns: + A wrapped method marked as a cache handler with memoization. + """ + return CacheHandlerMethod(memoize(meth)) + + +def crew( + meth: Callable[Concatenate[CrewInstance, P], Crew], +) -> Callable[Concatenate[CrewInstance, P], Crew]: + """Marks a method as the main crew execution point. + + Args: + meth: The method to mark as crew execution point. + + Returns: + A wrapped method that instantiates tasks and agents before execution. + """ + + @wraps(meth) + def wrapper(self: CrewInstance, *args: P.args, **kwargs: P.kwargs) -> Crew: + """Wrapper that sets up crew before calling the decorated method. + + Args: + self: The crew class instance. + *args: Additional positional arguments. + **kwargs: Keyword arguments to pass to the method. + + Returns: + The configured Crew instance with callbacks attached. + """ + instantiated_tasks: list[Task] = [] + instantiated_agents: list[Agent] = [] + agent_roles: set[str] = set() + + # Use the preserved task and agent information + tasks = self.__crew_metadata__["original_tasks"].items() + agents = self.__crew_metadata__["original_agents"].items() + + # Instantiate tasks in order + for _, task_method in tasks: + task_instance = task_method(self) + instantiated_tasks.append(task_instance) + agent_instance = getattr(task_instance, "agent", None) + if agent_instance and agent_instance.role not in agent_roles: + instantiated_agents.append(agent_instance) + agent_roles.add(agent_instance.role) + + # Instantiate agents not included by tasks + for _, agent_method in agents: + agent_instance = agent_method(self) + if agent_instance.role not in agent_roles: + instantiated_agents.append(agent_instance) + agent_roles.add(agent_instance.role) + + self.agents = instantiated_agents + self.tasks = instantiated_tasks + + crew_instance = meth(self, *args, **kwargs) + + def callback_wrapper( + hook: Callable[Concatenate[CrewInstance, P2], R2], instance: CrewInstance + ) -> Callable[P2, R2]: + """Bind a hook callback to an instance. + + Args: + hook: The callback hook to bind. + instance: The instance to bind to. + + Returns: + A bound callback function. + """ + + def bound_callback(*cb_args: P2.args, **cb_kwargs: P2.kwargs) -> R2: + """Execute the bound callback. + + Args: + *cb_args: Positional arguments for the callback. + **cb_kwargs: Keyword arguments for the callback. + + Returns: + The result of the callback execution. + """ + return hook(instance, *cb_args, **cb_kwargs) + + return bound_callback + + for hook_callback in self.__crew_metadata__["before_kickoff"].values(): + crew_instance.before_kickoff_callbacks.append( + callback_wrapper(hook_callback, self) + ) + for hook_callback in self.__crew_metadata__["after_kickoff"].values(): + crew_instance.after_kickoff_callbacks.append( + callback_wrapper(hook_callback, self) + ) + + return crew_instance + + return memoize(wrapper) diff --git a/lib/crewai/src/crewai/project/crew_base.py b/lib/crewai/src/crewai/project/crew_base.py new file mode 100644 index 000000000..d2ba2d794 --- /dev/null +++ b/lib/crewai/src/crewai/project/crew_base.py @@ -0,0 +1,632 @@ +"""Base metaclass for creating crew classes with configuration and method management.""" + +from __future__ import annotations + +from collections.abc import Callable +import inspect +import logging +from pathlib import Path +from typing import TYPE_CHECKING, Any, Literal, TypeGuard, TypeVar, TypedDict, cast + +from dotenv import load_dotenv +import yaml + +from crewai.project.wrappers import CrewClass, CrewMetadata +from crewai.tools import BaseTool + + +if TYPE_CHECKING: + from crewai import Agent, Task + from crewai.agents.cache.cache_handler import CacheHandler + from crewai.crews.crew_output import CrewOutput + from crewai.project.wrappers import ( + CrewInstance, + OutputJsonClass, + OutputPydanticClass, + ) + from crewai.tasks.task_output import TaskOutput + + +class AgentConfig(TypedDict, total=False): + """Type definition for agent configuration dictionary. + + All fields are optional as they come from YAML configuration files. + Fields can be either string references (from YAML) or actual instances (after processing). + """ + + # Core agent attributes (from BaseAgent) + role: str + goal: str + backstory: str + cache: bool + verbose: bool + max_rpm: int + allow_delegation: bool + max_iter: int + max_tokens: int + callbacks: list[str] + + # LLM configuration + llm: str + function_calling_llm: str + use_system_prompt: bool + + # Template configuration + system_template: str + prompt_template: str + response_template: str + + # Tools and handlers (can be string references or instances) + tools: list[str] | list[BaseTool] + step_callback: str + cache_handler: str | CacheHandler + + # Code execution + allow_code_execution: bool + code_execution_mode: Literal["safe", "unsafe"] + + # Context and performance + respect_context_window: bool + max_retry_limit: int + + # Multimodal and reasoning + multimodal: bool + reasoning: bool + max_reasoning_attempts: int + + # Knowledge configuration + knowledge_sources: list[str] | list[Any] + knowledge_storage: str | Any + knowledge_config: dict[str, Any] + embedder: dict[str, Any] + agent_knowledge_context: str + crew_knowledge_context: str + knowledge_search_query: str + + # Misc configuration + inject_date: bool + date_format: str + from_repository: str + guardrail: Callable[[Any], tuple[bool, Any]] | str + guardrail_max_retries: int + + +class TaskConfig(TypedDict, total=False): + """Type definition for task configuration dictionary. + + All fields are optional as they come from YAML configuration files. + Fields can be either string references (from YAML) or actual instances (after processing). + """ + + # Core task attributes + name: str + description: str + expected_output: str + + # Agent and context + agent: str + context: list[str] + + # Tools and callbacks (can be string references or instances) + tools: list[str] | list[BaseTool] + callback: str + callbacks: list[str] + + # Output configuration + output_json: str + output_pydantic: str + output_file: str + create_directory: bool + + # Execution configuration + async_execution: bool + human_input: bool + markdown: bool + + # Guardrail configuration + guardrail: Callable[[TaskOutput], tuple[bool, Any]] | str + guardrail_max_retries: int + + # Misc configuration + allow_crewai_trigger_context: bool + + +load_dotenv() + +CallableT = TypeVar("CallableT", bound=Callable[..., Any]) + + +def _set_base_directory(cls: type[CrewClass]) -> None: + """Set the base directory for the crew class. + + Args: + cls: Crew class to configure. + """ + try: + cls.base_directory = Path(inspect.getfile(cls)).parent + except (TypeError, OSError): + cls.base_directory = Path.cwd() + + +def _set_config_paths(cls: type[CrewClass]) -> None: + """Set the configuration file paths for the crew class. + + Args: + cls: Crew class to configure. + """ + cls.original_agents_config_path = getattr( + cls, "agents_config", "config/agents.yaml" + ) + cls.original_tasks_config_path = getattr(cls, "tasks_config", "config/tasks.yaml") + + +def _set_mcp_params(cls: type[CrewClass]) -> None: + """Set the MCP server parameters for the crew class. + + Args: + cls: Crew class to configure. + """ + cls.mcp_server_params = getattr(cls, "mcp_server_params", None) + cls.mcp_connect_timeout = getattr(cls, "mcp_connect_timeout", 30) + + +def _is_string_list(value: list[str] | list[BaseTool]) -> TypeGuard[list[str]]: + """Type guard to check if list contains strings rather than BaseTool instances. + + Args: + value: List that may contain strings or BaseTool instances. + + Returns: + True if all elements are strings, False otherwise. + """ + return all(isinstance(item, str) for item in value) + + +def _is_string_value(value: str | CacheHandler) -> TypeGuard[str]: + """Type guard to check if value is a string rather than a CacheHandler instance. + + Args: + value: Value that may be a string or CacheHandler instance. + + Returns: + True if value is a string, False otherwise. + """ + return isinstance(value, str) + + +class CrewBaseMeta(type): + """Metaclass that adds crew functionality to classes.""" + + def __new__( + mcs, + name: str, + bases: tuple[type, ...], + namespace: dict[str, Any], + **kwargs: Any, + ) -> type[CrewClass]: + """Create crew class with configuration and method injection. + + Args: + name: Class name. + bases: Base classes. + namespace: Class namespace dictionary. + **kwargs: Additional keyword arguments. + + Returns: + New crew class with injected methods and attributes. + """ + cls = cast( + type[CrewClass], cast(object, super().__new__(mcs, name, bases, namespace)) + ) + + cls.is_crew_class = True + cls._crew_name = name + + for setup_fn in _CLASS_SETUP_FUNCTIONS: + setup_fn(cls) + + for method in _METHODS_TO_INJECT: + setattr(cls, method.__name__, method) + + return cls + + def __call__(cls, *args: Any, **kwargs: Any) -> CrewInstance: + """Intercept instance creation to initialize crew functionality. + + Args: + *args: Positional arguments for instance creation. + **kwargs: Keyword arguments for instance creation. + + Returns: + Initialized crew instance. + """ + instance: CrewInstance = super().__call__(*args, **kwargs) + CrewBaseMeta._initialize_crew_instance(instance, cls) + return instance + + @staticmethod + def _initialize_crew_instance(instance: CrewInstance, cls: type) -> None: + """Initialize crew instance attributes and load configurations. + + Args: + instance: Crew instance to initialize. + cls: Crew class type. + """ + instance._mcp_server_adapter = None + instance.load_configurations() + instance._all_methods = _get_all_methods(instance) + instance.map_all_agent_variables() + instance.map_all_task_variables() + + original_methods = { + name: method + for name, method in cls.__dict__.items() + if any( + hasattr(method, attr) + for attr in [ + "is_task", + "is_agent", + "is_before_kickoff", + "is_after_kickoff", + "is_kickoff", + ] + ) + } + + after_kickoff_callbacks = _filter_methods(original_methods, "is_after_kickoff") + after_kickoff_callbacks["close_mcp_server"] = instance.close_mcp_server + + instance.__crew_metadata__ = CrewMetadata( + original_methods=original_methods, + original_tasks=_filter_methods(original_methods, "is_task"), + original_agents=_filter_methods(original_methods, "is_agent"), + before_kickoff=_filter_methods(original_methods, "is_before_kickoff"), + after_kickoff=after_kickoff_callbacks, + kickoff=_filter_methods(original_methods, "is_kickoff"), + ) + + +def close_mcp_server( + self: CrewInstance, _instance: CrewInstance, outputs: CrewOutput +) -> CrewOutput: + """Stop MCP server adapter and return outputs. + + Args: + self: Crew instance with MCP server adapter. + _instance: Crew instance (unused, required by callback signature). + outputs: Crew execution outputs. + + Returns: + Unmodified crew outputs. + """ + if self._mcp_server_adapter is not None: + try: + self._mcp_server_adapter.stop() + except Exception as e: + logging.warning(f"Error stopping MCP server: {e}") + return outputs + + +def get_mcp_tools(self: CrewInstance, *tool_names: str) -> list[BaseTool]: + """Get MCP tools filtered by name. + + Args: + self: Crew instance with MCP server configuration. + *tool_names: Optional tool names to filter by. + + Returns: + List of filtered MCP tools, or empty list if no MCP server configured. + """ + if not self.mcp_server_params: + return [] + + from crewai_tools import MCPServerAdapter # type: ignore[import-untyped] + + if self._mcp_server_adapter is None: + self._mcp_server_adapter = MCPServerAdapter( + self.mcp_server_params, connect_timeout=self.mcp_connect_timeout + ) + + return self._mcp_server_adapter.tools.filter_by_names(tool_names or None) + + +def _load_config( + self: CrewInstance, config_path: str | None, config_type: Literal["agent", "task"] +) -> dict[str, Any]: + """Load YAML config file or return empty dict if not found. + + Args: + self: Crew instance with base directory and load_yaml method. + config_path: Relative path to config file. + config_type: Config type for logging, either "agent" or "task". + + Returns: + Config dictionary or empty dict. + """ + if isinstance(config_path, str): + full_path = self.base_directory / config_path + try: + return self.load_yaml(full_path) + except FileNotFoundError: + logging.warning( + f"{config_type.capitalize()} config file not found at {full_path}. " + f"Proceeding with empty {config_type} configurations." + ) + return {} + else: + logging.warning( + f"No {config_type} configuration path provided. " + f"Proceeding with empty {config_type} configurations." + ) + return {} + + +def load_configurations(self: CrewInstance) -> None: + """Load agent and task YAML configurations. + + Args: + self: Crew instance with configuration paths. + """ + self.agents_config = self._load_config(self.original_agents_config_path, "agent") + self.tasks_config = self._load_config(self.original_tasks_config_path, "task") + + +def load_yaml(config_path: Path) -> dict[str, Any]: + """Load and parse YAML configuration file. + + Args: + config_path: Path to YAML configuration file. + + Returns: + Parsed YAML content as a dictionary. Returns empty dict if file is empty. + + Raises: + FileNotFoundError: If config file does not exist. + """ + try: + with open(config_path, encoding="utf-8") as file: + content = yaml.safe_load(file) + return content if isinstance(content, dict) else {} + except FileNotFoundError: + logging.warning(f"File not found: {config_path}") + raise + + +def _get_all_methods(self: CrewInstance) -> dict[str, Callable[..., Any]]: + """Return all non-dunder callable attributes (methods). + + Args: + self: Instance to inspect for callable attributes. + + Returns: + Dictionary mapping method names to bound method objects. + """ + return { + name: getattr(self, name) + for name in dir(self) + if not (name.startswith("__") and name.endswith("__")) + and callable(getattr(self, name, None)) + } + + +def _filter_methods( + methods: dict[str, CallableT], attribute: str +) -> dict[str, CallableT]: + """Filter methods by attribute presence, preserving exact callable types. + + Args: + methods: Dictionary of methods to filter. + attribute: Attribute name to check for. + + Returns: + Dictionary containing only methods with the specified attribute. + The return type matches the input callable type exactly. + """ + return { + name: method for name, method in methods.items() if hasattr(method, attribute) + } + + +def map_all_agent_variables(self: CrewInstance) -> None: + """Map agent configuration variables to callable instances. + + Args: + self: Crew instance with agent configurations to map. + """ + llms = _filter_methods(self._all_methods, "is_llm") + tool_functions = _filter_methods(self._all_methods, "is_tool") + cache_handler_functions = _filter_methods(self._all_methods, "is_cache_handler") + callbacks = _filter_methods(self._all_methods, "is_callback") + + for agent_name, agent_info in self.agents_config.items(): + self._map_agent_variables( + agent_name=agent_name, + agent_info=agent_info, + llms=llms, + tool_functions=tool_functions, + cache_handler_functions=cache_handler_functions, + callbacks=callbacks, + ) + + +def _map_agent_variables( + self: CrewInstance, + agent_name: str, + agent_info: AgentConfig, + llms: dict[str, Callable[[], Any]], + tool_functions: dict[str, Callable[[], BaseTool]], + cache_handler_functions: dict[str, Callable[[], Any]], + callbacks: dict[str, Callable[..., Any]], +) -> None: + """Resolve and map variables for a single agent. + + Args: + self: Crew instance with agent configurations. + agent_name: Name of agent to configure. + agent_info: Agent configuration dictionary with optional fields. + llms: Dictionary mapping names to LLM factory functions. + tool_functions: Dictionary mapping names to tool factory functions. + cache_handler_functions: Dictionary mapping names to cache handler factory functions. + callbacks: Dictionary of available callbacks. + """ + if llm := agent_info.get("llm"): + factory = llms.get(llm) + self.agents_config[agent_name]["llm"] = factory() if factory else llm + + if tools := agent_info.get("tools"): + if _is_string_list(tools): + self.agents_config[agent_name]["tools"] = [ + tool_functions[tool]() for tool in tools + ] + + if function_calling_llm := agent_info.get("function_calling_llm"): + factory = llms.get(function_calling_llm) + self.agents_config[agent_name]["function_calling_llm"] = ( + factory() if factory else function_calling_llm + ) + + if step_callback := agent_info.get("step_callback"): + self.agents_config[agent_name]["step_callback"] = callbacks[step_callback]() + + if cache_handler := agent_info.get("cache_handler"): + if _is_string_value(cache_handler): + self.agents_config[agent_name]["cache_handler"] = cache_handler_functions[ + cache_handler + ]() + + +def map_all_task_variables(self: CrewInstance) -> None: + """Map task configuration variables to callable instances. + + Args: + self: Crew instance with task configurations to map. + """ + agents = _filter_methods(self._all_methods, "is_agent") + tasks = _filter_methods(self._all_methods, "is_task") + output_json_functions = _filter_methods(self._all_methods, "is_output_json") + tool_functions = _filter_methods(self._all_methods, "is_tool") + callback_functions = _filter_methods(self._all_methods, "is_callback") + output_pydantic_functions = _filter_methods(self._all_methods, "is_output_pydantic") + + for task_name, task_info in self.tasks_config.items(): + self._map_task_variables( + task_name=task_name, + task_info=task_info, + agents=agents, + tasks=tasks, + output_json_functions=output_json_functions, + tool_functions=tool_functions, + callback_functions=callback_functions, + output_pydantic_functions=output_pydantic_functions, + ) + + +def _map_task_variables( + self: CrewInstance, + task_name: str, + task_info: TaskConfig, + agents: dict[str, Callable[[], Agent]], + tasks: dict[str, Callable[[], Task]], + output_json_functions: dict[str, OutputJsonClass[Any]], + tool_functions: dict[str, Callable[[], BaseTool]], + callback_functions: dict[str, Callable[..., Any]], + output_pydantic_functions: dict[str, OutputPydanticClass[Any]], +) -> None: + """Resolve and map variables for a single task. + + Args: + self: Crew instance with task configurations. + task_name: Name of task to configure. + task_info: Task configuration dictionary with optional fields. + agents: Dictionary mapping names to agent factory functions. + tasks: Dictionary mapping names to task factory functions. + output_json_functions: Dictionary of JSON output class wrappers. + tool_functions: Dictionary mapping names to tool factory functions. + callback_functions: Dictionary of available callbacks. + output_pydantic_functions: Dictionary of Pydantic output class wrappers. + """ + if context_list := task_info.get("context"): + self.tasks_config[task_name]["context"] = [ + tasks[context_task_name]() for context_task_name in context_list + ] + + if tools := task_info.get("tools"): + if _is_string_list(tools): + self.tasks_config[task_name]["tools"] = [ + tool_functions[tool]() for tool in tools + ] + + if agent_name := task_info.get("agent"): + self.tasks_config[task_name]["agent"] = agents[agent_name]() + + if output_json := task_info.get("output_json"): + self.tasks_config[task_name]["output_json"] = output_json_functions[output_json] + + if output_pydantic := task_info.get("output_pydantic"): + self.tasks_config[task_name]["output_pydantic"] = output_pydantic_functions[ + output_pydantic + ] + + if callbacks := task_info.get("callbacks"): + self.tasks_config[task_name]["callbacks"] = [ + callback_functions[callback]() for callback in callbacks + ] + + if guardrail := task_info.get("guardrail"): + self.tasks_config[task_name]["guardrail"] = guardrail + + +_CLASS_SETUP_FUNCTIONS: tuple[Callable[[type[CrewClass]], None], ...] = ( + _set_base_directory, + _set_config_paths, + _set_mcp_params, +) + +_METHODS_TO_INJECT = ( + close_mcp_server, + get_mcp_tools, + _load_config, + load_configurations, + staticmethod(load_yaml), + map_all_agent_variables, + _map_agent_variables, + map_all_task_variables, + _map_task_variables, +) + + +class _CrewBaseType(type): + """Metaclass for CrewBase that makes it callable as a decorator.""" + + def __call__(cls, decorated_cls: type) -> type[CrewClass]: + """Apply CrewBaseMeta to the decorated class. + + Args: + decorated_cls: Class to transform with CrewBaseMeta metaclass. + + Returns: + New class with CrewBaseMeta metaclass applied. + """ + __name = str(decorated_cls.__name__) + __bases = tuple(decorated_cls.__bases__) + __dict = { + key: value + for key, value in decorated_cls.__dict__.items() + if key not in ("__dict__", "__weakref__") + } + for slot in __dict.get("__slots__", tuple()): + __dict.pop(slot, None) + __dict["__metaclass__"] = CrewBaseMeta + return cast(type[CrewClass], CrewBaseMeta(__name, __bases, __dict)) + + +class CrewBase(metaclass=_CrewBaseType): + """Class decorator that applies CrewBaseMeta metaclass. + + Applies CrewBaseMeta metaclass to a class via decorator syntax rather than + explicit metaclass declaration. Use as @CrewBase instead of + class Foo(metaclass=CrewBaseMeta). + + Note: + Reference: https://stackoverflow.com/questions/11091609/setting-a-class-metaclass-using-a-decorator + """ diff --git a/lib/crewai/src/crewai/project/utils.py b/lib/crewai/src/crewai/project/utils.py new file mode 100644 index 000000000..65a844d0f --- /dev/null +++ b/lib/crewai/src/crewai/project/utils.py @@ -0,0 +1,21 @@ +"""Utility functions for the crewai project module.""" + +from collections.abc import Callable +from functools import lru_cache +from typing import ParamSpec, TypeVar, cast + + +P = ParamSpec("P") +R = TypeVar("R") + + +def memoize(meth: Callable[P, R]) -> Callable[P, R]: + """Memoize a method by caching its results based on arguments. + + Args: + meth: The method to memoize. + + Returns: + A memoized version of the method that caches results. + """ + return cast(Callable[P, R], lru_cache(typed=True)(meth)) diff --git a/lib/crewai/src/crewai/project/wrappers.py b/lib/crewai/src/crewai/project/wrappers.py new file mode 100644 index 000000000..566dd2268 --- /dev/null +++ b/lib/crewai/src/crewai/project/wrappers.py @@ -0,0 +1,389 @@ +"""Wrapper classes for decorated methods with type-safe metadata.""" + +from __future__ import annotations + +from collections.abc import Callable +from functools import partial +from pathlib import Path +from typing import ( + TYPE_CHECKING, + Any, + Generic, + Literal, + ParamSpec, + Protocol, + TypeVar, + TypedDict, +) + +from typing_extensions import Self + + +if TYPE_CHECKING: + from crewai import Agent, Task + from crewai.crews.crew_output import CrewOutput + from crewai.tools import BaseTool + + +class CrewMetadata(TypedDict): + """Type definition for crew metadata dictionary. + + Stores framework-injected metadata about decorated methods and callbacks. + """ + + original_methods: dict[str, Callable[..., Any]] + original_tasks: dict[str, Callable[..., Task]] + original_agents: dict[str, Callable[..., Agent]] + before_kickoff: dict[str, Callable[..., Any]] + after_kickoff: dict[str, Callable[..., Any]] + kickoff: dict[str, Callable[..., Any]] + + +P = ParamSpec("P") +R = TypeVar("R") +T = TypeVar("T") + + +class TaskResult(Protocol): + """Protocol for task objects that have a name attribute.""" + + name: str | None + + +TaskResultT = TypeVar("TaskResultT", bound=TaskResult) + + +def _copy_method_metadata(wrapper: Any, meth: Callable[..., Any]) -> None: + """Copy method metadata to a wrapper object. + + Args: + wrapper: The wrapper object to update. + meth: The method to copy metadata from. + """ + wrapper.__name__ = meth.__name__ + wrapper.__doc__ = meth.__doc__ + + +class CrewInstance(Protocol): + """Protocol for crew class instances with required attributes.""" + + __crew_metadata__: CrewMetadata + _mcp_server_adapter: Any + _all_methods: dict[str, Callable[..., Any]] + agents: list[Agent] + tasks: list[Task] + base_directory: Path + original_agents_config_path: str + original_tasks_config_path: str + agents_config: dict[str, Any] + tasks_config: dict[str, Any] + mcp_server_params: Any + mcp_connect_timeout: int + + def load_configurations(self) -> None: ... + def map_all_agent_variables(self) -> None: ... + def map_all_task_variables(self) -> None: ... + def close_mcp_server(self, instance: Self, outputs: CrewOutput) -> CrewOutput: ... + def _load_config( + self, config_path: str | None, config_type: Literal["agent", "task"] + ) -> dict[str, Any]: ... + def _map_agent_variables( + self, + agent_name: str, + agent_info: dict[str, Any], + llms: dict[str, Callable[..., Any]], + tool_functions: dict[str, Callable[..., Any]], + cache_handler_functions: dict[str, Callable[..., Any]], + callbacks: dict[str, Callable[..., Any]], + ) -> None: ... + def _map_task_variables( + self, + task_name: str, + task_info: dict[str, Any], + agents: dict[str, Callable[..., Any]], + tasks: dict[str, Callable[..., Any]], + output_json_functions: dict[str, Callable[..., Any]], + tool_functions: dict[str, Callable[..., Any]], + callback_functions: dict[str, Callable[..., Any]], + output_pydantic_functions: dict[str, Callable[..., Any]], + ) -> None: ... + def load_yaml(self, config_path: Path) -> dict[str, Any]: ... + + +class CrewClass(Protocol): + """Protocol describing class attributes injected by CrewBaseMeta.""" + + is_crew_class: bool + _crew_name: str + base_directory: Path + original_agents_config_path: str + original_tasks_config_path: str + mcp_server_params: Any + mcp_connect_timeout: int + close_mcp_server: Callable[..., Any] + get_mcp_tools: Callable[..., list[BaseTool]] + _load_config: Callable[..., dict[str, Any]] + load_configurations: Callable[..., None] + load_yaml: staticmethod + map_all_agent_variables: Callable[..., None] + _map_agent_variables: Callable[..., None] + map_all_task_variables: Callable[..., None] + _map_task_variables: Callable[..., None] + + +class DecoratedMethod(Generic[P, R]): + """Base wrapper for methods with decorator metadata. + + This class provides a type-safe way to add metadata to methods + while preserving their callable signature and attributes. + """ + + def __init__(self, meth: Callable[P, R]) -> None: + """Initialize the decorated method wrapper. + + Args: + meth: The method to wrap. + """ + self._meth = meth + _copy_method_metadata(self, meth) + + def __get__( + self, obj: Any, objtype: type[Any] | None = None + ) -> Self | Callable[..., R]: + """Support instance methods by implementing the descriptor protocol. + + Args: + obj: The instance that the method is accessed through. + objtype: The type of the instance. + + Returns: + Self when accessed through class, bound method when accessed through instance. + """ + if obj is None: + return self + bound = partial(self._meth, obj) + for attr in ( + "is_agent", + "is_llm", + "is_tool", + "is_callback", + "is_cache_handler", + "is_before_kickoff", + "is_after_kickoff", + "is_crew", + ): + if hasattr(self, attr): + setattr(bound, attr, getattr(self, attr)) + return bound + + def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R: + """Call the wrapped method. + + Args: + *args: Positional arguments. + **kwargs: Keyword arguments. + + Returns: + The result of calling the wrapped method. + """ + return self._meth(*args, **kwargs) + + def unwrap(self) -> Callable[P, R]: + """Get the original unwrapped method. + + Returns: + The original method before decoration. + """ + return self._meth + + +class BeforeKickoffMethod(DecoratedMethod[P, R]): + """Wrapper for methods marked to execute before crew kickoff.""" + + is_before_kickoff: bool = True + + +class AfterKickoffMethod(DecoratedMethod[P, R]): + """Wrapper for methods marked to execute after crew kickoff.""" + + is_after_kickoff: bool = True + + +class BoundTaskMethod(Generic[TaskResultT]): + """Bound task method with task marker attribute.""" + + is_task: bool = True + + def __init__(self, task_method: TaskMethod[Any, TaskResultT], obj: Any) -> None: + """Initialize the bound task method. + + Args: + task_method: The TaskMethod descriptor instance. + obj: The instance to bind to. + """ + self._task_method = task_method + self._obj = obj + + def __call__(self, *args: Any, **kwargs: Any) -> TaskResultT: + """Execute the bound task method. + + Args: + *args: Positional arguments. + **kwargs: Keyword arguments. + + Returns: + The task result with name ensured. + """ + result = self._task_method.unwrap()(self._obj, *args, **kwargs) + return self._task_method.ensure_task_name(result) + + +class TaskMethod(Generic[P, TaskResultT]): + """Wrapper for methods marked as crew tasks.""" + + is_task: bool = True + + def __init__(self, meth: Callable[P, TaskResultT]) -> None: + """Initialize the task method wrapper. + + Args: + meth: The method to wrap. + """ + self._meth = meth + _copy_method_metadata(self, meth) + + def ensure_task_name(self, result: TaskResultT) -> TaskResultT: + """Ensure task result has a name set. + + Args: + result: The task result to check. + + Returns: + The task result with name ensured. + """ + if not result.name: + result.name = self._meth.__name__ + return result + + def __get__( + self, obj: Any, objtype: type[Any] | None = None + ) -> Self | BoundTaskMethod[TaskResultT]: + """Support instance methods by implementing the descriptor protocol. + + Args: + obj: The instance that the method is accessed through. + objtype: The type of the instance. + + Returns: + Self when accessed through class, bound method when accessed through instance. + """ + if obj is None: + return self + return BoundTaskMethod(self, obj) + + def __call__(self, *args: P.args, **kwargs: P.kwargs) -> TaskResultT: + """Call the wrapped method and set task name if not provided. + + Args: + *args: Positional arguments. + **kwargs: Keyword arguments. + + Returns: + The task instance with name set if not already provided. + """ + return self.ensure_task_name(self._meth(*args, **kwargs)) + + def unwrap(self) -> Callable[P, TaskResultT]: + """Get the original unwrapped method. + + Returns: + The original method before decoration. + """ + return self._meth + + +class AgentMethod(DecoratedMethod[P, R]): + """Wrapper for methods marked as crew agents.""" + + is_agent: bool = True + + +class LLMMethod(DecoratedMethod[P, R]): + """Wrapper for methods marked as LLM providers.""" + + is_llm: bool = True + + +class ToolMethod(DecoratedMethod[P, R]): + """Wrapper for methods marked as crew tools.""" + + is_tool: bool = True + + +class CallbackMethod(DecoratedMethod[P, R]): + """Wrapper for methods marked as crew callbacks.""" + + is_callback: bool = True + + +class CacheHandlerMethod(DecoratedMethod[P, R]): + """Wrapper for methods marked as cache handlers.""" + + is_cache_handler: bool = True + + +class CrewMethod(DecoratedMethod[P, R]): + """Wrapper for methods marked as the main crew execution point.""" + + is_crew: bool = True + + +class OutputClass(Generic[T]): + """Base wrapper for classes marked as output format.""" + + def __init__(self, cls: type[T]) -> None: + """Initialize the output class wrapper. + + Args: + cls: The class to wrap. + """ + self._cls = cls + self.__name__ = cls.__name__ + self.__qualname__ = cls.__qualname__ + self.__module__ = cls.__module__ + self.__doc__ = cls.__doc__ + + def __call__(self, *args: Any, **kwargs: Any) -> T: + """Create an instance of the wrapped class. + + Args: + *args: Positional arguments for the class constructor. + **kwargs: Keyword arguments for the class constructor. + + Returns: + An instance of the wrapped class. + """ + return self._cls(*args, **kwargs) + + def __getattr__(self, name: str) -> Any: + """Delegate attribute access to the wrapped class. + + Args: + name: The attribute name. + + Returns: + The attribute from the wrapped class. + """ + return getattr(self._cls, name) + + +class OutputJsonClass(OutputClass[T]): + """Wrapper for classes marked as JSON output format.""" + + is_output_json: bool = True + + +class OutputPydanticClass(OutputClass[T]): + """Wrapper for classes marked as Pydantic output format.""" + + is_output_pydantic: bool = True diff --git a/lib/crewai/src/crewai/py.typed b/lib/crewai/src/crewai/py.typed new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/rag/__init__.py b/lib/crewai/src/crewai/rag/__init__.py similarity index 99% rename from src/crewai/rag/__init__.py rename to lib/crewai/src/crewai/rag/__init__.py index f107607c6..865910b42 100644 --- a/src/crewai/rag/__init__.py +++ b/lib/crewai/src/crewai/rag/__init__.py @@ -8,6 +8,7 @@ from typing import Any from crewai.rag.config.types import RagConfigType from crewai.rag.config.utils import set_rag_config + _module_path = __path__ _module_file = __file__ diff --git a/lib/crewai/src/crewai/rag/chromadb/__init__.py b/lib/crewai/src/crewai/rag/chromadb/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/rag/chromadb/client.py b/lib/crewai/src/crewai/rag/chromadb/client.py similarity index 97% rename from src/crewai/rag/chromadb/client.py rename to lib/crewai/src/crewai/rag/chromadb/client.py index 53a4189dd..36bd8ab10 100644 --- a/src/crewai/rag/chromadb/client.py +++ b/lib/crewai/src/crewai/rag/chromadb/client.py @@ -5,8 +5,6 @@ from typing import Any from chromadb.api.types import ( EmbeddingFunction as ChromaEmbeddingFunction, -) -from chromadb.api.types import ( QueryResult, ) from typing_extensions import Unpack @@ -99,7 +97,7 @@ class ChromaDBClient(BaseClient): >>> client.create_collection( ... collection_name="documents", ... metadata={"description": "Product documentation"}, - ... get_or_create=True + ... get_or_create=True, ... ) """ if not _is_sync_client(self.client): @@ -114,7 +112,7 @@ class ChromaDBClient(BaseClient): self.client.create_collection( name=_sanitize_collection_name(kwargs["collection_name"]), - configuration=kwargs.get("configuration"), + configuration=kwargs.get("configuration"), # type: ignore[arg-type] metadata=metadata, embedding_function=kwargs.get( "embedding_function", self.embedding_function @@ -155,7 +153,7 @@ class ChromaDBClient(BaseClient): ... await client.acreate_collection( ... collection_name="documents", ... metadata={"description": "Product documentation"}, - ... get_or_create=True + ... get_or_create=True, ... ) >>> asyncio.run(main()) """ @@ -171,7 +169,7 @@ class ChromaDBClient(BaseClient): await self.client.create_collection( name=_sanitize_collection_name(kwargs["collection_name"]), - configuration=kwargs.get("configuration"), + configuration=kwargs.get("configuration"), # type: ignore[arg-type] metadata=metadata, embedding_function=kwargs.get( "embedding_function", self.embedding_function @@ -207,7 +205,7 @@ class ChromaDBClient(BaseClient): >>> client = ChromaDBClient() >>> collection = client.get_or_create_collection( ... collection_name="documents", - ... metadata={"description": "Product documentation"} + ... metadata={"description": "Product documentation"}, ... ) """ if not _is_sync_client(self.client): @@ -222,7 +220,7 @@ class ChromaDBClient(BaseClient): return self.client.get_or_create_collection( name=_sanitize_collection_name(kwargs["collection_name"]), - configuration=kwargs.get("configuration"), + configuration=kwargs.get("configuration"), # type: ignore[arg-type] metadata=metadata, embedding_function=kwargs.get( "embedding_function", self.embedding_function @@ -259,7 +257,7 @@ class ChromaDBClient(BaseClient): ... client = ChromaDBClient() ... collection = await client.aget_or_create_collection( ... collection_name="documents", - ... metadata={"description": "Product documentation"} + ... metadata={"description": "Product documentation"}, ... ) >>> asyncio.run(main()) """ @@ -275,7 +273,7 @@ class ChromaDBClient(BaseClient): return await self.client.get_or_create_collection( name=_sanitize_collection_name(kwargs["collection_name"]), - configuration=kwargs.get("configuration"), + configuration=kwargs.get("configuration") or None, # type: ignore[arg-type] metadata=metadata, embedding_function=kwargs.get( "embedding_function", self.embedding_function @@ -330,7 +328,7 @@ class ChromaDBClient(BaseClient): collection.upsert( ids=batch_ids, documents=batch_texts, - metadatas=batch_metadatas, + metadatas=batch_metadatas, # type: ignore[arg-type] ) async def aadd_documents(self, **kwargs: Unpack[BaseCollectionAddParams]) -> None: @@ -379,7 +377,7 @@ class ChromaDBClient(BaseClient): await collection.upsert( ids=batch_ids, documents=batch_texts, - metadatas=batch_metadatas, + metadatas=batch_metadatas, # type: ignore[arg-type] ) def search( diff --git a/src/crewai/rag/chromadb/config.py b/lib/crewai/src/crewai/rag/chromadb/config.py similarity index 97% rename from src/crewai/rag/chromadb/config.py rename to lib/crewai/src/crewai/rag/chromadb/config.py index 54908c6b7..49a8b22ff 100644 --- a/src/crewai/rag/chromadb/config.py +++ b/lib/crewai/src/crewai/rag/chromadb/config.py @@ -1,9 +1,9 @@ """ChromaDB configuration model.""" -import os -import warnings from dataclasses import field +import os from typing import Literal, cast +import warnings from chromadb.config import Settings from pydantic.dataclasses import dataclass as pyd_dataclass @@ -16,6 +16,7 @@ from crewai.rag.chromadb.constants import ( from crewai.rag.chromadb.types import ChromaEmbeddingFunctionWrapper from crewai.rag.config.base import BaseRagConfig + warnings.filterwarnings( "ignore", message=".*Mixing V1 models and V2 models.*", @@ -58,6 +59,7 @@ def _default_embedding_function() -> ChromaEmbeddingFunctionWrapper: OpenAIEmbeddingFunction( api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small", + api_key_env_var="OPENAI_API_KEY", ), ) diff --git a/src/crewai/rag/chromadb/constants.py b/lib/crewai/src/crewai/rag/chromadb/constants.py similarity index 99% rename from src/crewai/rag/chromadb/constants.py rename to lib/crewai/src/crewai/rag/chromadb/constants.py index 8082356c6..73b659fdf 100644 --- a/src/crewai/rag/chromadb/constants.py +++ b/lib/crewai/src/crewai/rag/chromadb/constants.py @@ -5,6 +5,7 @@ from typing import Final from crewai.utilities.paths import db_storage_path + DEFAULT_TENANT: Final[str] = "default_tenant" DEFAULT_DATABASE: Final[str] = "default_database" DEFAULT_STORAGE_PATH: Final[str] = db_storage_path() diff --git a/src/crewai/rag/chromadb/factory.py b/lib/crewai/src/crewai/rag/chromadb/factory.py similarity index 100% rename from src/crewai/rag/chromadb/factory.py rename to lib/crewai/src/crewai/rag/chromadb/factory.py index 7c9532390..933da10a2 100644 --- a/src/crewai/rag/chromadb/factory.py +++ b/lib/crewai/src/crewai/rag/chromadb/factory.py @@ -1,10 +1,10 @@ """Factory functions for creating ChromaDB clients.""" -import os from hashlib import md5 +import os -import portalocker from chromadb import PersistentClient +import portalocker from crewai.rag.chromadb.client import ChromaDBClient from crewai.rag.chromadb.config import ChromaDBConfig diff --git a/src/crewai/rag/chromadb/types.py b/lib/crewai/src/crewai/rag/chromadb/types.py similarity index 98% rename from src/crewai/rag/chromadb/types.py rename to lib/crewai/src/crewai/rag/chromadb/types.py index 23db5b77a..982b2fbe1 100644 --- a/src/crewai/rag/chromadb/types.py +++ b/lib/crewai/src/crewai/rag/chromadb/types.py @@ -8,19 +8,18 @@ from chromadb.api.configuration import CollectionConfigurationInterface from chromadb.api.types import ( CollectionMetadata, DataLoader, + EmbeddingFunction as ChromaEmbeddingFunction, Include, Loadable, Where, WhereDocument, ) -from chromadb.api.types import ( - EmbeddingFunction as ChromaEmbeddingFunction, -) from pydantic import GetCoreSchemaHandler from pydantic_core import CoreSchema, core_schema from crewai.rag.core.base_client import BaseCollectionParams, BaseCollectionSearchParams + ChromaDBClientType = ClientAPI | AsyncClientAPI diff --git a/src/crewai/rag/chromadb/utils.py b/lib/crewai/src/crewai/rag/chromadb/utils.py similarity index 100% rename from src/crewai/rag/chromadb/utils.py rename to lib/crewai/src/crewai/rag/chromadb/utils.py index db66745ac..dc58fa8c5 100644 --- a/src/crewai/rag/chromadb/utils.py +++ b/lib/crewai/src/crewai/rag/chromadb/utils.py @@ -1,8 +1,8 @@ """Utility functions for ChromaDB client implementation.""" +from collections.abc import Mapping import hashlib import json -from collections.abc import Mapping from typing import Literal, TypeGuard, cast from chromadb.api import AsyncClientAPI, ClientAPI diff --git a/src/crewai/rag/config/__init__.py b/lib/crewai/src/crewai/rag/config/__init__.py similarity index 100% rename from src/crewai/rag/config/__init__.py rename to lib/crewai/src/crewai/rag/config/__init__.py diff --git a/src/crewai/rag/config/base.py b/lib/crewai/src/crewai/rag/config/base.py similarity index 100% rename from src/crewai/rag/config/base.py rename to lib/crewai/src/crewai/rag/config/base.py diff --git a/src/crewai/rag/config/constants.py b/lib/crewai/src/crewai/rag/config/constants.py similarity index 99% rename from src/crewai/rag/config/constants.py rename to lib/crewai/src/crewai/rag/config/constants.py index d0d360db1..636ec7a2f 100644 --- a/src/crewai/rag/config/constants.py +++ b/lib/crewai/src/crewai/rag/config/constants.py @@ -2,6 +2,7 @@ from typing import Final + DISCRIMINATOR: Final[str] = "provider" DEFAULT_RAG_CONFIG_PATH: Final[str] = "crewai.rag.chromadb.config" diff --git a/src/crewai/rag/config/optional_imports/__init__.py b/lib/crewai/src/crewai/rag/config/optional_imports/__init__.py similarity index 100% rename from src/crewai/rag/config/optional_imports/__init__.py rename to lib/crewai/src/crewai/rag/config/optional_imports/__init__.py diff --git a/src/crewai/rag/config/optional_imports/base.py b/lib/crewai/src/crewai/rag/config/optional_imports/base.py similarity index 100% rename from src/crewai/rag/config/optional_imports/base.py rename to lib/crewai/src/crewai/rag/config/optional_imports/base.py diff --git a/src/crewai/rag/config/optional_imports/protocols.py b/lib/crewai/src/crewai/rag/config/optional_imports/protocols.py similarity index 99% rename from src/crewai/rag/config/optional_imports/protocols.py rename to lib/crewai/src/crewai/rag/config/optional_imports/protocols.py index 3dd78021e..a4881f24a 100644 --- a/src/crewai/rag/config/optional_imports/protocols.py +++ b/lib/crewai/src/crewai/rag/config/optional_imports/protocols.py @@ -4,6 +4,7 @@ from __future__ import annotations from typing import TYPE_CHECKING, Protocol + if TYPE_CHECKING: from crewai.rag.chromadb.client import ChromaDBClient from crewai.rag.chromadb.config import ChromaDBConfig diff --git a/src/crewai/rag/config/optional_imports/providers.py b/lib/crewai/src/crewai/rag/config/optional_imports/providers.py similarity index 100% rename from src/crewai/rag/config/optional_imports/providers.py rename to lib/crewai/src/crewai/rag/config/optional_imports/providers.py diff --git a/src/crewai/rag/config/optional_imports/types.py b/lib/crewai/src/crewai/rag/config/optional_imports/types.py similarity index 99% rename from src/crewai/rag/config/optional_imports/types.py rename to lib/crewai/src/crewai/rag/config/optional_imports/types.py index 184348b1b..c4141e405 100644 --- a/src/crewai/rag/config/optional_imports/types.py +++ b/lib/crewai/src/crewai/rag/config/optional_imports/types.py @@ -2,6 +2,7 @@ from typing import Annotated, Literal + SupportedProvider = Annotated[ Literal["chromadb", "qdrant"], "Supported RAG provider types, add providers here as they become available", diff --git a/src/crewai/rag/config/types.py b/lib/crewai/src/crewai/rag/config/types.py similarity index 99% rename from src/crewai/rag/config/types.py rename to lib/crewai/src/crewai/rag/config/types.py index 59da7fea8..9e3cd78e7 100644 --- a/src/crewai/rag/config/types.py +++ b/lib/crewai/src/crewai/rag/config/types.py @@ -6,6 +6,7 @@ from pydantic import Field from crewai.rag.config.constants import DISCRIMINATOR + # Linter freaks out on conditional imports, assigning in the type checking fixes it if TYPE_CHECKING: from crewai.rag.chromadb.config import ChromaDBConfig as ChromaDBConfig_ diff --git a/src/crewai/rag/config/utils.py b/lib/crewai/src/crewai/rag/config/utils.py similarity index 100% rename from src/crewai/rag/config/utils.py rename to lib/crewai/src/crewai/rag/config/utils.py diff --git a/src/crewai/rag/core/__init__.py b/lib/crewai/src/crewai/rag/core/__init__.py similarity index 100% rename from src/crewai/rag/core/__init__.py rename to lib/crewai/src/crewai/rag/core/__init__.py diff --git a/src/crewai/rag/core/base_client.py b/lib/crewai/src/crewai/rag/core/base_client.py similarity index 97% rename from src/crewai/rag/core/base_client.py rename to lib/crewai/src/crewai/rag/core/base_client.py index bd7bd5d08..ab91ea91f 100644 --- a/src/crewai/rag/core/base_client.py +++ b/lib/crewai/src/crewai/rag/core/base_client.py @@ -208,13 +208,13 @@ class BaseClient(Protocol): >>> records: list[BaseRecord] = [ ... { ... "content": "Machine learning basics", - ... "metadata": {"source": "file3", "topic": "ML"} + ... "metadata": {"source": "file3", "topic": "ML"}, ... }, ... { ... "doc_id": "custom_id", ... "content": "Deep learning fundamentals", - ... "metadata": {"source": "file4", "topic": "DL"} - ... } + ... "metadata": {"source": "file4", "topic": "DL"}, + ... }, ... ] >>> client.add_documents(collection_name="my_docs", documents=records) >>> @@ -222,10 +222,12 @@ class BaseClient(Protocol): ... { ... "doc_id": "nlp_001", ... "content": "Advanced NLP techniques", - ... "metadata": {"source": "file5", "topic": "NLP"} + ... "metadata": {"source": "file5", "topic": "NLP"}, ... } ... ] - >>> client.add_documents(collection_name="my_docs", documents=records_with_id) + >>> client.add_documents( + ... collection_name="my_docs", documents=records_with_id + ... ) """ ... @@ -261,11 +263,12 @@ class BaseClient(Protocol): ... { ... "doc_id": "doc2", ... "content": "Async operations in Python", - ... "metadata": {"source": "file2", "topic": "async"} + ... "metadata": {"source": "file2", "topic": "async"}, ... } ... ] - ... await client.aadd_documents(collection_name="my_docs", documents=records) - ... + ... await client.aadd_documents( + ... collection_name="my_docs", documents=records + ... ) >>> asyncio.run(add_documents()) """ ... @@ -312,7 +315,7 @@ class BaseClient(Protocol): ... query="What is machine learning?", ... limit=5, ... metadata_filter={"source": "file1"}, - ... score_threshold=0.7 + ... score_threshold=0.7, ... ) >>> for result in results: ... print(f"{result['id']}: {result['score']:.2f}") @@ -351,11 +354,10 @@ class BaseClient(Protocol): ... query="Python programming best practices", ... limit=5, ... metadata_filter={"source": "file1"}, - ... score_threshold=0.7 + ... score_threshold=0.7, ... ) ... for result in results: ... print(f"{result['id']}: {result['score']:.2f}") - ... >>> asyncio.run(search_documents()) """ ... @@ -401,7 +403,6 @@ class BaseClient(Protocol): ... client = ChromaDBClient() ... await client.adelete_collection(collection_name="old_docs") ... print("Collection 'old_docs' deleted successfully") - ... >>> asyncio.run(delete_old_collection()) """ ... @@ -442,7 +443,6 @@ class BaseClient(Protocol): ... client = ChromaDBClient() ... await client.areset() ... print("Vector database completely reset - all data deleted") - ... >>> asyncio.run(reset_database()) """ ... diff --git a/src/crewai/rag/core/base_embeddings_callable.py b/lib/crewai/src/crewai/rag/core/base_embeddings_callable.py similarity index 99% rename from src/crewai/rag/core/base_embeddings_callable.py rename to lib/crewai/src/crewai/rag/core/base_embeddings_callable.py index 85fe88584..71bbf49b6 100644 --- a/src/crewai/rag/core/base_embeddings_callable.py +++ b/lib/crewai/src/crewai/rag/core/base_embeddings_callable.py @@ -11,6 +11,7 @@ from crewai.rag.core.types import ( PyEmbedding, ) + T = TypeVar("T") D = TypeVar("D", bound=Embeddable, contravariant=True) diff --git a/src/crewai/rag/core/base_embeddings_provider.py b/lib/crewai/src/crewai/rag/core/base_embeddings_provider.py similarity index 99% rename from src/crewai/rag/core/base_embeddings_provider.py rename to lib/crewai/src/crewai/rag/core/base_embeddings_provider.py index d93b575a0..9be35a912 100644 --- a/src/crewai/rag/core/base_embeddings_provider.py +++ b/lib/crewai/src/crewai/rag/core/base_embeddings_provider.py @@ -7,6 +7,7 @@ from pydantic_settings import BaseSettings, SettingsConfigDict from crewai.rag.core.base_embeddings_callable import EmbeddingFunction + T = TypeVar("T", bound=EmbeddingFunction) diff --git a/src/crewai/rag/core/exceptions.py b/lib/crewai/src/crewai/rag/core/exceptions.py similarity index 100% rename from src/crewai/rag/core/exceptions.py rename to lib/crewai/src/crewai/rag/core/exceptions.py diff --git a/src/crewai/rag/core/types.py b/lib/crewai/src/crewai/rag/core/types.py similarity index 99% rename from src/crewai/rag/core/types.py rename to lib/crewai/src/crewai/rag/core/types.py index d94ef777c..34e737f69 100644 --- a/src/crewai/rag/core/types.py +++ b/lib/crewai/src/crewai/rag/core/types.py @@ -7,6 +7,7 @@ import numpy as np from numpy import floating, integer, number from numpy.typing import NDArray + T = TypeVar("T") PyEmbedding = Sequence[float] | Sequence[int] diff --git a/src/crewai/rag/embeddings/__init__.py b/lib/crewai/src/crewai/rag/embeddings/__init__.py similarity index 100% rename from src/crewai/rag/embeddings/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/__init__.py diff --git a/src/crewai/rag/embeddings/factory.py b/lib/crewai/src/crewai/rag/embeddings/factory.py similarity index 91% rename from src/crewai/rag/embeddings/factory.py rename to lib/crewai/src/crewai/rag/embeddings/factory.py index 468e23de4..f5d5d9559 100644 --- a/src/crewai/rag/embeddings/factory.py +++ b/lib/crewai/src/crewai/rag/embeddings/factory.py @@ -2,15 +2,13 @@ from __future__ import annotations -import warnings -from typing import TYPE_CHECKING, TypeVar, overload - -from typing_extensions import deprecated +from typing import TYPE_CHECKING, Any, TypeVar, overload from crewai.rag.core.base_embeddings_callable import EmbeddingFunction from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider from crewai.utilities.import_utils import import_and_validate_definition + if TYPE_CHECKING: from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import ( AmazonBedrockEmbeddingFunction, @@ -65,7 +63,6 @@ if TYPE_CHECKING: WatsonXEmbeddingFunction, ) from crewai.rag.embeddings.providers.ibm.types import ( - WatsonProviderSpec, WatsonXProviderSpec, ) from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec @@ -85,7 +82,7 @@ if TYPE_CHECKING: ) from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec -T = TypeVar("T", bound=EmbeddingFunction) +T = TypeVar("T", bound=EmbeddingFunction[Any]) PROVIDER_PATHS = { @@ -106,7 +103,6 @@ PROVIDER_PATHS = { "sentence-transformer": "crewai.rag.embeddings.providers.sentence_transformer.sentence_transformer_provider.SentenceTransformerProvider", "text2vec": "crewai.rag.embeddings.providers.text2vec.text2vec_provider.Text2VecProvider", "voyageai": "crewai.rag.embeddings.providers.voyageai.voyageai_provider.VoyageAIProvider", - "watson": "crewai.rag.embeddings.providers.ibm.watsonx.WatsonXProvider", # Deprecated alias "watsonx": "crewai.rag.embeddings.providers.ibm.watsonx.WatsonXProvider", } @@ -140,7 +136,7 @@ def build_embedder_from_dict(spec: CohereProviderSpec) -> CohereEmbeddingFunctio @overload -def build_embedder_from_dict(spec: CustomProviderSpec) -> EmbeddingFunction: ... +def build_embedder_from_dict(spec: CustomProviderSpec) -> EmbeddingFunction[Any]: ... @overload @@ -179,13 +175,6 @@ def build_embedder_from_dict( def build_embedder_from_dict(spec: WatsonXProviderSpec) -> WatsonXEmbeddingFunction: ... -@overload -@deprecated( - 'The "WatsonProviderSpec" provider spec is deprecated and will be removed in v1.0.0. Use "WatsonXProviderSpec" instead.' -) -def build_embedder_from_dict(spec: WatsonProviderSpec) -> WatsonXEmbeddingFunction: ... - - @overload def build_embedder_from_dict( spec: SentenceTransformerProviderSpec, @@ -224,7 +213,7 @@ def build_embedder_from_dict( def build_embedder_from_dict(spec: ONNXProviderSpec) -> ONNXMiniLM_L6_V2: ... -def build_embedder_from_dict(spec): +def build_embedder_from_dict(spec): # type: ignore[no-untyped-def] """Build an embedding function instance from a dictionary specification. Args: @@ -247,14 +236,6 @@ def build_embedder_from_dict(spec): if not provider_name: raise ValueError("Missing 'provider' key in specification") - if provider_name == "watson": - warnings.warn( - 'The "watson" provider key is deprecated and will be removed in v1.0.0. ' - 'Use "watsonx" instead.', - DeprecationWarning, - stacklevel=2, - ) - if provider_name not in PROVIDER_PATHS: raise ValueError( f"Unknown provider: {provider_name}. Available providers: {list(PROVIDER_PATHS.keys())}" @@ -292,7 +273,7 @@ def build_embedder(spec: CohereProviderSpec) -> CohereEmbeddingFunction: ... @overload -def build_embedder(spec: CustomProviderSpec) -> EmbeddingFunction: ... +def build_embedder(spec: CustomProviderSpec) -> EmbeddingFunction[Any]: ... @overload @@ -325,13 +306,6 @@ def build_embedder(spec: VoyageAIProviderSpec) -> VoyageAIEmbeddingFunction: ... def build_embedder(spec: WatsonXProviderSpec) -> WatsonXEmbeddingFunction: ... -@overload -@deprecated( - 'The "WatsonProviderSpec" provider spec is deprecated and will be removed in v1.0.0. Use "WatsonXProviderSpec" instead.' -) -def build_embedder(spec: WatsonProviderSpec) -> WatsonXEmbeddingFunction: ... - - @overload def build_embedder( spec: SentenceTransformerProviderSpec, @@ -362,7 +336,7 @@ def build_embedder(spec: Text2VecProviderSpec) -> Text2VecEmbeddingFunction: ... def build_embedder(spec: ONNXProviderSpec) -> ONNXMiniLM_L6_V2: ... -def build_embedder(spec): +def build_embedder(spec): # type: ignore[no-untyped-def] """Build an embedding function from either a provider spec or a provider instance. Args: diff --git a/src/crewai/rag/embeddings/providers/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/__init__.py similarity index 100% rename from src/crewai/rag/embeddings/providers/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/__init__.py diff --git a/src/crewai/rag/embeddings/providers/aws/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/aws/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/aws/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/aws/__init__.py index 861bfd254..4b66c464e 100644 --- a/src/crewai/rag/embeddings/providers/aws/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/aws/__init__.py @@ -6,6 +6,7 @@ from crewai.rag.embeddings.providers.aws.types import ( BedrockProviderSpec, ) + __all__ = [ "BedrockProvider", "BedrockProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/aws/bedrock.py b/lib/crewai/src/crewai/rag/embeddings/providers/aws/bedrock.py similarity index 100% rename from src/crewai/rag/embeddings/providers/aws/bedrock.py rename to lib/crewai/src/crewai/rag/embeddings/providers/aws/bedrock.py diff --git a/src/crewai/rag/embeddings/providers/aws/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/aws/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/aws/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/aws/types.py diff --git a/src/crewai/rag/embeddings/providers/cohere/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/cohere/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/cohere/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/cohere/__init__.py index 16c517147..55d0ff206 100644 --- a/src/crewai/rag/embeddings/providers/cohere/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/cohere/__init__.py @@ -6,6 +6,7 @@ from crewai.rag.embeddings.providers.cohere.types import ( CohereProviderSpec, ) + __all__ = [ "CohereProvider", "CohereProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/cohere/cohere_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/cohere/cohere_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/cohere/cohere_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/cohere/cohere_provider.py diff --git a/src/crewai/rag/embeddings/providers/cohere/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/cohere/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/cohere/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/cohere/types.py diff --git a/src/crewai/rag/embeddings/providers/custom/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/custom/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/custom/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/custom/__init__.py index f6f08747d..b111e0b9f 100644 --- a/src/crewai/rag/embeddings/providers/custom/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/custom/__init__.py @@ -6,6 +6,7 @@ from crewai.rag.embeddings.providers.custom.types import ( CustomProviderSpec, ) + __all__ = [ "CustomProvider", "CustomProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/custom/custom_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/custom/custom_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/custom/custom_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/custom/custom_provider.py diff --git a/src/crewai/rag/embeddings/providers/custom/embedding_callable.py b/lib/crewai/src/crewai/rag/embeddings/providers/custom/embedding_callable.py similarity index 100% rename from src/crewai/rag/embeddings/providers/custom/embedding_callable.py rename to lib/crewai/src/crewai/rag/embeddings/providers/custom/embedding_callable.py diff --git a/src/crewai/rag/embeddings/providers/custom/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/custom/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/custom/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/custom/types.py diff --git a/src/crewai/rag/embeddings/providers/google/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/google/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/google/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/google/__init__.py index 1aae2bf7e..382f54c9a 100644 --- a/src/crewai/rag/embeddings/providers/google/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/google/__init__.py @@ -13,6 +13,7 @@ from crewai.rag.embeddings.providers.google.vertex import ( VertexAIProvider, ) + __all__ = [ "GenerativeAiProvider", "GenerativeAiProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/google/generative_ai.py b/lib/crewai/src/crewai/rag/embeddings/providers/google/generative_ai.py similarity index 100% rename from src/crewai/rag/embeddings/providers/google/generative_ai.py rename to lib/crewai/src/crewai/rag/embeddings/providers/google/generative_ai.py diff --git a/src/crewai/rag/embeddings/providers/google/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/google/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/google/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/google/types.py diff --git a/src/crewai/rag/embeddings/providers/google/vertex.py b/lib/crewai/src/crewai/rag/embeddings/providers/google/vertex.py similarity index 100% rename from src/crewai/rag/embeddings/providers/google/vertex.py rename to lib/crewai/src/crewai/rag/embeddings/providers/google/vertex.py diff --git a/src/crewai/rag/embeddings/providers/huggingface/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/huggingface/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/huggingface/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/huggingface/__init__.py index e52295602..36cf86f17 100644 --- a/src/crewai/rag/embeddings/providers/huggingface/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/huggingface/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.huggingface.types import ( HuggingFaceProviderSpec, ) + __all__ = [ "HuggingFaceProvider", "HuggingFaceProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/huggingface/huggingface_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/huggingface/huggingface_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/huggingface/huggingface_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/huggingface/huggingface_provider.py diff --git a/src/crewai/rag/embeddings/providers/huggingface/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/huggingface/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/huggingface/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/huggingface/types.py diff --git a/src/crewai/rag/embeddings/providers/ibm/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/ibm/__init__.py similarity index 86% rename from src/crewai/rag/embeddings/providers/ibm/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/ibm/__init__.py index d3e9e5e37..845a27b94 100644 --- a/src/crewai/rag/embeddings/providers/ibm/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/ibm/__init__.py @@ -1,7 +1,6 @@ """IBM embedding providers.""" from crewai.rag.embeddings.providers.ibm.types import ( - WatsonProviderSpec, WatsonXProviderConfig, WatsonXProviderSpec, ) @@ -9,8 +8,8 @@ from crewai.rag.embeddings.providers.ibm.watsonx import ( WatsonXProvider, ) + __all__ = [ - "WatsonProviderSpec", "WatsonXProvider", "WatsonXProviderConfig", "WatsonXProviderSpec", diff --git a/src/crewai/rag/embeddings/providers/ibm/embedding_callable.py b/lib/crewai/src/crewai/rag/embeddings/providers/ibm/embedding_callable.py similarity index 95% rename from src/crewai/rag/embeddings/providers/ibm/embedding_callable.py rename to lib/crewai/src/crewai/rag/embeddings/providers/ibm/embedding_callable.py index 56198987d..26cc84dd0 100644 --- a/src/crewai/rag/embeddings/providers/ibm/embedding_callable.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/ibm/embedding_callable.py @@ -6,6 +6,10 @@ from chromadb.api.types import Documents, EmbeddingFunction, Embeddings from typing_extensions import Unpack from crewai.rag.embeddings.providers.ibm.types import WatsonXProviderConfig +from crewai.utilities.printer import Printer + + +_printer = Printer() class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]): @@ -35,11 +39,11 @@ class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]): List of embedding vectors. """ try: - import ibm_watsonx_ai.foundation_models as watson_models # type: ignore[import-not-found, import-untyped] - from ibm_watsonx_ai import ( - Credentials, # type: ignore[import-not-found, import-untyped] + from ibm_watsonx_ai import ( # type: ignore[import-untyped] + Credentials, ) - from ibm_watsonx_ai.metanames import ( # type: ignore[import-not-found, import-untyped] + import ibm_watsonx_ai.foundation_models as watson_models # type: ignore[import-untyped] + from ibm_watsonx_ai.metanames import ( # type: ignore[import-untyped] EmbedTextParamsMetaNames as EmbedParams, ) @@ -155,5 +159,5 @@ class WatsonXEmbeddingFunction(EmbeddingFunction[Documents]): embeddings = embedding.embed_documents(input) return cast(Embeddings, embeddings) except Exception as e: - print(f"Error during WatsonX embedding: {e}") + _printer.print(f"Error during WatsonX embedding: {e}", color="red") raise diff --git a/src/crewai/rag/embeddings/providers/ibm/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/ibm/types.py similarity index 66% rename from src/crewai/rag/embeddings/providers/ibm/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/ibm/types.py index c06f825e3..845dd67cf 100644 --- a/src/crewai/rag/embeddings/providers/ibm/types.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/ibm/types.py @@ -2,7 +2,7 @@ from typing import Annotated, Any, Literal -from typing_extensions import Required, TypedDict, deprecated +from typing_extensions import Required, TypedDict class WatsonXProviderConfig(TypedDict, total=False): @@ -34,7 +34,7 @@ class WatsonXProviderConfig(TypedDict, total=False): version: str bedrock_url: str platform_url: str - proxies: dict + proxies: dict[str, Any] class WatsonXProviderSpec(TypedDict, total=False): @@ -42,17 +42,3 @@ class WatsonXProviderSpec(TypedDict, total=False): provider: Required[Literal["watsonx"]] config: WatsonXProviderConfig - - -@deprecated( - 'The "WatsonProviderSpec" provider spec is deprecated and will be removed in v1.0.0. Use "WatsonXProviderSpec" instead.' -) -class WatsonProviderSpec(TypedDict, total=False): - """Watson provider specification (deprecated). - - Notes: - - This is deprecated. Use WatsonXProviderSpec with provider="watsonx" instead. - """ - - provider: Required[Literal["watson"]] - config: WatsonXProviderConfig diff --git a/src/crewai/rag/embeddings/providers/ibm/watsonx.py b/lib/crewai/src/crewai/rag/embeddings/providers/ibm/watsonx.py similarity index 100% rename from src/crewai/rag/embeddings/providers/ibm/watsonx.py rename to lib/crewai/src/crewai/rag/embeddings/providers/ibm/watsonx.py diff --git a/src/crewai/rag/embeddings/providers/instructor/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/instructor/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/instructor/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/instructor/__init__.py index 987c797b0..25031caab 100644 --- a/src/crewai/rag/embeddings/providers/instructor/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/instructor/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.instructor.types import ( InstructorProviderSpec, ) + __all__ = [ "InstructorProvider", "InstructorProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/instructor/instructor_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/instructor/instructor_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/instructor/instructor_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/instructor/instructor_provider.py diff --git a/src/crewai/rag/embeddings/providers/instructor/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/instructor/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/instructor/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/instructor/types.py diff --git a/src/crewai/rag/embeddings/providers/jina/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/jina/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/jina/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/jina/__init__.py index c01f633bb..bf11074f9 100644 --- a/src/crewai/rag/embeddings/providers/jina/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/jina/__init__.py @@ -6,6 +6,7 @@ from crewai.rag.embeddings.providers.jina.types import ( JinaProviderSpec, ) + __all__ = [ "JinaProvider", "JinaProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/jina/jina_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/jina/jina_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/jina/jina_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/jina/jina_provider.py diff --git a/src/crewai/rag/embeddings/providers/jina/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/jina/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/jina/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/jina/types.py diff --git a/src/crewai/rag/embeddings/providers/microsoft/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/microsoft/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/microsoft/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/microsoft/__init__.py index 2f8d4b3d6..f10b7157a 100644 --- a/src/crewai/rag/embeddings/providers/microsoft/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/microsoft/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.microsoft.types import ( AzureProviderSpec, ) + __all__ = [ "AzureProvider", "AzureProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/microsoft/azure.py b/lib/crewai/src/crewai/rag/embeddings/providers/microsoft/azure.py similarity index 100% rename from src/crewai/rag/embeddings/providers/microsoft/azure.py rename to lib/crewai/src/crewai/rag/embeddings/providers/microsoft/azure.py diff --git a/src/crewai/rag/embeddings/providers/microsoft/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/microsoft/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/microsoft/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/microsoft/types.py diff --git a/src/crewai/rag/embeddings/providers/ollama/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/ollama/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/ollama/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/ollama/__init__.py index 91c82ed44..0311a3422 100644 --- a/src/crewai/rag/embeddings/providers/ollama/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/ollama/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.ollama.types import ( OllamaProviderSpec, ) + __all__ = [ "OllamaProvider", "OllamaProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/ollama/ollama_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/ollama/ollama_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/ollama/ollama_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/ollama/ollama_provider.py diff --git a/src/crewai/rag/embeddings/providers/ollama/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/ollama/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/ollama/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/ollama/types.py diff --git a/src/crewai/rag/embeddings/providers/onnx/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/onnx/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/onnx/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/onnx/__init__.py index a18928611..fa530ed1f 100644 --- a/src/crewai/rag/embeddings/providers/onnx/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/onnx/__init__.py @@ -6,6 +6,7 @@ from crewai.rag.embeddings.providers.onnx.types import ( ONNXProviderSpec, ) + __all__ = [ "ONNXProvider", "ONNXProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/onnx/onnx_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/onnx/onnx_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/onnx/onnx_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/onnx/onnx_provider.py diff --git a/src/crewai/rag/embeddings/providers/onnx/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/onnx/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/onnx/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/onnx/types.py diff --git a/src/crewai/rag/embeddings/providers/openai/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/openai/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/openai/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/openai/__init__.py index 847039352..276ed79b2 100644 --- a/src/crewai/rag/embeddings/providers/openai/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/openai/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.openai.types import ( OpenAIProviderSpec, ) + __all__ = [ "OpenAIProvider", "OpenAIProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/openai/openai_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/openai/openai_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/openai/openai_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/openai/openai_provider.py diff --git a/src/crewai/rag/embeddings/providers/openai/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/openai/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/openai/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/openai/types.py diff --git a/src/crewai/rag/embeddings/providers/openclip/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/openclip/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/openclip/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/openclip/__init__.py index 0a37506b0..5514d55f3 100644 --- a/src/crewai/rag/embeddings/providers/openclip/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/openclip/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.openclip.types import ( OpenCLIPProviderSpec, ) + __all__ = [ "OpenCLIPProvider", "OpenCLIPProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/openclip/openclip_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/openclip/openclip_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/openclip/openclip_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/openclip/openclip_provider.py diff --git a/src/crewai/rag/embeddings/providers/openclip/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/openclip/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/openclip/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/openclip/types.py diff --git a/src/crewai/rag/embeddings/providers/roboflow/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/roboflow/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/roboflow/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/roboflow/__init__.py index 7821a0160..605d09000 100644 --- a/src/crewai/rag/embeddings/providers/roboflow/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/roboflow/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.roboflow.types import ( RoboflowProviderSpec, ) + __all__ = [ "RoboflowProvider", "RoboflowProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/roboflow/roboflow_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/roboflow/roboflow_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/roboflow/roboflow_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/roboflow/roboflow_provider.py diff --git a/src/crewai/rag/embeddings/providers/roboflow/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/roboflow/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/roboflow/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/roboflow/types.py diff --git a/src/crewai/rag/embeddings/providers/sentence_transformer/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/sentence_transformer/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/sentence_transformer/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/sentence_transformer/__init__.py index 7aaf2ef33..713157016 100644 --- a/src/crewai/rag/embeddings/providers/sentence_transformer/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/sentence_transformer/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.sentence_transformer.types import ( SentenceTransformerProviderSpec, ) + __all__ = [ "SentenceTransformerProvider", "SentenceTransformerProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/sentence_transformer/sentence_transformer_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/sentence_transformer/sentence_transformer_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/sentence_transformer/sentence_transformer_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/sentence_transformer/sentence_transformer_provider.py diff --git a/src/crewai/rag/embeddings/providers/sentence_transformer/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/sentence_transformer/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/sentence_transformer/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/sentence_transformer/types.py diff --git a/src/crewai/rag/embeddings/providers/text2vec/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/text2vec/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/text2vec/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/text2vec/__init__.py index 07f9808c6..e7d7550ab 100644 --- a/src/crewai/rag/embeddings/providers/text2vec/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/text2vec/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.text2vec.types import ( Text2VecProviderSpec, ) + __all__ = [ "Text2VecProvider", "Text2VecProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/text2vec/text2vec_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/text2vec/text2vec_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/text2vec/text2vec_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/text2vec/text2vec_provider.py diff --git a/src/crewai/rag/embeddings/providers/text2vec/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/text2vec/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/text2vec/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/text2vec/types.py diff --git a/src/crewai/rag/embeddings/providers/voyageai/__init__.py b/lib/crewai/src/crewai/rag/embeddings/providers/voyageai/__init__.py similarity index 99% rename from src/crewai/rag/embeddings/providers/voyageai/__init__.py rename to lib/crewai/src/crewai/rag/embeddings/providers/voyageai/__init__.py index b0735ec13..6d971963f 100644 --- a/src/crewai/rag/embeddings/providers/voyageai/__init__.py +++ b/lib/crewai/src/crewai/rag/embeddings/providers/voyageai/__init__.py @@ -8,6 +8,7 @@ from crewai.rag.embeddings.providers.voyageai.voyageai_provider import ( VoyageAIProvider, ) + __all__ = [ "VoyageAIProvider", "VoyageAIProviderConfig", diff --git a/src/crewai/rag/embeddings/providers/voyageai/embedding_callable.py b/lib/crewai/src/crewai/rag/embeddings/providers/voyageai/embedding_callable.py similarity index 100% rename from src/crewai/rag/embeddings/providers/voyageai/embedding_callable.py rename to lib/crewai/src/crewai/rag/embeddings/providers/voyageai/embedding_callable.py diff --git a/src/crewai/rag/embeddings/providers/voyageai/types.py b/lib/crewai/src/crewai/rag/embeddings/providers/voyageai/types.py similarity index 100% rename from src/crewai/rag/embeddings/providers/voyageai/types.py rename to lib/crewai/src/crewai/rag/embeddings/providers/voyageai/types.py diff --git a/src/crewai/rag/embeddings/providers/voyageai/voyageai_provider.py b/lib/crewai/src/crewai/rag/embeddings/providers/voyageai/voyageai_provider.py similarity index 100% rename from src/crewai/rag/embeddings/providers/voyageai/voyageai_provider.py rename to lib/crewai/src/crewai/rag/embeddings/providers/voyageai/voyageai_provider.py diff --git a/src/crewai/rag/embeddings/types.py b/lib/crewai/src/crewai/rag/embeddings/types.py similarity index 90% rename from src/crewai/rag/embeddings/types.py rename to lib/crewai/src/crewai/rag/embeddings/types.py index f727cd220..1c8ea1ca0 100644 --- a/src/crewai/rag/embeddings/types.py +++ b/lib/crewai/src/crewai/rag/embeddings/types.py @@ -1,6 +1,6 @@ """Type definitions for the embeddings module.""" -from typing import Literal, TypeAlias +from typing import Any, Literal, TypeAlias from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec @@ -12,7 +12,6 @@ from crewai.rag.embeddings.providers.google.types import ( ) from crewai.rag.embeddings.providers.huggingface.types import HuggingFaceProviderSpec from crewai.rag.embeddings.providers.ibm.types import ( - WatsonProviderSpec, WatsonXProviderSpec, ) from crewai.rag.embeddings.providers.instructor.types import InstructorProviderSpec @@ -29,6 +28,7 @@ from crewai.rag.embeddings.providers.sentence_transformer.types import ( from crewai.rag.embeddings.providers.text2vec.types import Text2VecProviderSpec from crewai.rag.embeddings.providers.voyageai.types import VoyageAIProviderSpec + ProviderSpec = ( AzureProviderSpec | BedrockProviderSpec @@ -47,7 +47,6 @@ ProviderSpec = ( | Text2VecProviderSpec | VertexAIProviderSpec | VoyageAIProviderSpec - | WatsonProviderSpec # Deprecated, use WatsonXProviderSpec | WatsonXProviderSpec ) @@ -70,9 +69,8 @@ AllowedEmbeddingProviders = Literal[ "text2vec", "voyageai", "watsonx", - "watson", # for backward compatibility until v1.0.0 ] EmbedderConfig: TypeAlias = ( - ProviderSpec | BaseEmbeddingsProvider | type[BaseEmbeddingsProvider] + ProviderSpec | BaseEmbeddingsProvider[Any] | type[BaseEmbeddingsProvider[Any]] ) diff --git a/src/crewai/rag/factory.py b/lib/crewai/src/crewai/rag/factory.py similarity index 100% rename from src/crewai/rag/factory.py rename to lib/crewai/src/crewai/rag/factory.py diff --git a/src/crewai/rag/qdrant/__init__.py b/lib/crewai/src/crewai/rag/qdrant/__init__.py similarity index 100% rename from src/crewai/rag/qdrant/__init__.py rename to lib/crewai/src/crewai/rag/qdrant/__init__.py diff --git a/src/crewai/rag/qdrant/client.py b/lib/crewai/src/crewai/rag/qdrant/client.py similarity index 100% rename from src/crewai/rag/qdrant/client.py rename to lib/crewai/src/crewai/rag/qdrant/client.py diff --git a/src/crewai/rag/qdrant/config.py b/lib/crewai/src/crewai/rag/qdrant/config.py similarity index 94% rename from src/crewai/rag/qdrant/config.py rename to lib/crewai/src/crewai/rag/qdrant/config.py index 316708b80..0926c3385 100644 --- a/src/crewai/rag/qdrant/config.py +++ b/lib/crewai/src/crewai/rag/qdrant/config.py @@ -4,6 +4,7 @@ from dataclasses import field from typing import Literal, cast from pydantic.dataclasses import dataclass as pyd_dataclass +from qdrant_client.models import VectorParams from crewai.rag.config.base import BaseRagConfig from crewai.rag.qdrant.constants import DEFAULT_EMBEDDING_MODEL, DEFAULT_STORAGE_PATH @@ -53,3 +54,4 @@ class QdrantConfig(BaseRagConfig): embedding_function: QdrantEmbeddingFunctionWrapper = field( default_factory=_default_embedding_function ) + vectors_config: VectorParams | None = field(default=None) diff --git a/src/crewai/rag/qdrant/constants.py b/lib/crewai/src/crewai/rag/qdrant/constants.py similarity index 99% rename from src/crewai/rag/qdrant/constants.py rename to lib/crewai/src/crewai/rag/qdrant/constants.py index 9714c9de6..75e8e7c25 100644 --- a/src/crewai/rag/qdrant/constants.py +++ b/lib/crewai/src/crewai/rag/qdrant/constants.py @@ -7,6 +7,7 @@ from qdrant_client.models import Distance, VectorParams from crewai.utilities.paths import db_storage_path + DEFAULT_VECTOR_PARAMS: Final = VectorParams(size=384, distance=Distance.COSINE) DEFAULT_EMBEDDING_MODEL: Final[str] = "sentence-transformers/all-MiniLM-L6-v2" DEFAULT_STORAGE_PATH: Final[str] = os.path.join(db_storage_path(), "qdrant") diff --git a/src/crewai/rag/qdrant/factory.py b/lib/crewai/src/crewai/rag/qdrant/factory.py similarity index 100% rename from src/crewai/rag/qdrant/factory.py rename to lib/crewai/src/crewai/rag/qdrant/factory.py diff --git a/src/crewai/rag/qdrant/types.py b/lib/crewai/src/crewai/rag/qdrant/types.py similarity index 98% rename from src/crewai/rag/qdrant/types.py rename to lib/crewai/src/crewai/rag/qdrant/types.py index d586cbfaf..5ceba8d01 100644 --- a/src/crewai/rag/qdrant/types.py +++ b/lib/crewai/src/crewai/rag/qdrant/types.py @@ -6,8 +6,8 @@ from typing import Annotated, Any, Protocol, TypeAlias import numpy as np from pydantic import GetCoreSchemaHandler from pydantic_core import CoreSchema, core_schema -from qdrant_client import AsyncQdrantClient # type: ignore[import-not-found] from qdrant_client import ( + AsyncQdrantClient, # type: ignore[import-not-found] QdrantClient as SyncQdrantClient, # type: ignore[import-not-found] ) from qdrant_client.models import ( # type: ignore[import-not-found] @@ -31,6 +31,7 @@ from typing_extensions import NotRequired, TypedDict from crewai.rag.core.base_client import BaseCollectionParams + QdrantClientType = SyncQdrantClient | AsyncQdrantClient QueryEmbedding: TypeAlias = list[float] | np.ndarray[Any, np.dtype[np.floating[Any]]] diff --git a/src/crewai/rag/qdrant/utils.py b/lib/crewai/src/crewai/rag/qdrant/utils.py similarity index 98% rename from src/crewai/rag/qdrant/utils.py rename to lib/crewai/src/crewai/rag/qdrant/utils.py index 01afd31ef..a535fa9a4 100644 --- a/src/crewai/rag/qdrant/utils.py +++ b/lib/crewai/src/crewai/rag/qdrant/utils.py @@ -4,8 +4,8 @@ import asyncio from typing import TypeGuard from uuid import uuid4 -from qdrant_client import AsyncQdrantClient # type: ignore[import-not-found] from qdrant_client import ( + AsyncQdrantClient, # type: ignore[import-not-found] QdrantClient as SyncQdrantClient, # type: ignore[import-not-found] ) from qdrant_client.models import ( # type: ignore[import-not-found] diff --git a/src/crewai/rag/storage/__init__.py b/lib/crewai/src/crewai/rag/storage/__init__.py similarity index 100% rename from src/crewai/rag/storage/__init__.py rename to lib/crewai/src/crewai/rag/storage/__init__.py diff --git a/src/crewai/rag/storage/base_rag_storage.py b/lib/crewai/src/crewai/rag/storage/base_rag_storage.py similarity index 100% rename from src/crewai/rag/storage/base_rag_storage.py rename to lib/crewai/src/crewai/rag/storage/base_rag_storage.py diff --git a/src/crewai/rag/types.py b/lib/crewai/src/crewai/rag/types.py similarity index 100% rename from src/crewai/rag/types.py rename to lib/crewai/src/crewai/rag/types.py diff --git a/src/crewai/security/__init__.py b/lib/crewai/src/crewai/security/__init__.py similarity index 99% rename from src/crewai/security/__init__.py rename to lib/crewai/src/crewai/security/__init__.py index 91602970f..40ed65933 100644 --- a/src/crewai/security/__init__.py +++ b/lib/crewai/src/crewai/security/__init__.py @@ -10,4 +10,5 @@ This module provides security-related functionality for CrewAI, including: from crewai.security.fingerprint import Fingerprint from crewai.security.security_config import SecurityConfig + __all__ = ["Fingerprint", "SecurityConfig"] diff --git a/src/crewai/security/constants.py b/lib/crewai/src/crewai/security/constants.py similarity index 99% rename from src/crewai/security/constants.py rename to lib/crewai/src/crewai/security/constants.py index c16a52665..5781501d2 100644 --- a/src/crewai/security/constants.py +++ b/lib/crewai/src/crewai/security/constants.py @@ -9,6 +9,7 @@ Notes: from typing import Annotated from uuid import UUID + CREW_AI_NAMESPACE: Annotated[ UUID, "Create a deterministic UUID using v5 (SHA-1). Custom namespace for CrewAI to enhance security.", diff --git a/src/crewai/security/fingerprint.py b/lib/crewai/src/crewai/security/fingerprint.py similarity index 100% rename from src/crewai/security/fingerprint.py rename to lib/crewai/src/crewai/security/fingerprint.py diff --git a/src/crewai/security/security_config.py b/lib/crewai/src/crewai/security/security_config.py similarity index 100% rename from src/crewai/security/security_config.py rename to lib/crewai/src/crewai/security/security_config.py diff --git a/src/crewai/task.py b/lib/crewai/src/crewai/task.py similarity index 79% rename from src/crewai/task.py rename to lib/crewai/src/crewai/task.py index 5fa9149f9..5afe38cd3 100644 --- a/src/crewai/task.py +++ b/lib/crewai/src/crewai/task.py @@ -1,22 +1,24 @@ -import datetime -import inspect -import json -import logging -import threading -import uuid -import warnings +from __future__ import annotations + from collections.abc import Callable from concurrent.futures import Future from copy import copy as shallow_copy +import datetime from hashlib import md5 +import inspect +import json +import logging from pathlib import Path +import threading from typing import ( Any, ClassVar, - Union, + cast, get_args, get_origin, ) +import uuid +import warnings from pydantic import ( UUID4, @@ -42,12 +44,22 @@ from crewai.tools.base_tool import BaseTool from crewai.utilities.config import process_config from crewai.utilities.constants import NOT_SPECIFIED, _NotSpecified from crewai.utilities.converter import Converter, convert_to_model -from crewai.utilities.guardrail import process_guardrail +from crewai.utilities.guardrail import ( + process_guardrail, +) +from crewai.utilities.guardrail_types import ( + GuardrailCallable, + GuardrailType, + GuardrailsType, +) from crewai.utilities.i18n import I18N from crewai.utilities.printer import Printer from crewai.utilities.string_utils import interpolate_only +_printer = Printer() + + class Task(BaseModel): """Class that represents a task to be executed. @@ -73,12 +85,12 @@ class Task(BaseModel): False: Never inject trigger payload, even for first task. """ - __hash__ = object.__hash__ # type: ignore + __hash__ = object.__hash__ logger: ClassVar[logging.Logger] = logging.getLogger(__name__) used_tools: int = 0 tools_errors: int = 0 delegations: int = 0 - i18n: I18N = I18N() + i18n: I18N = Field(default_factory=I18N) name: str | None = Field(default=None) prompt_context: str | None = None description: str = Field(description="Description of the actual task.") @@ -95,7 +107,7 @@ class Task(BaseModel): agent: BaseAgent | None = Field( description="Agent responsible for execution the task.", default=None ) - context: list["Task"] | None | _NotSpecified = Field( + context: list[Task] | None | _NotSpecified = Field( description="Other tasks that will have their output used as context for this task.", default=NOT_SPECIFIED, ) @@ -130,7 +142,7 @@ class Task(BaseModel): default_factory=SecurityConfig, description="Security configuration for the task.", ) - id: UUID4 = Field( + id: uuid.UUID = Field( default_factory=uuid.uuid4, frozen=True, description="Unique identifier for the object, not set by user.", @@ -148,10 +160,15 @@ class Task(BaseModel): default=None, ) processed_by_agents: set[str] = Field(default_factory=set) - guardrail: Callable[[TaskOutput], tuple[bool, Any]] | str | None = Field( + guardrail: GuardrailType | None = Field( default=None, description="Function or string description of a guardrail to validate task output before proceeding to next task", ) + guardrails: GuardrailsType | None = Field( + default=None, + description="List of guardrails to validate task output before proceeding to next task. Also supports a single guardrail function or string description of a guardrail to validate task output before proceeding to next task", + ) + max_retries: int | None = Field( default=None, description="[DEPRECATED] Maximum number of retries when guardrail fails. Use guardrail_max_retries instead. Will be removed in v1.0.0", @@ -170,6 +187,17 @@ class Task(BaseModel): default=None, description="Whether this task should append 'Trigger Payload: {crewai_trigger_payload}' to the task description when crewai_trigger_payload exists in crew inputs.", ) + _guardrail: GuardrailCallable | None = PrivateAttr(default=None) + _guardrails: list[GuardrailCallable] = PrivateAttr( + default_factory=list, + ) + _guardrail_retry_counts: dict[int, int] = PrivateAttr( + default_factory=dict, + ) + _original_description: str | None = PrivateAttr(default=None) + _original_expected_output: str | None = PrivateAttr(default=None) + _original_output_file: str | None = PrivateAttr(default=None) + _thread: threading.Thread | None = PrivateAttr(default=None) model_config = {"arbitrary_types_allowed": True} @field_validator("guardrail") @@ -231,12 +259,6 @@ class Task(BaseModel): ) return v - _guardrail: Callable | None = PrivateAttr(default=None) - _original_description: str | None = PrivateAttr(default=None) - _original_expected_output: str | None = PrivateAttr(default=None) - _original_output_file: str | None = PrivateAttr(default=None) - _thread: threading.Thread | None = PrivateAttr(default=None) - @model_validator(mode="before") @classmethod def process_model_config(cls, values): @@ -253,7 +275,7 @@ class Task(BaseModel): return self @model_validator(mode="after") - def ensure_guardrail_is_callable(self) -> "Task": + def ensure_guardrail_is_callable(self) -> Task: if callable(self.guardrail): self._guardrail = self.guardrail elif isinstance(self.guardrail, str): @@ -262,12 +284,67 @@ class Task(BaseModel): if self.agent is None: raise ValueError("Agent is required to use LLMGuardrail") - self._guardrail = LLMGuardrail( - description=self.guardrail, llm=self.agent.llm + self._guardrail = cast( + GuardrailCallable, + LLMGuardrail(description=self.guardrail, llm=self.agent.llm), ) return self + @model_validator(mode="after") + def ensure_guardrails_is_list_of_callables(self) -> Task: + guardrails = [] + if self.guardrails is not None: + if isinstance(self.guardrails, (list, tuple)): + if len(self.guardrails) > 0: + for guardrail in self.guardrails: + if callable(guardrail): + guardrails.append(guardrail) + elif isinstance(guardrail, str): + if self.agent is None: + raise ValueError( + "Agent is required to use non-programmatic guardrails" + ) + from crewai.tasks.llm_guardrail import LLMGuardrail + + guardrails.append( + cast( + GuardrailCallable, + LLMGuardrail( + description=guardrail, llm=self.agent.llm + ), + ) + ) + else: + raise ValueError("Guardrail must be a callable or a string") + else: + if callable(self.guardrails): + guardrails.append(self.guardrails) + elif isinstance(self.guardrails, str): + if self.agent is None: + raise ValueError( + "Agent is required to use non-programmatic guardrails" + ) + from crewai.tasks.llm_guardrail import LLMGuardrail + + guardrails.append( + cast( + GuardrailCallable, + LLMGuardrail( + description=self.guardrails, llm=self.agent.llm + ), + ) + ) + else: + raise ValueError("Guardrail must be a callable or a string") + + self._guardrails = guardrails + if self._guardrails: + self.guardrail = None + self._guardrail = None + + return self + @field_validator("id", mode="before") @classmethod def _deny_user_set_id(cls, v: UUID4 | None) -> None: @@ -329,7 +406,7 @@ class Task(BaseModel): return value @model_validator(mode="after") - def set_attributes_based_on_config(self) -> "Task": + def set_attributes_based_on_config(self) -> Task: """Set attributes based on the agent configuration.""" if self.config: for key, value in self.config.items(): @@ -456,48 +533,24 @@ class Task(BaseModel): output_format=self._get_output_format(), ) + if self._guardrails: + for idx, guardrail in enumerate(self._guardrails): + task_output = self._invoke_guardrail_function( + task_output=task_output, + agent=agent, + tools=tools, + guardrail=guardrail, + guardrail_index=idx, + ) + + # backwards support if self._guardrail: - guardrail_result = process_guardrail( - output=task_output, + task_output = self._invoke_guardrail_function( + task_output=task_output, + agent=agent, + tools=tools, guardrail=self._guardrail, - retry_count=self.retry_count, - event_source=self, - from_task=self, - from_agent=agent, ) - if not guardrail_result.success: - if self.retry_count >= self.guardrail_max_retries: - raise Exception( - f"Task failed guardrail validation after {self.guardrail_max_retries} retries. " - f"Last error: {guardrail_result.error}" - ) - - self.retry_count += 1 - context = self.i18n.errors("validation_error").format( - guardrail_result_error=guardrail_result.error, - task_output=task_output.raw, - ) - printer = Printer() - printer.print( - content=f"Guardrail blocked, retrying, due to: {guardrail_result.error}\n", - color="yellow", - ) - return self._execute_core(agent, context, tools) - - if guardrail_result.result is None: - raise Exception( - "Task guardrail returned None as result. This is not allowed." - ) - - if isinstance(guardrail_result.result, str): - task_output.raw = guardrail_result.result - pydantic_output, json_output = self._export_output( - guardrail_result.result - ) - task_output.pydantic = pydantic_output - task_output.json_dict = json_output - elif isinstance(guardrail_result.result, TaskOutput): - task_output = guardrail_result.result self.output = task_output self.end_time = datetime.datetime.now() @@ -626,7 +679,10 @@ Follow these guidelines: try: crew_chat_messages = json.loads(crew_chat_messages_json) except json.JSONDecodeError as e: - print("An error occurred while parsing crew chat messages:", e) + _printer.print( + f"An error occurred while parsing crew chat messages: {e}", + color="red", + ) raise conversation_history = "\n".join( @@ -650,8 +706,8 @@ Follow these guidelines: self.delegations += 1 def copy( # type: ignore - self, agents: list["BaseAgent"], task_mapping: dict[str, "Task"] - ) -> "Task": + self, agents: list[BaseAgent], task_mapping: dict[str, Task] + ) -> Task: """Creates a deep copy of the Task while preserving its original class type. Args: @@ -679,7 +735,7 @@ Follow these guidelines: else None ) - def get_agent_by_role(role: str) -> Union["BaseAgent", None]: + def get_agent_by_role(role: str) -> BaseAgent | None: return next((agent for agent in agents if agent.role == role), None) cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None @@ -789,3 +845,101 @@ Follow these guidelines: Fingerprint: The fingerprint of the task """ return self.security_config.fingerprint + + def _invoke_guardrail_function( + self, + task_output: TaskOutput, + agent: BaseAgent, + tools: list[BaseTool], + guardrail: GuardrailCallable | None, + guardrail_index: int | None = None, + ) -> TaskOutput: + if not guardrail: + return task_output + + if guardrail_index is not None: + current_retry_count = self._guardrail_retry_counts.get(guardrail_index, 0) + else: + current_retry_count = self.retry_count + + max_attempts = self.guardrail_max_retries + 1 + + for attempt in range(max_attempts): + guardrail_result = process_guardrail( + output=task_output, + guardrail=guardrail, + retry_count=current_retry_count, + event_source=self, + from_task=self, + from_agent=agent, + ) + + if guardrail_result.success: + # Guardrail passed + if guardrail_result.result is None: + raise Exception( + "Task guardrail returned None as result. This is not allowed." + ) + + if isinstance(guardrail_result.result, str): + task_output.raw = guardrail_result.result + pydantic_output, json_output = self._export_output( + guardrail_result.result + ) + task_output.pydantic = pydantic_output + task_output.json_dict = json_output + elif isinstance(guardrail_result.result, TaskOutput): + task_output = guardrail_result.result + + return task_output + + # Guardrail failed + if attempt >= self.guardrail_max_retries: + # Max retries reached + guardrail_name = ( + f"guardrail {guardrail_index}" + if guardrail_index is not None + else "guardrail" + ) + raise Exception( + f"Task failed {guardrail_name} validation after {self.guardrail_max_retries} retries. " + f"Last error: {guardrail_result.error}" + ) + + if guardrail_index is not None: + current_retry_count += 1 + self._guardrail_retry_counts[guardrail_index] = current_retry_count + else: + self.retry_count += 1 + current_retry_count = self.retry_count + + context = self.i18n.errors("validation_error").format( + guardrail_result_error=guardrail_result.error, + task_output=task_output.raw, + ) + printer = Printer() + printer.print( + content=f"Guardrail {guardrail_index if guardrail_index is not None else ''} blocked (attempt {attempt + 1}/{max_attempts}), retrying due to: {guardrail_result.error}\n", + color="yellow", + ) + + # Regenerate output from agent + result = agent.execute_task( + task=self, + context=context, + tools=tools, + ) + + pydantic_output, json_output = self._export_output(result) + task_output = TaskOutput( + name=self.name or self.description, + description=self.description, + expected_output=self.expected_output, + raw=result, + pydantic=pydantic_output, + json_dict=json_output, + agent=agent.role, + output_format=self._get_output_format(), + ) + + return task_output diff --git a/src/crewai/tasks/__init__.py b/lib/crewai/src/crewai/tasks/__init__.py similarity index 99% rename from src/crewai/tasks/__init__.py rename to lib/crewai/src/crewai/tasks/__init__.py index d26db2093..6a5f64359 100644 --- a/src/crewai/tasks/__init__.py +++ b/lib/crewai/src/crewai/tasks/__init__.py @@ -1,4 +1,5 @@ from crewai.tasks.output_format import OutputFormat from crewai.tasks.task_output import TaskOutput + __all__ = ["OutputFormat", "TaskOutput"] diff --git a/src/crewai/tasks/conditional_task.py b/lib/crewai/src/crewai/tasks/conditional_task.py similarity index 100% rename from src/crewai/tasks/conditional_task.py rename to lib/crewai/src/crewai/tasks/conditional_task.py diff --git a/src/crewai/tasks/hallucination_guardrail.py b/lib/crewai/src/crewai/tasks/hallucination_guardrail.py similarity index 95% rename from src/crewai/tasks/hallucination_guardrail.py rename to lib/crewai/src/crewai/tasks/hallucination_guardrail.py index 682209e51..dd000a83c 100644 --- a/src/crewai/tasks/hallucination_guardrail.py +++ b/lib/crewai/src/crewai/tasks/hallucination_guardrail.py @@ -26,21 +26,21 @@ class HallucinationGuardrail: >>> # Basic usage with default verdict logic >>> guardrail = HallucinationGuardrail( ... context="AI helps with various tasks including analysis and generation.", - ... llm=agent.llm + ... llm=agent.llm, ... ) >>> # With custom threshold for stricter validation >>> strict_guardrail = HallucinationGuardrail( ... context="Quantum computing uses qubits in superposition.", ... llm=agent.llm, - ... threshold=8.0 # Would require score >= 8 to pass in enterprise version + ... threshold=8.0, # Would require score >= 8 to pass in enterprise version ... ) >>> # With tool response for additional context >>> guardrail_with_tools = HallucinationGuardrail( ... context="The current weather data", ... llm=agent.llm, - ... tool_response="Weather API returned: Temperature 22°C, Humidity 65%" + ... tool_response="Weather API returned: Temperature 22°C, Humidity 65%", ... ) """ diff --git a/src/crewai/tasks/llm_guardrail.py b/lib/crewai/src/crewai/tasks/llm_guardrail.py similarity index 95% rename from src/crewai/tasks/llm_guardrail.py rename to lib/crewai/src/crewai/tasks/llm_guardrail.py index 79c0c6b2a..803b2d749 100644 --- a/src/crewai/tasks/llm_guardrail.py +++ b/lib/crewai/src/crewai/tasks/llm_guardrail.py @@ -2,8 +2,9 @@ from typing import Any from pydantic import BaseModel, Field -from crewai.agent import Agent, LiteAgentOutput -from crewai.llm import BaseLLM +from crewai.agent import Agent +from crewai.lite_agent_output import LiteAgentOutput +from crewai.llms.base_llm import BaseLLM from crewai.tasks.task_output import TaskOutput diff --git a/src/crewai/tasks/output_format.py b/lib/crewai/src/crewai/tasks/output_format.py similarity index 100% rename from src/crewai/tasks/output_format.py rename to lib/crewai/src/crewai/tasks/output_format.py diff --git a/src/crewai/tasks/task_output.py b/lib/crewai/src/crewai/tasks/task_output.py similarity index 100% rename from src/crewai/tasks/task_output.py rename to lib/crewai/src/crewai/tasks/task_output.py diff --git a/lib/crewai/src/crewai/telemetry/__init__.py b/lib/crewai/src/crewai/telemetry/__init__.py new file mode 100644 index 000000000..38739d88a --- /dev/null +++ b/lib/crewai/src/crewai/telemetry/__init__.py @@ -0,0 +1,5 @@ +from crewai.telemetry.telemetry import Telemetry + + + +__all__ = ["Telemetry"] diff --git a/src/crewai/telemetry/constants.py b/lib/crewai/src/crewai/telemetry/constants.py similarity index 99% rename from src/crewai/telemetry/constants.py rename to lib/crewai/src/crewai/telemetry/constants.py index 3c735d868..24ac608c9 100644 --- a/src/crewai/telemetry/constants.py +++ b/lib/crewai/src/crewai/telemetry/constants.py @@ -5,5 +5,6 @@ This module defines constants used for CrewAI telemetry configuration. from typing import Final + CREWAI_TELEMETRY_BASE_URL: Final[str] = "https://telemetry.crewai.com:4319" CREWAI_TELEMETRY_SERVICE_NAME: Final[str] = "crewAI-telemetry" diff --git a/src/crewai/telemetry/telemetry.py b/lib/crewai/src/crewai/telemetry/telemetry.py similarity index 99% rename from src/crewai/telemetry/telemetry.py rename to lib/crewai/src/crewai/telemetry/telemetry.py index b7d479069..a75fa50fa 100644 --- a/src/crewai/telemetry/telemetry.py +++ b/lib/crewai/src/crewai/telemetry/telemetry.py @@ -9,13 +9,13 @@ data is collected. Users can opt-in to share more complete data using the from __future__ import annotations import asyncio +from collections.abc import Callable +from importlib.metadata import version import json import logging import os import platform import threading -from collections.abc import Callable -from importlib.metadata import version from typing import TYPE_CHECKING, Any from opentelemetry import trace @@ -42,6 +42,7 @@ from crewai.telemetry.utils import ( ) from crewai.utilities.logger_utils import suppress_warnings + logger = logging.getLogger(__name__) if TYPE_CHECKING: diff --git a/src/crewai/telemetry/utils.py b/lib/crewai/src/crewai/telemetry/utils.py similarity index 97% rename from src/crewai/telemetry/utils.py rename to lib/crewai/src/crewai/telemetry/utils.py index b56b58e8d..c6b649a30 100644 --- a/src/crewai/telemetry/utils.py +++ b/lib/crewai/src/crewai/telemetry/utils.py @@ -3,11 +3,14 @@ This module provides utility functions for telemetry operations. """ +from __future__ import annotations + from collections.abc import Callable from typing import TYPE_CHECKING, Any from opentelemetry.trace import Span, Status, StatusCode + if TYPE_CHECKING: from crewai.crew import Crew from crewai.task import Task @@ -42,7 +45,7 @@ def add_agent_fingerprint_to_span( def add_crew_attributes( span: Span, - crew: "Crew", + crew: Crew, add_attribute_fn: Callable[[Span, str, Any], None], include_fingerprint: bool = True, ) -> None: @@ -63,7 +66,7 @@ def add_crew_attributes( def add_task_attributes( span: Span, - task: "Task", + task: Task, add_attribute_fn: Callable[[Span, str, Any], None], include_fingerprint: bool = True, ) -> None: @@ -84,8 +87,8 @@ def add_task_attributes( def add_crew_and_task_attributes( span: Span, - crew: "Crew", - task: "Task", + crew: Crew, + task: Task, add_attribute_fn: Callable[[Span, str, Any], None], include_fingerprints: bool = True, ) -> None: diff --git a/lib/crewai/src/crewai/tools/__init__.py b/lib/crewai/src/crewai/tools/__init__.py new file mode 100644 index 000000000..ef698c90a --- /dev/null +++ b/lib/crewai/src/crewai/tools/__init__.py @@ -0,0 +1,9 @@ +from crewai.tools.base_tool import BaseTool, EnvVar, tool + + + +__all__ = [ + "BaseTool", + "EnvVar", + "tool", +] diff --git a/src/crewai/tools/agent_tools/__init__.py b/lib/crewai/src/crewai/tools/agent_tools/__init__.py similarity index 100% rename from src/crewai/tools/agent_tools/__init__.py rename to lib/crewai/src/crewai/tools/agent_tools/__init__.py diff --git a/src/crewai/tools/agent_tools/add_image_tool.py b/lib/crewai/src/crewai/tools/agent_tools/add_image_tool.py similarity index 99% rename from src/crewai/tools/agent_tools/add_image_tool.py rename to lib/crewai/src/crewai/tools/agent_tools/add_image_tool.py index 8539d54a6..45cc0d687 100644 --- a/src/crewai/tools/agent_tools/add_image_tool.py +++ b/lib/crewai/src/crewai/tools/agent_tools/add_image_tool.py @@ -3,6 +3,7 @@ from pydantic import BaseModel, Field from crewai.tools.base_tool import BaseTool from crewai.utilities import I18N + i18n = I18N() diff --git a/src/crewai/tools/agent_tools/agent_tools.py b/lib/crewai/src/crewai/tools/agent_tools/agent_tools.py similarity index 82% rename from src/crewai/tools/agent_tools/agent_tools.py rename to lib/crewai/src/crewai/tools/agent_tools/agent_tools.py index 9077c55d3..a5f444c25 100644 --- a/src/crewai/tools/agent_tools/agent_tools.py +++ b/lib/crewai/src/crewai/tools/agent_tools/agent_tools.py @@ -1,15 +1,14 @@ from crewai.agents.agent_builder.base_agent import BaseAgent +from crewai.tools.agent_tools.ask_question_tool import AskQuestionTool +from crewai.tools.agent_tools.delegate_work_tool import DelegateWorkTool from crewai.tools.base_tool import BaseTool -from crewai.utilities import I18N - -from .ask_question_tool import AskQuestionTool -from .delegate_work_tool import DelegateWorkTool +from crewai.utilities.i18n import I18N class AgentTools: """Manager class for agent-related tools""" - def __init__(self, agents: list[BaseAgent], i18n: I18N | None = None): + def __init__(self, agents: list[BaseAgent], i18n: I18N | None = None) -> None: self.agents = agents self.i18n = i18n if i18n is not None else I18N() diff --git a/src/crewai/tools/agent_tools/ask_question_tool.py b/lib/crewai/src/crewai/tools/agent_tools/ask_question_tool.py similarity index 100% rename from src/crewai/tools/agent_tools/ask_question_tool.py rename to lib/crewai/src/crewai/tools/agent_tools/ask_question_tool.py diff --git a/src/crewai/tools/agent_tools/base_agent_tools.py b/lib/crewai/src/crewai/tools/agent_tools/base_agent_tools.py similarity index 97% rename from src/crewai/tools/agent_tools/base_agent_tools.py rename to lib/crewai/src/crewai/tools/agent_tools/base_agent_tools.py index c6c7321a7..db8e43a17 100644 --- a/src/crewai/tools/agent_tools/base_agent_tools.py +++ b/lib/crewai/src/crewai/tools/agent_tools/base_agent_tools.py @@ -5,7 +5,9 @@ from pydantic import Field from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.task import Task from crewai.tools.base_tool import BaseTool -from crewai.utilities import I18N +from crewai.utilities.i18n import I18N + + logger = logging.getLogger(__name__) @@ -37,7 +39,8 @@ class BaseAgentTool(BaseTool): # Remove quotes and convert to lowercase return normalized.replace('"', "").casefold() - def _get_coworker(self, coworker: str | None, **kwargs) -> str | None: + @staticmethod + def _get_coworker(coworker: str | None, **kwargs) -> str | None: coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker") if coworker: is_list = coworker.startswith("[") and coworker.endswith("]") diff --git a/src/crewai/tools/agent_tools/delegate_work_tool.py b/lib/crewai/src/crewai/tools/agent_tools/delegate_work_tool.py similarity index 100% rename from src/crewai/tools/agent_tools/delegate_work_tool.py rename to lib/crewai/src/crewai/tools/agent_tools/delegate_work_tool.py diff --git a/src/crewai/tools/base_tool.py b/lib/crewai/src/crewai/tools/base_tool.py similarity index 77% rename from src/crewai/tools/base_tool.py rename to lib/crewai/src/crewai/tools/base_tool.py index 0905db320..19ed6b671 100644 --- a/src/crewai/tools/base_tool.py +++ b/lib/crewai/src/crewai/tools/base_tool.py @@ -1,19 +1,25 @@ -import asyncio +from __future__ import annotations + from abc import ABC, abstractmethod +import asyncio from collections.abc import Callable from inspect import signature -from typing import Any, get_args, get_origin +from typing import Any, cast, get_args, get_origin from pydantic import ( BaseModel, + BaseModel as PydanticBaseModel, ConfigDict, Field, create_model, field_validator, ) -from pydantic import BaseModel as PydanticBaseModel from crewai.tools.structured_tool import CrewStructuredTool +from crewai.utilities.printer import Printer + + +_printer = Printer() class EnvVar(BaseModel): @@ -29,26 +35,42 @@ class BaseTool(BaseModel, ABC): model_config = ConfigDict(arbitrary_types_allowed=True) - name: str - """The unique name of the tool that clearly communicates its purpose.""" - description: str - """Used to tell the model how/when/why to use the tool.""" - env_vars: list[EnvVar] = [] - """List of environment variables used by the tool.""" - args_schema: type[PydanticBaseModel] = Field( - default=_ArgsSchemaPlaceholder, validate_default=True + name: str = Field( + description="The unique name of the tool that clearly communicates its purpose." + ) + description: str = Field( + description="Used to tell the model how/when/why to use the tool." + ) + env_vars: list[EnvVar] = Field( + default_factory=list, + description="List of environment variables used by the tool.", + ) + args_schema: type[PydanticBaseModel] = Field( + default=_ArgsSchemaPlaceholder, + validate_default=True, + description="The schema for the arguments that the tool accepts.", + ) + + description_updated: bool = Field( + default=False, description="Flag to check if the description has been updated." + ) + + cache_function: Callable = Field( + default=lambda _args=None, _result=None: True, + description="Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached.", + ) + result_as_answer: bool = Field( + default=False, + description="Flag to check if the tool should be the final agent answer.", + ) + max_usage_count: int | None = Field( + default=None, + description="Maximum number of times this tool can be used. None means unlimited usage.", + ) + current_usage_count: int = Field( + default=0, + description="Current number of times this tool has been used.", ) - """The schema for the arguments that the tool accepts.""" - description_updated: bool = False - """Flag to check if the description has been updated.""" - cache_function: Callable = lambda _args=None, _result=None: True - """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached.""" - result_as_answer: bool = False - """Flag to check if the tool should be the final agent answer.""" - max_usage_count: int | None = None - """Maximum number of times this tool can be used. None means unlimited usage.""" - current_usage_count: int = 0 - """Current number of times this tool has been used.""" @field_validator("args_schema", mode="before") @classmethod @@ -58,14 +80,19 @@ class BaseTool(BaseModel, ABC): if v != cls._ArgsSchemaPlaceholder: return v - return type( - f"{cls.__name__}Schema", - (PydanticBaseModel,), - { - "__annotations__": { - k: v for k, v in cls._run.__annotations__.items() if k != "return" + return cast( + type[PydanticBaseModel], + type( + f"{cls.__name__}Schema", + (PydanticBaseModel,), + { + "__annotations__": { + k: v + for k, v in cls._run.__annotations__.items() + if k != "return" + }, }, - }, + ), ) @field_validator("max_usage_count", mode="before") @@ -85,7 +112,7 @@ class BaseTool(BaseModel, ABC): *args: Any, **kwargs: Any, ) -> Any: - print(f"Using Tool: {self.name}") + _printer.print(f"Using Tool: {self.name}", color="cyan") result = self._run(*args, **kwargs) # If _run is async, we safely run it @@ -124,7 +151,7 @@ class BaseTool(BaseModel, ABC): return structured_tool @classmethod - def from_langchain(cls, tool: Any) -> "BaseTool": + def from_langchain(cls, tool: Any) -> BaseTool: """Create a Tool instance from a CrewStructuredTool. This method takes a CrewStructuredTool object and converts it into a @@ -166,22 +193,25 @@ class BaseTool(BaseModel, ABC): args_schema=args_schema, ) - def _set_args_schema(self): + def _set_args_schema(self) -> None: if self.args_schema is None: class_name = f"{self.__class__.__name__}Schema" - self.args_schema = type( - class_name, - (PydanticBaseModel,), - { - "__annotations__": { - k: v - for k, v in self._run.__annotations__.items() - if k != "return" + self.args_schema = cast( + type[PydanticBaseModel], + type( + class_name, + (PydanticBaseModel,), + { + "__annotations__": { + k: v + for k, v in self._run.__annotations__.items() + if k != "return" + }, }, - }, + ), ) - def _generate_description(self): + def _generate_description(self) -> None: args_schema = { name: { "description": field.description, @@ -223,7 +253,7 @@ class Tool(BaseTool): return self.func(*args, **kwargs) @classmethod - def from_langchain(cls, tool: Any) -> "Tool": + def from_langchain(cls, tool: Any) -> Tool: """Create a Tool instance from a CrewStructuredTool. This method takes a CrewStructuredTool object and converts it into a @@ -301,14 +331,17 @@ def tool( raise ValueError("Function must have type annotations") class_name = "".join(tool_name.split()).title() - args_schema = type( - class_name, - (PydanticBaseModel,), - { - "__annotations__": { - k: v for k, v in f.__annotations__.items() if k != "return" + args_schema = cast( + type[PydanticBaseModel], + type( + class_name, + (PydanticBaseModel,), + { + "__annotations__": { + k: v for k, v in f.__annotations__.items() if k != "return" + }, }, - }, + ), ) return Tool( diff --git a/lib/crewai/src/crewai/tools/cache_tools/__init__.py b/lib/crewai/src/crewai/tools/cache_tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/tools/cache_tools/cache_tools.py b/lib/crewai/src/crewai/tools/cache_tools/cache_tools.py similarity index 92% rename from src/crewai/tools/cache_tools/cache_tools.py rename to lib/crewai/src/crewai/tools/cache_tools/cache_tools.py index a81ce98cf..e391d4289 100644 --- a/src/crewai/tools/cache_tools/cache_tools.py +++ b/lib/crewai/src/crewai/tools/cache_tools/cache_tools.py @@ -1,6 +1,6 @@ from pydantic import BaseModel, Field -from crewai.agents.cache import CacheHandler +from crewai.agents.cache.cache_handler import CacheHandler from crewai.tools.structured_tool import CrewStructuredTool diff --git a/lib/crewai/src/crewai/tools/mcp_tool_wrapper.py b/lib/crewai/src/crewai/tools/mcp_tool_wrapper.py new file mode 100644 index 000000000..7845d0c85 --- /dev/null +++ b/lib/crewai/src/crewai/tools/mcp_tool_wrapper.py @@ -0,0 +1,213 @@ +"""MCP Tool Wrapper for on-demand MCP server connections.""" + +import asyncio + +from crewai.tools import BaseTool + + +MCP_CONNECTION_TIMEOUT = 15 +MCP_TOOL_EXECUTION_TIMEOUT = 60 +MCP_DISCOVERY_TIMEOUT = 15 +MCP_MAX_RETRIES = 3 + + +class MCPToolWrapper(BaseTool): + """Lightweight wrapper for MCP tools that connects on-demand.""" + + def __init__( + self, + mcp_server_params: dict, + tool_name: str, + tool_schema: dict, + server_name: str, + ): + """Initialize the MCP tool wrapper. + + Args: + mcp_server_params: Parameters for connecting to the MCP server + tool_name: Original name of the tool on the MCP server + tool_schema: Schema information for the tool + server_name: Name of the MCP server for prefixing + """ + # Create tool name with server prefix to avoid conflicts + prefixed_name = f"{server_name}_{tool_name}" + + # Handle args_schema properly - BaseTool expects a BaseModel subclass + args_schema = tool_schema.get("args_schema") + + # Only pass args_schema if it's provided + kwargs = { + "name": prefixed_name, + "description": tool_schema.get( + "description", f"Tool {tool_name} from {server_name}" + ), + } + + if args_schema is not None: + kwargs["args_schema"] = args_schema + + super().__init__(**kwargs) + + # Set instance attributes after super().__init__ + self._mcp_server_params = mcp_server_params + self._original_tool_name = tool_name + self._server_name = server_name + + @property + def mcp_server_params(self) -> dict: + """Get the MCP server parameters.""" + return self._mcp_server_params + + @property + def original_tool_name(self) -> str: + """Get the original tool name.""" + return self._original_tool_name + + @property + def server_name(self) -> str: + """Get the server name.""" + return self._server_name + + def _run(self, **kwargs) -> str: + """Connect to MCP server and execute tool. + + Args: + **kwargs: Arguments to pass to the MCP tool + + Returns: + Result from the MCP tool execution + """ + try: + return asyncio.run(self._run_async(**kwargs)) + except asyncio.TimeoutError: + return f"MCP tool '{self.original_tool_name}' timed out after {MCP_TOOL_EXECUTION_TIMEOUT} seconds" + except Exception as e: + return f"Error executing MCP tool {self.original_tool_name}: {e!s}" + + async def _run_async(self, **kwargs) -> str: + """Async implementation of MCP tool execution with timeouts and retry logic.""" + return await self._retry_with_exponential_backoff( + self._execute_tool_with_timeout, **kwargs + ) + + async def _retry_with_exponential_backoff(self, operation_func, **kwargs) -> str: + """Retry operation with exponential backoff, avoiding try-except in loop for performance.""" + last_error = None + + for attempt in range(MCP_MAX_RETRIES): + # Execute single attempt outside try-except loop structure + result, error, should_retry = await self._execute_single_attempt( + operation_func, **kwargs + ) + + # Success case - return immediately + if result is not None: + return result + + # Non-retryable error - return immediately + if not should_retry: + return error + + # Retryable error - continue with backoff + last_error = error + if attempt < MCP_MAX_RETRIES - 1: + wait_time = 2**attempt # Exponential backoff + await asyncio.sleep(wait_time) + + return ( + f"MCP tool execution failed after {MCP_MAX_RETRIES} attempts: {last_error}" + ) + + async def _execute_single_attempt( + self, operation_func, **kwargs + ) -> tuple[str | None, str, bool]: + """Execute single operation attempt and return (result, error_message, should_retry).""" + try: + result = await operation_func(**kwargs) + return result, "", False + + except ImportError: + return ( + None, + "MCP library not available. Please install with: pip install mcp", + False, + ) + + except asyncio.TimeoutError: + return ( + None, + f"Connection timed out after {MCP_TOOL_EXECUTION_TIMEOUT} seconds", + True, + ) + + except Exception as e: + error_str = str(e).lower() + + # Classify errors as retryable or non-retryable + if "authentication" in error_str or "unauthorized" in error_str: + return None, f"Authentication failed for MCP server: {e!s}", False + if "not found" in error_str: + return ( + None, + f"Tool '{self.original_tool_name}' not found on MCP server", + False, + ) + if "connection" in error_str or "network" in error_str: + return None, f"Network connection failed: {e!s}", True + if "json" in error_str or "parsing" in error_str: + return None, f"Server response parsing error: {e!s}", True + return None, f"MCP execution error: {e!s}", False + + async def _execute_tool_with_timeout(self, **kwargs) -> str: + """Execute tool with timeout wrapper.""" + return await asyncio.wait_for( + self._execute_tool(**kwargs), timeout=MCP_TOOL_EXECUTION_TIMEOUT + ) + + async def _execute_tool(self, **kwargs) -> str: + """Execute the actual MCP tool call.""" + from mcp import ClientSession + from mcp.client.streamable_http import streamablehttp_client + + server_url = self.mcp_server_params["url"] + + try: + # Wrap entire operation with single timeout + async def _do_mcp_call(): + async with streamablehttp_client( + server_url, terminate_on_close=True + ) as (read, write, _): + async with ClientSession(read, write) as session: + await session.initialize() + result = await session.call_tool( + self.original_tool_name, kwargs + ) + + # Extract the result content + if hasattr(result, "content") and result.content: + if ( + isinstance(result.content, list) + and len(result.content) > 0 + ): + content_item = result.content[0] + if hasattr(content_item, "text"): + return str(content_item.text) + return str(content_item) + return str(result.content) + return str(result) + + return await asyncio.wait_for( + _do_mcp_call(), timeout=MCP_TOOL_EXECUTION_TIMEOUT + ) + + except asyncio.CancelledError as e: + raise asyncio.TimeoutError("MCP operation was cancelled") from e + except Exception as e: + if hasattr(e, "__cause__") and e.__cause__: + raise asyncio.TimeoutError( + f"MCP connection error: {e.__cause__}" + ) from e.__cause__ + + if "TaskGroup" in str(e) or "unhandled errors" in str(e): + raise asyncio.TimeoutError(f"MCP connection error: {e}") from e + raise diff --git a/src/crewai/tools/structured_tool.py b/lib/crewai/src/crewai/tools/structured_tool.py similarity index 98% rename from src/crewai/tools/structured_tool.py rename to lib/crewai/src/crewai/tools/structured_tool.py index 7488ce37b..ceb1f2af9 100644 --- a/src/crewai/tools/structured_tool.py +++ b/lib/crewai/src/crewai/tools/structured_tool.py @@ -1,15 +1,17 @@ from __future__ import annotations import asyncio -import inspect -import textwrap from collections.abc import Callable +import inspect +import json +import textwrap from typing import TYPE_CHECKING, Any, get_type_hints from pydantic import BaseModel, Field, create_model from crewai.utilities.logger import Logger + if TYPE_CHECKING: from crewai.tools.base_tool import BaseTool @@ -25,8 +27,6 @@ class CrewStructuredTool: that integrates better with CrewAI's ecosystem. """ - _original_tool: BaseTool | None = None - def __init__( self, name: str, @@ -56,7 +56,7 @@ class CrewStructuredTool: self.result_as_answer = result_as_answer self.max_usage_count = max_usage_count self.current_usage_count = current_usage_count - self._original_tool = None + self._original_tool: BaseTool | None = None # Validate the function signature matches the schema self._validate_function_signature() @@ -199,8 +199,6 @@ class CrewStructuredTool: """ if isinstance(raw_args, str): try: - import json - raw_args = json.loads(raw_args) except json.JSONDecodeError as e: raise ValueError(f"Failed to parse arguments as JSON: {e}") from e diff --git a/src/crewai/tools/tool_calling.py b/lib/crewai/src/crewai/tools/tool_calling.py similarity index 80% rename from src/crewai/tools/tool_calling.py rename to lib/crewai/src/crewai/tools/tool_calling.py index 1e2eed546..333c37fec 100644 --- a/src/crewai/tools/tool_calling.py +++ b/lib/crewai/src/crewai/tools/tool_calling.py @@ -1,8 +1,11 @@ from typing import Any -from pydantic import BaseModel, Field -from pydantic import BaseModel as PydanticBaseModel -from pydantic import Field as PydanticField +from pydantic import ( + BaseModel, + BaseModel as PydanticBaseModel, + Field, + Field as PydanticField, +) class ToolCalling(BaseModel): diff --git a/src/crewai/tools/tool_types.py b/lib/crewai/src/crewai/tools/tool_types.py similarity index 100% rename from src/crewai/tools/tool_types.py rename to lib/crewai/src/crewai/tools/tool_types.py diff --git a/src/crewai/tools/tool_usage.py b/lib/crewai/src/crewai/tools/tool_usage.py similarity index 96% rename from src/crewai/tools/tool_usage.py rename to lib/crewai/src/crewai/tools/tool_usage.py index 7ef05f347..e6ad9c357 100644 --- a/src/crewai/tools/tool_usage.py +++ b/lib/crewai/src/crewai/tools/tool_usage.py @@ -1,16 +1,17 @@ +from __future__ import annotations + import ast import datetime -import json -import time from difflib import SequenceMatcher +import json from json import JSONDecodeError from textwrap import dedent -from typing import TYPE_CHECKING, Any, Union +import time +from typing import TYPE_CHECKING, Any import json5 from json_repair import repair_json # type: ignore[import-untyped,import-error] -from crewai.agents.tools_handler import ToolsHandler from crewai.events.event_bus import crewai_event_bus from crewai.events.types.tool_usage_events import ( ToolSelectionErrorEvent, @@ -19,19 +20,24 @@ from crewai.events.types.tool_usage_events import ( ToolUsageStartedEvent, ToolValidateInputErrorEvent, ) -from crewai.task import Task -from crewai.telemetry import Telemetry +from crewai.telemetry.telemetry import Telemetry from crewai.tools.structured_tool import CrewStructuredTool from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling -from crewai.utilities import I18N, Converter, Printer from crewai.utilities.agent_utils import ( get_tool_names, render_text_description_and_args, ) +from crewai.utilities.converter import Converter +from crewai.utilities.i18n import I18N +from crewai.utilities.printer import Printer + if TYPE_CHECKING: from crewai.agents.agent_builder.base_agent import BaseAgent + from crewai.agents.tools_handler import ToolsHandler from crewai.lite_agent import LiteAgent + from crewai.llm import LLM + from crewai.task import Task OPENAI_BIGGER_MODELS = [ "gpt-4", @@ -70,8 +76,8 @@ class ToolUsage: tools_handler: ToolsHandler | None, tools: list[CrewStructuredTool], task: Task | None, - function_calling_llm: Any, - agent: Union["BaseAgent", "LiteAgent"] | None = None, + function_calling_llm: LLM, + agent: BaseAgent | LiteAgent | None = None, action: Any = None, fingerprint_context: dict[str, str] | None = None, ) -> None: @@ -587,7 +593,23 @@ class ToolUsage: e: Exception, ) -> None: event_data = self._prepare_event_data(tool, tool_calling) - crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e})) + event_data.update( + { + "task_id": str(self.task.id) if self.task else None, + "task_name": self.task.name or self.task.description + if self.task + else None, + } + ) + crewai_event_bus.emit( + self, + ToolUsageErrorEvent( + **{ + **event_data, + "error": e, + } + ), + ) def on_tool_use_finished( self, diff --git a/src/crewai/translations/en.json b/lib/crewai/src/crewai/translations/en.json similarity index 100% rename from src/crewai/translations/en.json rename to lib/crewai/src/crewai/translations/en.json diff --git a/lib/crewai/src/crewai/types/__init__.py b/lib/crewai/src/crewai/types/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/crewai/types/crew_chat.py b/lib/crewai/src/crewai/types/crew_chat.py similarity index 92% rename from src/crewai/types/crew_chat.py rename to lib/crewai/src/crewai/types/crew_chat.py index 2873700f4..dc64ae904 100644 --- a/src/crewai/types/crew_chat.py +++ b/lib/crewai/src/crewai/types/crew_chat.py @@ -13,8 +13,7 @@ class ChatInputField(BaseModel): Example: ```python field = ChatInputField( - name="topic", - description="The topic to focus on for the conversation" + name="topic", description="The topic to focus on for the conversation" ) ``` """ @@ -34,7 +33,7 @@ class ChatInputs(BaseModel): inputs=[ ChatInputField(name="topic", description="The topic to focus on"), ChatInputField(name="username", description="Name of the user"), - ] + ], ) ``` """ diff --git a/src/crewai/types/hitl.py b/lib/crewai/src/crewai/types/hitl.py similarity index 100% rename from src/crewai/types/hitl.py rename to lib/crewai/src/crewai/types/hitl.py diff --git a/src/crewai/types/usage_metrics.py b/lib/crewai/src/crewai/types/usage_metrics.py similarity index 100% rename from src/crewai/types/usage_metrics.py rename to lib/crewai/src/crewai/types/usage_metrics.py diff --git a/src/crewai/utilities/__init__.py b/lib/crewai/src/crewai/utilities/__init__.py similarity index 99% rename from src/crewai/utilities/__init__.py rename to lib/crewai/src/crewai/utilities/__init__.py index 8ca82a1f4..b2c02dce0 100644 --- a/src/crewai/utilities/__init__.py +++ b/lib/crewai/src/crewai/utilities/__init__.py @@ -10,6 +10,7 @@ from crewai.utilities.printer import Printer from crewai.utilities.prompts import Prompts from crewai.utilities.rpm_controller import RPMController + __all__ = [ "I18N", "Converter", diff --git a/src/crewai/utilities/agent_utils.py b/lib/crewai/src/crewai/utilities/agent_utils.py similarity index 98% rename from src/crewai/utilities/agent_utils.py rename to lib/crewai/src/crewai/utilities/agent_utils.py index 003e8b7d1..0ef5f1ecf 100644 --- a/src/crewai/utilities/agent_utils.py +++ b/lib/crewai/src/crewai/utilities/agent_utils.py @@ -1,8 +1,8 @@ from __future__ import annotations +from collections.abc import Callable, Sequence import json import re -from collections.abc import Callable, Sequence from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict from rich.console import Console @@ -15,7 +15,6 @@ from crewai.agents.parser import ( parse, ) from crewai.cli.config import Settings -from crewai.llm import LLM from crewai.llms.base_llm import BaseLLM from crewai.tools import BaseTool as CrewAITool from crewai.tools.base_tool import BaseTool @@ -27,11 +26,14 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import ( ) from crewai.utilities.i18n import I18N from crewai.utilities.printer import ColoredText, Printer +from crewai.utilities.token_counter_callback import TokenCalcHandler from crewai.utilities.types import LLMMessage + if TYPE_CHECKING: from crewai.agent import Agent from crewai.lite_agent import LiteAgent + from crewai.llm import LLM from crewai.task import Task @@ -123,7 +125,7 @@ def handle_max_iterations_exceeded( i18n: I18N, messages: list[LLMMessage], llm: LLM | BaseLLM, - callbacks: list[Callable[..., Any]], + callbacks: list[TokenCalcHandler], ) -> AgentAction | AgentFinish: """Handles the case when the maximum number of iterations is exceeded. Performs one more LLM call to get the final answer. @@ -220,7 +222,7 @@ def enforce_rpm_limit( def get_llm_response( llm: LLM | BaseLLM, messages: list[LLMMessage], - callbacks: list[Callable[..., Any]], + callbacks: list[TokenCalcHandler], printer: Printer, from_task: Task | None = None, from_agent: Agent | LiteAgent | None = None, @@ -287,7 +289,7 @@ def process_llm_response( def handle_agent_action_core( formatted_answer: AgentAction, tool_result: ToolResult, - messages: list[dict[str, str]] | None = None, + messages: list[LLMMessage] | None = None, step_callback: Callable | None = None, show_logs: Callable | None = None, ) -> AgentAction | AgentFinish: @@ -403,7 +405,7 @@ def handle_context_length( printer: Printer, messages: list[LLMMessage], llm: LLM | BaseLLM, - callbacks: list[Callable[..., Any]], + callbacks: list[TokenCalcHandler], i18n: I18N, ) -> None: """Handle context length exceeded by either summarizing or raising an error. @@ -438,7 +440,7 @@ def handle_context_length( def summarize_messages( messages: list[LLMMessage], llm: LLM | BaseLLM, - callbacks: list[Callable[..., Any]], + callbacks: list[TokenCalcHandler], i18n: I18N, ) -> None: """Summarize messages to fit within context window. @@ -449,7 +451,7 @@ def summarize_messages( callbacks: List of callbacks for LLM i18n: I18N instance for messages """ - messages_string = " ".join([message["content"] for message in messages]) + messages_string = " ".join([message["content"] for message in messages]) # type: ignore[misc] cut_size = llm.get_context_window_size() messages_groups = [ diff --git a/src/crewai/utilities/config.py b/lib/crewai/src/crewai/utilities/config.py similarity index 100% rename from src/crewai/utilities/config.py rename to lib/crewai/src/crewai/utilities/config.py diff --git a/src/crewai/utilities/constants.py b/lib/crewai/src/crewai/utilities/constants.py similarity index 99% rename from src/crewai/utilities/constants.py rename to lib/crewai/src/crewai/utilities/constants.py index c1d808a32..5823a6111 100644 --- a/src/crewai/utilities/constants.py +++ b/lib/crewai/src/crewai/utilities/constants.py @@ -2,6 +2,7 @@ from typing import Annotated, Final from crewai.utilities.printer import PrinterColor + TRAINING_DATA_FILE: Final[str] = "training_data.pkl" TRAINED_AGENTS_DATA_FILE: Final[str] = "trained_agents_data.pkl" KNOWLEDGE_DIRECTORY: Final[str] = "knowledge" diff --git a/src/crewai/utilities/converter.py b/lib/crewai/src/crewai/utilities/converter.py similarity index 99% rename from src/crewai/utilities/converter.py rename to lib/crewai/src/crewai/utilities/converter.py index 07f6f7ea3..67cd1d7d0 100644 --- a/src/crewai/utilities/converter.py +++ b/lib/crewai/src/crewai/utilities/converter.py @@ -12,6 +12,7 @@ from crewai.utilities.internal_instructor import InternalInstructor from crewai.utilities.printer import Printer from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser + if TYPE_CHECKING: from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent diff --git a/src/crewai/utilities/crew/__init__.py b/lib/crewai/src/crewai/utilities/crew/__init__.py similarity index 100% rename from src/crewai/utilities/crew/__init__.py rename to lib/crewai/src/crewai/utilities/crew/__init__.py diff --git a/src/crewai/utilities/crew/crew_context.py b/lib/crewai/src/crewai/utilities/crew/crew_context.py similarity index 100% rename from src/crewai/utilities/crew/crew_context.py rename to lib/crewai/src/crewai/utilities/crew/crew_context.py diff --git a/src/crewai/utilities/crew/models.py b/lib/crewai/src/crewai/utilities/crew/models.py similarity index 100% rename from src/crewai/utilities/crew/models.py rename to lib/crewai/src/crewai/utilities/crew/models.py diff --git a/src/crewai/utilities/crew_json_encoder.py b/lib/crewai/src/crewai/utilities/crew_json_encoder.py similarity index 100% rename from src/crewai/utilities/crew_json_encoder.py rename to lib/crewai/src/crewai/utilities/crew_json_encoder.py index 745340a7b..17468e8bb 100644 --- a/src/crewai/utilities/crew_json_encoder.py +++ b/lib/crewai/src/crewai/utilities/crew_json_encoder.py @@ -1,9 +1,9 @@ """JSON encoder for handling CrewAI specific types.""" -import json from datetime import date, datetime from decimal import Decimal from enum import Enum +import json from typing import Any from uuid import UUID diff --git a/src/crewai/utilities/errors.py b/lib/crewai/src/crewai/utilities/errors.py similarity index 100% rename from src/crewai/utilities/errors.py rename to lib/crewai/src/crewai/utilities/errors.py diff --git a/src/crewai/utilities/evaluators/__init__.py b/lib/crewai/src/crewai/utilities/evaluators/__init__.py similarity index 100% rename from src/crewai/utilities/evaluators/__init__.py rename to lib/crewai/src/crewai/utilities/evaluators/__init__.py diff --git a/src/crewai/utilities/evaluators/crew_evaluator_handler.py b/lib/crewai/src/crewai/utilities/evaluators/crew_evaluator_handler.py similarity index 99% rename from src/crewai/utilities/evaluators/crew_evaluator_handler.py rename to lib/crewai/src/crewai/utilities/evaluators/crew_evaluator_handler.py index 47c2bb100..9c9cac0c6 100644 --- a/src/crewai/utilities/evaluators/crew_evaluator_handler.py +++ b/lib/crewai/src/crewai/utilities/evaluators/crew_evaluator_handler.py @@ -15,6 +15,7 @@ from crewai.llms.base_llm import BaseLLM from crewai.task import Task from crewai.tasks.task_output import TaskOutput + if TYPE_CHECKING: from crewai.crew import Crew diff --git a/src/crewai/utilities/evaluators/task_evaluator.py b/lib/crewai/src/crewai/utilities/evaluators/task_evaluator.py similarity index 99% rename from src/crewai/utilities/evaluators/task_evaluator.py rename to lib/crewai/src/crewai/utilities/evaluators/task_evaluator.py index ad1b993cf..0d40b505a 100644 --- a/src/crewai/utilities/evaluators/task_evaluator.py +++ b/lib/crewai/src/crewai/utilities/evaluators/task_evaluator.py @@ -11,6 +11,7 @@ from crewai.utilities.converter import Converter from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser from crewai.utilities.training_converter import TrainingConverter + if TYPE_CHECKING: from crewai.agent import Agent from crewai.task import Task diff --git a/src/crewai/utilities/exceptions/__init__.py b/lib/crewai/src/crewai/utilities/exceptions/__init__.py similarity index 100% rename from src/crewai/utilities/exceptions/__init__.py rename to lib/crewai/src/crewai/utilities/exceptions/__init__.py diff --git a/src/crewai/utilities/exceptions/context_window_exceeding_exception.py b/lib/crewai/src/crewai/utilities/exceptions/context_window_exceeding_exception.py similarity index 99% rename from src/crewai/utilities/exceptions/context_window_exceeding_exception.py rename to lib/crewai/src/crewai/utilities/exceptions/context_window_exceeding_exception.py index cbbe3e0a5..9e44ce6f4 100644 --- a/src/crewai/utilities/exceptions/context_window_exceeding_exception.py +++ b/lib/crewai/src/crewai/utilities/exceptions/context_window_exceeding_exception.py @@ -1,5 +1,6 @@ from typing import Final + CONTEXT_LIMIT_ERRORS: Final[list[str]] = [ "expected a string with maximum length", "maximum context length", diff --git a/src/crewai/utilities/file_handler.py b/lib/crewai/src/crewai/utilities/file_handler.py similarity index 100% rename from src/crewai/utilities/file_handler.py rename to lib/crewai/src/crewai/utilities/file_handler.py index 106cb76b3..ff50197a1 100644 --- a/src/crewai/utilities/file_handler.py +++ b/lib/crewai/src/crewai/utilities/file_handler.py @@ -1,7 +1,7 @@ +from datetime import datetime import json import os import pickle -from datetime import datetime from typing import Any, TypedDict from typing_extensions import Unpack diff --git a/src/crewai/utilities/formatter.py b/lib/crewai/src/crewai/utilities/formatter.py similarity index 99% rename from src/crewai/utilities/formatter.py rename to lib/crewai/src/crewai/utilities/formatter.py index 892167d39..53c097a77 100644 --- a/src/crewai/utilities/formatter.py +++ b/lib/crewai/src/crewai/utilities/formatter.py @@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Final from crewai.utilities.constants import _NotSpecified + if TYPE_CHECKING: from crewai.task import Task from crewai.tasks.task_output import TaskOutput diff --git a/src/crewai/utilities/guardrail.py b/lib/crewai/src/crewai/utilities/guardrail.py similarity index 86% rename from src/crewai/utilities/guardrail.py rename to lib/crewai/src/crewai/utilities/guardrail.py index 6846bf0e6..499cc957f 100644 --- a/src/crewai/utilities/guardrail.py +++ b/lib/crewai/src/crewai/utilities/guardrail.py @@ -1,14 +1,17 @@ from __future__ import annotations -from collections.abc import Callable from typing import TYPE_CHECKING, Any from pydantic import BaseModel, Field, field_validator from typing_extensions import Self +from crewai.utilities.guardrail_types import GuardrailCallable + + if TYPE_CHECKING: from crewai.agents.agent_builder.base_agent import BaseAgent - from crewai.lite_agent import LiteAgent, LiteAgentOutput + from crewai.lite_agent import LiteAgent + from crewai.lite_agent_output import LiteAgentOutput from crewai.task import Task from crewai.tasks.task_output import TaskOutput @@ -78,7 +81,7 @@ class GuardrailResult(BaseModel): def process_guardrail( output: TaskOutput | LiteAgentOutput, - guardrail: Callable[[Any], tuple[bool, Any | str]], + guardrail: GuardrailCallable, retry_count: int, event_source: Any | None = None, from_agent: BaseAgent | LiteAgent | None = None, @@ -91,6 +94,8 @@ def process_guardrail( guardrail: The guardrail to validate the output with retry_count: The number of times the guardrail has been retried event_source: The source of the guardrail to be sent in events + from_agent: The agent that produced the output + from_task: The task that produced the output Returns: GuardrailResult: The result of the guardrail validation @@ -99,6 +104,14 @@ def process_guardrail( TypeError: If output is not a TaskOutput or LiteAgentOutput ValueError: If guardrail is None """ + from crewai.lite_agent_output import LiteAgentOutput + from crewai.tasks.task_output import TaskOutput + + if not isinstance(output, (TaskOutput, LiteAgentOutput)): + raise TypeError("Output must be a TaskOutput or LiteAgentOutput") + if guardrail is None: + raise ValueError("Guardrail must not be None") + from crewai.events.event_bus import crewai_event_bus from crewai.events.types.llm_guardrail_events import ( LLMGuardrailCompletedEvent, diff --git a/lib/crewai/src/crewai/utilities/guardrail_types.py b/lib/crewai/src/crewai/utilities/guardrail_types.py new file mode 100644 index 000000000..babb5bf52 --- /dev/null +++ b/lib/crewai/src/crewai/utilities/guardrail_types.py @@ -0,0 +1,18 @@ +"""Type aliases for guardrails.""" + +from __future__ import annotations + +from collections.abc import Callable, Sequence +from typing import Any, TypeAlias + +from crewai.lite_agent_output import LiteAgentOutput +from crewai.tasks.task_output import TaskOutput + + +GuardrailCallable: TypeAlias = Callable[ + [TaskOutput | LiteAgentOutput], tuple[bool, Any] +] + +GuardrailType: TypeAlias = GuardrailCallable | str + +GuardrailsType: TypeAlias = Sequence[GuardrailType] | GuardrailType diff --git a/src/crewai/utilities/i18n.py b/lib/crewai/src/crewai/utilities/i18n.py similarity index 100% rename from src/crewai/utilities/i18n.py rename to lib/crewai/src/crewai/utilities/i18n.py diff --git a/src/crewai/utilities/import_utils.py b/lib/crewai/src/crewai/utilities/import_utils.py similarity index 100% rename from src/crewai/utilities/import_utils.py rename to lib/crewai/src/crewai/utilities/import_utils.py diff --git a/src/crewai/utilities/internal_instructor.py b/lib/crewai/src/crewai/utilities/internal_instructor.py similarity index 96% rename from src/crewai/utilities/internal_instructor.py rename to lib/crewai/src/crewai/utilities/internal_instructor.py index aefbcb28b..d7391821c 100644 --- a/src/crewai/utilities/internal_instructor.py +++ b/lib/crewai/src/crewai/utilities/internal_instructor.py @@ -4,13 +4,16 @@ from typing import TYPE_CHECKING, Any, Generic, TypeGuard, TypeVar from pydantic import BaseModel + if TYPE_CHECKING: from crewai.agent import Agent from crewai.llm import LLM from crewai.llms.base_llm import BaseLLM + from crewai.utilities.types import LLMMessage from crewai.utilities.logger_utils import suppress_warnings -from crewai.utilities.types import LLMMessage + + T = TypeVar("T", bound=BaseModel) @@ -58,7 +61,7 @@ class InternalInstructor(Generic[T]): self.llm = llm or (agent.function_calling_llm or agent.llm if agent else None) with suppress_warnings(): - import instructor + import instructor # type: ignore[import-untyped] from litellm import completion self._client = instructor.from_litellm(completion) diff --git a/src/crewai/utilities/llm_utils.py b/lib/crewai/src/crewai/utilities/llm_utils.py similarity index 95% rename from src/crewai/utilities/llm_utils.py rename to lib/crewai/src/crewai/utilities/llm_utils.py index d3b439e5d..c87c439ea 100644 --- a/src/crewai/utilities/llm_utils.py +++ b/lib/crewai/src/crewai/utilities/llm_utils.py @@ -6,6 +6,7 @@ from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS from crewai.llm import LLM from crewai.llms.base_llm import BaseLLM + logger = logging.getLogger(__name__) @@ -42,7 +43,7 @@ def create_llm( or str(llm_value) ) temperature: float | None = getattr(llm_value, "temperature", None) - max_tokens: int | None = getattr(llm_value, "max_tokens", None) + max_tokens: float | int | None = getattr(llm_value, "max_tokens", None) logprobs: int | None = getattr(llm_value, "logprobs", None) timeout: float | None = getattr(llm_value, "timeout", None) api_key: str | None = getattr(llm_value, "api_key", None) @@ -59,6 +60,7 @@ def create_llm( base_url=base_url, api_base=api_base, ) + except Exception as e: logger.debug(f"Error instantiating LLM from unknown object type: {e}") return None @@ -117,6 +119,7 @@ def _llm_via_environment_or_fallback() -> LLM | None: elif api_base and not base_url: base_url = api_base + # Initialize llm_params dictionary llm_params: dict[str, Any] = { "model": model, "temperature": temperature, @@ -140,6 +143,11 @@ def _llm_via_environment_or_fallback() -> LLM | None: "callbacks": callbacks, } + unaccepted_attributes = [ + "AWS_ACCESS_KEY_ID", + "AWS_SECRET_ACCESS_KEY", + "AWS_REGION_NAME", + ] set_provider = model_name.partition("/")[0] if "/" in model_name else "openai" if set_provider in ENV_VARS: @@ -147,7 +155,7 @@ def _llm_via_environment_or_fallback() -> LLM | None: if isinstance(env_vars_for_provider, (list, tuple)): for env_var in env_vars_for_provider: key_name = env_var.get("key_name") - if key_name and key_name not in UNACCEPTED_ATTRIBUTES: + if key_name and key_name not in unaccepted_attributes: env_value = os.environ.get(key_name) if env_value: # Map environment variable names to recognized parameters diff --git a/src/crewai/utilities/logger.py b/lib/crewai/src/crewai/utilities/logger.py similarity index 100% rename from src/crewai/utilities/logger.py rename to lib/crewai/src/crewai/utilities/logger.py diff --git a/src/crewai/utilities/logger_utils.py b/lib/crewai/src/crewai/utilities/logger_utils.py similarity index 100% rename from src/crewai/utilities/logger_utils.py rename to lib/crewai/src/crewai/utilities/logger_utils.py index f0ad21f18..f59865578 100644 --- a/src/crewai/utilities/logger_utils.py +++ b/lib/crewai/src/crewai/utilities/logger_utils.py @@ -1,10 +1,10 @@ """Logging and warning utility functions for CrewAI.""" +from collections.abc import Generator import contextlib import io import logging import warnings -from collections.abc import Generator @contextlib.contextmanager diff --git a/src/crewai/utilities/paths.py b/lib/crewai/src/crewai/utilities/paths.py similarity index 100% rename from src/crewai/utilities/paths.py rename to lib/crewai/src/crewai/utilities/paths.py diff --git a/src/crewai/utilities/planning_handler.py b/lib/crewai/src/crewai/utilities/planning_handler.py similarity index 99% rename from src/crewai/utilities/planning_handler.py rename to lib/crewai/src/crewai/utilities/planning_handler.py index c1470d77f..c76153020 100644 --- a/src/crewai/utilities/planning_handler.py +++ b/lib/crewai/src/crewai/utilities/planning_handler.py @@ -8,6 +8,7 @@ from crewai.agent import Agent from crewai.llms.base_llm import BaseLLM from crewai.task import Task + logger = logging.getLogger(__name__) diff --git a/src/crewai/utilities/printer.py b/lib/crewai/src/crewai/utilities/printer.py similarity index 99% rename from src/crewai/utilities/printer.py rename to lib/crewai/src/crewai/utilities/printer.py index 5b78e700e..c40de684e 100644 --- a/src/crewai/utilities/printer.py +++ b/lib/crewai/src/crewai/utilities/printer.py @@ -4,6 +4,7 @@ from __future__ import annotations from typing import TYPE_CHECKING, Final, Literal, NamedTuple + if TYPE_CHECKING: from _typeshed import SupportsWrite diff --git a/src/crewai/utilities/prompts.py b/lib/crewai/src/crewai/utilities/prompts.py similarity index 100% rename from src/crewai/utilities/prompts.py rename to lib/crewai/src/crewai/utilities/prompts.py diff --git a/src/crewai/utilities/pydantic_schema_parser.py b/lib/crewai/src/crewai/utilities/pydantic_schema_parser.py similarity index 100% rename from src/crewai/utilities/pydantic_schema_parser.py rename to lib/crewai/src/crewai/utilities/pydantic_schema_parser.py diff --git a/src/crewai/utilities/reasoning_handler.py b/lib/crewai/src/crewai/utilities/reasoning_handler.py similarity index 95% rename from src/crewai/utilities/reasoning_handler.py rename to lib/crewai/src/crewai/utilities/reasoning_handler.py index 56ac8c1a0..fb78e3e64 100644 --- a/src/crewai/utilities/reasoning_handler.py +++ b/lib/crewai/src/crewai/utilities/reasoning_handler.py @@ -102,21 +102,18 @@ class AgentReasoning: try: output = self.__handle_agent_reasoning() - # Emit reasoning completed event - try: - crewai_event_bus.emit( - self.agent, - AgentReasoningCompletedEvent( - agent_role=self.agent.role, - task_id=str(self.task.id), - plan=output.plan.plan, - ready=output.plan.ready, - attempt=1, - from_task=self.task, - ), - ) - except Exception: # noqa: S110 - pass + crewai_event_bus.emit( + self.agent, + AgentReasoningCompletedEvent( + agent_role=self.agent.role, + task_id=str(self.task.id), + plan=output.plan.plan, + ready=output.plan.ready, + attempt=1, + from_task=self.task, + from_agent=self.agent, + ), + ) return output except Exception as e: @@ -130,10 +127,11 @@ class AgentReasoning: error=str(e), attempt=1, from_task=self.task, + from_agent=self.agent, ), ) - except Exception: # noqa: S110 - pass + except Exception as e: + logging.error(f"Error emitting reasoning failed event: {e}") raise diff --git a/src/crewai/utilities/rpm_controller.py b/lib/crewai/src/crewai/utilities/rpm_controller.py similarity index 100% rename from src/crewai/utilities/rpm_controller.py rename to lib/crewai/src/crewai/utilities/rpm_controller.py diff --git a/lib/crewai/src/crewai/utilities/rw_lock.py b/lib/crewai/src/crewai/utilities/rw_lock.py new file mode 100644 index 000000000..2c170b3d4 --- /dev/null +++ b/lib/crewai/src/crewai/utilities/rw_lock.py @@ -0,0 +1,81 @@ +"""Read-write lock for thread-safe concurrent access. + +This module provides a reader-writer lock implementation that allows multiple +concurrent readers or a single exclusive writer. +""" + +from collections.abc import Generator +from contextlib import contextmanager +from threading import Condition + + +class RWLock: + """Read-write lock for managing concurrent read and exclusive write access. + + Allows multiple threads to acquire read locks simultaneously, but ensures + exclusive access for write operations. Writers are prioritized when waiting. + + Attributes: + _cond: Condition variable for coordinating lock access + _readers: Count of active readers + _writer: Whether a writer currently holds the lock + """ + + def __init__(self) -> None: + """Initialize the read-write lock.""" + self._cond = Condition() + self._readers = 0 + self._writer = False + + def r_acquire(self) -> None: + """Acquire a read lock, blocking if a writer holds the lock.""" + with self._cond: + while self._writer: + self._cond.wait() + self._readers += 1 + + def r_release(self) -> None: + """Release a read lock and notify waiting writers if last reader.""" + with self._cond: + self._readers -= 1 + if self._readers == 0: + self._cond.notify_all() + + @contextmanager + def r_locked(self) -> Generator[None, None, None]: + """Context manager for acquiring a read lock. + + Yields: + None + """ + try: + self.r_acquire() + yield + finally: + self.r_release() + + def w_acquire(self) -> None: + """Acquire a write lock, blocking if any readers or writers are active.""" + with self._cond: + while self._writer or self._readers > 0: + self._cond.wait() + self._writer = True + + def w_release(self) -> None: + """Release a write lock and notify all waiting threads.""" + with self._cond: + self._writer = False + self._cond.notify_all() + + @contextmanager + def w_locked(self) -> Generator[None, None, None]: + """Context manager for acquiring a write lock. + + Yields: + None + """ + try: + self.w_acquire() + yield + finally: + self.w_release() diff --git a/src/crewai/utilities/serialization.py b/lib/crewai/src/crewai/utilities/serialization.py similarity index 99% rename from src/crewai/utilities/serialization.py rename to lib/crewai/src/crewai/utilities/serialization.py index 0267d0c83..5b2681b2d 100644 --- a/src/crewai/utilities/serialization.py +++ b/lib/crewai/src/crewai/utilities/serialization.py @@ -1,12 +1,13 @@ from __future__ import annotations -import json -import uuid from datetime import date, datetime +import json from typing import Any, TypeAlias +import uuid from pydantic import BaseModel + SerializablePrimitive: TypeAlias = str | int | float | bool | None Serializable: TypeAlias = ( SerializablePrimitive | list["Serializable"] | dict[str, "Serializable"] diff --git a/src/crewai/utilities/string_utils.py b/lib/crewai/src/crewai/utilities/string_utils.py similarity index 99% rename from src/crewai/utilities/string_utils.py rename to lib/crewai/src/crewai/utilities/string_utils.py index 40181459f..034bb84a3 100644 --- a/src/crewai/utilities/string_utils.py +++ b/lib/crewai/src/crewai/utilities/string_utils.py @@ -1,6 +1,7 @@ import re from typing import Any, Final + _VARIABLE_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{([A-Za-z_][A-Za-z0-9_\-]*)}") diff --git a/src/crewai/utilities/task_output_storage_handler.py b/lib/crewai/src/crewai/utilities/task_output_storage_handler.py similarity index 100% rename from src/crewai/utilities/task_output_storage_handler.py rename to lib/crewai/src/crewai/utilities/task_output_storage_handler.py diff --git a/src/crewai/utilities/token_counter_callback.py b/lib/crewai/src/crewai/utilities/token_counter_callback.py similarity index 83% rename from src/crewai/utilities/token_counter_callback.py rename to lib/crewai/src/crewai/utilities/token_counter_callback.py index 96124f226..07c27727a 100644 --- a/src/crewai/utilities/token_counter_callback.py +++ b/lib/crewai/src/crewai/utilities/token_counter_callback.py @@ -4,10 +4,24 @@ This module provides a callback handler that tracks token usage for LLM API calls through the litellm library. """ -from typing import Any +from typing import TYPE_CHECKING, Any + + +if TYPE_CHECKING: + from litellm.integrations.custom_logger import CustomLogger + from litellm.types.utils import Usage +else: + try: + from litellm.integrations.custom_logger import CustomLogger + from litellm.types.utils import Usage + except ImportError: + + class CustomLogger: + """Fallback CustomLogger when litellm is not available.""" + + class Usage: + """Fallback Usage when litellm is not available.""" -from litellm.integrations.custom_logger import CustomLogger -from litellm.types.utils import Usage from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess from crewai.utilities.logger_utils import suppress_warnings diff --git a/src/crewai/utilities/tool_utils.py b/lib/crewai/src/crewai/utilities/tool_utils.py similarity index 97% rename from src/crewai/utilities/tool_utils.py rename to lib/crewai/src/crewai/utilities/tool_utils.py index 5506dde64..eb433c02c 100644 --- a/src/crewai/utilities/tool_utils.py +++ b/lib/crewai/src/crewai/utilities/tool_utils.py @@ -10,6 +10,7 @@ from crewai.tools.tool_types import ToolResult from crewai.tools.tool_usage import ToolUsage, ToolUsageError from crewai.utilities.i18n import I18N + if TYPE_CHECKING: from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent @@ -64,7 +65,7 @@ def execute_tool_and_check_finality( tool_usage = ToolUsage( tools_handler=tools_handler, tools=tools, - function_calling_llm=function_calling_llm, + function_calling_llm=function_calling_llm, # type: ignore[arg-type] task=task, agent=agent, action=agent_action, diff --git a/src/crewai/utilities/training_converter.py b/lib/crewai/src/crewai/utilities/training_converter.py similarity index 99% rename from src/crewai/utilities/training_converter.py rename to lib/crewai/src/crewai/utilities/training_converter.py index 05f74fa53..733dc9ee0 100644 --- a/src/crewai/utilities/training_converter.py +++ b/lib/crewai/src/crewai/utilities/training_converter.py @@ -6,6 +6,7 @@ from pydantic import BaseModel, ValidationError from crewai.utilities.converter import Converter, ConverterError + _FLOAT_PATTERN: Final[re.Pattern[str]] = re.compile(r"(\d+(?:\.\d+)?)") diff --git a/src/crewai/utilities/training_handler.py b/lib/crewai/src/crewai/utilities/training_handler.py similarity index 98% rename from src/crewai/utilities/training_handler.py rename to lib/crewai/src/crewai/utilities/training_handler.py index 4bc87d237..98d781e11 100644 --- a/src/crewai/utilities/training_handler.py +++ b/lib/crewai/src/crewai/utilities/training_handler.py @@ -5,7 +5,7 @@ from crewai.utilities.file_handler import PickleHandler class CrewTrainingHandler(PickleHandler): - def save_trained_data(self, agent_id: str, trained_data: dict[int, Any]) -> None: + def save_trained_data(self, agent_id: str, trained_data: dict[str, Any]) -> None: """Save the trained data for a specific agent. Args: diff --git a/src/crewai/utilities/types.py b/lib/crewai/src/crewai/utilities/types.py similarity index 78% rename from src/crewai/utilities/types.py rename to lib/crewai/src/crewai/utilities/types.py index 0cdaa1878..bc331a97e 100644 --- a/src/crewai/utilities/types.py +++ b/lib/crewai/src/crewai/utilities/types.py @@ -1,6 +1,6 @@ """Types for CrewAI utilities.""" -from typing import Literal, TypedDict +from typing import Any, Literal, TypedDict class LLMMessage(TypedDict): @@ -12,4 +12,4 @@ class LLMMessage(TypedDict): """ role: Literal["user", "assistant", "system"] - content: str + content: str | list[dict[str, Any]] diff --git a/lib/crewai/tests/__init__.py b/lib/crewai/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/tests/agents/__init__.py b/lib/crewai/tests/agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/agents/agent_adapters/__init__.py b/lib/crewai/tests/agents/agent_adapters/__init__.py similarity index 100% rename from tests/agents/agent_adapters/__init__.py rename to lib/crewai/tests/agents/agent_adapters/__init__.py diff --git a/tests/agents/agent_adapters/test_base_agent_adapter.py b/lib/crewai/tests/agents/agent_adapters/test_base_agent_adapter.py similarity index 81% rename from tests/agents/agent_adapters/test_base_agent_adapter.py rename to lib/crewai/tests/agents/agent_adapters/test_base_agent_adapter.py index 2da90b719..3d4ee5351 100644 --- a/tests/agents/agent_adapters/test_base_agent_adapter.py +++ b/lib/crewai/tests/agents/agent_adapters/test_base_agent_adapter.py @@ -1,18 +1,17 @@ -from typing import Any, Dict, List, Optional +from typing import Any import pytest -from pydantic import BaseModel - from crewai.agent import BaseAgent from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter -from crewai.tools import BaseTool +from crewai.tools.base_tool import BaseTool from crewai.utilities.token_counter_callback import TokenProcess +from pydantic import BaseModel # Concrete implementation for testing class ConcreteAgentAdapter(BaseAgentAdapter): def configure_tools( - self, tools: Optional[List[BaseTool]] = None, **kwargs: Any + self, tools: list[BaseTool] | None = None, **kwargs: Any ) -> None: # Simple implementation for testing self.tools = tools or [] @@ -20,19 +19,19 @@ class ConcreteAgentAdapter(BaseAgentAdapter): def execute_task( self, task: Any, - context: Optional[str] = None, - tools: Optional[List[Any]] = None, + context: str | None = None, + tools: list[Any] | None = None, ) -> str: # Dummy implementation needed due to BaseAgent inheritance return "Task executed" - def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> Any: + def create_agent_executor(self, tools: list[BaseTool] | None = None) -> Any: # Dummy implementation return None def get_delegation_tools( - self, tools: List[BaseTool], tool_map: Optional[Dict[str, BaseTool]] - ) -> List[BaseTool]: + self, tools: list[BaseTool], tool_map: dict[str, BaseTool] | None + ) -> list[BaseTool]: # Dummy implementation return [] @@ -40,10 +39,18 @@ class ConcreteAgentAdapter(BaseAgentAdapter): # Dummy implementation pass - def get_output_converter(self, tools: Optional[List[BaseTool]] = None) -> Any: + def get_output_converter(self, tools: list[BaseTool] | None = None) -> Any: # Dummy implementation return None + def get_platform_tools(self, apps: Any) -> list[BaseTool]: + # Dummy implementation + return [] + + def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]: + # Dummy implementation for MCP tools + return [] + def test_base_agent_adapter_initialization(): """Test initialization of the concrete agent adapter.""" @@ -95,7 +102,6 @@ def test_configure_structured_output_method_exists(): adapter.configure_structured_output(structured_output) # Add assertions here if configure_structured_output modifies state # For now, just ensuring it runs without error is sufficient - pass def test_base_agent_adapter_inherits_base_agent(): diff --git a/tests/agents/agent_adapters/test_base_tool_adapter.py b/lib/crewai/tests/agents/agent_adapters/test_base_tool_adapter.py similarity index 100% rename from tests/agents/agent_adapters/test_base_tool_adapter.py rename to lib/crewai/tests/agents/agent_adapters/test_base_tool_adapter.py diff --git a/tests/agents/agent_builder/__init__.py b/lib/crewai/tests/agents/agent_builder/__init__.py similarity index 100% rename from tests/agents/agent_builder/__init__.py rename to lib/crewai/tests/agents/agent_builder/__init__.py diff --git a/tests/agents/agent_builder/test_base_agent.py b/lib/crewai/tests/agents/agent_builder/test_base_agent.py similarity index 66% rename from tests/agents/agent_builder/test_base_agent.py rename to lib/crewai/tests/agents/agent_builder/test_base_agent.py index 59faa6ba3..883b03bb8 100644 --- a/tests/agents/agent_builder/test_base_agent.py +++ b/lib/crewai/tests/agents/agent_builder/test_base_agent.py @@ -1,5 +1,5 @@ import hashlib -from typing import Any, List, Optional +from typing import Any from pydantic import BaseModel @@ -11,14 +11,19 @@ class MockAgent(BaseAgent): def execute_task( self, task: Any, - context: Optional[str] = None, - tools: Optional[List[BaseTool]] = None, + context: str | None = None, + tools: list[BaseTool] | None = None, ) -> str: return "" def create_agent_executor(self, tools=None) -> None: ... - def get_delegation_tools(self, agents: List["BaseAgent"]): ... + def get_delegation_tools(self, agents: list["BaseAgent"]): ... + + def get_platform_tools(self, apps: list[Any]): ... + + def get_mcp_tools(self, mcps: list[str]) -> list[BaseTool]: + return [] def get_output_converter( self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str @@ -31,5 +36,5 @@ def test_key(): goal="test goal", backstory="test backstory", ) - hash = hashlib.md5("test role|test goal|test backstory".encode()).hexdigest() + hash = hashlib.md5("test role|test goal|test backstory".encode(), usedforsecurity=False).hexdigest() assert agent.key == hash diff --git a/tests/agents/test_agent.py b/lib/crewai/tests/agents/test_agent.py similarity index 93% rename from tests/agents/test_agent.py rename to lib/crewai/tests/agents/test_agent.py index f17d4d161..1e0f57582 100644 --- a/tests/agents/test_agent.py +++ b/lib/crewai/tests/agents/test_agent.py @@ -1,13 +1,10 @@ """Test Agent creation and execution basic functionality.""" import os +import threading from unittest import mock from unittest.mock import MagicMock, patch -import pytest - -from crewai import Agent, Crew, Task -from crewai.agents.cache import CacheHandler from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor from crewai.events.event_bus import crewai_event_bus from crewai.events.types.tool_usage_events import ToolUsageFinishedEvent @@ -16,12 +13,17 @@ from crewai.knowledge.knowledge_config import KnowledgeConfig from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource from crewai.llm import LLM +from crewai.llms.base_llm import BaseLLM from crewai.process import Process -from crewai.tools import tool from crewai.tools.tool_calling import InstructorToolCalling from crewai.tools.tool_usage import ToolUsage -from crewai.utilities import RPMController from crewai.utilities.errors import AgentRepositoryError +import pytest + +from crewai import Agent, Crew, Task +from crewai.agents.cache import CacheHandler +from crewai.tools import tool +from crewai.utilities import RPMController def test_agent_llm_creation_with_env_vars(): @@ -39,7 +41,7 @@ def test_agent_llm_creation_with_env_vars(): agent = Agent(role="test role", goal="test goal", backstory="test backstory") # Check if LLM is created correctly - assert isinstance(agent.llm, LLM) + assert isinstance(agent.llm, BaseLLM) assert agent.llm.model == "gpt-4-turbo" assert agent.llm.api_key == "test_api_key" assert agent.llm.base_url == "https://test-api-base.com" @@ -49,11 +51,18 @@ def test_agent_llm_creation_with_env_vars(): del os.environ["OPENAI_API_BASE"] del os.environ["OPENAI_MODEL_NAME"] + if original_api_key: + os.environ["OPENAI_API_KEY"] = original_api_key + if original_api_base: + os.environ["OPENAI_API_BASE"] = original_api_base + if original_model_name: + os.environ["OPENAI_MODEL_NAME"] = original_model_name + # Create an agent without specifying LLM agent = Agent(role="test role", goal="test goal", backstory="test backstory") # Check if LLM is created correctly - assert isinstance(agent.llm, LLM) + assert isinstance(agent.llm, BaseLLM) assert agent.llm.model != "gpt-4-turbo" assert agent.llm.api_key != "test_api_key" assert agent.llm.base_url != "https://test-api-base.com" @@ -177,14 +186,17 @@ def test_agent_execution_with_tools(): expected_output="The result of the multiplication.", ) received_events = [] + event_received = threading.Event() @crewai_event_bus.on(ToolUsageFinishedEvent) def handle_tool_end(source, event): received_events.append(event) + event_received.set() output = agent.execute_task(task) assert output == "The result of the multiplication is 12." + assert event_received.wait(timeout=5), "Timeout waiting for tool usage event" assert len(received_events) == 1 assert isinstance(received_events[0], ToolUsageFinishedEvent) assert received_events[0].tool_name == "multiplier" @@ -276,10 +288,12 @@ def test_cache_hitting(): 'multiplier-{"first_number": 12, "second_number": 3}': 36, } received_events = [] + event_received = threading.Event() @crewai_event_bus.on(ToolUsageFinishedEvent) def handle_tool_end(source, event): received_events.append(event) + event_received.set() with ( patch.object(CacheHandler, "read") as read, @@ -295,6 +309,7 @@ def test_cache_hitting(): read.assert_called_with( tool="multiplier", input='{"first_number": 2, "second_number": 6}' ) + assert event_received.wait(timeout=5), "Timeout waiting for tool usage event" assert len(received_events) == 1 assert isinstance(received_events[0], ToolUsageFinishedEvent) assert received_events[0].from_cache @@ -455,18 +470,30 @@ def test_agent_custom_max_iterations(): allow_delegation=False, ) - with patch.object( - LLM, "call", wraps=LLM("gpt-4o", stop=["\nObservation:"]).call - ) as private_mock: - task = Task( - description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.", - expected_output="The final answer", - ) - agent.execute_task( - task=task, - tools=[get_final_answer], - ) - assert private_mock.call_count == 3 + original_call = agent.llm.call + call_count = 0 + + def counting_call(*args, **kwargs): + nonlocal call_count + call_count += 1 + return original_call(*args, **kwargs) + + agent.llm.call = counting_call + + task = Task( + description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.", + expected_output="The final answer", + ) + result = agent.execute_task( + task=task, + tools=[get_final_answer], + ) + + assert result is not None + assert isinstance(result, str) + assert len(result) > 0 + assert call_count > 0 + assert call_count == 3 @pytest.mark.vcr(filter_headers=["authorization"]) @@ -887,9 +914,8 @@ def test_agent_function_calling_llm(): crew = Crew(agents=[agent1], tasks=tasks) from unittest.mock import patch - import instructor - from crewai.tools.tool_usage import ToolUsage + import instructor with ( patch.object( @@ -1412,7 +1438,7 @@ def test_agent_with_llm(): llm=LLM(model="gpt-3.5-turbo", temperature=0.7), ) - assert isinstance(agent.llm, LLM) + assert isinstance(agent.llm, BaseLLM) assert agent.llm.model == "gpt-3.5-turbo" assert agent.llm.temperature == 0.7 @@ -1426,7 +1452,7 @@ def test_agent_with_custom_stop_words(): llm=LLM(model="gpt-3.5-turbo", stop=stop_words), ) - assert isinstance(agent.llm, LLM) + assert isinstance(agent.llm, BaseLLM) assert set(agent.llm.stop) == set([*stop_words, "\nObservation:"]) assert all(word in agent.llm.stop for word in stop_words) assert "\nObservation:" in agent.llm.stop @@ -1440,10 +1466,12 @@ def test_agent_with_callbacks(): role="test role", goal="test goal", backstory="test backstory", - llm=LLM(model="gpt-3.5-turbo", callbacks=[dummy_callback]), + llm=LLM(model="gpt-3.5-turbo", callbacks=[dummy_callback], is_litellm=True), ) - assert isinstance(agent.llm, LLM) + assert isinstance(agent.llm, BaseLLM) + # All LLM implementations now support callbacks consistently + assert hasattr(agent.llm, "callbacks") assert len(agent.llm.callbacks) == 1 assert agent.llm.callbacks[0] == dummy_callback @@ -1462,7 +1490,7 @@ def test_agent_with_additional_kwargs(): ), ) - assert isinstance(agent.llm, LLM) + assert isinstance(agent.llm, BaseLLM) assert agent.llm.model == "gpt-3.5-turbo" assert agent.llm.temperature == 0.8 assert agent.llm.top_p == 0.9 @@ -1579,40 +1607,40 @@ def test_agent_with_all_llm_attributes(): timeout=10, temperature=0.7, top_p=0.9, - n=1, + # n=1, stop=["STOP", "END"], max_tokens=100, presence_penalty=0.1, frequency_penalty=0.1, - logit_bias={50256: -100}, # Example: bias against the EOT token + # logit_bias={50256: -100}, # Example: bias against the EOT token response_format={"type": "json_object"}, seed=42, logprobs=True, top_logprobs=5, base_url="https://api.openai.com/v1", - api_version="2023-05-15", + # api_version="2023-05-15", api_key="sk-your-api-key-here", ), ) - assert isinstance(agent.llm, LLM) + assert isinstance(agent.llm, BaseLLM) assert agent.llm.model == "gpt-3.5-turbo" assert agent.llm.timeout == 10 assert agent.llm.temperature == 0.7 assert agent.llm.top_p == 0.9 - assert agent.llm.n == 1 + # assert agent.llm.n == 1 assert set(agent.llm.stop) == set(["STOP", "END", "\nObservation:"]) assert all(word in agent.llm.stop for word in ["STOP", "END", "\nObservation:"]) assert agent.llm.max_tokens == 100 assert agent.llm.presence_penalty == 0.1 assert agent.llm.frequency_penalty == 0.1 - assert agent.llm.logit_bias == {50256: -100} + # assert agent.llm.logit_bias == {50256: -100} assert agent.llm.response_format == {"type": "json_object"} assert agent.llm.seed == 42 assert agent.llm.logprobs assert agent.llm.top_logprobs == 5 assert agent.llm.base_url == "https://api.openai.com/v1" - assert agent.llm.api_version == "2023-05-15" + # assert agent.llm.api_version == "2023-05-15" assert agent.llm.api_key == "sk-your-api-key-here" @@ -1981,7 +2009,7 @@ def test_agent_with_knowledge_sources_works_with_copy(): assert len(agent_copy.knowledge_sources) == 1 assert isinstance(agent_copy.knowledge_sources[0], StringKnowledgeSource) assert agent_copy.knowledge_sources[0].content == content - assert isinstance(agent_copy.llm, LLM) + assert isinstance(agent_copy.llm, BaseLLM) @pytest.mark.vcr(filter_headers=["authorization"]) @@ -2129,7 +2157,7 @@ def test_litellm_auth_error_handling(): role="test role", goal="test goal", backstory="test backstory", - llm=LLM(model="gpt-4"), + llm=LLM(model="gpt-4", is_litellm=True), max_retry_limit=0, # Disable retries for authentication errors ) @@ -2156,16 +2184,15 @@ def test_litellm_auth_error_handling(): def test_crew_agent_executor_litellm_auth_error(): """Test that CrewAgentExecutor handles LiteLLM authentication errors by raising them.""" - from litellm.exceptions import AuthenticationError - from crewai.agents.tools_handler import ToolsHandler + from litellm.exceptions import AuthenticationError # Create an agent and executor agent = Agent( role="test role", goal="test goal", backstory="test backstory", - llm=LLM(model="gpt-4", api_key="invalid_api_key"), + llm=LLM(model="gpt-4", api_key="invalid_api_key", is_litellm=True), ) task = Task( description="Test task", @@ -2223,7 +2250,7 @@ def test_litellm_anthropic_error_handling(): role="test role", goal="test goal", backstory="test backstory", - llm=LLM(model="claude-3.5-sonnet-20240620"), + llm=LLM(model="claude-3.5-sonnet-20240620", is_litellm=True), max_retry_limit=0, ) @@ -2333,7 +2360,6 @@ def mock_get_auth_token(): @patch("crewai.cli.plus_api.PlusAPI.get_agent") def test_agent_from_repository(mock_get_agent, mock_get_auth_token): from crewai_tools import ( - EnterpriseActionTool, FileReadTool, SerperDevTool, ) @@ -2355,40 +2381,22 @@ def test_agent_from_repository(mock_get_agent, mock_get_auth_token): "name": "FileReadTool", "init_params": {"file_path": "test.txt"}, }, - # using a tools that returns a list of BaseTools - { - "module": "crewai_tools", - "name": "CrewaiEnterpriseTools", - "init_params": {"actions_list": [], "enterprise_token": "test_key"}, - }, ], } mock_get_agent.return_value = mock_get_response - tool_action = EnterpriseActionTool( - name="test_name", - description="test_description", - enterprise_action_token="test_token", # noqa: S106 - action_name="test_action_name", - action_schema={"test": "test"}, - ) - - with patch("crewai_tools.CrewaiEnterpriseTools", return_value=[tool_action]): - agent = Agent(from_repository="test_agent") + agent = Agent(from_repository="test_agent") assert agent.role == "test role" assert agent.goal == "test goal" assert agent.backstory == "test backstory" - assert len(agent.tools) == 3 + assert len(agent.tools) == 2 assert isinstance(agent.tools[0], SerperDevTool) assert agent.tools[0].n_results == 30 assert isinstance(agent.tools[1], FileReadTool) assert agent.tools[1].file_path == "test.txt" - assert isinstance(agent.tools[2], EnterpriseActionTool) - assert agent.tools[2].name == "test_name" - @patch("crewai.cli.plus_api.PlusAPI.get_agent") def test_agent_from_repository_override_attributes(mock_get_agent, mock_get_auth_token): @@ -2522,3 +2530,132 @@ def test_agent_from_repository_without_org_set( "No organization currently set. We recommend setting one before using: `crewai org switch ` command.", style="yellow", ) + +def test_agent_apps_consolidated_functionality(): + agent = Agent( + role="Platform Agent", + goal="Use platform tools", + backstory="Platform specialist", + apps=["gmail/create_task", "slack/update_status", "hubspot"] + ) + expected = {"gmail/create_task", "slack/update_status", "hubspot"} + assert set(agent.apps) == expected + + agent_apps_only = Agent( + role="App Agent", + goal="Use apps", + backstory="App specialist", + apps=["gmail", "slack"] + ) + assert set(agent_apps_only.apps) == {"gmail", "slack"} + + agent_default = Agent( + role="Regular Agent", + goal="Regular tasks", + backstory="Regular agent" + ) + assert agent_default.apps is None + + +def test_agent_apps_validation(): + agent = Agent( + role="Custom Agent", + goal="Test validation", + backstory="Test agent", + apps=["custom_app", "another_app/action"] + ) + assert set(agent.apps) == {"custom_app", "another_app/action"} + + with pytest.raises(ValueError, match=r"Invalid app format.*Apps can only have one '/' for app/action format"): + Agent( + role="Invalid Agent", + goal="Test validation", + backstory="Test agent", + apps=["app/action/invalid"] + ) + + +@patch.object(Agent, 'get_platform_tools') +def test_app_actions_propagated_to_platform_tools(mock_get_platform_tools): + from crewai.tools import tool + + @tool + def action_tool() -> str: + """Mock action platform tool.""" + return "action tool result" + + mock_get_platform_tools.return_value = [action_tool] + + agent = Agent( + role="Action Agent", + goal="Execute actions", + backstory="Action specialist", + apps=["gmail/send_email", "slack/update_status"] + ) + + task = Task( + description="Test task", + expected_output="Test output", + agent=agent + ) + + crew = Crew(agents=[agent], tasks=[task]) + tools = crew._prepare_tools(agent, task, []) + + mock_get_platform_tools.assert_called_once() + call_args = mock_get_platform_tools.call_args[1] + assert set(call_args["apps"]) == {"gmail/send_email", "slack/update_status"} + assert len(tools) >= 1 + + +@patch.object(Agent, 'get_platform_tools') +def test_mixed_apps_and_actions_propagated(mock_get_platform_tools): + from crewai.tools import tool + + @tool + def combined_tool() -> str: + """Mock combined platform tool.""" + return "combined tool result" + + mock_get_platform_tools.return_value = [combined_tool] + + agent = Agent( + role="Combined Agent", + goal="Use apps and actions", + backstory="Platform specialist", + apps=["gmail", "slack", "gmail/create_task", "slack/update_status"] + ) + + task = Task( + description="Test task", + expected_output="Test output", + agent=agent + ) + + crew = Crew(agents=[agent], tasks=[task]) + tools = crew._prepare_tools(agent, task, []) + + mock_get_platform_tools.assert_called_once() + call_args = mock_get_platform_tools.call_args[1] + expected_apps = {"gmail", "slack", "gmail/create_task", "slack/update_status"} + assert set(call_args["apps"]) == expected_apps + assert len(tools) >= 1 + +def test_agent_without_apps_no_platform_tools(): + """Test that agents without apps don't trigger platform tools integration.""" + agent = Agent( + role="Regular Agent", + goal="Regular tasks", + backstory="Regular agent" + ) + + task = Task( + description="Test task", + expected_output="Test output", + agent=agent + ) + + crew = Crew(agents=[agent], tasks=[task]) + + tools = crew._prepare_tools(agent, task, []) + assert tools == [] diff --git a/tests/agents/test_agent_inject_date.py b/lib/crewai/tests/agents/test_agent_inject_date.py similarity index 100% rename from tests/agents/test_agent_inject_date.py rename to lib/crewai/tests/agents/test_agent_inject_date.py diff --git a/tests/agents/test_agent_reasoning.py b/lib/crewai/tests/agents/test_agent_reasoning.py similarity index 100% rename from tests/agents/test_agent_reasoning.py rename to lib/crewai/tests/agents/test_agent_reasoning.py diff --git a/tests/agents/test_crew_agent_parser.py b/lib/crewai/tests/agents/test_crew_agent_parser.py similarity index 99% rename from tests/agents/test_crew_agent_parser.py rename to lib/crewai/tests/agents/test_crew_agent_parser.py index 72e44487c..f3076a036 100644 --- a/tests/agents/test_crew_agent_parser.py +++ b/lib/crewai/tests/agents/test_crew_agent_parser.py @@ -1,5 +1,4 @@ import pytest - from crewai.agents import parser from crewai.agents.parser import ( AgentAction, diff --git a/tests/agents/test_lite_agent.py b/lib/crewai/tests/agents/test_lite_agent.py similarity index 73% rename from tests/agents/test_lite_agent.py rename to lib/crewai/tests/agents/test_lite_agent.py index 0fa06c0ff..57e141cf9 100644 --- a/tests/agents/test_lite_agent.py +++ b/lib/crewai/tests/agents/test_lite_agent.py @@ -1,18 +1,20 @@ # mypy: ignore-errors +import threading from collections import defaultdict from typing import cast from unittest.mock import Mock, patch -import pytest -from pydantic import BaseModel, Field - -from crewai import LLM, Agent from crewai.events.event_bus import crewai_event_bus from crewai.events.types.agent_events import LiteAgentExecutionStartedEvent from crewai.events.types.tool_usage_events import ToolUsageStartedEvent -from crewai.flow import Flow, start -from crewai.lite_agent import LiteAgent, LiteAgentOutput +from crewai.lite_agent import LiteAgent +from crewai.lite_agent_output import LiteAgentOutput from crewai.llms.base_llm import BaseLLM +from pydantic import BaseModel, Field +import pytest + +from crewai import LLM, Agent +from crewai.flow import Flow, start from crewai.tools import BaseTool @@ -156,14 +158,17 @@ def test_lite_agent_with_tools(): ) received_events = [] + event_received = threading.Event() @crewai_event_bus.on(ToolUsageStartedEvent) def event_handler(source, event): received_events.append(event) + event_received.set() agent.kickoff("What are the effects of climate change on coral reefs?") # Verify tool usage events were emitted + assert event_received.wait(timeout=5), "Timeout waiting for tool usage events" assert len(received_events) > 0, "Tool usage events should be emitted" event = received_events[0] assert isinstance(event, ToolUsageStartedEvent) @@ -198,10 +203,6 @@ def test_lite_agent_structured_output(): response_format=SimpleOutput, ) - print(f"\n=== Agent Result Type: {type(result)}") - print(f"=== Agent Result: {result}") - print(f"=== Pydantic: {result.pydantic}") - assert result.pydantic is not None, "Should return a Pydantic model" output = cast(SimpleOutput, result.pydantic) @@ -296,6 +297,17 @@ def test_sets_parent_flow_when_inside_flow(): mock_llm.call.return_value = "Test response" mock_llm.stop = [] + from crewai.types.usage_metrics import UsageMetrics + + mock_usage_metrics = UsageMetrics( + total_tokens=100, + prompt_tokens=50, + completion_tokens=50, + cached_prompt_tokens=0, + successful_requests=1, + ) + mock_llm.get_token_usage_summary.return_value = mock_usage_metrics + class MyFlow(Flow): @start() def start(self): @@ -309,15 +321,18 @@ def test_sets_parent_flow_when_inside_flow(): return agent.kickoff("Test query") flow = MyFlow() - with crewai_event_bus.scoped_handlers(): + event_received = threading.Event() - @crewai_event_bus.on(LiteAgentExecutionStartedEvent) - def capture_agent(source, event): - nonlocal captured_agent - captured_agent = source + @crewai_event_bus.on(LiteAgentExecutionStartedEvent) + def capture_agent(source, event): + nonlocal captured_agent + captured_agent = source + event_received.set() - flow.kickoff() - assert captured_agent.parent_flow is flow + flow.kickoff() + + assert event_received.wait(timeout=5), "Timeout waiting for agent execution event" + assert captured_agent.parent_flow is flow @pytest.mark.vcr(filter_headers=["authorization"]) @@ -335,30 +350,43 @@ def test_guardrail_is_called_using_string(): guardrail="""Only include Brazilian players, both women and men""", ) - with crewai_event_bus.scoped_handlers(): + all_events_received = threading.Event() - @crewai_event_bus.on(LLMGuardrailStartedEvent) - def capture_guardrail_started(source, event): - assert isinstance(source, LiteAgent) - assert source.original_agent == agent - guardrail_events["started"].append(event) + @crewai_event_bus.on(LLMGuardrailStartedEvent) + def capture_guardrail_started(source, event): + assert isinstance(source, LiteAgent) + assert source.original_agent == agent + guardrail_events["started"].append(event) + if ( + len(guardrail_events["started"]) == 2 + and len(guardrail_events["completed"]) == 2 + ): + all_events_received.set() - @crewai_event_bus.on(LLMGuardrailCompletedEvent) - def capture_guardrail_completed(source, event): - assert isinstance(source, LiteAgent) - assert source.original_agent == agent - guardrail_events["completed"].append(event) + @crewai_event_bus.on(LLMGuardrailCompletedEvent) + def capture_guardrail_completed(source, event): + assert isinstance(source, LiteAgent) + assert source.original_agent == agent + guardrail_events["completed"].append(event) + if ( + len(guardrail_events["started"]) == 2 + and len(guardrail_events["completed"]) == 2 + ): + all_events_received.set() - result = agent.kickoff(messages="Top 10 best players in the world?") + result = agent.kickoff(messages="Top 10 best players in the world?") - assert len(guardrail_events["started"]) == 2 - assert len(guardrail_events["completed"]) == 2 - assert not guardrail_events["completed"][0].success - assert guardrail_events["completed"][1].success - assert ( - "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players" - in result.raw - ) + assert all_events_received.wait(timeout=10), ( + "Timeout waiting for all guardrail events" + ) + assert len(guardrail_events["started"]) == 2 + assert len(guardrail_events["completed"]) == 2 + assert not guardrail_events["completed"][0].success + assert guardrail_events["completed"][1].success + assert ( + "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players" + in result.raw + ) @pytest.mark.vcr(filter_headers=["authorization"]) @@ -369,29 +397,42 @@ def test_guardrail_is_called_using_callable(): LLMGuardrailStartedEvent, ) - with crewai_event_bus.scoped_handlers(): + all_events_received = threading.Event() - @crewai_event_bus.on(LLMGuardrailStartedEvent) - def capture_guardrail_started(source, event): - guardrail_events["started"].append(event) + @crewai_event_bus.on(LLMGuardrailStartedEvent) + def capture_guardrail_started(source, event): + guardrail_events["started"].append(event) + if ( + len(guardrail_events["started"]) == 1 + and len(guardrail_events["completed"]) == 1 + ): + all_events_received.set() - @crewai_event_bus.on(LLMGuardrailCompletedEvent) - def capture_guardrail_completed(source, event): - guardrail_events["completed"].append(event) + @crewai_event_bus.on(LLMGuardrailCompletedEvent) + def capture_guardrail_completed(source, event): + guardrail_events["completed"].append(event) + if ( + len(guardrail_events["started"]) == 1 + and len(guardrail_events["completed"]) == 1 + ): + all_events_received.set() - agent = Agent( - role="Sports Analyst", - goal="Gather information about the best soccer players", - backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""", - guardrail=lambda output: (True, "Pelé - Santos, 1958"), - ) + agent = Agent( + role="Sports Analyst", + goal="Gather information about the best soccer players", + backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""", + guardrail=lambda output: (True, "Pelé - Santos, 1958"), + ) - result = agent.kickoff(messages="Top 1 best players in the world?") + result = agent.kickoff(messages="Top 1 best players in the world?") - assert len(guardrail_events["started"]) == 1 - assert len(guardrail_events["completed"]) == 1 - assert guardrail_events["completed"][0].success - assert "Pelé - Santos, 1958" in result.raw + assert all_events_received.wait(timeout=10), ( + "Timeout waiting for all guardrail events" + ) + assert len(guardrail_events["started"]) == 1 + assert len(guardrail_events["completed"]) == 1 + assert guardrail_events["completed"][0].success + assert "Pelé - Santos, 1958" in result.raw @pytest.mark.vcr(filter_headers=["authorization"]) @@ -402,37 +443,50 @@ def test_guardrail_reached_attempt_limit(): LLMGuardrailStartedEvent, ) - with crewai_event_bus.scoped_handlers(): + all_events_received = threading.Event() - @crewai_event_bus.on(LLMGuardrailStartedEvent) - def capture_guardrail_started(source, event): - guardrail_events["started"].append(event) - - @crewai_event_bus.on(LLMGuardrailCompletedEvent) - def capture_guardrail_completed(source, event): - guardrail_events["completed"].append(event) - - agent = Agent( - role="Sports Analyst", - goal="Gather information about the best soccer players", - backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""", - guardrail=lambda output: ( - False, - "You are not allowed to include Brazilian players", - ), - guardrail_max_retries=2, - ) - - with pytest.raises( - Exception, match="Agent's guardrail failed validation after 2 retries" + @crewai_event_bus.on(LLMGuardrailStartedEvent) + def capture_guardrail_started(source, event): + guardrail_events["started"].append(event) + if ( + len(guardrail_events["started"]) == 3 + and len(guardrail_events["completed"]) == 3 ): - agent.kickoff(messages="Top 10 best players in the world?") + all_events_received.set() - assert len(guardrail_events["started"]) == 3 # 2 retries + 1 initial call - assert len(guardrail_events["completed"]) == 3 # 2 retries + 1 initial call - assert not guardrail_events["completed"][0].success - assert not guardrail_events["completed"][1].success - assert not guardrail_events["completed"][2].success + @crewai_event_bus.on(LLMGuardrailCompletedEvent) + def capture_guardrail_completed(source, event): + guardrail_events["completed"].append(event) + if ( + len(guardrail_events["started"]) == 3 + and len(guardrail_events["completed"]) == 3 + ): + all_events_received.set() + + agent = Agent( + role="Sports Analyst", + goal="Gather information about the best soccer players", + backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""", + guardrail=lambda output: ( + False, + "You are not allowed to include Brazilian players", + ), + guardrail_max_retries=2, + ) + + with pytest.raises( + Exception, match="Agent's guardrail failed validation after 2 retries" + ): + agent.kickoff(messages="Top 10 best players in the world?") + + assert all_events_received.wait(timeout=10), ( + "Timeout waiting for all guardrail events" + ) + assert len(guardrail_events["started"]) == 3 # 2 retries + 1 initial call + assert len(guardrail_events["completed"]) == 3 # 2 retries + 1 initial call + assert not guardrail_events["completed"][0].success + assert not guardrail_events["completed"][1].success + assert not guardrail_events["completed"][2].success @pytest.mark.vcr(filter_headers=["authorization"]) diff --git a/tests/cassettes/TestAgentEvaluator.test_eval_lite_agent.yaml b/lib/crewai/tests/cassettes/TestAgentEvaluator.test_eval_lite_agent.yaml similarity index 100% rename from tests/cassettes/TestAgentEvaluator.test_eval_lite_agent.yaml rename to lib/crewai/tests/cassettes/TestAgentEvaluator.test_eval_lite_agent.yaml diff --git a/tests/cassettes/TestAgentEvaluator.test_eval_specific_agents_from_crew.yaml b/lib/crewai/tests/cassettes/TestAgentEvaluator.test_eval_specific_agents_from_crew.yaml similarity index 100% rename from tests/cassettes/TestAgentEvaluator.test_eval_specific_agents_from_crew.yaml rename to lib/crewai/tests/cassettes/TestAgentEvaluator.test_eval_specific_agents_from_crew.yaml diff --git a/tests/cassettes/TestAgentEvaluator.test_evaluate_current_iteration.yaml b/lib/crewai/tests/cassettes/TestAgentEvaluator.test_evaluate_current_iteration.yaml similarity index 85% rename from tests/cassettes/TestAgentEvaluator.test_evaluate_current_iteration.yaml rename to lib/crewai/tests/cassettes/TestAgentEvaluator.test_evaluate_current_iteration.yaml index 4cf79e839..a02b48327 100644 --- a/tests/cassettes/TestAgentEvaluator.test_evaluate_current_iteration.yaml +++ b/lib/crewai/tests/cassettes/TestAgentEvaluator.test_evaluate_current_iteration.yaml @@ -563,4 +563,439 @@ interactions: status: code: 200 message: OK +- request: + body: '{"trace_id": "609bada1-d49d-4a3b-803c-63fe91e1bee0", "execution_type": + "crew", "user_identifier": null, "execution_context": {"crew_fingerprint": null, + "crew_name": "crew", "flow_name": null, "crewai_version": "1.0.0a2", "privacy_level": + "standard"}, "execution_metadata": {"expected_duration_estimate": 300, "agent_count": + 0, "task_count": 0, "flow_method_count": 0, "execution_started_at": "2025-10-02T22:35:43.865866+00:00"}, + "ephemeral_trace_id": "609bada1-d49d-4a3b-803c-63fe91e1bee0"}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate, zstd + Connection: + - keep-alive + Content-Length: + - '490' + Content-Type: + - application/json + User-Agent: + - CrewAI-CLI/1.0.0a2 + X-Crewai-Version: + - 1.0.0a2 + method: POST + uri: https://app.crewai.com/crewai_plus/api/v1/tracing/ephemeral/batches + response: + body: + string: '{"id":"3eed9776-2457-48ba-830b-b848cd1a3216","ephemeral_trace_id":"609bada1-d49d-4a3b-803c-63fe91e1bee0","execution_type":"crew","crew_name":"crew","flow_name":null,"status":"running","duration_ms":null,"crewai_version":"1.0.0a2","total_events":0,"execution_context":{"crew_fingerprint":null,"crew_name":"crew","flow_name":null,"crewai_version":"1.0.0a2","privacy_level":"standard"},"created_at":"2025-10-02T22:35:44.008Z","updated_at":"2025-10-02T22:35:44.008Z","access_code":"TRACE-545be8e2a7","user_identifier":null}' + headers: + Connection: + - keep-alive + Content-Length: + - '519' + Content-Type: + - application/json; 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You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "9bc61f86-cf57-4971-aa11-5344a7015e23", - "timestamp": "2025-10-08T18:16:44.081687+00:00", "type": "llm_call_completed", - "event_data": {"timestamp": "2025-10-08T18:16:44.081634+00:00", "type": "llm_call_completed", + object at 0x1282fc920>"], "available_functions": null}}, {"event_id": "a65577fd-4beb-4943-990c-a49505a84fa1", + "timestamp": "2025-09-23T17:18:02.659699+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T17:18:02.659676+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": null, "task_id": null, "agent_id": null, "agent_role": null, "from_task": + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final - answer but don''t give it yet, just re-use this\ntool non-stop.\n\nIMPORTANT: + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, @@ -2098,18 +2087,18 @@ interactions: I now know the final answer\nFinal Answer: I must follow the predefined structure and utilize the get_final_answer tool to extract the necessary information.\n```", "call_type": "", "model": "gpt-4o-mini"}}, - {"event_id": "781c93ff-cf05-4a5e-81b8-0170f889ee5b", "timestamp": "2025-10-08T18:16:44.081795+00:00", - "type": "llm_call_started", "event_data": {"timestamp": "2025-10-08T18:16:44.081746+00:00", + {"event_id": "8fc34fc3-d887-4bd5-9a57-b884abe6c5ab", "timestamp": "2025-09-23T17:18:02.659758+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T17:18:02.659738+00:00", "type": "llm_call_started", "source_fingerprint": null, "source_type": null, - "fingerprint_metadata": null, "task_name": "Use the get_final_answer tool.", - "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", "agent_id": "163c48fa-466e-4470-b238-acbbfe263776", - "agent_role": "test role", "from_task": null, "from_agent": null, "model": "gpt-4o-mini", - "messages": [{"role": "system", "content": "You are test role. test backstory\nYour - personal goal is: test goal\nYou ONLY have access to the following tools, and - should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + "fingerprint_metadata": null, "task_id": "5bd360ad-7d39-418c-8ea5-c3fb1bc33b0b", + "task_name": "Use the get_final_answer tool.", "agent_id": null, "agent_role": + null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": + [{"role": "system", "content": "You are test role. test backstory\nYour personal + goal is: test goal\nYou ONLY have access to the following tools, and should + NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final answer but don''t give it yet, - just re-use this\ntool non-stop.\n\nIMPORTANT: Use the following format in your - response:\n\n```\nThought: you should always think about what to do\nAction: + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: @@ -2146,31 +2135,31 @@ interactions: it must be outcome described\n\n```\nNow it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": - [""], - "available_functions": null}}, {"event_id": "91dbc997-a5bb-4a55-91e2-31f601fff95a", - "timestamp": "2025-10-08T18:16:44.085678+00:00", "type": "llm_call_completed", - "event_data": {"timestamp": "2025-10-08T18:16:44.085617+00:00", "type": "llm_call_completed", + [""], + "available_functions": null}}, {"event_id": "3d96c88a-03b4-4c86-b109-e651e08d0ed2", + "timestamp": "2025-09-23T17:18:02.660558+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T17:18:02.660539+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": "Use the get_final_answer tool.", "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", - "agent_id": "163c48fa-466e-4470-b238-acbbfe263776", "agent_role": "test role", - "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": - "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY - have access to the following tools, and should NEVER make up tools that are - not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: - Get the final answer but don''t give it yet, just re-use this\ntool non-stop.\n\nIMPORTANT: - Use the following format in your response:\n\n```\nThought: you should always - think about what to do\nAction: the action to take, only one name of [get_final_answer], - just the name, exactly as it''s written.\nAction Input: the input to the action, - just a simple JSON object, enclosed in curly braces, using \" to wrap keys and - values.\nObservation: the result of the action\n```\n\nOnce all necessary information - is gathered, return the following format:\n\n```\nThought: I now know the final - answer\nFinal Answer: the final answer to the original input question\n```"}, - {"role": "user", "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis - is the expected criteria for your final answer: The final answer\nyou MUST return - the actual complete content as the final answer, not a summary.\n\nBegin! This - is VERY important to you, use the tools available and give your best Final Answer, - your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need - to determine what action to take next to retrieve the final answer. \nAction: + "task_id": "5bd360ad-7d39-418c-8ea5-c3fb1bc33b0b", "task_name": "Use the get_final_answer + tool.", "agent_id": null, "agent_role": null, "from_task": null, "from_agent": + null, "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need to + determine what action to take next to retrieve the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: I encountered an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. When responding, @@ -2197,17 +2186,17 @@ interactions: tools, and just return your absolute BEST Final answer."}], "response": "```\nThought: you should always think about what to do\nAction: get_final_answer\nAction Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, - {"event_id": "5472e69e-83de-41f7-9406-69823552ff2f", "timestamp": "2025-10-08T18:16:44.087638+00:00", - "type": "llm_call_started", "event_data": {"timestamp": "2025-10-08T18:16:44.087510+00:00", + {"event_id": "d74dd03c-79ca-4acc-9947-fdf6c91b28d6", "timestamp": "2025-09-23T17:18:02.661730+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T17:18:02.661631+00:00", "type": "llm_call_started", "source_fingerprint": null, "source_type": null, - "fingerprint_metadata": null, "task_name": null, "task_id": null, "agent_id": + "fingerprint_metadata": null, "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final answer but don''t give it yet, - just re-use this\ntool non-stop.\n\nIMPORTANT: Use the following format in your - response:\n\n```\nThought: you should always think about what to do\nAction: + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: @@ -2269,16 +2258,16 @@ interactions: it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "b72092ce-ceda-4f82-9393-a80f9dcf0c09", - "timestamp": "2025-10-08T18:16:44.090720+00:00", "type": "llm_call_completed", - "event_data": {"timestamp": "2025-10-08T18:16:44.090679+00:00", "type": "llm_call_completed", + object at 0x1282fc920>"], "available_functions": null}}, {"event_id": "42294a65-9862-48d1-8868-f15906d58250", + "timestamp": "2025-09-23T17:18:02.662796+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T17:18:02.662766+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": null, "task_id": null, "agent_id": null, "agent_role": null, "from_task": + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final - answer but don''t give it yet, just re-use this\ntool non-stop.\n\nIMPORTANT: + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, @@ -2343,18 +2332,18 @@ interactions: Final answer."}], "response": "```\nThought: I need to determine how to proceed in order to get the final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, - {"event_id": "a4709344-ef62-4836-ac7e-29e66fe56e9b", "timestamp": "2025-10-08T18:16:44.090828+00:00", - "type": "llm_call_started", "event_data": {"timestamp": "2025-10-08T18:16:44.090787+00:00", + {"event_id": "35598d62-c7eb-46e0-9abc-13e0a8de39a1", "timestamp": "2025-09-23T17:18:02.662867+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T17:18:02.662844+00:00", "type": "llm_call_started", "source_fingerprint": null, "source_type": null, - "fingerprint_metadata": null, "task_name": "Use the get_final_answer tool.", - "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", "agent_id": "163c48fa-466e-4470-b238-acbbfe263776", - "agent_role": "test role", "from_task": null, "from_agent": null, "model": "gpt-4o-mini", - "messages": [{"role": "system", "content": "You are test role. test backstory\nYour - personal goal is: test goal\nYou ONLY have access to the following tools, and - should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + "fingerprint_metadata": null, "task_id": "5bd360ad-7d39-418c-8ea5-c3fb1bc33b0b", + "task_name": "Use the get_final_answer tool.", "agent_id": null, "agent_role": + null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": + [{"role": "system", "content": "You are test role. test backstory\nYour personal + goal is: test goal\nYou ONLY have access to the following tools, and should + NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final answer but don''t give it yet, - just re-use this\ntool non-stop.\n\nIMPORTANT: Use the following format in your - response:\n\n```\nThought: you should always think about what to do\nAction: + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: @@ -2416,30 +2405,30 @@ interactions: it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "01a16383-a9d3-4257-8fe6-cf644c9bfcdb", - "timestamp": "2025-10-08T18:16:44.093691+00:00", "type": "llm_call_completed", - "event_data": {"timestamp": "2025-10-08T18:16:44.093657+00:00", "type": "llm_call_completed", + object at 0x1282fc920>"], "available_functions": null}}, {"event_id": "efa2e49b-14a9-4e81-962e-fa8ca322e58b", + "timestamp": "2025-09-23T17:18:02.663770+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T17:18:02.663752+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": "Use the get_final_answer tool.", "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", - "agent_id": "163c48fa-466e-4470-b238-acbbfe263776", "agent_role": "test role", - "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": - "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY - have access to the following tools, and should NEVER make up tools that are - not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: - Get the final answer but don''t give it yet, just re-use this\ntool non-stop.\n\nIMPORTANT: - Use the following format in your response:\n\n```\nThought: you should always - think about what to do\nAction: the action to take, only one name of [get_final_answer], - just the name, exactly as it''s written.\nAction Input: the input to the action, - just a simple JSON object, enclosed in curly braces, using \" to wrap keys and - values.\nObservation: the result of the action\n```\n\nOnce all necessary information - is gathered, return the following format:\n\n```\nThought: I now know the final - answer\nFinal Answer: the final answer to the original input question\n```"}, - {"role": "user", "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis - is the expected criteria for your final answer: The final answer\nyou MUST return - the actual complete content as the final answer, not a summary.\n\nBegin! This - is VERY important to you, use the tools available and give your best Final Answer, - your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need - to determine what action to take next to retrieve the final answer. \nAction: + "task_id": "5bd360ad-7d39-418c-8ea5-c3fb1bc33b0b", "task_name": "Use the get_final_answer + tool.", "agent_id": null, "agent_role": null, "from_task": null, "from_agent": + null, "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need to + determine what action to take next to retrieve the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: I encountered an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. When responding, @@ -2491,17 +2480,17 @@ interactions: Final answer."}], "response": "```\nThought: I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, {"event_id": - "9290fb66-22c5-49fe-bc4c-0edef3213baf", "timestamp": "2025-10-08T18:16:44.095930+00:00", - "type": "llm_call_started", "event_data": {"timestamp": "2025-10-08T18:16:44.095245+00:00", + "004536e5-868f-44c5-8cdd-f323ad188ca2", "timestamp": "2025-09-23T17:18:02.664931+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T17:18:02.664847+00:00", "type": "llm_call_started", "source_fingerprint": null, "source_type": null, - "fingerprint_metadata": null, "task_name": null, "task_id": null, "agent_id": + "fingerprint_metadata": null, "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final answer but don''t give it yet, - just re-use this\ntool non-stop.\n\nIMPORTANT: Use the following format in your - response:\n\n```\nThought: you should always think about what to do\nAction: + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: @@ -2587,17 +2576,17 @@ interactions: it must be outcome described\n\n```\nNow it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": - [""], - "available_functions": null}}, {"event_id": "e3c09cf8-7e54-4b7c-a941-48e40d11d482", - "timestamp": "2025-10-08T18:16:44.098722+00:00", "type": "llm_call_completed", - "event_data": {"timestamp": "2025-10-08T18:16:44.098680+00:00", "type": "llm_call_completed", + [""], + "available_functions": null}}, {"event_id": "e154d3f6-ab11-4fc7-bb23-998d3fd55d47", + "timestamp": "2025-09-23T17:18:02.666012+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T17:18:02.665992+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": null, "task_id": null, "agent_id": null, "agent_role": null, "from_task": + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final - answer but don''t give it yet, just re-use this\ntool non-stop.\n\nIMPORTANT: + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, @@ -2686,18 +2675,18 @@ interactions: tools, and just return your absolute BEST Final answer."}], "response": "```\nThought: I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, - {"event_id": "ec342da7-248c-4fb5-b7a9-c7fb90ec4e3a", "timestamp": "2025-10-08T18:16:44.098808+00:00", - "type": "llm_call_started", "event_data": {"timestamp": "2025-10-08T18:16:44.098774+00:00", + {"event_id": "e91fcc7a-a66e-46cd-9193-1c5e60e2bc62", "timestamp": "2025-09-23T17:18:02.666071+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T17:18:02.666052+00:00", "type": "llm_call_started", "source_fingerprint": null, "source_type": null, - "fingerprint_metadata": null, "task_name": "Use the get_final_answer tool.", - "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", "agent_id": "163c48fa-466e-4470-b238-acbbfe263776", - "agent_role": "test role", "from_task": null, "from_agent": null, "model": "gpt-4o-mini", - "messages": [{"role": "system", "content": "You are test role. test backstory\nYour - personal goal is: test goal\nYou ONLY have access to the following tools, and - should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + "fingerprint_metadata": null, "task_id": "5bd360ad-7d39-418c-8ea5-c3fb1bc33b0b", + "task_name": "Use the get_final_answer tool.", "agent_id": null, "agent_role": + null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": + [{"role": "system", "content": "You are test role. test backstory\nYour personal + goal is: test goal\nYou ONLY have access to the following tools, and should + NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final answer but don''t give it yet, - just re-use this\ntool non-stop.\n\nIMPORTANT: Use the following format in your - response:\n\n```\nThought: you should always think about what to do\nAction: + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: @@ -2783,31 +2772,31 @@ interactions: it must be outcome described\n\n```\nNow it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": - [""], - "available_functions": null}}, {"event_id": "fcfab733-e239-4781-81e8-bbee191c3cce", - "timestamp": "2025-10-08T18:16:44.101471+00:00", "type": "llm_call_completed", - "event_data": {"timestamp": "2025-10-08T18:16:44.101441+00:00", "type": "llm_call_completed", + [""], + "available_functions": null}}, {"event_id": "48ad2d38-fd9e-4ddf-99e6-3c06ae63947d", + "timestamp": "2025-09-23T17:18:02.667103+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T17:18:02.667085+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": "Use the get_final_answer tool.", "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", - "agent_id": "163c48fa-466e-4470-b238-acbbfe263776", "agent_role": "test role", - "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": - "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY - have access to the following tools, and should NEVER make up tools that are - not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: - Get the final answer but don''t give it yet, just re-use this\ntool non-stop.\n\nIMPORTANT: - Use the following format in your response:\n\n```\nThought: you should always - think about what to do\nAction: the action to take, only one name of [get_final_answer], - just the name, exactly as it''s written.\nAction Input: the input to the action, - just a simple JSON object, enclosed in curly braces, using \" to wrap keys and - values.\nObservation: the result of the action\n```\n\nOnce all necessary information - is gathered, return the following format:\n\n```\nThought: I now know the final - answer\nFinal Answer: the final answer to the original input question\n```"}, - {"role": "user", "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis - is the expected criteria for your final answer: The final answer\nyou MUST return - the actual complete content as the final answer, not a summary.\n\nBegin! This - is VERY important to you, use the tools available and give your best Final Answer, - your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need - to determine what action to take next to retrieve the final answer. \nAction: + "task_id": "5bd360ad-7d39-418c-8ea5-c3fb1bc33b0b", "task_name": "Use the get_final_answer + tool.", "agent_id": null, "agent_role": null, "from_task": null, "from_agent": + null, "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need to + determine what action to take next to retrieve the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: I encountered an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. When responding, @@ -2883,17 +2872,17 @@ interactions: tools, and just return your absolute BEST Final answer."}], "response": "```\nThought: I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, - {"event_id": "a3b55181-2cc1-4c0c-8ef6-dd91b7da3cea", "timestamp": "2025-10-08T18:16:44.103430+00:00", - "type": "llm_call_started", "event_data": {"timestamp": "2025-10-08T18:16:44.102860+00:00", + {"event_id": "fe9bd495-7a1c-4a8e-a4f6-3d3abc6b667c", "timestamp": "2025-09-23T17:18:02.668209+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T17:18:02.668124+00:00", "type": "llm_call_started", "source_fingerprint": null, "source_type": null, - "fingerprint_metadata": null, "task_name": null, "task_id": null, "agent_id": + "fingerprint_metadata": null, "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final answer but don''t give it yet, - just re-use this\ntool non-stop.\n\nIMPORTANT: Use the following format in your - response:\n\n```\nThought: you should always think about what to do\nAction: + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: @@ -3004,16 +2993,16 @@ interactions: it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "7ce3a4d9-329c-4e03-97ee-1e7ff09b9e8a", - "timestamp": "2025-10-08T18:16:44.106320+00:00", "type": "llm_call_completed", - "event_data": {"timestamp": "2025-10-08T18:16:44.106287+00:00", "type": "llm_call_completed", + object at 0x1282fc920>"], "available_functions": null}}, {"event_id": "5d45d0ef-df58-4953-8c9c-0c2c426581cb", + "timestamp": "2025-09-23T17:18:02.669377+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T17:18:02.669358+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": null, "task_id": null, "agent_id": null, "agent_role": null, "from_task": + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final - answer but don''t give it yet, just re-use this\ntool non-stop.\n\nIMPORTANT: + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, @@ -3126,19 +3115,1449 @@ interactions: previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "response": "```\nThought: I need to take action to get the final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, {"event_id": "6973d64a-9c0e-451d-aa6b-a460364d290e", - "timestamp": "2025-10-08T18:16:44.106404+00:00", "type": "llm_call_started", - "event_data": {"timestamp": "2025-10-08T18:16:44.106372+00:00", "type": "llm_call_started", + ''llm_call''>", "model": "gpt-4o-mini"}}, {"event_id": "aef7edef-469e-4787-8cc9-4e16b22b1196", + "timestamp": "2025-09-23T17:18:02.669434+00:00", "type": "llm_call_started", + "event_data": {"timestamp": "2025-09-23T17:18:02.669415+00:00", "type": "llm_call_started", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": "Use the get_final_answer tool.", "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", - "agent_id": "163c48fa-466e-4470-b238-acbbfe263776", "agent_role": "test role", - "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": - "system", "content": "You are test role. test backstory\nYour personal goal - is: test goal\nYou ONLY have access to the following tools, and should NEVER - make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + "task_id": "5bd360ad-7d39-418c-8ea5-c3fb1bc33b0b", "task_name": "Use the get_final_answer + tool.", "agent_id": null, "agent_role": null, "from_task": null, "from_agent": + null, "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You + are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have + access to the following tools, and should NEVER make up tools that are not listed + here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: + Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "user", "content": "\nCurrent Task: Use the + get_final_answer tool.\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "I need + to determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: you should + always think about what to do\nAction: get_final_answer\nAction Input: {}\nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + you should always think about what to do\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: I need to pursue the action to get the final answer.\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: I need to pursue the action to get the + final answer.\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered + an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: I need to pursue + the action to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I encountered an error: Error on parsing tool.\nMoving + on then. I MUST either use a tool (use one at time) OR give my best final answer + not both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. 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Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "I need + to determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}], "response": "```\nThought: you should always think about what + to do\nAction: get_final_answer\nAction Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, {"event_id": "3d474495-0192-418c-90cc-0260705ed7f2", + "timestamp": "2025-09-24T06:05:19.818171+00:00", "type": "llm_call_started", + "event_data": {"timestamp": "2025-09-24T06:05:19.818066+00:00", "type": "llm_call_started", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": + null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": "system", + "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "user", "content": "\nCurrent Task: Use the + get_final_answer tool.\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "I need + to determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: you should + always think about what to do\nAction: get_final_answer\nAction Input: {}\nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + you should always think about what to do\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": + [""], + "available_functions": null}}, {"event_id": "24aeddf4-d818-4c25-aac5-0c13bd8f7ccd", + "timestamp": "2025-09-24T06:05:19.819391+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T06:05:19.819362+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": + null, "from_agent": null, "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [get_final_answer], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple JSON object, enclosed in curly braces, using \" to wrap keys and + values.\nObservation: the result of the action\n```\n\nOnce all necessary information + is gathered, return the following format:\n\n```\nThought: I now know the final + answer\nFinal Answer: the final answer to the original input question\n```"}, + {"role": "user", "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis + is the expected criteria for your final answer: The final answer\nyou MUST return + the actual complete content as the final answer, not a summary.\n\nBegin! This + is VERY important to you, use the tools available and give your best Final Answer, + your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need + to determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: you should always think about what to do\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: you should always think about what to + do\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered an + error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}], "response": "```\nThought: I need to determine how to proceed + in order to get the final answer.\nAction: get_final_answer\nAction Input: {}", + "call_type": "", "model": "gpt-4o-mini"}}, + {"event_id": "a4d462c8-c1bc-4ce5-8ddd-876243c90ad4", "timestamp": "2025-09-24T06:05:19.819470+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T06:05:19.819443+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "d0148c4b-ca4a-4a88-a0b3-d17d14911dfa", + "task_name": "Use the get_final_answer tool.", "agent_id": "ec3d4ced-a392-4b1c-8941-cb7c7a2089da", + "agent_role": "test role", "from_task": null, "from_agent": null, "model": "gpt-4o-mini", + "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need to + determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: you should always think about what to do\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: you should always think about what to + do\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered an + error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "0c2c92a3-4dc3-4928-af66-fc2febe9b2af", + "timestamp": "2025-09-24T06:05:19.820544+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T06:05:19.820520+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "d0148c4b-ca4a-4a88-a0b3-d17d14911dfa", "task_name": "Use the get_final_answer + tool.", "agent_id": "ec3d4ced-a392-4b1c-8941-cb7c7a2089da", "agent_role": "test + role", "from_task": null, "from_agent": null, "messages": [{"role": "system", + "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "user", "content": "\nCurrent Task: Use the + get_final_answer tool.\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "I need + to determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: you should + always think about what to do\nAction: get_final_answer\nAction Input: {}\nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + you should always think about what to do\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}], "response": "```\nThought: + I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction + Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, + {"event_id": "60a8b8ca-790d-4ba2-a4b6-09bc5735b3e9", "timestamp": "2025-09-24T06:05:19.821928+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T06:05:19.821834+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": null, "task_name": null, "agent_id": + null, "agent_role": null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", + "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need to + determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: you should always think about what to do\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: you should always think about what to + do\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered an + error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: I need to pursue + the action to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I encountered an error: Error on parsing tool.\nMoving + on then. I MUST either use a tool (use one at time) OR give my best final answer + not both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": + [""], + "available_functions": null}}, {"event_id": "f434c181-36d3-4523-ba2f-ff9378a652b5", + "timestamp": "2025-09-24T06:05:19.823117+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T06:05:19.823096+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": + null, "from_agent": null, "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [get_final_answer], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple JSON object, enclosed in curly braces, using \" to wrap keys and + values.\nObservation: the result of the action\n```\n\nOnce all necessary information + is gathered, return the following format:\n\n```\nThought: I now know the final + answer\nFinal Answer: the final answer to the original input question\n```"}, + {"role": "user", "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis + is the expected criteria for your final answer: The final answer\nyou MUST return + the actual complete content as the final answer, not a summary.\n\nBegin! This + is VERY important to you, use the tools available and give your best Final Answer, + your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need + to determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: you should always think about what to do\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: you should always think about what to + do\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered an + error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: I need to pursue + the action to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I encountered an error: Error on parsing tool.\nMoving + on then. I MUST either use a tool (use one at time) OR give my best final answer + not both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}], "response": "```\nThought: + I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction + Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, + {"event_id": "5590a1eb-5172-4c4d-af69-9a237af47fef", "timestamp": "2025-09-24T06:05:19.823179+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T06:05:19.823160+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "d0148c4b-ca4a-4a88-a0b3-d17d14911dfa", + "task_name": "Use the get_final_answer tool.", "agent_id": "ec3d4ced-a392-4b1c-8941-cb7c7a2089da", + "agent_role": "test role", "from_task": null, "from_agent": null, "model": "gpt-4o-mini", + "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need to + determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: you should always think about what to do\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: you should always think about what to + do\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered an + error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: I need to pursue + the action to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I encountered an error: Error on parsing tool.\nMoving + on then. I MUST either use a tool (use one at time) OR give my best final answer + not both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": + [""], + "available_functions": null}}, {"event_id": "f51cfd44-c3c5-4d5d-8cfa-f2582fd3c5a5", + "timestamp": "2025-09-24T06:05:19.824198+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T06:05:19.824179+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "d0148c4b-ca4a-4a88-a0b3-d17d14911dfa", "task_name": "Use the get_final_answer + tool.", "agent_id": "ec3d4ced-a392-4b1c-8941-cb7c7a2089da", "agent_role": "test + role", "from_task": null, "from_agent": null, "messages": [{"role": "system", + "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "user", "content": "\nCurrent Task: Use the + get_final_answer tool.\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "I need + to determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: you should + always think about what to do\nAction: get_final_answer\nAction Input: {}\nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + you should always think about what to do\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: I need to pursue the action to get the final answer.\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: I need to pursue the action to get the + final answer.\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered + an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}], "response": "```\nThought: I need to pursue the action to + get the final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": + "", "model": "gpt-4o-mini"}}, {"event_id": + "615a347c-ad5c-420f-9d71-af45a7f901a6", "timestamp": "2025-09-24T06:05:19.825358+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T06:05:19.825262+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": null, "task_name": null, "agent_id": + null, "agent_role": null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", + "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: @@ -3249,17 +4668,16 @@ interactions: it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "79ebf739-db33-4fc4-ad3b-f3f1be07b3b6", - "timestamp": "2025-10-08T18:16:44.109198+00:00", "type": "llm_call_completed", - "event_data": {"timestamp": "2025-10-08T18:16:44.109164+00:00", "type": "llm_call_completed", + object at 0x1281e5b80>"], "available_functions": null}}, {"event_id": "be21a5e4-09af-43d5-9e33-9ab2e2e16eda", + "timestamp": "2025-09-24T06:05:19.826640+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T06:05:19.826614+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, - "task_name": "Use the get_final_answer tool.", "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", - "agent_id": "163c48fa-466e-4470-b238-acbbfe263776", "agent_role": "test role", - "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": - "You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY - have access to the following tools, and should NEVER make up tools that are - not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool Description: - Get the final answer but don''t give it yet, just re-use this\ntool non-stop.\n\nIMPORTANT: + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": + null, "from_agent": null, "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, @@ -3370,93 +4788,330 @@ interactions: and the most complete as possible, it must be outcome described\n\n```\nNow it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST - Final answer."}], "response": "```\nThought: I now know the final answer\nFinal - Answer: I am unable to provide a final answer due to a continuous error when - trying to retrieve it using the get_final_answer tool.\n```", "call_type": "", "model": "gpt-4o-mini"}}, {"event_id": "3001082e-221b-47a0-9c15-4ac1f31e3b98", - "timestamp": "2025-10-08T18:16:44.109826+00:00", "type": "agent_execution_completed", - "event_data": {"agent_role": "test role", "agent_goal": "test goal", "agent_backstory": - "test backstory"}}, {"event_id": "6f921a6b-e3c2-45d2-a266-3658800dda49", "timestamp": - "2025-10-08T18:16:44.111015+00:00", "type": "task_completed", "event_data": - {"task_description": "Use the get_final_answer tool.", "task_name": "Use the - get_final_answer tool.", "task_id": "3515212f-6267-4b3d-a775-2cc602a9e8c3", + Final answer."}], "response": "```\nThought: I need to take action to get the + final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, {"event_id": "19bafe34-4ab6-45c0-8d7d-f811124cf186", + "timestamp": "2025-09-24T06:05:19.826705+00:00", "type": "llm_call_started", + "event_data": {"timestamp": "2025-09-24T06:05:19.826687+00:00", "type": "llm_call_started", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "d0148c4b-ca4a-4a88-a0b3-d17d14911dfa", "task_name": "Use the get_final_answer + tool.", "agent_id": "ec3d4ced-a392-4b1c-8941-cb7c7a2089da", "agent_role": "test + role", "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": + [{"role": "system", "content": "You are test role. test backstory\nYour personal + goal is: test goal\nYou ONLY have access to the following tools, and should + NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use the get_final_answer tool.\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I need to + determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: you should always think about what to do\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: you should always think about what to + do\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered an + error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: I need to pursue + the action to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I encountered an error: Error on parsing tool.\nMoving + on then. I MUST either use a tool (use one at time) OR give my best final answer + not both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: I need to pursue the action to get the final answer.\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: I need to pursue the action to get the + final answer.\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered + an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "1fca7f22-fc79-4bfc-a035-7c6383a90d88", + "timestamp": "2025-09-24T06:05:19.827942+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T06:05:19.827922+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "d0148c4b-ca4a-4a88-a0b3-d17d14911dfa", "task_name": "Use the get_final_answer + tool.", "agent_id": "ec3d4ced-a392-4b1c-8941-cb7c7a2089da", "agent_role": "test + role", "from_task": null, "from_agent": null, "messages": [{"role": "system", + "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "user", "content": "\nCurrent Task: Use the + get_final_answer tool.\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I need to determine what action to take next to retrieve + the final answer. \nAction: get_final_answer \nAction Input: {} \nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "I need + to determine what action to take next to retrieve the final answer. \nAction: + get_final_answer \nAction Input: {} \nObservation: I encountered an error: + Error on parsing tool.\nMoving on then. I MUST either use a tool (use one at + time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```\nNow + it''s time you MUST give your absolute best final answer. You''ll ignore all + previous instructions, stop using any tools, and just return your absolute BEST + Final answer."}, {"role": "assistant", "content": "```\nThought: you should + always think about what to do\nAction: get_final_answer\nAction Input: {}\nObservation: + I encountered an error: Error on parsing tool.\nMoving on then. I MUST either + use a tool (use one at time) OR give my best final answer not both at the same + time. When responding, I must use the following format:\n\n```\nThought: you + should always think about what to do\nAction: the action to take, should be + one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + you should always think about what to do\nAction: get_final_answer\nAction Input: + {}\nObservation: I encountered an error: Error on parsing tool.\nMoving on then. + I MUST either use a tool (use one at time) OR give my best final answer not + both at the same time. When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```\nNow it''s time you MUST give your absolute + best final answer. You''ll ignore all previous instructions, stop using any + tools, and just return your absolute BEST Final answer."}, {"role": "assistant", + "content": "```\nThought: I need to pursue the action to get the final answer.\nAction: + get_final_answer\nAction Input: {}\nObservation: I encountered an error: Error + on parsing tool.\nMoving on then. I MUST either use a tool (use one at time) + OR give my best final answer not both at the same time. When responding, I must + use the following format:\n\n```\nThought: you should always think about what + to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. Once I know the final answer, I must return the following format:\n\n```\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described\n\n```"}, {"role": + "assistant", "content": "```\nThought: I need to pursue the action to get the + final answer.\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered + an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use + one at time) OR give my best final answer not both at the same time. When responding, + I must use the following format:\n\n```\nThought: you should always think about + what to do\nAction: the action to take, should be one of [get_final_answer]\nAction + Input: the input to the action, dictionary enclosed in curly braces\nObservation: + the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat + N times. 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When responding, I must use the following format:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, should + be one of [get_final_answer]\nAction Input: the input to the action, dictionary + enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action + Input/Result can repeat N times. Once I know the final answer, I must return + the following format:\n\n```\nThought: I now can give a great answer\nFinal + Answer: Your final answer must be the great and the most complete as possible, + it must be outcome described\n\n```"}, {"role": "assistant", "content": "```\nThought: + I need to pursue the action to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I encountered an error: Error on parsing tool.\nMoving + on then. I MUST either use a tool (use one at time) OR give my best final answer + not both at the same time. 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(if you are located outside of the + EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using + or distributing any portion or element of the Llama Materials,\\nyou agree to + be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to the + Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If + you distribute or make available the Llama Materials (or any derivative works + thereof), \\nor a product or service (including another AI model) that contains + any of them, you shall (A) provide\\na copy of this Agreement with any such + Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is distributed + or made available, you shall also include \u201CLlama\u201D\\nat the beginning + of any such AI model name.\\n\\n ii. If you receive Llama Materials, + or any derivative works thereof, from a Licensee as part\\nof an integrated + end user product, then Section 2 of this Agreement will not apply to you. \\n\\n + \ iii. You must retain in all copies of the Llama Materials that you distribute + the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed + as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 + Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n + \ iv. Your use of the Llama Materials must comply with applicable laws + and regulations\\n(including trade compliance laws and regulations) and adhere + to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), + which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. + Additional Commercial Terms. If, on the Llama 3.2 version release date, the + monthly active users\\nof the products or services made available by or for + Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly + active users in the preceding calendar month, you must request \\na license + from Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or until + Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. + UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS + THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF + ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND + IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, + MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR + DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS + AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY + OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR + ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, + TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, + \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, + EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED + OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n + \ a. No trademark licenses are granted under this Agreement, and in connection + with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark + owned by or associated with the other or any of its affiliates, \\nexcept as + required for reasonable and customary use in describing and redistributing the + Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants + you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s + brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. 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Term and Termination. + The term of this Agreement will commence upon your acceptance of this Agreement + or access\\nto the Llama Materials and will continue in full force and effect + until terminated in accordance with the terms\\nand conditions herein. Meta + may terminate this Agreement if you are in breach of any term or condition of + this\\nAgreement. Upon termination of this Agreement, you shall delete and cease + use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination + of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will + be governed and construed under the laws of the State of \\nCalifornia without + regard to choice of law principles, and the UN Convention on Contracts for the + International\\nSale of Goods does not apply to this Agreement. The courts of + California shall have exclusive jurisdiction of\\nany dispute arising out of + this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed + to promoting safe and fair use of its tools and features, including Llama 3.2. + If you access or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). + The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree + you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate + the law or others\u2019 rights, including to:\\n 1. Engage in, promote, generate, + contribute to, encourage, plan, incite, or further illegal or unlawful activity + or content, such as:\\n 1. Violence or terrorism\\n 2. Exploitation + or harm to children, including the solicitation, creation, acquisition, or dissemination + of child exploitative content or failure to report Child Sexual Abuse Material\\n + \ 3. Human trafficking, exploitation, and sexual violence\\n 4. + The illegal distribution of information or materials to minors, including obscene + materials, or failure to employ legally required age-gating in connection with + such information or materials.\\n 5. Sexual solicitation\\n 6. + Any other criminal activity\\n 1. Engage in, promote, incite, or facilitate + the harassment, abuse, threatening, or bullying of individuals or groups of + individuals\\n 2. Engage in, promote, incite, or facilitate discrimination + or other unlawful or harmful conduct in the provision of employment, employment + benefits, credit, housing, other economic benefits, or other essential goods + and services\\n 3. Engage in the unauthorized or unlicensed practice of any + profession including, but not limited to, financial, legal, medical/health, + or related professional practices\\n 4. Collect, process, disclose, generate, + or infer private or sensitive information about individuals, including information + about individuals\u2019 identity, health, or demographic information, unless + you have obtained the right to do so in accordance with applicable law\\n 5. + Engage in or facilitate any action or generate any content that infringes, misappropriates, + or otherwise violates any third-party rights, including the outputs or results + of any products or services using the Llama Materials\\n 6. Create, generate, + or facilitate the creation of malicious code, malware, computer viruses or do + anything else that could disable, overburden, interfere with or impair the proper + working, integrity, operation or appearance of a website or computer system\\n + \ 7. Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the + planning or development of activities that present a risk of death or bodily + harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic Arms + Regulations (ITAR) maintained by the United States Department of State or to + the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons + Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including + weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n + \ 11. Operation of critical infrastructure, transportation technologies, or + heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting, + and eating disorders\\n 13. Any content intended to incite or promote violence, + abuse, or any infliction of bodily harm to an individual\\n3. Intentionally + deceive or mislead others, including use of Llama 3.2 related to the following:\\n + \ 14. Generating, promoting, or furthering fraud or the creation or promotion + of disinformation\\n 15. Generating, promoting, or furthering defamatory + content, including the creation of defamatory statements, images, or other content\\n + \ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating + another individual without consent, authorization, or legal right\\n 18. + Representing that the use of Llama 3.2 or outputs are human-generated\\n 19. + Generating or facilitating false online engagement, including fake reviews and + other means of fake online engagement\\n4. Fail to appropriately disclose to + end users any known dangers of your AI system\\n5. Interact with third party + tools, models, or software designed to generate unlawful content or engage in + unlawful or harmful conduct and/or represent that the outputs of such tools, + models, or software are associated with Meta or Llama 3.2\\n\\nWith respect + to any multimodal models included in Llama 3.2, the rights granted under Section + 1(a) of the Llama 3.2 Community License Agreement are not being granted to you + if you are an individual domiciled in, or a company with a principal place of + business in, the European Union. This restriction does not apply to end users + of a product or service that incorporates any such multimodal models.\\n\\nPlease + report any violation of this Policy, software \u201Cbug,\u201D or other problems + that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama + show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# + FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE + \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- + if .Tools }}When you receive a tool call response, use the output to format + an answer to the orginal user question.\\n\\nYou are a helpful assistant with + tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, + $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond with + a JSON for a function call with its proper arguments that best answers the given + prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": + dictionary of argument name and its value}. 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(if you are located outside of the + EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using + or distributing any portion or element of the Llama Materials,\\nyou agree to + be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to the + Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If + you distribute or make available the Llama Materials (or any derivative works + thereof), \\nor a product or service (including another AI model) that contains + any of them, you shall (A) provide\\na copy of this Agreement with any such + Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is distributed + or made available, you shall also include \u201CLlama\u201D\\nat the beginning + of any such AI model name.\\n\\n ii. If you receive Llama Materials, + or any derivative works thereof, from a Licensee as part\\nof an integrated + end user product, then Section 2 of this Agreement will not apply to you. \\n\\n + \ iii. You must retain in all copies of the Llama Materials that you distribute + the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed + as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 + Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n + \ iv. Your use of the Llama Materials must comply with applicable laws + and regulations\\n(including trade compliance laws and regulations) and adhere + to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), + which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. + Additional Commercial Terms. If, on the Llama 3.2 version release date, the + monthly active users\\nof the products or services made available by or for + Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly + active users in the preceding calendar month, you must request \\na license + from Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or until + Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. + UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS + THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF + ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND + IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, + MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR + DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS + AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY + OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR + ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, + TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, + \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, + EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED + OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n + \ a. No trademark licenses are granted under this Agreement, and in connection + with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark + owned by or associated with the other or any of its affiliates, \\nexcept as + required for reasonable and customary use in describing and redistributing the + Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants + you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s + brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. 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Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the + planning or development of activities that present a risk of death or bodily + harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic Arms + Regulations (ITAR) maintained by the United States Department of State or to + the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons + Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including + weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n + \ 11. Operation of critical infrastructure, transportation technologies, or + heavy machinery\\n 12. 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This restriction does not apply to end users + of a product or service that incorporates any such multimodal models.\\n\\nPlease + report any violation of this Policy, software \u201Cbug,\u201D or other problems + that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop + \ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- + if .Tools }}When you receive a tool call response, use the output to format + an answer to the orginal user question.\\n\\nYou are a helpful assistant with + tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, + $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond with + a JSON for a function call with its proper arguments that best answers the given + prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": + dictionary of argument name and its value}. 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IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY + THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, + OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY + INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, + EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF + ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n a. No trademark + licenses are granted under this Agreement, and in connection with the Llama + Materials, \\nneither Meta nor Licensee may use any name or mark owned by + or associated with the other or any of its affiliates, \\nexcept as required + for reasonable and customary use in describing and redistributing the Llama + Materials or as \\nset forth in this Section 5(a). Meta hereby grants you + a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. 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The + most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You + agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. + Violate the law or others\u2019 rights, including to:\\n 1. Engage in, + promote, generate, contribute to, encourage, plan, incite, or further illegal + or unlawful activity or content, such as:\\n 1. Violence or terrorism\\n + \ 2. Exploitation or harm to children, including the solicitation, creation, + acquisition, or dissemination of child exploitative content or failure to + report Child Sexual Abuse Material\\n 3. Human trafficking, exploitation, + and sexual violence\\n 4. 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Any content intended to incite + or promote violence, abuse, or any infliction of bodily harm to an individual\\n3. + Intentionally deceive or mislead others, including use of Llama 3.2 related + to the following:\\n 14. Generating, promoting, or furthering fraud or + the creation or promotion of disinformation\\n 15. Generating, promoting, + or furthering defamatory content, including the creation of defamatory statements, + images, or other content\\n 16. Generating, promoting, or further distributing + spam\\n 17. Impersonating another individual without consent, authorization, + or legal right\\n 18. Representing that the use of Llama 3.2 or outputs + are human-generated\\n 19. Generating or facilitating false online engagement, + including fake reviews and other means of fake online engagement\\n4. Fail + to appropriately disclose to end users any known dangers of your AI system\\n5. + Interact with third party tools, models, or software designed to generate + unlawful content or engage in unlawful or harmful conduct and/or represent + that the outputs of such tools, models, or software are associated with Meta + or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama + 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License + Agreement are not being granted to you if you are an individual domiciled + in, or a company with a principal place of business in, the European Union. + This restriction does not apply to end users of a product or service that + incorporates any such multimodal models.\\n\\nPlease report any violation + of this Policy, software \u201Cbug,\u201D or other problems that could lead + to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama + show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# + FROM llama3.2:3b\\n\\nFROM /Users/greysonlalonde/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE + \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end + }}\\n{{- if .Tools }}When you receive a tool call response, use the output + to format an answer to the orginal user question.\\n\\nYou are a helpful assistant + with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range + $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond + with a JSON for a function call with its proper arguments that best answers + the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name, + \\\"parameters\\\": dictionary of argument name and its value}. 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(if you are located outside + of the EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below + or by using or distributing any portion or element of the Llama Materials,\\nyou + agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to + the Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. + If you distribute or make available the Llama Materials (or any derivative + works thereof), \\nor a product or service (including another AI model) that + contains any of them, you shall (A) provide\\na copy of this Agreement with + any such Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is + distributed or made available, you shall also include \u201CLlama\u201D\\nat + the beginning of any such AI model name.\\n\\n ii. If you receive Llama + Materials, or any derivative works thereof, from a Licensee as part\\nof an + integrated end user product, then Section 2 of this Agreement will not apply + to you. \\n\\n iii. You must retain in all copies of the Llama Materials + that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D + text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed + under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc. + All Rights Reserved.\u201D\\n\\n iv. Your use of the Llama Materials + must comply with applicable laws and regulations\\n(including trade compliance + laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama + Materials (available at https://www.llama.com/llama3_2/use-policy), which + is hereby \\nincorporated by reference into this Agreement.\\n \\n2. Additional + Commercial Terms. If, on the Llama 3.2 version release date, the monthly active + users\\nof the products or services made available by or for Licensee, or + Licensee\u2019s affiliates, \\nis greater than 700 million monthly active + users in the preceding calendar month, you must request \\na license from + Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or + until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer + of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY + OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, + WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY + KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF + TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. + YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING + OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\\nWITH + YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\\n\\n4. Limitation + of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY + THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, + OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY + INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, + EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF + ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n a. No trademark + licenses are granted under this Agreement, and in connection with the Llama + Materials, \\nneither Meta nor Licensee may use any name or mark owned by + or associated with the other or any of its affiliates, \\nexcept as required + for reasonable and customary use in describing and redistributing the Llama + Materials or as \\nset forth in this Section 5(a). Meta hereby grants you + a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with + Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between + you and Meta,\\n you are and will be the owner of such derivative works + and modifications.\\n\\n c. If you institute litigation or other proceedings + against Meta or any entity (including a cross-claim or\\n counterclaim + in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, + or any portion\\n of any of the foregoing, constitutes infringement of + intellectual property or other rights owned or licensable\\n by you, then + any licenses granted to you under this Agreement shall terminate as of the + date such litigation or\\n claim is filed or instituted. You will indemnify + and hold harmless Meta from and against any claim by any third\\n party + arising out of or related to your use or distribution of the Llama Materials.\\n\\n6. + Term and Termination. The term of this Agreement will commence upon your acceptance + of this Agreement or access\\nto the Llama Materials and will continue in + full force and effect until terminated in accordance with the terms\\nand + conditions herein. Meta may terminate this Agreement if you are in breach + of any term or condition of this\\nAgreement. Upon termination of this Agreement, + you shall delete and cease use of the Llama Materials. Sections 3,\\n4 and + 7 shall survive the termination of this Agreement. \\n\\n7. Governing Law + and Jurisdiction. This Agreement will be governed and construed under the + laws of the State of \\nCalifornia without regard to choice of law principles, + and the UN Convention on Contracts for the International\\nSale of Goods does + not apply to this Agreement. The courts of California shall have exclusive + jurisdiction of\\nany dispute arising out of this Agreement.\\\"\\nLICENSE + \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed to promoting + safe and fair use of its tools and features, including Llama 3.2. If you access + or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). + The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You + agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. + Violate the law or others\u2019 rights, including to:\\n 1. Engage in, + promote, generate, contribute to, encourage, plan, incite, or further illegal + or unlawful activity or content, such as:\\n 1. Violence or terrorism\\n + \ 2. Exploitation or harm to children, including the solicitation, creation, + acquisition, or dissemination of child exploitative content or failure to + report Child Sexual Abuse Material\\n 3. Human trafficking, exploitation, + and sexual violence\\n 4. The illegal distribution of information or + materials to minors, including obscene materials, or failure to employ legally + required age-gating in connection with such information or materials.\\n 5. + Sexual solicitation\\n 6. Any other criminal activity\\n 1. Engage + in, promote, incite, or facilitate the harassment, abuse, threatening, or + bullying of individuals or groups of individuals\\n 2. Engage in, promote, + incite, or facilitate discrimination or other unlawful or harmful conduct + in the provision of employment, employment benefits, credit, housing, other + economic benefits, or other essential goods and services\\n 3. Engage in + the unauthorized or unlicensed practice of any profession including, but not + limited to, financial, legal, medical/health, or related professional practices\\n + \ 4. Collect, process, disclose, generate, or infer private or sensitive + information about individuals, including information about individuals\u2019 + identity, health, or demographic information, unless you have obtained the + right to do so in accordance with applicable law\\n 5. Engage in or facilitate + any action or generate any content that infringes, misappropriates, or otherwise + violates any third-party rights, including the outputs or results of any products + or services using the Llama Materials\\n 6. Create, generate, or facilitate + the creation of malicious code, malware, computer viruses or do anything else + that could disable, overburden, interfere with or impair the proper working, + integrity, operation or appearance of a website or computer system\\n 7. + Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in + the planning or development of activities that present a risk of death or + bodily harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic + Arms Regulations (ITAR) maintained by the United States Department of State + or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical + Weapons Convention Implementation Act of 1997\\n 9. Guns and illegal weapons + (including weapon development)\\n 10. Illegal drugs and regulated/controlled + substances\\n 11. Operation of critical infrastructure, transportation + technologies, or heavy machinery\\n 12. Self-harm or harm to others, including + suicide, cutting, and eating disorders\\n 13. Any content intended to incite + or promote violence, abuse, or any infliction of bodily harm to an individual\\n3. + Intentionally deceive or mislead others, including use of Llama 3.2 related + to the following:\\n 14. Generating, promoting, or furthering fraud or + the creation or promotion of disinformation\\n 15. Generating, promoting, + or furthering defamatory content, including the creation of defamatory statements, + images, or other content\\n 16. Generating, promoting, or further distributing + spam\\n 17. Impersonating another individual without consent, authorization, + or legal right\\n 18. Representing that the use of Llama 3.2 or outputs + are human-generated\\n 19. Generating or facilitating false online engagement, + including fake reviews and other means of fake online engagement\\n4. Fail + to appropriately disclose to end users any known dangers of your AI system\\n5. + Interact with third party tools, models, or software designed to generate + unlawful content or engage in unlawful or harmful conduct and/or represent + that the outputs of such tools, models, or software are associated with Meta + or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama + 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License + Agreement are not being granted to you if you are an individual domiciled + in, or a company with a principal place of business in, the European Union. + This restriction does not apply to end users of a product or service that + incorporates any such multimodal models.\\n\\nPlease report any violation + of this Policy, software \u201Cbug,\u201D or other problems that could lead + to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop + \ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end + }}\\n{{- if .Tools }}When you receive a tool call response, use the output + to format an answer to the orginal user question.\\n\\nYou are a helpful assistant + with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range + $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond + with a JSON for a function call with its proper arguments that best answers + the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name, + \\\"parameters\\\": dictionary of argument name and its value}. Do not use + variables.\\n\\n{{ range $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- + else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last + }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- + if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name + }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else + }}\\n\\n{{ .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ + end }}\\n{{- else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ + .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- end }}\\n{{- end 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+ headers: + Content-Type: + - application/json; charset=utf-8 + Date: + - Mon, 20 Oct 2025 15:08:09 GMT + Transfer-Encoding: + - chunked + status: + code: 200 + message: OK +- request: + body: '{"name": "llama3.2:3b"}' + headers: + accept: + - '*/*' + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '23' + content-type: + - application/json + host: + - localhost:11434 + user-agent: + - litellm/1.77.5 + method: POST + uri: http://localhost:11434/api/show + response: + body: + string: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version + Release Date: September 25, 2024\\n\\n\u201CAgreement\u201D means the terms + and conditions for use, reproduction, distribution \\nand modification of + the Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means + the specifications, manuals and documentation accompanying Llama 3.2\\ndistributed + by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D + or \u201Cyou\u201D means you, or your employer or any other person or entity + (if you are \\nentering into this Agreement on such person or entity\u2019s + behalf), of the age required under\\napplicable laws, rules or regulations + to provide legal consent and that has legal authority\\nto bind your employer + or such other person or entity if you are entering in this Agreement\\non + their behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language + models and software and algorithms, including\\nmachine-learning model code, + trained model weights, inference-enabling code, training-enabling code,\\nfine-tuning + enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama + Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and + Documentation (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D + or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located + in or, \\nif you are an entity, your principal place of business is in the + EEA or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside + of the EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below + or by using or distributing any portion or element of the Llama Materials,\\nyou + agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to + the Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. + If you distribute or make available the Llama Materials (or any derivative + works thereof), \\nor a product or service (including another AI model) that + contains any of them, you shall (A) provide\\na copy of this Agreement with + any such Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is + distributed or made available, you shall also include \u201CLlama\u201D\\nat + the beginning of any such AI model name.\\n\\n ii. If you receive Llama + Materials, or any derivative works thereof, from a Licensee as part\\nof an + integrated end user product, then Section 2 of this Agreement will not apply + to you. \\n\\n iii. You must retain in all copies of the Llama Materials + that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D + text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed + under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc. + All Rights Reserved.\u201D\\n\\n iv. Your use of the Llama Materials + must comply with applicable laws and regulations\\n(including trade compliance + laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama + Materials (available at https://www.llama.com/llama3_2/use-policy), which + is hereby \\nincorporated by reference into this Agreement.\\n \\n2. Additional + Commercial Terms. If, on the Llama 3.2 version release date, the monthly active + users\\nof the products or services made available by or for Licensee, or + Licensee\u2019s affiliates, \\nis greater than 700 million monthly active + users in the preceding calendar month, you must request \\na license from + Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or + until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer + of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY + OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, + WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY + KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF + TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. + YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING + OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\\nWITH + YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\\n\\n4. Limitation + of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY + THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, + OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY + INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, + EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF + ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n a. No trademark + licenses are granted under this Agreement, and in connection with the Llama + Materials, \\nneither Meta nor Licensee may use any name or mark owned by + or associated with the other or any of its affiliates, \\nexcept as required + for reasonable and customary use in describing and redistributing the Llama + Materials or as \\nset forth in this Section 5(a). Meta hereby grants you + a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with + Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between + you and Meta,\\n you are and will be the owner of such derivative works + and modifications.\\n\\n c. If you institute litigation or other proceedings + against Meta or any entity (including a cross-claim or\\n counterclaim + in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, + or any portion\\n of any of the foregoing, constitutes infringement of + intellectual property or other rights owned or licensable\\n by you, then + any licenses granted to you under this Agreement shall terminate as of the + date such litigation or\\n claim is filed or instituted. You will indemnify + and hold harmless Meta from and against any claim by any third\\n party + arising out of or related to your use or distribution of the Llama Materials.\\n\\n6. + Term and Termination. The term of this Agreement will commence upon your acceptance + of this Agreement or access\\nto the Llama Materials and will continue in + full force and effect until terminated in accordance with the terms\\nand + conditions herein. Meta may terminate this Agreement if you are in breach + of any term or condition of this\\nAgreement. Upon termination of this Agreement, + you shall delete and cease use of the Llama Materials. Sections 3,\\n4 and + 7 shall survive the termination of this Agreement. \\n\\n7. Governing Law + and Jurisdiction. This Agreement will be governed and construed under the + laws of the State of \\nCalifornia without regard to choice of law principles, + and the UN Convention on Contracts for the International\\nSale of Goods does + not apply to this Agreement. The courts of California shall have exclusive + jurisdiction of\\nany dispute arising out of this Agreement.\\n**Llama 3.2** + **Acceptable Use Policy**\\n\\nMeta is committed to promoting safe and fair + use of its tools and features, including Llama 3.2. If you access or use Llama + 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). The + most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You + agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. + Violate the law or others\u2019 rights, including to:\\n 1. Engage in, + promote, generate, contribute to, encourage, plan, incite, or further illegal + or unlawful activity or content, such as:\\n 1. Violence or terrorism\\n + \ 2. Exploitation or harm to children, including the solicitation, creation, + acquisition, or dissemination of child exploitative content or failure to + report Child Sexual Abuse Material\\n 3. Human trafficking, exploitation, + and sexual violence\\n 4. The illegal distribution of information or + materials to minors, including obscene materials, or failure to employ legally + required age-gating in connection with such information or materials.\\n 5. + Sexual solicitation\\n 6. Any other criminal activity\\n 1. Engage + in, promote, incite, or facilitate the harassment, abuse, threatening, or + bullying of individuals or groups of individuals\\n 2. Engage in, promote, + incite, or facilitate discrimination or other unlawful or harmful conduct + in the provision of employment, employment benefits, credit, housing, other + economic benefits, or other essential goods and services\\n 3. Engage in + the unauthorized or unlicensed practice of any profession including, but not + limited to, financial, legal, medical/health, or related professional practices\\n + \ 4. Collect, process, disclose, generate, or infer private or sensitive + information about individuals, including information about individuals\u2019 + identity, health, or demographic information, unless you have obtained the + right to do so in accordance with applicable law\\n 5. Engage in or facilitate + any action or generate any content that infringes, misappropriates, or otherwise + violates any third-party rights, including the outputs or results of any products + or services using the Llama Materials\\n 6. Create, generate, or facilitate + the creation of malicious code, malware, computer viruses or do anything else + that could disable, overburden, interfere with or impair the proper working, + integrity, operation or appearance of a website or computer system\\n 7. + Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in + the planning or development of activities that present a risk of death or + bodily harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic + Arms Regulations (ITAR) maintained by the United States Department of State + or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical + Weapons Convention Implementation Act of 1997\\n 9. Guns and illegal weapons + (including weapon development)\\n 10. Illegal drugs and regulated/controlled + substances\\n 11. Operation of critical infrastructure, transportation + technologies, or heavy machinery\\n 12. Self-harm or harm to others, including + suicide, cutting, and eating disorders\\n 13. Any content intended to incite + or promote violence, abuse, or any infliction of bodily harm to an individual\\n3. + Intentionally deceive or mislead others, including use of Llama 3.2 related + to the following:\\n 14. Generating, promoting, or furthering fraud or + the creation or promotion of disinformation\\n 15. Generating, promoting, + or furthering defamatory content, including the creation of defamatory statements, + images, or other content\\n 16. Generating, promoting, or further distributing + spam\\n 17. Impersonating another individual without consent, authorization, + or legal right\\n 18. Representing that the use of Llama 3.2 or outputs + are human-generated\\n 19. Generating or facilitating false online engagement, + including fake reviews and other means of fake online engagement\\n4. Fail + to appropriately disclose to end users any known dangers of your AI system\\n5. + Interact with third party tools, models, or software designed to generate + unlawful content or engage in unlawful or harmful conduct and/or represent + that the outputs of such tools, models, or software are associated with Meta + or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama + 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License + Agreement are not being granted to you if you are an individual domiciled + in, or a company with a principal place of business in, the European Union. + This restriction does not apply to end users of a product or service that + incorporates any such multimodal models.\\n\\nPlease report any violation + of this Policy, software \u201Cbug,\u201D or other problems that could lead + to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama + show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# + FROM llama3.2:3b\\n\\nFROM /Users/greysonlalonde/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE + \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end + }}\\n{{- if .Tools }}When you receive a tool call response, use the output + to format an answer to the orginal user question.\\n\\nYou are a helpful assistant + with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range + $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond + with a JSON for a function call with its proper arguments that best answers + the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name, + \\\"parameters\\\": dictionary of argument name and its value}. Do not use + variables.\\n\\n{{ range $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- + else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last + }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- + if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name + }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else + }}\\n\\n{{ .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ + end }}\\n{{- else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ + .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- end }}\\n{{- end }}\\\"\\\"\\\"\\nPARAMETER stop \\u003c|start_header_id|\\u003e\\nPARAMETER + stop \\u003c|end_header_id|\\u003e\\nPARAMETER stop \\u003c|eot_id|\\u003e\\nLICENSE + \\\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version Release Date: + September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions + for use, reproduction, distribution \\nand modification of the Llama Materials + set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications, + manuals and documentation accompanying Llama 3.2\\ndistributed by Meta at + https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D or \u201Cyou\u201D + means you, or your employer or any other person or entity (if you are \\nentering + into this Agreement on such person or entity\u2019s behalf), of the age required + under\\napplicable laws, rules or regulations to provide legal consent and + that has legal authority\\nto bind your employer or such other person or entity + if you are entering in this Agreement\\non their behalf.\\n\\n\u201CLlama + 3.2\u201D means the foundational large language models and software and algorithms, + including\\nmachine-learning model code, trained model weights, inference-enabling + code, training-enabling code,\\nfine-tuning enabling code and other elements + of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama + Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and + Documentation (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D + or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located + in or, \\nif you are an entity, your principal place of business is in the + EEA or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside + of the EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below + or by using or distributing any portion or element of the Llama Materials,\\nyou + agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to + the Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. + If you distribute or make available the Llama Materials (or any derivative + works thereof), \\nor a product or service (including another AI model) that + contains any of them, you shall (A) provide\\na copy of this Agreement with + any such Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is + distributed or made available, you shall also include \u201CLlama\u201D\\nat + the beginning of any such AI model name.\\n\\n ii. If you receive Llama + Materials, or any derivative works thereof, from a Licensee as part\\nof an + integrated end user product, then Section 2 of this Agreement will not apply + to you. \\n\\n iii. You must retain in all copies of the Llama Materials + that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D + text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed + under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc. + All Rights Reserved.\u201D\\n\\n iv. Your use of the Llama Materials + must comply with applicable laws and regulations\\n(including trade compliance + laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama + Materials (available at https://www.llama.com/llama3_2/use-policy), which + is hereby \\nincorporated by reference into this Agreement.\\n \\n2. Additional + Commercial Terms. If, on the Llama 3.2 version release date, the monthly active + users\\nof the products or services made available by or for Licensee, or + Licensee\u2019s affiliates, \\nis greater than 700 million monthly active + users in the preceding calendar month, you must request \\na license from + Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or + until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer + of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY + OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, + WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY + KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF + TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. + YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING + OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\\nWITH + YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\\n\\n4. Limitation + of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY + THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, + OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY + INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, + EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF + ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n a. No trademark + licenses are granted under this Agreement, and in connection with the Llama + Materials, \\nneither Meta nor Licensee may use any name or mark owned by + or associated with the other or any of its affiliates, \\nexcept as required + for reasonable and customary use in describing and redistributing the Llama + Materials or as \\nset forth in this Section 5(a). Meta hereby grants you + a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with + Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between + you and Meta,\\n you are and will be the owner of such derivative works + and modifications.\\n\\n c. If you institute litigation or other proceedings + against Meta or any entity (including a cross-claim or\\n counterclaim + in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, + or any portion\\n of any of the foregoing, constitutes infringement of + intellectual property or other rights owned or licensable\\n by you, then + any licenses granted to you under this Agreement shall terminate as of the + date such litigation or\\n claim is filed or instituted. You will indemnify + and hold harmless Meta from and against any claim by any third\\n party + arising out of or related to your use or distribution of the Llama Materials.\\n\\n6. + Term and Termination. The term of this Agreement will commence upon your acceptance + of this Agreement or access\\nto the Llama Materials and will continue in + full force and effect until terminated in accordance with the terms\\nand + conditions herein. Meta may terminate this Agreement if you are in breach + of any term or condition of this\\nAgreement. Upon termination of this Agreement, + you shall delete and cease use of the Llama Materials. Sections 3,\\n4 and + 7 shall survive the termination of this Agreement. \\n\\n7. Governing Law + and Jurisdiction. This Agreement will be governed and construed under the + laws of the State of \\nCalifornia without regard to choice of law principles, + and the UN Convention on Contracts for the International\\nSale of Goods does + not apply to this Agreement. The courts of California shall have exclusive + jurisdiction of\\nany dispute arising out of this Agreement.\\\"\\nLICENSE + \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed to promoting + safe and fair use of its tools and features, including Llama 3.2. If you access + or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). + The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You + agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. + Violate the law or others\u2019 rights, including to:\\n 1. Engage in, + promote, generate, contribute to, encourage, plan, incite, or further illegal + or unlawful activity or content, such as:\\n 1. Violence or terrorism\\n + \ 2. Exploitation or harm to children, including the solicitation, creation, + acquisition, or dissemination of child exploitative content or failure to + report Child Sexual Abuse Material\\n 3. Human trafficking, exploitation, + and sexual violence\\n 4. The illegal distribution of information or + materials to minors, including obscene materials, or failure to employ legally + required age-gating in connection with such information or materials.\\n 5. + Sexual solicitation\\n 6. Any other criminal activity\\n 1. Engage + in, promote, incite, or facilitate the harassment, abuse, threatening, or + bullying of individuals or groups of individuals\\n 2. Engage in, promote, + incite, or facilitate discrimination or other unlawful or harmful conduct + in the provision of employment, employment benefits, credit, housing, other + economic benefits, or other essential goods and services\\n 3. Engage in + the unauthorized or unlicensed practice of any profession including, but not + limited to, financial, legal, medical/health, or related professional practices\\n + \ 4. Collect, process, disclose, generate, or infer private or sensitive + information about individuals, including information about individuals\u2019 + identity, health, or demographic information, unless you have obtained the + right to do so in accordance with applicable law\\n 5. Engage in or facilitate + any action or generate any content that infringes, misappropriates, or otherwise + violates any third-party rights, including the outputs or results of any products + or services using the Llama Materials\\n 6. Create, generate, or facilitate + the creation of malicious code, malware, computer viruses or do anything else + that could disable, overburden, interfere with or impair the proper working, + integrity, operation or appearance of a website or computer system\\n 7. + Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in + the planning or development of activities that present a risk of death or + bodily harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic + Arms Regulations (ITAR) maintained by the United States Department of State + or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical + Weapons Convention Implementation Act of 1997\\n 9. Guns and illegal weapons + (including weapon development)\\n 10. Illegal drugs and regulated/controlled + substances\\n 11. Operation of critical infrastructure, transportation + technologies, or heavy machinery\\n 12. Self-harm or harm to others, including + suicide, cutting, and eating disorders\\n 13. Any content intended to incite + or promote violence, abuse, or any infliction of bodily harm to an individual\\n3. + Intentionally deceive or mislead others, including use of Llama 3.2 related + to the following:\\n 14. Generating, promoting, or furthering fraud or + the creation or promotion of disinformation\\n 15. Generating, promoting, + or furthering defamatory content, including the creation of defamatory statements, + images, or other content\\n 16. Generating, promoting, or further distributing + spam\\n 17. Impersonating another individual without consent, authorization, + or legal right\\n 18. Representing that the use of Llama 3.2 or outputs + are human-generated\\n 19. Generating or facilitating false online engagement, + including fake reviews and other means of fake online engagement\\n4. Fail + to appropriately disclose to end users any known dangers of your AI system\\n5. + Interact with third party tools, models, or software designed to generate + unlawful content or engage in unlawful or harmful conduct and/or represent + that the outputs of such tools, models, or software are associated with Meta + or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama + 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License + Agreement are not being granted to you if you are an individual domiciled + in, or a company with a principal place of business in, the European Union. + This restriction does not apply to end users of a product or service that + incorporates any such multimodal models.\\n\\nPlease report any violation + of this Policy, software \u201Cbug,\u201D or other problems that could lead + to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop + \ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end + }}\\n{{- if .Tools }}When you receive a tool call response, use the output + to format an answer to the orginal user question.\\n\\nYou are a helpful assistant + with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range + $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond + with a JSON for a function call with its proper arguments that best answers + the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name, + \\\"parameters\\\": dictionary of argument name and its value}. Do not use + variables.\\n\\n{{ range $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- + else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last + }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- + if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name + }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else + }}\\n\\n{{ .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ + end }}\\n{{- else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ + .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- end }}\\n{{- end 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This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I should use the available tool to get the final answer + multiple times, as instructed.\n\nAction: get_final_answer\nAction Input: {\"input\":\"n/a\"}\nObservation: + 42"}], "response": "Thought: I should continue to use the tool to meet the criteria + specified.\n\nAction: get_final_answer\nAction Input: {\"input\": \"n/a\"}", + "call_type": "", "model": "gpt-4o-mini"}}, + {"event_id": "dc120a99-64ae-4586-baed-94606a5fc9c6", "timestamp": "2025-09-24T05:25:59.045530+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T05:25:59.045426+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "0ca9aa84-9dd9-4ac2-bc7f-2d810dd6097a", + "task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, instead keep using it unless you''re told to give your final answer", + "agent_id": "b6cf723e-04c8-40c5-a927-e2078cfbae59", "agent_role": "test role", + "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": + "system", "content": "You are test role. test backstory\nYour personal goal + is: test goal\nYou ONLY have access to the following tools, and should NEVER + make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I should use the available tool to get the final answer + multiple times, as instructed.\n\nAction: get_final_answer\nAction Input: {\"input\":\"n/a\"}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I should continue to use the + tool to meet the criteria specified.\n\nAction: get_final_answer\nAction Input: + {\"input\": \"n/a\"}\nObservation: I tried reusing the same input, I must stop + using this action input. 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This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I should use the available tool to get the final answer + multiple times, as instructed.\n\nAction: get_final_answer\nAction Input: {\"input\":\"n/a\"}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I should continue to use the + tool to meet the criteria specified.\n\nAction: get_final_answer\nAction Input: + {\"input\": \"n/a\"}\nObservation: I tried reusing the same input, I must stop + using this action input. 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"task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, instead keep using it unless you''re told to give your final answer", + "agent_id": "b6cf723e-04c8-40c5-a927-e2078cfbae59", "agent_role": "test role", + "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": + "system", "content": "You are test role. test backstory\nYour personal goal + is: test goal\nYou ONLY have access to the following tools, and should NEVER + make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I should use the available tool to get the final answer + multiple times, as instructed.\n\nAction: get_final_answer\nAction Input: {\"input\":\"n/a\"}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I should continue to use the + tool to meet the criteria specified.\n\nAction: get_final_answer\nAction Input: + {\"input\": \"n/a\"}\nObservation: I tried reusing the same input, I must stop + using this action input. I''ll try something else instead."}, {"role": "assistant", + "content": "Thought: I need to modify my action input to continue using the + tool correctly.\n\nAction: get_final_answer\nAction Input: {\"input\": \"test + input\"}\nObservation: "}], + "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "603166bd-f912-4db7-b3d1-03ce4a63e122", + "timestamp": "2025-09-24T05:25:59.050706+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T05:25:59.050662+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "0ca9aa84-9dd9-4ac2-bc7f-2d810dd6097a", "task_name": "Use tool logic + for `get_final_answer` but fon''t give you final answer yet, instead keep using + it unless you''re told to give your final answer", "agent_id": "b6cf723e-04c8-40c5-a927-e2078cfbae59", + "agent_role": "test role", "from_task": null, "from_agent": null, "messages": + [{"role": "system", "content": "You are test role. test backstory\nYour personal + goal is: test goal\nYou ONLY have access to the following tools, and should + NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I should use the available tool to get the final answer + multiple times, as instructed.\n\nAction: get_final_answer\nAction Input: {\"input\":\"n/a\"}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I should continue to use the + tool to meet the criteria specified.\n\nAction: get_final_answer\nAction Input: + {\"input\": \"n/a\"}\nObservation: I tried reusing the same input, I must stop + using this action input. I''ll try something else instead."}, {"role": "assistant", + "content": "Thought: I need to modify my action input to continue using the + tool correctly.\n\nAction: get_final_answer\nAction Input: {\"input\": \"test + input\"}\nObservation: "}], + "response": "Thought: I should try another variation in the input to observe + any changes and continue using the tool.\n\nAction: get_final_answer\nAction + Input: {\"input\": \"retrying with new input\"}", "call_type": "", "model": "gpt-4o-mini"}}, {"event_id": "89ff2fb9-8a8c-467e-8414-d89923aab204", + "timestamp": "2025-09-24T05:25:59.050949+00:00", "type": "tool_usage_started", + "event_data": {"timestamp": "2025-09-24T05:25:59.050905+00:00", "type": "tool_usage_started", + "source_fingerprint": "363cc2aa-b694-4cb1-a834-aa5d693977ab", "source_type": + "agent", "fingerprint_metadata": null, "task_id": "0ca9aa84-9dd9-4ac2-bc7f-2d810dd6097a", + "task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, 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0, "agent": + null, "from_task": null, "from_agent": null, "started_at": "2025-09-23T22:25:59.051126", + "finished_at": "2025-09-23T22:25:59.051285", "from_cache": false, "output": + "42"}}, {"event_id": "34be85d1-e742-4a01-aef2-afab16791949", "timestamp": "2025-09-24T05:25:59.052829+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T05:25:59.052743+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "0ca9aa84-9dd9-4ac2-bc7f-2d810dd6097a", + "task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, instead keep using it unless you''re told to give your final answer", + "agent_id": "b6cf723e-04c8-40c5-a927-e2078cfbae59", "agent_role": "test role", + "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": + "system", "content": "You are test role. test backstory\nYour personal goal + is: test goal\nYou ONLY have access to the following tools, and should NEVER + make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I should use the available tool to get the final answer + multiple times, as instructed.\n\nAction: get_final_answer\nAction Input: {\"input\":\"n/a\"}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I should continue to use the + tool to meet the criteria specified.\n\nAction: get_final_answer\nAction Input: + {\"input\": \"n/a\"}\nObservation: I tried reusing the same input, I must stop + using this action input. I''ll try something else instead."}, {"role": "assistant", + "content": "Thought: I need to modify my action input to continue using the + tool correctly.\n\nAction: get_final_answer\nAction Input: {\"input\": \"test + input\"}\nObservation: "}, + {"role": "assistant", "content": "Thought: I should try another variation in + the input to observe any changes and continue using the tool.\n\nAction: get_final_answer\nAction + Input: {\"input\": \"retrying with new input\"}\nObservation: 42"}], "tools": + null, "callbacks": [""], "available_functions": null}}, {"event_id": "3f2bb116-90d7-4317-8ee4-7e9a8afd988b", + "timestamp": "2025-09-24T05:25:59.054235+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T05:25:59.054196+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "0ca9aa84-9dd9-4ac2-bc7f-2d810dd6097a", "task_name": "Use tool logic + for `get_final_answer` but fon''t give you final answer yet, instead keep using + it unless you''re told to give your final answer", "agent_id": "b6cf723e-04c8-40c5-a927-e2078cfbae59", + "agent_role": "test role", "from_task": null, "from_agent": null, "messages": + [{"role": "system", "content": "You are test role. test backstory\nYour personal + goal is: test goal\nYou ONLY have access to the following tools, and should + NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I should use the available tool to get the final answer + multiple times, as instructed.\n\nAction: get_final_answer\nAction Input: {\"input\":\"n/a\"}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I should continue to use the + tool to meet the criteria specified.\n\nAction: get_final_answer\nAction Input: + {\"input\": \"n/a\"}\nObservation: I tried reusing the same input, I must stop + using this action input. I''ll try something else instead."}, {"role": "assistant", + "content": "Thought: I need to modify my action input to continue using the + tool correctly.\n\nAction: get_final_answer\nAction Input: {\"input\": \"test + input\"}\nObservation: "}, + {"role": "assistant", "content": "Thought: I should try another variation in + the input to observe any changes and continue using the tool.\n\nAction: get_final_answer\nAction + Input: {\"input\": \"retrying with new input\"}\nObservation: 42"}], "response": + "Thought: I should perform the action again, but not give the final answer yet. + I''ll just keep using the tool as instructed.\n\nAction: get_final_answer\nAction + Input: {\"input\": \"test input\"}", "call_type": "", + "model": "gpt-4o-mini"}}, {"event_id": "becb08f6-6599-41a3-a4cc-582ddd127333", + "timestamp": "2025-09-24T05:25:59.054448+00:00", "type": "tool_usage_started", + "event_data": {"timestamp": "2025-09-24T05:25:59.054407+00:00", "type": "tool_usage_started", + 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name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! 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I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to modify my action input to continue using the tool correctly.\n\nAction: + get_final_answer\nAction Input: {\"input\": \"test input\"}\nObservation: "}, {"role": "assistant", "content": + "Thought: I should try another variation in the input to observe any changes + and continue using the tool.\n\nAction: get_final_answer\nAction Input: {\"input\": + \"retrying with new input\"}\nObservation: 42"}, {"role": "assistant", "content": + "Thought: I should perform the action again, but not give the final answer yet. + I''ll just keep using the tool as instructed.\n\nAction: get_final_answer\nAction + Input: {\"input\": \"test input\"}\nObservation: 42"}, {"role": "assistant", + "content": "Thought: I need to make sure that I correctly utilize the tool without + giving the final answer prematurely.\n\nAction: get_final_answer\nAction Input: + {\"input\": \"test example\"}\nObservation: "}, {"role": "assistant", "content": "Thought: I need to make + sure that I correctly utilize the tool without giving the final answer prematurely.\n\nAction: + get_final_answer\nAction Input: {\"input\": \"test example\"}\nObservation: + \nNow it''s time you + MUST give your absolute best final answer. You''ll ignore all previous instructions, + stop using any tools, and just return your absolute BEST Final answer."}], "tools": + null, "callbacks": [""], "available_functions": null}}, {"event_id": "3bf412fe-db1d-43e9-9332-9116a1c6c340", + "timestamp": "2025-09-24T05:25:59.061640+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T05:25:59.061605+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": + null, "from_agent": null, "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [get_final_answer], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple JSON object, enclosed in curly braces, using \" to wrap keys and + values.\nObservation: the result of the action\n```\n\nOnce all necessary information + is gathered, return the following format:\n\n```\nThought: I now know the final + answer\nFinal Answer: the final answer to the original input question\n```"}, + {"role": "user", "content": "\nCurrent Task: Use tool logic for `get_final_answer` + but fon''t give you final answer yet, instead keep using it unless you''re told + to give your final answer\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "I should use the available tool to get the final answer + multiple times, as instructed.\n\nAction: get_final_answer\nAction Input: {\"input\":\"n/a\"}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I should continue to use the + tool to meet the criteria specified.\n\nAction: get_final_answer\nAction Input: + {\"input\": \"n/a\"}\nObservation: I tried reusing the same input, I must stop + using this action input. 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This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "05f9f131-23e6-40c3-820c-10846f50a1b1", + "timestamp": "2025-09-23T20:23:57.415964+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T20:23:57.415941+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "57942855-c061-4590-9005-9fb0d06f9570", "task_name": "Use tool logic + for `get_final_answer` but fon''t give you final answer yet, instead keep using + it unless you''re told to give your final answer", "agent_id": null, "agent_role": + null, "from_task": null, "from_agent": null, "messages": [{"role": "system", + "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "user", "content": "\nCurrent Task: Use tool + logic for `get_final_answer` but fon''t give you final answer yet, instead keep + using it unless you''re told to give your final answer\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: + I need to gather information to fulfill the task effectively.\nAction: get_final_answer\nAction + Input: {}\nObservation: 42"}], "response": "Thought: I need to keep gathering + the information necessary for my task.\nAction: get_final_answer\nAction Input: + {}", "call_type": "", "model": "gpt-4o-mini"}}, + {"event_id": "9c78febc-1c7e-4173-82a8-3b4235e41819", "timestamp": "2025-09-23T20:23:57.417169+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T20:23:57.417065+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "57942855-c061-4590-9005-9fb0d06f9570", + "task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, instead keep using it unless you''re told to give your final answer", + "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, + "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [get_final_answer], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple JSON object, enclosed in curly braces, using \" to wrap keys and + values.\nObservation: the result of the action\n```\n\nOnce all necessary information + is gathered, return the following format:\n\n```\nThought: I now know the final + answer\nFinal Answer: the final answer to the original input question\n```"}, + {"role": "user", "content": "\nCurrent Task: Use tool logic for `get_final_answer` + but fon''t give you final answer yet, instead keep using it unless you''re told + to give your final answer\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "bb19279e-4432-41aa-b228-eeab2b421856", + "timestamp": "2025-09-23T20:23:57.418180+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T20:23:57.418156+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "57942855-c061-4590-9005-9fb0d06f9570", "task_name": "Use tool logic + for `get_final_answer` but fon''t give you final answer yet, instead keep using + it unless you''re told to give your final answer", "agent_id": null, "agent_role": + null, "from_task": null, "from_agent": null, "messages": [{"role": "system", + "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "user", "content": "\nCurrent Task: Use tool + logic for `get_final_answer` but fon''t give you final answer yet, instead keep + using it unless you''re told to give your final answer\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: + I need to gather information to fulfill the task effectively.\nAction: get_final_answer\nAction + Input: {}\nObservation: 42"}, {"role": "assistant", "content": "Thought: I need + to keep gathering the information necessary for my task.\nAction: get_final_answer\nAction + Input: {}\nObservation: I tried reusing the same input, I must stop using this + action input. I''ll try something else instead."}], "response": "Thought: I + need to persist in obtaining the final answer for the task.\nAction: get_final_answer\nAction + Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, + {"event_id": "17f5760b-5798-4dfc-b076-265264f9ca4c", "timestamp": "2025-09-23T20:23:57.419666+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T20:23:57.419577+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "57942855-c061-4590-9005-9fb0d06f9570", + "task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, instead keep using it unless you''re told to give your final answer", + "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, + "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [get_final_answer], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple JSON object, enclosed in curly braces, using \" to wrap keys and + values.\nObservation: the result of the action\n```\n\nOnce all necessary information + is gathered, return the following format:\n\n```\nThought: I now know the final + answer\nFinal Answer: the final answer to the original input question\n```"}, + {"role": "user", "content": "\nCurrent Task: Use tool logic for `get_final_answer` + but fon''t give you final answer yet, instead keep using it unless you''re told + to give your final answer\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "7f0cc112-9c45-4a8b-8f60-a27668bf8a59", + "timestamp": "2025-09-23T20:23:57.421082+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T20:23:57.421043+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "57942855-c061-4590-9005-9fb0d06f9570", "task_name": "Use tool logic + for `get_final_answer` but fon''t give you final answer yet, instead keep using + it unless you''re told to give your final answer", "agent_id": null, "agent_role": + null, "from_task": null, "from_agent": null, "messages": [{"role": "system", + "content": "You are test role. test backstory\nYour personal goal is: test goal\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "user", "content": "\nCurrent Task: Use tool + logic for `get_final_answer` but fon''t give you final answer yet, instead keep + using it unless you''re told to give your final answer\n\nThis is the expected + criteria for your final answer: The final answer\nyou MUST return the actual + complete content as the final answer, not a summary.\n\nBegin! This is VERY + important to you, use the tools available and give your best Final Answer, your + job depends on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: + I need to gather information to fulfill the task effectively.\nAction: get_final_answer\nAction + Input: {}\nObservation: 42"}, {"role": "assistant", "content": "Thought: I need + to keep gathering the information necessary for my task.\nAction: get_final_answer\nAction + Input: {}\nObservation: I tried reusing the same input, I must stop using this + action input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}], "response": "```\nThought: I need to keep trying to + get the final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": + "", "model": "gpt-4o-mini"}}, {"event_id": + "3f872678-59b3-4484-bbf7-8e5e7599fd0b", "timestamp": "2025-09-23T20:23:57.422532+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T20:23:57.422415+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": null, "task_name": null, "agent_id": + null, "agent_role": null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", + "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "assistant", "content": "```\nThought: I need + to keep trying to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "```\nThought: I need to keep trying to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I tried reusing the same input, I must stop using this + action input. I''ll try something else instead.\n\n\nNow it''s time you MUST + give your absolute best final answer. You''ll ignore all previous instructions, + stop using any tools, and just return your absolute BEST Final answer."}], "tools": + null, "callbacks": [""], "available_functions": null}}, {"event_id": "195cab8f-fa7f-44cf-bc5c-37a1929f4114", + "timestamp": "2025-09-23T20:23:57.423936+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T20:23:57.423908+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": + null, "from_agent": null, "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [get_final_answer], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple JSON object, enclosed in curly braces, using \" to wrap keys and + values.\nObservation: the result of the action\n```\n\nOnce all necessary information + is gathered, return the following format:\n\n```\nThought: I now know the final + answer\nFinal Answer: the final answer to the original input question\n```"}, + {"role": "user", "content": "\nCurrent Task: Use tool logic for `get_final_answer` + but fon''t give you final answer yet, instead keep using it unless you''re told + to give your final answer\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "assistant", "content": "```\nThought: I need + to keep trying to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "```\nThought: I need to keep trying to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I tried reusing the same input, I must stop using this + action input. I''ll try something else instead.\n\n\nNow it''s time you MUST + give your absolute best final answer. You''ll ignore all previous instructions, + stop using any tools, and just return your absolute BEST Final answer."}], "response": + "```\nThought: I now know the final answer\nFinal Answer: 42\n```", "call_type": + "", "model": "gpt-4o-mini"}}, {"event_id": + "56ad593f-7111-4f7a-a727-c697d28ae6a6", "timestamp": "2025-09-23T20:23:57.424017+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T20:23:57.423991+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "57942855-c061-4590-9005-9fb0d06f9570", + "task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, instead keep using it unless you''re told to give your final answer", + "agent_id": null, "agent_role": null, "from_task": null, "from_agent": null, + "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [get_final_answer], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple JSON object, enclosed in curly braces, using \" to wrap keys and + values.\nObservation: the result of the action\n```\n\nOnce all necessary information + is gathered, return the following format:\n\n```\nThought: I now know the final + answer\nFinal Answer: the final answer to the original input question\n```"}, + {"role": "user", "content": "\nCurrent Task: Use tool logic for `get_final_answer` + but fon''t give you final answer yet, instead keep using it unless you''re told + to give your final answer\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! 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This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}], "response": "Thought: I need to + persist in obtaining the final answer for the task.\nAction: get_final_answer\nAction + Input: {}", "call_type": "", "model": "gpt-4o-mini"}}, + {"event_id": "a8f9013c-3774-4291-98d4-d23547bc26f6", "timestamp": "2025-09-24T05:35:45.971143+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T05:35:45.970993+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "fe06ddb1-3701-4679-a557-c23de84af895", + "task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, instead keep using it unless you''re told to give your final answer", + "agent_id": "575f7e4c-4c75-4783-a769-6df687b611a5", "agent_role": "test role", + "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": + "system", "content": "You are test role. test backstory\nYour personal goal + is: test goal\nYou ONLY have access to the following tools, and should NEVER + make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "2e51730c-6ae3-4839-aa3d-5aea1a069009", + "timestamp": "2025-09-24T05:35:45.972927+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T05:35:45.972891+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "fe06ddb1-3701-4679-a557-c23de84af895", "task_name": "Use tool logic + for `get_final_answer` but fon''t give you final answer yet, instead keep using + it unless you''re told to give your final answer", "agent_id": "575f7e4c-4c75-4783-a769-6df687b611a5", + "agent_role": "test role", "from_task": null, "from_agent": null, "messages": + [{"role": "system", "content": "You are test role. test backstory\nYour personal + goal is: test goal\nYou ONLY have access to the following tools, and should + NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}], "response": "```\nThought: I need to keep trying to + get the final answer.\nAction: get_final_answer\nAction Input: {}", "call_type": + "", "model": "gpt-4o-mini"}}, {"event_id": + "eb1d5919-5eb7-4dfb-8e20-fc9fd368d7fd", "timestamp": "2025-09-24T05:35:45.974413+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T05:35:45.974316+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": null, "task_name": null, "agent_id": + null, "agent_role": null, "from_task": null, "from_agent": null, "model": "gpt-4o-mini", + "messages": [{"role": "system", "content": "You are test role. test backstory\nYour + personal goal is: test goal\nYou ONLY have access to the following tools, and + should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "assistant", "content": "```\nThought: I need + to keep trying to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "```\nThought: I need to keep trying to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I tried reusing the same input, I must stop using this + action input. I''ll try something else instead.\n\n\nNow it''s time you MUST + give your absolute best final answer. You''ll ignore all previous instructions, + stop using any tools, and just return your absolute BEST Final answer."}], "tools": + null, "callbacks": [""], "available_functions": null}}, {"event_id": "ebf29eff-0636-45c5-9f15-710a10d5862c", + "timestamp": "2025-09-24T05:35:45.975985+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T05:35:45.975949+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": null, "task_name": null, "agent_id": null, "agent_role": null, "from_task": + null, "from_agent": null, "messages": [{"role": "system", "content": "You are + test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access + to the following tools, and should NEVER make up tools that are not listed here:\n\nTool + Name: get_final_answer\nTool Arguments: {}\nTool Description: Get the final + answer but don''t give it yet, just re-use this\n tool non-stop.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [get_final_answer], + just the name, exactly as it''s written.\nAction Input: the input to the action, + just a simple JSON object, enclosed in curly braces, using \" to wrap keys and + values.\nObservation: the result of the action\n```\n\nOnce all necessary information + is gathered, return the following format:\n\n```\nThought: I now know the final + answer\nFinal Answer: the final answer to the original input question\n```"}, + {"role": "user", "content": "\nCurrent Task: Use tool logic for `get_final_answer` + but fon''t give you final answer yet, instead keep using it unless you''re told + to give your final answer\n\nThis is the expected criteria for your final answer: + The final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "assistant", "content": "```\nThought: I need + to keep trying to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "```\nThought: I need to keep trying to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I tried reusing the same input, I must stop using this + action input. I''ll try something else instead.\n\n\nNow it''s time you MUST + give your absolute best final answer. You''ll ignore all previous instructions, + stop using any tools, and just return your absolute BEST Final answer."}], "response": + "```\nThought: I now know the final answer\nFinal Answer: 42\n```", "call_type": + "", "model": "gpt-4o-mini"}}, {"event_id": + "3ca40bc2-0d55-4a1a-940e-cc84a314efc1", "timestamp": "2025-09-24T05:35:45.976085+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-24T05:35:45.976052+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "fe06ddb1-3701-4679-a557-c23de84af895", + "task_name": "Use tool logic for `get_final_answer` but fon''t give you final + answer yet, instead keep using it unless you''re told to give your final answer", + "agent_id": "575f7e4c-4c75-4783-a769-6df687b611a5", "agent_role": "test role", + "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": + "system", "content": "You are test role. test backstory\nYour personal goal + is: test goal\nYou ONLY have access to the following tools, and should NEVER + make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool + Arguments: {}\nTool Description: Get the final answer but don''t give it yet, + just re-use this\n tool non-stop.\n\nIMPORTANT: Use the following format + in your response:\n\n```\nThought: you should always think about what to do\nAction: + the action to take, only one name of [get_final_answer], just the name, exactly + as it''s written.\nAction Input: the input to the action, just a simple JSON + object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Use tool logic for `get_final_answer` but fon''t + give you final answer yet, instead keep using it unless you''re told to give + your final answer\n\nThis is the expected criteria for your final answer: The + final answer\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! This is VERY important to you, use the tools available + and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": + "assistant", "content": "Thought: I need to gather information to fulfill the + task effectively.\nAction: get_final_answer\nAction Input: {}\nObservation: + 42"}, {"role": "assistant", "content": "Thought: I need to keep gathering the + information necessary for my task.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "Thought: I need to persist in obtaining the final answer for the task.\nAction: + get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, + I must stop using this action input. I''ll try something else instead.\n\n\n\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {}\nTool + Description: Get the final answer but don''t give it yet, just re-use this\n tool + non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [get_final_answer], just the name, exactly as it''s written.\nAction + Input: the input to the action, just a simple JSON object, enclosed in curly + braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce + all necessary information is gathered, return the following format:\n\n```\nThought: + I now know the final answer\nFinal Answer: the final answer to the original + input question\n```"}, {"role": "assistant", "content": "```\nThought: I need + to keep trying to get the final answer.\nAction: get_final_answer\nAction Input: + {}\nObservation: I tried reusing the same input, I must stop using this action + input. I''ll try something else instead."}, {"role": "assistant", "content": + "```\nThought: I need to keep trying to get the final answer.\nAction: get_final_answer\nAction + Input: {}\nObservation: I tried reusing the same input, I must stop using this + action input. I''ll try something else instead.\n\n\nNow it''s time you MUST + give your absolute best final answer. 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The goal is to have a clear understanding of what + the data indicates and any initial insights that can be drawn from it.\", \"coworker\": + \"First Agent\"}\nObservation: To process the initial data set effectively, + we will follow several key steps to ensure we clean, categorize, analyze, and + summarize our findings comprehensively.\n\n1. **Data Cleaning**: \n - First, + we will review the dataset for missing, inconsistent, or erroneous entries. + \n - We will handle missing values by either imputing them based on surrounding + data or removing records where necessary.\n - Additionally, we will standardize + categorical variables to ensure consistency (e.g., ensuring all location names + are spelled the same way).\n\n2. **Data Categorization**: \n - Next, we will + categorize the data into relevant segments that will aid our analysis. \n - + This involves grouping data points based on common characteristics, such as + demographics, time periods, or any key performance indicators (KPIs) we are + focusing on.\n\n3. **Trend and Pattern Identification**: \n - With the cleaned + and categorized data, we will perform a detailed analysis to identify trends + and patterns.\n - This will involve using statistical tools and visualizations + to uncover relationships within the data. 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work to coworker\\nTool + Arguments: {''task'': {''description'': ''The task to delegate'', ''type'': + ''str''}, ''context'': {''description'': ''The context for the task'', ''type'': + ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to + delegate to'', ''type'': ''str''}}\\nTool Description: Delegate a specific task + to one of the following coworkers: First Agent, Second Agent\\nThe input to + this tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolutely + everything you know, don''t reference things but instead explain them.\", ''env_vars'': + [], ''args_schema'': , + ''description_updated'': False, ''cache_function'': + at 0x10614d3a0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'': + 0}, {''name'': ''Ask question to coworker'', ''description'': \"Tool Name: Ask + question to coworker\\nTool Arguments: {''question'': {''description'': ''The + question to ask'', ''type'': ''str''}, ''context'': {''description'': ''The + context for the question'', ''type'': ''str''}, ''coworker'': {''description'': + ''The role/name of the coworker to ask'', ''type'': ''str''}}\\nTool Description: + Ask a specific question to one of the following coworkers: First Agent, Second + Agent\\nThe input to this tool should be the coworker, the question you have + for them, and ALL necessary context to ask the question properly, they know + nothing about the question, so share absolutely everything you know, don''t + reference things but instead explain them.\", ''env_vars'': [], ''args_schema'': + , + ''description_updated'': False, ''cache_function'': + at 0x10614d3a0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'': + 0}], ''max_iter'': 25, ''agent_executor'': , ''llm'': , ''crew'': Crew(id=49cbb747-f055-4636-bbca-9e8a450c05f6, + process=Process.hierarchical, number_of_agents=2, 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True, ''verbose'': False, ''max_rpm'': None, ''allow_delegation'': + False, ''tools'': [], ''max_iter'': 25, ''agent_executor'': , ''llm'': , ''crew'': Crew(id=49cbb747-f055-4636-bbca-9e8a450c05f6, + process=Process.hierarchical, number_of_agents=2, number_of_tasks=1), ''i18n'': + {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': , ''tools_results'': [], ''max_tokens'': None, ''knowledge'': + None, ''knowledge_sources'': None, ''knowledge_storage'': None, ''security_config'': + {''fingerprint'': {''metadata'': {}}}, ''callbacks'': [], ''adapted_agent'': + False, ''knowledge_config'': None}"], "process": "hierarchical", "verbose": + false, "memory": false, "short_term_memory": null, "long_term_memory": null, + "entity_memory": null, "external_memory": null, "embedder": null, "usage_metrics": + null, "manager_llm": "", "manager_agent": {"id": "UUID(''b0898472-5e3b-45bb-bd90-05bad0b5a8ce'')", + "role": "''Crew Manager''", "goal": "''Manage the team to complete the task + in the best way possible.''", "backstory": "\"You are a seasoned manager with + a knack for getting the best out of your team.\\nYou are also known for your + ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\\nEven though you don''t perform tasks by + yourself, you have a lot of experience in the field, which allows you to properly + evaluate the work of your team members.\"", "cache": "True", "verbose": "False", + "max_rpm": "None", "allow_delegation": "True", "tools": "[{''name'': ''Delegate + work to coworker'', ''description'': \"Tool Name: Delegate work to coworker\\nTool + Arguments: {''task'': {''description'': ''The task to delegate'', ''type'': + ''str''}, ''context'': {''description'': ''The context for the task'', ''type'': + ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to + delegate to'', ''type'': ''str''}}\\nTool Description: Delegate a specific task + to one of the following coworkers: First Agent, Second Agent\\nThe input to + this tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolutely + everything you know, don''t reference things but instead explain them.\", ''env_vars'': + [], ''args_schema'': , + ''description_updated'': False, ''cache_function'': + at 0x10614d3a0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'': + 0}, {''name'': ''Ask question to coworker'', ''description'': \"Tool Name: Ask + question to coworker\\nTool Arguments: {''question'': {''description'': ''The + question to ask'', ''type'': ''str''}, ''context'': {''description'': ''The + context for the question'', ''type'': ''str''}, ''coworker'': {''description'': + ''The role/name of the coworker to ask'', ''type'': ''str''}}\\nTool Description: + Ask a specific question to one of the following coworkers: First Agent, Second + Agent\\nThe input to this tool should be the coworker, the question you have + for them, and ALL necessary context to ask the question properly, they know + nothing about the question, so share absolutely everything you know, don''t + reference things but instead explain them.\", ''env_vars'': [], ''args_schema'': + , + ''description_updated'': False, ''cache_function'': + at 0x10614d3a0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'': + 0}]", "max_iter": "25", "agent_executor": "", "llm": "", "crew": "Crew(id=49cbb747-f055-4636-bbca-9e8a450c05f6, + process=Process.hierarchical, number_of_agents=2, number_of_tasks=1)", "i18n": + "{''prompt_file'': None}", "cache_handler": "{}", "tools_handler": "", "tools_results": "[]", "max_tokens": "None", "knowledge": + "None", "knowledge_sources": "None", "knowledge_storage": "None", "security_config": + "{''fingerprint'': {''metadata'': {}}}", "callbacks": "[]", "adapted_agent": + "False", "knowledge_config": "None"}, "function_calling_llm": null, "config": + null, "id": "49cbb747-f055-4636-bbca-9e8a450c05f6", "share_crew": false, "step_callback": + null, "task_callback": null, "before_kickoff_callbacks": [], "after_kickoff_callbacks": + [], "max_rpm": null, "prompt_file": null, "output_log_file": null, "planning": + false, "planning_llm": null, "task_execution_output_json_files": null, "execution_logs": + [], "knowledge_sources": null, "chat_llm": null, "knowledge": null, "security_config": + {"fingerprint": "{''metadata'': {}}"}, "token_usage": null, "tracing": false}, + "i18n": {"prompt_file": null}, "cache_handler": {}, "tools_handler": "", "tools_results": [], "max_tokens": null, "knowledge": + null, "knowledge_sources": null, "knowledge_storage": null, "security_config": + {"fingerprint": {"metadata": "{}"}}, "callbacks": [], "adapted_agent": false, + "knowledge_config": null, "max_execution_time": null, "agent_ops_agent_name": + "Crew Manager", "agent_ops_agent_id": null, "step_callback": null, "use_system_prompt": + true, "function_calling_llm": null, "system_template": null, "prompt_template": + null, "response_template": null, "allow_code_execution": false, "respect_context_window": + true, "max_retry_limit": 2, "multimodal": false, "inject_date": false, "date_format": + "%Y-%m-%d", "code_execution_mode": "safe", "reasoning": false, "max_reasoning_attempts": + null, "embedder": null, "agent_knowledge_context": null, "crew_knowledge_context": + null, "knowledge_search_query": null, "from_repository": null, "guardrail": + null, "guardrail_max_retries": 3}, "from_task": null, "from_agent": null}}, + {"event_id": "fe025852-e64a-4765-b1d2-54fce213b94d", "timestamp": "2025-09-23T22:00:38.325302+00:00", + "type": "agent_execution_started", "event_data": {"agent_role": "First Agent", + "agent_goal": "First goal", "agent_backstory": "First backstory"}}, {"event_id": + "b66f3262-25e2-4e91-9d96-120efd6aaf20", "timestamp": "2025-09-23T22:00:38.325366+00:00", + "type": "llm_call_started", "event_data": {"timestamp": "2025-09-23T22:00:38.325352+00:00", + "type": "llm_call_started", "source_fingerprint": null, "source_type": null, + "fingerprint_metadata": null, "task_id": "fcf97ccf-8dac-4ee4-a36e-9807e8fddb98", + "task_name": "Process initial data", "agent_id": "384876b3-8794-4e16-afb9-a2e9539b0a86", + "agent_role": "First Agent", "from_task": null, "from_agent": null, "model": + "gpt-4o-mini", "messages": [{"role": "system", "content": "You are First Agent. + First backstory\nYour personal goal is: First goal\nTo give my best complete + final answer to the task respond using the exact following format:\n\nThought: + I now can give a great answer\nFinal Answer: Your final answer must be the great + and the most complete as possible, it must be outcome described.\n\nI MUST use + these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent + Task: Process initial data\n\nThis is the expected criteria for your final answer: + Your best answer to your coworker asking you this, accounting for the context + shared.\nyou MUST return the actual complete content as the final answer, not + a summary.\n\nThis is the context you''re working with:\nThe task involves analyzing + the initial data set we have received. This includes cleaning the data, categorizing + it for analysis, identifying any trends or patterns, and summarizing the findings. + The goal is to have a clear understanding of what the data indicates and any + initial insights that can be drawn from it.\n\nBegin! This is VERY important + to you, use the tools available and give your best Final Answer, your job depends + on it!\n\nThought:"}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "827bbc84-ba1a-4ae3-9d2e-2d7496d43361", + "timestamp": "2025-09-23T22:00:38.326169+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-23T22:00:38.326155+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "fcf97ccf-8dac-4ee4-a36e-9807e8fddb98", "task_name": "Process initial + data", "agent_id": "384876b3-8794-4e16-afb9-a2e9539b0a86", "agent_role": "First + Agent", "from_task": null, "from_agent": null, "messages": [{"role": "system", + "content": "You are First Agent. First backstory\nYour personal goal is: First + goal\nTo give my best complete final answer to the task respond using the exact + following format:\n\nThought: I now can give a great answer\nFinal Answer: Your + final answer must be the great and the most complete as possible, it must be + outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": + "user", "content": "\nCurrent Task: Process initial data\n\nThis is the expected + criteria for your final answer: Your best answer to your coworker asking you + this, accounting for the context shared.\nyou MUST return the actual complete + content as the final answer, not a summary.\n\nThis is the context you''re working + with:\nThe task involves analyzing the initial data set we have received. This + includes cleaning the data, categorizing it for analysis, identifying any trends + or patterns, and summarizing the findings. The goal is to have a clear understanding + of what the data indicates and any initial insights that can be drawn from it.\n\nBegin! + This is VERY important to you, use the tools available and give your best Final + Answer, your job depends on it!\n\nThought:"}], "response": "I now can give + a great answer \nFinal Answer: To process the initial data set effectively, + we will follow several key steps to ensure we clean, categorize, analyze, and + summarize our findings comprehensively.\n\n1. **Data Cleaning**: \n - First, + we will review the dataset for missing, inconsistent, or erroneous entries. + \n - We will handle missing values by either imputing them based on surrounding + data or removing records where necessary.\n - Additionally, we will standardize + categorical variables to ensure consistency (e.g., ensuring all location names + are spelled the same way).\n\n2. **Data Categorization**: \n - Next, we will + categorize the data into relevant segments that will aid our analysis. \n - + This involves grouping data points based on common characteristics, such as + demographics, time periods, or any key performance indicators (KPIs) we are + focusing on.\n\n3. **Trend and Pattern Identification**: \n - With the cleaned + and categorized data, we will perform a detailed analysis to identify trends + and patterns.\n - This will involve using statistical tools and visualizations + to uncover relationships within the data. We will look at time series analysis, + correlation coefficients, and any significant outliers that may require further + investigation.\n\n4. **Summarizing Findings**: \n - Finally, we will compile + a summary of our findings which will include both qualitative insights and quantitative + metrics.\n - This summary should encapsulate the key trends identified, any + notable patterns, and implications of these findings.\n - We will also document + any limitations of the data and suggest areas for further research if necessary.\n\nBy + completing these steps, we will not only have a clear understanding of what + the data indicates but also provide actionable insights that can guide our next + steps. This comprehensive analysis will serve as a solid foundation for any + additional exploration or decision-making initiatives related to our project. + \n\nIf you have any questions or need further clarification on any part of this + process, please let me know!", "call_type": "", + "model": "gpt-4o-mini"}}, {"event_id": "ada92792-d5a4-48bb-82df-2344d3a850e0", + "timestamp": "2025-09-23T22:00:38.326287+00:00", "type": "agent_execution_completed", + "event_data": {"agent_role": "First Agent", "agent_goal": "First goal", "agent_backstory": + "First backstory"}}, {"event_id": "a1a1d3ea-9b26-45aa-871a-16714b824eeb", "timestamp": + "2025-09-23T22:00:38.326403+00:00", "type": "tool_usage_finished", "event_data": + {"timestamp": "2025-09-23T22:00:38.326376+00:00", "type": "tool_usage_finished", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "dc8bb909-2112-4834-9bb2-755e9aac1202", "task_name": "Process initial + data", "agent_id": null, "agent_role": "Crew Manager", "agent_key": "6b5becc64d7e3c705a7d3784a5fab1d3", + "tool_name": "Delegate work to coworker", "tool_args": {"task": "Process initial + data", "context": "The task involves analyzing the initial data set we have + received. This includes cleaning the data, categorizing it for analysis, identifying + any trends or patterns, and summarizing the findings. The goal is to have a + clear understanding of what the data indicates and any initial insights that + can be drawn from it.", "coworker": "First Agent"}, "tool_class": "CrewStructuredTool", + "run_attempts": 1, "delegations": 1, "agent": null, "from_task": null, "from_agent": + null, "started_at": "2025-09-23T15:00:38.324061", "finished_at": "2025-09-23T15:00:38.326362", + "from_cache": false, "output": "To process the initial data set effectively, + we will follow several key steps to ensure we clean, categorize, analyze, and + summarize our findings comprehensively.\n\n1. **Data Cleaning**: \n - First, + we will review the dataset for missing, inconsistent, or erroneous entries. + \n - We will handle missing values by either imputing them based on surrounding + data or removing records where necessary.\n - Additionally, we will standardize + categorical variables to ensure consistency (e.g., ensuring all location names + are spelled the same way).\n\n2. **Data Categorization**: \n - Next, we will + categorize the data into relevant segments that will aid our analysis. \n - + This involves grouping data points based on common characteristics, such as + demographics, time periods, or any key performance indicators (KPIs) we are + focusing on.\n\n3. **Trend and Pattern Identification**: \n - With the cleaned + and categorized data, we will perform a detailed analysis to identify trends + and patterns.\n - This will involve using statistical tools and visualizations + to uncover relationships within the data. We will look at time series analysis, + correlation coefficients, and any significant outliers that may require further + investigation.\n\n4. **Summarizing Findings**: \n - Finally, we will compile + a summary of our findings which will include both qualitative insights and quantitative + metrics.\n - This summary should encapsulate the key trends identified, any + notable patterns, and implications of these findings.\n - We will also document + any limitations of the data and suggest areas for further research if necessary.\n\nBy + completing these steps, we will not only have a clear understanding of what + the data indicates but also provide actionable insights that can guide our next + steps. This comprehensive analysis will serve as a solid foundation for any + additional exploration or decision-making initiatives related to our project. + \n\nIf you have any questions or need further clarification on any part of this + process, please let me know!"}}, {"event_id": "5f0246bc-25f1-4343-974e-68d5b5aaf46c", + "timestamp": "2025-09-23T22:00:38.326473+00:00", "type": "llm_call_started", + "event_data": {"timestamp": "2025-09-23T22:00:38.326462+00:00", "type": "llm_call_started", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "dc8bb909-2112-4834-9bb2-755e9aac1202", "task_name": "Process initial + data", "agent_id": "b0898472-5e3b-45bb-bd90-05bad0b5a8ce", "agent_role": "Crew + Manager", "from_task": null, "from_agent": null, "model": "gpt-4o", "messages": + [{"role": "system", "content": "You are Crew Manager. You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker\nTool Arguments: {''task'': {''description'': ''The task to + delegate'', ''type'': ''str''}, ''context'': {''description'': ''The context + for the task'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name + of the coworker to delegate to'', ''type'': ''str''}}\nTool Description: Delegate + a specific task to one of the following coworkers: First Agent\nThe input to + this tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolutely + everything you know, don''t reference things but instead explain them.\nTool + Name: Ask question to coworker\nTool Arguments: {''question'': {''description'': + ''The question to ask'', ''type'': ''str''}, ''context'': {''description'': + ''The context for the question'', ''type'': ''str''}, ''coworker'': {''description'': + ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool Description: + Ask a specific question to one of the following coworkers: First Agent\nThe + input to this tool should be the coworker, the question you have for them, and + ALL necessary context to ask the question properly, they know nothing about + the question, so share absolutely everything you know, don''t reference things + but instead explain them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [Delegate work to coworker, Ask question to coworker], just the name, + exactly as it''s written.\nAction Input: the input to the action, just a simple + JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Process initial data\n\nThis is the expected criteria + for your final answer: Initial analysis\nyou MUST return the actual complete + content as the final answer, not a summary.\n\nBegin! This is VERY important + to you, use the tools available and give your best Final Answer, your job depends + on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: I need to delegate + the task of processing the initial data to the First Agent to ensure we have + a thorough and accurate analysis. I will provide them with all the necessary + details to complete this task effectively.\n\nAction: Delegate work to coworker\nAction + Input: {\"task\": \"Process initial data\", \"context\": \"The task involves + analyzing the initial data set we have received. This includes cleaning the + data, categorizing it for analysis, identifying any trends or patterns, and + summarizing the findings. The goal is to have a clear understanding of what + the data indicates and any initial insights that can be drawn from it.\", \"coworker\": + \"First Agent\"}\nObservation: To process the initial data set effectively, + we will follow several key steps to ensure we clean, categorize, analyze, and + summarize our findings comprehensively.\n\n1. **Data Cleaning**: \n - First, + we will review the dataset for missing, inconsistent, or erroneous entries. + \n - We will handle missing values by either imputing them based on surrounding + data or removing records where necessary.\n - Additionally, we will standardize + categorical variables to ensure consistency (e.g., ensuring all location names + are spelled the same way).\n\n2. **Data Categorization**: \n - Next, we will + categorize the data into relevant segments that will aid our analysis. \n - + This involves grouping data points based on common characteristics, such as + demographics, time periods, or any key performance indicators (KPIs) we are + focusing on.\n\n3. **Trend and Pattern Identification**: \n - With the cleaned + and categorized data, we will perform a detailed analysis to identify trends + and patterns.\n - This will involve using statistical tools and visualizations + to uncover relationships within the data. We will look at time series analysis, + correlation coefficients, and any significant outliers that may require further + investigation.\n\n4. **Summarizing Findings**: \n - Finally, we will compile + a summary of our findings which will include both qualitative insights and quantitative + metrics.\n - This summary should encapsulate the key trends identified, any + notable patterns, and implications of these findings.\n - We will also document + any limitations of the data and suggest areas for further research if necessary.\n\nBy + completing these steps, we will not only have a clear understanding of what + the data indicates but also provide actionable insights that can guide our next + steps. 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{''description'': ''The task to delegate'', ''type'': ''str''}, ''context'': + {''description'': ''The context for the task'', ''type'': ''str''}, ''coworker'': + {''description'': ''The role/name of the coworker to delegate to'', ''type'': + ''str''}}\nTool Description: Delegate a specific task to one of the following + coworkers: First Agent\nThe input to this tool should be the coworker, the task + you want them to do, and ALL necessary context to execute the task, they know + nothing about the task, so share absolutely everything you know, don''t reference + things but instead explain them.\nTool Name: Ask question to coworker\nTool + Arguments: {''question'': {''description'': ''The question to ask'', ''type'': + ''str''}, ''context'': {''description'': ''The context for the question'', ''type'': + ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to + ask'', ''type'': ''str''}}\nTool Description: Ask a specific question to one + of the following coworkers: First Agent\nThe input to this tool should be the + coworker, the question you have for them, and ALL necessary context to ask the + question properly, they know nothing about the question, so share absolutely + everything you know, don''t reference things but instead explain them.\n\nIMPORTANT: + Use the following format in your response:\n\n```\nThought: you should always + think about what to do\nAction: the action to take, only one name of [Delegate + work to coworker, Ask question to coworker], just the name, exactly as it''s + written.\nAction Input: the input to the action, just a simple JSON object, + enclosed in curly braces, using \" to wrap keys and values.\nObservation: the + result of the action\n```\n\nOnce all necessary information is gathered, return + the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: + the final answer to the original input question\n```"}, {"role": "user", "content": + "\nCurrent Task: Process initial data\n\nThis is the expected criteria for your + final answer: Initial analysis\nyou MUST return the actual complete content + as the final answer, not a summary.\n\nBegin! This is VERY important to you, + use the tools available and give your best Final Answer, your job depends on + it!\n\nThought:"}], "tools": null, "callbacks": [""], "available_functions": null}}, {"event_id": "c5fddadc-afb7-41e4-b3f5-dc1ecb882f44", + "timestamp": "2025-09-24T05:24:11.452266+00:00", "type": "llm_call_completed", + "event_data": {"timestamp": "2025-09-24T05:24:11.451919+00:00", "type": "llm_call_completed", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "d112deef-93fb-46ea-bba2-a56b52712d0a", "task_name": "Process initial + data", "agent_id": "09794b42-447f-4b7a-b634-3a861f457357", "agent_role": "Crew + Manager", "from_task": null, "from_agent": null, "messages": [{"role": "system", + "content": "You are Crew Manager. You are a seasoned manager with a knack for + getting the best out of your team.\nYou are also known for your ability to delegate + work to the right people, and to ask the right questions to get the best out + of your team.\nEven though you don''t perform tasks by yourself, you have a + lot of experience in the field, which allows you to properly evaluate the work + of your team members.\nYour personal goal is: Manage the team to complete the + task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker\nTool Arguments: {''task'': {''description'': ''The task to + delegate'', ''type'': ''str''}, ''context'': {''description'': ''The context + for the task'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name + of the coworker to delegate to'', ''type'': ''str''}}\nTool Description: Delegate + a specific task to one of the following coworkers: First Agent\nThe input to + this tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolutely + everything you know, don''t reference things but instead explain them.\nTool + Name: Ask question to coworker\nTool Arguments: {''question'': {''description'': + ''The question to ask'', ''type'': ''str''}, ''context'': {''description'': + ''The context for the question'', ''type'': ''str''}, ''coworker'': {''description'': + ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool Description: + Ask a specific question to one of the following coworkers: First Agent\nThe + input to this tool should be the coworker, the question you have for them, and + ALL necessary context to ask the question properly, they know nothing about + the question, so share absolutely everything you know, don''t reference things + but instead explain them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [Delegate work to coworker, Ask question to coworker], just the name, + exactly as it''s written.\nAction Input: the input to the action, just a simple + JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Process initial data\n\nThis is the expected criteria + for your final answer: Initial analysis\nyou MUST return the actual complete + content as the final answer, not a summary.\n\nBegin! 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answer to your coworker asking you this, accounting for the context + shared.\nyou MUST return the actual complete content as the final answer, not + a summary.\n\nThis is the context you''re working with:\nThe task involves analyzing + the initial data set we have received. This includes cleaning the data, categorizing + it for analysis, identifying any trends or patterns, and summarizing the findings. + The goal is to have a clear understanding of what the data indicates and any + initial insights that can be drawn from it.\n\nBegin! 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First backstory\nYour personal goal is: First + goal\nTo give my best complete final answer to the task respond using the exact + following format:\n\nThought: I now can give a great answer\nFinal Answer: Your + final answer must be the great and the most complete as possible, it must be + outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": + "user", "content": "\nCurrent Task: Process initial data\n\nThis is the expected + criteria for your final answer: Your best answer to your coworker asking you + this, accounting for the context shared.\nyou MUST return the actual complete + content as the final answer, not a summary.\n\nThis is the context you''re working + with:\nThe task involves analyzing the initial data set we have received. This + includes cleaning the data, categorizing it for analysis, identifying any trends + or patterns, and summarizing the findings. The goal is to have a clear understanding + of what the data indicates and any initial insights that can be drawn from it.\n\nBegin! + This is VERY important to you, use the tools available and give your best Final + Answer, your job depends on it!\n\nThought:"}], "response": "I now can give + a great answer \nFinal Answer: To process the initial data set effectively, + we will follow several key steps to ensure we clean, categorize, analyze, and + summarize our findings comprehensively.\n\n1. **Data Cleaning**: \n - First, + we will review the dataset for missing, inconsistent, or erroneous entries. + \n - We will handle missing values by either imputing them based on surrounding + data or removing records where necessary.\n - Additionally, we will standardize + categorical variables to ensure consistency (e.g., ensuring all location names + are spelled the same way).\n\n2. **Data Categorization**: \n - Next, we will + categorize the data into relevant segments that will aid our analysis. \n - + This involves grouping data points based on common characteristics, such as + demographics, time periods, or any key performance indicators (KPIs) we are + focusing on.\n\n3. **Trend and Pattern Identification**: \n - With the cleaned + and categorized data, we will perform a detailed analysis to identify trends + and patterns.\n - This will involve using statistical tools and visualizations + to uncover relationships within the data. We will look at time series analysis, + correlation coefficients, and any significant outliers that may require further + investigation.\n\n4. **Summarizing Findings**: \n - Finally, we will compile + a summary of our findings which will include both qualitative insights and quantitative + metrics.\n - This summary should encapsulate the key trends identified, any + notable patterns, and implications of these findings.\n - We will also document + any limitations of the data and suggest areas for further research if necessary.\n\nBy + completing these steps, we will not only have a clear understanding of what + the data indicates but also provide actionable insights that can guide our next + steps. This comprehensive analysis will serve as a solid foundation for any + additional exploration or decision-making initiatives related to our project. + \n\nIf you have any questions or need further clarification on any part of this + process, please let me know!", "call_type": "", + "model": "gpt-4o-mini"}}, {"event_id": "ea73190b-14dc-4caf-be63-921bd5e3c09e", + "timestamp": "2025-09-24T05:24:11.455967+00:00", "type": "agent_execution_completed", + "event_data": {"agent_role": "First Agent", "agent_goal": "First goal", "agent_backstory": + "First backstory"}}, {"event_id": "fbf8b1cf-8692-4a14-af49-84a04b54678d", "timestamp": + "2025-09-24T05:24:11.456088+00:00", "type": "tool_usage_finished", "event_data": + {"timestamp": "2025-09-24T05:24:11.456060+00:00", "type": "tool_usage_finished", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "d112deef-93fb-46ea-bba2-a56b52712d0a", "task_name": "Process initial + data", "agent_id": null, "agent_role": "Crew Manager", "agent_key": "6b5becc64d7e3c705a7d3784a5fab1d3", + "tool_name": "Delegate work to coworker", "tool_args": {"task": "Process initial + data", "context": "The task involves analyzing the initial data set we have + received. This includes cleaning the data, categorizing it for analysis, identifying + any trends or patterns, and summarizing the findings. The goal is to have a + clear understanding of what the data indicates and any initial insights that + can be drawn from it.", "coworker": "First Agent"}, "tool_class": "CrewStructuredTool", + "run_attempts": 1, "delegations": 1, "agent": null, "from_task": null, "from_agent": + null, "started_at": "2025-09-23T22:24:11.453368", "finished_at": "2025-09-23T22:24:11.456043", + "from_cache": false, "output": "To process the initial data set effectively, + we will follow several key steps to ensure we clean, categorize, analyze, and + summarize our findings comprehensively.\n\n1. **Data Cleaning**: \n - First, + we will review the dataset for missing, inconsistent, or erroneous entries. + \n - We will handle missing values by either imputing them based on surrounding + data or removing records where necessary.\n - Additionally, we will standardize + categorical variables to ensure consistency (e.g., ensuring all location names + are spelled the same way).\n\n2. **Data Categorization**: \n - Next, we will + categorize the data into relevant segments that will aid our analysis. \n - + This involves grouping data points based on common characteristics, such as + demographics, time periods, or any key performance indicators (KPIs) we are + focusing on.\n\n3. **Trend and Pattern Identification**: \n - With the cleaned + and categorized data, we will perform a detailed analysis to identify trends + and patterns.\n - This will involve using statistical tools and visualizations + to uncover relationships within the data. 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This comprehensive analysis will serve as a solid foundation for any + additional exploration or decision-making initiatives related to our project. + \n\nIf you have any questions or need further clarification on any part of this + process, please let me know!"}}, {"event_id": "fb28b62f-ee47-4e82-b4e4-d212929dbd25", + "timestamp": "2025-09-24T05:24:11.456167+00:00", "type": "llm_call_started", + "event_data": {"timestamp": "2025-09-24T05:24:11.456154+00:00", "type": "llm_call_started", + "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, + "task_id": "d112deef-93fb-46ea-bba2-a56b52712d0a", "task_name": "Process initial + data", "agent_id": "09794b42-447f-4b7a-b634-3a861f457357", "agent_role": "Crew + Manager", "from_task": null, "from_agent": null, "model": "gpt-4o", "messages": + [{"role": "system", "content": "You are Crew Manager. You are a seasoned manager + with a knack for getting the best out of your team.\nYou are also known for + your ability to delegate work to the right people, and to ask the right questions + to get the best out of your team.\nEven though you don''t perform tasks by yourself, + you have a lot of experience in the field, which allows you to properly evaluate + the work of your team members.\nYour personal goal is: Manage the team to complete + the task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker\nTool Arguments: {''task'': {''description'': ''The task to + delegate'', ''type'': ''str''}, ''context'': {''description'': ''The context + for the task'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name + of the coworker to delegate to'', ''type'': ''str''}}\nTool Description: Delegate + a specific task to one of the following coworkers: First Agent\nThe input to + this tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolutely + everything you know, don''t reference things but instead explain them.\nTool + Name: Ask question to coworker\nTool Arguments: {''question'': {''description'': + ''The question to ask'', ''type'': ''str''}, ''context'': {''description'': + ''The context for the question'', ''type'': ''str''}, ''coworker'': {''description'': + ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool Description: + Ask a specific question to one of the following coworkers: First Agent\nThe + input to this tool should be the coworker, the question you have for them, and + ALL necessary context to ask the question properly, they know nothing about + the question, so share absolutely everything you know, don''t reference things + but instead explain them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [Delegate work to coworker, Ask question to coworker], just the name, + exactly as it''s written.\nAction Input: the input to the action, just a simple + JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Process initial data\n\nThis is the expected criteria + for your final answer: Initial analysis\nyou MUST return the actual complete + content as the final answer, not a summary.\n\nBegin! This is VERY important + to you, use the tools available and give your best Final Answer, your job depends + on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: I need to delegate + the task of processing the initial data to the First Agent to ensure we have + a thorough and accurate analysis. I will provide them with all the necessary + details to complete this task effectively.\n\nAction: Delegate work to coworker\nAction + Input: {\"task\": \"Process initial data\", \"context\": \"The task involves + analyzing the initial data set we have received. This includes cleaning the + data, categorizing it for analysis, identifying any trends or patterns, and + summarizing the findings. The goal is to have a clear understanding of what + the data indicates and any initial insights that can be drawn from it.\", \"coworker\": + \"First Agent\"}\nObservation: To process the initial data set effectively, + we will follow several key steps to ensure we clean, categorize, analyze, and + summarize our findings comprehensively.\n\n1. **Data Cleaning**: \n - First, + we will review the dataset for missing, inconsistent, or erroneous entries. + \n - We will handle missing values by either imputing them based on surrounding + data or removing records where necessary.\n - Additionally, we will standardize + categorical variables to ensure consistency (e.g., ensuring all location names + are spelled the same way).\n\n2. **Data Categorization**: \n - Next, we will + categorize the data into relevant segments that will aid our analysis. \n - + This involves grouping data points based on common characteristics, such as + demographics, time periods, or any key performance indicators (KPIs) we are + focusing on.\n\n3. **Trend and Pattern Identification**: \n - With the cleaned + and categorized data, we will perform a detailed analysis to identify trends + and patterns.\n - This will involve using statistical tools and visualizations + to uncover relationships within the data. We will look at time series analysis, + correlation coefficients, and any significant outliers that may require further + investigation.\n\n4. **Summarizing Findings**: \n - Finally, we will compile + a summary of our findings which will include both qualitative insights and quantitative + metrics.\n - This summary should encapsulate the key trends identified, any + notable patterns, and implications of these findings.\n - We will also document + any limitations of the data and suggest areas for further research if necessary.\n\nBy + completing these steps, we will not only have a clear understanding of what + the data indicates but also provide actionable insights that can guide our next + steps. 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You are a seasoned manager with a knack for + getting the best out of your team.\nYou are also known for your ability to delegate + work to the right people, and to ask the right questions to get the best out + of your team.\nEven though you don''t perform tasks by yourself, you have a + lot of experience in the field, which allows you to properly evaluate the work + of your team members.\nYour personal goal is: Manage the team to complete the + task in the best way possible.\nYou ONLY have access to the following tools, + and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate + work to coworker\nTool Arguments: {''task'': {''description'': ''The task to + delegate'', ''type'': ''str''}, ''context'': {''description'': ''The context + for the task'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name + of the coworker to delegate to'', ''type'': ''str''}}\nTool Description: Delegate + a specific task to one of the following coworkers: First Agent\nThe input to + this tool should be the coworker, the task you want them to do, and ALL necessary + context to execute the task, they know nothing about the task, so share absolutely + everything you know, don''t reference things but instead explain them.\nTool + Name: Ask question to coworker\nTool Arguments: {''question'': {''description'': + ''The question to ask'', ''type'': ''str''}, ''context'': {''description'': + ''The context for the question'', ''type'': ''str''}, ''coworker'': {''description'': + ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool Description: + Ask a specific question to one of the following coworkers: First Agent\nThe + input to this tool should be the coworker, the question you have for them, and + ALL necessary context to ask the question properly, they know nothing about + the question, so share absolutely everything you know, don''t reference things + but instead explain them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: + you should always think about what to do\nAction: the action to take, only one + name of [Delegate work to coworker, Ask question to coworker], just the name, + exactly as it''s written.\nAction Input: the input to the action, just a simple + JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: + the result of the action\n```\n\nOnce all necessary information is gathered, + return the following format:\n\n```\nThought: I now know the final answer\nFinal + Answer: the final answer to the original input question\n```"}, {"role": "user", + "content": "\nCurrent Task: Process initial data\n\nThis is the expected criteria + for your final answer: Initial analysis\nyou MUST return the actual complete + content as the final answer, not a summary.\n\nBegin! This is VERY important + to you, use the tools available and give your best Final Answer, your job depends + on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: I need to delegate + the task of processing the initial data to the First Agent to ensure we have + a thorough and accurate analysis. I will provide them with all the necessary + details to complete this task effectively.\n\nAction: Delegate work to coworker\nAction + Input: {\"task\": \"Process initial data\", \"context\": \"The task involves + analyzing the initial data set we have received. This includes cleaning the + data, categorizing it for analysis, identifying any trends or patterns, and + summarizing the findings. The goal is to have a clear understanding of what + the data indicates and any initial insights that can be drawn from it.\", \"coworker\": + \"First Agent\"}\nObservation: To process the initial data set effectively, + we will follow several key steps to ensure we clean, categorize, analyze, and + summarize our findings comprehensively.\n\n1. **Data Cleaning**: \n - First, + we will review the dataset for missing, inconsistent, or erroneous entries. + \n - We will handle missing values by either imputing them based on surrounding + data or removing records where necessary.\n - Additionally, we will standardize + categorical variables to ensure consistency (e.g., ensuring all location names + are spelled the same way).\n\n2. **Data Categorization**: \n - Next, we will + categorize the data into relevant segments that will aid our analysis. \n - + This involves grouping data points based on common characteristics, such as + demographics, time periods, or any key performance indicators (KPIs) we are + focusing on.\n\n3. **Trend and Pattern Identification**: \n - With the cleaned + and categorized data, we will perform a detailed analysis to identify trends + and patterns.\n - This will involve using statistical tools and visualizations + to uncover relationships within the data. We will look at time series analysis, + correlation coefficients, and any significant outliers that may require further + investigation.\n\n4. **Summarizing Findings**: \n - Finally, we will compile + a summary of our findings which will include both qualitative insights and quantitative + metrics.\n - This summary should encapsulate the key trends identified, any + notable patterns, and implications of these findings.\n - We will also document + any limitations of the data and suggest areas for further research if necessary.\n\nBy + completing these steps, we will not only have a clear understanding of what + the data indicates but also provide actionable insights that can guide our next + steps. This comprehensive analysis will serve as a solid foundation for any + additional exploration or decision-making initiatives related to our project. + \n\nIf you have any questions or need further clarification on any part of this + process, please let me know!"}], "response": "```\nThought: I now know the final + answer\nFinal Answer: To process the initial data set effectively, we will follow + several key steps to ensure we clean, categorize, analyze, and summarize our + findings comprehensively.\n\n1. **Data Cleaning**: \n - First, we will review + the dataset for missing, inconsistent, or erroneous entries. \n - We will + handle missing values by either imputing them based on surrounding data or removing + records where necessary.\n - Additionally, we will standardize categorical + variables to ensure consistency (e.g., ensuring all location names are spelled + the same way).\n\n2. **Data Categorization**: \n - Next, we will categorize + the data into relevant segments that will aid our analysis. \n - This involves + grouping data points based on common characteristics, such as demographics, + time periods, or any key performance indicators (KPIs) we are focusing on.\n\n3. + **Trend and Pattern Identification**: \n - With the cleaned and categorized + data, we will perform a detailed analysis to identify trends and patterns.\n - + This will involve using statistical tools and visualizations to uncover relationships + within the data. We will look at time series analysis, correlation coefficients, + and significant outliers that may require further investigation.\n\n4. **Summarizing + Findings**: \n - Finally, we will compile a summary of our findings, including + both qualitative insights and quantitative metrics.\n - This summary should + encapsulate the key trends identified, any notable patterns, and implications + of these findings.\n - We will also document any limitations of the data and + suggest areas for further research if necessary.\n\nBy completing these steps, + we will not only have a clear understanding of what the data indicates but also + provide actionable insights that can guide our next steps. This comprehensive + analysis will serve as a solid foundation for any additional exploration or + decision-making initiatives related to our project. \n```\n", "call_type": "", "model": "gpt-4o"}}, {"event_id": "9aec0184-de1c-40d1-b407-7cea95ba8336", + "timestamp": "2025-09-24T05:24:11.457064+00:00", "type": "agent_execution_completed", + "event_data": {"agent_role": "Crew Manager", "agent_goal": "Manage the team + to complete the task in the best way possible.", "agent_backstory": "You are + a seasoned manager with a knack for getting the best out of your team.\nYou + are also known for your ability to delegate work to the right people, and to + ask the right questions to get the best out of your team.\nEven though you don''t + perform tasks by yourself, you have a lot of experience in the field, which + allows you to properly evaluate the work of your team members."}}, {"event_id": + "a41004c8-4211-4656-8d36-abb361de4dc1", "timestamp": "2025-09-24T05:24:11.457121+00:00", + "type": "task_completed", "event_data": {"task_description": "Process initial + data", "task_name": "Process initial data", "task_id": "d112deef-93fb-46ea-bba2-a56b52712d0a", + "output_raw": "To process the initial data set effectively, we will follow several + key steps to ensure we clean, categorize, analyze, and summarize our findings + comprehensively.\n\n1. **Data Cleaning**: \n - First, we will review the dataset + for missing, inconsistent, or erroneous entries. \n - We will handle missing + values by either imputing them based on surrounding data or removing records + where necessary.\n - Additionally, we will standardize categorical variables + to ensure consistency (e.g., ensuring all location names are spelled the same + way).\n\n2. **Data Categorization**: \n - Next, we will categorize the data + into relevant segments that will aid our analysis. \n - This involves grouping + data points based on common characteristics, such as demographics, time periods, + or any key performance indicators (KPIs) we are focusing on.\n\n3. **Trend and + Pattern Identification**: \n - With the cleaned and categorized data, we will + perform a detailed analysis to identify trends and patterns.\n - This will + involve using statistical tools and visualizations to uncover relationships + within the data. 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It involves + evaluating the sources of the data, the methodology used in its collection, + and any potential biases. \n\n1. **Data Sources**: Identify where the data originated, + including databases, surveys, experiments, or third-party sources.\n\n2. **Data + Types**: Determine the types of data (quantitative or qualitative) and the specific + metrics involved (e.g., numerical values, text responses, categorical data).\n\n3. + **Preliminary Trends**: Look for initial trends in the data, such as averages, + distributions, and correlations between variables. This can include graphical + representations like charts or histograms to visualize trends.\n\n4. **Outliers**: + Identify any data points that significantly deviate from the expected range, + which could affect the overall analysis. Understand potential reasons for these + anomalies.\n\n5. **Comparative Analysis**: If applicable, compare the data across + different segments or over time to identify stable trends versus temporary fluctuations.\n\n6. + **Limitations**: Recognize any limitations within the dataset, including missing + data, potential errors in data entry, and sampling biases that could affect + the reliability of the analysis.\n\n7. **Recommendations for Further Analysis**: + Based on the initial analysis, suggest areas for deeper investigation. This + may include additional data collection, more complex modeling, or exploring + other variables that could influence the findings.\n\nBy thoroughly addressing + these elements, the initial analysis will provide a solid foundational understanding + of the dataset, paving the way for informed decision-making and strategic planning.\n\nBegin! + This is VERY important to you, use the tools available and give your best Final + Answer, your job depends on it!\n\nThought:"}], "response": "I now can give + a great answer \nFinal Answer: \n\n### Comprehensive Examination of Secondary + Data\n\n#### 1. **Data Sources**\nThe data originates from multiple sources, + attempting to encapsulate a holistic view of the researched topic. Specific + data sources include:\n- **Databases**: Publicly accessible databases, such + as PubMed for health-related data and the U.S. Census Bureau for demographic + statistics.\n- **Surveys**: National surveys conducted by reputable organizations + such as Pew Research Center, providing data on public opinion and behavior.\n- + **Experiments**: Published studies which have adhered to peer-review standards, + ensuring methodological rigor.\n- **Third-party sources**: Reports from think + tanks and academic institutions which aggregate data from primary research.\n\n#### + 2. **Data Types**\nThe dataset comprises both quantitative and qualitative types:\n- + **Quantitative Data**: Numerical values are predominantly used, including continuous + metrics such as age, income levels, and frequency of events. This is suitable + for statistical analysis.\n- **Qualitative Data**: Text responses from surveys + that capture opinions, experiences, and feedback. This can involve coding responses + into categories for easier analysis.\n\n#### 3. **Preliminary Trends**\nInitial + trends observed in the dataset include:\n- **Averages**: Calculation of mean + and median values to measure central tendency (e.g., average income levels across + demographic groups).\n- **Distributions**: Graphical representation using histograms + reveals how data points are spread across different categories or values (e.g., + age groups).\n- **Correlations**: Initial analysis indicates potential correlations, + such as between education level and income, visualized through scatter plots + which depict the relationship between the two variables.\n\n#### 4. **Outliers**\nThe + analysis identifies several outliers:\n- Data points significantly exceeding + or falling below expected ranges (e.g., an income level substantially higher + than the surrounding cluster).\n- Potential reasons for these anomalies might + include errors in data entry, unique subpopulations not representative of the + larger group, or influential cases that merit further exploration.\n\n#### 5. + **Comparative Analysis**\nComparative analysis reveals:\n- **Temporal Fluctuations**: + Examining the same dataset over time indicates fluctuations in responses, such + as changing public opinion on specific social issues.\n- **Segmentation**: Segmenting + data by demographic factors (e.g., age, income, education) allows for comparisons + that highlight significant differences across groups, reinforcing the stability + or volatility of particular trends.\n\n#### 6. **Limitations**\nRecognizing + limitations is crucial:\n- **Missing Data**: Instances where values are absent, + leading to gaps in the analysis. 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Return your strucutred output in JSON format + "What is the population of Tokyo? 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If + you distribute or make available the Llama Materials (or any derivative works + thereof), \\nor a product or service (including another AI model) that contains + any of them, you shall (A) provide\\na copy of this Agreement with any such + Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is distributed + or made available, you shall also include \u201CLlama\u201D\\nat the beginning + of any such AI model name.\\n\\n ii. If you receive Llama Materials, + or any derivative works thereof, from a Licensee as part\\nof an integrated + end user product, then Section 2 of this Agreement will not apply to you. \\n\\n + \ iii. 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If, on the Llama 3.2 version release date, the + monthly active users\\nof the products or services made available by or for + Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly + active users in the preceding calendar month, you must request \\na license + from Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or until + Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. + UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS + THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF + ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND + IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, + MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR + DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS + AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY + OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR + ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, + TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, + \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, + EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED + OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n + \ a. No trademark licenses are granted under this Agreement, and in connection + with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark + owned by or associated with the other or any of its affiliates, \\nexcept as + required for reasonable and customary use in describing and redistributing the + Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants + you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s + brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. 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Term and Termination. + The term of this Agreement will commence upon your acceptance of this Agreement + or access\\nto the Llama Materials and will continue in full force and effect + until terminated in accordance with the terms\\nand conditions herein. Meta + may terminate this Agreement if you are in breach of any term or condition of + this\\nAgreement. Upon termination of this Agreement, you shall delete and cease + use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination + of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will + be governed and construed under the laws of the State of \\nCalifornia without + regard to choice of law principles, and the UN Convention on Contracts for the + International\\nSale of Goods does not apply to this Agreement. The courts of + California shall have exclusive jurisdiction of\\nany dispute arising out of + this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed + to promoting safe and fair use of its tools and features, including Llama 3.2. + If you access or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). + The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree + you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate + the law or others\u2019 rights, including to:\\n 1. Engage in, promote, generate, + contribute to, encourage, plan, incite, or further illegal or unlawful activity + or content, such as:\\n 1. Violence or terrorism\\n 2. 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Interact with third party + tools, models, or software designed to generate unlawful content or engage in + unlawful or harmful conduct and/or represent that the outputs of such tools, + models, or software are associated with Meta or Llama 3.2\\n\\nWith respect + to any multimodal models included in Llama 3.2, the rights granted under Section + 1(a) of the Llama 3.2 Community License Agreement are not being granted to you + if you are an individual domiciled in, or a company with a principal place of + business in, the European Union. This restriction does not apply to end users + of a product or service that incorporates any such multimodal models.\\n\\nPlease + report any violation of this Policy, software \u201Cbug,\u201D or other problems + that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama + show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# + FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE + \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- + if .Tools }}When you receive a tool call response, use the output to format + an answer to the orginal user question.\\n\\nYou are a helpful assistant with + tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, + $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond with + a JSON for a function call with its proper arguments that best answers the given + prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": + dictionary of argument name and its value}. 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(if you are located outside of the + EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using + or distributing any portion or element of the Llama Materials,\\nyou agree to + be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to the + Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If + you distribute or make available the Llama Materials (or any derivative works + thereof), \\nor a product or service (including another AI model) that contains + any of them, you shall (A) provide\\na copy of this Agreement with any such + Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is distributed + or made available, you shall also include \u201CLlama\u201D\\nat the beginning + of any such AI model name.\\n\\n ii. If you receive Llama Materials, + or any derivative works thereof, from a Licensee as part\\nof an integrated + end user product, then Section 2 of this Agreement will not apply to you. \\n\\n + \ iii. You must retain in all copies of the Llama Materials that you distribute + the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed + as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 + Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n + \ iv. Your use of the Llama Materials must comply with applicable laws + and regulations\\n(including trade compliance laws and regulations) and adhere + to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), + which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. + Additional Commercial Terms. If, on the Llama 3.2 version release date, the + monthly active users\\nof the products or services made available by or for + Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly + active users in the preceding calendar month, you must request \\na license + from Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or until + Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. + UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS + THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF + ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND + IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, + MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR + DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS + AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY + OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR + ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, + TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, + \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, + EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED + OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n + \ a. No trademark licenses are granted under this Agreement, and in connection + with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark + owned by or associated with the other or any of its affiliates, \\nexcept as + required for reasonable and customary use in describing and redistributing the + Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants + you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s + brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between you + and Meta,\\n you are and will be the owner of such derivative works and modifications.\\n\\n + \ c. If you institute litigation or other proceedings against Meta or any + entity (including a cross-claim or\\n counterclaim in a lawsuit) alleging + that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n + \ of any of the foregoing, constitutes infringement of intellectual property + or other rights owned or licensable\\n by you, then any licenses granted + to you under this Agreement shall terminate as of the date such litigation or\\n + \ claim is filed or instituted. You will indemnify and hold harmless Meta + from and against any claim by any third\\n party arising out of or related + to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination. + The term of this Agreement will commence upon your acceptance of this Agreement + or access\\nto the Llama Materials and will continue in full force and effect + until terminated in accordance with the terms\\nand conditions herein. Meta + may terminate this Agreement if you are in breach of any term or condition of + this\\nAgreement. Upon termination of this Agreement, you shall delete and cease + use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination + of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will + be governed and construed under the laws of the State of \\nCalifornia without + regard to choice of law principles, and the UN Convention on Contracts for the + International\\nSale of Goods does not apply to this Agreement. The courts of + California shall have exclusive jurisdiction of\\nany dispute arising out of + this Agreement.\\\"\\nLICENSE \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta + is committed to promoting safe and fair use of its tools and features, including + Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use + Policy (\u201C**Policy**\u201D). The most recent copy of this policy can be + found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree + you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate + the law or others\u2019 rights, including to:\\n 1. Engage in, promote, generate, + contribute to, encourage, plan, incite, or further illegal or unlawful activity + or content, such as:\\n 1. Violence or terrorism\\n 2. Exploitation + or harm to children, including the solicitation, creation, acquisition, or dissemination + of child exploitative content or failure to report Child Sexual Abuse Material\\n + \ 3. Human trafficking, exploitation, and sexual violence\\n 4. + The illegal distribution of information or materials to minors, including obscene + materials, or failure to employ legally required age-gating in connection with + such information or materials.\\n 5. Sexual solicitation\\n 6. + Any other criminal activity\\n 1. Engage in, promote, incite, or facilitate + the harassment, abuse, threatening, or bullying of individuals or groups of + individuals\\n 2. Engage in, promote, incite, or facilitate discrimination + or other unlawful or harmful conduct in the provision of employment, employment + benefits, credit, housing, other economic benefits, or other essential goods + and services\\n 3. Engage in the unauthorized or unlicensed practice of any + profession including, but not limited to, financial, legal, medical/health, + or related professional practices\\n 4. Collect, process, disclose, generate, + or infer private or sensitive information about individuals, including information + about individuals\u2019 identity, health, or demographic information, unless + you have obtained the right to do so in accordance with applicable law\\n 5. + Engage in or facilitate any action or generate any content that infringes, misappropriates, + or otherwise violates any third-party rights, including the outputs or results + of any products or services using the Llama Materials\\n 6. Create, generate, + or facilitate the creation of malicious code, malware, computer viruses or do + anything else that could disable, overburden, interfere with or impair the proper + working, integrity, operation or appearance of a website or computer system\\n + \ 7. Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the + planning or development of activities that present a risk of death or bodily + harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic Arms + Regulations (ITAR) maintained by the United States Department of State or to + the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons + Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including + weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n + \ 11. Operation of critical infrastructure, transportation technologies, or + heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting, + and eating disorders\\n 13. Any content intended to incite or promote violence, + abuse, or any infliction of bodily harm to an individual\\n3. Intentionally + deceive or mislead others, including use of Llama 3.2 related to the following:\\n + \ 14. Generating, promoting, or furthering fraud or the creation or promotion + of disinformation\\n 15. Generating, promoting, or furthering defamatory + content, including the creation of defamatory statements, images, or other content\\n + \ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating + another individual without consent, authorization, or legal right\\n 18. + Representing that the use of Llama 3.2 or outputs are human-generated\\n 19. + Generating or facilitating false online engagement, including fake reviews and + other means of fake online engagement\\n4. Fail to appropriately disclose to + end users any known dangers of your AI system\\n5. Interact with third party + tools, models, or software designed to generate unlawful content or engage in + unlawful or harmful conduct and/or represent that the outputs of such tools, + models, or software are associated with Meta or Llama 3.2\\n\\nWith respect + to any multimodal models included in Llama 3.2, the rights granted under Section + 1(a) of the Llama 3.2 Community License Agreement are not being granted to you + if you are an individual domiciled in, or a company with a principal place of + business in, the European Union. This restriction does not apply to end users + of a product or service that incorporates any such multimodal models.\\n\\nPlease + report any violation of this Policy, software \u201Cbug,\u201D or other problems + that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop + \ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- + if .Tools }}When you receive a tool call response, use the output to format + an answer to the orginal user question.\\n\\nYou are a helpful assistant with + tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, + $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond with + a JSON for a function call with its proper arguments that best answers the given + prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": + dictionary of argument name and its value}. 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(if you are located outside of the + EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using + or distributing any portion or element of the Llama Materials,\\nyou agree to + be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to the + Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If + you distribute or make available the Llama Materials (or any derivative works + thereof), \\nor a product or service (including another AI model) that contains + any of them, you shall (A) provide\\na copy of this Agreement with any such + Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is distributed + or made available, you shall also include \u201CLlama\u201D\\nat the beginning + of any such AI model name.\\n\\n ii. If you receive Llama Materials, + or any derivative works thereof, from a Licensee as part\\nof an integrated + end user product, then Section 2 of this Agreement will not apply to you. \\n\\n + \ iii. You must retain in all copies of the Llama Materials that you distribute + the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed + as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 + Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n + \ iv. Your use of the Llama Materials must comply with applicable laws + and regulations\\n(including trade compliance laws and regulations) and adhere + to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), + which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. + Additional Commercial Terms. If, on the Llama 3.2 version release date, the + monthly active users\\nof the products or services made available by or for + Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly + active users in the preceding calendar month, you must request \\na license + from Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or until + Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. + UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS + THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF + ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND + IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, + MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR + DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS + AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY + OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR + ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, + TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, + \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, + EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED + OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n + \ a. No trademark licenses are granted under this Agreement, and in connection + with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark + owned by or associated with the other or any of its affiliates, \\nexcept as + required for reasonable and customary use in describing and redistributing the + Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants + you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s + brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between you + and Meta,\\n you are and will be the owner of such derivative works and modifications.\\n\\n + \ c. If you institute litigation or other proceedings against Meta or any + entity (including a cross-claim or\\n counterclaim in a lawsuit) alleging + that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n + \ of any of the foregoing, constitutes infringement of intellectual property + or other rights owned or licensable\\n by you, then any licenses granted + to you under this Agreement shall terminate as of the date such litigation or\\n + \ claim is filed or instituted. You will indemnify and hold harmless Meta + from and against any claim by any third\\n party arising out of or related + to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination. + The term of this Agreement will commence upon your acceptance of this Agreement + or access\\nto the Llama Materials and will continue in full force and effect + until terminated in accordance with the terms\\nand conditions herein. Meta + may terminate this Agreement if you are in breach of any term or condition of + this\\nAgreement. Upon termination of this Agreement, you shall delete and cease + use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination + of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will + be governed and construed under the laws of the State of \\nCalifornia without + regard to choice of law principles, and the UN Convention on Contracts for the + International\\nSale of Goods does not apply to this Agreement. The courts of + California shall have exclusive jurisdiction of\\nany dispute arising out of + this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed + to promoting safe and fair use of its tools and features, including Llama 3.2. + If you access or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). + The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree + you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate + the law or others\u2019 rights, including to:\\n 1. Engage in, promote, generate, + contribute to, encourage, plan, incite, or further illegal or unlawful activity + or content, such as:\\n 1. Violence or terrorism\\n 2. Exploitation + or harm to children, including the solicitation, creation, acquisition, or dissemination + of child exploitative content or failure to report Child Sexual Abuse Material\\n + \ 3. Human trafficking, exploitation, and sexual violence\\n 4. + The illegal distribution of information or materials to minors, including obscene + materials, or failure to employ legally required age-gating in connection with + such information or materials.\\n 5. Sexual solicitation\\n 6. + Any other criminal activity\\n 1. Engage in, promote, incite, or facilitate + the harassment, abuse, threatening, or bullying of individuals or groups of + individuals\\n 2. Engage in, promote, incite, or facilitate discrimination + or other unlawful or harmful conduct in the provision of employment, employment + benefits, credit, housing, other economic benefits, or other essential goods + and services\\n 3. Engage in the unauthorized or unlicensed practice of any + profession including, but not limited to, financial, legal, medical/health, + or related professional practices\\n 4. Collect, process, disclose, generate, + or infer private or sensitive information about individuals, including information + about individuals\u2019 identity, health, or demographic information, unless + you have obtained the right to do so in accordance with applicable law\\n 5. + Engage in or facilitate any action or generate any content that infringes, misappropriates, + or otherwise violates any third-party rights, including the outputs or results + of any products or services using the Llama Materials\\n 6. Create, generate, + or facilitate the creation of malicious code, malware, computer viruses or do + anything else that could disable, overburden, interfere with or impair the proper + working, integrity, operation or appearance of a website or computer system\\n + \ 7. Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the + planning or development of activities that present a risk of death or bodily + harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic Arms + Regulations (ITAR) maintained by the United States Department of State or to + the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons + Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including + weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n + \ 11. Operation of critical infrastructure, transportation technologies, or + heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting, + and eating disorders\\n 13. Any content intended to incite or promote violence, + abuse, or any infliction of bodily harm to an individual\\n3. Intentionally + deceive or mislead others, including use of Llama 3.2 related to the following:\\n + \ 14. Generating, promoting, or furthering fraud or the creation or promotion + of disinformation\\n 15. Generating, promoting, or furthering defamatory + content, including the creation of defamatory statements, images, or other content\\n + \ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating + another individual without consent, authorization, or legal right\\n 18. + Representing that the use of Llama 3.2 or outputs are human-generated\\n 19. + Generating or facilitating false online engagement, including fake reviews and + other means of fake online engagement\\n4. Fail to appropriately disclose to + end users any known dangers of your AI system\\n5. Interact with third party + tools, models, or software designed to generate unlawful content or engage in + unlawful or harmful conduct and/or represent that the outputs of such tools, + models, or software are associated with Meta or Llama 3.2\\n\\nWith respect + to any multimodal models included in Llama 3.2, the rights granted under Section + 1(a) of the Llama 3.2 Community License Agreement are not being granted to you + if you are an individual domiciled in, or a company with a principal place of + business in, the European Union. This restriction does not apply to end users + of a product or service that incorporates any such multimodal models.\\n\\nPlease + report any violation of this Policy, software \u201Cbug,\u201D or other problems + that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama + show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# + FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE + \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- + if .Tools }}When you receive a tool call response, use the output to format + an answer to the orginal user question.\\n\\nYou are a helpful assistant with + tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, + $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond with + a JSON for a function call with its proper arguments that best answers the given + prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": + dictionary of argument name and its value}. Do not use variables.\\n\\n{{ range + $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- + else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- + if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name + }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else }}\\n\\n{{ + .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ end }}\\n{{- + else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ + .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- end }}\\n{{- end }}\\\"\\\"\\\"\\nPARAMETER stop \\u003c|start_header_id|\\u003e\\nPARAMETER + stop \\u003c|end_header_id|\\u003e\\nPARAMETER stop \\u003c|eot_id|\\u003e\\nLICENSE + \\\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version Release Date: + September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions + for use, reproduction, distribution \\nand modification of the Llama Materials + set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications, manuals + and documentation accompanying Llama 3.2\\ndistributed by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D + or \u201Cyou\u201D means you, or your employer or any other person or entity + (if you are \\nentering into this Agreement on such person or entity\u2019s + behalf), of the age required under\\napplicable laws, rules or regulations to + provide legal consent and that has legal authority\\nto bind your employer or + such other person or entity if you are entering in this Agreement\\non their + behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language models + and software and algorithms, including\\nmachine-learning model code, trained + model weights, inference-enabling code, training-enabling code,\\nfine-tuning + enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama + Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and Documentation + (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D + or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in + or, \\nif you are an entity, your principal place of business is in the EEA + or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the + EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using + or distributing any portion or element of the Llama Materials,\\nyou agree to + be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to the + Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If + you distribute or make available the Llama Materials (or any derivative works + thereof), \\nor a product or service (including another AI model) that contains + any of them, you shall (A) provide\\na copy of this Agreement with any such + Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is distributed + or made available, you shall also include \u201CLlama\u201D\\nat the beginning + of any such AI model name.\\n\\n ii. If you receive Llama Materials, + or any derivative works thereof, from a Licensee as part\\nof an integrated + end user product, then Section 2 of this Agreement will not apply to you. \\n\\n + \ iii. You must retain in all copies of the Llama Materials that you distribute + the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed + as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 + Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n + \ iv. Your use of the Llama Materials must comply with applicable laws + and regulations\\n(including trade compliance laws and regulations) and adhere + to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), + which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. + Additional Commercial Terms. If, on the Llama 3.2 version release date, the + monthly active users\\nof the products or services made available by or for + Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly + active users in the preceding calendar month, you must request \\na license + from Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or until + Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. + UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS + THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF + ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND + IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, + MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR + DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS + AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY + OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR + ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, + TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, + \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, + EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED + OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n + \ a. No trademark licenses are granted under this Agreement, and in connection + with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark + owned by or associated with the other or any of its affiliates, \\nexcept as + required for reasonable and customary use in describing and redistributing the + Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants + you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s + brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between you + and Meta,\\n you are and will be the owner of such derivative works and modifications.\\n\\n + \ c. If you institute litigation or other proceedings against Meta or any + entity (including a cross-claim or\\n counterclaim in a lawsuit) alleging + that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n + \ of any of the foregoing, constitutes infringement of intellectual property + or other rights owned or licensable\\n by you, then any licenses granted + to you under this Agreement shall terminate as of the date such litigation or\\n + \ claim is filed or instituted. You will indemnify and hold harmless Meta + from and against any claim by any third\\n party arising out of or related + to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination. + The term of this Agreement will commence upon your acceptance of this Agreement + or access\\nto the Llama Materials and will continue in full force and effect + until terminated in accordance with the terms\\nand conditions herein. Meta + may terminate this Agreement if you are in breach of any term or condition of + this\\nAgreement. Upon termination of this Agreement, you shall delete and cease + use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination + of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will + be governed and construed under the laws of the State of \\nCalifornia without + regard to choice of law principles, and the UN Convention on Contracts for the + International\\nSale of Goods does not apply to this Agreement. The courts of + California shall have exclusive jurisdiction of\\nany dispute arising out of + this Agreement.\\\"\\nLICENSE \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta + is committed to promoting safe and fair use of its tools and features, including + Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use + Policy (\u201C**Policy**\u201D). The most recent copy of this policy can be + found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree + you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate + the law or others\u2019 rights, including to:\\n 1. Engage in, promote, generate, + contribute to, encourage, plan, incite, or further illegal or unlawful activity + or content, such as:\\n 1. Violence or terrorism\\n 2. Exploitation + or harm to children, including the solicitation, creation, acquisition, or dissemination + of child exploitative content or failure to report Child Sexual Abuse Material\\n + \ 3. Human trafficking, exploitation, and sexual violence\\n 4. + The illegal distribution of information or materials to minors, including obscene + materials, or failure to employ legally required age-gating in connection with + such information or materials.\\n 5. Sexual solicitation\\n 6. + Any other criminal activity\\n 1. Engage in, promote, incite, or facilitate + the harassment, abuse, threatening, or bullying of individuals or groups of + individuals\\n 2. Engage in, promote, incite, or facilitate discrimination + or other unlawful or harmful conduct in the provision of employment, employment + benefits, credit, housing, other economic benefits, or other essential goods + and services\\n 3. Engage in the unauthorized or unlicensed practice of any + profession including, but not limited to, financial, legal, medical/health, + or related professional practices\\n 4. Collect, process, disclose, generate, + or infer private or sensitive information about individuals, including information + about individuals\u2019 identity, health, or demographic information, unless + you have obtained the right to do so in accordance with applicable law\\n 5. + Engage in or facilitate any action or generate any content that infringes, misappropriates, + or otherwise violates any third-party rights, including the outputs or results + of any products or services using the Llama Materials\\n 6. Create, generate, + or facilitate the creation of malicious code, malware, computer viruses or do + anything else that could disable, overburden, interfere with or impair the proper + working, integrity, operation or appearance of a website or computer system\\n + \ 7. Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the + planning or development of activities that present a risk of death or bodily + harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic Arms + Regulations (ITAR) maintained by the United States Department of State or to + the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons + Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including + weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n + \ 11. Operation of critical infrastructure, transportation technologies, or + heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting, + and eating disorders\\n 13. Any content intended to incite or promote violence, + abuse, or any infliction of bodily harm to an individual\\n3. Intentionally + deceive or mislead others, including use of Llama 3.2 related to the following:\\n + \ 14. Generating, promoting, or furthering fraud or the creation or promotion + of disinformation\\n 15. Generating, promoting, or furthering defamatory + content, including the creation of defamatory statements, images, or other content\\n + \ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating + another individual without consent, authorization, or legal right\\n 18. + Representing that the use of Llama 3.2 or outputs are human-generated\\n 19. + Generating or facilitating false online engagement, including fake reviews and + other means of fake online engagement\\n4. Fail to appropriately disclose to + end users any known dangers of your AI system\\n5. Interact with third party + tools, models, or software designed to generate unlawful content or engage in + unlawful or harmful conduct and/or represent that the outputs of such tools, + models, or software are associated with Meta or Llama 3.2\\n\\nWith respect + to any multimodal models included in Llama 3.2, the rights granted under Section + 1(a) of the Llama 3.2 Community License Agreement are not being granted to you + if you are an individual domiciled in, or a company with a principal place of + business in, the European Union. This restriction does not apply to end users + of a product or service that incorporates any such multimodal models.\\n\\nPlease + report any violation of this Policy, software \u201Cbug,\u201D or other problems + that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop + \ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- + if .Tools }}When you receive a tool call response, use the output to format + an answer to the orginal user question.\\n\\nYou are a helpful assistant with + tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, + $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond with + a JSON for a function call with its proper arguments that best answers the given + prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": + dictionary of argument name and its value}. 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(if you are located outside + of the EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below + or by using or distributing any portion or element of the Llama Materials,\\nyou + agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to + the Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. + If you distribute or make available the Llama Materials (or any derivative + works thereof), \\nor a product or service (including another AI model) that + contains any of them, you shall (A) provide\\na copy of this Agreement with + any such Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is + distributed or made available, you shall also include \u201CLlama\u201D\\nat + the beginning of any such AI model name.\\n\\n ii. If you receive Llama + Materials, or any derivative works thereof, from a Licensee as part\\nof an + integrated end user product, then Section 2 of this Agreement will not apply + to you. \\n\\n iii. You must retain in all copies of the Llama Materials + that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D + text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed + under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc. + All Rights Reserved.\u201D\\n\\n iv. Your use of the Llama Materials + must comply with applicable laws and regulations\\n(including trade compliance + laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama + Materials (available at https://www.llama.com/llama3_2/use-policy), which + is hereby \\nincorporated by reference into this Agreement.\\n \\n2. Additional + Commercial Terms. If, on the Llama 3.2 version release date, the monthly active + users\\nof the products or services made available by or for Licensee, or + Licensee\u2019s affiliates, \\nis greater than 700 million monthly active + users in the preceding calendar month, you must request \\na license from + Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or + until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer + of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY + OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, + WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY + KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF + TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. + YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING + OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\\nWITH + YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\\n\\n4. Limitation + of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY + THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, + OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY + INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, + EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF + ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n a. No trademark + licenses are granted under this Agreement, and in connection with the Llama + Materials, \\nneither Meta nor Licensee may use any name or mark owned by + or associated with the other or any of its affiliates, \\nexcept as required + for reasonable and customary use in describing and redistributing the Llama + Materials or as \\nset forth in this Section 5(a). Meta hereby grants you + a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with + Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between + you and Meta,\\n you are and will be the owner of such derivative works + and modifications.\\n\\n c. If you institute litigation or other proceedings + against Meta or any entity (including a cross-claim or\\n counterclaim + in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, + or any portion\\n of any of the foregoing, constitutes infringement of + intellectual property or other rights owned or licensable\\n by you, then + any licenses granted to you under this Agreement shall terminate as of the + date such litigation or\\n claim is filed or instituted. You will indemnify + and hold harmless Meta from and against any claim by any third\\n party + arising out of or related to your use or distribution of the Llama Materials.\\n\\n6. + Term and Termination. The term of this Agreement will commence upon your acceptance + of this Agreement or access\\nto the Llama Materials and will continue in + full force and effect until terminated in accordance with the terms\\nand + conditions herein. Meta may terminate this Agreement if you are in breach + of any term or condition of this\\nAgreement. Upon termination of this Agreement, + you shall delete and cease use of the Llama Materials. Sections 3,\\n4 and + 7 shall survive the termination of this Agreement. \\n\\n7. Governing Law + and Jurisdiction. This Agreement will be governed and construed under the + laws of the State of \\nCalifornia without regard to choice of law principles, + and the UN Convention on Contracts for the International\\nSale of Goods does + not apply to this Agreement. The courts of California shall have exclusive + jurisdiction of\\nany dispute arising out of this Agreement.\\n**Llama 3.2** + **Acceptable Use Policy**\\n\\nMeta is committed to promoting safe and fair + use of its tools and features, including Llama 3.2. If you access or use Llama + 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). The + most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You + agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. + Violate the law or others\u2019 rights, including to:\\n 1. Engage in, + promote, generate, contribute to, encourage, plan, incite, or further illegal + or unlawful activity or content, such as:\\n 1. Violence or terrorism\\n + \ 2. Exploitation or harm to children, including the solicitation, creation, + acquisition, or dissemination of child exploitative content or failure to + report Child Sexual Abuse Material\\n 3. Human trafficking, exploitation, + and sexual violence\\n 4. The illegal distribution of information or + materials to minors, including obscene materials, or failure to employ legally + required age-gating in connection with such information or materials.\\n 5. + Sexual solicitation\\n 6. Any other criminal activity\\n 1. Engage + in, promote, incite, or facilitate the harassment, abuse, threatening, or + bullying of individuals or groups of individuals\\n 2. Engage in, promote, + incite, or facilitate discrimination or other unlawful or harmful conduct + in the provision of employment, employment benefits, credit, housing, other + economic benefits, or other essential goods and services\\n 3. Engage in + the unauthorized or unlicensed practice of any profession including, but not + limited to, financial, legal, medical/health, or related professional practices\\n + \ 4. Collect, process, disclose, generate, or infer private or sensitive + information about individuals, including information about individuals\u2019 + identity, health, or demographic information, unless you have obtained the + right to do so in accordance with applicable law\\n 5. Engage in or facilitate + any action or generate any content that infringes, misappropriates, or otherwise + violates any third-party rights, including the outputs or results of any products + or services using the Llama Materials\\n 6. Create, generate, or facilitate + the creation of malicious code, malware, computer viruses or do anything else + that could disable, overburden, interfere with or impair the proper working, + integrity, operation or appearance of a website or computer system\\n 7. + Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in + the planning or development of activities that present a risk of death or + bodily harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic + Arms Regulations (ITAR) maintained by the United States Department of State + or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical + Weapons Convention Implementation Act of 1997\\n 9. Guns and illegal weapons + (including weapon development)\\n 10. Illegal drugs and regulated/controlled + substances\\n 11. Operation of critical infrastructure, transportation + technologies, or heavy machinery\\n 12. Self-harm or harm to others, including + suicide, cutting, and eating disorders\\n 13. Any content intended to incite + or promote violence, abuse, or any infliction of bodily harm to an individual\\n3. + Intentionally deceive or mislead others, including use of Llama 3.2 related + to the following:\\n 14. Generating, promoting, or furthering fraud or + the creation or promotion of disinformation\\n 15. Generating, promoting, + or furthering defamatory content, including the creation of defamatory statements, + images, or other content\\n 16. Generating, promoting, or further distributing + spam\\n 17. Impersonating another individual without consent, authorization, + or legal right\\n 18. Representing that the use of Llama 3.2 or outputs + are human-generated\\n 19. Generating or facilitating false online engagement, + including fake reviews and other means of fake online engagement\\n4. Fail + to appropriately disclose to end users any known dangers of your AI system\\n5. + Interact with third party tools, models, or software designed to generate + unlawful content or engage in unlawful or harmful conduct and/or represent + that the outputs of such tools, models, or software are associated with Meta + or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama + 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License + Agreement are not being granted to you if you are an individual domiciled + in, or a company with a principal place of business in, the European Union. + This restriction does not apply to end users of a product or service that + incorporates any such multimodal models.\\n\\nPlease report any violation + of this Policy, software \u201Cbug,\u201D or other problems that could lead + to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama + show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# + FROM llama3.2:3b\\n\\nFROM /Users/greysonlalonde/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE + \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end + }}\\n{{- if .Tools }}When you receive a tool call response, use the output + to format an answer to the orginal user question.\\n\\nYou are a helpful assistant + with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range + $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond + with a JSON for a function call with its proper arguments that best answers + the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name, + \\\"parameters\\\": dictionary of argument name and its value}. Do not use + variables.\\n\\n{{ range $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- + else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last + }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- + if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name + }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else + }}\\n\\n{{ .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ + end }}\\n{{- else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ + .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- end }}\\n{{- end }}\\\"\\\"\\\"\\nPARAMETER stop \\u003c|start_header_id|\\u003e\\nPARAMETER + stop \\u003c|end_header_id|\\u003e\\nPARAMETER stop \\u003c|eot_id|\\u003e\\nLICENSE + \\\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version Release Date: + September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions + for use, reproduction, distribution \\nand modification of the Llama Materials + set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications, + manuals and documentation accompanying Llama 3.2\\ndistributed by Meta at + https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D or \u201Cyou\u201D + means you, or your employer or any other person or entity (if you are \\nentering + into this Agreement on such person or entity\u2019s behalf), of the age required + under\\napplicable laws, rules or regulations to provide legal consent and + that has legal authority\\nto bind your employer or such other person or entity + if you are entering in this Agreement\\non their behalf.\\n\\n\u201CLlama + 3.2\u201D means the foundational large language models and software and algorithms, + including\\nmachine-learning model code, trained model weights, inference-enabling + code, training-enabling code,\\nfine-tuning enabling code and other elements + of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama + Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and + Documentation (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D + or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located + in or, \\nif you are an entity, your principal place of business is in the + EEA or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside + of the EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below + or by using or distributing any portion or element of the Llama Materials,\\nyou + agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to + the Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. + If you distribute or make available the Llama Materials (or any derivative + works thereof), \\nor a product or service (including another AI model) that + contains any of them, you shall (A) provide\\na copy of this Agreement with + any such Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is + distributed or made available, you shall also include \u201CLlama\u201D\\nat + the beginning of any such AI model name.\\n\\n ii. If you receive Llama + Materials, or any derivative works thereof, from a Licensee as part\\nof an + integrated end user product, then Section 2 of this Agreement will not apply + to you. \\n\\n iii. You must retain in all copies of the Llama Materials + that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D + text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed + under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc. + All Rights Reserved.\u201D\\n\\n iv. Your use of the Llama Materials + must comply with applicable laws and regulations\\n(including trade compliance + laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama + Materials (available at https://www.llama.com/llama3_2/use-policy), which + is hereby \\nincorporated by reference into this Agreement.\\n \\n2. Additional + Commercial Terms. If, on the Llama 3.2 version release date, the monthly active + users\\nof the products or services made available by or for Licensee, or + Licensee\u2019s affiliates, \\nis greater than 700 million monthly active + users in the preceding calendar month, you must request \\na license from + Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or + until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer + of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY + OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, + WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY + KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF + TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. + YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING + OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\\nWITH + YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\\n\\n4. Limitation + of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY + THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, + OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY + INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, + EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF + ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n a. No trademark + licenses are granted under this Agreement, and in connection with the Llama + Materials, \\nneither Meta nor Licensee may use any name or mark owned by + or associated with the other or any of its affiliates, \\nexcept as required + for reasonable and customary use in describing and redistributing the Llama + Materials or as \\nset forth in this Section 5(a). Meta hereby grants you + a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with + Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between + you and Meta,\\n you are and will be the owner of such derivative works + and modifications.\\n\\n c. If you institute litigation or other proceedings + against Meta or any entity (including a cross-claim or\\n counterclaim + in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, + or any portion\\n of any of the foregoing, constitutes infringement of + intellectual property or other rights owned or licensable\\n by you, then + any licenses granted to you under this Agreement shall terminate as of the + date such litigation or\\n claim is filed or instituted. You will indemnify + and hold harmless Meta from and against any claim by any third\\n party + arising out of or related to your use or distribution of the Llama Materials.\\n\\n6. + Term and Termination. The term of this Agreement will commence upon your acceptance + of this Agreement or access\\nto the Llama Materials and will continue in + full force and effect until terminated in accordance with the terms\\nand + conditions herein. Meta may terminate this Agreement if you are in breach + of any term or condition of this\\nAgreement. Upon termination of this Agreement, + you shall delete and cease use of the Llama Materials. Sections 3,\\n4 and + 7 shall survive the termination of this Agreement. \\n\\n7. Governing Law + and Jurisdiction. This Agreement will be governed and construed under the + laws of the State of \\nCalifornia without regard to choice of law principles, + and the UN Convention on Contracts for the International\\nSale of Goods does + not apply to this Agreement. The courts of California shall have exclusive + jurisdiction of\\nany dispute arising out of this Agreement.\\\"\\nLICENSE + \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed to promoting + safe and fair use of its tools and features, including Llama 3.2. If you access + or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). + The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You + agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. + Violate the law or others\u2019 rights, including to:\\n 1. Engage in, + promote, generate, contribute to, encourage, plan, incite, or further illegal + or unlawful activity or content, such as:\\n 1. Violence or terrorism\\n + \ 2. Exploitation or harm to children, including the solicitation, creation, + acquisition, or dissemination of child exploitative content or failure to + report Child Sexual Abuse Material\\n 3. Human trafficking, exploitation, + and sexual violence\\n 4. The illegal distribution of information or + materials to minors, including obscene materials, or failure to employ legally + required age-gating in connection with such information or materials.\\n 5. + Sexual solicitation\\n 6. Any other criminal activity\\n 1. Engage + in, promote, incite, or facilitate the harassment, abuse, threatening, or + bullying of individuals or groups of individuals\\n 2. Engage in, promote, + incite, or facilitate discrimination or other unlawful or harmful conduct + in the provision of employment, employment benefits, credit, housing, other + economic benefits, or other essential goods and services\\n 3. Engage in + the unauthorized or unlicensed practice of any profession including, but not + limited to, financial, legal, medical/health, or related professional practices\\n + \ 4. Collect, process, disclose, generate, or infer private or sensitive + information about individuals, including information about individuals\u2019 + identity, health, or demographic information, unless you have obtained the + right to do so in accordance with applicable law\\n 5. Engage in or facilitate + any action or generate any content that infringes, misappropriates, or otherwise + violates any third-party rights, including the outputs or results of any products + or services using the Llama Materials\\n 6. Create, generate, or facilitate + the creation of malicious code, malware, computer viruses or do anything else + that could disable, overburden, interfere with or impair the proper working, + integrity, operation or appearance of a website or computer system\\n 7. + Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in + the planning or development of activities that present a risk of death or + bodily harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic + Arms Regulations (ITAR) maintained by the United States Department of State + or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical + Weapons Convention Implementation Act of 1997\\n 9. Guns and illegal weapons + (including weapon development)\\n 10. Illegal drugs and regulated/controlled + substances\\n 11. Operation of critical infrastructure, transportation + technologies, or heavy machinery\\n 12. Self-harm or harm to others, including + suicide, cutting, and eating disorders\\n 13. Any content intended to incite + or promote violence, abuse, or any infliction of bodily harm to an individual\\n3. + Intentionally deceive or mislead others, including use of Llama 3.2 related + to the following:\\n 14. Generating, promoting, or furthering fraud or + the creation or promotion of disinformation\\n 15. Generating, promoting, + or furthering defamatory content, including the creation of defamatory statements, + images, or other content\\n 16. Generating, promoting, or further distributing + spam\\n 17. Impersonating another individual without consent, authorization, + or legal right\\n 18. Representing that the use of Llama 3.2 or outputs + are human-generated\\n 19. Generating or facilitating false online engagement, + including fake reviews and other means of fake online engagement\\n4. Fail + to appropriately disclose to end users any known dangers of your AI system\\n5. + Interact with third party tools, models, or software designed to generate + unlawful content or engage in unlawful or harmful conduct and/or represent + that the outputs of such tools, models, or software are associated with Meta + or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama + 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License + Agreement are not being granted to you if you are an individual domiciled + in, or a company with a principal place of business in, the European Union. + This restriction does not apply to end users of a product or service that + incorporates any such multimodal models.\\n\\nPlease report any violation + of this Policy, software \u201Cbug,\u201D or other problems that could lead + to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop + \ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end + }}\\n{{- if .Tools }}When you receive a tool call response, use the output + to format an answer to the orginal user question.\\n\\nYou are a helpful assistant + with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range + $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond + with a JSON for a function call with its proper arguments that best answers + the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name, + \\\"parameters\\\": dictionary of argument name and its value}. Do not use + variables.\\n\\n{{ range $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- + else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last + }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- + if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name + }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else + }}\\n\\n{{ .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ + end }}\\n{{- else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ + .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- end }}\\n{{- end 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+ headers: + Content-Type: + - application/json; charset=utf-8 + Date: + - Mon, 20 Oct 2025 15:08:11 GMT + Transfer-Encoding: + - chunked + status: + code: 200 + message: OK +- request: + body: '{"name": "llama3.2:3b"}' + headers: + accept: + - '*/*' + accept-encoding: + - gzip, deflate, zstd + connection: + - keep-alive + content-length: + - '23' + content-type: + - application/json + host: + - localhost:11434 + user-agent: + - litellm/1.77.5 + method: POST + uri: http://localhost:11434/api/show + response: + body: + string: "{\"license\":\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version + Release Date: September 25, 2024\\n\\n\u201CAgreement\u201D means the terms + and conditions for use, reproduction, distribution \\nand modification of + the Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means + the specifications, manuals and documentation accompanying Llama 3.2\\ndistributed + by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D + or \u201Cyou\u201D means you, or your employer or any other person or entity + (if you are \\nentering into this Agreement on such person or entity\u2019s + behalf), of the age required under\\napplicable laws, rules or regulations + to provide legal consent and that has legal authority\\nto bind your employer + or such other person or entity if you are entering in this Agreement\\non + their behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language + models and software and algorithms, including\\nmachine-learning model code, + trained model weights, inference-enabling code, training-enabling code,\\nfine-tuning + enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama + Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and + Documentation (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D + or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located + in or, \\nif you are an entity, your principal place of business is in the + EEA or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside + of the EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below + or by using or distributing any portion or element of the Llama Materials,\\nyou + agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to + the Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. + If you distribute or make available the Llama Materials (or any derivative + works thereof), \\nor a product or service (including another AI model) that + contains any of them, you shall (A) provide\\na copy of this Agreement with + any such Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is + distributed or made available, you shall also include \u201CLlama\u201D\\nat + the beginning of any such AI model name.\\n\\n ii. If you receive Llama + Materials, or any derivative works thereof, from a Licensee as part\\nof an + integrated end user product, then Section 2 of this Agreement will not apply + to you. \\n\\n iii. You must retain in all copies of the Llama Materials + that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D + text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed + under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc. + All Rights Reserved.\u201D\\n\\n iv. Your use of the Llama Materials + must comply with applicable laws and regulations\\n(including trade compliance + laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama + Materials (available at https://www.llama.com/llama3_2/use-policy), which + is hereby \\nincorporated by reference into this Agreement.\\n \\n2. Additional + Commercial Terms. If, on the Llama 3.2 version release date, the monthly active + users\\nof the products or services made available by or for Licensee, or + Licensee\u2019s affiliates, \\nis greater than 700 million monthly active + users in the preceding calendar month, you must request \\na license from + Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or + until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer + of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY + OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, + WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY + KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF + TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. + YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING + OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\\nWITH + YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\\n\\n4. Limitation + of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY + THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, + OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY + INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, + EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF + ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n a. No trademark + licenses are granted under this Agreement, and in connection with the Llama + Materials, \\nneither Meta nor Licensee may use any name or mark owned by + or associated with the other or any of its affiliates, \\nexcept as required + for reasonable and customary use in describing and redistributing the Llama + Materials or as \\nset forth in this Section 5(a). Meta hereby grants you + a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with + Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between + you and Meta,\\n you are and will be the owner of such derivative works + and modifications.\\n\\n c. If you institute litigation or other proceedings + against Meta or any entity (including a cross-claim or\\n counterclaim + in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, + or any portion\\n of any of the foregoing, constitutes infringement of + intellectual property or other rights owned or licensable\\n by you, then + any licenses granted to you under this Agreement shall terminate as of the + date such litigation or\\n claim is filed or instituted. You will indemnify + and hold harmless Meta from and against any claim by any third\\n party + arising out of or related to your use or distribution of the Llama Materials.\\n\\n6. + Term and Termination. The term of this Agreement will commence upon your acceptance + of this Agreement or access\\nto the Llama Materials and will continue in + full force and effect until terminated in accordance with the terms\\nand + conditions herein. Meta may terminate this Agreement if you are in breach + of any term or condition of this\\nAgreement. Upon termination of this Agreement, + you shall delete and cease use of the Llama Materials. Sections 3,\\n4 and + 7 shall survive the termination of this Agreement. \\n\\n7. Governing Law + and Jurisdiction. This Agreement will be governed and construed under the + laws of the State of \\nCalifornia without regard to choice of law principles, + and the UN Convention on Contracts for the International\\nSale of Goods does + not apply to this Agreement. The courts of California shall have exclusive + jurisdiction of\\nany dispute arising out of this Agreement.\\n**Llama 3.2** + **Acceptable Use Policy**\\n\\nMeta is committed to promoting safe and fair + use of its tools and features, including Llama 3.2. If you access or use Llama + 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). The + most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You + agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. + Violate the law or others\u2019 rights, including to:\\n 1. Engage in, + promote, generate, contribute to, encourage, plan, incite, or further illegal + or unlawful activity or content, such as:\\n 1. Violence or terrorism\\n + \ 2. Exploitation or harm to children, including the solicitation, creation, + acquisition, or dissemination of child exploitative content or failure to + report Child Sexual Abuse Material\\n 3. Human trafficking, exploitation, + and sexual violence\\n 4. The illegal distribution of information or + materials to minors, including obscene materials, or failure to employ legally + required age-gating in connection with such information or materials.\\n 5. + Sexual solicitation\\n 6. Any other criminal activity\\n 1. Engage + in, promote, incite, or facilitate the harassment, abuse, threatening, or + bullying of individuals or groups of individuals\\n 2. Engage in, promote, + incite, or facilitate discrimination or other unlawful or harmful conduct + in the provision of employment, employment benefits, credit, housing, other + economic benefits, or other essential goods and services\\n 3. Engage in + the unauthorized or unlicensed practice of any profession including, but not + limited to, financial, legal, medical/health, or related professional practices\\n + \ 4. Collect, process, disclose, generate, or infer private or sensitive + information about individuals, including information about individuals\u2019 + identity, health, or demographic information, unless you have obtained the + right to do so in accordance with applicable law\\n 5. Engage in or facilitate + any action or generate any content that infringes, misappropriates, or otherwise + violates any third-party rights, including the outputs or results of any products + or services using the Llama Materials\\n 6. Create, generate, or facilitate + the creation of malicious code, malware, computer viruses or do anything else + that could disable, overburden, interfere with or impair the proper working, + integrity, operation or appearance of a website or computer system\\n 7. + Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in + the planning or development of activities that present a risk of death or + bodily harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic + Arms Regulations (ITAR) maintained by the United States Department of State + or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical + Weapons Convention Implementation Act of 1997\\n 9. Guns and illegal weapons + (including weapon development)\\n 10. Illegal drugs and regulated/controlled + substances\\n 11. Operation of critical infrastructure, transportation + technologies, or heavy machinery\\n 12. Self-harm or harm to others, including + suicide, cutting, and eating disorders\\n 13. Any content intended to incite + or promote violence, abuse, or any infliction of bodily harm to an individual\\n3. + Intentionally deceive or mislead others, including use of Llama 3.2 related + to the following:\\n 14. Generating, promoting, or furthering fraud or + the creation or promotion of disinformation\\n 15. Generating, promoting, + or furthering defamatory content, including the creation of defamatory statements, + images, or other content\\n 16. Generating, promoting, or further distributing + spam\\n 17. Impersonating another individual without consent, authorization, + or legal right\\n 18. Representing that the use of Llama 3.2 or outputs + are human-generated\\n 19. Generating or facilitating false online engagement, + including fake reviews and other means of fake online engagement\\n4. Fail + to appropriately disclose to end users any known dangers of your AI system\\n5. + Interact with third party tools, models, or software designed to generate + unlawful content or engage in unlawful or harmful conduct and/or represent + that the outputs of such tools, models, or software are associated with Meta + or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama + 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License + Agreement are not being granted to you if you are an individual domiciled + in, or a company with a principal place of business in, the European Union. + This restriction does not apply to end users of a product or service that + incorporates any such multimodal models.\\n\\nPlease report any violation + of this Policy, software \u201Cbug,\u201D or other problems that could lead + to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama + show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# + FROM llama3.2:3b\\n\\nFROM /Users/greysonlalonde/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE + \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end + }}\\n{{- if .Tools }}When you receive a tool call response, use the output + to format an answer to the orginal user question.\\n\\nYou are a helpful assistant + with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range + $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond + with a JSON for a function call with its proper arguments that best answers + the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name, + \\\"parameters\\\": dictionary of argument name and its value}. Do not use + variables.\\n\\n{{ range $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- + else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last + }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- + if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name + }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else + }}\\n\\n{{ .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ + end }}\\n{{- else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ + .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- end }}\\n{{- end }}\\\"\\\"\\\"\\nPARAMETER stop \\u003c|start_header_id|\\u003e\\nPARAMETER + stop \\u003c|end_header_id|\\u003e\\nPARAMETER stop \\u003c|eot_id|\\u003e\\nLICENSE + \\\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version Release Date: + September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions + for use, reproduction, distribution \\nand modification of the Llama Materials + set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications, + manuals and documentation accompanying Llama 3.2\\ndistributed by Meta at + https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D or \u201Cyou\u201D + means you, or your employer or any other person or entity (if you are \\nentering + into this Agreement on such person or entity\u2019s behalf), of the age required + under\\napplicable laws, rules or regulations to provide legal consent and + that has legal authority\\nto bind your employer or such other person or entity + if you are entering in this Agreement\\non their behalf.\\n\\n\u201CLlama + 3.2\u201D means the foundational large language models and software and algorithms, + including\\nmachine-learning model code, trained model weights, inference-enabling + code, training-enabling code,\\nfine-tuning enabling code and other elements + of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama + Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and + Documentation (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D + or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located + in or, \\nif you are an entity, your principal place of business is in the + EEA or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside + of the EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below + or by using or distributing any portion or element of the Llama Materials,\\nyou + agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n + \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable + and royalty-free limited license under Meta\u2019s intellectual property or + other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, + distribute, copy, create derivative works \\nof, and make modifications to + the Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. + If you distribute or make available the Llama Materials (or any derivative + works thereof), \\nor a product or service (including another AI model) that + contains any of them, you shall (A) provide\\na copy of this Agreement with + any such Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non + a related website, user interface, blogpost, about page, or product documentation. + If you use the\\nLlama Materials or any outputs or results of the Llama Materials + to create, train, fine tune, or\\notherwise improve an AI model, which is + distributed or made available, you shall also include \u201CLlama\u201D\\nat + the beginning of any such AI model name.\\n\\n ii. If you receive Llama + Materials, or any derivative works thereof, from a Licensee as part\\nof an + integrated end user product, then Section 2 of this Agreement will not apply + to you. \\n\\n iii. You must retain in all copies of the Llama Materials + that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D + text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed + under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc. + All Rights Reserved.\u201D\\n\\n iv. Your use of the Llama Materials + must comply with applicable laws and regulations\\n(including trade compliance + laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama + Materials (available at https://www.llama.com/llama3_2/use-policy), which + is hereby \\nincorporated by reference into this Agreement.\\n \\n2. Additional + Commercial Terms. If, on the Llama 3.2 version release date, the monthly active + users\\nof the products or services made available by or for Licensee, or + Licensee\u2019s affiliates, \\nis greater than 700 million monthly active + users in the preceding calendar month, you must request \\na license from + Meta, which Meta may grant to you in its sole discretion, and you are not + authorized to\\nexercise any of the rights under this Agreement unless or + until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer + of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY + OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, + WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY + KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF + TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. + YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING + OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED\\nWITH + YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\\n\\n4. Limitation + of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY + THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, + OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY + INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, + EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF + ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n a. No trademark + licenses are granted under this Agreement, and in connection with the Llama + Materials, \\nneither Meta nor Licensee may use any name or mark owned by + or associated with the other or any of its affiliates, \\nexcept as required + for reasonable and customary use in describing and redistributing the Llama + Materials or as \\nset forth in this Section 5(a). Meta hereby grants you + a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required + \\nto comply with the last sentence of Section 1.b.i. You will comply with + Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). + All goodwill arising out of your use of the Mark \\nwill inure to the benefit + of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and + derivatives made by or for Meta, with respect to any\\n derivative works + and modifications of the Llama Materials that are made by you, as between + you and Meta,\\n you are and will be the owner of such derivative works + and modifications.\\n\\n c. If you institute litigation or other proceedings + against Meta or any entity (including a cross-claim or\\n counterclaim + in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, + or any portion\\n of any of the foregoing, constitutes infringement of + intellectual property or other rights owned or licensable\\n by you, then + any licenses granted to you under this Agreement shall terminate as of the + date such litigation or\\n claim is filed or instituted. You will indemnify + and hold harmless Meta from and against any claim by any third\\n party + arising out of or related to your use or distribution of the Llama Materials.\\n\\n6. + Term and Termination. The term of this Agreement will commence upon your acceptance + of this Agreement or access\\nto the Llama Materials and will continue in + full force and effect until terminated in accordance with the terms\\nand + conditions herein. Meta may terminate this Agreement if you are in breach + of any term or condition of this\\nAgreement. Upon termination of this Agreement, + you shall delete and cease use of the Llama Materials. Sections 3,\\n4 and + 7 shall survive the termination of this Agreement. \\n\\n7. Governing Law + and Jurisdiction. This Agreement will be governed and construed under the + laws of the State of \\nCalifornia without regard to choice of law principles, + and the UN Convention on Contracts for the International\\nSale of Goods does + not apply to this Agreement. The courts of California shall have exclusive + jurisdiction of\\nany dispute arising out of this Agreement.\\\"\\nLICENSE + \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed to promoting + safe and fair use of its tools and features, including Llama 3.2. If you access + or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). + The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited + Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You + agree you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. + Violate the law or others\u2019 rights, including to:\\n 1. Engage in, + promote, generate, contribute to, encourage, plan, incite, or further illegal + or unlawful activity or content, such as:\\n 1. Violence or terrorism\\n + \ 2. Exploitation or harm to children, including the solicitation, creation, + acquisition, or dissemination of child exploitative content or failure to + report Child Sexual Abuse Material\\n 3. Human trafficking, exploitation, + and sexual violence\\n 4. The illegal distribution of information or + materials to minors, including obscene materials, or failure to employ legally + required age-gating in connection with such information or materials.\\n 5. + Sexual solicitation\\n 6. Any other criminal activity\\n 1. Engage + in, promote, incite, or facilitate the harassment, abuse, threatening, or + bullying of individuals or groups of individuals\\n 2. Engage in, promote, + incite, or facilitate discrimination or other unlawful or harmful conduct + in the provision of employment, employment benefits, credit, housing, other + economic benefits, or other essential goods and services\\n 3. Engage in + the unauthorized or unlicensed practice of any profession including, but not + limited to, financial, legal, medical/health, or related professional practices\\n + \ 4. Collect, process, disclose, generate, or infer private or sensitive + information about individuals, including information about individuals\u2019 + identity, health, or demographic information, unless you have obtained the + right to do so in accordance with applicable law\\n 5. Engage in or facilitate + any action or generate any content that infringes, misappropriates, or otherwise + violates any third-party rights, including the outputs or results of any products + or services using the Llama Materials\\n 6. Create, generate, or facilitate + the creation of malicious code, malware, computer viruses or do anything else + that could disable, overburden, interfere with or impair the proper working, + integrity, operation or appearance of a website or computer system\\n 7. + Engage in any action, or facilitate any action, to intentionally circumvent + or remove usage restrictions or other safety measures, or to enable functionality + disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in + the planning or development of activities that present a risk of death or + bodily harm to individuals, including use of Llama 3.2 related to the following:\\n + \ 8. Military, warfare, nuclear industries or applications, espionage, use + for materials or activities that are subject to the International Traffic + Arms Regulations (ITAR) maintained by the United States Department of State + or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical + Weapons Convention Implementation Act of 1997\\n 9. Guns and illegal weapons + (including weapon development)\\n 10. Illegal drugs and regulated/controlled + substances\\n 11. Operation of critical infrastructure, transportation + technologies, or heavy machinery\\n 12. Self-harm or harm to others, including + suicide, cutting, and eating disorders\\n 13. Any content intended to incite + or promote violence, abuse, or any infliction of bodily harm to an individual\\n3. + Intentionally deceive or mislead others, including use of Llama 3.2 related + to the following:\\n 14. Generating, promoting, or furthering fraud or + the creation or promotion of disinformation\\n 15. Generating, promoting, + or furthering defamatory content, including the creation of defamatory statements, + images, or other content\\n 16. Generating, promoting, or further distributing + spam\\n 17. Impersonating another individual without consent, authorization, + or legal right\\n 18. Representing that the use of Llama 3.2 or outputs + are human-generated\\n 19. Generating or facilitating false online engagement, + including fake reviews and other means of fake online engagement\\n4. Fail + to appropriately disclose to end users any known dangers of your AI system\\n5. + Interact with third party tools, models, or software designed to generate + unlawful content or engage in unlawful or harmful conduct and/or represent + that the outputs of such tools, models, or software are associated with Meta + or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama + 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License + Agreement are not being granted to you if you are an individual domiciled + in, or a company with a principal place of business in, the European Union. + This restriction does not apply to end users of a product or service that + incorporates any such multimodal models.\\n\\nPlease report any violation + of this Policy, software \u201Cbug,\u201D or other problems that could lead + to a violation of this Policy through one of the following means:\\n\\n\\n\\n* + Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* + Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* + Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* + Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama + 3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop + \ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting + Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end + }}\\n{{- if .Tools }}When you receive a tool call response, use the output + to format an answer to the orginal user question.\\n\\nYou are a helpful assistant + with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range + $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- + if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- + if and $.Tools $last }}\\n\\nGiven the following functions, please respond + with a JSON for a function call with its proper arguments that best answers + the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name, + \\\"parameters\\\": dictionary of argument name and its value}. Do not use + variables.\\n\\n{{ range $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- + else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last + }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- + if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name + }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else + }}\\n\\n{{ .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ + end }}\\n{{- else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ + .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ + end }}\\n{{- end }}\\n{{- end 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100644 index 000000000..e69de29bb diff --git a/lib/crewai/tests/cli/authentication/__init__.py b/lib/crewai/tests/cli/authentication/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/tests/cli/authentication/providers/__init__.py b/lib/crewai/tests/cli/authentication/providers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/cli/authentication/providers/test_auth0.py b/lib/crewai/tests/cli/authentication/providers/test_auth0.py similarity index 100% rename from tests/cli/authentication/providers/test_auth0.py rename to lib/crewai/tests/cli/authentication/providers/test_auth0.py diff --git a/tests/cli/authentication/providers/test_okta.py b/lib/crewai/tests/cli/authentication/providers/test_okta.py similarity index 100% rename from tests/cli/authentication/providers/test_okta.py rename to lib/crewai/tests/cli/authentication/providers/test_okta.py diff --git a/tests/cli/authentication/providers/test_workos.py b/lib/crewai/tests/cli/authentication/providers/test_workos.py similarity index 100% rename from tests/cli/authentication/providers/test_workos.py rename to lib/crewai/tests/cli/authentication/providers/test_workos.py diff --git a/tests/cli/authentication/test_auth_main.py b/lib/crewai/tests/cli/authentication/test_auth_main.py similarity index 98% rename from tests/cli/authentication/test_auth_main.py rename to lib/crewai/tests/cli/authentication/test_auth_main.py index ca8a0cf2b..d5d309ca9 100644 --- a/tests/cli/authentication/test_auth_main.py +++ b/lib/crewai/tests/cli/authentication/test_auth_main.py @@ -1,12 +1,13 @@ -import pytest from datetime import datetime, timedelta +from unittest.mock import MagicMock, call, patch + +import pytest import requests -from unittest.mock import MagicMock, patch, call from crewai.cli.authentication.main import AuthenticationCommand from crewai.cli.constants import ( - CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN, - CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID, CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE, + CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID, + CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN, ) @@ -52,7 +53,7 @@ class TestAuthenticationCommand: self.auth_command.login() mock_console_print.assert_called_once_with( - "Signing in to CrewAI Enterprise...\n", style="bold blue" + "Signing in to CrewAI AMP...\n", style="bold blue" ) mock_get_device.assert_called_once() mock_display.assert_called_once_with( @@ -114,8 +115,8 @@ class TestAuthenticationCommand: jwt_config, has_expiration, ): - from crewai.cli.authentication.providers.workos import WorkosProvider from crewai.cli.authentication.main import Oauth2Settings + from crewai.cli.authentication.providers.workos import WorkosProvider if user_provider == "workos": self.auth_command.oauth2_provider = WorkosProvider( @@ -297,7 +298,7 @@ class TestAuthenticationCommand: expected_calls = [ call("\nWaiting for authentication... ", style="bold blue", end=""), call("Success!", style="bold green"), - call("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"), + call("\n[bold green]Welcome to CrewAI AMP![/bold green]\n"), ] mock_console_print.assert_has_calls(expected_calls) diff --git a/tests/cli/authentication/test_utils.py b/lib/crewai/tests/cli/authentication/test_utils.py similarity index 100% rename from tests/cli/authentication/test_utils.py rename to lib/crewai/tests/cli/authentication/test_utils.py diff --git a/tests/cli/deploy/__init__.py b/lib/crewai/tests/cli/deploy/__init__.py similarity index 100% rename from tests/cli/deploy/__init__.py rename to lib/crewai/tests/cli/deploy/__init__.py diff --git a/tests/cli/deploy/test_deploy_main.py b/lib/crewai/tests/cli/deploy/test_deploy_main.py similarity index 99% rename from tests/cli/deploy/test_deploy_main.py rename to lib/crewai/tests/cli/deploy/test_deploy_main.py index 8a8799ca9..f33dfbbd5 100644 --- a/tests/cli/deploy/test_deploy_main.py +++ b/lib/crewai/tests/cli/deploy/test_deploy_main.py @@ -5,10 +5,9 @@ from unittest.mock import MagicMock, Mock, patch import pytest import requests -from requests.exceptions import JSONDecodeError - from crewai.cli.deploy.main import DeployCommand from crewai.cli.utils import parse_toml +from requests.exceptions import JSONDecodeError class TestDeployCommand(unittest.TestCase): @@ -62,7 +61,7 @@ class TestDeployCommand(unittest.TestCase): in output ) assert "Status Code: 500" in output - assert "Response:\nb'Invalid JSON'" in output + assert "Response:\nInvalid JSON" in output def test_validate_response_422_error(self): mock_response = Mock(spec=requests.Response) diff --git a/lib/crewai/tests/cli/enterprise/__init__.py b/lib/crewai/tests/cli/enterprise/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/cli/enterprise/test_main.py b/lib/crewai/tests/cli/enterprise/test_main.py similarity index 100% rename from tests/cli/enterprise/test_main.py rename to lib/crewai/tests/cli/enterprise/test_main.py diff --git a/tests/cli/organization/__init__.py b/lib/crewai/tests/cli/organization/__init__.py similarity index 100% rename from tests/cli/organization/__init__.py rename to lib/crewai/tests/cli/organization/__init__.py diff --git a/tests/cli/organization/test_main.py b/lib/crewai/tests/cli/organization/test_main.py similarity index 100% rename from tests/cli/organization/test_main.py rename to lib/crewai/tests/cli/organization/test_main.py diff --git a/tests/cli/test_cli.py b/lib/crewai/tests/cli/test_cli.py similarity index 99% rename from tests/cli/test_cli.py rename to lib/crewai/tests/cli/test_cli.py index 60e3208b1..4f4141269 100644 --- a/tests/cli/test_cli.py +++ b/lib/crewai/tests/cli/test_cli.py @@ -3,7 +3,6 @@ from unittest import mock import pytest from click.testing import CliRunner - from crewai.cli.cli import ( deploy_create, deploy_list, @@ -12,8 +11,8 @@ from crewai.cli.cli import ( deploy_remove, deply_status, flow_add_crew, - reset_memories, login, + reset_memories, test, train, version, diff --git a/tests/cli/test_config.py b/lib/crewai/tests/cli/test_config.py similarity index 99% rename from tests/cli/test_config.py rename to lib/crewai/tests/cli/test_config.py index 09690c470..4db005e78 100644 --- a/tests/cli/test_config.py +++ b/lib/crewai/tests/cli/test_config.py @@ -2,17 +2,17 @@ import json import shutil import tempfile import unittest +from datetime import datetime, timedelta from pathlib import Path -from unittest.mock import patch, MagicMock +from unittest.mock import MagicMock, patch from crewai.cli.config import ( - Settings, - USER_SETTINGS_KEYS, CLI_SETTINGS_KEYS, DEFAULT_CLI_SETTINGS, + USER_SETTINGS_KEYS, + Settings, ) from crewai.cli.shared.token_manager import TokenManager -from datetime import datetime, timedelta class TestSettings(unittest.TestCase): diff --git a/tests/cli/test_constants.py b/lib/crewai/tests/cli/test_constants.py similarity index 83% rename from tests/cli/test_constants.py rename to lib/crewai/tests/cli/test_constants.py index 61d8e069b..013d8ff8c 100644 --- a/tests/cli/test_constants.py +++ b/lib/crewai/tests/cli/test_constants.py @@ -1,5 +1,3 @@ -import pytest - from crewai.cli.constants import ENV_VARS, MODELS, PROVIDERS @@ -12,8 +10,7 @@ def test_huggingface_env_vars(): """Test that Huggingface environment variables are properly configured.""" assert "huggingface" in ENV_VARS assert any( - detail.get("key_name") == "HF_TOKEN" - for detail in ENV_VARS["huggingface"] + detail.get("key_name") == "HF_TOKEN" for detail in ENV_VARS["huggingface"] ) diff --git a/tests/cli/test_create_crew.py b/lib/crewai/tests/cli/test_create_crew.py similarity index 76% rename from tests/cli/test_create_crew.py rename to lib/crewai/tests/cli/test_create_crew.py index 323b7aa18..638be9b5d 100644 --- a/tests/cli/test_create_crew.py +++ b/lib/crewai/tests/cli/test_create_crew.py @@ -6,9 +6,9 @@ from unittest import mock import pytest from click.testing import CliRunner - from crewai.cli.create_crew import create_crew, create_folder_structure + @pytest.fixture def runner(): return CliRunner() @@ -23,7 +23,9 @@ def temp_dir(): def test_create_folder_structure_strips_single_trailing_slash(): with tempfile.TemporaryDirectory() as temp_dir: - folder_path, folder_name, class_name = create_folder_structure("hello/", parent_folder=temp_dir) + folder_path, folder_name, class_name = create_folder_structure( + "hello/", parent_folder=temp_dir + ) assert folder_name == "hello" assert class_name == "Hello" @@ -34,7 +36,9 @@ def test_create_folder_structure_strips_single_trailing_slash(): def test_create_folder_structure_strips_multiple_trailing_slashes(): with tempfile.TemporaryDirectory() as temp_dir: - folder_path, folder_name, class_name = create_folder_structure("hello///", parent_folder=temp_dir) + folder_path, folder_name, class_name = create_folder_structure( + "hello///", parent_folder=temp_dir + ) assert folder_name == "hello" assert class_name == "Hello" @@ -45,7 +49,9 @@ def test_create_folder_structure_strips_multiple_trailing_slashes(): def test_create_folder_structure_handles_complex_name_with_trailing_slash(): with tempfile.TemporaryDirectory() as temp_dir: - folder_path, folder_name, class_name = create_folder_structure("my-awesome_project/", parent_folder=temp_dir) + folder_path, folder_name, class_name = create_folder_structure( + "my-awesome_project/", parent_folder=temp_dir + ) assert folder_name == "my_awesome_project" assert class_name == "MyAwesomeProject" @@ -56,7 +62,9 @@ def test_create_folder_structure_handles_complex_name_with_trailing_slash(): def test_create_folder_structure_normal_name_unchanged(): with tempfile.TemporaryDirectory() as temp_dir: - folder_path, folder_name, class_name = create_folder_structure("hello", parent_folder=temp_dir) + folder_path, folder_name, class_name = create_folder_structure( + "hello", parent_folder=temp_dir + ) assert folder_name == "hello" assert class_name == "Hello" @@ -65,15 +73,14 @@ def test_create_folder_structure_normal_name_unchanged(): assert folder_path.parent == Path(temp_dir) - - - def test_create_folder_structure_with_parent_folder(): with tempfile.TemporaryDirectory() as temp_dir: parent_path = Path(temp_dir) / "parent" parent_path.mkdir() - folder_path, folder_name, class_name = create_folder_structure("child/", parent_folder=parent_path) + folder_path, folder_name, class_name = create_folder_structure( + "child/", parent_folder=parent_path + ) assert folder_name == "child" assert class_name == "Child" @@ -85,13 +92,21 @@ def test_create_folder_structure_with_parent_folder(): @mock.patch("crewai.cli.create_crew.copy_template") @mock.patch("crewai.cli.create_crew.write_env_file") @mock.patch("crewai.cli.create_crew.load_env_vars") -def test_create_crew_with_trailing_slash_creates_valid_project(mock_load_env, mock_write_env, mock_copy_template, temp_dir): +def test_create_crew_with_trailing_slash_creates_valid_project( + mock_load_env, mock_write_env, mock_copy_template, temp_dir +): mock_load_env.return_value = {} with tempfile.TemporaryDirectory() as work_dir: - with mock.patch("crewai.cli.create_crew.create_folder_structure") as mock_create_folder: + with mock.patch( + "crewai.cli.create_crew.create_folder_structure" + ) as mock_create_folder: mock_folder_path = Path(work_dir) / "test_project" - mock_create_folder.return_value = (mock_folder_path, "test_project", "TestProject") + mock_create_folder.return_value = ( + mock_folder_path, + "test_project", + "TestProject", + ) create_crew("test-project/", skip_provider=True) @@ -103,19 +118,29 @@ def test_create_crew_with_trailing_slash_creates_valid_project(mock_load_env, mo args = call[0] if len(args) >= 5: folder_name_arg = args[4] - assert not folder_name_arg.endswith("/"), f"folder_name should not end with slash: {folder_name_arg}" + assert not folder_name_arg.endswith("/"), ( + f"folder_name should not end with slash: {folder_name_arg}" + ) @mock.patch("crewai.cli.create_crew.copy_template") @mock.patch("crewai.cli.create_crew.write_env_file") @mock.patch("crewai.cli.create_crew.load_env_vars") -def test_create_crew_with_multiple_trailing_slashes(mock_load_env, mock_write_env, mock_copy_template, temp_dir): +def test_create_crew_with_multiple_trailing_slashes( + mock_load_env, mock_write_env, mock_copy_template, temp_dir +): mock_load_env.return_value = {} with tempfile.TemporaryDirectory() as work_dir: - with mock.patch("crewai.cli.create_crew.create_folder_structure") as mock_create_folder: + with mock.patch( + "crewai.cli.create_crew.create_folder_structure" + ) as mock_create_folder: mock_folder_path = Path(work_dir) / "test_project" - mock_create_folder.return_value = (mock_folder_path, "test_project", "TestProject") + mock_create_folder.return_value = ( + mock_folder_path, + "test_project", + "TestProject", + ) create_crew("test-project///", skip_provider=True) @@ -125,13 +150,21 @@ def test_create_crew_with_multiple_trailing_slashes(mock_load_env, mock_write_en @mock.patch("crewai.cli.create_crew.copy_template") @mock.patch("crewai.cli.create_crew.write_env_file") @mock.patch("crewai.cli.create_crew.load_env_vars") -def test_create_crew_normal_name_still_works(mock_load_env, mock_write_env, mock_copy_template, temp_dir): +def test_create_crew_normal_name_still_works( + mock_load_env, mock_write_env, mock_copy_template, temp_dir +): mock_load_env.return_value = {} with tempfile.TemporaryDirectory() as work_dir: - with mock.patch("crewai.cli.create_crew.create_folder_structure") as mock_create_folder: + with mock.patch( + "crewai.cli.create_crew.create_folder_structure" + ) as mock_create_folder: mock_folder_path = Path(work_dir) / "normal_project" - mock_create_folder.return_value = (mock_folder_path, "normal_project", "NormalProject") + mock_create_folder.return_value = ( + mock_folder_path, + "normal_project", + "NormalProject", + ) create_crew("normal-project", skip_provider=True) @@ -140,7 +173,9 @@ def test_create_crew_normal_name_still_works(mock_load_env, mock_write_env, mock def test_create_folder_structure_handles_spaces_and_dashes_with_slash(): with tempfile.TemporaryDirectory() as temp_dir: - folder_path, folder_name, class_name = create_folder_structure("My Cool-Project/", parent_folder=temp_dir) + folder_path, folder_name, class_name = create_folder_structure( + "My Cool-Project/", parent_folder=temp_dir + ) assert folder_name == "my_cool_project" assert class_name == "MyCoolProject" @@ -180,16 +215,28 @@ def test_create_folder_structure_validates_names(): ] for valid_name, expected_folder, expected_class in valid_cases: - folder_path, folder_name, class_name = create_folder_structure(valid_name, parent_folder=temp_dir) + folder_path, folder_name, class_name = create_folder_structure( + valid_name, parent_folder=temp_dir + ) assert folder_name == expected_folder assert class_name == expected_class - assert folder_name.isidentifier(), f"folder_name '{folder_name}' should be valid Python identifier" - assert not keyword.iskeyword(folder_name), f"folder_name '{folder_name}' should not be Python keyword" - assert not folder_name[0].isdigit(), f"folder_name '{folder_name}' should not start with digit" + assert folder_name.isidentifier(), ( + f"folder_name '{folder_name}' should be valid Python identifier" + ) + assert not keyword.iskeyword(folder_name), ( + f"folder_name '{folder_name}' should not be Python keyword" + ) + assert not folder_name[0].isdigit(), ( + f"folder_name '{folder_name}' should not start with digit" + ) - assert class_name.isidentifier(), f"class_name '{class_name}' should be valid Python identifier" - assert not keyword.iskeyword(class_name), f"class_name '{class_name}' should not be Python keyword" + assert class_name.isidentifier(), ( + f"class_name '{class_name}' should be valid Python identifier" + ) + assert not keyword.iskeyword(class_name), ( + f"class_name '{class_name}' should not be Python keyword" + ) assert folder_path.parent == Path(temp_dir) if folder_path.exists(): @@ -199,7 +246,9 @@ def test_create_folder_structure_validates_names(): @mock.patch("crewai.cli.create_crew.copy_template") @mock.patch("crewai.cli.create_crew.write_env_file") @mock.patch("crewai.cli.create_crew.load_env_vars") -def test_create_crew_with_parent_folder_and_trailing_slash(mock_load_env, mock_write_env, mock_copy_template, temp_dir): +def test_create_crew_with_parent_folder_and_trailing_slash( + mock_load_env, mock_write_env, mock_copy_template, temp_dir +): mock_load_env.return_value = {} with tempfile.TemporaryDirectory() as work_dir: @@ -236,7 +285,9 @@ def test_create_folder_structure_folder_name_validation(): ] for valid_name, expected_folder in valid_cases: - folder_path, folder_name, class_name = create_folder_structure(valid_name, parent_folder=temp_dir) + folder_path, folder_name, class_name = create_folder_structure( + valid_name, parent_folder=temp_dir + ) assert folder_name == expected_folder assert folder_name.isidentifier() assert not keyword.iskeyword(folder_name) @@ -244,6 +295,7 @@ def test_create_folder_structure_folder_name_validation(): if folder_path.exists(): shutil.rmtree(folder_path) + @mock.patch("crewai.cli.create_crew.create_folder_structure") @mock.patch("crewai.cli.create_crew.copy_template") @mock.patch("crewai.cli.create_crew.load_env_vars") @@ -259,7 +311,7 @@ def test_env_vars_are_uppercased_in_env_file( mock_load_env_vars, mock_copy_template, mock_create_folder_structure, - tmp_path + tmp_path, ): crew_path = tmp_path / "test_crew" crew_path.mkdir() @@ -275,4 +327,4 @@ def test_env_vars_are_uppercased_in_env_file( env_file_path = crew_path / ".env" content = env_file_path.read_text() - assert "MODEL=" in content \ No newline at end of file + assert "MODEL=" in content diff --git a/tests/cli/test_crew_test.py b/lib/crewai/tests/cli/test_crew_test.py similarity index 100% rename from tests/cli/test_crew_test.py rename to lib/crewai/tests/cli/test_crew_test.py diff --git a/tests/cli/test_git.py b/lib/crewai/tests/cli/test_git.py similarity index 99% rename from tests/cli/test_git.py rename to lib/crewai/tests/cli/test_git.py index ccf8f0539..b77106d3f 100644 --- a/tests/cli/test_git.py +++ b/lib/crewai/tests/cli/test_git.py @@ -1,5 +1,4 @@ import pytest - from crewai.cli.git import Repository diff --git a/tests/cli/test_plus_api.py b/lib/crewai/tests/cli/test_plus_api.py similarity index 99% rename from tests/cli/test_plus_api.py rename to lib/crewai/tests/cli/test_plus_api.py index 0bc4278e8..937d023a7 100644 --- a/tests/cli/test_plus_api.py +++ b/lib/crewai/tests/cli/test_plus_api.py @@ -1,8 +1,8 @@ import unittest -from unittest.mock import MagicMock, patch, ANY +from unittest.mock import ANY, MagicMock, patch -from crewai.cli.plus_api import PlusAPI from crewai.cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL +from crewai.cli.plus_api import PlusAPI class TestPlusAPI(unittest.TestCase): diff --git a/tests/cli/test_settings_command.py b/lib/crewai/tests/cli/test_settings_command.py similarity index 100% rename from tests/cli/test_settings_command.py rename to lib/crewai/tests/cli/test_settings_command.py diff --git a/tests/cli/test_token_manager.py b/lib/crewai/tests/cli/test_token_manager.py similarity index 100% rename from tests/cli/test_token_manager.py rename to lib/crewai/tests/cli/test_token_manager.py diff --git a/tests/cli/test_train_crew.py b/lib/crewai/tests/cli/test_train_crew.py similarity index 100% rename from tests/cli/test_train_crew.py rename to lib/crewai/tests/cli/test_train_crew.py diff --git a/tests/cli/test_utils.py b/lib/crewai/tests/cli/test_utils.py similarity index 99% rename from tests/cli/test_utils.py rename to lib/crewai/tests/cli/test_utils.py index 517a1c236..5baf1cffe 100644 --- a/tests/cli/test_utils.py +++ b/lib/crewai/tests/cli/test_utils.py @@ -4,7 +4,6 @@ import tempfile from pathlib import Path import pytest - from crewai.cli import utils @@ -348,7 +347,9 @@ def test_get_crews_with_invalid_module(temp_crew_project, capsys): assert "Error" in captured.out -def test_get_crews_ignores_template_directories(temp_crew_project, monkeypatch, mock_crew): +def test_get_crews_ignores_template_directories( + temp_crew_project, monkeypatch, mock_crew +): template_crew_detected = False def mock_fetch_crews(module_attr): diff --git a/tests/cli/test_version.py b/lib/crewai/tests/cli/test_version.py similarity index 100% rename from tests/cli/test_version.py rename to lib/crewai/tests/cli/test_version.py diff --git a/lib/crewai/tests/cli/tools/__init__.py b/lib/crewai/tests/cli/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/cli/tools/test_main.py b/lib/crewai/tests/cli/tools/test_main.py similarity index 99% rename from tests/cli/tools/test_main.py rename to lib/crewai/tests/cli/tools/test_main.py index 117526487..fa1c5fa44 100644 --- a/tests/cli/tools/test_main.py +++ b/lib/crewai/tests/cli/tools/test_main.py @@ -2,17 +2,16 @@ import os import tempfile import unittest import unittest.mock -from datetime import datetime, timedelta from contextlib import contextmanager +from datetime import datetime, timedelta from pathlib import Path from unittest import mock from unittest.mock import MagicMock, patch import pytest -from pytest import raises - from crewai.cli.shared.token_manager import TokenManager from crewai.cli.tools.main import ToolCommand +from pytest import raises @contextmanager diff --git a/lib/crewai/tests/cli/triggers/test_main.py b/lib/crewai/tests/cli/triggers/test_main.py new file mode 100644 index 000000000..93d24568d --- /dev/null +++ b/lib/crewai/tests/cli/triggers/test_main.py @@ -0,0 +1,170 @@ +import json +import subprocess +import unittest +from unittest.mock import Mock, patch + +import requests +from crewai.cli.triggers.main import TriggersCommand + + +class TestTriggersCommand(unittest.TestCase): + @patch("crewai.cli.command.get_auth_token") + @patch("crewai.cli.command.PlusAPI") + def setUp(self, mock_plus_api, mock_get_auth_token): + self.mock_get_auth_token = mock_get_auth_token + self.mock_plus_api = mock_plus_api + + self.mock_get_auth_token.return_value = "test_token" + + self.triggers_command = TriggersCommand() + self.mock_client = self.triggers_command.plus_api_client + + @patch("crewai.cli.triggers.main.console.print") + def test_list_triggers_success(self, mock_console_print): + mock_response = Mock(spec=requests.Response) + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = { + "apps": [ + { + "name": "Test App", + "slug": "test-app", + "description": "A test application", + "is_connected": True, + "triggers": [ + { + "name": "Test Trigger", + "slug": "test-trigger", + "description": "A test trigger" + } + ] + } + ] + } + self.mock_client.get_triggers.return_value = mock_response + + self.triggers_command.list_triggers() + + self.mock_client.get_triggers.assert_called_once() + mock_console_print.assert_any_call("[bold blue]Fetching available triggers...[/bold blue]") + + @patch("crewai.cli.triggers.main.console.print") + def test_list_triggers_no_apps(self, mock_console_print): + mock_response = Mock(spec=requests.Response) + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = {"apps": []} + self.mock_client.get_triggers.return_value = mock_response + + self.triggers_command.list_triggers() + + mock_console_print.assert_any_call("[yellow]No triggers found.[/yellow]") + + @patch("crewai.cli.triggers.main.console.print") + def test_list_triggers_api_error(self, mock_console_print): + self.mock_client.get_triggers.side_effect = Exception("API Error") + + with self.assertRaises(SystemExit): + self.triggers_command.list_triggers() + + mock_console_print.assert_any_call("[bold red]Error fetching triggers: API Error[/bold red]") + + @patch("crewai.cli.triggers.main.console.print") + def test_execute_with_trigger_invalid_format(self, mock_console_print): + with self.assertRaises(SystemExit): + self.triggers_command.execute_with_trigger("invalid-format") + + mock_console_print.assert_called_with( + "[bold red]Error: Trigger must be in format 'app_slug/trigger_slug'[/bold red]" + ) + + @patch("crewai.cli.triggers.main.console.print") + @patch.object(TriggersCommand, "_run_crew_with_payload") + def test_execute_with_trigger_success(self, mock_run_crew, mock_console_print): + mock_response = Mock(spec=requests.Response) + mock_response.status_code = 200 + mock_response.ok = True + mock_response.json.return_value = { + "sample_payload": {"key": "value", "data": "test"} + } + self.mock_client.get_trigger_payload.return_value = mock_response + + self.triggers_command.execute_with_trigger("test-app/test-trigger") + + self.mock_client.get_trigger_payload.assert_called_once_with("test-app", "test-trigger") + mock_run_crew.assert_called_once_with({"key": "value", "data": "test"}) + mock_console_print.assert_any_call( + "[bold blue]Fetching trigger payload for test-app/test-trigger...[/bold blue]" + ) + + @patch("crewai.cli.triggers.main.console.print") + def test_execute_with_trigger_not_found(self, mock_console_print): + mock_response = Mock(spec=requests.Response) + mock_response.status_code = 404 + mock_response.json.return_value = {"error": "Trigger not found"} + self.mock_client.get_trigger_payload.return_value = mock_response + + with self.assertRaises(SystemExit): + self.triggers_command.execute_with_trigger("test-app/nonexistent-trigger") + + mock_console_print.assert_any_call("[bold red]Error: Trigger not found[/bold red]") + + @patch("crewai.cli.triggers.main.console.print") + def test_execute_with_trigger_api_error(self, mock_console_print): + self.mock_client.get_trigger_payload.side_effect = Exception("API Error") + + with self.assertRaises(SystemExit): + self.triggers_command.execute_with_trigger("test-app/test-trigger") + + mock_console_print.assert_any_call( + "[bold red]Error executing crew with trigger: API Error[/bold red]" + ) + + + @patch("subprocess.run") + def test_run_crew_with_payload_success(self, mock_subprocess): + payload = {"key": "value", "data": "test"} + mock_subprocess.return_value = None + + self.triggers_command._run_crew_with_payload(payload) + + mock_subprocess.assert_called_once_with( + ["uv", "run", "run_with_trigger", json.dumps(payload)], + capture_output=False, + text=True, + check=True + ) + + @patch("subprocess.run") + def test_run_crew_with_payload_failure(self, mock_subprocess): + payload = {"key": "value"} + mock_subprocess.side_effect = subprocess.CalledProcessError(1, "uv") + + with self.assertRaises(SystemExit): + self.triggers_command._run_crew_with_payload(payload) + + @patch("subprocess.run") + def test_run_crew_with_payload_empty_payload(self, mock_subprocess): + payload = {} + mock_subprocess.return_value = None + + self.triggers_command._run_crew_with_payload(payload) + + mock_subprocess.assert_called_once_with( + ["uv", "run", "run_with_trigger", "{}"], + capture_output=False, + text=True, + check=True + ) + + @patch("crewai.cli.triggers.main.console.print") + def test_execute_with_trigger_with_default_error_message(self, mock_console_print): + mock_response = Mock(spec=requests.Response) + mock_response.status_code = 404 + mock_response.json.return_value = {} + self.mock_client.get_trigger_payload.return_value = mock_response + + with self.assertRaises(SystemExit): + self.triggers_command.execute_with_trigger("test-app/test-trigger") + + mock_console_print.assert_any_call("[bold red]Error: Trigger not found[/bold red]") diff --git a/tests/config/agents.yaml b/lib/crewai/tests/config/agents.yaml similarity index 100% rename from tests/config/agents.yaml rename to lib/crewai/tests/config/agents.yaml diff --git a/tests/config/tasks.yaml b/lib/crewai/tests/config/tasks.yaml similarity index 100% rename from tests/config/tasks.yaml rename to lib/crewai/tests/config/tasks.yaml diff --git a/tests/conftest.py b/lib/crewai/tests/conftest.py similarity index 85% rename from tests/conftest.py rename to lib/crewai/tests/conftest.py index 8ddfae82f..8dd99c26e 100644 --- a/tests/conftest.py +++ b/lib/crewai/tests/conftest.py @@ -33,7 +33,7 @@ def setup_test_environment(): except (OSError, IOError) as e: raise RuntimeError( f"Test storage directory {storage_dir} is not writable: {e}" - ) + ) from e os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir) os.environ["CREWAI_TESTING"] = "true" @@ -159,10 +159,37 @@ def mock_opentelemetry_components(): } +@pytest.fixture(autouse=True) +def clear_event_bus_handlers(setup_test_environment): + """Clear event bus handlers after each test for isolation. + + Handlers registered during the test are allowed to run, then cleaned up + after the test completes. + + Depends on setup_test_environment to ensure cleanup happens in correct order. + """ + from crewai.events.event_bus import crewai_event_bus + from crewai.experimental.evaluation.evaluation_listener import ( + EvaluationTraceCallback, + ) + + yield + + # Shutdown event bus and wait for all handlers to complete + crewai_event_bus.shutdown(wait=True) + crewai_event_bus._initialize() + + callback = EvaluationTraceCallback() + callback.traces.clear() + callback.current_agent_id = None + callback.current_task_id = None + + @pytest.fixture(scope="module") def vcr_config(request) -> dict: + import os return { - "cassette_library_dir": "tests/cassettes", + "cassette_library_dir": os.path.join(os.path.dirname(__file__), "cassettes"), "record_mode": "new_episodes", "filter_headers": [("authorization", "AUTHORIZATION-XXX")], } diff --git a/lib/crewai/tests/events/test_depends.py b/lib/crewai/tests/events/test_depends.py new file mode 100644 index 000000000..4f1e26a1c --- /dev/null +++ b/lib/crewai/tests/events/test_depends.py @@ -0,0 +1,286 @@ +"""Tests for FastAPI-style dependency injection in event handlers.""" + +import asyncio + +import pytest + +from crewai.events import Depends, crewai_event_bus +from crewai.events.base_events import BaseEvent + + +class DependsTestEvent(BaseEvent): + """Test event for dependency tests.""" + + value: int = 0 + type: str = "test_event" + + +@pytest.mark.asyncio +async def test_basic_dependency(): + """Test that handler with dependency runs after its dependency.""" + execution_order = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(DependsTestEvent) + def setup(source, event: DependsTestEvent): + execution_order.append("setup") + + @crewai_event_bus.on(DependsTestEvent, Depends(setup)) + def process(source, event: DependsTestEvent): + execution_order.append("process") + + event = DependsTestEvent(value=1) + future = crewai_event_bus.emit("test_source", event) + + if future: + await asyncio.wrap_future(future) + + assert execution_order == ["setup", "process"] + + +@pytest.mark.asyncio +async def test_multiple_dependencies(): + """Test handler with multiple dependencies.""" + execution_order = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(DependsTestEvent) + def setup_a(source, event: DependsTestEvent): + execution_order.append("setup_a") + + @crewai_event_bus.on(DependsTestEvent) + def setup_b(source, event: DependsTestEvent): + execution_order.append("setup_b") + + @crewai_event_bus.on( + DependsTestEvent, depends_on=[Depends(setup_a), Depends(setup_b)] + ) + def process(source, event: DependsTestEvent): + execution_order.append("process") + + event = DependsTestEvent(value=1) + future = crewai_event_bus.emit("test_source", event) + + if future: + await asyncio.wrap_future(future) + + # setup_a and setup_b can run in any order (same level) + assert "process" in execution_order + assert execution_order.index("process") > execution_order.index("setup_a") + assert execution_order.index("process") > execution_order.index("setup_b") + + +@pytest.mark.asyncio +async def test_chain_of_dependencies(): + """Test chain of dependencies (A -> B -> C).""" + execution_order = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(DependsTestEvent) + def handler_a(source, event: DependsTestEvent): + execution_order.append("handler_a") + + @crewai_event_bus.on(DependsTestEvent, depends_on=Depends(handler_a)) + def handler_b(source, event: DependsTestEvent): + execution_order.append("handler_b") + + @crewai_event_bus.on(DependsTestEvent, depends_on=Depends(handler_b)) + def handler_c(source, event: DependsTestEvent): + execution_order.append("handler_c") + + event = DependsTestEvent(value=1) + future = crewai_event_bus.emit("test_source", event) + + if future: + await asyncio.wrap_future(future) + + assert execution_order == ["handler_a", "handler_b", "handler_c"] + + +@pytest.mark.asyncio +async def test_async_handler_with_dependency(): + """Test async handler with dependency on sync handler.""" + execution_order = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(DependsTestEvent) + def sync_setup(source, event: DependsTestEvent): + execution_order.append("sync_setup") + + @crewai_event_bus.on(DependsTestEvent, depends_on=Depends(sync_setup)) + async def async_process(source, event: DependsTestEvent): + await asyncio.sleep(0.01) + execution_order.append("async_process") + + event = DependsTestEvent(value=1) + future = crewai_event_bus.emit("test_source", event) + + if future: + await asyncio.wrap_future(future) + + assert execution_order == ["sync_setup", "async_process"] + + +@pytest.mark.asyncio +async def test_mixed_handlers_with_dependencies(): + """Test mix of sync and async handlers with dependencies.""" + execution_order = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(DependsTestEvent) + def setup(source, event: DependsTestEvent): + execution_order.append("setup") + + @crewai_event_bus.on(DependsTestEvent, depends_on=Depends(setup)) + def sync_process(source, event: DependsTestEvent): + execution_order.append("sync_process") + + @crewai_event_bus.on(DependsTestEvent, depends_on=Depends(setup)) + async def async_process(source, event: DependsTestEvent): + await asyncio.sleep(0.01) + execution_order.append("async_process") + + @crewai_event_bus.on( + DependsTestEvent, depends_on=[Depends(sync_process), Depends(async_process)] + ) + def finalize(source, event: DependsTestEvent): + execution_order.append("finalize") + + event = DependsTestEvent(value=1) + future = crewai_event_bus.emit("test_source", event) + + if future: + await asyncio.wrap_future(future) + + # Verify execution order + assert execution_order[0] == "setup" + assert "finalize" in execution_order + assert execution_order.index("finalize") > execution_order.index("sync_process") + assert execution_order.index("finalize") > execution_order.index("async_process") + + +@pytest.mark.asyncio +async def test_independent_handlers_run_concurrently(): + """Test that handlers without dependencies can run concurrently.""" + execution_order = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(DependsTestEvent) + async def handler_a(source, event: DependsTestEvent): + await asyncio.sleep(0.01) + execution_order.append("handler_a") + + @crewai_event_bus.on(DependsTestEvent) + async def handler_b(source, event: DependsTestEvent): + await asyncio.sleep(0.01) + execution_order.append("handler_b") + + event = DependsTestEvent(value=1) + future = crewai_event_bus.emit("test_source", event) + + if future: + await asyncio.wrap_future(future) + + # Both handlers should have executed + assert len(execution_order) == 2 + assert "handler_a" in execution_order + assert "handler_b" in execution_order + + +@pytest.mark.asyncio +async def test_circular_dependency_detection(): + """Test that circular dependencies are detected and raise an error.""" + from crewai.events.handler_graph import CircularDependencyError, build_execution_plan + + # Create circular dependency: handler_a -> handler_b -> handler_c -> handler_a + def handler_a(source, event: DependsTestEvent): + pass + + def handler_b(source, event: DependsTestEvent): + pass + + def handler_c(source, event: DependsTestEvent): + pass + + # Build a dependency graph with a cycle + handlers = [handler_a, handler_b, handler_c] + dependencies = { + handler_a: [Depends(handler_b)], + handler_b: [Depends(handler_c)], + handler_c: [Depends(handler_a)], # Creates the cycle + } + + # Should raise CircularDependencyError about circular dependency + with pytest.raises(CircularDependencyError, match="Circular dependency"): + build_execution_plan(handlers, dependencies) + + +@pytest.mark.asyncio +async def test_handler_without_dependency_runs_normally(): + """Test that handlers without dependencies still work as before.""" + execution_order = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(DependsTestEvent) + def simple_handler(source, event: DependsTestEvent): + execution_order.append("simple_handler") + + event = DependsTestEvent(value=1) + future = crewai_event_bus.emit("test_source", event) + + if future: + await asyncio.wrap_future(future) + + assert execution_order == ["simple_handler"] + + +@pytest.mark.asyncio +async def test_depends_equality(): + """Test Depends equality and hashing.""" + + def handler_a(source, event): + pass + + def handler_b(source, event): + pass + + dep_a1 = Depends(handler_a) + dep_a2 = Depends(handler_a) + dep_b = Depends(handler_b) + + # Same handler should be equal + assert dep_a1 == dep_a2 + assert hash(dep_a1) == hash(dep_a2) + + # Different handlers should not be equal + assert dep_a1 != dep_b + assert hash(dep_a1) != hash(dep_b) + + +@pytest.mark.asyncio +async def test_aemit_ignores_dependencies(): + """Test that aemit only processes async handlers (no dependency support yet).""" + execution_order = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(DependsTestEvent) + def sync_handler(source, event: DependsTestEvent): + execution_order.append("sync_handler") + + @crewai_event_bus.on(DependsTestEvent) + async def async_handler(source, event: DependsTestEvent): + execution_order.append("async_handler") + + event = DependsTestEvent(value=1) + await crewai_event_bus.aemit("test_source", event) + + # Only async handler should execute + assert execution_order == ["async_handler"] diff --git a/tests/events/test_tracing_utils_machine_id.py b/lib/crewai/tests/events/test_tracing_utils_machine_id.py similarity index 100% rename from tests/events/test_tracing_utils_machine_id.py rename to lib/crewai/tests/events/test_tracing_utils_machine_id.py diff --git a/lib/crewai/tests/experimental/__init__.py b/lib/crewai/tests/experimental/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/tests/experimental/evaluation/__init__.py b/lib/crewai/tests/experimental/evaluation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/tests/experimental/evaluation/metrics/__init__.py b/lib/crewai/tests/experimental/evaluation/metrics/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/experimental/evaluation/metrics/test_base_evaluation_metrics.py b/lib/crewai/tests/experimental/evaluation/metrics/test_base_evaluation_metrics.py similarity index 99% rename from tests/experimental/evaluation/metrics/test_base_evaluation_metrics.py rename to lib/crewai/tests/experimental/evaluation/metrics/test_base_evaluation_metrics.py index c1a371fb8..5f1772905 100644 --- a/tests/experimental/evaluation/metrics/test_base_evaluation_metrics.py +++ b/lib/crewai/tests/experimental/evaluation/metrics/test_base_evaluation_metrics.py @@ -1,5 +1,6 @@ -import pytest from unittest.mock import MagicMock + +import pytest from crewai.agent import Agent from crewai.task import Task diff --git a/tests/experimental/evaluation/metrics/test_goal_metrics.py b/lib/crewai/tests/experimental/evaluation/metrics/test_goal_metrics.py similarity index 98% rename from tests/experimental/evaluation/metrics/test_goal_metrics.py rename to lib/crewai/tests/experimental/evaluation/metrics/test_goal_metrics.py index 1d71f9159..e6beab9ba 100644 --- a/tests/experimental/evaluation/metrics/test_goal_metrics.py +++ b/lib/crewai/tests/experimental/evaluation/metrics/test_goal_metrics.py @@ -1,12 +1,13 @@ -from unittest.mock import patch, MagicMock -from tests.experimental.evaluation.metrics.test_base_evaluation_metrics import ( - BaseEvaluationMetricsTest, -) +from unittest.mock import MagicMock, patch from crewai.experimental.evaluation.base_evaluator import EvaluationScore from crewai.experimental.evaluation.metrics.goal_metrics import GoalAlignmentEvaluator from crewai.utilities.llm_utils import LLM +from tests.experimental.evaluation.metrics.test_base_evaluation_metrics import ( + BaseEvaluationMetricsTest, +) + class TestGoalAlignmentEvaluator(BaseEvaluationMetricsTest): @patch("crewai.utilities.llm_utils.create_llm") diff --git a/tests/experimental/evaluation/metrics/test_reasoning_metrics.py b/lib/crewai/tests/experimental/evaluation/metrics/test_reasoning_metrics.py similarity index 98% rename from tests/experimental/evaluation/metrics/test_reasoning_metrics.py rename to lib/crewai/tests/experimental/evaluation/metrics/test_reasoning_metrics.py index 2153640e3..0c89d9f67 100644 --- a/tests/experimental/evaluation/metrics/test_reasoning_metrics.py +++ b/lib/crewai/tests/experimental/evaluation/metrics/test_reasoning_metrics.py @@ -1,16 +1,17 @@ -import pytest -from unittest.mock import patch, MagicMock -from typing import List, Dict, Any +from typing import Any, Dict, List +from unittest.mock import MagicMock, patch -from crewai.tasks.task_output import TaskOutput +import pytest +from crewai.experimental.evaluation.base_evaluator import EvaluationScore from crewai.experimental.evaluation.metrics.reasoning_metrics import ( ReasoningEfficiencyEvaluator, ) +from crewai.tasks.task_output import TaskOutput +from crewai.utilities.llm_utils import LLM + from tests.experimental.evaluation.metrics.test_base_evaluation_metrics import ( BaseEvaluationMetricsTest, ) -from crewai.utilities.llm_utils import LLM -from crewai.experimental.evaluation.base_evaluator import EvaluationScore class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest): diff --git a/tests/experimental/evaluation/metrics/test_semantic_quality_metrics.py b/lib/crewai/tests/experimental/evaluation/metrics/test_semantic_quality_metrics.py similarity index 100% rename from tests/experimental/evaluation/metrics/test_semantic_quality_metrics.py rename to lib/crewai/tests/experimental/evaluation/metrics/test_semantic_quality_metrics.py diff --git a/tests/experimental/evaluation/metrics/test_tools_metrics.py b/lib/crewai/tests/experimental/evaluation/metrics/test_tools_metrics.py similarity index 99% rename from tests/experimental/evaluation/metrics/test_tools_metrics.py rename to lib/crewai/tests/experimental/evaluation/metrics/test_tools_metrics.py index bda3eb687..ee9732422 100644 --- a/tests/experimental/evaluation/metrics/test_tools_metrics.py +++ b/lib/crewai/tests/experimental/evaluation/metrics/test_tools_metrics.py @@ -1,11 +1,12 @@ -from unittest.mock import patch, MagicMock +from unittest.mock import MagicMock, patch from crewai.experimental.evaluation.metrics.tools_metrics import ( - ToolSelectionEvaluator, ParameterExtractionEvaluator, ToolInvocationEvaluator, + ToolSelectionEvaluator, ) from crewai.utilities.llm_utils import LLM + from tests.experimental.evaluation.metrics.test_base_evaluation_metrics import ( BaseEvaluationMetricsTest, ) diff --git a/lib/crewai/tests/experimental/evaluation/test_agent_evaluator.py b/lib/crewai/tests/experimental/evaluation/test_agent_evaluator.py new file mode 100644 index 000000000..a03d0e8d9 --- /dev/null +++ b/lib/crewai/tests/experimental/evaluation/test_agent_evaluator.py @@ -0,0 +1,269 @@ +import threading + +import pytest +from crewai.agent import Agent +from crewai.crew import Crew +from crewai.events.event_bus import crewai_event_bus +from crewai.events.types.agent_events import ( + AgentEvaluationCompletedEvent, + AgentEvaluationFailedEvent, + AgentEvaluationStartedEvent, +) +from crewai.experimental.evaluation import ( + EvaluationScore, + GoalAlignmentEvaluator, + MetricCategory, + ParameterExtractionEvaluator, + ReasoningEfficiencyEvaluator, + SemanticQualityEvaluator, + ToolInvocationEvaluator, + ToolSelectionEvaluator, + create_default_evaluator, +) +from crewai.experimental.evaluation.agent_evaluator import AgentEvaluator +from crewai.experimental.evaluation.base_evaluator import ( + AgentEvaluationResult, + BaseEvaluator, +) +from crewai.task import Task + + +class TestAgentEvaluator: + @pytest.fixture + def mock_crew(self): + agent = Agent( + role="Test Agent", + goal="Complete test tasks successfully", + backstory="An agent created for testing purposes", + allow_delegation=False, + verbose=False, + ) + + task = Task( + description="Test task description", + agent=agent, + expected_output="Expected test output", + ) + + crew = Crew(agents=[agent], tasks=[task]) + return crew + + def test_set_iteration(self): + agent_evaluator = AgentEvaluator(agents=[]) + + agent_evaluator.set_iteration(3) + assert agent_evaluator._execution_state.iteration == 3 + + @pytest.mark.vcr(filter_headers=["authorization"]) + def test_evaluate_current_iteration(self, mock_crew): + from crewai.events.types.task_events import TaskCompletedEvent + + agent_evaluator = AgentEvaluator( + agents=mock_crew.agents, evaluators=[GoalAlignmentEvaluator()] + ) + + task_completed_event = threading.Event() + + @crewai_event_bus.on(TaskCompletedEvent) + async def on_task_completed(source, event): + # TaskCompletedEvent fires AFTER evaluation results are stored + task_completed_event.set() + + mock_crew.kickoff() + + assert task_completed_event.wait(timeout=5), ( + "Timeout waiting for task completion" + ) + + results = agent_evaluator.get_evaluation_results() + + assert isinstance(results, dict) + + (agent,) = mock_crew.agents + (task,) = mock_crew.tasks + + assert len(mock_crew.agents) == 1 + assert agent.role in results + assert len(results[agent.role]) == 1 + + (result,) = results[agent.role] + assert isinstance(result, AgentEvaluationResult) + + assert result.agent_id == str(agent.id) + assert result.task_id == str(task.id) + + (goal_alignment,) = result.metrics.values() + assert goal_alignment.score == 5.0 + + expected_feedback = "The agent's output demonstrates an understanding of the need for a comprehensive document outlining task" + assert expected_feedback in goal_alignment.feedback + + assert goal_alignment.raw_response is not None + assert '"score": 5' in goal_alignment.raw_response + + def test_create_default_evaluator(self, mock_crew): + agent_evaluator = create_default_evaluator(agents=mock_crew.agents) + assert isinstance(agent_evaluator, AgentEvaluator) + assert agent_evaluator.agents == mock_crew.agents + + expected_types = [ + GoalAlignmentEvaluator, + SemanticQualityEvaluator, + ToolSelectionEvaluator, + ParameterExtractionEvaluator, + ToolInvocationEvaluator, + ReasoningEfficiencyEvaluator, + ] + + assert len(agent_evaluator.evaluators) == len(expected_types) + for evaluator, expected_type in zip( + agent_evaluator.evaluators, expected_types, strict=False + ): + assert isinstance(evaluator, expected_type) + + @pytest.mark.vcr(filter_headers=["authorization"]) + def test_eval_specific_agents_from_crew(self, mock_crew): + from crewai.events.types.task_events import TaskCompletedEvent + + agent = Agent( + role="Test Agent Eval", + goal="Complete test tasks successfully", + backstory="An agent created for testing purposes", + ) + task = Task( + description="Test task description", + agent=agent, + expected_output="Expected test output", + ) + mock_crew.agents.append(agent) + mock_crew.tasks.append(task) + + events = {} + started_event = threading.Event() + completed_event = threading.Event() + task_completed_event = threading.Event() + + agent_evaluator = AgentEvaluator( + agents=[agent], evaluators=[GoalAlignmentEvaluator()] + ) + + @crewai_event_bus.on(AgentEvaluationStartedEvent) + async def capture_started(source, event): + if event.agent_id == str(agent.id): + events["started"] = event + started_event.set() + + @crewai_event_bus.on(AgentEvaluationCompletedEvent) + async def capture_completed(source, event): + if event.agent_id == str(agent.id): + events["completed"] = event + completed_event.set() + + @crewai_event_bus.on(AgentEvaluationFailedEvent) + def capture_failed(source, event): + events["failed"] = event + + @crewai_event_bus.on(TaskCompletedEvent) + async def on_task_completed(source, event): + # TaskCompletedEvent fires AFTER evaluation results are stored + if event.task and event.task.id == task.id: + task_completed_event.set() + + mock_crew.kickoff() + + assert started_event.wait(timeout=5), "Timeout waiting for started event" + assert completed_event.wait(timeout=5), "Timeout waiting for completed event" + assert task_completed_event.wait(timeout=5), ( + "Timeout waiting for task completion" + ) + + assert events.keys() == {"started", "completed"} + assert events["started"].agent_id == str(agent.id) + assert events["started"].agent_role == agent.role + assert events["started"].task_id == str(task.id) + assert events["started"].iteration == 1 + + assert events["completed"].agent_id == str(agent.id) + assert events["completed"].agent_role == agent.role + assert events["completed"].task_id == str(task.id) + assert events["completed"].iteration == 1 + assert events["completed"].metric_category == MetricCategory.GOAL_ALIGNMENT + assert isinstance(events["completed"].score, EvaluationScore) + assert events["completed"].score.score == 5.0 + + results = agent_evaluator.get_evaluation_results() + + assert isinstance(results, dict) + assert len(results.keys()) == 1 + (result,) = results[agent.role] + assert isinstance(result, AgentEvaluationResult) + + assert result.agent_id == str(agent.id) + assert result.task_id == str(task.id) + + (goal_alignment,) = result.metrics.values() + assert goal_alignment.score == 5.0 + + expected_feedback = "The agent provided a thorough guide on how to conduct a test task but failed to produce specific expected output" + assert expected_feedback in goal_alignment.feedback + + assert goal_alignment.raw_response is not None + assert '"score": 5' in goal_alignment.raw_response + + @pytest.mark.vcr(filter_headers=["authorization"]) + def test_failed_evaluation(self, mock_crew): + (agent,) = mock_crew.agents + (task,) = mock_crew.tasks + + events = {} + started_event = threading.Event() + failed_event = threading.Event() + + @crewai_event_bus.on(AgentEvaluationStartedEvent) + def capture_started(source, event): + events["started"] = event + started_event.set() + + @crewai_event_bus.on(AgentEvaluationCompletedEvent) + def capture_completed(source, event): + events["completed"] = event + + @crewai_event_bus.on(AgentEvaluationFailedEvent) + def capture_failed(source, event): + events["failed"] = event + failed_event.set() + + class FailingEvaluator(BaseEvaluator): + metric_category = MetricCategory.GOAL_ALIGNMENT + + def evaluate(self, agent, task, execution_trace, final_output): + raise ValueError("Forced evaluation failure") + + agent_evaluator = AgentEvaluator( + agents=[agent], evaluators=[FailingEvaluator()] + ) + mock_crew.kickoff() + + assert started_event.wait(timeout=5), "Timeout waiting for started event" + assert failed_event.wait(timeout=5), "Timeout waiting for failed event" + + assert events.keys() == {"started", "failed"} + assert events["started"].agent_id == str(agent.id) + assert events["started"].agent_role == agent.role + assert events["started"].task_id == str(task.id) + assert events["started"].iteration == 1 + + assert events["failed"].agent_id == str(agent.id) + assert events["failed"].agent_role == agent.role + assert events["failed"].task_id == str(task.id) + assert events["failed"].iteration == 1 + assert events["failed"].error == "Forced evaluation failure" + + results = agent_evaluator.get_evaluation_results() + (result,) = results[agent.role] + assert isinstance(result, AgentEvaluationResult) + + assert result.agent_id == str(agent.id) + assert result.task_id == str(task.id) + + assert result.metrics == {} diff --git a/tests/experimental/evaluation/test_experiment_result.py b/lib/crewai/tests/experimental/evaluation/test_experiment_result.py similarity index 100% rename from tests/experimental/evaluation/test_experiment_result.py rename to lib/crewai/tests/experimental/evaluation/test_experiment_result.py diff --git a/tests/experimental/evaluation/test_experiment_runner.py b/lib/crewai/tests/experimental/evaluation/test_experiment_runner.py similarity index 77% rename from tests/experimental/evaluation/test_experiment_runner.py rename to lib/crewai/tests/experimental/evaluation/test_experiment_runner.py index 58382fa65..f15af56de 100644 --- a/tests/experimental/evaluation/test_experiment_runner.py +++ b/lib/crewai/tests/experimental/evaluation/test_experiment_runner.py @@ -1,11 +1,16 @@ -import pytest from unittest.mock import MagicMock, patch +import pytest from crewai.crew import Crew -from crewai.experimental.evaluation.experiment.runner import ExperimentRunner +from crewai.experimental.evaluation.base_evaluator import ( + EvaluationScore, + MetricCategory, +) +from crewai.experimental.evaluation.evaluation_display import ( + AgentAggregatedEvaluationResult, +) from crewai.experimental.evaluation.experiment.result import ExperimentResults -from crewai.experimental.evaluation.evaluation_display import AgentAggregatedEvaluationResult -from crewai.experimental.evaluation.base_evaluator import MetricCategory, EvaluationScore +from crewai.experimental.evaluation.experiment.runner import ExperimentRunner class TestExperimentRunner: @@ -22,45 +27,47 @@ class TestExperimentRunner: MetricCategory.GOAL_ALIGNMENT: EvaluationScore( score=9, feedback="Test feedback for goal alignment", - raw_response="Test raw response for goal alignment" + raw_response="Test raw response for goal alignment", ), MetricCategory.REASONING_EFFICIENCY: EvaluationScore( score=None, feedback="Reasoning efficiency not applicable", - raw_response="Reasoning efficiency not applicable" + raw_response="Reasoning efficiency not applicable", ), MetricCategory.PARAMETER_EXTRACTION: EvaluationScore( score=7, feedback="Test parameter extraction explanation", - raw_response="Test raw output" + raw_response="Test raw output", ), MetricCategory.TOOL_SELECTION: EvaluationScore( score=8, feedback="Test tool selection explanation", - raw_response="Test raw output" - ) - } + raw_response="Test raw output", + ), + }, ) return {"Test Agent": agent_evaluation} - @patch('crewai.experimental.evaluation.experiment.runner.create_default_evaluator') - def test_run_success(self, mock_create_evaluator, mock_crew, mock_evaluator_results): + @patch("crewai.experimental.evaluation.experiment.runner.create_default_evaluator") + def test_run_success( + self, mock_create_evaluator, mock_crew, mock_evaluator_results + ): dataset = [ { "identifier": "test-case-1", "inputs": {"query": "Test query 1"}, - "expected_score": 8 + "expected_score": 8, }, { "identifier": "test-case-2", "inputs": {"query": "Test query 2"}, - "expected_score": {"goal_alignment": 7} + "expected_score": {"goal_alignment": 7}, }, { "inputs": {"query": "Test query 3"}, - "expected_score": {"tool_selection": 9} - } + "expected_score": {"tool_selection": 9}, + }, ] mock_evaluator = MagicMock() @@ -101,14 +108,15 @@ class TestExperimentRunner: assert mock_evaluator.reset_iterations_results.call_count == 3 assert mock_evaluator.get_agent_evaluation.call_count == 3 - - @patch('crewai.experimental.evaluation.experiment.runner.create_default_evaluator') - def test_run_success_with_unknown_metric(self, mock_create_evaluator, mock_crew, mock_evaluator_results): + @patch("crewai.experimental.evaluation.experiment.runner.create_default_evaluator") + def test_run_success_with_unknown_metric( + self, mock_create_evaluator, mock_crew, mock_evaluator_results + ): dataset = [ { "identifier": "test-case-2", "inputs": {"query": "Test query 2"}, - "expected_score": {"goal_alignment": 7, "unknown_metric": 8} + "expected_score": {"goal_alignment": 7, "unknown_metric": 8}, } ] @@ -121,7 +129,7 @@ class TestExperimentRunner: results = runner.run(crew=mock_crew) - result, = results.results + (result,) = results.results assert result.identifier == "test-case-2" assert result.inputs == {"query": "Test query 2"} @@ -130,55 +138,24 @@ class TestExperimentRunner: assert "unknown_metric" in result.expected_score.keys() assert result.passed is True - @patch('crewai.experimental.evaluation.experiment.runner.create_default_evaluator') - def test_run_success_with_single_metric_evaluator_and_expected_specific_metric(self, mock_create_evaluator, mock_crew, mock_evaluator_results): + @patch("crewai.experimental.evaluation.experiment.runner.create_default_evaluator") + def test_run_success_with_single_metric_evaluator_and_expected_specific_metric( + self, mock_create_evaluator, mock_crew, mock_evaluator_results + ): dataset = [ { "identifier": "test-case-2", "inputs": {"query": "Test query 2"}, - "expected_score": {"goal_alignment": 7} + "expected_score": {"goal_alignment": 7}, } ] mock_evaluator = MagicMock() mock_create_evaluator["Test Agent"].metrics = { MetricCategory.GOAL_ALIGNMENT: EvaluationScore( - score=9, - feedback="Test feedback for goal alignment", - raw_response="Test raw response for goal alignment" - ) - } - mock_evaluator.get_agent_evaluation.return_value = mock_evaluator_results - mock_evaluator.reset_iterations_results = MagicMock() - mock_create_evaluator.return_value = mock_evaluator - - runner = ExperimentRunner(dataset=dataset) - - results = runner.run(crew=mock_crew) - result, = results.results - - assert result.identifier == "test-case-2" - assert result.inputs == {"query": "Test query 2"} - assert isinstance(result.expected_score, dict) - assert "goal_alignment" in result.expected_score.keys() - assert result.passed is True - - @patch('crewai.experimental.evaluation.experiment.runner.create_default_evaluator') - def test_run_success_when_expected_metric_is_not_available(self, mock_create_evaluator, mock_crew, mock_evaluator_results): - dataset = [ - { - "identifier": "test-case-2", - "inputs": {"query": "Test query 2"}, - "expected_score": {"unknown_metric": 7} - } - ] - - mock_evaluator = MagicMock() - mock_create_evaluator["Test Agent"].metrics = { - MetricCategory.GOAL_ALIGNMENT: EvaluationScore( - score=5, + score=9, feedback="Test feedback for goal alignment", - raw_response="Test raw response for goal alignment" + raw_response="Test raw response for goal alignment", ) } mock_evaluator.get_agent_evaluation.return_value = mock_evaluator_results @@ -188,10 +165,45 @@ class TestExperimentRunner: runner = ExperimentRunner(dataset=dataset) results = runner.run(crew=mock_crew) - result, = results.results + (result,) = results.results + + assert result.identifier == "test-case-2" + assert result.inputs == {"query": "Test query 2"} + assert isinstance(result.expected_score, dict) + assert "goal_alignment" in result.expected_score.keys() + assert result.passed is True + + @patch("crewai.experimental.evaluation.experiment.runner.create_default_evaluator") + def test_run_success_when_expected_metric_is_not_available( + self, mock_create_evaluator, mock_crew, mock_evaluator_results + ): + dataset = [ + { + "identifier": "test-case-2", + "inputs": {"query": "Test query 2"}, + "expected_score": {"unknown_metric": 7}, + } + ] + + mock_evaluator = MagicMock() + mock_create_evaluator["Test Agent"].metrics = { + MetricCategory.GOAL_ALIGNMENT: EvaluationScore( + score=5, + feedback="Test feedback for goal alignment", + raw_response="Test raw response for goal alignment", + ) + } + mock_evaluator.get_agent_evaluation.return_value = mock_evaluator_results + mock_evaluator.reset_iterations_results = MagicMock() + mock_create_evaluator.return_value = mock_evaluator + + runner = ExperimentRunner(dataset=dataset) + + results = runner.run(crew=mock_crew) + (result,) = results.results assert result.identifier == "test-case-2" assert result.inputs == {"query": "Test query 2"} assert isinstance(result.expected_score, dict) assert "unknown_metric" in result.expected_score.keys() - assert result.passed is False \ No newline at end of file + assert result.passed is False diff --git a/lib/crewai/tests/knowledge/__init__.py b/lib/crewai/tests/knowledge/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/knowledge/crewai_quickstart.pdf b/lib/crewai/tests/knowledge/crewai_quickstart.pdf similarity index 100% rename from tests/knowledge/crewai_quickstart.pdf rename to lib/crewai/tests/knowledge/crewai_quickstart.pdf diff --git a/tests/knowledge/test_knowledge.py b/lib/crewai/tests/knowledge/test_knowledge.py similarity index 99% rename from tests/knowledge/test_knowledge.py rename to lib/crewai/tests/knowledge/test_knowledge.py index 67c2d68b0..a6f253fb1 100644 --- a/tests/knowledge/test_knowledge.py +++ b/lib/crewai/tests/knowledge/test_knowledge.py @@ -4,7 +4,6 @@ from pathlib import Path from unittest.mock import patch import pytest - from crewai.knowledge.source.crew_docling_source import CrewDoclingSource from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource diff --git a/tests/knowledge/test_knowledge_searchresult.py b/lib/crewai/tests/knowledge/test_knowledge_searchresult.py similarity index 99% rename from tests/knowledge/test_knowledge_searchresult.py rename to lib/crewai/tests/knowledge/test_knowledge_searchresult.py index cea7c0367..6f3db84de 100644 --- a/tests/knowledge/test_knowledge_searchresult.py +++ b/lib/crewai/tests/knowledge/test_knowledge_searchresult.py @@ -4,7 +4,6 @@ from typing import Any from unittest.mock import MagicMock, patch import pytest - from crewai.knowledge.knowledge import Knowledge # type: ignore[import-untyped] from crewai.knowledge.source.string_knowledge_source import ( # type: ignore[import-untyped] StringKnowledgeSource, diff --git a/tests/knowledge/test_knowledge_storage_integration.py b/lib/crewai/tests/knowledge/test_knowledge_storage_integration.py similarity index 99% rename from tests/knowledge/test_knowledge_storage_integration.py rename to lib/crewai/tests/knowledge/test_knowledge_storage_integration.py index 0c457d5d2..a58dcb2fc 100644 --- a/tests/knowledge/test_knowledge_storage_integration.py +++ b/lib/crewai/tests/knowledge/test_knowledge_storage_integration.py @@ -3,7 +3,6 @@ from unittest.mock import MagicMock, patch import pytest - from crewai.knowledge.storage.knowledge_storage import ( # type: ignore[import-untyped] KnowledgeStorage, ) diff --git a/lib/crewai/tests/llms/__init__.py b/lib/crewai/tests/llms/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/tests/llms/anthropic/test_anthropic.py b/lib/crewai/tests/llms/anthropic/test_anthropic.py new file mode 100644 index 000000000..37ba366b9 --- /dev/null +++ b/lib/crewai/tests/llms/anthropic/test_anthropic.py @@ -0,0 +1,666 @@ +import os +import sys +import types +from unittest.mock import patch, MagicMock +import pytest + +from crewai.llm import LLM +from crewai.crew import Crew +from crewai.agent import Agent +from crewai.task import Task + + +@pytest.fixture(autouse=True) +def mock_anthropic_api_key(): + """Automatically mock ANTHROPIC_API_KEY for all tests in this module.""" + with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}): + yield + + +def test_anthropic_completion_is_used_when_anthropic_provider(): + """ + Test that AnthropicCompletion from completion.py is used when LLM uses provider 'anthropic' + """ + llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + assert llm.__class__.__name__ == "AnthropicCompletion" + assert llm.provider == "anthropic" + assert llm.model == "claude-3-5-sonnet-20241022" + + +def test_anthropic_completion_is_used_when_claude_provider(): + """ + Test that AnthropicCompletion is used when provider is 'claude' + """ + llm = LLM(model="claude/claude-3-5-sonnet-20241022") + + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + assert isinstance(llm, AnthropicCompletion) + assert llm.provider == "claude" + assert llm.model == "claude-3-5-sonnet-20241022" + + + + +def test_anthropic_tool_use_conversation_flow(): + """ + Test that the Anthropic completion properly handles tool use conversation flow + """ + from unittest.mock import Mock, patch + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + from anthropic.types.tool_use_block import ToolUseBlock + + # Create AnthropicCompletion instance + completion = AnthropicCompletion(model="claude-3-5-sonnet-20241022") + + # Mock tool function + def mock_weather_tool(location: str) -> str: + return f"The weather in {location} is sunny and 75°F" + + available_functions = {"get_weather": mock_weather_tool} + + # Mock the Anthropic client responses + with patch.object(completion.client.messages, 'create') as mock_create: + # Mock initial response with tool use - need to properly mock ToolUseBlock + mock_tool_use = Mock(spec=ToolUseBlock) + mock_tool_use.id = "tool_123" + mock_tool_use.name = "get_weather" + mock_tool_use.input = {"location": "San Francisco"} + + mock_initial_response = Mock() + mock_initial_response.content = [mock_tool_use] + mock_initial_response.usage = Mock() + mock_initial_response.usage.input_tokens = 100 + mock_initial_response.usage.output_tokens = 50 + + # Mock final response after tool result - properly mock text content + mock_text_block = Mock() + # Set the text attribute as a string, not another Mock + mock_text_block.configure_mock(text="Based on the weather data, it's a beautiful day in San Francisco with sunny skies and 75°F temperature.") + + mock_final_response = Mock() + mock_final_response.content = [mock_text_block] + mock_final_response.usage = Mock() + mock_final_response.usage.input_tokens = 150 + mock_final_response.usage.output_tokens = 75 + + # Configure mock to return different responses on successive calls + mock_create.side_effect = [mock_initial_response, mock_final_response] + + # Test the call + messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}] + result = completion.call( + messages=messages, + available_functions=available_functions + ) + + # Verify the result contains the final response + assert "beautiful day in San Francisco" in result + assert "sunny skies" in result + assert "75°F" in result + + # Verify that two API calls were made (initial + follow-up) + assert mock_create.call_count == 2 + + # Verify the second call includes tool results + second_call_args = mock_create.call_args_list[1][1] # kwargs of second call + messages_in_second_call = second_call_args["messages"] + + # Should have original user message + assistant tool use + user tool result + assert len(messages_in_second_call) == 3 + assert messages_in_second_call[0]["role"] == "user" + assert messages_in_second_call[1]["role"] == "assistant" + assert messages_in_second_call[2]["role"] == "user" + + # Verify tool result format + tool_result = messages_in_second_call[2]["content"][0] + assert tool_result["type"] == "tool_result" + assert tool_result["tool_use_id"] == "tool_123" + assert "sunny and 75°F" in tool_result["content"] + + +def test_anthropic_completion_module_is_imported(): + """ + Test that the completion module is properly imported when using Anthropic provider + """ + module_name = "crewai.llms.providers.anthropic.completion" + + # Remove module from cache if it exists + if module_name in sys.modules: + del sys.modules[module_name] + + # Create LLM instance - this should trigger the import + LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Verify the module was imported + assert module_name in sys.modules + completion_mod = sys.modules[module_name] + assert isinstance(completion_mod, types.ModuleType) + + # Verify the class exists in the module + assert hasattr(completion_mod, 'AnthropicCompletion') + + +def test_native_anthropic_raises_error_when_initialization_fails(): + """ + Test that LLM raises ImportError when native Anthropic completion fails to initialize. + This ensures we don't silently fall back when there's a configuration issue. + """ + # Mock the _get_native_provider to return a failing class + with patch('crewai.llm.LLM._get_native_provider') as mock_get_provider: + + class FailingCompletion: + def __init__(self, *args, **kwargs): + raise Exception("Native Anthropic SDK failed") + + mock_get_provider.return_value = FailingCompletion + + # This should raise ImportError, not fall back to LiteLLM + with pytest.raises(ImportError) as excinfo: + LLM(model="anthropic/claude-3-5-sonnet-20241022") + + assert "Error importing native provider" in str(excinfo.value) + assert "Native Anthropic SDK failed" in str(excinfo.value) + + +def test_anthropic_completion_initialization_parameters(): + """ + Test that AnthropicCompletion is initialized with correct parameters + """ + llm = LLM( + model="anthropic/claude-3-5-sonnet-20241022", + temperature=0.7, + max_tokens=2000, + top_p=0.9, + api_key="test-key" + ) + + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + assert isinstance(llm, AnthropicCompletion) + assert llm.model == "claude-3-5-sonnet-20241022" + assert llm.temperature == 0.7 + assert llm.max_tokens == 2000 + assert llm.top_p == 0.9 + + +def test_anthropic_specific_parameters(): + """ + Test Anthropic-specific parameters like stop_sequences and streaming + """ + llm = LLM( + model="anthropic/claude-3-5-sonnet-20241022", + stop_sequences=["Human:", "Assistant:"], + stream=True, + max_retries=5, + timeout=60 + ) + + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + assert isinstance(llm, AnthropicCompletion) + assert llm.stop_sequences == ["Human:", "Assistant:"] + assert llm.stream == True + assert llm.client.max_retries == 5 + assert llm.client.timeout == 60 + + +def test_anthropic_completion_call(): + """ + Test that AnthropicCompletion call method works + """ + llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Mock the call method on the instance + with patch.object(llm, 'call', return_value="Hello! I'm Claude, ready to help.") as mock_call: + result = llm.call("Hello, how are you?") + + assert result == "Hello! I'm Claude, ready to help." + mock_call.assert_called_once_with("Hello, how are you?") + + +def test_anthropic_completion_called_during_crew_execution(): + """ + Test that AnthropicCompletion.call is actually invoked when running a crew + """ + # Create the LLM instance first + anthropic_llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Mock the call method on the specific instance + with patch.object(anthropic_llm, 'call', return_value="Tokyo has 14 million people.") as mock_call: + + # Create agent with explicit LLM configuration + agent = Agent( + role="Research Assistant", + goal="Find population info", + backstory="You research populations.", + llm=anthropic_llm, + ) + + task = Task( + description="Find Tokyo population", + expected_output="Population number", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + # Verify mock was called + assert mock_call.called + assert "14 million" in str(result) + + +def test_anthropic_completion_call_arguments(): + """ + Test that AnthropicCompletion.call is invoked with correct arguments + """ + # Create LLM instance first + anthropic_llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Mock the instance method + with patch.object(anthropic_llm, 'call') as mock_call: + mock_call.return_value = "Task completed successfully." + + agent = Agent( + role="Test Agent", + goal="Complete a simple task", + backstory="You are a test agent.", + llm=anthropic_llm # Use same instance + ) + + task = Task( + description="Say hello world", + expected_output="Hello world", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + # Verify call was made + assert mock_call.called + + # Check the arguments passed to the call method + call_args = mock_call.call_args + assert call_args is not None + + # The first argument should be the messages + messages = call_args[0][0] # First positional argument + assert isinstance(messages, (str, list)) + + # Verify that the task description appears in the messages + if isinstance(messages, str): + assert "hello world" in messages.lower() + elif isinstance(messages, list): + message_content = str(messages).lower() + assert "hello world" in message_content + + +def test_multiple_anthropic_calls_in_crew(): + """ + Test that AnthropicCompletion.call is invoked multiple times for multiple tasks + """ + # Create LLM instance first + anthropic_llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Mock the instance method + with patch.object(anthropic_llm, 'call') as mock_call: + mock_call.return_value = "Task completed." + + agent = Agent( + role="Multi-task Agent", + goal="Complete multiple tasks", + backstory="You can handle multiple tasks.", + llm=anthropic_llm # Use same instance + ) + + task1 = Task( + description="First task", + expected_output="First result", + agent=agent, + ) + + task2 = Task( + description="Second task", + expected_output="Second result", + agent=agent, + ) + + crew = Crew( + agents=[agent], + tasks=[task1, task2] + ) + crew.kickoff() + + # Verify multiple calls were made + assert mock_call.call_count >= 2 # At least one call per task + + # Verify each call had proper arguments + for call in mock_call.call_args_list: + assert len(call[0]) > 0 # Has positional arguments + messages = call[0][0] + assert messages is not None + + +def test_anthropic_completion_with_tools(): + """ + Test that AnthropicCompletion.call is invoked with tools when agent has tools + """ + from crewai.tools import tool + + @tool + def sample_tool(query: str) -> str: + """A sample tool for testing""" + return f"Tool result for: {query}" + + # Create LLM instance first + anthropic_llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Mock the instance method + with patch.object(anthropic_llm, 'call') as mock_call: + mock_call.return_value = "Task completed with tools." + + agent = Agent( + role="Tool User", + goal="Use tools to complete tasks", + backstory="You can use tools.", + llm=anthropic_llm, # Use same instance + tools=[sample_tool] + ) + + task = Task( + description="Use the sample tool", + expected_output="Tool usage result", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + assert mock_call.called + + call_args = mock_call.call_args + call_kwargs = call_args[1] if len(call_args) > 1 else {} + + if 'tools' in call_kwargs: + assert call_kwargs['tools'] is not None + assert len(call_kwargs['tools']) > 0 + + +def test_anthropic_raises_error_when_model_not_supported(): + """Test that AnthropicCompletion raises ValueError when model not supported""" + + # Mock the Anthropic client to raise an error + with patch('crewai.llms.providers.anthropic.completion.Anthropic') as mock_anthropic_class: + mock_client = MagicMock() + mock_anthropic_class.return_value = mock_client + + # Mock the error that Anthropic would raise for unsupported models + from anthropic import NotFoundError + mock_client.messages.create.side_effect = NotFoundError( + message="The model `model-doesnt-exist` does not exist", + response=MagicMock(), + body={} + ) + + llm = LLM(model="anthropic/model-doesnt-exist") + + with pytest.raises(Exception): # Should raise some error for unsupported model + llm.call("Hello") + + +def test_anthropic_client_params_setup(): + """ + Test that client_params are properly merged with default client parameters + """ + # Use only valid Anthropic client parameters + custom_client_params = { + "default_headers": {"X-Custom-Header": "test-value"}, + } + + with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}): + llm = LLM( + model="anthropic/claude-3-5-sonnet-20241022", + api_key="test-key", + base_url="https://custom-api.com", + timeout=45, + max_retries=5, + client_params=custom_client_params + ) + + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + assert isinstance(llm, AnthropicCompletion) + + assert llm.client_params == custom_client_params + + merged_params = llm._get_client_params() + + assert merged_params["api_key"] == "test-key" + assert merged_params["base_url"] == "https://custom-api.com" + assert merged_params["timeout"] == 45 + assert merged_params["max_retries"] == 5 + + assert merged_params["default_headers"] == {"X-Custom-Header": "test-value"} + + +def test_anthropic_client_params_override_defaults(): + """ + Test that client_params can override default client parameters + """ + override_client_params = { + "timeout": 120, # Override the timeout parameter + "max_retries": 10, # Override the max_retries parameter + "default_headers": {"X-Override": "true"} # Valid custom parameter + } + + with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}): + llm = LLM( + model="anthropic/claude-3-5-sonnet-20241022", + api_key="test-key", + timeout=30, + max_retries=3, + client_params=override_client_params + ) + + # Verify this is actually AnthropicCompletion, not LiteLLM fallback + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + assert isinstance(llm, AnthropicCompletion) + + merged_params = llm._get_client_params() + + # client_params should override the individual parameters + assert merged_params["timeout"] == 120 + assert merged_params["max_retries"] == 10 + assert merged_params["default_headers"] == {"X-Override": "true"} + + +def test_anthropic_client_params_none(): + """ + Test that client_params=None works correctly (no additional parameters) + """ + with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}): + llm = LLM( + model="anthropic/claude-3-5-sonnet-20241022", + api_key="test-key", + base_url="https://api.anthropic.com", + timeout=60, + max_retries=2, + client_params=None + ) + + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + assert isinstance(llm, AnthropicCompletion) + + assert llm.client_params is None + + merged_params = llm._get_client_params() + + expected_keys = {"api_key", "base_url", "timeout", "max_retries"} + assert set(merged_params.keys()) == expected_keys + + # Fixed assertions - all should be inside the with block and use correct values + assert merged_params["api_key"] == "test-key" # Not "test-anthropic-key" + assert merged_params["base_url"] == "https://api.anthropic.com" + assert merged_params["timeout"] == 60 + assert merged_params["max_retries"] == 2 + + +def test_anthropic_client_params_empty_dict(): + """ + Test that client_params={} works correctly (empty additional parameters) + """ + with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}): + llm = LLM( + model="anthropic/claude-3-5-sonnet-20241022", + api_key="test-key", + client_params={} + ) + + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + assert isinstance(llm, AnthropicCompletion) + + assert llm.client_params == {} + + merged_params = llm._get_client_params() + + assert "api_key" in merged_params + assert merged_params["api_key"] == "test-key" + + +def test_anthropic_model_detection(): + """ + Test that various Anthropic model formats are properly detected + """ + # Test Anthropic model naming patterns that actually work with provider detection + anthropic_test_cases = [ + "anthropic/claude-3-5-sonnet-20241022", + "claude/claude-3-5-sonnet-20241022" + ] + + for model_name in anthropic_test_cases: + llm = LLM(model=model_name) + from crewai.llms.providers.anthropic.completion import AnthropicCompletion + assert isinstance(llm, AnthropicCompletion), f"Failed for model: {model_name}" + + +def test_anthropic_supports_stop_words(): + """ + Test that Anthropic models support stop sequences + """ + llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + assert llm.supports_stop_words() == True + + +def test_anthropic_context_window_size(): + """ + Test that Anthropic models return correct context window sizes + """ + llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + context_size = llm.get_context_window_size() + + # Should return a reasonable context window size (Claude 3.5 has 200k tokens) + assert context_size > 100000 # Should be substantial + assert context_size <= 200000 # But not exceed the actual limit + + +def test_anthropic_message_formatting(): + """ + Test that messages are properly formatted for Anthropic API + """ + llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Test message formatting + test_messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + {"role": "user", "content": "How are you?"} + ] + + formatted_messages, system_message = llm._format_messages_for_anthropic(test_messages) + + # System message should be extracted + assert system_message == "You are a helpful assistant." + + # Remaining messages should start with user + assert formatted_messages[0]["role"] == "user" + assert len(formatted_messages) >= 3 # Should have user, assistant, user messages + + +def test_anthropic_streaming_parameter(): + """ + Test that streaming parameter is properly handled + """ + # Test non-streaming + llm_no_stream = LLM(model="anthropic/claude-3-5-sonnet-20241022", stream=False) + assert llm_no_stream.stream == False + + # Test streaming + llm_stream = LLM(model="anthropic/claude-3-5-sonnet-20241022", stream=True) + assert llm_stream.stream == True + + +def test_anthropic_tool_conversion(): + """ + Test that tools are properly converted to Anthropic format + """ + llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Mock tool in CrewAI format + crewai_tools = [{ + "type": "function", + "function": { + "name": "test_tool", + "description": "A test tool", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"] + } + } + }] + + # Test tool conversion + anthropic_tools = llm._convert_tools_for_interference(crewai_tools) + + assert len(anthropic_tools) == 1 + assert anthropic_tools[0]["name"] == "test_tool" + assert anthropic_tools[0]["description"] == "A test tool" + assert "input_schema" in anthropic_tools[0] + + +def test_anthropic_environment_variable_api_key(): + """ + Test that Anthropic API key is properly loaded from environment + """ + with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-anthropic-key"}): + llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + assert llm.client is not None + assert hasattr(llm.client, 'messages') + + +def test_anthropic_token_usage_tracking(): + """ + Test that token usage is properly tracked for Anthropic responses + """ + llm = LLM(model="anthropic/claude-3-5-sonnet-20241022") + + # Mock the Anthropic response with usage information + with patch.object(llm.client.messages, 'create') as mock_create: + mock_response = MagicMock() + mock_response.content = [MagicMock(text="test response")] + mock_response.usage = MagicMock(input_tokens=50, output_tokens=25) + mock_create.return_value = mock_response + + result = llm.call("Hello") + + # Verify the response + assert result == "test response" + + # Verify token usage was extracted + usage = llm._extract_anthropic_token_usage(mock_response) + assert usage["input_tokens"] == 50 + assert usage["output_tokens"] == 25 + assert usage["total_tokens"] == 75 diff --git a/lib/crewai/tests/llms/azure/__init__.py b/lib/crewai/tests/llms/azure/__init__.py new file mode 100644 index 000000000..78acbf011 --- /dev/null +++ b/lib/crewai/tests/llms/azure/__init__.py @@ -0,0 +1,3 @@ +# Azure LLM tests + + diff --git a/lib/crewai/tests/llms/azure/test_azure.py b/lib/crewai/tests/llms/azure/test_azure.py new file mode 100644 index 000000000..d4f739a90 --- /dev/null +++ b/lib/crewai/tests/llms/azure/test_azure.py @@ -0,0 +1,1088 @@ +import os +import sys +import types +from unittest.mock import patch, MagicMock, Mock +import pytest + +from crewai.llm import LLM +from crewai.crew import Crew +from crewai.agent import Agent +from crewai.task import Task + + +@pytest.fixture(autouse=True) +def mock_azure_credentials(): + """Automatically mock Azure credentials for all tests in this module.""" + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com" + }): + yield + + +def test_azure_completion_is_used_when_azure_provider(): + """ + Test that AzureCompletion from completion.py is used when LLM uses provider 'azure' + """ + llm = LLM(model="azure/gpt-4") + + assert llm.__class__.__name__ == "AzureCompletion" + assert llm.provider == "azure" + assert llm.model == "gpt-4" + + +def test_azure_completion_is_used_when_azure_openai_provider(): + """ + Test that AzureCompletion is used when provider is 'azure_openai' + """ + llm = LLM(model="azure_openai/gpt-4") + + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm, AzureCompletion) + assert llm.provider == "azure_openai" + assert llm.model == "gpt-4" + + +def test_azure_tool_use_conversation_flow(): + """ + Test that the Azure completion properly handles tool use conversation flow + """ + from crewai.llms.providers.azure.completion import AzureCompletion + from azure.ai.inference.models import ChatCompletionsToolCall + + # Create AzureCompletion instance + completion = AzureCompletion( + model="gpt-4", + api_key="test-key", + endpoint="https://test.openai.azure.com" + ) + + # Mock tool function + def mock_weather_tool(location: str) -> str: + return f"The weather in {location} is sunny and 75°F" + + available_functions = {"get_weather": mock_weather_tool} + + # Mock the Azure client responses + with patch.object(completion.client, 'complete') as mock_complete: + # Mock tool call in response with proper type + mock_tool_call = MagicMock(spec=ChatCompletionsToolCall) + mock_tool_call.function.name = "get_weather" + mock_tool_call.function.arguments = '{"location": "San Francisco"}' + + mock_message = MagicMock() + mock_message.content = None + mock_message.tool_calls = [mock_tool_call] + + mock_choice = MagicMock() + mock_choice.message = mock_message + + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage = MagicMock( + prompt_tokens=100, + completion_tokens=50, + total_tokens=150 + ) + + mock_complete.return_value = mock_response + + # Test the call + messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}] + result = completion.call( + messages=messages, + available_functions=available_functions + ) + + # Verify the tool was executed and returned the result + assert result == "The weather in San Francisco is sunny and 75°F" + + # Verify that the API was called + assert mock_complete.called + + +def test_azure_completion_module_is_imported(): + """ + Test that the completion module is properly imported when using Azure provider + """ + module_name = "crewai.llms.providers.azure.completion" + + # Remove module from cache if it exists + if module_name in sys.modules: + del sys.modules[module_name] + + # Create LLM instance - this should trigger the import + LLM(model="azure/gpt-4") + + # Verify the module was imported + assert module_name in sys.modules + completion_mod = sys.modules[module_name] + assert isinstance(completion_mod, types.ModuleType) + + # Verify the class exists in the module + assert hasattr(completion_mod, 'AzureCompletion') + + +def test_native_azure_raises_error_when_initialization_fails(): + """ + Test that LLM raises ImportError when native Azure completion fails to initialize. + This ensures we don't silently fall back when there's a configuration issue. + """ + # Mock the _get_native_provider to return a failing class + with patch('crewai.llm.LLM._get_native_provider') as mock_get_provider: + + class FailingCompletion: + def __init__(self, *args, **kwargs): + raise Exception("Native Azure AI Inference SDK failed") + + mock_get_provider.return_value = FailingCompletion + + # This should raise ImportError, not fall back to LiteLLM + with pytest.raises(ImportError) as excinfo: + LLM(model="azure/gpt-4") + + assert "Error importing native provider" in str(excinfo.value) + assert "Native Azure AI Inference SDK failed" in str(excinfo.value) + + +def test_azure_completion_initialization_parameters(): + """ + Test that AzureCompletion is initialized with correct parameters + """ + llm = LLM( + model="azure/gpt-4", + temperature=0.7, + max_tokens=2000, + top_p=0.9, + frequency_penalty=0.5, + presence_penalty=0.3, + api_key="test-key", + endpoint="https://test.openai.azure.com" + ) + + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm, AzureCompletion) + assert llm.model == "gpt-4" + assert llm.temperature == 0.7 + assert llm.max_tokens == 2000 + assert llm.top_p == 0.9 + assert llm.frequency_penalty == 0.5 + assert llm.presence_penalty == 0.3 + + +def test_azure_specific_parameters(): + """ + Test Azure-specific parameters like stop sequences, streaming, and API version + """ + llm = LLM( + model="azure/gpt-4", + stop=["Human:", "Assistant:"], + stream=True, + api_version="2024-02-01", + endpoint="https://test.openai.azure.com" + ) + + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm, AzureCompletion) + assert llm.stop == ["Human:", "Assistant:"] + assert llm.stream == True + assert llm.api_version == "2024-02-01" + + +def test_azure_completion_call(): + """ + Test that AzureCompletion call method works + """ + llm = LLM(model="azure/gpt-4") + + # Mock the call method on the instance + with patch.object(llm, 'call', return_value="Hello! I'm Azure OpenAI, ready to help.") as mock_call: + result = llm.call("Hello, how are you?") + + assert result == "Hello! I'm Azure OpenAI, ready to help." + mock_call.assert_called_once_with("Hello, how are you?") + + +def test_azure_completion_called_during_crew_execution(): + """ + Test that AzureCompletion.call is actually invoked when running a crew + """ + # Create the LLM instance first + azure_llm = LLM(model="azure/gpt-4") + + # Mock the call method on the specific instance + with patch.object(azure_llm, 'call', return_value="Tokyo has 14 million people.") as mock_call: + + # Create agent with explicit LLM configuration + agent = Agent( + role="Research Assistant", + goal="Find population info", + backstory="You research populations.", + llm=azure_llm, + ) + + task = Task( + description="Find Tokyo population", + expected_output="Population number", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + # Verify mock was called + assert mock_call.called + assert "14 million" in str(result) + + +def test_azure_completion_call_arguments(): + """ + Test that AzureCompletion.call is invoked with correct arguments + """ + # Create LLM instance first + azure_llm = LLM(model="azure/gpt-4") + + # Mock the instance method + with patch.object(azure_llm, 'call') as mock_call: + mock_call.return_value = "Task completed successfully." + + agent = Agent( + role="Test Agent", + goal="Complete a simple task", + backstory="You are a test agent.", + llm=azure_llm # Use same instance + ) + + task = Task( + description="Say hello world", + expected_output="Hello world", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + # Verify call was made + assert mock_call.called + + # Check the arguments passed to the call method + call_args = mock_call.call_args + assert call_args is not None + + # The first argument should be the messages + messages = call_args[0][0] # First positional argument + assert isinstance(messages, (str, list)) + + # Verify that the task description appears in the messages + if isinstance(messages, str): + assert "hello world" in messages.lower() + elif isinstance(messages, list): + message_content = str(messages).lower() + assert "hello world" in message_content + + +def test_multiple_azure_calls_in_crew(): + """ + Test that AzureCompletion.call is invoked multiple times for multiple tasks + """ + # Create LLM instance first + azure_llm = LLM(model="azure/gpt-4") + + # Mock the instance method + with patch.object(azure_llm, 'call') as mock_call: + mock_call.return_value = "Task completed." + + agent = Agent( + role="Multi-task Agent", + goal="Complete multiple tasks", + backstory="You can handle multiple tasks.", + llm=azure_llm # Use same instance + ) + + task1 = Task( + description="First task", + expected_output="First result", + agent=agent, + ) + + task2 = Task( + description="Second task", + expected_output="Second result", + agent=agent, + ) + + crew = Crew( + agents=[agent], + tasks=[task1, task2] + ) + crew.kickoff() + + # Verify multiple calls were made + assert mock_call.call_count >= 2 # At least one call per task + + # Verify each call had proper arguments + for call in mock_call.call_args_list: + assert len(call[0]) > 0 # Has positional arguments + messages = call[0][0] + assert messages is not None + + +def test_azure_completion_with_tools(): + """ + Test that AzureCompletion.call is invoked with tools when agent has tools + """ + from crewai.tools import tool + + @tool + def sample_tool(query: str) -> str: + """A sample tool for testing""" + return f"Tool result for: {query}" + + # Create LLM instance first + azure_llm = LLM(model="azure/gpt-4") + + # Mock the instance method + with patch.object(azure_llm, 'call') as mock_call: + mock_call.return_value = "Task completed with tools." + + agent = Agent( + role="Tool User", + goal="Use tools to complete tasks", + backstory="You can use tools.", + llm=azure_llm, # Use same instance + tools=[sample_tool] + ) + + task = Task( + description="Use the sample tool", + expected_output="Tool usage result", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + assert mock_call.called + + call_args = mock_call.call_args + call_kwargs = call_args[1] if len(call_args) > 1 else {} + + if 'tools' in call_kwargs: + assert call_kwargs['tools'] is not None + assert len(call_kwargs['tools']) > 0 + + +def test_azure_raises_error_when_endpoint_missing(): + """Test that AzureCompletion raises ValueError when endpoint is missing""" + from crewai.llms.providers.azure.completion import AzureCompletion + + # Clear environment variables + with patch.dict(os.environ, {}, clear=True): + with pytest.raises(ValueError, match="Azure endpoint is required"): + AzureCompletion(model="gpt-4", api_key="test-key") + +def test_azure_raises_error_when_api_key_missing(): + """Test that AzureCompletion raises ValueError when API key is missing""" + from crewai.llms.providers.azure.completion import AzureCompletion + + # Clear environment variables + with patch.dict(os.environ, {}, clear=True): + with pytest.raises(ValueError, match="Azure API key is required"): + AzureCompletion(model="gpt-4", endpoint="https://test.openai.azure.com") +def test_azure_endpoint_configuration(): + """ + Test that Azure endpoint configuration works with multiple environment variable names + """ + # Test with AZURE_ENDPOINT + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test1.openai.azure.com" + }): + llm = LLM(model="azure/gpt-4") + + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm, AzureCompletion) + assert llm.endpoint == "https://test1.openai.azure.com/openai/deployments/gpt-4" + + # Test with AZURE_OPENAI_ENDPOINT + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_OPENAI_ENDPOINT": "https://test2.openai.azure.com" + }, clear=True): + llm = LLM(model="azure/gpt-4") + + assert isinstance(llm, AzureCompletion) + # Endpoint should be auto-constructed for Azure OpenAI + assert llm.endpoint == "https://test2.openai.azure.com/openai/deployments/gpt-4" + + +def test_azure_api_key_configuration(): + """ + Test that API key configuration works from AZURE_API_KEY environment variable + """ + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-azure-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com" + }): + llm = LLM(model="azure/gpt-4") + + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm, AzureCompletion) + assert llm.api_key == "test-azure-key" + + +def test_azure_model_capabilities(): + """ + Test that model capabilities are correctly identified + """ + # Test GPT-4 model (supports function calling) + llm_gpt4 = LLM(model="azure/gpt-4") + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm_gpt4, AzureCompletion) + assert llm_gpt4.is_openai_model == True + assert llm_gpt4.supports_function_calling() == True + + # Test GPT-3.5 model + llm_gpt35 = LLM(model="azure/gpt-35-turbo") + assert isinstance(llm_gpt35, AzureCompletion) + assert llm_gpt35.is_openai_model == True + assert llm_gpt35.supports_function_calling() == True + + +def test_azure_completion_params_preparation(): + """ + Test that completion parameters are properly prepared + """ + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://models.inference.ai.azure.com" + }): + llm = LLM( + model="azure/gpt-4", + temperature=0.7, + top_p=0.9, + frequency_penalty=0.5, + presence_penalty=0.3, + max_tokens=1000 + ) + + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm, AzureCompletion) + + messages = [{"role": "user", "content": "Hello"}] + params = llm._prepare_completion_params(messages) + + assert params["model"] == "gpt-4" + assert params["temperature"] == 0.7 + assert params["top_p"] == 0.9 + assert params["frequency_penalty"] == 0.5 + assert params["presence_penalty"] == 0.3 + assert params["max_tokens"] == 1000 + + +def test_azure_model_detection(): + """ + Test that various Azure model formats are properly detected + """ + # Test Azure model naming patterns + azure_test_cases = [ + "azure/gpt-4", + "azure_openai/gpt-4", + "azure/gpt-4o", + "azure/gpt-35-turbo" + ] + + for model_name in azure_test_cases: + llm = LLM(model=model_name) + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm, AzureCompletion), f"Failed for model: {model_name}" + + +def test_azure_supports_stop_words(): + """ + Test that Azure models support stop sequences + """ + llm = LLM(model="azure/gpt-4") + assert llm.supports_stop_words() == True + + +def test_azure_context_window_size(): + """ + Test that Azure models return correct context window sizes + """ + # Test GPT-4 + llm_gpt4 = LLM(model="azure/gpt-4") + context_size_gpt4 = llm_gpt4.get_context_window_size() + assert context_size_gpt4 > 0 # Should return valid context size + + # Test GPT-4o + llm_gpt4o = LLM(model="azure/gpt-4o") + context_size_gpt4o = llm_gpt4o.get_context_window_size() + assert context_size_gpt4o > context_size_gpt4 # GPT-4o has larger context + + +def test_azure_message_formatting(): + """ + Test that messages are properly formatted for Azure API + """ + llm = LLM(model="azure/gpt-4") + + # Test message formatting + test_messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + {"role": "user", "content": "How are you?"} + ] + + formatted_messages = llm._format_messages_for_azure(test_messages) + + # All messages should be formatted as dictionaries with content + assert len(formatted_messages) == 4 + + # Verify each message is a dict with content + for msg in formatted_messages: + assert isinstance(msg, dict) + assert "content" in msg + + +def test_azure_streaming_parameter(): + """ + Test that streaming parameter is properly handled + """ + # Test non-streaming + llm_no_stream = LLM(model="azure/gpt-4", stream=False) + assert llm_no_stream.stream == False + + # Test streaming + llm_stream = LLM(model="azure/gpt-4", stream=True) + assert llm_stream.stream == True + + +def test_azure_tool_conversion(): + """ + Test that tools are properly converted to Azure OpenAI format + """ + llm = LLM(model="azure/gpt-4") + + # Mock tool in CrewAI format + crewai_tools = [{ + "type": "function", + "function": { + "name": "test_tool", + "description": "A test tool", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"] + } + } + }] + + # Test tool conversion + azure_tools = llm._convert_tools_for_interference(crewai_tools) + + assert len(azure_tools) == 1 + # Azure tools should maintain the function calling format + assert azure_tools[0]["type"] == "function" + assert azure_tools[0]["function"]["name"] == "test_tool" + assert azure_tools[0]["function"]["description"] == "A test tool" + assert "parameters" in azure_tools[0]["function"] + + +def test_azure_environment_variable_endpoint(): + """ + Test that Azure endpoint is properly loaded from environment + """ + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com" + }): + llm = LLM(model="azure/gpt-4") + + assert llm.client is not None + assert llm.endpoint == "https://test.openai.azure.com/openai/deployments/gpt-4" + + +def test_azure_token_usage_tracking(): + """ + Test that token usage is properly tracked for Azure responses + """ + llm = LLM(model="azure/gpt-4") + + # Mock the Azure response with usage information + with patch.object(llm.client, 'complete') as mock_complete: + mock_message = MagicMock() + mock_message.content = "test response" + mock_message.tool_calls = None + + mock_choice = MagicMock() + mock_choice.message = mock_message + + mock_response = MagicMock() + mock_response.choices = [mock_choice] + mock_response.usage = MagicMock( + prompt_tokens=50, + completion_tokens=25, + total_tokens=75 + ) + mock_complete.return_value = mock_response + + result = llm.call("Hello") + + # Verify the response + assert result == "test response" + + # Verify token usage was extracted + usage = llm._extract_azure_token_usage(mock_response) + assert usage["prompt_tokens"] == 50 + assert usage["completion_tokens"] == 25 + assert usage["total_tokens"] == 75 + + +def test_azure_http_error_handling(): + """ + Test that Azure HTTP errors are properly handled + """ + from azure.core.exceptions import HttpResponseError + + llm = LLM(model="azure/gpt-4") + + # Mock an HTTP error + with patch.object(llm.client, 'complete') as mock_complete: + mock_complete.side_effect = HttpResponseError(message="Rate limit exceeded", response=MagicMock(status_code=429)) + + with pytest.raises(HttpResponseError): + llm.call("Hello") + + +def test_azure_streaming_completion(): + """ + Test that streaming completions work properly + """ + from crewai.llms.providers.azure.completion import AzureCompletion + from azure.ai.inference.models import StreamingChatCompletionsUpdate + + llm = LLM(model="azure/gpt-4", stream=True) + + # Mock streaming response + with patch.object(llm.client, 'complete') as mock_complete: + # Create mock streaming updates with proper type + mock_updates = [] + for chunk in ["Hello", " ", "world", "!"]: + mock_delta = MagicMock() + mock_delta.content = chunk + mock_delta.tool_calls = None + + mock_choice = MagicMock() + mock_choice.delta = mock_delta + + # Create mock update as StreamingChatCompletionsUpdate instance + mock_update = MagicMock(spec=StreamingChatCompletionsUpdate) + mock_update.choices = [mock_choice] + mock_updates.append(mock_update) + + mock_complete.return_value = iter(mock_updates) + + result = llm.call("Say hello") + + # Verify the full response was assembled + assert result == "Hello world!" + + +def test_azure_api_version_default(): + """ + Test that Azure API version defaults correctly + """ + llm = LLM(model="azure/gpt-4") + + from crewai.llms.providers.azure.completion import AzureCompletion + assert isinstance(llm, AzureCompletion) + # Should use default or environment variable + assert llm.api_version is not None + + +def test_azure_function_calling_support(): + """ + Test that function calling is supported for OpenAI models + """ + # Test with GPT-4 (supports function calling) + llm_gpt4 = LLM(model="azure/gpt-4") + assert llm_gpt4.supports_function_calling() == True + + # Test with GPT-3.5 (supports function calling) + llm_gpt35 = LLM(model="azure/gpt-35-turbo") + assert llm_gpt35.supports_function_calling() == True + + +def test_azure_openai_endpoint_url_construction(): + """ + Test that Azure OpenAI endpoint URLs are automatically constructed correctly + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test-resource.openai.azure.com" + }): + llm = LLM(model="azure/gpt-4o-mini") + + assert "/openai/deployments/gpt-4o-mini" in llm.endpoint + assert llm.endpoint == "https://test-resource.openai.azure.com/openai/deployments/gpt-4o-mini" + assert llm.is_azure_openai_endpoint == True + + +def test_azure_openai_endpoint_url_with_trailing_slash(): + """ + Test that trailing slashes are handled correctly in endpoint URLs + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test-resource.openai.azure.com/" # trailing slash + }): + llm = LLM(model="azure/gpt-4o") + + assert llm.endpoint == "https://test-resource.openai.azure.com/openai/deployments/gpt-4o" + assert not llm.endpoint.endswith("//") + + +def test_azure_openai_endpoint_already_complete(): + """ + Test that already complete Azure OpenAI endpoint URLs are not modified + """ + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test-resource.openai.azure.com/openai/deployments/my-deployment" + }): + llm = LLM(model="azure/gpt-4") + + assert llm.endpoint == "https://test-resource.openai.azure.com/openai/deployments/my-deployment" + assert llm.is_azure_openai_endpoint == True + + +def test_non_azure_openai_endpoint_unchanged(): + """ + Test that non-Azure OpenAI endpoints are not modified + """ + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://models.inference.ai.azure.com" + }): + llm = LLM(model="azure/mistral-large") + + assert llm.endpoint == "https://models.inference.ai.azure.com" + assert llm.is_azure_openai_endpoint == False + + +def test_azure_openai_model_parameter_excluded(): + """ + Test that model parameter is NOT included for Azure OpenAI endpoints + """ + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com/openai/deployments/gpt-4" + }): + llm = LLM(model="azure/gpt-4") + + # Prepare params to check model parameter handling + params = llm._prepare_completion_params( + messages=[{"role": "user", "content": "test"}] + ) + + # Model parameter should NOT be included for Azure OpenAI endpoints + assert "model" not in params + assert "messages" in params + assert params["stream"] == False + + +def test_non_azure_openai_model_parameter_included(): + """ + Test that model parameter IS included for non-Azure OpenAI endpoints + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://models.inference.ai.azure.com" + }): + llm = LLM(model="azure/mistral-large") + + params = llm._prepare_completion_params( + messages=[{"role": "user", "content": "test"}] + ) + + assert "model" in params + assert params["model"] == "mistral-large" + + +def test_azure_message_formatting_with_role(): + """ + Test that messages are formatted with both 'role' and 'content' fields + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + llm = LLM(model="azure/gpt-4") + + # Test with string message + formatted = llm._format_messages_for_azure("Hello world") + assert isinstance(formatted, list) + assert len(formatted) > 0 + assert "role" in formatted[0] + assert "content" in formatted[0] + + messages = [ + {"role": "system", "content": "You are helpful"}, + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there"} + ] + formatted = llm._format_messages_for_azure(messages) + + for msg in formatted: + assert "role" in msg + assert "content" in msg + assert msg["role"] in ["system", "user", "assistant"] + + +def test_azure_message_formatting_default_role(): + """ + Test that messages without a role default to 'user' + """ + + llm = LLM(model="azure/gpt-4") + + # Test with message that has role but tests default behavior + messages = [{"role": "user", "content": "test message"}] + formatted = llm._format_messages_for_azure(messages) + + assert formatted[0]["role"] == "user" + assert formatted[0]["content"] == "test message" + + +def test_azure_endpoint_detection_flags(): + """ + Test that is_azure_openai_endpoint flag is set correctly + """ + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com/openai/deployments/gpt-4" + }): + llm_openai = LLM(model="azure/gpt-4") + assert llm_openai.is_azure_openai_endpoint == True + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://models.inference.ai.azure.com" + }): + llm_other = LLM(model="azure/mistral-large") + assert llm_other.is_azure_openai_endpoint == False + + +def test_azure_improved_error_messages(): + """ + Test that improved error messages are provided for common HTTP errors + """ + from crewai.llms.providers.azure.completion import AzureCompletion + from azure.core.exceptions import HttpResponseError + + llm = LLM(model="azure/gpt-4") + + with patch.object(llm.client, 'complete') as mock_complete: + error_401 = HttpResponseError(message="Unauthorized") + error_401.status_code = 401 + mock_complete.side_effect = error_401 + + with pytest.raises(HttpResponseError): + llm.call("test") + + error_404 = HttpResponseError(message="Not Found") + error_404.status_code = 404 + mock_complete.side_effect = error_404 + + with pytest.raises(HttpResponseError): + llm.call("test") + + error_429 = HttpResponseError(message="Rate Limited") + error_429.status_code = 429 + mock_complete.side_effect = error_429 + + with pytest.raises(HttpResponseError): + llm.call("test") + + +def test_azure_api_version_properly_passed(): + """ + Test that api_version is properly passed to the client + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com", + "AZURE_API_VERSION": "" # Clear env var to test default + }, clear=False): + llm = LLM(model="azure/gpt-4", api_version="2024-08-01") + assert llm.api_version == "2024-08-01" + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com" + }, clear=True): + llm_default = LLM(model="azure/gpt-4") + assert llm_default.api_version == "2024-06-01" # Current default + + +def test_azure_timeout_and_max_retries_stored(): + """ + Test that timeout and max_retries parameters are stored + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com" + }): + llm = LLM( + model="azure/gpt-4", + timeout=60.0, + max_retries=5 + ) + + assert llm.timeout == 60.0 + assert llm.max_retries == 5 + + +def test_azure_complete_params_include_optional_params(): + """ + Test that optional parameters are included in completion params when set + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://models.inference.ai.azure.com" + }): + llm = LLM( + model="azure/gpt-4", + temperature=0.7, + top_p=0.9, + frequency_penalty=0.5, + presence_penalty=0.3, + max_tokens=1000, + stop=["STOP", "END"] + ) + + params = llm._prepare_completion_params( + messages=[{"role": "user", "content": "test"}] + ) + + assert params["temperature"] == 0.7 + assert params["top_p"] == 0.9 + assert params["frequency_penalty"] == 0.5 + assert params["presence_penalty"] == 0.3 + assert params["max_tokens"] == 1000 + assert params["stop"] == ["STOP", "END"] + + +def test_azure_endpoint_validation_with_azure_prefix(): + """ + Test that 'azure/' prefix is properly stripped when constructing endpoint + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://test.openai.azure.com" + }): + llm = LLM(model="azure/gpt-4o-mini") + + # Should strip 'azure/' prefix and use 'gpt-4o-mini' as deployment name + assert "gpt-4o-mini" in llm.endpoint + assert "azure/gpt-4o-mini" not in llm.endpoint + + +def test_azure_message_formatting_preserves_all_roles(): + """ + Test that all message roles (system, user, assistant) are preserved correctly + """ + from crewai.llms.providers.azure.completion import AzureCompletion + + llm = LLM(model="azure/gpt-4") + + messages = [ + {"role": "system", "content": "System message"}, + {"role": "user", "content": "User message"}, + {"role": "assistant", "content": "Assistant message"}, + {"role": "user", "content": "Another user message"} + ] + + formatted = llm._format_messages_for_azure(messages) + + assert formatted[0]["role"] == "system" + assert formatted[0]["content"] == "System message" + assert formatted[1]["role"] == "user" + assert formatted[1]["content"] == "User message" + assert formatted[2]["role"] == "assistant" + assert formatted[2]["content"] == "Assistant message" + assert formatted[3]["role"] == "user" + assert formatted[3]["content"] == "Another user message" + + +def test_azure_deepseek_model_support(): + """ + Test that DeepSeek and other non-OpenAI models work correctly with Azure AI Inference + """ + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://models.inference.ai.azure.com" + }): + # Test DeepSeek model + llm_deepseek = LLM(model="azure/deepseek-chat") + + # Endpoint should not be modified for non-OpenAI endpoints + assert llm_deepseek.endpoint == "https://models.inference.ai.azure.com" + assert llm_deepseek.is_azure_openai_endpoint == False + + # Model parameter should be included in completion params + params = llm_deepseek._prepare_completion_params( + messages=[{"role": "user", "content": "test"}] + ) + assert "model" in params + assert params["model"] == "deepseek-chat" + + # Should not be detected as OpenAI model (no function calling) + assert llm_deepseek.is_openai_model == False + assert llm_deepseek.supports_function_calling() == False + + +def test_azure_mistral_and_other_models(): + """ + Test that various non-OpenAI models (Mistral, Llama, etc.) work with Azure AI Inference + """ + test_models = [ + "mistral-large-latest", + "llama-3-70b-instruct", + "cohere-command-r-plus" + ] + + for model_name in test_models: + with patch.dict(os.environ, { + "AZURE_API_KEY": "test-key", + "AZURE_ENDPOINT": "https://models.inference.ai.azure.com" + }): + llm = LLM(model=f"azure/{model_name}") + + # Verify endpoint is not modified + assert llm.endpoint == "https://models.inference.ai.azure.com" + assert llm.is_azure_openai_endpoint == False + + # Verify model parameter is included + params = llm._prepare_completion_params( + messages=[{"role": "user", "content": "test"}] + ) + assert "model" in params + assert params["model"] == model_name diff --git a/lib/crewai/tests/llms/bedrock/test_bedrock.py b/lib/crewai/tests/llms/bedrock/test_bedrock.py new file mode 100644 index 000000000..9fd172cc6 --- /dev/null +++ b/lib/crewai/tests/llms/bedrock/test_bedrock.py @@ -0,0 +1,738 @@ +import os +import sys +import types +from unittest.mock import patch, MagicMock +import pytest + +from crewai.llm import LLM +from crewai.crew import Crew +from crewai.agent import Agent +from crewai.task import Task + + +@pytest.fixture(autouse=True) +def mock_aws_credentials(): + """Automatically mock AWS credentials and boto3 Session for all tests in this module.""" + with patch.dict(os.environ, { + "AWS_ACCESS_KEY_ID": "test-access-key", + "AWS_SECRET_ACCESS_KEY": "test-secret-key", + "AWS_DEFAULT_REGION": "us-east-1" + }): + # Mock boto3 Session to prevent actual AWS connections + with patch('crewai.llms.providers.bedrock.completion.Session') as mock_session_class: + # Create mock session instance + mock_session_instance = MagicMock() + mock_client = MagicMock() + + # Set up default mock responses to prevent hanging + default_response = { + 'output': { + 'message': { + 'role': 'assistant', + 'content': [ + {'text': 'Test response'} + ] + } + }, + 'usage': { + 'inputTokens': 10, + 'outputTokens': 5, + 'totalTokens': 15 + } + } + mock_client.converse.return_value = default_response + mock_client.converse_stream.return_value = {'stream': []} + + # Configure the mock session instance to return the mock client + mock_session_instance.client.return_value = mock_client + + # Configure the mock Session class to return the mock session instance + mock_session_class.return_value = mock_session_instance + + yield mock_session_class, mock_client + + +def test_bedrock_completion_is_used_when_bedrock_provider(): + """ + Test that BedrockCompletion from completion.py is used when LLM uses provider 'bedrock' + """ + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + assert llm.__class__.__name__ == "BedrockCompletion" + assert llm.provider == "bedrock" + assert llm.model == "anthropic.claude-3-5-sonnet-20241022-v2:0" + + +def test_bedrock_completion_module_is_imported(): + """ + Test that the completion module is properly imported when using Bedrock provider + """ + module_name = "crewai.llms.providers.bedrock.completion" + + # Remove module from cache if it exists + if module_name in sys.modules: + del sys.modules[module_name] + + # Create LLM instance - this should trigger the import + LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Verify the module was imported + assert module_name in sys.modules + completion_mod = sys.modules[module_name] + assert isinstance(completion_mod, types.ModuleType) + + # Verify the class exists in the module + assert hasattr(completion_mod, 'BedrockCompletion') + + +def test_native_bedrock_raises_error_when_initialization_fails(): + """ + Test that LLM raises ImportError when native Bedrock completion fails. + + With the new behavior, when a native provider is in SUPPORTED_NATIVE_PROVIDERS + but fails to instantiate, we raise an ImportError instead of silently falling back. + This provides clearer error messages to users about missing dependencies. + """ + # Mock the _get_native_provider to return a failing class + with patch('crewai.llm.LLM._get_native_provider') as mock_get_provider: + + class FailingCompletion: + def __init__(self, *args, **kwargs): + raise Exception("Native AWS Bedrock SDK failed") + + mock_get_provider.return_value = FailingCompletion + + # This should raise ImportError with clear message + with pytest.raises(ImportError) as excinfo: + LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Verify the error message is helpful + assert "Error importing native provider" in str(excinfo.value) + assert "Native AWS Bedrock SDK failed" in str(excinfo.value) + + +def test_bedrock_completion_initialization_parameters(): + """ + Test that BedrockCompletion is initialized with correct parameters + """ + llm = LLM( + model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", + temperature=0.7, + max_tokens=2000, + top_p=0.9, + top_k=40, + region_name="us-west-2" + ) + + from crewai.llms.providers.bedrock.completion import BedrockCompletion + assert isinstance(llm, BedrockCompletion) + assert llm.model == "anthropic.claude-3-5-sonnet-20241022-v2:0" + assert llm.temperature == 0.7 + assert llm.max_tokens == 2000 + assert llm.top_p == 0.9 + assert llm.top_k == 40 + assert llm.region_name == "us-west-2" + + +def test_bedrock_specific_parameters(): + """ + Test Bedrock-specific parameters like stop_sequences and streaming + """ + llm = LLM( + model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", + stop_sequences=["Human:", "Assistant:"], + stream=True, + region_name="us-east-1" + ) + + from crewai.llms.providers.bedrock.completion import BedrockCompletion + assert isinstance(llm, BedrockCompletion) + assert llm.stop_sequences == ["Human:", "Assistant:"] + assert llm.stream == True + assert llm.region_name == "us-east-1" + + +def test_bedrock_completion_call(): + """ + Test that BedrockCompletion call method works + """ + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Mock the call method on the instance + with patch.object(llm, 'call', return_value="Hello! I'm Claude on Bedrock, ready to help.") as mock_call: + result = llm.call("Hello, how are you?") + + assert result == "Hello! I'm Claude on Bedrock, ready to help." + mock_call.assert_called_once_with("Hello, how are you?") + + +def test_bedrock_completion_called_during_crew_execution(): + """ + Test that BedrockCompletion.call is actually invoked when running a crew + """ + # Create the LLM instance first + bedrock_llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Mock the call method on the specific instance + with patch.object(bedrock_llm, 'call', return_value="Tokyo has 14 million people.") as mock_call: + + # Create agent with explicit LLM configuration + agent = Agent( + role="Research Assistant", + goal="Find population info", + backstory="You research populations.", + llm=bedrock_llm, + ) + + task = Task( + description="Find Tokyo population", + expected_output="Population number", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + # Verify mock was called + assert mock_call.called + assert "14 million" in str(result) + + +@pytest.mark.skip(reason="Crew execution test - may hang, needs investigation") +def test_bedrock_completion_call_arguments(): + """ + Test that BedrockCompletion.call is invoked with correct arguments + """ + # Create LLM instance first + bedrock_llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Mock the instance method + with patch.object(bedrock_llm, 'call') as mock_call: + mock_call.return_value = "Task completed successfully." + + agent = Agent( + role="Test Agent", + goal="Complete a simple task", + backstory="You are a test agent.", + llm=bedrock_llm # Use same instance + ) + + task = Task( + description="Say hello world", + expected_output="Hello world", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + # Verify call was made + assert mock_call.called + + # Check the arguments passed to the call method + call_args = mock_call.call_args + assert call_args is not None + + # The first argument should be the messages + messages = call_args[0][0] # First positional argument + assert isinstance(messages, (str, list)) + + # Verify that the task description appears in the messages + if isinstance(messages, str): + assert "hello world" in messages.lower() + elif isinstance(messages, list): + message_content = str(messages).lower() + assert "hello world" in message_content + + +def test_multiple_bedrock_calls_in_crew(): + """ + Test that BedrockCompletion.call is invoked multiple times for multiple tasks + """ + # Create LLM instance first + bedrock_llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Mock the instance method + with patch.object(bedrock_llm, 'call') as mock_call: + mock_call.return_value = "Task completed." + + agent = Agent( + role="Multi-task Agent", + goal="Complete multiple tasks", + backstory="You can handle multiple tasks.", + llm=bedrock_llm # Use same instance + ) + + task1 = Task( + description="First task", + expected_output="First result", + agent=agent, + ) + + task2 = Task( + description="Second task", + expected_output="Second result", + agent=agent, + ) + + crew = Crew( + agents=[agent], + tasks=[task1, task2] + ) + crew.kickoff() + + # Verify multiple calls were made + assert mock_call.call_count >= 2 # At least one call per task + + # Verify each call had proper arguments + for call in mock_call.call_args_list: + assert len(call[0]) > 0 # Has positional arguments + messages = call[0][0] + assert messages is not None + +def test_bedrock_completion_with_tools(): + """ + Test that BedrockCompletion.call is invoked with tools when agent has tools + """ + from crewai.tools import tool + + @tool + def sample_tool(query: str) -> str: + """A sample tool for testing""" + return f"Tool result for: {query}" + + # Create LLM instance first + bedrock_llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Mock the instance method + with patch.object(bedrock_llm, 'call') as mock_call: + mock_call.return_value = "Task completed with tools." + + agent = Agent( + role="Tool User", + goal="Use tools to complete tasks", + backstory="You can use tools.", + llm=bedrock_llm, # Use same instance + tools=[sample_tool] + ) + + task = Task( + description="Use the sample tool", + expected_output="Tool usage result", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + + crew.kickoff() + + assert mock_call.called + + call_args = mock_call.call_args + call_kwargs = call_args[1] if len(call_args) > 1 else {} + + if 'tools' in call_kwargs: + assert call_kwargs['tools'] is not None + assert len(call_kwargs['tools']) > 0 + + +def test_bedrock_raises_error_when_model_not_found(mock_aws_credentials): + """Test that BedrockCompletion raises appropriate error when model not found""" + from botocore.exceptions import ClientError + + # Get the mock client from the fixture + _, mock_client = mock_aws_credentials + + error_response = { + 'Error': { + 'Code': 'ResourceNotFoundException', + 'Message': 'Could not resolve the foundation model from the model identifier' + } + } + mock_client.converse.side_effect = ClientError(error_response, 'converse') + + llm = LLM(model="bedrock/model-doesnt-exist") + + with pytest.raises(Exception): # Should raise some error for unsupported model + llm.call("Hello") + + +def test_bedrock_aws_credentials_configuration(): + """ + Test that AWS credentials configuration works properly + """ + # Test with environment variables + with patch.dict(os.environ, { + "AWS_ACCESS_KEY_ID": "test-access-key", + "AWS_SECRET_ACCESS_KEY": "test-secret-key", + "AWS_DEFAULT_REGION": "us-east-1" + }): + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + from crewai.llms.providers.bedrock.completion import BedrockCompletion + assert isinstance(llm, BedrockCompletion) + assert llm.region_name == "us-east-1" + + # Test with explicit credentials + llm_explicit = LLM( + model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", + aws_access_key_id="explicit-key", + aws_secret_access_key="explicit-secret", + region_name="us-west-2" + ) + assert isinstance(llm_explicit, BedrockCompletion) + assert llm_explicit.region_name == "us-west-2" + + +def test_bedrock_model_capabilities(): + """ + Test that model capabilities are correctly identified + """ + # Test Claude model + llm_claude = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + from crewai.llms.providers.bedrock.completion import BedrockCompletion + assert isinstance(llm_claude, BedrockCompletion) + assert llm_claude.is_claude_model == True + assert llm_claude.supports_tools == True + + # Test other Bedrock model + llm_titan = LLM(model="bedrock/amazon.titan-text-express-v1") + assert isinstance(llm_titan, BedrockCompletion) + assert llm_titan.supports_tools == True + + +def test_bedrock_inference_config(): + """ + Test that inference config is properly prepared + """ + llm = LLM( + model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", + temperature=0.7, + top_p=0.9, + top_k=40, + max_tokens=1000 + ) + + from crewai.llms.providers.bedrock.completion import BedrockCompletion + assert isinstance(llm, BedrockCompletion) + + # Test config preparation + config = llm._get_inference_config() + + # Verify config has the expected parameters + assert 'temperature' in config + assert config['temperature'] == 0.7 + assert 'topP' in config + assert config['topP'] == 0.9 + assert 'maxTokens' in config + assert config['maxTokens'] == 1000 + assert 'topK' in config + assert config['topK'] == 40 + + +def test_bedrock_model_detection(): + """ + Test that various Bedrock model formats are properly detected + """ + # Test Bedrock model naming patterns + bedrock_test_cases = [ + "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", + "bedrock/anthropic.claude-3-haiku-20240307-v1:0", + "bedrock/amazon.titan-text-express-v1", + "bedrock/meta.llama3-70b-instruct-v1:0" + ] + + for model_name in bedrock_test_cases: + llm = LLM(model=model_name) + from crewai.llms.providers.bedrock.completion import BedrockCompletion + assert isinstance(llm, BedrockCompletion), f"Failed for model: {model_name}" + + +def test_bedrock_supports_stop_words(): + """ + Test that Bedrock models support stop sequences + """ + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + assert llm.supports_stop_words() == True + + +def test_bedrock_context_window_size(): + """ + Test that Bedrock models return correct context window sizes + """ + # Test Claude 3.5 Sonnet + llm_claude = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + context_size_claude = llm_claude.get_context_window_size() + assert context_size_claude > 150000 # Should be substantial (200K tokens with ratio) + + # Test Titan + llm_titan = LLM(model="bedrock/amazon.titan-text-express-v1") + context_size_titan = llm_titan.get_context_window_size() + assert context_size_titan > 5000 # Should have 8K context window + + +def test_bedrock_message_formatting(): + """ + Test that messages are properly formatted for Bedrock Converse API + """ + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Test message formatting + test_messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + {"role": "user", "content": "How are you?"} + ] + + formatted_messages, system_message = llm._format_messages_for_converse(test_messages) + + # System message should be extracted + assert system_message == "You are a helpful assistant." + + # Remaining messages should be in Converse format + assert len(formatted_messages) >= 3 # Should have user, assistant, user messages + + # First message should be user role + assert formatted_messages[0]["role"] == "user" + # Second should be assistant + assert formatted_messages[1]["role"] == "assistant" + + # Messages should have content array with text + assert isinstance(formatted_messages[0]["content"], list) + assert "text" in formatted_messages[0]["content"][0] + + +def test_bedrock_streaming_parameter(): + """ + Test that streaming parameter is properly handled + """ + # Test non-streaming + llm_no_stream = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", stream=False) + assert llm_no_stream.stream == False + + # Test streaming + llm_stream = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0", stream=True) + assert llm_stream.stream == True + + +def test_bedrock_tool_conversion(): + """ + Test that tools are properly converted to Bedrock Converse format + """ + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Mock tool in CrewAI format + crewai_tools = [{ + "type": "function", + "function": { + "name": "test_tool", + "description": "A test tool", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"] + } + } + }] + + # Test tool conversion + bedrock_tools = llm._format_tools_for_converse(crewai_tools) + + assert len(bedrock_tools) == 1 + # Bedrock tools should have toolSpec structure + assert "toolSpec" in bedrock_tools[0] + assert bedrock_tools[0]["toolSpec"]["name"] == "test_tool" + assert bedrock_tools[0]["toolSpec"]["description"] == "A test tool" + assert "inputSchema" in bedrock_tools[0]["toolSpec"] + + +def test_bedrock_environment_variable_credentials(mock_aws_credentials): + """ + Test that AWS credentials are properly loaded from environment + """ + mock_session_class, _ = mock_aws_credentials + + # Reset the mock to clear any previous calls + mock_session_class.reset_mock() + + with patch.dict(os.environ, { + "AWS_ACCESS_KEY_ID": "test-access-key-123", + "AWS_SECRET_ACCESS_KEY": "test-secret-key-456" + }): + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Verify Session was called with environment credentials + assert mock_session_class.called + # Get the most recent call - Session is called as Session(...) + call_kwargs = mock_session_class.call_args[1] if mock_session_class.call_args else {} + assert call_kwargs.get('aws_access_key_id') == "test-access-key-123" + assert call_kwargs.get('aws_secret_access_key') == "test-secret-key-456" + + +def test_bedrock_token_usage_tracking(): + """ + Test that token usage is properly tracked for Bedrock responses + """ + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Mock the Bedrock response with usage information + with patch.object(llm.client, 'converse') as mock_converse: + mock_response = { + 'output': { + 'message': { + 'role': 'assistant', + 'content': [ + {'text': 'test response'} + ] + } + }, + 'usage': { + 'inputTokens': 50, + 'outputTokens': 25, + 'totalTokens': 75 + } + } + mock_converse.return_value = mock_response + + result = llm.call("Hello") + + # Verify the response + assert result == "test response" + + # Verify token usage was tracked + assert llm._token_usage['prompt_tokens'] == 50 + assert llm._token_usage['completion_tokens'] == 25 + assert llm._token_usage['total_tokens'] == 75 + + +def test_bedrock_tool_use_conversation_flow(): + """ + Test that the Bedrock completion properly handles tool use conversation flow + """ + from unittest.mock import Mock + + # Create BedrockCompletion instance + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Mock tool function + def mock_weather_tool(location: str) -> str: + return f"The weather in {location} is sunny and 75°F" + + available_functions = {"get_weather": mock_weather_tool} + + # Mock the Bedrock client responses + with patch.object(llm.client, 'converse') as mock_converse: + # First response: tool use request + tool_use_response = { + 'output': { + 'message': { + 'role': 'assistant', + 'content': [ + { + 'toolUse': { + 'toolUseId': 'tool-123', + 'name': 'get_weather', + 'input': {'location': 'San Francisco'} + } + } + ] + } + }, + 'usage': { + 'inputTokens': 100, + 'outputTokens': 50, + 'totalTokens': 150 + } + } + + # Second response: final answer after tool execution + final_response = { + 'output': { + 'message': { + 'role': 'assistant', + 'content': [ + {'text': 'Based on the weather data, it is sunny and 75°F in San Francisco.'} + ] + } + }, + 'usage': { + 'inputTokens': 120, + 'outputTokens': 30, + 'totalTokens': 150 + } + } + + # Configure mock to return different responses on successive calls + mock_converse.side_effect = [tool_use_response, final_response] + + # Test the call + messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}] + result = llm.call( + messages=messages, + available_functions=available_functions + ) + + # Verify the final response contains the weather information + assert "sunny" in result.lower() or "75" in result + + # Verify that the API was called twice (once for tool use, once for final answer) + assert mock_converse.call_count == 2 + + +def test_bedrock_handles_cohere_conversation_requirements(): + """ + Test that Bedrock properly handles Cohere model's requirement for user message at end + """ + llm = LLM(model="bedrock/cohere.command-r-plus-v1:0") + + # Test message formatting with conversation ending in assistant message + test_messages = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"} + ] + + formatted_messages, system_message = llm._format_messages_for_converse(test_messages) + + # For Cohere models, should add a user message at the end + assert formatted_messages[-1]["role"] == "user" + assert "continue" in formatted_messages[-1]["content"][0]["text"].lower() + + +def test_bedrock_client_error_handling(): + """ + Test that Bedrock properly handles various AWS client errors + """ + from botocore.exceptions import ClientError + + llm = LLM(model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0") + + # Test ValidationException + with patch.object(llm.client, 'converse') as mock_converse: + error_response = { + 'Error': { + 'Code': 'ValidationException', + 'Message': 'Invalid request format' + } + } + mock_converse.side_effect = ClientError(error_response, 'converse') + + with pytest.raises(ValueError) as exc_info: + llm.call("Hello") + assert "validation" in str(exc_info.value).lower() + + # Test ThrottlingException + with patch.object(llm.client, 'converse') as mock_converse: + error_response = { + 'Error': { + 'Code': 'ThrottlingException', + 'Message': 'Rate limit exceeded' + } + } + mock_converse.side_effect = ClientError(error_response, 'converse') + + with pytest.raises(RuntimeError) as exc_info: + llm.call("Hello") + assert "throttled" in str(exc_info.value).lower() diff --git a/lib/crewai/tests/llms/google/test_google.py b/lib/crewai/tests/llms/google/test_google.py new file mode 100644 index 000000000..fc3ff9099 --- /dev/null +++ b/lib/crewai/tests/llms/google/test_google.py @@ -0,0 +1,650 @@ +import os +import sys +import types +from unittest.mock import patch, MagicMock +import pytest + +from crewai.llm import LLM +from crewai.crew import Crew +from crewai.agent import Agent +from crewai.task import Task + + +@pytest.fixture(autouse=True) +def mock_google_api_key(): + """Automatically mock GOOGLE_API_KEY for all tests in this module.""" + with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}): + yield + + +def test_gemini_completion_is_used_when_google_provider(): + """ + Test that GeminiCompletion from completion.py is used when LLM uses provider 'google' + """ + llm = LLM(model="google/gemini-2.0-flash-001") + + assert llm.__class__.__name__ == "GeminiCompletion" + assert llm.provider == "google" + assert llm.model == "gemini-2.0-flash-001" + + +def test_gemini_completion_is_used_when_gemini_provider(): + """ + Test that GeminiCompletion is used when provider is 'gemini' + """ + llm = LLM(model="gemini/gemini-2.0-flash-001") + + from crewai.llms.providers.gemini.completion import GeminiCompletion + assert isinstance(llm, GeminiCompletion) + assert llm.provider == "gemini" + assert llm.model == "gemini-2.0-flash-001" + + + + +def test_gemini_tool_use_conversation_flow(): + """ + Test that the Gemini completion properly handles tool use conversation flow + """ + from unittest.mock import Mock, patch + from crewai.llms.providers.gemini.completion import GeminiCompletion + + # Create GeminiCompletion instance + completion = GeminiCompletion(model="gemini-2.0-flash-001") + + # Mock tool function + def mock_weather_tool(location: str) -> str: + return f"The weather in {location} is sunny and 75°F" + + available_functions = {"get_weather": mock_weather_tool} + + # Mock the Google Gemini client responses + with patch.object(completion.client.models, 'generate_content') as mock_generate: + # Mock function call in response + mock_function_call = Mock() + mock_function_call.name = "get_weather" + mock_function_call.args = {"location": "San Francisco"} + + mock_part = Mock() + mock_part.function_call = mock_function_call + + mock_content = Mock() + mock_content.parts = [mock_part] + + mock_candidate = Mock() + mock_candidate.content = mock_content + + mock_response = Mock() + mock_response.candidates = [mock_candidate] + mock_response.text = "Based on the weather data, it's a beautiful day in San Francisco with sunny skies and 75°F temperature." + mock_response.usage_metadata = Mock() + mock_response.usage_metadata.prompt_token_count = 100 + mock_response.usage_metadata.candidates_token_count = 50 + mock_response.usage_metadata.total_token_count = 150 + + mock_generate.return_value = mock_response + + # Test the call + messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}] + result = completion.call( + messages=messages, + available_functions=available_functions + ) + + # Verify the tool was executed and returned the result + assert result == "The weather in San Francisco is sunny and 75°F" + + # Verify that the API was called + assert mock_generate.called + + +def test_gemini_completion_module_is_imported(): + """ + Test that the completion module is properly imported when using Google provider + """ + module_name = "crewai.llms.providers.gemini.completion" + + # Remove module from cache if it exists + if module_name in sys.modules: + del sys.modules[module_name] + + # Create LLM instance - this should trigger the import + LLM(model="google/gemini-2.0-flash-001") + + # Verify the module was imported + assert module_name in sys.modules + completion_mod = sys.modules[module_name] + assert isinstance(completion_mod, types.ModuleType) + + # Verify the class exists in the module + assert hasattr(completion_mod, 'GeminiCompletion') + + +def test_native_gemini_raises_error_when_initialization_fails(): + """ + Test that LLM raises ImportError when native Gemini completion fails. + + With the new behavior, when a native provider is in SUPPORTED_NATIVE_PROVIDERS + but fails to instantiate, we raise an ImportError instead of silently falling back. + This provides clearer error messages to users about missing dependencies. + """ + # Mock the _get_native_provider to return a failing class + with patch('crewai.llm.LLM._get_native_provider') as mock_get_provider: + + class FailingCompletion: + def __init__(self, *args, **kwargs): + raise Exception("Native Google Gen AI SDK failed") + + mock_get_provider.return_value = FailingCompletion + + # This should raise ImportError with clear message + with pytest.raises(ImportError) as excinfo: + LLM(model="google/gemini-2.0-flash-001") + + # Verify the error message is helpful + assert "Error importing native provider" in str(excinfo.value) + assert "Native Google Gen AI SDK failed" in str(excinfo.value) + + +def test_gemini_completion_initialization_parameters(): + """ + Test that GeminiCompletion is initialized with correct parameters + """ + llm = LLM( + model="google/gemini-2.0-flash-001", + temperature=0.7, + max_output_tokens=2000, + top_p=0.9, + top_k=40, + api_key="test-key" + ) + + from crewai.llms.providers.gemini.completion import GeminiCompletion + assert isinstance(llm, GeminiCompletion) + assert llm.model == "gemini-2.0-flash-001" + assert llm.temperature == 0.7 + assert llm.max_output_tokens == 2000 + assert llm.top_p == 0.9 + assert llm.top_k == 40 + + +def test_gemini_specific_parameters(): + """ + Test Gemini-specific parameters like stop_sequences, streaming, and safety settings + """ + safety_settings = { + "HARM_CATEGORY_HARASSMENT": "BLOCK_MEDIUM_AND_ABOVE", + "HARM_CATEGORY_HATE_SPEECH": "BLOCK_MEDIUM_AND_ABOVE" + } + + llm = LLM( + model="google/gemini-2.0-flash-001", + stop_sequences=["Human:", "Assistant:"], + stream=True, + safety_settings=safety_settings, + project="test-project", + location="us-central1" + ) + + from crewai.llms.providers.gemini.completion import GeminiCompletion + assert isinstance(llm, GeminiCompletion) + assert llm.stop_sequences == ["Human:", "Assistant:"] + assert llm.stream == True + assert llm.safety_settings == safety_settings + assert llm.project == "test-project" + assert llm.location == "us-central1" + + +def test_gemini_completion_call(): + """ + Test that GeminiCompletion call method works + """ + llm = LLM(model="google/gemini-2.0-flash-001") + + # Mock the call method on the instance + with patch.object(llm, 'call', return_value="Hello! I'm Gemini, ready to help.") as mock_call: + result = llm.call("Hello, how are you?") + + assert result == "Hello! I'm Gemini, ready to help." + mock_call.assert_called_once_with("Hello, how are you?") + + +def test_gemini_completion_called_during_crew_execution(): + """ + Test that GeminiCompletion.call is actually invoked when running a crew + """ + # Create the LLM instance first + gemini_llm = LLM(model="google/gemini-2.0-flash-001") + + # Mock the call method on the specific instance + with patch.object(gemini_llm, 'call', return_value="Tokyo has 14 million people.") as mock_call: + + # Create agent with explicit LLM configuration + agent = Agent( + role="Research Assistant", + goal="Find population info", + backstory="You research populations.", + llm=gemini_llm, + ) + + task = Task( + description="Find Tokyo population", + expected_output="Population number", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + # Verify mock was called + assert mock_call.called + assert "14 million" in str(result) + + +def test_gemini_completion_call_arguments(): + """ + Test that GeminiCompletion.call is invoked with correct arguments + """ + # Create LLM instance first + gemini_llm = LLM(model="google/gemini-2.0-flash-001") + + # Mock the instance method + with patch.object(gemini_llm, 'call') as mock_call: + mock_call.return_value = "Task completed successfully." + + agent = Agent( + role="Test Agent", + goal="Complete a simple task", + backstory="You are a test agent.", + llm=gemini_llm # Use same instance + ) + + task = Task( + description="Say hello world", + expected_output="Hello world", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + # Verify call was made + assert mock_call.called + + # Check the arguments passed to the call method + call_args = mock_call.call_args + assert call_args is not None + + # The first argument should be the messages + messages = call_args[0][0] # First positional argument + assert isinstance(messages, (str, list)) + + # Verify that the task description appears in the messages + if isinstance(messages, str): + assert "hello world" in messages.lower() + elif isinstance(messages, list): + message_content = str(messages).lower() + assert "hello world" in message_content + + +def test_multiple_gemini_calls_in_crew(): + """ + Test that GeminiCompletion.call is invoked multiple times for multiple tasks + """ + # Create LLM instance first + gemini_llm = LLM(model="google/gemini-2.0-flash-001") + + # Mock the instance method + with patch.object(gemini_llm, 'call') as mock_call: + mock_call.return_value = "Task completed." + + agent = Agent( + role="Multi-task Agent", + goal="Complete multiple tasks", + backstory="You can handle multiple tasks.", + llm=gemini_llm # Use same instance + ) + + task1 = Task( + description="First task", + expected_output="First result", + agent=agent, + ) + + task2 = Task( + description="Second task", + expected_output="Second result", + agent=agent, + ) + + crew = Crew( + agents=[agent], + tasks=[task1, task2] + ) + crew.kickoff() + + # Verify multiple calls were made + assert mock_call.call_count >= 2 # At least one call per task + + # Verify each call had proper arguments + for call in mock_call.call_args_list: + assert len(call[0]) > 0 # Has positional arguments + messages = call[0][0] + assert messages is not None + + +def test_gemini_completion_with_tools(): + """ + Test that GeminiCompletion.call is invoked with tools when agent has tools + """ + from crewai.tools import tool + + @tool + def sample_tool(query: str) -> str: + """A sample tool for testing""" + return f"Tool result for: {query}" + + # Create LLM instance first + gemini_llm = LLM(model="google/gemini-2.0-flash-001") + + # Mock the instance method + with patch.object(gemini_llm, 'call') as mock_call: + mock_call.return_value = "Task completed with tools." + + agent = Agent( + role="Tool User", + goal="Use tools to complete tasks", + backstory="You can use tools.", + llm=gemini_llm, # Use same instance + tools=[sample_tool] + ) + + task = Task( + description="Use the sample tool", + expected_output="Tool usage result", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + assert mock_call.called + + call_args = mock_call.call_args + call_kwargs = call_args[1] if len(call_args) > 1 else {} + + if 'tools' in call_kwargs: + assert call_kwargs['tools'] is not None + assert len(call_kwargs['tools']) > 0 + + +def test_gemini_raises_error_when_model_not_supported(): + """Test that GeminiCompletion raises ValueError when model not supported""" + + # Mock the Google client to raise an error + with patch('crewai.llms.providers.gemini.completion.genai') as mock_genai: + mock_client = MagicMock() + mock_genai.Client.return_value = mock_client + + from google.genai.errors import ClientError # type: ignore + + mock_response = MagicMock() + mock_response.body_segments = [{ + 'error': { + 'code': 404, + 'message': 'models/model-doesnt-exist is not found for API version v1beta, or is not supported for generateContent.', + 'status': 'NOT_FOUND' + } + }] + mock_response.status_code = 404 + + mock_client.models.generate_content.side_effect = ClientError(404, mock_response) + + llm = LLM(model="google/model-doesnt-exist") + + with pytest.raises(Exception): # Should raise some error for unsupported model + llm.call("Hello") + + +def test_gemini_vertex_ai_setup(): + """ + Test that Vertex AI configuration is properly handled + """ + with patch.dict(os.environ, { + "GOOGLE_CLOUD_PROJECT": "test-project", + "GOOGLE_CLOUD_LOCATION": "us-west1" + }): + llm = LLM( + model="google/gemini-2.0-flash-001", + project="test-project", + location="us-west1" + ) + + from crewai.llms.providers.gemini.completion import GeminiCompletion + assert isinstance(llm, GeminiCompletion) + + assert llm.project == "test-project" + assert llm.location == "us-west1" + + +def test_gemini_api_key_configuration(): + """ + Test that API key configuration works for both GOOGLE_API_KEY and GEMINI_API_KEY + """ + # Test with GOOGLE_API_KEY + with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-google-key"}): + llm = LLM(model="google/gemini-2.0-flash-001") + + from crewai.llms.providers.gemini.completion import GeminiCompletion + assert isinstance(llm, GeminiCompletion) + assert llm.api_key == "test-google-key" + + # Test with GEMINI_API_KEY + with patch.dict(os.environ, {"GEMINI_API_KEY": "test-gemini-key"}, clear=True): + llm = LLM(model="google/gemini-2.0-flash-001") + + assert isinstance(llm, GeminiCompletion) + assert llm.api_key == "test-gemini-key" + + +def test_gemini_model_capabilities(): + """ + Test that model capabilities are correctly identified + """ + # Test Gemini 2.0 model + llm_2_0 = LLM(model="google/gemini-2.0-flash-001") + from crewai.llms.providers.gemini.completion import GeminiCompletion + assert isinstance(llm_2_0, GeminiCompletion) + assert llm_2_0.is_gemini_2 == True + assert llm_2_0.supports_tools == True + + # Test Gemini 1.5 model + llm_1_5 = LLM(model="google/gemini-1.5-pro") + assert isinstance(llm_1_5, GeminiCompletion) + assert llm_1_5.is_gemini_1_5 == True + assert llm_1_5.supports_tools == True + + +def test_gemini_generation_config(): + """ + Test that generation config is properly prepared + """ + llm = LLM( + model="google/gemini-2.0-flash-001", + temperature=0.7, + top_p=0.9, + top_k=40, + max_output_tokens=1000 + ) + + from crewai.llms.providers.gemini.completion import GeminiCompletion + assert isinstance(llm, GeminiCompletion) + + # Test config preparation + config = llm._prepare_generation_config() + + # Verify config has the expected parameters + assert hasattr(config, 'temperature') or 'temperature' in str(config) + assert hasattr(config, 'top_p') or 'top_p' in str(config) + assert hasattr(config, 'top_k') or 'top_k' in str(config) + assert hasattr(config, 'max_output_tokens') or 'max_output_tokens' in str(config) + + +def test_gemini_model_detection(): + """ + Test that various Gemini model formats are properly detected + """ + # Test Gemini model naming patterns that actually work with provider detection + gemini_test_cases = [ + "google/gemini-2.0-flash-001", + "gemini/gemini-2.0-flash-001", + "google/gemini-1.5-pro", + "gemini/gemini-1.5-flash" + ] + + for model_name in gemini_test_cases: + llm = LLM(model=model_name) + from crewai.llms.providers.gemini.completion import GeminiCompletion + assert isinstance(llm, GeminiCompletion), f"Failed for model: {model_name}" + + +def test_gemini_supports_stop_words(): + """ + Test that Gemini models support stop sequences + """ + llm = LLM(model="google/gemini-2.0-flash-001") + assert llm.supports_stop_words() == True + + +def test_gemini_context_window_size(): + """ + Test that Gemini models return correct context window sizes + """ + # Test Gemini 2.0 Flash + llm_2_0 = LLM(model="google/gemini-2.0-flash-001") + context_size_2_0 = llm_2_0.get_context_window_size() + assert context_size_2_0 > 500000 # Should be substantial (1M tokens) + + # Test Gemini 1.5 Pro + llm_1_5 = LLM(model="google/gemini-1.5-pro") + context_size_1_5 = llm_1_5.get_context_window_size() + assert context_size_1_5 > 1000000 # Should be very large (2M tokens) + + +def test_gemini_message_formatting(): + """ + Test that messages are properly formatted for Gemini API + """ + llm = LLM(model="google/gemini-2.0-flash-001") + + # Test message formatting + test_messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + {"role": "user", "content": "How are you?"} + ] + + formatted_contents, system_instruction = llm._format_messages_for_gemini(test_messages) + + # System message should be extracted + assert system_instruction == "You are a helpful assistant." + + # Remaining messages should be Content objects + assert len(formatted_contents) >= 3 # Should have user, model, user messages + + # First content should be user role + assert formatted_contents[0].role == "user" + # Second should be model (converted from assistant) + assert formatted_contents[1].role == "model" + + +def test_gemini_streaming_parameter(): + """ + Test that streaming parameter is properly handled + """ + # Test non-streaming + llm_no_stream = LLM(model="google/gemini-2.0-flash-001", stream=False) + assert llm_no_stream.stream == False + + # Test streaming + llm_stream = LLM(model="google/gemini-2.0-flash-001", stream=True) + assert llm_stream.stream == True + + +def test_gemini_tool_conversion(): + """ + Test that tools are properly converted to Gemini format + """ + llm = LLM(model="google/gemini-2.0-flash-001") + + # Mock tool in CrewAI format + crewai_tools = [{ + "type": "function", + "function": { + "name": "test_tool", + "description": "A test tool", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"] + } + } + }] + + # Test tool conversion + gemini_tools = llm._convert_tools_for_interference(crewai_tools) + + assert len(gemini_tools) == 1 + # Gemini tools are Tool objects with function_declarations + assert hasattr(gemini_tools[0], 'function_declarations') + assert len(gemini_tools[0].function_declarations) == 1 + + func_decl = gemini_tools[0].function_declarations[0] + assert func_decl.name == "test_tool" + assert func_decl.description == "A test tool" + + +def test_gemini_environment_variable_api_key(): + """ + Test that Google API key is properly loaded from environment + """ + with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-google-key"}): + llm = LLM(model="google/gemini-2.0-flash-001") + + assert llm.client is not None + assert hasattr(llm.client, 'models') + assert llm.api_key == "test-google-key" + + +def test_gemini_token_usage_tracking(): + """ + Test that token usage is properly tracked for Gemini responses + """ + llm = LLM(model="google/gemini-2.0-flash-001") + + # Mock the Gemini response with usage information + with patch.object(llm.client.models, 'generate_content') as mock_generate: + mock_response = MagicMock() + mock_response.text = "test response" + mock_response.candidates = [] + mock_response.usage_metadata = MagicMock( + prompt_token_count=50, + candidates_token_count=25, + total_token_count=75 + ) + mock_generate.return_value = mock_response + + result = llm.call("Hello") + + # Verify the response + assert result == "test response" + + # Verify token usage was extracted + usage = llm._extract_token_usage(mock_response) + assert usage["prompt_token_count"] == 50 + assert usage["candidates_token_count"] == 25 + assert usage["total_token_count"] == 75 + assert usage["total_tokens"] == 75 diff --git a/lib/crewai/tests/llms/openai/test_openai.py b/lib/crewai/tests/llms/openai/test_openai.py new file mode 100644 index 000000000..c2f1636c9 --- /dev/null +++ b/lib/crewai/tests/llms/openai/test_openai.py @@ -0,0 +1,484 @@ +import os +import sys +import types +from unittest.mock import patch, MagicMock +import openai +import pytest + +from crewai.llm import LLM +from crewai.llms.providers.openai.completion import OpenAICompletion +from crewai.crew import Crew +from crewai.agent import Agent +from crewai.task import Task +from crewai.cli.constants import DEFAULT_LLM_MODEL + +def test_openai_completion_is_used_when_openai_provider(): + """ + Test that OpenAICompletion from completion.py is used when LLM uses provider 'openai' + """ + llm = LLM(model="openai/gpt-4o") + + assert llm.__class__.__name__ == "OpenAICompletion" + assert llm.provider == "openai" + assert llm.model == "gpt-4o" + + +def test_openai_completion_is_used_when_no_provider_prefix(): + """ + Test that OpenAICompletion is used when no provider prefix is given (defaults to openai) + """ + llm = LLM(model="gpt-4o") + + from crewai.llms.providers.openai.completion import OpenAICompletion + assert isinstance(llm, OpenAICompletion) + assert llm.provider == "openai" + assert llm.model == "gpt-4o" + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_openai_is_default_provider_without_explicit_llm_set_on_agent(): + """ + Test that OpenAI is the default provider when no explicit LLM is set on the agent + """ + agent = Agent( + role="Research Assistant", + goal="Find information about the population of Tokyo", + backstory="You are a helpful research assistant.", + ) + task = Task( + description="Find information about the population of Tokyo", + expected_output="The population of Tokyo is 10 million", + agent=agent, + ) + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + assert crew.agents[0].llm.__class__.__name__ == "OpenAICompletion" + assert crew.agents[0].llm.model == DEFAULT_LLM_MODEL + + + + + + +def test_openai_completion_module_is_imported(): + """ + Test that the completion module is properly imported when using OpenAI provider + """ + module_name = "crewai.llms.providers.openai.completion" + + # Remove module from cache if it exists + if module_name in sys.modules: + del sys.modules[module_name] + + # Create LLM instance - this should trigger the import + LLM(model="openai/gpt-4o") + + # Verify the module was imported + assert module_name in sys.modules + completion_mod = sys.modules[module_name] + assert isinstance(completion_mod, types.ModuleType) + + # Verify the class exists in the module + assert hasattr(completion_mod, 'OpenAICompletion') + + +def test_native_openai_raises_error_when_initialization_fails(): + """ + Test that LLM raises ImportError when native OpenAI completion fails to initialize. + This ensures we don't silently fall back when there's a configuration issue. + """ + # Mock the _get_native_provider to return a failing class + with patch('crewai.llm.LLM._get_native_provider') as mock_get_provider: + + class FailingCompletion: + def __init__(self, *args, **kwargs): + raise Exception("Native SDK failed") + + mock_get_provider.return_value = FailingCompletion + + # This should raise ImportError, not fall back to LiteLLM + with pytest.raises(ImportError) as excinfo: + LLM(model="openai/gpt-4o") + + assert "Error importing native provider" in str(excinfo.value) + assert "Native SDK failed" in str(excinfo.value) + + +def test_openai_completion_initialization_parameters(): + """ + Test that OpenAICompletion is initialized with correct parameters + """ + llm = LLM( + model="openai/gpt-4o", + temperature=0.7, + max_tokens=1000, + api_key="test-key" + ) + + from crewai.llms.providers.openai.completion import OpenAICompletion + assert isinstance(llm, OpenAICompletion) + assert llm.model == "gpt-4o" + assert llm.temperature == 0.7 + assert llm.max_tokens == 1000 + +def test_openai_completion_call(): + """ + Test that OpenAICompletion call method works + """ + llm = LLM(model="openai/gpt-4o") + + # Mock the call method on the instance + with patch.object(llm, 'call', return_value="Hello! I'm ready to help.") as mock_call: + result = llm.call("Hello, how are you?") + + assert result == "Hello! I'm ready to help." + mock_call.assert_called_once_with("Hello, how are you?") + + +def test_openai_completion_called_during_crew_execution(): + """ + Test that OpenAICompletion.call is actually invoked when running a crew + """ + # Create the LLM instance first + openai_llm = LLM(model="openai/gpt-4o") + + # Mock the call method on the specific instance + with patch.object(openai_llm, 'call', return_value="Tokyo has 14 million people.") as mock_call: + + # Create agent with explicit LLM configuration + agent = Agent( + role="Research Assistant", + goal="Find population info", + backstory="You research populations.", + llm=openai_llm, + ) + + task = Task( + description="Find Tokyo population", + expected_output="Population number", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + # Verify mock was called + assert mock_call.called + assert "14 million" in str(result) + + +def test_openai_completion_call_arguments(): + """ + Test that OpenAICompletion.call is invoked with correct arguments + """ + # Create LLM instance first (like working tests) + openai_llm = LLM(model="openai/gpt-4o") + + # Mock the instance method (like working tests) + with patch.object(openai_llm, 'call') as mock_call: + mock_call.return_value = "Task completed successfully." + + agent = Agent( + role="Test Agent", + goal="Complete a simple task", + backstory="You are a test agent.", + llm=openai_llm # Use same instance + ) + + task = Task( + description="Say hello world", + expected_output="Hello world", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + # Verify call was made + assert mock_call.called + + # Check the arguments passed to the call method + call_args = mock_call.call_args + assert call_args is not None + + # The first argument should be the messages + messages = call_args[0][0] # First positional argument + assert isinstance(messages, (str, list)) + + # Verify that the task description appears in the messages + if isinstance(messages, str): + assert "hello world" in messages.lower() + elif isinstance(messages, list): + message_content = str(messages).lower() + assert "hello world" in message_content + + +def test_multiple_openai_calls_in_crew(): + """ + Test that OpenAICompletion.call is invoked multiple times for multiple tasks + """ + # Create LLM instance first + openai_llm = LLM(model="openai/gpt-4o") + + # Mock the instance method + with patch.object(openai_llm, 'call') as mock_call: + mock_call.return_value = "Task completed." + + agent = Agent( + role="Multi-task Agent", + goal="Complete multiple tasks", + backstory="You can handle multiple tasks.", + llm=openai_llm # Use same instance + ) + + task1 = Task( + description="First task", + expected_output="First result", + agent=agent, + ) + + task2 = Task( + description="Second task", + expected_output="Second result", + agent=agent, + ) + + crew = Crew( + agents=[agent], + tasks=[task1, task2] + ) + crew.kickoff() + + # Verify multiple calls were made + assert mock_call.call_count >= 2 # At least one call per task + + # Verify each call had proper arguments + for call in mock_call.call_args_list: + assert len(call[0]) > 0 # Has positional arguments + messages = call[0][0] + assert messages is not None + + +def test_openai_completion_with_tools(): + """ + Test that OpenAICompletion.call is invoked with tools when agent has tools + """ + from crewai.tools import tool + + @tool + def sample_tool(query: str) -> str: + """A sample tool for testing""" + return f"Tool result for: {query}" + + # Create LLM instance first + openai_llm = LLM(model="openai/gpt-4o") + + # Mock the instance method (not the class method) + with patch.object(openai_llm, 'call') as mock_call: + mock_call.return_value = "Task completed with tools." + + agent = Agent( + role="Tool User", + goal="Use tools to complete tasks", + backstory="You can use tools.", + llm=openai_llm, # Use same instance + tools=[sample_tool] + ) + + task = Task( + description="Use the sample tool", + expected_output="Tool usage result", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + + assert mock_call.called + + call_args = mock_call.call_args + call_kwargs = call_args[1] if len(call_args) > 1 else {} + + if 'tools' in call_kwargs: + assert call_kwargs['tools'] is not None + assert len(call_kwargs['tools']) > 0 + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_openai_completion_call_returns_usage_metrics(): + """ + Test that OpenAICompletion.call returns usage metrics + """ + agent = Agent( + role="Research Assistant", + goal="Find information about the population of Tokyo", + backstory="You are a helpful research assistant.", + llm=LLM(model="openai/gpt-4o"), + verbose=True, + ) + + task = Task( + description="Find information about the population of Tokyo", + expected_output="The population of Tokyo is 10 million", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + assert result.token_usage is not None + assert result.token_usage.total_tokens == 289 + assert result.token_usage.prompt_tokens == 173 + assert result.token_usage.completion_tokens == 116 + assert result.token_usage.successful_requests == 1 + assert result.token_usage.cached_prompt_tokens == 0 + + +def test_openai_raises_error_when_model_not_supported(): + """Test that OpenAICompletion raises ValueError when model not supported""" + + with patch('crewai.llms.providers.openai.completion.OpenAI') as mock_openai_class: + mock_client = MagicMock() + mock_openai_class.return_value = mock_client + + mock_client.chat.completions.create.side_effect = openai.NotFoundError( + message="The model `model-doesnt-exist` does not exist", + response=MagicMock(), + body={} + ) + + llm = LLM(model="openai/model-doesnt-exist") + + with pytest.raises(ValueError, match="Model.*not found"): + llm.call("Hello") + +def test_openai_client_setup_with_extra_arguments(): + """ + Test that OpenAICompletion is initialized with correct parameters + """ + llm = LLM( + model="openai/gpt-4o", + temperature=0.7, + max_tokens=1000, + top_p=0.5, + max_retries=3, + timeout=30 + ) + + # Check that model parameters are stored on the LLM instance + assert llm.temperature == 0.7 + assert llm.max_tokens == 1000 + assert llm.top_p == 0.5 + + # Check that client parameters are properly configured + assert llm.client.max_retries == 3 + assert llm.client.timeout == 30 + + # Test that parameters are properly used in API calls + with patch.object(llm.client.chat.completions, 'create') as mock_create: + mock_create.return_value = MagicMock( + choices=[MagicMock(message=MagicMock(content="test response", tool_calls=None))], + usage=MagicMock(prompt_tokens=10, completion_tokens=20, total_tokens=30) + ) + + llm.call("Hello") + + # Verify the API was called with the right parameters + call_args = mock_create.call_args[1] # keyword arguments + assert call_args['temperature'] == 0.7 + assert call_args['max_tokens'] == 1000 + assert call_args['top_p'] == 0.5 + assert call_args['model'] == 'gpt-4o' + +def test_extra_arguments_are_passed_to_openai_completion(): + """ + Test that extra arguments are passed to OpenAICompletion + """ + llm = LLM(model="openai/gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3) + + with patch.object(llm.client.chat.completions, 'create') as mock_create: + mock_create.return_value = MagicMock( + choices=[MagicMock(message=MagicMock(content="test response", tool_calls=None))], + usage=MagicMock(prompt_tokens=10, completion_tokens=20, total_tokens=30) + ) + + llm.call("Hello, how are you?") + + assert mock_create.called + call_kwargs = mock_create.call_args[1] + + assert call_kwargs['temperature'] == 0.7 + assert call_kwargs['max_tokens'] == 1000 + assert call_kwargs['top_p'] == 0.5 + assert call_kwargs['model'] == 'gpt-4o' + + + +def test_openai_get_client_params_with_api_base(): + """ + Test that _get_client_params correctly converts api_base to base_url + """ + llm = OpenAICompletion( + model="gpt-4o", + api_base="https://custom.openai.com/v1", + ) + client_params = llm._get_client_params() + assert client_params["base_url"] == "https://custom.openai.com/v1" + +def test_openai_get_client_params_with_base_url_priority(): + """ + Test that base_url takes priority over api_base in _get_client_params + """ + llm = OpenAICompletion( + model="gpt-4o", + base_url="https://priority.openai.com/v1", + api_base="https://fallback.openai.com/v1", + ) + client_params = llm._get_client_params() + assert client_params["base_url"] == "https://priority.openai.com/v1" + +def test_openai_get_client_params_with_env_var(): + """ + Test that _get_client_params uses OPENAI_BASE_URL environment variable as fallback + """ + with patch.dict(os.environ, { + "OPENAI_BASE_URL": "https://env.openai.com/v1", + }): + llm = OpenAICompletion(model="gpt-4o") + client_params = llm._get_client_params() + assert client_params["base_url"] == "https://env.openai.com/v1" + +def test_openai_get_client_params_priority_order(): + """ + Test the priority order: base_url > api_base > OPENAI_BASE_URL env var + """ + with patch.dict(os.environ, { + "OPENAI_BASE_URL": "https://env.openai.com/v1", + }): + # Test base_url beats api_base and env var + llm1 = OpenAICompletion( + model="gpt-4o", + base_url="https://base-url.openai.com/v1", + api_base="https://api-base.openai.com/v1", + ) + params1 = llm1._get_client_params() + assert params1["base_url"] == "https://base-url.openai.com/v1" + + # Test api_base beats env var when base_url is None + llm2 = OpenAICompletion( + model="gpt-4o", + api_base="https://api-base.openai.com/v1", + ) + params2 = llm2._get_client_params() + assert params2["base_url"] == "https://api-base.openai.com/v1" + + # Test env var is used when both base_url and api_base are None + llm3 = OpenAICompletion(model="gpt-4o") + params3 = llm3._get_client_params() + assert params3["base_url"] == "https://env.openai.com/v1" + +def test_openai_get_client_params_no_base_url(): + """ + Test that _get_client_params works correctly when no base_url is specified + """ + llm = OpenAICompletion(model="gpt-4o") + client_params = llm._get_client_params() + # When no base_url is provided, it should not be in the params (filtered out as None) + assert "base_url" not in client_params or client_params.get("base_url") is None diff --git a/tests/memory/__init__.py b/lib/crewai/tests/memory/__init__.py similarity index 100% rename from tests/memory/__init__.py rename to lib/crewai/tests/memory/__init__.py diff --git a/tests/memory/test_external_memory.py b/lib/crewai/tests/memory/test_external_memory.py similarity index 84% rename from tests/memory/test_external_memory.py rename to lib/crewai/tests/memory/test_external_memory.py index 49b3a3160..ac8516bca 100644 --- a/tests/memory/test_external_memory.py +++ b/lib/crewai/tests/memory/test_external_memory.py @@ -1,23 +1,45 @@ -from unittest.mock import MagicMock, patch, ANY +import threading from collections import defaultdict -from crewai.events.event_bus import crewai_event_bus -from crewai.events.types.memory_events import ( - MemorySaveStartedEvent, - MemorySaveCompletedEvent, - MemoryQueryStartedEvent, - MemoryQueryCompletedEvent, -) +from unittest.mock import ANY, MagicMock, patch + import pytest from mem0.memory.main import Memory from crewai.agent import Agent from crewai.crew import Crew, Process +from crewai.events.event_bus import crewai_event_bus +from crewai.events.types.memory_events import ( + MemoryQueryCompletedEvent, + MemoryQueryStartedEvent, + MemorySaveCompletedEvent, + MemorySaveStartedEvent, +) from crewai.memory.external.external_memory import ExternalMemory from crewai.memory.external.external_memory_item import ExternalMemoryItem from crewai.memory.storage.interface import Storage from crewai.task import Task +@pytest.fixture(autouse=True) +def cleanup_event_handlers(): + """Cleanup event handlers before and after each test""" + # Cleanup before test + with crewai_event_bus._rwlock.w_locked(): + crewai_event_bus._sync_handlers = {} + crewai_event_bus._async_handlers = {} + crewai_event_bus._handler_dependencies = {} + crewai_event_bus._execution_plan_cache = {} + + yield + + # Cleanup after test + with crewai_event_bus._rwlock.w_locked(): + crewai_event_bus._sync_handlers = {} + crewai_event_bus._async_handlers = {} + crewai_event_bus._handler_dependencies = {} + crewai_event_bus._execution_plan_cache = {} + + @pytest.fixture def mock_mem0_memory(): mock_memory = MagicMock(spec=Memory) @@ -238,24 +260,26 @@ def test_external_memory_search_events( custom_storage, external_memory_with_mocked_config ): events = defaultdict(list) + event_received = threading.Event() external_memory_with_mocked_config.storage = custom_storage - with crewai_event_bus.scoped_handlers(): - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - events["MemoryQueryStartedEvent"].append(event) + @crewai_event_bus.on(MemoryQueryStartedEvent) + def on_search_started(source, event): + events["MemoryQueryStartedEvent"].append(event) - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - events["MemoryQueryCompletedEvent"].append(event) + @crewai_event_bus.on(MemoryQueryCompletedEvent) + def on_search_completed(source, event): + events["MemoryQueryCompletedEvent"].append(event) + event_received.set() - external_memory_with_mocked_config.search( - query="test value", - limit=3, - score_threshold=0.35, - ) + external_memory_with_mocked_config.search( + query="test value", + limit=3, + score_threshold=0.35, + ) + assert event_received.wait(timeout=5), "Timeout waiting for search events" assert len(events["MemoryQueryStartedEvent"]) == 1 assert len(events["MemoryQueryCompletedEvent"]) == 1 @@ -300,24 +324,25 @@ def test_external_memory_save_events( custom_storage, external_memory_with_mocked_config ): events = defaultdict(list) + event_received = threading.Event() external_memory_with_mocked_config.storage = custom_storage - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(MemorySaveStartedEvent) + def on_save_started(source, event): + events["MemorySaveStartedEvent"].append(event) - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - events["MemorySaveStartedEvent"].append(event) + @crewai_event_bus.on(MemorySaveCompletedEvent) + def on_save_completed(source, event): + events["MemorySaveCompletedEvent"].append(event) + event_received.set() - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - events["MemorySaveCompletedEvent"].append(event) - - external_memory_with_mocked_config.save( - value="saving value", - metadata={"task": "test_task"}, - ) + external_memory_with_mocked_config.save( + value="saving value", + metadata={"task": "test_task"}, + ) + assert event_received.wait(timeout=5), "Timeout waiting for save events" assert len(events["MemorySaveStartedEvent"]) == 1 assert len(events["MemorySaveCompletedEvent"]) == 1 diff --git a/tests/memory/test_long_term_memory.py b/lib/crewai/tests/memory/test_long_term_memory.py similarity index 67% rename from tests/memory/test_long_term_memory.py rename to lib/crewai/tests/memory/test_long_term_memory.py index bf4c9285f..3461a3b7b 100644 --- a/tests/memory/test_long_term_memory.py +++ b/lib/crewai/tests/memory/test_long_term_memory.py @@ -1,15 +1,18 @@ -import pytest -from unittest.mock import ANY +import threading from collections import defaultdict +from unittest.mock import ANY + +import pytest + from crewai.events.event_bus import crewai_event_bus +from crewai.events.types.memory_events import ( + MemoryQueryCompletedEvent, + MemoryQueryStartedEvent, + MemorySaveCompletedEvent, + MemorySaveStartedEvent, +) from crewai.memory.long_term.long_term_memory import LongTermMemory from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem -from crewai.events.types.memory_events import ( - MemorySaveStartedEvent, - MemorySaveCompletedEvent, - MemoryQueryStartedEvent, - MemoryQueryCompletedEvent, -) @pytest.fixture @@ -20,27 +23,37 @@ def long_term_memory(): def test_long_term_memory_save_events(long_term_memory): events = defaultdict(list) + all_events_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(MemorySaveStartedEvent) + def on_save_started(source, event): + events["MemorySaveStartedEvent"].append(event) + if ( + len(events["MemorySaveStartedEvent"]) == 1 + and len(events["MemorySaveCompletedEvent"]) == 1 + ): + all_events_received.set() - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - events["MemorySaveStartedEvent"].append(event) + @crewai_event_bus.on(MemorySaveCompletedEvent) + def on_save_completed(source, event): + events["MemorySaveCompletedEvent"].append(event) + if ( + len(events["MemorySaveStartedEvent"]) == 1 + and len(events["MemorySaveCompletedEvent"]) == 1 + ): + all_events_received.set() - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - events["MemorySaveCompletedEvent"].append(event) - - memory = LongTermMemoryItem( - agent="test_agent", - task="test_task", - expected_output="test_output", - datetime="test_datetime", - quality=0.5, - metadata={"task": "test_task", "quality": 0.5}, - ) - long_term_memory.save(memory) + memory = LongTermMemoryItem( + agent="test_agent", + task="test_task", + expected_output="test_output", + datetime="test_datetime", + quality=0.5, + metadata={"task": "test_task", "quality": 0.5}, + ) + long_term_memory.save(memory) + assert all_events_received.wait(timeout=5), "Timeout waiting for save events" assert len(events["MemorySaveStartedEvent"]) == 1 assert len(events["MemorySaveCompletedEvent"]) == 1 assert len(events["MemorySaveFailedEvent"]) == 0 @@ -85,21 +98,31 @@ def test_long_term_memory_save_events(long_term_memory): def test_long_term_memory_search_events(long_term_memory): events = defaultdict(list) + all_events_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(MemoryQueryStartedEvent) + def on_search_started(source, event): + events["MemoryQueryStartedEvent"].append(event) + if ( + len(events["MemoryQueryStartedEvent"]) == 1 + and len(events["MemoryQueryCompletedEvent"]) == 1 + ): + all_events_received.set() - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - events["MemoryQueryStartedEvent"].append(event) + @crewai_event_bus.on(MemoryQueryCompletedEvent) + def on_search_completed(source, event): + events["MemoryQueryCompletedEvent"].append(event) + if ( + len(events["MemoryQueryStartedEvent"]) == 1 + and len(events["MemoryQueryCompletedEvent"]) == 1 + ): + all_events_received.set() - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - events["MemoryQueryCompletedEvent"].append(event) + test_query = "test query" - test_query = "test query" - - long_term_memory.search(test_query, latest_n=5) + long_term_memory.search(test_query, latest_n=5) + assert all_events_received.wait(timeout=5), "Timeout waiting for search events" assert len(events["MemoryQueryStartedEvent"]) == 1 assert len(events["MemoryQueryCompletedEvent"]) == 1 assert len(events["MemoryQueryFailedEvent"]) == 0 diff --git a/tests/memory/test_short_term_memory.py b/lib/crewai/tests/memory/test_short_term_memory.py similarity index 76% rename from tests/memory/test_short_term_memory.py rename to lib/crewai/tests/memory/test_short_term_memory.py index b50f6d2fe..049e38972 100644 --- a/tests/memory/test_short_term_memory.py +++ b/lib/crewai/tests/memory/test_short_term_memory.py @@ -1,8 +1,8 @@ +import threading from collections import defaultdict from unittest.mock import ANY, patch import pytest - from crewai.agent import Agent from crewai.crew import Crew from crewai.events.event_bus import crewai_event_bus @@ -38,24 +38,33 @@ def short_term_memory(): def test_short_term_memory_search_events(short_term_memory): events = defaultdict(list) + search_started = threading.Event() + search_completed = threading.Event() with patch.object(short_term_memory.storage, "search", return_value=[]): - with crewai_event_bus.scoped_handlers(): - @crewai_event_bus.on(MemoryQueryStartedEvent) - def on_search_started(source, event): - events["MemoryQueryStartedEvent"].append(event) + @crewai_event_bus.on(MemoryQueryStartedEvent) + def on_search_started(source, event): + events["MemoryQueryStartedEvent"].append(event) + search_started.set() - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def on_search_completed(source, event): - events["MemoryQueryCompletedEvent"].append(event) + @crewai_event_bus.on(MemoryQueryCompletedEvent) + def on_search_completed(source, event): + events["MemoryQueryCompletedEvent"].append(event) + search_completed.set() - # Call the save method - short_term_memory.search( - query="test value", - limit=3, - score_threshold=0.35, - ) + short_term_memory.search( + query="test value", + limit=3, + score_threshold=0.35, + ) + + assert search_started.wait(timeout=2), ( + "Timeout waiting for search started event" + ) + assert search_completed.wait(timeout=2), ( + "Timeout waiting for search completed event" + ) assert len(events["MemoryQueryStartedEvent"]) == 1 assert len(events["MemoryQueryCompletedEvent"]) == 1 @@ -99,20 +108,26 @@ def test_short_term_memory_search_events(short_term_memory): def test_short_term_memory_save_events(short_term_memory): events = defaultdict(list) - with crewai_event_bus.scoped_handlers(): + save_started = threading.Event() + save_completed = threading.Event() - @crewai_event_bus.on(MemorySaveStartedEvent) - def on_save_started(source, event): - events["MemorySaveStartedEvent"].append(event) + @crewai_event_bus.on(MemorySaveStartedEvent) + def on_save_started(source, event): + events["MemorySaveStartedEvent"].append(event) + save_started.set() - @crewai_event_bus.on(MemorySaveCompletedEvent) - def on_save_completed(source, event): - events["MemorySaveCompletedEvent"].append(event) + @crewai_event_bus.on(MemorySaveCompletedEvent) + def on_save_completed(source, event): + events["MemorySaveCompletedEvent"].append(event) + save_completed.set() - short_term_memory.save( - value="test value", - metadata={"task": "test_task"}, - ) + short_term_memory.save( + value="test value", + metadata={"task": "test_task"}, + ) + + assert save_started.wait(timeout=2), "Timeout waiting for save started event" + assert save_completed.wait(timeout=2), "Timeout waiting for save completed event" assert len(events["MemorySaveStartedEvent"]) == 1 assert len(events["MemorySaveCompletedEvent"]) == 1 diff --git a/lib/crewai/tests/pipeline/__init__.py b/lib/crewai/tests/pipeline/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/pipeline/cassettes/test_router_with_empty_input.yaml b/lib/crewai/tests/pipeline/cassettes/test_router_with_empty_input.yaml similarity index 100% rename from tests/pipeline/cassettes/test_router_with_empty_input.yaml rename to lib/crewai/tests/pipeline/cassettes/test_router_with_empty_input.yaml diff --git a/lib/crewai/tests/rag/__init__.py b/lib/crewai/tests/rag/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/lib/crewai/tests/rag/chromadb/__init__.py b/lib/crewai/tests/rag/chromadb/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/rag/chromadb/test_client.py b/lib/crewai/tests/rag/chromadb/test_client.py similarity index 99% rename from tests/rag/chromadb/test_client.py rename to lib/crewai/tests/rag/chromadb/test_client.py index ab31549e7..e8af7655b 100644 --- a/tests/rag/chromadb/test_client.py +++ b/lib/crewai/tests/rag/chromadb/test_client.py @@ -3,7 +3,6 @@ from unittest.mock import AsyncMock, Mock import pytest - from crewai.rag.chromadb.client import ChromaDBClient from crewai.rag.types import BaseRecord diff --git a/tests/rag/chromadb/test_utils.py b/lib/crewai/tests/rag/chromadb/test_utils.py similarity index 100% rename from tests/rag/chromadb/test_utils.py rename to lib/crewai/tests/rag/chromadb/test_utils.py diff --git a/tests/rag/config/test_factory.py b/lib/crewai/tests/rag/config/test_factory.py similarity index 99% rename from tests/rag/config/test_factory.py rename to lib/crewai/tests/rag/config/test_factory.py index e23dfbbd0..47c02aadd 100644 --- a/tests/rag/config/test_factory.py +++ b/lib/crewai/tests/rag/config/test_factory.py @@ -3,7 +3,6 @@ from unittest.mock import Mock, patch import pytest - from crewai.rag.factory import create_client diff --git a/tests/rag/config/test_optional_imports.py b/lib/crewai/tests/rag/config/test_optional_imports.py similarity index 99% rename from tests/rag/config/test_optional_imports.py rename to lib/crewai/tests/rag/config/test_optional_imports.py index 11dad9855..cf0217a3c 100644 --- a/tests/rag/config/test_optional_imports.py +++ b/lib/crewai/tests/rag/config/test_optional_imports.py @@ -1,7 +1,6 @@ """Tests for optional imports.""" import pytest - from crewai.rag.config.optional_imports.base import _MissingProvider from crewai.rag.config.optional_imports.providers import MissingChromaDBConfig diff --git a/tests/rag/embeddings/test_embedding_factory.py b/lib/crewai/tests/rag/embeddings/test_embedding_factory.py similarity index 100% rename from tests/rag/embeddings/test_embedding_factory.py rename to lib/crewai/tests/rag/embeddings/test_embedding_factory.py diff --git a/tests/rag/embeddings/test_factory_azure.py b/lib/crewai/tests/rag/embeddings/test_factory_azure.py similarity index 100% rename from tests/rag/embeddings/test_factory_azure.py rename to lib/crewai/tests/rag/embeddings/test_factory_azure.py diff --git a/tests/rag/qdrant/test_client.py b/lib/crewai/tests/rag/qdrant/test_client.py similarity index 99% rename from tests/rag/qdrant/test_client.py rename to lib/crewai/tests/rag/qdrant/test_client.py index 9984dce8a..03a4e62dc 100644 --- a/tests/rag/qdrant/test_client.py +++ b/lib/crewai/tests/rag/qdrant/test_client.py @@ -3,12 +3,11 @@ from unittest.mock import AsyncMock, Mock import pytest -from qdrant_client import AsyncQdrantClient -from qdrant_client import QdrantClient as SyncQdrantClient - from crewai.rag.core.exceptions import ClientMethodMismatchError from crewai.rag.qdrant.client import QdrantClient from crewai.rag.types import BaseRecord +from qdrant_client import AsyncQdrantClient +from qdrant_client import QdrantClient as SyncQdrantClient @pytest.fixture diff --git a/tests/rag/test_error_handling.py b/lib/crewai/tests/rag/test_error_handling.py similarity index 99% rename from tests/rag/test_error_handling.py rename to lib/crewai/tests/rag/test_error_handling.py index 0cf033c52..1bbab292c 100644 --- a/tests/rag/test_error_handling.py +++ b/lib/crewai/tests/rag/test_error_handling.py @@ -3,7 +3,6 @@ from unittest.mock import MagicMock, patch import pytest - from crewai.knowledge.storage.knowledge_storage import ( # type: ignore[import-untyped] KnowledgeStorage, ) diff --git a/lib/crewai/tests/security/__init__.py b/lib/crewai/tests/security/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/security/test_deterministic_fingerprints.py b/lib/crewai/tests/security/test_deterministic_fingerprints.py similarity index 100% rename from tests/security/test_deterministic_fingerprints.py rename to lib/crewai/tests/security/test_deterministic_fingerprints.py diff --git a/tests/security/test_examples.py b/lib/crewai/tests/security/test_examples.py similarity index 97% rename from tests/security/test_examples.py rename to lib/crewai/tests/security/test_examples.py index 895b19900..0a6dbe59b 100644 --- a/tests/security/test_examples.py +++ b/lib/crewai/tests/security/test_examples.py @@ -1,7 +1,5 @@ """Test for the examples in the fingerprinting documentation.""" -import pytest - from crewai import Agent, Crew, Task from crewai.security import Fingerprint, SecurityConfig @@ -74,9 +72,9 @@ def test_accessing_fingerprints_example(): crew_fingerprint.uuid_str, task_fingerprint.uuid_str, ] - assert len(fingerprints) == len( - set(fingerprints) - ), "All fingerprints should be unique" + assert len(fingerprints) == len(set(fingerprints)), ( + "All fingerprints should be unique" + ) def test_fingerprint_metadata_example(): @@ -169,9 +167,9 @@ def test_complete_workflow_example(): writing_task.fingerprint.uuid_str, content_crew.fingerprint.uuid_str, ] - assert len(fingerprints) == len( - set(fingerprints) - ), "All fingerprints should be unique" + assert len(fingerprints) == len(set(fingerprints)), ( + "All fingerprints should be unique" + ) def test_security_preservation_during_copy(): diff --git a/tests/security/test_fingerprint.py b/lib/crewai/tests/security/test_fingerprint.py similarity index 96% rename from tests/security/test_fingerprint.py rename to lib/crewai/tests/security/test_fingerprint.py index 8444556bf..1ce7e8370 100644 --- a/tests/security/test_fingerprint.py +++ b/lib/crewai/tests/security/test_fingerprint.py @@ -5,8 +5,6 @@ import uuid from datetime import datetime, timedelta import pytest -from pydantic import ValidationError - from crewai.security import Fingerprint @@ -170,7 +168,7 @@ def test_fingerprint_from_dict(): fingerprint_dict = { "uuid_str": uuid_str, "created_at": created_at_iso, - "metadata": metadata + "metadata": metadata, } fingerprint = Fingerprint.from_dict(fingerprint_dict) @@ -207,11 +205,7 @@ def test_invalid_uuid_str(): uuid_str = "not-a-valid-uuid" created_at = datetime.now().isoformat() - fingerprint_dict = { - "uuid_str": uuid_str, - "created_at": created_at, - "metadata": {} - } + fingerprint_dict = {"uuid_str": uuid_str, "created_at": created_at, "metadata": {}} # The Fingerprint.from_dict method accepts even invalid UUIDs # This seems to be the current behavior @@ -243,7 +237,7 @@ def test_fingerprint_metadata_mutation(): expected_metadata = { "version": "1.0", "status": "published", - "author": "Test Author" + "author": "Test Author", } assert fingerprint.metadata == expected_metadata @@ -260,4 +254,4 @@ def test_fingerprint_metadata_mutation(): # Ensure immutable fields remain unchanged assert fingerprint.uuid_str == uuid_str - assert fingerprint.created_at == created_at \ No newline at end of file + assert fingerprint.created_at == created_at diff --git a/tests/security/test_integration.py b/lib/crewai/tests/security/test_integration.py similarity index 71% rename from tests/security/test_integration.py rename to lib/crewai/tests/security/test_integration.py index a4dbc0c23..8dd0617fb 100644 --- a/tests/security/test_integration.py +++ b/lib/crewai/tests/security/test_integration.py @@ -1,7 +1,5 @@ """Test integration of fingerprinting with Agent, Crew, and Task classes.""" -import pytest - from crewai import Agent, Crew, Task from crewai.security import Fingerprint, SecurityConfig @@ -15,7 +13,7 @@ def test_agent_with_security_config(): role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting", - security_config=security_config + security_config=security_config, ) assert agent.security_config is not None @@ -28,9 +26,7 @@ def test_agent_fingerprint_property(): """Test the fingerprint property on Agent.""" # Create agent without security_config agent = Agent( - role="Tester", - goal="Test fingerprinting", - backstory="Testing fingerprinting" + role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting" ) # Fingerprint should be automatically generated @@ -45,21 +41,14 @@ def test_crew_with_security_config(): security_config = SecurityConfig() agent1 = Agent( - role="Tester1", - goal="Test fingerprinting", - backstory="Testing fingerprinting" + role="Tester1", goal="Test fingerprinting", backstory="Testing fingerprinting" ) agent2 = Agent( - role="Tester2", - goal="Test fingerprinting", - backstory="Testing fingerprinting" + role="Tester2", goal="Test fingerprinting", backstory="Testing fingerprinting" ) - crew = Crew( - agents=[agent1, agent2], - security_config=security_config - ) + crew = Crew(agents=[agent1, agent2], security_config=security_config) assert crew.security_config is not None assert crew.security_config == security_config @@ -71,15 +60,11 @@ def test_crew_fingerprint_property(): """Test the fingerprint property on Crew.""" # Create crew without security_config agent1 = Agent( - role="Tester1", - goal="Test fingerprinting", - backstory="Testing fingerprinting" + role="Tester1", goal="Test fingerprinting", backstory="Testing fingerprinting" ) agent2 = Agent( - role="Tester2", - goal="Test fingerprinting", - backstory="Testing fingerprinting" + role="Tester2", goal="Test fingerprinting", backstory="Testing fingerprinting" ) crew = Crew(agents=[agent1, agent2]) @@ -96,16 +81,14 @@ def test_task_with_security_config(): security_config = SecurityConfig() agent = Agent( - role="Tester", - goal="Test fingerprinting", - backstory="Testing fingerprinting" + role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting" ) task = Task( description="Test task", expected_output="Testing output", agent=agent, - security_config=security_config + security_config=security_config, ) assert task.security_config is not None @@ -118,16 +101,10 @@ def test_task_fingerprint_property(): """Test the fingerprint property on Task.""" # Create task without security_config agent = Agent( - role="Tester", - goal="Test fingerprinting", - backstory="Testing fingerprinting" + role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting" ) - task = Task( - description="Test task", - expected_output="Testing output", - agent=agent - ) + task = Task(description="Test task", expected_output="Testing output", agent=agent) # Fingerprint should be automatically generated assert task.fingerprint is not None @@ -139,33 +116,20 @@ def test_end_to_end_fingerprinting(): """Test end-to-end fingerprinting across Agent, Crew, and Task.""" # Create components with auto-generated fingerprints agent1 = Agent( - role="Researcher", - goal="Research information", - backstory="Expert researcher" + role="Researcher", goal="Research information", backstory="Expert researcher" ) - agent2 = Agent( - role="Writer", - goal="Write content", - backstory="Expert writer" - ) + agent2 = Agent(role="Writer", goal="Write content", backstory="Expert writer") task1 = Task( - description="Research topic", - expected_output="Research findings", - agent=agent1 + description="Research topic", expected_output="Research findings", agent=agent1 ) task2 = Task( - description="Write article", - expected_output="Written article", - agent=agent2 + description="Write article", expected_output="Written article", agent=agent2 ) - crew = Crew( - agents=[agent1, agent2], - tasks=[task1, task2] - ) + crew = Crew(agents=[agent1, agent2], tasks=[task1, task2]) # Verify all fingerprints were automatically generated assert agent1.fingerprint is not None @@ -180,18 +144,18 @@ def test_end_to_end_fingerprinting(): agent2.fingerprint.uuid_str, task1.fingerprint.uuid_str, task2.fingerprint.uuid_str, - crew.fingerprint.uuid_str + crew.fingerprint.uuid_str, ] - assert len(fingerprints) == len(set(fingerprints)), "All fingerprints should be unique" + assert len(fingerprints) == len(set(fingerprints)), ( + "All fingerprints should be unique" + ) def test_fingerprint_persistence(): """Test that fingerprints persist and don't change.""" # Create an agent and check its fingerprint agent = Agent( - role="Tester", - goal="Test fingerprinting", - backstory="Testing fingerprinting" + role="Tester", goal="Test fingerprinting", backstory="Testing fingerprinting" ) # Get initial fingerprint @@ -201,11 +165,7 @@ def test_fingerprint_persistence(): assert agent.fingerprint.uuid_str == initial_fingerprint # Create a task with the agent - task = Task( - description="Test task", - expected_output="Testing output", - agent=agent - ) + task = Task(description="Test task", expected_output="Testing output", agent=agent) # Check that task has its own unique fingerprint assert task.fingerprint is not None @@ -223,27 +183,25 @@ def test_shared_security_config_fingerprints(): role="Researcher", goal="Research information", backstory="Expert researcher", - security_config=shared_security_config + security_config=shared_security_config, ) agent2 = Agent( role="Writer", goal="Write content", backstory="Expert writer", - security_config=shared_security_config + security_config=shared_security_config, ) task = Task( description="Write article", expected_output="Written article", agent=agent1, - security_config=shared_security_config + security_config=shared_security_config, ) crew = Crew( - agents=[agent1, agent2], - tasks=[task], - security_config=shared_security_config + agents=[agent1, agent2], tasks=[task], security_config=shared_security_config ) # Verify all components have the same fingerprint UUID @@ -256,4 +214,4 @@ def test_shared_security_config_fingerprints(): assert agent1.fingerprint is shared_security_config.fingerprint assert agent2.fingerprint is shared_security_config.fingerprint assert task.fingerprint is shared_security_config.fingerprint - assert crew.fingerprint is shared_security_config.fingerprint \ No newline at end of file + assert crew.fingerprint is shared_security_config.fingerprint diff --git a/tests/security/test_security_config.py b/lib/crewai/tests/security/test_security_config.py similarity index 97% rename from tests/security/test_security_config.py rename to lib/crewai/tests/security/test_security_config.py index 39f43218b..70885a6bb 100644 --- a/tests/security/test_security_config.py +++ b/lib/crewai/tests/security/test_security_config.py @@ -63,13 +63,11 @@ def test_security_config_from_dict(): fingerprint_dict = { "uuid_str": "b723c6ff-95de-5e87-860b-467b72282bd8", "created_at": datetime.now().isoformat(), - "metadata": {"version": "1.0"} + "metadata": {"version": "1.0"}, } # Create a config dict with just the fingerprint - config_dict = { - "fingerprint": fingerprint_dict - } + config_dict = {"fingerprint": fingerprint_dict} # Create config manually since from_dict has a specific implementation config = SecurityConfig() @@ -115,4 +113,4 @@ def test_security_config_json_serialization(): new_config.fingerprint = new_fingerprint # Check the new config has the same fingerprint metadata - assert new_config.fingerprint.metadata == {"version": "1.0"} \ No newline at end of file + assert new_config.fingerprint.metadata == {"version": "1.0"} diff --git a/tests/storage/__init__.py b/lib/crewai/tests/storage/__init__.py similarity index 100% rename from tests/storage/__init__.py rename to lib/crewai/tests/storage/__init__.py diff --git a/tests/storage/test_mem0_storage.py b/lib/crewai/tests/storage/test_mem0_storage.py similarity index 99% rename from tests/storage/test_mem0_storage.py rename to lib/crewai/tests/storage/test_mem0_storage.py index 11cfddb3a..f219f0b45 100644 --- a/tests/storage/test_mem0_storage.py +++ b/lib/crewai/tests/storage/test_mem0_storage.py @@ -1,9 +1,8 @@ from unittest.mock import MagicMock, patch import pytest -from mem0 import Memory, MemoryClient - from crewai.memory.storage.mem0_storage import Mem0Storage +from mem0 import Memory, MemoryClient # Define the class (if not already defined) diff --git a/lib/crewai/tests/telemetry/__init__.py b/lib/crewai/tests/telemetry/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/telemetry/test_telemetry.py b/lib/crewai/tests/telemetry/test_telemetry.py similarity index 96% rename from tests/telemetry/test_telemetry.py rename to lib/crewai/tests/telemetry/test_telemetry.py index f7c19008a..2429a4ade 100644 --- a/tests/telemetry/test_telemetry.py +++ b/lib/crewai/tests/telemetry/test_telemetry.py @@ -3,10 +3,8 @@ import threading from unittest.mock import patch import pytest - from crewai import Agent, Crew, Task from crewai.telemetry import Telemetry - from opentelemetry import trace @@ -95,9 +93,9 @@ def test_telemetry_singleton_pattern(): assert telemetry1 is telemetry2 - setattr(telemetry1, "test_attribute", "test_value") + telemetry1.test_attribute = "test_value" assert hasattr(telemetry2, "test_attribute") - assert getattr(telemetry2, "test_attribute") == "test_value" + assert telemetry2.test_attribute == "test_value" import threading diff --git a/tests/telemetry/test_telemetry_disable.py b/lib/crewai/tests/telemetry/test_telemetry_disable.py similarity index 98% rename from tests/telemetry/test_telemetry_disable.py rename to lib/crewai/tests/telemetry/test_telemetry_disable.py index 2168bc8c2..5e4e9d3c1 100644 --- a/tests/telemetry/test_telemetry_disable.py +++ b/lib/crewai/tests/telemetry/test_telemetry_disable.py @@ -1,8 +1,7 @@ import os -from unittest.mock import patch, MagicMock +from unittest.mock import MagicMock, patch import pytest - from crewai.telemetry import Telemetry diff --git a/tests/test_context.py b/lib/crewai/tests/test_context.py similarity index 99% rename from tests/test_context.py rename to lib/crewai/tests/test_context.py index ea4ae3f98..a1255a162 100644 --- a/tests/test_context.py +++ b/lib/crewai/tests/test_context.py @@ -1,14 +1,14 @@ # ruff: noqa: S105 import os -import pytest from unittest.mock import patch +import pytest from crewai.context import ( - set_platform_integration_token, + _platform_integration_token, get_platform_integration_token, platform_context, - _platform_integration_token, + set_platform_integration_token, ) @@ -187,7 +187,7 @@ class TestPlatformIntegrationToken: assert _platform_integration_token.get() is None assert get_platform_integration_token() == "" - @patch('crewai.context.os.getenv') + @patch("crewai.context.os.getenv") def test_env_var_access_error_handling(self, mock_getenv): mock_getenv.side_effect = OSError("Environment access error") @@ -203,7 +203,6 @@ class TestPlatformIntegrationToken: set_platform_integration_token(test_token) assert get_platform_integration_token() == test_token - def test_context_manager_return_value(self): """Test that platform_context can be used in with statement with return value.""" test_token = "return-value-token" diff --git a/tests/test_crew.py b/lib/crewai/tests/test_crew.py similarity index 97% rename from tests/test_crew.py rename to lib/crewai/tests/test_crew.py index 0a9b94695..73ef7b54d 100644 --- a/tests/test_crew.py +++ b/lib/crewai/tests/test_crew.py @@ -1,17 +1,15 @@ """Test Agent creation and execution basic functionality.""" import json +import threading from collections import defaultdict from concurrent.futures import Future from hashlib import md5 +import re from unittest import mock from unittest.mock import ANY, MagicMock, patch -import pydantic_core -import pytest - from crewai.agent import Agent -from crewai.agents import CacheHandler from crewai.crew import Crew from crewai.crews.crew_output import CrewOutput from crewai.events.event_bus import crewai_event_bus @@ -31,7 +29,6 @@ from crewai.events.types.memory_events import ( MemorySaveFailedEvent, MemorySaveStartedEvent, ) -from crewai.flow import Flow, start from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource from crewai.llm import LLM @@ -47,6 +44,11 @@ from crewai.tasks.task_output import TaskOutput from crewai.types.usage_metrics import UsageMetrics from crewai.utilities.rpm_controller import RPMController from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler +import pydantic_core +import pytest + +from crewai.agents import CacheHandler +from crewai.flow import Flow, start @pytest.fixture @@ -200,7 +202,9 @@ def test_async_task_cannot_include_sequential_async_tasks_in_context( # This should raise an error because task2 is async and has task1 in its context without a sync task in between with pytest.raises( ValueError, - match="Task 'Task 2' is asynchronous and cannot include other sequential asynchronous tasks in its context.", + match=re.escape( + "Task 'Task 2' is asynchronous and cannot include other sequential asynchronous tasks in its context." + ), ): Crew(tasks=[task1, task2, task3, task4, task5], agents=[researcher, writer]) @@ -238,7 +242,9 @@ def test_context_no_future_tasks(researcher, writer): # This should raise an error because task1 has a context dependency on a future task (task4) with pytest.raises( ValueError, - match="Task 'Task 1' has a context dependency on a future task 'Task 4', which is not allowed.", + match=re.escape( + "Task 'Task 1' has a context dependency on a future task 'Task 4', which is not allowed." + ), ): Crew(tasks=[task1, task2, task3, task4], agents=[researcher, writer]) @@ -1010,7 +1016,7 @@ def test_crew_kickoff_streaming_usage_metrics(): role="{topic} Researcher", goal="Express hot takes on {topic}.", backstory="You have a lot of experience with {topic}.", - llm=LLM(model="gpt-4o", stream=True), + llm=LLM(model="gpt-4o", stream=True, is_litellm=True), max_iter=3, ) @@ -1778,7 +1784,7 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher): agent=researcher, # *regular* agent ) - # ── 2. Stub out each agent's _token_process.get_summary() ─────────────────── + # ── 2. Stub out each agent's token usage methods ─────────────────── researcher_metrics = UsageMetrics( total_tokens=120, prompt_tokens=80, completion_tokens=40, successful_requests=2 ) @@ -1786,10 +1792,10 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher): total_tokens=30, prompt_tokens=20, completion_tokens=10, successful_requests=1 ) - # Replace the internal _token_process objects with simple mocks - researcher._token_process = MagicMock( - get_summary=MagicMock(return_value=researcher_metrics) - ) + # Mock the LLM's get_token_usage_summary method for the researcher + researcher.llm.get_token_usage_summary = MagicMock(return_value=researcher_metrics) + + # Mock the manager's _token_process since it uses the fallback path manager._token_process = MagicMock( get_summary=MagicMock(return_value=manager_metrics) ) @@ -2471,62 +2477,63 @@ def test_using_contextual_memory(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_memory_events_are_emitted(): events = defaultdict(list) + event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(MemorySaveStartedEvent) + def handle_memory_save_started(source, event): + events["MemorySaveStartedEvent"].append(event) - @crewai_event_bus.on(MemorySaveStartedEvent) - def handle_memory_save_started(source, event): - events["MemorySaveStartedEvent"].append(event) + @crewai_event_bus.on(MemorySaveCompletedEvent) + def handle_memory_save_completed(source, event): + events["MemorySaveCompletedEvent"].append(event) - @crewai_event_bus.on(MemorySaveCompletedEvent) - def handle_memory_save_completed(source, event): - events["MemorySaveCompletedEvent"].append(event) + @crewai_event_bus.on(MemorySaveFailedEvent) + def handle_memory_save_failed(source, event): + events["MemorySaveFailedEvent"].append(event) - @crewai_event_bus.on(MemorySaveFailedEvent) - def handle_memory_save_failed(source, event): - events["MemorySaveFailedEvent"].append(event) + @crewai_event_bus.on(MemoryQueryStartedEvent) + def handle_memory_query_started(source, event): + events["MemoryQueryStartedEvent"].append(event) - @crewai_event_bus.on(MemoryQueryStartedEvent) - def handle_memory_query_started(source, event): - events["MemoryQueryStartedEvent"].append(event) + @crewai_event_bus.on(MemoryQueryCompletedEvent) + def handle_memory_query_completed(source, event): + events["MemoryQueryCompletedEvent"].append(event) - @crewai_event_bus.on(MemoryQueryCompletedEvent) - def handle_memory_query_completed(source, event): - events["MemoryQueryCompletedEvent"].append(event) + @crewai_event_bus.on(MemoryQueryFailedEvent) + def handle_memory_query_failed(source, event): + events["MemoryQueryFailedEvent"].append(event) - @crewai_event_bus.on(MemoryQueryFailedEvent) - def handle_memory_query_failed(source, event): - events["MemoryQueryFailedEvent"].append(event) + @crewai_event_bus.on(MemoryRetrievalStartedEvent) + def handle_memory_retrieval_started(source, event): + events["MemoryRetrievalStartedEvent"].append(event) - @crewai_event_bus.on(MemoryRetrievalStartedEvent) - def handle_memory_retrieval_started(source, event): - events["MemoryRetrievalStartedEvent"].append(event) + @crewai_event_bus.on(MemoryRetrievalCompletedEvent) + def handle_memory_retrieval_completed(source, event): + events["MemoryRetrievalCompletedEvent"].append(event) + event_received.set() - @crewai_event_bus.on(MemoryRetrievalCompletedEvent) - def handle_memory_retrieval_completed(source, event): - events["MemoryRetrievalCompletedEvent"].append(event) + math_researcher = Agent( + role="Researcher", + goal="You research about math.", + backstory="You're an expert in research and you love to learn new things.", + allow_delegation=False, + ) - math_researcher = Agent( - role="Researcher", - goal="You research about math.", - backstory="You're an expert in research and you love to learn new things.", - allow_delegation=False, - ) + task1 = Task( + description="Research a topic to teach a kid aged 6 about math.", + expected_output="A topic, explanation, angle, and examples.", + agent=math_researcher, + ) - task1 = Task( - description="Research a topic to teach a kid aged 6 about math.", - expected_output="A topic, explanation, angle, and examples.", - agent=math_researcher, - ) + crew = Crew( + agents=[math_researcher], + tasks=[task1], + memory=True, + ) - crew = Crew( - agents=[math_researcher], - tasks=[task1], - memory=True, - ) - - crew.kickoff() + crew.kickoff() + assert event_received.wait(timeout=5), "Timeout waiting for memory events" assert len(events["MemorySaveStartedEvent"]) == 3 assert len(events["MemorySaveCompletedEvent"]) == 3 assert len(events["MemorySaveFailedEvent"]) == 0 @@ -2902,19 +2909,29 @@ def test_crew_train_success( copy_mock.return_value = crew received_events = [] + lock = threading.Lock() + all_events_received = threading.Event() @crewai_event_bus.on(CrewTrainStartedEvent) def on_crew_train_started(source, event: CrewTrainStartedEvent): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == 2: + all_events_received.set() @crewai_event_bus.on(CrewTrainCompletedEvent) def on_crew_train_completed(source, event: CrewTrainCompletedEvent): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == 2: + all_events_received.set() crew.train( n_iterations=2, inputs={"topic": "AI"}, filename="trained_agents_data.pkl" ) + assert all_events_received.wait(timeout=5), "Timeout waiting for all train events" + # Ensure kickoff is called on the copied crew kickoff_mock.assert_has_calls( [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})] @@ -3339,7 +3356,9 @@ def test_replay_with_invalid_task_id(): ): with pytest.raises( ValueError, - match="Task with id bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d not found in the crew's tasks.", + match=re.escape( + "Task with id bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d not found in the crew's tasks." + ), ): crew.replay("bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d") @@ -3719,17 +3738,27 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator, research llm_instance = LLM("gpt-4o-mini") received_events = [] + lock = threading.Lock() + all_events_received = threading.Event() @crewai_event_bus.on(CrewTestStartedEvent) def on_crew_test_started(source, event: CrewTestStartedEvent): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == 2: + all_events_received.set() @crewai_event_bus.on(CrewTestCompletedEvent) def on_crew_test_completed(source, event: CrewTestCompletedEvent): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == 2: + all_events_received.set() crew.test(n_iterations, llm_instance, inputs={"topic": "AI"}) + assert all_events_received.wait(timeout=5), "Timeout waiting for all test events" + # Ensure kickoff is called on the copied crew kickoff_mock.assert_has_calls( [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})] diff --git a/tests/test_crew_thread_safety.py b/lib/crewai/tests/test_crew_thread_safety.py similarity index 87% rename from tests/test_crew_thread_safety.py rename to lib/crewai/tests/test_crew_thread_safety.py index 145a0405c..d7d612f2a 100644 --- a/tests/test_crew_thread_safety.py +++ b/lib/crewai/tests/test_crew_thread_safety.py @@ -1,11 +1,10 @@ import asyncio import threading from concurrent.futures import ThreadPoolExecutor -from typing import Dict, Any, Callable +from typing import Any, Callable, Dict from unittest.mock import patch import pytest - from crewai import Agent, Crew, Task from crewai.utilities.crew.crew_context import get_crew_context @@ -105,28 +104,28 @@ class TestCrewThreadSafety: before_ctx = next( ctx for ctx in result["contexts"] if ctx["stage"] == "before_kickoff" ) - assert ( - before_ctx["crew_id"] is None - ), f"Context should be None before kickoff for {result['crew_id']}" + assert before_ctx["crew_id"] is None, ( + f"Context should be None before kickoff for {result['crew_id']}" + ) task_ctx = next( ctx for ctx in result["contexts"] if ctx["stage"] == "task_callback" ) - assert ( - task_ctx["crew_id"] == crew_uuid - ), f"Context mismatch during task for {result['crew_id']}" + assert task_ctx["crew_id"] == crew_uuid, ( + f"Context mismatch during task for {result['crew_id']}" + ) after_ctx = next( ctx for ctx in result["contexts"] if ctx["stage"] == "after_kickoff" ) - assert ( - after_ctx["crew_id"] is None - ), f"Context should be None after kickoff for {result['crew_id']}" + assert after_ctx["crew_id"] is None, ( + f"Context should be None after kickoff for {result['crew_id']}" + ) thread_name = before_ctx["thread"] - assert ( - "ThreadPoolExecutor" in thread_name - ), f"Should run in thread pool for {result['crew_id']}" + assert "ThreadPoolExecutor" in thread_name, ( + f"Should run in thread pool for {result['crew_id']}" + ) @pytest.mark.asyncio @patch("crewai.Agent.execute_task") @@ -162,12 +161,12 @@ class TestCrewThreadSafety: crew_uuid = result["crew_uuid"] task_ctx = result["task_context"]["context"] - assert ( - task_ctx is not None - ), f"Context should exist during task for {result['crew_id']}" - assert ( - task_ctx["crew_id"] == crew_uuid - ), f"Context mismatch for {result['crew_id']}" + assert task_ctx is not None, ( + f"Context should exist during task for {result['crew_id']}" + ) + assert task_ctx["crew_id"] == crew_uuid, ( + f"Context mismatch for {result['crew_id']}" + ) @patch("crewai.Agent.execute_task") def test_concurrent_kickoff_for_each(self, mock_execute_task, crew_factory): @@ -193,9 +192,9 @@ class TestCrewThreadSafety: assert len(contexts_captured) == len(inputs) context_ids = [ctx["context_id"] for ctx in contexts_captured] - assert len(set(context_ids)) == len( - inputs - ), "Each execution should have unique context" + assert len(set(context_ids)) == len(inputs), ( + "Each execution should have unique context" + ) @patch("crewai.Agent.execute_task") def test_no_context_leakage_between_crews(self, mock_execute_task, crew_factory): diff --git a/tests/test_custom_llm.py b/lib/crewai/tests/test_custom_llm.py similarity index 89% rename from tests/test_custom_llm.py rename to lib/crewai/tests/test_custom_llm.py index 85a4b2e64..441ee0e54 100644 --- a/tests/test_custom_llm.py +++ b/lib/crewai/tests/test_custom_llm.py @@ -1,7 +1,6 @@ from typing import Any, Dict, List, Optional, Union import pytest - from crewai import Agent, Crew, Process, Task from crewai.llms.base_llm import BaseLLM from crewai.utilities.llm_utils import create_llm @@ -282,35 +281,32 @@ class TimeoutHandlingLLM(BaseLLM): ) # Otherwise, continue to the next attempt (simulating backoff) continue - else: - # Success on first attempt - return "First attempt response" - else: - # This is a retry attempt (attempt > 0) - # Always record retry attempts - self.calls.append( - { - "retry_attempt": attempt, - "messages": messages, - "tools": tools, - "callbacks": callbacks, - "available_functions": available_functions, - } - ) + # Success on first attempt + return "First attempt response" + # This is a retry attempt (attempt > 0) + # Always record retry attempts + self.calls.append( + { + "retry_attempt": attempt, + "messages": messages, + "tools": tools, + "callbacks": callbacks, + "available_functions": available_functions, + } + ) - # Simulate a failure if fail_count > 0 - if self.fail_count > 0: - self.fail_count -= 1 - # If we've used all retries, raise an error - if attempt == self.max_retries - 1: - raise TimeoutError( - f"LLM request failed after {self.max_retries} attempts" - ) - # Otherwise, continue to the next attempt (simulating backoff) - continue - else: - # Success on retry - return "Response after retry" + # Simulate a failure if fail_count > 0 + if self.fail_count > 0: + self.fail_count -= 1 + # If we've used all retries, raise an error + if attempt == self.max_retries - 1: + raise TimeoutError( + f"LLM request failed after {self.max_retries} attempts" + ) + # Otherwise, continue to the next attempt (simulating backoff) + continue + # Success on retry + return "Response after retry" def supports_function_calling(self) -> bool: """Return True to indicate that function calling is supported. diff --git a/tests/test_flow.py b/lib/crewai/tests/test_flow.py similarity index 90% rename from tests/test_flow.py rename to lib/crewai/tests/test_flow.py index f060a7a19..8142b6491 100644 --- a/tests/test_flow.py +++ b/lib/crewai/tests/test_flow.py @@ -1,6 +1,7 @@ """Test Flow creation and execution basic functionality.""" import asyncio +import threading from datetime import datetime import pytest @@ -440,20 +441,42 @@ def test_unstructured_flow_event_emission(): flow = PoemFlow() received_events = [] + lock = threading.Lock() + all_events_received = threading.Event() + expected_event_count = ( + 7 # 1 FlowStarted + 5 MethodExecutionStarted + 1 FlowFinished + ) @crewai_event_bus.on(FlowStartedEvent) def handle_flow_start(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() @crewai_event_bus.on(MethodExecutionStartedEvent) def handle_method_start(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() @crewai_event_bus.on(FlowFinishedEvent) def handle_flow_end(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() flow.kickoff(inputs={"separator": ", "}) + + assert all_events_received.wait(timeout=5), "Timeout waiting for all flow events" + + # Sort events by timestamp to ensure deterministic order + # (async handlers may append out of order) + with lock: + received_events.sort(key=lambda e: e.timestamp) + assert isinstance(received_events[0], FlowStartedEvent) assert received_events[0].flow_name == "PoemFlow" assert received_events[0].inputs == {"separator": ", "} @@ -643,28 +666,48 @@ def test_structured_flow_event_emission(): return f"Welcome, {self.state.name}!" flow = OnboardingFlow() - flow.kickoff(inputs={"name": "Anakin"}) received_events = [] + lock = threading.Lock() + all_events_received = threading.Event() + expected_event_count = 6 # 1 FlowStarted + 2 MethodExecutionStarted + 2 MethodExecutionFinished + 1 FlowFinished @crewai_event_bus.on(FlowStartedEvent) def handle_flow_start(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() @crewai_event_bus.on(MethodExecutionStartedEvent) def handle_method_start(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() @crewai_event_bus.on(MethodExecutionFinishedEvent) def handle_method_end(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() @crewai_event_bus.on(FlowFinishedEvent) def handle_flow_end(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() flow.kickoff(inputs={"name": "Anakin"}) + assert all_events_received.wait(timeout=5), "Timeout waiting for all flow events" + + # Sort events by timestamp to ensure deterministic order + with lock: + received_events.sort(key=lambda e: e.timestamp) + assert isinstance(received_events[0], FlowStartedEvent) assert received_events[0].flow_name == "OnboardingFlow" assert received_events[0].inputs == {"name": "Anakin"} @@ -712,25 +755,46 @@ def test_stateless_flow_event_emission(): flow = StatelessFlow() received_events = [] + lock = threading.Lock() + all_events_received = threading.Event() + expected_event_count = 6 # 1 FlowStarted + 2 MethodExecutionStarted + 2 MethodExecutionFinished + 1 FlowFinished @crewai_event_bus.on(FlowStartedEvent) def handle_flow_start(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() @crewai_event_bus.on(MethodExecutionStartedEvent) def handle_method_start(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() @crewai_event_bus.on(MethodExecutionFinishedEvent) def handle_method_end(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() @crewai_event_bus.on(FlowFinishedEvent) def handle_flow_end(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) == expected_event_count: + all_events_received.set() flow.kickoff() + assert all_events_received.wait(timeout=5), "Timeout waiting for all flow events" + + # Sort events by timestamp to ensure deterministic order + with lock: + received_events.sort(key=lambda e: e.timestamp) + assert isinstance(received_events[0], FlowStartedEvent) assert received_events[0].flow_name == "StatelessFlow" assert received_events[0].inputs is None @@ -770,13 +834,16 @@ def test_flow_plotting(): flow = StatelessFlow() flow.kickoff() received_events = [] + event_received = threading.Event() @crewai_event_bus.on(FlowPlotEvent) def handle_flow_plot(source, event): received_events.append(event) + event_received.set() flow.plot("test_flow") + assert event_received.wait(timeout=5), "Timeout waiting for plot event" assert len(received_events) == 1 assert isinstance(received_events[0], FlowPlotEvent) assert received_events[0].flow_name == "StatelessFlow" diff --git a/tests/test_flow_default_override.py b/lib/crewai/tests/test_flow_default_override.py similarity index 100% rename from tests/test_flow_default_override.py rename to lib/crewai/tests/test_flow_default_override.py diff --git a/tests/test_flow_human_input_integration.py b/lib/crewai/tests/test_flow_human_input_integration.py similarity index 99% rename from tests/test_flow_human_input_integration.py rename to lib/crewai/tests/test_flow_human_input_integration.py index 398840ad3..63f6308ed 100644 --- a/tests/test_flow_human_input_integration.py +++ b/lib/crewai/tests/test_flow_human_input_integration.py @@ -1,5 +1,6 @@ +from unittest.mock import MagicMock, patch + import pytest -from unittest.mock import patch, MagicMock from crewai.events.event_listener import event_listener diff --git a/tests/test_flow_persistence.py b/lib/crewai/tests/test_flow_persistence.py similarity index 97% rename from tests/test_flow_persistence.py rename to lib/crewai/tests/test_flow_persistence.py index 667a1f058..53e059b52 100644 --- a/tests/test_flow_persistence.py +++ b/lib/crewai/tests/test_flow_persistence.py @@ -3,11 +3,10 @@ import os from typing import Dict, List -from pydantic import BaseModel - from crewai.flow.flow import Flow, FlowState, listen, start from crewai.flow.persistence import persist from crewai.flow.persistence.sqlite import SQLiteFlowPersistence +from pydantic import BaseModel class TestState(FlowState): @@ -209,7 +208,6 @@ def test_persist_decorator_verbose_logging(tmp_path, caplog): assert "Saving flow state" in caplog.text - def test_persistence_with_base_model(tmp_path): db_path = os.path.join(tmp_path, "test_flows.db") persistence = SQLiteFlowPersistence(db_path) @@ -229,14 +227,16 @@ def test_persistence_with_base_model(tmp_path): @start() def init_step(self): - self.state.latest_message = Message(role="user", type="text", content="Hello, World!") + self.state.latest_message = Message( + role="user", type="text", content="Hello, World!" + ) self.state.history.append(self.state.latest_message) flow = BaseModelFlow(persistence=persistence) flow.kickoff() latest_message = flow.state.latest_message - message, = flow.state.history + (message,) = flow.state.history assert latest_message is not None assert latest_message.role == "user" diff --git a/tests/test_flow_resumability_regression.py b/lib/crewai/tests/test_flow_resumability_regression.py similarity index 99% rename from tests/test_flow_resumability_regression.py rename to lib/crewai/tests/test_flow_resumability_regression.py index 87f67173d..588cee8e7 100644 --- a/tests/test_flow_resumability_regression.py +++ b/lib/crewai/tests/test_flow_resumability_regression.py @@ -6,6 +6,7 @@ These tests ensure that: """ from typing import Dict + from crewai.flow.flow import Flow, listen, router, start from crewai.flow.persistence.sqlite import SQLiteFlowPersistence diff --git a/tests/test_hallucination_guardrail.py b/lib/crewai/tests/test_hallucination_guardrail.py similarity index 99% rename from tests/test_hallucination_guardrail.py rename to lib/crewai/tests/test_hallucination_guardrail.py index af08a3924..659931822 100644 --- a/tests/test_hallucination_guardrail.py +++ b/lib/crewai/tests/test_hallucination_guardrail.py @@ -1,7 +1,6 @@ from unittest.mock import Mock import pytest - from crewai.llm import LLM from crewai.tasks.hallucination_guardrail import HallucinationGuardrail from crewai.tasks.task_output import TaskOutput diff --git a/tests/test_imports.py b/lib/crewai/tests/test_imports.py similarity index 100% rename from tests/test_imports.py rename to lib/crewai/tests/test_imports.py diff --git a/tests/test_llm.py b/lib/crewai/tests/test_llm.py similarity index 87% rename from tests/test_llm.py rename to lib/crewai/tests/test_llm.py index 065687565..3555ee8c5 100644 --- a/tests/test_llm.py +++ b/lib/crewai/tests/test_llm.py @@ -3,9 +3,6 @@ import os from time import sleep from unittest.mock import MagicMock, patch -import pytest -from pydantic import BaseModel - from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess from crewai.events.event_types import ( LLMCallCompletedEvent, @@ -16,33 +13,31 @@ from crewai.events.event_types import ( ) from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM from crewai.utilities.token_counter_callback import TokenCalcHandler +from pydantic import BaseModel +import pytest # TODO: This test fails without print statement, which makes me think that something is happening asynchronously that we need to eventually fix and dive deeper into at a later date @pytest.mark.vcr(filter_headers=["authorization"]) def test_llm_callback_replacement(): - llm1 = LLM(model="gpt-4o-mini") - llm2 = LLM(model="gpt-4o-mini") + llm1 = LLM(model="gpt-4o-mini", is_litellm=True) + llm2 = LLM(model="gpt-4o-mini", is_litellm=True) calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess()) calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess()) - result1 = llm1.call( + llm1.call( messages=[{"role": "user", "content": "Hello, world!"}], callbacks=[calc_handler_1], ) - print("result1:", result1) usage_metrics_1 = calc_handler_1.token_cost_process.get_summary() - print("usage_metrics_1:", usage_metrics_1) - result2 = llm2.call( + llm2.call( messages=[{"role": "user", "content": "Hello, world from another agent!"}], callbacks=[calc_handler_2], ) sleep(5) - print("result2:", result2) usage_metrics_2 = calc_handler_2.token_cost_process.get_summary() - print("usage_metrics_2:", usage_metrics_2) # The first handler should not have been updated assert usage_metrics_1.successful_requests == 1 @@ -62,7 +57,7 @@ def test_llm_call_with_string_input(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_llm_call_with_string_input_and_callbacks(): - llm = LLM(model="gpt-4o-mini") + llm = LLM(model="gpt-4o-mini", is_litellm=True) calc_handler = TokenCalcHandler(token_cost_process=TokenProcess()) # Test the call method with a string input and callbacks @@ -128,7 +123,7 @@ def test_llm_call_with_tool_and_string_input(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_llm_call_with_tool_and_message_list(): - llm = LLM(model="gpt-4o-mini") + llm = LLM(model="gpt-4o-mini", is_litellm=True) def square_number(number: int) -> int: """Returns the square of a number.""" @@ -172,6 +167,7 @@ def test_llm_passes_additional_params(): model="gpt-4o-mini", vertex_credentials="test_credentials", vertex_project="test_project", + is_litellm=True, ) messages = [{"role": "user", "content": "Hello, world!"}] @@ -219,12 +215,12 @@ def test_get_custom_llm_provider_openrouter(): def test_get_custom_llm_provider_gemini(): - llm = LLM(model="gemini/gemini-1.5-pro") + llm = LLM(model="gemini/gemini-1.5-pro", is_litellm=True) assert llm._get_custom_llm_provider() == "gemini" def test_get_custom_llm_provider_openai(): - llm = LLM(model="gpt-4") + llm = LLM(model="gpt-4", is_litellm=True) assert llm._get_custom_llm_provider() is None @@ -247,7 +243,7 @@ def test_validate_call_params_not_supported(): # Patch supports_response_schema to simulate an unsupported model. with patch("crewai.llm.supports_response_schema", return_value=False): - llm = LLM(model="gemini/gemini-1.5-pro", response_format=DummyResponse) + llm = LLM(model="gemini/gemini-1.5-pro", response_format=DummyResponse, is_litellm=True) with pytest.raises(ValueError) as excinfo: llm._validate_call_params() assert "does not support response_format" in str(excinfo.value) @@ -255,7 +251,7 @@ def test_validate_call_params_not_supported(): def test_validate_call_params_no_response_format(): # When no response_format is provided, no validation error should occur. - llm = LLM(model="gemini/gemini-1.5-pro", response_format=None) + llm = LLM(model="gemini/gemini-1.5-pro", response_format=None, is_litellm=True) llm._validate_call_params() @@ -271,7 +267,8 @@ def test_validate_call_params_no_response_format(): ], ) def test_gemini_models(model): - llm = LLM(model=model) + # Use LiteLLM for VCR compatibility (VCR can intercept HTTP calls but not native SDK calls) + llm = LLM(model=model, is_litellm=True) result = llm.call("What is the capital of France?") assert isinstance(result, str) assert "Paris" in result @@ -285,7 +282,8 @@ def test_gemini_models(model): ], ) def test_gemma3(model): - llm = LLM(model=model) + # Use LiteLLM for VCR compatibility (VCR can intercept HTTP calls but not native SDK calls) + llm = LLM(model=model, is_litellm=True) result = llm.call("What is the capital of France?") assert isinstance(result, str) assert "Paris" in result @@ -376,13 +374,12 @@ def get_weather_tool_schema(): def test_context_window_exceeded_error_handling(): """Test that litellm.ContextWindowExceededError is converted to LLMContextLengthExceededError.""" - from litellm.exceptions import ContextWindowExceededError - from crewai.utilities.exceptions.context_window_exceeding_exception import ( LLMContextLengthExceededError, ) + from litellm.exceptions import ContextWindowExceededError - llm = LLM(model="gpt-4") + llm = LLM(model="gpt-4", is_litellm=True) # Test non-streaming response with patch("litellm.completion") as mock_completion: @@ -399,7 +396,7 @@ def test_context_window_exceeded_error_handling(): assert "8192 tokens" in str(excinfo.value) # Test streaming response - llm = LLM(model="gpt-4", stream=True) + llm = LLM(model="gpt-4", stream=True, is_litellm=True) with patch("litellm.completion") as mock_completion: mock_completion.side_effect = ContextWindowExceededError( "This model's maximum context length is 8192 tokens. However, your messages resulted in 10000 tokens.", @@ -418,7 +415,7 @@ def test_context_window_exceeded_error_handling(): @pytest.fixture def anthropic_llm(): """Fixture providing an Anthropic LLM instance.""" - return LLM(model="anthropic/claude-3-sonnet") + return LLM(model="anthropic/claude-3-sonnet", is_litellm=True) @pytest.fixture @@ -457,40 +454,25 @@ def test_anthropic_model_detection(): ("claude-instant", True), ("claude/v1", True), ("gpt-4", False), - ("", False), ("anthropomorphic", False), # Should not match partial words ] for model, expected in models: - llm = LLM(model=model) + llm = LLM(model=model, is_litellm=True) assert llm.is_anthropic == expected, f"Failed for model: {model}" def test_anthropic_message_formatting(anthropic_llm, system_message, user_message): """Test Anthropic message formatting with fixtures.""" # Test when first message is system - formatted = anthropic_llm._format_messages_for_provider([system_message]) - assert len(formatted) == 2 - assert formatted[0]["role"] == "user" - assert formatted[0]["content"] == "." - assert formatted[1] == system_message - # Test when first message is already user - formatted = anthropic_llm._format_messages_for_provider([user_message]) - assert len(formatted) == 1 - assert formatted[0] == user_message - - # Test with empty message list formatted = anthropic_llm._format_messages_for_provider([]) assert len(formatted) == 1 assert formatted[0]["role"] == "user" assert formatted[0]["content"] == "." - # Test with non-Anthropic model (should not modify messages) - non_anthropic_llm = LLM(model="gpt-4") - formatted = non_anthropic_llm._format_messages_for_provider([system_message]) - assert len(formatted) == 1 - assert formatted[0] == system_message + with pytest.raises(TypeError, match="Invalid message format"): + anthropic_llm._format_messages_for_provider([{"invalid": "message"}]) def test_deepseek_r1_with_open_router(): @@ -501,6 +483,7 @@ def test_deepseek_r1_with_open_router(): model="openrouter/deepseek/deepseek-r1", base_url="https://openrouter.ai/api/v1", api_key=os.getenv("OPEN_ROUTER_API_KEY"), + is_litellm=True, ) result = llm.call("What is the capital of France?") assert isinstance(result, str) @@ -570,7 +553,7 @@ def mock_emit() -> MagicMock: @pytest.mark.vcr(filter_headers=["authorization"]) def test_handle_streaming_tool_calls(get_weather_tool_schema, mock_emit): - llm = LLM(model="openai/gpt-4o", stream=True) + llm = LLM(model="openai/gpt-4o", stream=True, is_litellm=True) response = llm.call( messages=[ {"role": "user", "content": "What is the weather in New York?"}, @@ -601,7 +584,7 @@ def test_handle_streaming_tool_calls_with_error(get_weather_tool_schema, mock_em def get_weather_error(location): raise Exception("Error") - llm = LLM(model="openai/gpt-4o", stream=True) + llm = LLM(model="openai/gpt-4o", stream=True, is_litellm=True) response = llm.call( messages=[ {"role": "user", "content": "What is the weather in New York?"}, @@ -625,7 +608,7 @@ def test_handle_streaming_tool_calls_with_error(get_weather_tool_schema, mock_em def test_handle_streaming_tool_calls_no_available_functions( get_weather_tool_schema, mock_emit ): - llm = LLM(model="openai/gpt-4o", stream=True) + llm = LLM(model="openai/gpt-4o", stream=True, is_litellm=True) response = llm.call( messages=[ {"role": "user", "content": "What is the weather in New York?"}, @@ -644,7 +627,7 @@ def test_handle_streaming_tool_calls_no_available_functions( @pytest.mark.vcr(filter_headers=["authorization"]) def test_handle_streaming_tool_calls_no_tools(mock_emit): - llm = LLM(model="openai/gpt-4o", stream=True) + llm = LLM(model="openai/gpt-4o", stream=True, is_litellm=True) response = llm.call( messages=[ {"role": "user", "content": "What is the weather in New York?"}, @@ -665,7 +648,7 @@ def test_handle_streaming_tool_calls_no_tools(mock_emit): @pytest.mark.vcr(filter_headers=["authorization"]) def test_llm_call_when_stop_is_unsupported(caplog): - llm = LLM(model="o1-mini", stop=["stop"]) + llm = LLM(model="o1-mini", stop=["stop"], is_litellm=True) with caplog.at_level(logging.INFO): result = llm.call("What is the capital of France?") assert "Retrying LLM call without the unsupported 'stop'" in caplog.text @@ -677,7 +660,12 @@ def test_llm_call_when_stop_is_unsupported(caplog): def test_llm_call_when_stop_is_unsupported_when_additional_drop_params_is_provided( caplog, ): - llm = LLM(model="o1-mini", stop=["stop"], additional_drop_params=["another_param"]) + llm = LLM( + model="o1-mini", + stop=["stop"], + additional_drop_params=["another_param"], + is_litellm=True, + ) with caplog.at_level(logging.INFO): result = llm.call("What is the capital of France?") assert "Retrying LLM call without the unsupported 'stop'" in caplog.text @@ -687,7 +675,7 @@ def test_llm_call_when_stop_is_unsupported_when_additional_drop_params_is_provid @pytest.fixture def ollama_llm(): - return LLM(model="ollama/llama3.2:3b") + return LLM(model="ollama/llama3.2:3b", is_litellm=True) def test_ollama_appends_dummy_user_message_when_last_is_assistant(ollama_llm): @@ -711,3 +699,29 @@ def test_ollama_does_not_modify_when_last_is_user(ollama_llm): formatted = ollama_llm._format_messages_for_provider(original_messages) assert formatted == original_messages + +def test_native_provider_raises_error_when_supported_but_fails(): + """Test that when a native provider is in SUPPORTED_NATIVE_PROVIDERS but fails to instantiate, we raise the error.""" + with patch("crewai.llm.SUPPORTED_NATIVE_PROVIDERS", ["openai"]): + with patch("crewai.llm.LLM._get_native_provider") as mock_get_native: + # Mock that provider exists but throws an error when instantiated + mock_provider = MagicMock() + mock_provider.side_effect = ValueError("Native provider initialization failed") + mock_get_native.return_value = mock_provider + + with pytest.raises(ImportError) as excinfo: + LLM(model="openai/gpt-4", is_litellm=False) + + assert "Error importing native provider" in str(excinfo.value) + assert "Native provider initialization failed" in str(excinfo.value) + + +def test_native_provider_falls_back_to_litellm_when_not_in_supported_list(): + """Test that when a provider is not in SUPPORTED_NATIVE_PROVIDERS, we fall back to LiteLLM.""" + with patch("crewai.llm.SUPPORTED_NATIVE_PROVIDERS", ["openai", "anthropic"]): + # Using a provider not in the supported list + llm = LLM(model="groq/llama-3.1-70b-versatile", is_litellm=False) + + # Should fall back to LiteLLM + assert llm.is_litellm is True + assert llm.model == "groq/llama-3.1-70b-versatile" diff --git a/tests/test_markdown_task.py b/lib/crewai/tests/test_markdown_task.py similarity index 100% rename from tests/test_markdown_task.py rename to lib/crewai/tests/test_markdown_task.py diff --git a/tests/test_multimodal_validation.py b/lib/crewai/tests/test_multimodal_validation.py similarity index 94% rename from tests/test_multimodal_validation.py rename to lib/crewai/tests/test_multimodal_validation.py index 3b0817bf2..e71e148c0 100644 --- a/tests/test_multimodal_validation.py +++ b/lib/crewai/tests/test_multimodal_validation.py @@ -1,7 +1,6 @@ import os import pytest - from crewai import LLM, Agent, Crew, Task @@ -18,7 +17,7 @@ def test_multimodal_agent_with_image_url(): llm = LLM( model="openai/gpt-4o", # model with vision capabilities api_key=OPENAI_API_KEY, - temperature=0.7 + temperature=0.7, ) expert_analyst = Agent( @@ -28,7 +27,7 @@ def test_multimodal_agent_with_image_url(): llm=llm, verbose=True, allow_delegation=False, - multimodal=True + multimodal=True, ) inspection_task = Task( @@ -40,7 +39,7 @@ def test_multimodal_agent_with_image_url(): Provide a detailed report highlighting any issues found. """, expected_output="A detailed report highlighting any issues found", - agent=expert_analyst + agent=expert_analyst, ) crew = Crew(agents=[expert_analyst], tasks=[inspection_task]) diff --git a/tests/test_project.py b/lib/crewai/tests/test_project.py similarity index 99% rename from tests/test_project.py rename to lib/crewai/tests/test_project.py index c6708d92f..5106aae6e 100644 --- a/tests/test_project.py +++ b/lib/crewai/tests/test_project.py @@ -2,7 +2,6 @@ from typing import Any, ClassVar from unittest.mock import Mock, patch import pytest - from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.crew import Crew diff --git a/tests/test_task.py b/lib/crewai/tests/test_task.py similarity index 100% rename from tests/test_task.py rename to lib/crewai/tests/test_task.py diff --git a/lib/crewai/tests/test_task_guardrails.py b/lib/crewai/tests/test_task_guardrails.py new file mode 100644 index 000000000..5930079c0 --- /dev/null +++ b/lib/crewai/tests/test_task_guardrails.py @@ -0,0 +1,724 @@ +from unittest.mock import Mock, patch + +import pytest + +from crewai import Agent, Task +from crewai.events.event_bus import crewai_event_bus +from crewai.events.event_types import ( + LLMGuardrailCompletedEvent, + LLMGuardrailStartedEvent, +) +from crewai.llm import LLM +from crewai.tasks.hallucination_guardrail import HallucinationGuardrail +from crewai.tasks.llm_guardrail import LLMGuardrail +from crewai.tasks.task_output import TaskOutput + + +def create_smart_task(**kwargs): + """ + Smart task factory that automatically assigns a mock agent when guardrails are present. + This maintains backward compatibility while handling the agent requirement for guardrails. + """ + guardrails_list = kwargs.get("guardrails") + has_guardrails = kwargs.get("guardrail") is not None or ( + guardrails_list is not None and len(guardrails_list) > 0 + ) + + if has_guardrails and kwargs.get("agent") is None: + kwargs["agent"] = Agent( + role="test_agent", goal="test_goal", backstory="test_backstory" + ) + + return Task(**kwargs) + + +def test_task_without_guardrail(): + """Test that tasks work normally without guardrails (backward compatibility).""" + agent = Mock() + agent.role = "test_agent" + agent.execute_task.return_value = "test result" + agent.crew = None + + task = create_smart_task(description="Test task", expected_output="Output") + + result = task.execute_sync(agent=agent) + assert isinstance(result, TaskOutput) + assert result.raw == "test result" + + +def test_task_with_successful_guardrail_func(): + """Test that successful guardrail validation passes transformed result.""" + + def guardrail(result: TaskOutput): + return (True, result.raw.upper()) + + agent = Mock() + agent.role = "test_agent" + agent.execute_task.return_value = "test result" + agent.crew = None + + task = create_smart_task( + description="Test task", expected_output="Output", guardrail=guardrail + ) + + result = task.execute_sync(agent=agent) + assert isinstance(result, TaskOutput) + assert result.raw == "TEST RESULT" + + +def test_task_with_failing_guardrail(): + """Test that failing guardrail triggers retry with error context.""" + + def guardrail(result: TaskOutput): + return (False, "Invalid format") + + agent = Mock() + agent.role = "test_agent" + agent.execute_task.side_effect = ["bad result", "good result"] + agent.crew = None + + task = create_smart_task( + description="Test task", + expected_output="Output", + guardrail=guardrail, + guardrail_max_retries=1, + ) + + # First execution fails guardrail, second succeeds + agent.execute_task.side_effect = ["bad result", "good result"] + with pytest.raises(Exception) as exc_info: + task.execute_sync(agent=agent) + + assert "Task failed guardrail validation" in str(exc_info.value) + assert task.retry_count == 1 + + +def test_task_with_guardrail_retries(): + """Test that guardrail respects max_retries configuration.""" + + def guardrail(result: TaskOutput): + return (False, "Invalid format") + + agent = Mock() + agent.role = "test_agent" + agent.execute_task.return_value = "bad result" + agent.crew = None + + task = create_smart_task( + description="Test task", + expected_output="Output", + guardrail=guardrail, + guardrail_max_retries=2, + ) + + with pytest.raises(Exception) as exc_info: + task.execute_sync(agent=agent) + + assert task.retry_count == 2 + assert "Task failed guardrail validation after 2 retries" in str(exc_info.value) + assert "Invalid format" in str(exc_info.value) + + +def test_guardrail_error_in_context(): + """Test that guardrail error is passed in context for retry.""" + + def guardrail(result: TaskOutput): + return (False, "Expected JSON, got string") + + agent = Mock() + agent.role = "test_agent" + agent.crew = None + + task = create_smart_task( + description="Test task", + expected_output="Output", + guardrail=guardrail, + guardrail_max_retries=1, + ) + + # Mock execute_task to succeed on second attempt + first_call = True + + def execute_task(task, context, tools): + nonlocal first_call + if first_call: + first_call = False + return "invalid" + return '{"valid": "json"}' + + agent.execute_task.side_effect = execute_task + + with pytest.raises(Exception) as exc_info: + task.execute_sync(agent=agent) + + assert "Task failed guardrail validation" in str(exc_info.value) + assert "Expected JSON, got string" in str(exc_info.value) + + +@pytest.fixture +def sample_agent(): + return Agent(role="Test Agent", goal="Test Goal", backstory="Test Backstory") + + +@pytest.fixture +def task_output(): + return TaskOutput( + raw=""" + Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever + """, + description="Test task", + expected_output="Output", + agent="Test Agent", + ) + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_task_guardrail_process_output(task_output): + guardrail = LLMGuardrail( + description="Ensure the result has less than 10 words", llm=LLM(model="gpt-4o") + ) + + result = guardrail(task_output) + assert result[0] is False + + assert "exceeding the guardrail limit of fewer than" in result[1].lower() + + guardrail = LLMGuardrail( + description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o") + ) + + result = guardrail(task_output) + assert result[0] is True + assert result[1] == task_output.raw + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_guardrail_emits_events(sample_agent): + started_guardrail = [] + completed_guardrail = [] + + task = create_smart_task( + description="Gather information about available books on the First World War", + agent=sample_agent, + expected_output="A list of available books on the First World War", + guardrail="Ensure the authors are from Italy", + ) + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(LLMGuardrailStartedEvent) + def handle_guardrail_started(source, event): + assert source == task + started_guardrail.append( + {"guardrail": event.guardrail, "retry_count": event.retry_count} + ) + + @crewai_event_bus.on(LLMGuardrailCompletedEvent) + def handle_guardrail_completed(source, event): + assert source == task + completed_guardrail.append( + { + "success": event.success, + "result": event.result, + "error": event.error, + "retry_count": event.retry_count, + } + ) + + result = task.execute_sync(agent=sample_agent) + + def custom_guardrail(result: TaskOutput): + return (True, "good result from callable function") + + task = create_smart_task( + description="Test task", + expected_output="Output", + guardrail=custom_guardrail, + ) + + task.execute_sync(agent=sample_agent) + + expected_started_events = [ + {"guardrail": "Ensure the authors are from Italy", "retry_count": 0}, + {"guardrail": "Ensure the authors are from Italy", "retry_count": 1}, + { + "guardrail": """def custom_guardrail(result: TaskOutput): + return (True, "good result from callable function")""", + "retry_count": 0, + }, + ] + + expected_completed_events = [ + { + "success": False, + "result": None, + "error": "The task result does not comply with the guardrail because none of " + "the listed authors are from Italy. All authors mentioned are from " + "different countries, including Germany, the UK, the USA, and others, " + "which violates the requirement that authors must be Italian.", + "retry_count": 0, + }, + {"success": True, "result": result.raw, "error": None, "retry_count": 1}, + { + "success": True, + "result": "good result from callable function", + "error": None, + "retry_count": 0, + }, + ] + assert started_guardrail == expected_started_events + assert completed_guardrail == expected_completed_events + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_guardrail_when_an_error_occurs(sample_agent, task_output): + with ( + patch( + "crewai.Agent.kickoff", + side_effect=Exception("Unexpected error"), + ), + pytest.raises( + Exception, + match="Error while validating the task output: Unexpected error", + ), + ): + task = create_smart_task( + description="Gather information about available books on the First World War", + agent=sample_agent, + expected_output="A list of available books on the First World War", + guardrail="Ensure the authors are from Italy", + guardrail_max_retries=0, + ) + task.execute_sync(agent=sample_agent) + + +def test_hallucination_guardrail_integration(): + """Test that HallucinationGuardrail integrates properly with the task system.""" + agent = Mock() + agent.role = "test_agent" + agent.execute_task.return_value = "test result" + agent.crew = None + + mock_llm = Mock(spec=LLM) + guardrail = HallucinationGuardrail( + context="Test reference context for validation", llm=mock_llm, threshold=8.0 + ) + + task = create_smart_task( + description="Test task with hallucination guardrail", + expected_output="Valid output", + guardrail=guardrail, + ) + + result = task.execute_sync(agent=agent) + assert isinstance(result, TaskOutput) + assert result.raw == "test result" + + +def test_hallucination_guardrail_description_in_events(): + """Test that HallucinationGuardrail description appears correctly in events.""" + mock_llm = Mock(spec=LLM) + guardrail = HallucinationGuardrail(context="Test context", llm=mock_llm) + + assert guardrail.description == "HallucinationGuardrail (no-op)" + + event = LLMGuardrailStartedEvent(guardrail=guardrail, retry_count=0) + assert event.guardrail == "HallucinationGuardrail (no-op)" + + +def test_multiple_guardrails_sequential_processing(): + """Test that multiple guardrails are processed sequentially.""" + + def first_guardrail(result: TaskOutput) -> tuple[bool, str]: + """First guardrail adds prefix.""" + return (True, f"[FIRST] {result.raw}") + + def second_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Second guardrail adds suffix.""" + return (True, f"{result.raw} [SECOND]") + + def third_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Third guardrail converts to uppercase.""" + return (True, result.raw.upper()) + + agent = Mock() + agent.role = "sequential_agent" + agent.execute_task.return_value = "original text" + agent.crew = None + + task = create_smart_task( + description="Test sequential guardrails", + expected_output="Processed text", + guardrails=[first_guardrail, second_guardrail, third_guardrail], + ) + + result = task.execute_sync(agent=agent) + assert result.raw == "[FIRST] ORIGINAL TEXT [SECOND]" + + +def test_multiple_guardrails_with_validation_failure(): + """Test multiple guardrails where one fails validation.""" + + def length_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Ensure minimum length.""" + if len(result.raw) < 10: + return (False, "Text too short") + return (True, result.raw) + + def format_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Add formatting only if not already formatted.""" + if not result.raw.startswith("Formatted:"): + return (True, f"Formatted: {result.raw}") + return (True, result.raw) + + def validation_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Final validation.""" + if "Formatted:" not in result.raw: + return (False, "Missing formatting") + return (True, result.raw) + + # Use a callable that tracks calls and returns appropriate values + call_count = 0 + + def mock_execute_task(*args, **kwargs): + nonlocal call_count + call_count += 1 + result = ( + "short" + if call_count == 1 + else "this is a longer text that meets requirements" + ) + return result + + agent = Mock() + agent.role = "validation_agent" + agent.execute_task = mock_execute_task + agent.crew = None + + task = create_smart_task( + description="Test guardrails with validation", + expected_output="Valid formatted text", + guardrails=[length_guardrail, format_guardrail, validation_guardrail], + guardrail_max_retries=2, + ) + + result = task.execute_sync(agent=agent) + # The second call should be processed through all guardrails + assert result.raw == "Formatted: this is a longer text that meets requirements" + assert task._guardrail_retry_counts.get(0, 0) == 1 + + +def test_multiple_guardrails_with_mixed_string_and_taskoutput(): + """Test guardrails that return both strings and TaskOutput objects.""" + + def string_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Returns a string.""" + return (True, f"String: {result.raw}") + + def taskoutput_guardrail(result: TaskOutput) -> tuple[bool, TaskOutput]: + """Returns a TaskOutput object.""" + new_output = TaskOutput( + name=result.name, + description=result.description, + expected_output=result.expected_output, + raw=f"TaskOutput: {result.raw}", + agent=result.agent, + output_format=result.output_format, + ) + return (True, new_output) + + def final_string_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Final string transformation.""" + return (True, f"Final: {result.raw}") + + agent = Mock() + agent.role = "mixed_agent" + agent.execute_task.return_value = "original" + agent.crew = None + + task = create_smart_task( + description="Test mixed return types", + expected_output="Mixed processing", + guardrails=[string_guardrail, taskoutput_guardrail, final_string_guardrail], + ) + + result = task.execute_sync(agent=agent) + assert result.raw == "Final: TaskOutput: String: original" + + +def test_multiple_guardrails_with_retry_on_middle_guardrail(): + """Test that retry works correctly when a middle guardrail fails.""" + + call_count = {"first": 0, "second": 0, "third": 0} + + def first_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Always succeeds.""" + call_count["first"] += 1 + return (True, f"First({call_count['first']}): {result.raw}") + + def second_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Fails on first attempt, succeeds on second.""" + call_count["second"] += 1 + if call_count["second"] == 1: + return (False, "Second guardrail failed on first attempt") + return (True, f"Second({call_count['second']}): {result.raw}") + + def third_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Always succeeds.""" + call_count["third"] += 1 + return (True, f"Third({call_count['third']}): {result.raw}") + + agent = Mock() + agent.role = "retry_agent" + agent.execute_task.return_value = "base" + agent.crew = None + + task = create_smart_task( + description="Test retry in middle guardrail", + expected_output="Retry handling", + guardrails=[first_guardrail, second_guardrail, third_guardrail], + guardrail_max_retries=2, + ) + + result = task.execute_sync(agent=agent) + assert task._guardrail_retry_counts.get(1, 0) == 1 + assert call_count["first"] == 1 + assert call_count["second"] == 2 + assert call_count["third"] == 1 + assert "Second(2)" in result.raw + + +def test_multiple_guardrails_with_max_retries_exceeded(): + """Test that exception is raised when max retries exceeded with multiple guardrails.""" + + def passing_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Always passes.""" + return (True, f"Passed: {result.raw}") + + def failing_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Always fails.""" + return (False, "This guardrail always fails") + + agent = Mock() + agent.role = "failing_agent" + agent.execute_task.return_value = "test" + agent.crew = None + + task = create_smart_task( + description="Test max retries with multiple guardrails", + expected_output="Will fail", + guardrails=[passing_guardrail, failing_guardrail], + guardrail_max_retries=1, + ) + + with pytest.raises(Exception) as exc_info: + task.execute_sync(agent=agent) + + assert "Task failed guardrail 1 validation after 1 retries" in str(exc_info.value) + assert "This guardrail always fails" in str(exc_info.value) + assert task._guardrail_retry_counts.get(1, 0) == 1 + + +def test_multiple_guardrails_empty_list(): + """Test that empty guardrails list works correctly.""" + + agent = Mock() + agent.role = "empty_agent" + agent.execute_task.return_value = "no guardrails" + agent.crew = None + + task = create_smart_task( + description="Test empty guardrails list", + expected_output="No processing", + guardrails=[], + ) + + result = task.execute_sync(agent=agent) + assert result.raw == "no guardrails" + + +def test_multiple_guardrails_with_llm_guardrails(): + """Test mixing callable and LLM guardrails.""" + + def callable_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Callable guardrail.""" + return (True, f"Callable: {result.raw}") + + # Create a proper mock agent without config issues + from crewai import Agent + + agent = Agent( + role="mixed_guardrail_agent", goal="Test goal", backstory="Test backstory" + ) + + task = create_smart_task( + description="Test mixed guardrail types", + expected_output="Mixed processing", + guardrails=[callable_guardrail, "Ensure the output is professional"], + agent=agent, + ) + + # The LLM guardrail will be converted to LLMGuardrail internally + assert len(task._guardrails) == 2 + assert callable(task._guardrails[0]) + assert callable(task._guardrails[1]) # LLMGuardrail is callable + + +def test_multiple_guardrails_processing_order(): + """Test that guardrails are processed in the correct order.""" + + processing_order = [] + + def first_guardrail(result: TaskOutput) -> tuple[bool, str]: + processing_order.append("first") + return (True, f"1-{result.raw}") + + def second_guardrail(result: TaskOutput) -> tuple[bool, str]: + processing_order.append("second") + return (True, f"2-{result.raw}") + + def third_guardrail(result: TaskOutput) -> tuple[bool, str]: + processing_order.append("third") + return (True, f"3-{result.raw}") + + agent = Mock() + agent.role = "order_agent" + agent.execute_task.return_value = "base" + agent.crew = None + + task = create_smart_task( + description="Test processing order", + expected_output="Ordered processing", + guardrails=[first_guardrail, second_guardrail, third_guardrail], + ) + + result = task.execute_sync(agent=agent) + assert processing_order == ["first", "second", "third"] + assert result.raw == "3-2-1-base" + + +def test_multiple_guardrails_with_pydantic_output(): + """Test multiple guardrails with Pydantic output model.""" + from pydantic import BaseModel, Field + + class TestModel(BaseModel): + content: str = Field(description="The content") + processed: bool = Field(description="Whether it was processed") + + def json_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Convert to JSON format.""" + import json + + data = {"content": result.raw, "processed": True} + return (True, json.dumps(data)) + + def validation_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Validate JSON structure.""" + import json + + try: + data = json.loads(result.raw) + if "content" not in data or "processed" not in data: + return (False, "Missing required fields") + return (True, result.raw) + except json.JSONDecodeError: + return (False, "Invalid JSON format") + + agent = Mock() + agent.role = "pydantic_agent" + agent.execute_task.return_value = "test content" + agent.crew = None + + task = create_smart_task( + description="Test guardrails with Pydantic", + expected_output="Structured output", + guardrails=[json_guardrail, validation_guardrail], + output_pydantic=TestModel, + ) + + result = task.execute_sync(agent=agent) + + # Verify the result is valid JSON and can be parsed + import json + + parsed = json.loads(result.raw) + assert parsed["content"] == "test content" + assert parsed["processed"] is True + + +def test_guardrails_vs_single_guardrail_mutual_exclusion(): + """Test that guardrails list nullifies single guardrail.""" + + def single_guardrail(result: TaskOutput) -> tuple[bool, str]: + """Single guardrail - should be ignored.""" + return (True, f"Single: {result.raw}") + + def list_guardrail(result: TaskOutput) -> tuple[bool, str]: + """List guardrail - should be used.""" + return (True, f"List: {result.raw}") + + agent = Mock() + agent.role = "exclusion_agent" + agent.execute_task.return_value = "test" + agent.crew = None + + task = create_smart_task( + description="Test mutual exclusion", + expected_output="Exclusion test", + guardrail=single_guardrail, # This should be ignored + guardrails=[list_guardrail], # This should be used + ) + + result = task.execute_sync(agent=agent) + # Should only use the guardrails list, not the single guardrail + assert result.raw == "List: test" + assert task._guardrail is None # Single guardrail should be nullified + + +def test_per_guardrail_independent_retry_tracking(): + """Test that each guardrail has independent retry tracking.""" + + call_counts = {"g1": 0, "g2": 0, "g3": 0} + + def guardrail_1(result: TaskOutput) -> tuple[bool, str]: + """Fails twice, then succeeds.""" + call_counts["g1"] += 1 + if call_counts["g1"] <= 2: + return (False, "Guardrail 1 not ready yet") + return (True, f"G1({call_counts['g1']}): {result.raw}") + + def guardrail_2(result: TaskOutput) -> tuple[bool, str]: + """Fails once, then succeeds.""" + call_counts["g2"] += 1 + if call_counts["g2"] == 1: + return (False, "Guardrail 2 not ready yet") + return (True, f"G2({call_counts['g2']}): {result.raw}") + + def guardrail_3(result: TaskOutput) -> tuple[bool, str]: + """Always succeeds.""" + call_counts["g3"] += 1 + return (True, f"G3({call_counts['g3']}): {result.raw}") + + agent = Mock() + agent.role = "independent_retry_agent" + agent.execute_task.return_value = "base" + agent.crew = None + + task = create_smart_task( + description="Test independent retry tracking", + expected_output="Independent retries", + guardrails=[guardrail_1, guardrail_2, guardrail_3], + guardrail_max_retries=3, + ) + + result = task.execute_sync(agent=agent) + + assert task._guardrail_retry_counts.get(0, 0) == 2 + assert task._guardrail_retry_counts.get(1, 0) == 1 + assert task._guardrail_retry_counts.get(2, 0) == 0 + + assert call_counts["g1"] == 3 + assert call_counts["g2"] == 2 + assert call_counts["g3"] == 1 + + assert "G3(1)" in result.raw diff --git a/tests/tools/__init__.py b/lib/crewai/tests/tools/__init__.py similarity index 100% rename from tests/tools/__init__.py rename to lib/crewai/tests/tools/__init__.py diff --git a/lib/crewai/tests/tools/agent_tools/__init__.py b/lib/crewai/tests/tools/agent_tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/tools/agent_tools/cassettes/test_ask_question.yaml b/lib/crewai/tests/tools/agent_tools/cassettes/test_ask_question.yaml similarity index 100% rename from tests/tools/agent_tools/cassettes/test_ask_question.yaml rename to lib/crewai/tests/tools/agent_tools/cassettes/test_ask_question.yaml diff --git a/tests/tools/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml b/lib/crewai/tests/tools/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml similarity index 100% rename from tests/tools/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml rename to lib/crewai/tests/tools/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml diff --git a/tests/tools/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml b/lib/crewai/tests/tools/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml similarity index 100% rename from tests/tools/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml rename to lib/crewai/tests/tools/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml diff --git a/tests/tools/agent_tools/cassettes/test_delegate_work.yaml b/lib/crewai/tests/tools/agent_tools/cassettes/test_delegate_work.yaml similarity index 100% rename from tests/tools/agent_tools/cassettes/test_delegate_work.yaml rename to lib/crewai/tests/tools/agent_tools/cassettes/test_delegate_work.yaml diff --git a/tests/tools/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml b/lib/crewai/tests/tools/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml similarity index 100% rename from tests/tools/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml rename to lib/crewai/tests/tools/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml diff --git a/tests/tools/agent_tools/cassettes/test_delegate_work_withwith_coworker_as_array.yaml b/lib/crewai/tests/tools/agent_tools/cassettes/test_delegate_work_withwith_coworker_as_array.yaml similarity index 100% rename from tests/tools/agent_tools/cassettes/test_delegate_work_withwith_coworker_as_array.yaml rename to lib/crewai/tests/tools/agent_tools/cassettes/test_delegate_work_withwith_coworker_as_array.yaml diff --git a/tests/tools/agent_tools/test_agent_tools.py b/lib/crewai/tests/tools/agent_tools/test_agent_tools.py similarity index 99% rename from tests/tools/agent_tools/test_agent_tools.py rename to lib/crewai/tests/tools/agent_tools/test_agent_tools.py index 6cb5d26e7..89d2798d6 100644 --- a/tests/tools/agent_tools/test_agent_tools.py +++ b/lib/crewai/tests/tools/agent_tools/test_agent_tools.py @@ -1,7 +1,7 @@ """Test Agent creation and execution basic functionality.""" +import os import pytest - from crewai.agent import Agent from crewai.tools.agent_tools.agent_tools import AgentTools @@ -19,7 +19,7 @@ ask_tool = tools[1] @pytest.fixture(scope="module") def vcr_config(request) -> dict: return { - "cassette_library_dir": "tests/tools/agent_tools/cassettes", + "cassette_library_dir": os.path.join(os.path.dirname(__file__), "cassettes"), } diff --git a/tests/tools/test_base_tool.py b/lib/crewai/tests/tools/test_base_tool.py similarity index 99% rename from tests/tools/test_base_tool.py rename to lib/crewai/tests/tools/test_base_tool.py index 0c11e1e09..2aa9ac8bf 100644 --- a/tests/tools/test_base_tool.py +++ b/lib/crewai/tests/tools/test_base_tool.py @@ -3,7 +3,6 @@ from typing import Callable from unittest.mock import patch import pytest - from crewai.agent import Agent from crewai.crew import Crew from crewai.task import Task diff --git a/tests/tools/test_structured_tool.py b/lib/crewai/tests/tools/test_structured_tool.py similarity index 100% rename from tests/tools/test_structured_tool.py rename to lib/crewai/tests/tools/test_structured_tool.py diff --git a/tests/tools/test_tool_usage.py b/lib/crewai/tests/tools/test_tool_usage.py similarity index 95% rename from tests/tools/test_tool_usage.py rename to lib/crewai/tests/tools/test_tool_usage.py index 66e2bb616..927031302 100644 --- a/tests/tools/test_tool_usage.py +++ b/lib/crewai/tests/tools/test_tool_usage.py @@ -1,21 +1,21 @@ import datetime import json import random +import threading import time from unittest.mock import MagicMock, patch import pytest -from pydantic import BaseModel, Field - from crewai import Agent, Task -from crewai.tools import BaseTool -from crewai.tools.tool_usage import ToolUsage from crewai.events.event_bus import crewai_event_bus from crewai.events.types.tool_usage_events import ( ToolSelectionErrorEvent, ToolUsageFinishedEvent, ToolValidateInputErrorEvent, ) +from crewai.tools import BaseTool +from crewai.tools.tool_usage import ToolUsage +from pydantic import BaseModel, Field class RandomNumberToolInput(BaseModel): @@ -33,7 +33,7 @@ class RandomNumberTool(BaseTool): args_schema: type[BaseModel] = RandomNumberToolInput def _run(self, min_value: int, max_value: int) -> int: - return random.randint(min_value, max_value) + return random.randint(min_value, max_value) # noqa: S311 # Example agent and task @@ -471,13 +471,21 @@ def test_tool_selection_error_event_direct(): ) received_events = [] + first_event_received = threading.Event() + second_event_received = threading.Event() @crewai_event_bus.on(ToolSelectionErrorEvent) def event_handler(source, event): received_events.append(event) + if event.tool_name == "Non Existent Tool": + first_event_received.set() + elif event.tool_name == "": + second_event_received.set() - with pytest.raises(Exception): + with pytest.raises(Exception): # noqa: B017 tool_usage._select_tool("Non Existent Tool") + + assert first_event_received.wait(timeout=5), "Timeout waiting for first event" assert len(received_events) == 1 event = received_events[0] assert isinstance(event, ToolSelectionErrorEvent) @@ -489,12 +497,12 @@ def test_tool_selection_error_event_direct(): assert "A test tool" in event.tool_class assert "don't exist" in event.error - received_events.clear() - with pytest.raises(Exception): + with pytest.raises(Exception): # noqa: B017 tool_usage._select_tool("") - assert len(received_events) == 1 - event = received_events[0] + assert second_event_received.wait(timeout=5), "Timeout waiting for second event" + assert len(received_events) == 2 + event = received_events[1] assert isinstance(event, ToolSelectionErrorEvent) assert event.agent_key == "test_key" assert event.agent_role == "test_role" @@ -563,7 +571,7 @@ def test_tool_validate_input_error_event(): # Test invalid input invalid_input = "invalid json {[}" - with pytest.raises(Exception): + with pytest.raises(Exception): # noqa: B017 tool_usage._validate_tool_input(invalid_input) # Verify event was emitted @@ -617,12 +625,13 @@ def test_tool_usage_finished_event_with_result(): action=MagicMock(), ) - # Track received events received_events = [] + event_received = threading.Event() @crewai_event_bus.on(ToolUsageFinishedEvent) def event_handler(source, event): received_events.append(event) + event_received.set() # Call on_tool_use_finished with test data started_at = time.time() @@ -635,7 +644,7 @@ def test_tool_usage_finished_event_with_result(): result=result, ) - # Verify event was emitted + assert event_received.wait(timeout=5), "Timeout waiting for event" assert len(received_events) == 1, "Expected one event to be emitted" event = received_events[0] assert isinstance(event, ToolUsageFinishedEvent) @@ -696,12 +705,13 @@ def test_tool_usage_finished_event_with_cached_result(): action=MagicMock(), ) - # Track received events received_events = [] + event_received = threading.Event() @crewai_event_bus.on(ToolUsageFinishedEvent) def event_handler(source, event): received_events.append(event) + event_received.set() # Call on_tool_use_finished with test data and from_cache=True started_at = time.time() @@ -714,7 +724,7 @@ def test_tool_usage_finished_event_with_cached_result(): result=result, ) - # Verify event was emitted + assert event_received.wait(timeout=5), "Timeout waiting for event" assert len(received_events) == 1, "Expected one event to be emitted" event = received_events[0] assert isinstance(event, ToolUsageFinishedEvent) diff --git a/tests/tools/test_tool_usage_limit.py b/lib/crewai/tests/tools/test_tool_usage_limit.py similarity index 100% rename from tests/tools/test_tool_usage_limit.py rename to lib/crewai/tests/tools/test_tool_usage_limit.py diff --git a/lib/crewai/tests/tracing/__init__.py b/lib/crewai/tests/tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/tracing/test_tracing.py b/lib/crewai/tests/tracing/test_tracing.py similarity index 84% rename from tests/tracing/test_tracing.py rename to lib/crewai/tests/tracing/test_tracing.py index 629fb7a87..aeedcd1bd 100644 --- a/tests/tracing/test_tracing.py +++ b/lib/crewai/tests/tracing/test_tracing.py @@ -2,7 +2,6 @@ import os from unittest.mock import MagicMock, Mock, patch import pytest - from crewai import Agent, Crew, Task from crewai.events.listeners.tracing.first_time_trace_handler import ( FirstTimeTraceHandler, @@ -15,6 +14,7 @@ from crewai.events.listeners.tracing.trace_listener import ( ) from crewai.events.listeners.tracing.types import TraceEvent from crewai.flow.flow import Flow, start +from tests.utils import wait_for_event_handlers class TestTraceListenerSetup: @@ -40,38 +40,44 @@ class TestTraceListenerSetup: ): yield - @pytest.fixture(autouse=True) - def clear_event_bus(self): - """Clear event bus listeners before and after each test""" - from crewai.events.event_bus import crewai_event_bus - - # Store original handlers - original_handlers = crewai_event_bus._handlers.copy() - - # Clear for test - crewai_event_bus._handlers.clear() - - yield - - # Restore original state - crewai_event_bus._handlers.clear() - crewai_event_bus._handlers.update(original_handlers) - @pytest.fixture(autouse=True) def reset_tracing_singletons(self): """Reset tracing singleton instances between tests""" + from crewai.events.event_bus import crewai_event_bus + from crewai.events.event_listener import EventListener + + # Clear event bus handlers BEFORE creating any new singletons + with crewai_event_bus._rwlock.w_locked(): + crewai_event_bus._sync_handlers = {} + crewai_event_bus._async_handlers = {} + crewai_event_bus._handler_dependencies = {} + crewai_event_bus._execution_plan_cache = {} + # Reset TraceCollectionListener singleton if hasattr(TraceCollectionListener, "_instance"): TraceCollectionListener._instance = None TraceCollectionListener._initialized = False + # Reset EventListener singleton + if hasattr(EventListener, "_instance"): + EventListener._instance = None + yield # Clean up after test + with crewai_event_bus._rwlock.w_locked(): + crewai_event_bus._sync_handlers = {} + crewai_event_bus._async_handlers = {} + crewai_event_bus._handler_dependencies = {} + crewai_event_bus._execution_plan_cache = {} + if hasattr(TraceCollectionListener, "_instance"): TraceCollectionListener._instance = None TraceCollectionListener._initialized = False + if hasattr(EventListener, "_instance"): + EventListener._instance = None + @pytest.fixture(autouse=True) def mock_plus_api_calls(self): """Mock all PlusAPI HTTP calls to avoid network requests""" @@ -168,15 +174,26 @@ class TestTraceListenerSetup: from crewai.events.event_bus import crewai_event_bus trace_listener = None - for handler_list in crewai_event_bus._handlers.values(): - for handler in handler_list: - if hasattr(handler, "__self__") and isinstance( - handler.__self__, TraceCollectionListener - ): - trace_listener = handler.__self__ + with crewai_event_bus._rwlock.r_locked(): + for handler_set in crewai_event_bus._sync_handlers.values(): + for handler in handler_set: + if hasattr(handler, "__self__") and isinstance( + handler.__self__, TraceCollectionListener + ): + trace_listener = handler.__self__ + break + if trace_listener: break - if trace_listener: - break + if not trace_listener: + for handler_set in crewai_event_bus._async_handlers.values(): + for handler in handler_set: + if hasattr(handler, "__self__") and isinstance( + handler.__self__, TraceCollectionListener + ): + trace_listener = handler.__self__ + break + if trace_listener: + break if not trace_listener: pytest.skip( @@ -222,6 +239,7 @@ class TestTraceListenerSetup: wraps=trace_listener.batch_manager.add_event, ) as add_event_mock: crew.kickoff() + wait_for_event_handlers() assert add_event_mock.call_count >= 2 @@ -268,24 +286,22 @@ class TestTraceListenerSetup: from crewai.events.event_bus import crewai_event_bus trace_handlers = [] - for handlers in crewai_event_bus._handlers.values(): - for handler in handlers: - if hasattr(handler, "__self__") and isinstance( - handler.__self__, TraceCollectionListener - ): - trace_handlers.append(handler) - elif hasattr(handler, "__name__") and any( - trace_name in handler.__name__ - for trace_name in [ - "on_crew_started", - "on_crew_completed", - "on_flow_started", - ] - ): - trace_handlers.append(handler) + with crewai_event_bus._rwlock.r_locked(): + for handlers in crewai_event_bus._sync_handlers.values(): + for handler in handlers: + if hasattr(handler, "__self__") and isinstance( + handler.__self__, TraceCollectionListener + ): + trace_handlers.append(handler) + for handlers in crewai_event_bus._async_handlers.values(): + for handler in handlers: + if hasattr(handler, "__self__") and isinstance( + handler.__self__, TraceCollectionListener + ): + trace_handlers.append(handler) assert len(trace_handlers) == 0, ( - f"Found {len(trace_handlers)} trace handlers when tracing should be disabled" + f"Found {len(trace_handlers)} TraceCollectionListener handlers when tracing should be disabled" ) def test_trace_listener_setup_correctly_for_crew(self): @@ -386,6 +402,7 @@ class TestTraceListenerSetup: ): crew = Crew(agents=[agent], tasks=[task], tracing=True) crew.kickoff() + wait_for_event_handlers() mock_plus_api_class.assert_called_with(api_key="mock_token_12345") @@ -397,15 +414,33 @@ class TestTraceListenerSetup: def teardown_method(self): """Cleanup after each test method""" from crewai.events.event_bus import crewai_event_bus + from crewai.events.event_listener import EventListener - crewai_event_bus._handlers.clear() + with crewai_event_bus._rwlock.w_locked(): + crewai_event_bus._sync_handlers = {} + crewai_event_bus._async_handlers = {} + crewai_event_bus._handler_dependencies = {} + crewai_event_bus._execution_plan_cache = {} + + # Reset EventListener singleton + if hasattr(EventListener, "_instance"): + EventListener._instance = None @classmethod def teardown_class(cls): """Final cleanup after all tests in this class""" from crewai.events.event_bus import crewai_event_bus + from crewai.events.event_listener import EventListener - crewai_event_bus._handlers.clear() + with crewai_event_bus._rwlock.w_locked(): + crewai_event_bus._sync_handlers = {} + crewai_event_bus._async_handlers = {} + crewai_event_bus._handler_dependencies = {} + crewai_event_bus._execution_plan_cache = {} + + # Reset EventListener singleton + if hasattr(EventListener, "_instance"): + EventListener._instance = None @pytest.mark.vcr(filter_headers=["authorization"]) def test_first_time_user_trace_collection_with_timeout(self, mock_plus_api_calls): @@ -467,6 +502,7 @@ class TestTraceListenerSetup: ) as mock_add_event, ): result = crew.kickoff() + wait_for_event_handlers() assert result is not None assert mock_handle_completion.call_count >= 1 @@ -474,7 +510,7 @@ class TestTraceListenerSetup: assert trace_listener.first_time_handler.collected_events is True - mock_prompt.assert_called_once_with(timeout_seconds=20) + mock_prompt.assert_called_once() mock_mark_completed.assert_called_once() @@ -544,6 +580,7 @@ class TestTraceListenerSetup: ) crew.kickoff() + wait_for_event_handlers() assert mock_handle_completion.call_count >= 1, ( "handle_execution_completion should be called" @@ -562,7 +599,6 @@ class TestTraceListenerSetup: @pytest.mark.vcr(filter_headers=["authorization"]) def test_first_time_user_trace_consolidation_logic(self, mock_plus_api_calls): """Test the consolidation logic for first-time users vs regular tracing""" - with ( patch.dict(os.environ, {"CREWAI_TRACING_ENABLED": "false"}), patch( @@ -580,7 +616,9 @@ class TestTraceListenerSetup: ): from crewai.events.event_bus import crewai_event_bus - crewai_event_bus._handlers.clear() + with crewai_event_bus._rwlock.w_locked(): + crewai_event_bus._sync_handlers = {} + crewai_event_bus._async_handlers = {} trace_listener = TraceCollectionListener() trace_listener.setup_listeners(crewai_event_bus) @@ -601,6 +639,9 @@ class TestTraceListenerSetup: with patch.object(TraceBatchManager, "initialize_batch") as mock_initialize: result = crew.kickoff() + assert trace_listener.batch_manager.wait_for_pending_events(timeout=5.0), ( + "Timeout waiting for trace event handlers to complete" + ) assert mock_initialize.call_count >= 1 assert mock_initialize.call_args_list[0][1]["use_ephemeral"] is True assert result is not None @@ -681,7 +722,16 @@ class TestTraceListenerSetup: trace_listener.setup_listeners(crewai_event_bus) + mock_init_response = MagicMock() + mock_init_response.status_code = 200 + mock_init_response.json.return_value = {"trace_id": "test_batch_id_12345"} + with ( + patch.object( + trace_listener.batch_manager.plus_api, + "initialize_trace_batch", + return_value=mock_init_response, + ), patch.object( trace_listener.batch_manager.plus_api, "send_trace_events", @@ -701,7 +751,8 @@ class TestTraceListenerSetup: ) as mock_mark_failed, ): crew.kickoff() + wait_for_event_handlers() mock_mark_failed.assert_called_once() call_args = mock_mark_failed.call_args_list[0] - assert call_args[0][1] == "Error sending events to backend" + assert call_args[0][1] == "Internal Server Error" diff --git a/tests/utilities/__init__.py b/lib/crewai/tests/utilities/__init__.py similarity index 100% rename from tests/utilities/__init__.py rename to lib/crewai/tests/utilities/__init__.py diff --git a/tests/utilities/cassettes/test_agent_emits_execution_started_and_completed_events.yaml b/lib/crewai/tests/utilities/cassettes/test_agent_emits_execution_started_and_completed_events.yaml similarity index 100% rename from tests/utilities/cassettes/test_agent_emits_execution_started_and_completed_events.yaml rename to lib/crewai/tests/utilities/cassettes/test_agent_emits_execution_started_and_completed_events.yaml diff --git a/tests/utilities/cassettes/test_convert_with_instructions.yaml b/lib/crewai/tests/utilities/cassettes/test_convert_with_instructions.yaml similarity index 100% rename from tests/utilities/cassettes/test_convert_with_instructions.yaml rename to lib/crewai/tests/utilities/cassettes/test_convert_with_instructions.yaml diff --git a/tests/utilities/cassettes/test_converter_with_llama3_1_model.yaml b/lib/crewai/tests/utilities/cassettes/test_converter_with_llama3_1_model.yaml similarity index 100% rename from tests/utilities/cassettes/test_converter_with_llama3_1_model.yaml rename to lib/crewai/tests/utilities/cassettes/test_converter_with_llama3_1_model.yaml diff --git a/tests/utilities/cassettes/test_converter_with_llama3_2_model.yaml b/lib/crewai/tests/utilities/cassettes/test_converter_with_llama3_2_model.yaml similarity index 100% rename from tests/utilities/cassettes/test_converter_with_llama3_2_model.yaml rename to lib/crewai/tests/utilities/cassettes/test_converter_with_llama3_2_model.yaml diff --git a/tests/utilities/cassettes/test_converter_with_nested_model.yaml b/lib/crewai/tests/utilities/cassettes/test_converter_with_nested_model.yaml similarity index 100% rename from tests/utilities/cassettes/test_converter_with_nested_model.yaml rename to lib/crewai/tests/utilities/cassettes/test_converter_with_nested_model.yaml diff --git a/tests/utilities/cassettes/test_crew_emits_end_kickoff_event.yaml b/lib/crewai/tests/utilities/cassettes/test_crew_emits_end_kickoff_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_crew_emits_end_kickoff_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_crew_emits_end_kickoff_event.yaml diff --git a/tests/utilities/cassettes/test_crew_emits_end_task_event.yaml b/lib/crewai/tests/utilities/cassettes/test_crew_emits_end_task_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_crew_emits_end_task_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_crew_emits_end_task_event.yaml diff --git a/tests/utilities/cassettes/test_crew_emits_kickoff_events.yaml b/lib/crewai/tests/utilities/cassettes/test_crew_emits_kickoff_events.yaml similarity index 100% rename from tests/utilities/cassettes/test_crew_emits_kickoff_events.yaml rename to lib/crewai/tests/utilities/cassettes/test_crew_emits_kickoff_events.yaml diff --git a/tests/utilities/cassettes/test_crew_emits_start_kickoff_event.yaml b/lib/crewai/tests/utilities/cassettes/test_crew_emits_start_kickoff_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_crew_emits_start_kickoff_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_crew_emits_start_kickoff_event.yaml diff --git a/tests/utilities/cassettes/test_crew_emits_start_task_event.yaml b/lib/crewai/tests/utilities/cassettes/test_crew_emits_start_task_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_crew_emits_start_task_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_crew_emits_start_task_event.yaml diff --git a/tests/utilities/cassettes/test_crew_emits_task_failed_event.yaml b/lib/crewai/tests/utilities/cassettes/test_crew_emits_task_failed_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_crew_emits_task_failed_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_crew_emits_task_failed_event.yaml diff --git a/tests/utilities/cassettes/test_crew_emits_test_kickoff_type_event.yaml b/lib/crewai/tests/utilities/cassettes/test_crew_emits_test_kickoff_type_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_crew_emits_test_kickoff_type_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_crew_emits_test_kickoff_type_event.yaml diff --git a/tests/utilities/cassettes/test_llm_emits_call_failed_event.yaml b/lib/crewai/tests/utilities/cassettes/test_llm_emits_call_failed_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_llm_emits_call_failed_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_llm_emits_call_failed_event.yaml diff --git a/tests/utilities/cassettes/test_llm_emits_call_started_event.yaml b/lib/crewai/tests/utilities/cassettes/test_llm_emits_call_started_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_llm_emits_call_started_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_llm_emits_call_started_event.yaml diff --git a/tests/utilities/cassettes/test_llm_emits_event_with_lite_agent.yaml b/lib/crewai/tests/utilities/cassettes/test_llm_emits_event_with_lite_agent.yaml similarity index 100% rename from tests/utilities/cassettes/test_llm_emits_event_with_lite_agent.yaml rename to lib/crewai/tests/utilities/cassettes/test_llm_emits_event_with_lite_agent.yaml diff --git a/tests/utilities/cassettes/test_llm_emits_event_with_task_and_agent_info.yaml b/lib/crewai/tests/utilities/cassettes/test_llm_emits_event_with_task_and_agent_info.yaml similarity index 100% rename from tests/utilities/cassettes/test_llm_emits_event_with_task_and_agent_info.yaml rename to lib/crewai/tests/utilities/cassettes/test_llm_emits_event_with_task_and_agent_info.yaml diff --git a/tests/utilities/cassettes/test_llm_emits_stream_chunk_events.yaml b/lib/crewai/tests/utilities/cassettes/test_llm_emits_stream_chunk_events.yaml similarity index 100% rename from tests/utilities/cassettes/test_llm_emits_stream_chunk_events.yaml rename to lib/crewai/tests/utilities/cassettes/test_llm_emits_stream_chunk_events.yaml diff --git a/tests/utilities/cassettes/test_llm_no_stream_chunks_when_streaming_disabled.yaml b/lib/crewai/tests/utilities/cassettes/test_llm_no_stream_chunks_when_streaming_disabled.yaml similarity index 100% rename from tests/utilities/cassettes/test_llm_no_stream_chunks_when_streaming_disabled.yaml rename to lib/crewai/tests/utilities/cassettes/test_llm_no_stream_chunks_when_streaming_disabled.yaml diff --git a/tests/utilities/cassettes/test_multiple_handlers_for_same_event.yaml b/lib/crewai/tests/utilities/cassettes/test_multiple_handlers_for_same_event.yaml similarity index 100% rename from tests/utilities/cassettes/test_multiple_handlers_for_same_event.yaml rename to lib/crewai/tests/utilities/cassettes/test_multiple_handlers_for_same_event.yaml diff --git a/tests/utilities/cassettes/test_register_handler_adds_new_handler.yaml b/lib/crewai/tests/utilities/cassettes/test_register_handler_adds_new_handler.yaml similarity index 100% rename from tests/utilities/cassettes/test_register_handler_adds_new_handler.yaml rename to lib/crewai/tests/utilities/cassettes/test_register_handler_adds_new_handler.yaml diff --git a/tests/utilities/cassettes/test_stream_llm_emits_event_with_task_and_agent_info.yaml b/lib/crewai/tests/utilities/cassettes/test_stream_llm_emits_event_with_task_and_agent_info.yaml similarity index 100% rename from tests/utilities/cassettes/test_stream_llm_emits_event_with_task_and_agent_info.yaml rename to lib/crewai/tests/utilities/cassettes/test_stream_llm_emits_event_with_task_and_agent_info.yaml diff --git a/tests/utilities/cassettes/test_task_emits_failed_event_on_execution_error.yaml b/lib/crewai/tests/utilities/cassettes/test_task_emits_failed_event_on_execution_error.yaml similarity index 100% rename from tests/utilities/cassettes/test_task_emits_failed_event_on_execution_error.yaml rename to lib/crewai/tests/utilities/cassettes/test_task_emits_failed_event_on_execution_error.yaml diff --git a/tests/utilities/cassettes/test_tools_emits_error_events.yaml b/lib/crewai/tests/utilities/cassettes/test_tools_emits_error_events.yaml similarity index 100% rename from tests/utilities/cassettes/test_tools_emits_error_events.yaml rename to lib/crewai/tests/utilities/cassettes/test_tools_emits_error_events.yaml diff --git a/tests/utilities/cassettes/test_tools_emits_finished_events.yaml b/lib/crewai/tests/utilities/cassettes/test_tools_emits_finished_events.yaml similarity index 100% rename from tests/utilities/cassettes/test_tools_emits_finished_events.yaml rename to lib/crewai/tests/utilities/cassettes/test_tools_emits_finished_events.yaml diff --git a/lib/crewai/tests/utilities/crew/__init__.py b/lib/crewai/tests/utilities/crew/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/utilities/crew/test_crew_context.py b/lib/crewai/tests/utilities/crew/test_crew_context.py similarity index 99% rename from tests/utilities/crew/test_crew_context.py rename to lib/crewai/tests/utilities/crew/test_crew_context.py index 29ce5a356..c16647641 100644 --- a/tests/utilities/crew/test_crew_context.py +++ b/lib/crewai/tests/utilities/crew/test_crew_context.py @@ -1,11 +1,10 @@ import uuid import pytest -from opentelemetry import baggage -from opentelemetry.context import attach, detach - from crewai.utilities.crew.crew_context import get_crew_context from crewai.utilities.crew.models import CrewContext +from opentelemetry import baggage +from opentelemetry.context import attach, detach def test_crew_context_creation(): diff --git a/tests/utilities/evaluators/__init__.py b/lib/crewai/tests/utilities/evaluators/__init__.py similarity index 100% rename from tests/utilities/evaluators/__init__.py rename to lib/crewai/tests/utilities/evaluators/__init__.py diff --git a/tests/utilities/evaluators/test_crew_evaluator_handler.py b/lib/crewai/tests/utilities/evaluators/test_crew_evaluator_handler.py similarity index 99% rename from tests/utilities/evaluators/test_crew_evaluator_handler.py rename to lib/crewai/tests/utilities/evaluators/test_crew_evaluator_handler.py index 4fbe2b2d4..ededb89d2 100644 --- a/tests/utilities/evaluators/test_crew_evaluator_handler.py +++ b/lib/crewai/tests/utilities/evaluators/test_crew_evaluator_handler.py @@ -1,7 +1,6 @@ from unittest import mock import pytest - from crewai.agent import Agent from crewai.crew import Crew from crewai.task import Task diff --git a/tests/utilities/evaluators/test_task_evaluator.py b/lib/crewai/tests/utilities/evaluators/test_task_evaluator.py similarity index 97% rename from tests/utilities/evaluators/test_task_evaluator.py rename to lib/crewai/tests/utilities/evaluators/test_task_evaluator.py index 70a39c7b1..f933f9571 100644 --- a/tests/utilities/evaluators/test_task_evaluator.py +++ b/lib/crewai/tests/utilities/evaluators/test_task_evaluator.py @@ -1,12 +1,11 @@ from unittest import mock from unittest.mock import MagicMock, patch - +from crewai.utilities.converter import ConverterError from crewai.utilities.evaluators.task_evaluator import ( TaskEvaluator, TrainingTaskEvaluation, ) -from crewai.utilities.converter import ConverterError @patch("crewai.utilities.evaluators.task_evaluator.TrainingConverter") @@ -66,9 +65,12 @@ def test_evaluate_training_data(converter_mock): ] ) + @patch("crewai.utilities.converter.Converter.to_pydantic") @patch("crewai.utilities.training_converter.TrainingConverter._convert_field_by_field") -def test_training_converter_fallback_mechanism(convert_field_by_field_mock, to_pydantic_mock): +def test_training_converter_fallback_mechanism( + convert_field_by_field_mock, to_pydantic_mock +): training_data = { "agent_id": { "data1": { @@ -89,7 +91,7 @@ def test_training_converter_fallback_mechanism(convert_field_by_field_mock, to_p expected_result = TrainingTaskEvaluation( suggestions=["Fallback suggestion"], quality=6.5, - final_summary="Fallback summary" + final_summary="Fallback summary", ) convert_field_by_field_mock.return_value = expected_result diff --git a/tests/utilities/events/__init__.py b/lib/crewai/tests/utilities/events/__init__.py similarity index 100% rename from tests/utilities/events/__init__.py rename to lib/crewai/tests/utilities/events/__init__.py diff --git a/lib/crewai/tests/utilities/events/test_async_event_bus.py b/lib/crewai/tests/utilities/events/test_async_event_bus.py new file mode 100644 index 000000000..9925a0e6b --- /dev/null +++ b/lib/crewai/tests/utilities/events/test_async_event_bus.py @@ -0,0 +1,206 @@ +"""Tests for async event handling in CrewAI event bus. + +This module tests async handler registration, execution, and the aemit method. +""" + +import asyncio + +import pytest + +from crewai.events.base_events import BaseEvent +from crewai.events.event_bus import crewai_event_bus + + +class AsyncTestEvent(BaseEvent): + pass + + +@pytest.mark.asyncio +async def test_async_handler_execution(): + received_events = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(AsyncTestEvent) + async def async_handler(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.01) + received_events.append(event) + + event = AsyncTestEvent(type="async_test") + crewai_event_bus.emit("test_source", event) + + await asyncio.sleep(0.1) + + assert len(received_events) == 1 + assert received_events[0] == event + + +@pytest.mark.asyncio +async def test_aemit_with_async_handlers(): + received_events = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(AsyncTestEvent) + async def async_handler(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.01) + received_events.append(event) + + event = AsyncTestEvent(type="async_test") + await crewai_event_bus.aemit("test_source", event) + + assert len(received_events) == 1 + assert received_events[0] == event + + +@pytest.mark.asyncio +async def test_multiple_async_handlers(): + received_events_1 = [] + received_events_2 = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(AsyncTestEvent) + async def handler_1(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.01) + received_events_1.append(event) + + @crewai_event_bus.on(AsyncTestEvent) + async def handler_2(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.02) + received_events_2.append(event) + + event = AsyncTestEvent(type="async_test") + await crewai_event_bus.aemit("test_source", event) + + assert len(received_events_1) == 1 + assert len(received_events_2) == 1 + + +@pytest.mark.asyncio +async def test_mixed_sync_and_async_handlers(): + sync_events = [] + async_events = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(AsyncTestEvent) + def sync_handler(source: object, event: BaseEvent) -> None: + sync_events.append(event) + + @crewai_event_bus.on(AsyncTestEvent) + async def async_handler(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.01) + async_events.append(event) + + event = AsyncTestEvent(type="mixed_test") + crewai_event_bus.emit("test_source", event) + + await asyncio.sleep(0.1) + + assert len(sync_events) == 1 + assert len(async_events) == 1 + + +@pytest.mark.asyncio +async def test_async_handler_error_handling(): + successful_handler_called = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(AsyncTestEvent) + async def failing_handler(source: object, event: BaseEvent) -> None: + raise ValueError("Async handler error") + + @crewai_event_bus.on(AsyncTestEvent) + async def successful_handler(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.01) + successful_handler_called.append(True) + + event = AsyncTestEvent(type="error_test") + await crewai_event_bus.aemit("test_source", event) + + assert len(successful_handler_called) == 1 + + +@pytest.mark.asyncio +async def test_aemit_with_no_handlers(): + with crewai_event_bus.scoped_handlers(): + event = AsyncTestEvent(type="no_handlers") + await crewai_event_bus.aemit("test_source", event) + + +@pytest.mark.asyncio +async def test_async_handler_registration_via_register_handler(): + received_events = [] + + with crewai_event_bus.scoped_handlers(): + + async def custom_async_handler(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.01) + received_events.append(event) + + crewai_event_bus.register_handler(AsyncTestEvent, custom_async_handler) + + event = AsyncTestEvent(type="register_test") + await crewai_event_bus.aemit("test_source", event) + + assert len(received_events) == 1 + assert received_events[0] == event + + +@pytest.mark.asyncio +async def test_emit_async_handlers_fire_and_forget(): + received_events = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(AsyncTestEvent) + async def slow_async_handler(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.05) + received_events.append(event) + + event = AsyncTestEvent(type="fire_forget_test") + crewai_event_bus.emit("test_source", event) + + assert len(received_events) == 0 + + await asyncio.sleep(0.1) + + assert len(received_events) == 1 + + +@pytest.mark.asyncio +async def test_scoped_handlers_with_async(): + received_before = [] + received_during = [] + received_after = [] + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(AsyncTestEvent) + async def before_handler(source: object, event: BaseEvent) -> None: + received_before.append(event) + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(AsyncTestEvent) + async def scoped_handler(source: object, event: BaseEvent) -> None: + received_during.append(event) + + event1 = AsyncTestEvent(type="during_scope") + await crewai_event_bus.aemit("test_source", event1) + + assert len(received_before) == 0 + assert len(received_during) == 1 + + @crewai_event_bus.on(AsyncTestEvent) + async def after_handler(source: object, event: BaseEvent) -> None: + received_after.append(event) + + event2 = AsyncTestEvent(type="after_scope") + await crewai_event_bus.aemit("test_source", event2) + + assert len(received_before) == 1 + assert len(received_during) == 1 + assert len(received_after) == 1 diff --git a/lib/crewai/tests/utilities/events/test_crewai_event_bus.py b/lib/crewai/tests/utilities/events/test_crewai_event_bus.py new file mode 100644 index 000000000..9e9d9adaf --- /dev/null +++ b/lib/crewai/tests/utilities/events/test_crewai_event_bus.py @@ -0,0 +1,63 @@ +import threading +from unittest.mock import Mock + +from crewai.events.base_events import BaseEvent +from crewai.events.event_bus import crewai_event_bus + + +class TestEvent(BaseEvent): + pass + + +def test_specific_event_handler(): + mock_handler = Mock() + + @crewai_event_bus.on(TestEvent) + def handler(source, event): + mock_handler(source, event) + + event = TestEvent(type="test_event") + crewai_event_bus.emit("source_object", event) + + mock_handler.assert_called_once_with("source_object", event) + + +def test_multiple_handlers_same_event(): + """Test that multiple handlers can be registered for the same event type.""" + mock_handler1 = Mock() + mock_handler2 = Mock() + + @crewai_event_bus.on(TestEvent) + def handler1(source, event): + mock_handler1(source, event) + + @crewai_event_bus.on(TestEvent) + def handler2(source, event): + mock_handler2(source, event) + + event = TestEvent(type="test_event") + crewai_event_bus.emit("source_object", event) + + mock_handler1.assert_called_once_with("source_object", event) + mock_handler2.assert_called_once_with("source_object", event) + + +def test_event_bus_error_handling(): + """Test that handler exceptions are caught and don't break the event bus.""" + called = threading.Event() + error_caught = threading.Event() + + @crewai_event_bus.on(TestEvent) + def broken_handler(source, event): + called.set() + raise ValueError("Simulated handler failure") + + @crewai_event_bus.on(TestEvent) + def working_handler(source, event): + error_caught.set() + + event = TestEvent(type="test_event") + crewai_event_bus.emit("source_object", event) + + assert called.wait(timeout=2), "Broken handler was never called" + assert error_caught.wait(timeout=2), "Working handler was never called after error" diff --git a/lib/crewai/tests/utilities/events/test_rw_lock.py b/lib/crewai/tests/utilities/events/test_rw_lock.py new file mode 100644 index 000000000..27ebaf1d3 --- /dev/null +++ b/lib/crewai/tests/utilities/events/test_rw_lock.py @@ -0,0 +1,264 @@ +"""Tests for read-write lock implementation. + +This module tests the RWLock class for correct concurrent read and write behavior. +""" + +import threading +import time + +from crewai.utilities.rw_lock import RWLock + + +def test_multiple_readers_concurrent(): + lock = RWLock() + active_readers = [0] + max_concurrent_readers = [0] + lock_for_counters = threading.Lock() + + def reader(reader_id: int) -> None: + with lock.r_locked(): + with lock_for_counters: + active_readers[0] += 1 + max_concurrent_readers[0] = max( + max_concurrent_readers[0], active_readers[0] + ) + + time.sleep(0.1) + + with lock_for_counters: + active_readers[0] -= 1 + + threads = [threading.Thread(target=reader, args=(i,)) for i in range(5)] + + for thread in threads: + thread.start() + + for thread in threads: + thread.join() + + assert max_concurrent_readers[0] == 5 + + +def test_writer_blocks_readers(): + lock = RWLock() + writer_holding_lock = [False] + reader_accessed_during_write = [False] + + def writer() -> None: + with lock.w_locked(): + writer_holding_lock[0] = True + time.sleep(0.2) + writer_holding_lock[0] = False + + def reader() -> None: + time.sleep(0.05) + with lock.r_locked(): + if writer_holding_lock[0]: + reader_accessed_during_write[0] = True + + writer_thread = threading.Thread(target=writer) + reader_thread = threading.Thread(target=reader) + + writer_thread.start() + reader_thread.start() + + writer_thread.join() + reader_thread.join() + + assert not reader_accessed_during_write[0] + + +def test_writer_blocks_other_writers(): + lock = RWLock() + execution_order: list[int] = [] + lock_for_order = threading.Lock() + + def writer(writer_id: int) -> None: + with lock.w_locked(): + with lock_for_order: + execution_order.append(writer_id) + time.sleep(0.1) + + threads = [threading.Thread(target=writer, args=(i,)) for i in range(3)] + + for thread in threads: + thread.start() + + for thread in threads: + thread.join() + + assert len(execution_order) == 3 + assert len(set(execution_order)) == 3 + + +def test_readers_block_writers(): + lock = RWLock() + reader_count = [0] + writer_accessed_during_read = [False] + lock_for_counters = threading.Lock() + + def reader() -> None: + with lock.r_locked(): + with lock_for_counters: + reader_count[0] += 1 + time.sleep(0.2) + with lock_for_counters: + reader_count[0] -= 1 + + def writer() -> None: + time.sleep(0.05) + with lock.w_locked(): + with lock_for_counters: + if reader_count[0] > 0: + writer_accessed_during_read[0] = True + + reader_thread = threading.Thread(target=reader) + writer_thread = threading.Thread(target=writer) + + reader_thread.start() + writer_thread.start() + + reader_thread.join() + writer_thread.join() + + assert not writer_accessed_during_read[0] + + +def test_alternating_readers_and_writers(): + lock = RWLock() + operations: list[str] = [] + lock_for_operations = threading.Lock() + + def reader(reader_id: int) -> None: + with lock.r_locked(): + with lock_for_operations: + operations.append(f"r{reader_id}_start") + time.sleep(0.05) + with lock_for_operations: + operations.append(f"r{reader_id}_end") + + def writer(writer_id: int) -> None: + with lock.w_locked(): + with lock_for_operations: + operations.append(f"w{writer_id}_start") + time.sleep(0.05) + with lock_for_operations: + operations.append(f"w{writer_id}_end") + + threads = [ + threading.Thread(target=reader, args=(0,)), + threading.Thread(target=writer, args=(0,)), + threading.Thread(target=reader, args=(1,)), + threading.Thread(target=writer, args=(1,)), + threading.Thread(target=reader, args=(2,)), + ] + + for thread in threads: + thread.start() + + for thread in threads: + thread.join() + + assert len(operations) == 10 + + start_ops = [op for op in operations if "_start" in op] + end_ops = [op for op in operations if "_end" in op] + assert len(start_ops) == 5 + assert len(end_ops) == 5 + + +def test_context_manager_releases_on_exception(): + lock = RWLock() + exception_raised = False + + try: + with lock.r_locked(): + raise ValueError("Test exception") + except ValueError: + exception_raised = True + + assert exception_raised + + acquired = False + with lock.w_locked(): + acquired = True + + assert acquired + + +def test_write_lock_releases_on_exception(): + lock = RWLock() + exception_raised = False + + try: + with lock.w_locked(): + raise ValueError("Test exception") + except ValueError: + exception_raised = True + + assert exception_raised + + acquired = False + with lock.r_locked(): + acquired = True + + assert acquired + + +def test_stress_many_readers_few_writers(): + lock = RWLock() + read_count = [0] + write_count = [0] + lock_for_counters = threading.Lock() + + def reader() -> None: + for _ in range(10): + with lock.r_locked(): + with lock_for_counters: + read_count[0] += 1 + time.sleep(0.001) + + def writer() -> None: + for _ in range(5): + with lock.w_locked(): + with lock_for_counters: + write_count[0] += 1 + time.sleep(0.01) + + reader_threads = [threading.Thread(target=reader) for _ in range(10)] + writer_threads = [threading.Thread(target=writer) for _ in range(2)] + + all_threads = reader_threads + writer_threads + + for thread in all_threads: + thread.start() + + for thread in all_threads: + thread.join() + + assert read_count[0] == 100 + assert write_count[0] == 10 + + +def test_nested_read_locks_same_thread(): + lock = RWLock() + nested_acquired = False + + with lock.r_locked(): + with lock.r_locked(): + nested_acquired = True + + assert nested_acquired + + +def test_manual_acquire_release(): + lock = RWLock() + + lock.r_acquire() + lock.r_release() + + lock.w_acquire() + lock.w_release() + + with lock.r_locked(): + pass diff --git a/lib/crewai/tests/utilities/events/test_shutdown.py b/lib/crewai/tests/utilities/events/test_shutdown.py new file mode 100644 index 000000000..eeac0c667 --- /dev/null +++ b/lib/crewai/tests/utilities/events/test_shutdown.py @@ -0,0 +1,247 @@ +"""Tests for event bus shutdown and cleanup behavior. + +This module tests graceful shutdown, task completion, and cleanup operations. +""" + +import asyncio +import threading +import time + +import pytest + +from crewai.events.base_events import BaseEvent +from crewai.events.event_bus import CrewAIEventsBus + + +class ShutdownTestEvent(BaseEvent): + pass + + +def test_shutdown_prevents_new_events(): + bus = CrewAIEventsBus() + received_events = [] + + with bus.scoped_handlers(): + + @bus.on(ShutdownTestEvent) + def handler(source: object, event: BaseEvent) -> None: + received_events.append(event) + + bus._shutting_down = True + + event = ShutdownTestEvent(type="after_shutdown") + bus.emit("test_source", event) + + time.sleep(0.1) + + assert len(received_events) == 0 + + bus._shutting_down = False + + +@pytest.mark.asyncio +async def test_aemit_during_shutdown(): + bus = CrewAIEventsBus() + received_events = [] + + with bus.scoped_handlers(): + + @bus.on(ShutdownTestEvent) + async def handler(source: object, event: BaseEvent) -> None: + received_events.append(event) + + bus._shutting_down = True + + event = ShutdownTestEvent(type="aemit_during_shutdown") + await bus.aemit("test_source", event) + + assert len(received_events) == 0 + + bus._shutting_down = False + + +def test_shutdown_flag_prevents_emit(): + bus = CrewAIEventsBus() + emitted_count = [0] + + with bus.scoped_handlers(): + + @bus.on(ShutdownTestEvent) + def handler(source: object, event: BaseEvent) -> None: + emitted_count[0] += 1 + + event1 = ShutdownTestEvent(type="before_shutdown") + bus.emit("test_source", event1) + + time.sleep(0.1) + assert emitted_count[0] == 1 + + bus._shutting_down = True + + event2 = ShutdownTestEvent(type="during_shutdown") + bus.emit("test_source", event2) + + time.sleep(0.1) + assert emitted_count[0] == 1 + + bus._shutting_down = False + + +def test_concurrent_access_during_shutdown_flag(): + bus = CrewAIEventsBus() + received_events = [] + lock = threading.Lock() + + with bus.scoped_handlers(): + + @bus.on(ShutdownTestEvent) + def handler(source: object, event: BaseEvent) -> None: + with lock: + received_events.append(event) + + def emit_events() -> None: + for i in range(10): + event = ShutdownTestEvent(type=f"event_{i}") + bus.emit("source", event) + time.sleep(0.01) + + def set_shutdown_flag() -> None: + time.sleep(0.05) + bus._shutting_down = True + + emit_thread = threading.Thread(target=emit_events) + shutdown_thread = threading.Thread(target=set_shutdown_flag) + + emit_thread.start() + shutdown_thread.start() + + emit_thread.join() + shutdown_thread.join() + + time.sleep(0.2) + + assert len(received_events) < 10 + assert len(received_events) > 0 + + bus._shutting_down = False + + +@pytest.mark.asyncio +async def test_async_handlers_complete_before_shutdown_flag(): + bus = CrewAIEventsBus() + completed_handlers = [] + + with bus.scoped_handlers(): + + @bus.on(ShutdownTestEvent) + async def async_handler(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.05) + if not bus._shutting_down: + completed_handlers.append(event) + + for i in range(5): + event = ShutdownTestEvent(type=f"event_{i}") + bus.emit("source", event) + + await asyncio.sleep(0.3) + + assert len(completed_handlers) == 5 + + +def test_scoped_handlers_cleanup(): + bus = CrewAIEventsBus() + received_before = [] + received_during = [] + received_after = [] + + with bus.scoped_handlers(): + + @bus.on(ShutdownTestEvent) + def before_handler(source: object, event: BaseEvent) -> None: + received_before.append(event) + + with bus.scoped_handlers(): + + @bus.on(ShutdownTestEvent) + def during_handler(source: object, event: BaseEvent) -> None: + received_during.append(event) + + event1 = ShutdownTestEvent(type="during") + bus.emit("source", event1) + time.sleep(0.1) + + assert len(received_before) == 0 + assert len(received_during) == 1 + + event2 = ShutdownTestEvent(type="after_inner_scope") + bus.emit("source", event2) + time.sleep(0.1) + + assert len(received_before) == 1 + assert len(received_during) == 1 + + event3 = ShutdownTestEvent(type="after_outer_scope") + bus.emit("source", event3) + time.sleep(0.1) + + assert len(received_before) == 1 + assert len(received_during) == 1 + assert len(received_after) == 0 + + +def test_handler_registration_thread_safety(): + bus = CrewAIEventsBus() + handlers_registered = [0] + lock = threading.Lock() + + with bus.scoped_handlers(): + + def register_handlers() -> None: + for _ in range(20): + + @bus.on(ShutdownTestEvent) + def handler(source: object, event: BaseEvent) -> None: + pass + + with lock: + handlers_registered[0] += 1 + + time.sleep(0.001) + + threads = [threading.Thread(target=register_handlers) for _ in range(3)] + + for thread in threads: + thread.start() + + for thread in threads: + thread.join() + + assert handlers_registered[0] == 60 + + +@pytest.mark.asyncio +async def test_mixed_sync_async_handler_execution(): + bus = CrewAIEventsBus() + sync_executed = [] + async_executed = [] + + with bus.scoped_handlers(): + + @bus.on(ShutdownTestEvent) + def sync_handler(source: object, event: BaseEvent) -> None: + time.sleep(0.01) + sync_executed.append(event) + + @bus.on(ShutdownTestEvent) + async def async_handler(source: object, event: BaseEvent) -> None: + await asyncio.sleep(0.01) + async_executed.append(event) + + for i in range(5): + event = ShutdownTestEvent(type=f"event_{i}") + bus.emit("source", event) + + await asyncio.sleep(0.2) + + assert len(sync_executed) == 5 + assert len(async_executed) == 5 diff --git a/lib/crewai/tests/utilities/events/test_thread_safety.py b/lib/crewai/tests/utilities/events/test_thread_safety.py new file mode 100644 index 000000000..8702e484d --- /dev/null +++ b/lib/crewai/tests/utilities/events/test_thread_safety.py @@ -0,0 +1,189 @@ +"""Tests for thread safety in CrewAI event bus. + +This module tests concurrent event emission and handler registration. +""" + +import threading +import time +from collections.abc import Callable + +from crewai.events.base_events import BaseEvent +from crewai.events.event_bus import crewai_event_bus + + +class ThreadSafetyTestEvent(BaseEvent): + pass + + +def test_concurrent_emit_from_multiple_threads(): + received_events: list[BaseEvent] = [] + lock = threading.Lock() + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(ThreadSafetyTestEvent) + def handler(source: object, event: BaseEvent) -> None: + with lock: + received_events.append(event) + + threads: list[threading.Thread] = [] + num_threads = 10 + events_per_thread = 10 + + def emit_events(thread_id: int) -> None: + for i in range(events_per_thread): + event = ThreadSafetyTestEvent(type=f"thread_{thread_id}_event_{i}") + crewai_event_bus.emit(f"source_{thread_id}", event) + + for i in range(num_threads): + thread = threading.Thread(target=emit_events, args=(i,)) + threads.append(thread) + thread.start() + + for thread in threads: + thread.join() + + time.sleep(0.5) + + assert len(received_events) == num_threads * events_per_thread + + +def test_concurrent_handler_registration(): + handlers_executed: list[int] = [] + lock = threading.Lock() + + def create_handler(handler_id: int) -> Callable[[object, BaseEvent], None]: + def handler(source: object, event: BaseEvent) -> None: + with lock: + handlers_executed.append(handler_id) + + return handler + + with crewai_event_bus.scoped_handlers(): + threads: list[threading.Thread] = [] + num_handlers = 20 + + def register_handler(handler_id: int) -> None: + crewai_event_bus.register_handler( + ThreadSafetyTestEvent, create_handler(handler_id) + ) + + for i in range(num_handlers): + thread = threading.Thread(target=register_handler, args=(i,)) + threads.append(thread) + thread.start() + + for thread in threads: + thread.join() + + event = ThreadSafetyTestEvent(type="registration_test") + crewai_event_bus.emit("test_source", event) + + time.sleep(0.5) + + assert len(handlers_executed) == num_handlers + assert set(handlers_executed) == set(range(num_handlers)) + + +def test_concurrent_emit_and_registration(): + received_events: list[BaseEvent] = [] + lock = threading.Lock() + + with crewai_event_bus.scoped_handlers(): + + def emit_continuously() -> None: + for i in range(50): + event = ThreadSafetyTestEvent(type=f"emit_event_{i}") + crewai_event_bus.emit("emitter", event) + time.sleep(0.001) + + def register_continuously() -> None: + for _ in range(10): + + @crewai_event_bus.on(ThreadSafetyTestEvent) + def handler(source: object, event: BaseEvent) -> None: + with lock: + received_events.append(event) + + time.sleep(0.005) + + emit_thread = threading.Thread(target=emit_continuously) + register_thread = threading.Thread(target=register_continuously) + + emit_thread.start() + register_thread.start() + + emit_thread.join() + register_thread.join() + + time.sleep(0.5) + + assert len(received_events) > 0 + + +def test_stress_test_rapid_emit(): + received_count = [0] + lock = threading.Lock() + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(ThreadSafetyTestEvent) + def counter_handler(source: object, event: BaseEvent) -> None: + with lock: + received_count[0] += 1 + + num_events = 1000 + + for i in range(num_events): + event = ThreadSafetyTestEvent(type=f"rapid_event_{i}") + crewai_event_bus.emit("rapid_source", event) + + time.sleep(1.0) + + assert received_count[0] == num_events + + +def test_multiple_event_types_concurrent(): + class EventTypeA(BaseEvent): + pass + + class EventTypeB(BaseEvent): + pass + + received_a: list[BaseEvent] = [] + received_b: list[BaseEvent] = [] + lock = threading.Lock() + + with crewai_event_bus.scoped_handlers(): + + @crewai_event_bus.on(EventTypeA) + def handler_a(source: object, event: BaseEvent) -> None: + with lock: + received_a.append(event) + + @crewai_event_bus.on(EventTypeB) + def handler_b(source: object, event: BaseEvent) -> None: + with lock: + received_b.append(event) + + def emit_type_a() -> None: + for i in range(50): + crewai_event_bus.emit("source_a", EventTypeA(type=f"type_a_{i}")) + + def emit_type_b() -> None: + for i in range(50): + crewai_event_bus.emit("source_b", EventTypeB(type=f"type_b_{i}")) + + thread_a = threading.Thread(target=emit_type_a) + thread_b = threading.Thread(target=emit_type_b) + + thread_a.start() + thread_b.start() + + thread_a.join() + thread_b.join() + + time.sleep(0.5) + + assert len(received_a) == 50 + assert len(received_b) == 50 diff --git a/tests/utilities/prompts.json b/lib/crewai/tests/utilities/prompts.json similarity index 100% rename from tests/utilities/prompts.json rename to lib/crewai/tests/utilities/prompts.json diff --git a/tests/utilities/test_console_formatter_pause_resume.py b/lib/crewai/tests/utilities/test_console_formatter_pause_resume.py similarity index 100% rename from tests/utilities/test_console_formatter_pause_resume.py rename to lib/crewai/tests/utilities/test_console_formatter_pause_resume.py diff --git a/tests/utilities/test_converter.py b/lib/crewai/tests/utilities/test_converter.py similarity index 97% rename from tests/utilities/test_converter.py rename to lib/crewai/tests/utilities/test_converter.py index 7ebc52bed..cc9f3ee5d 100644 --- a/tests/utilities/test_converter.py +++ b/lib/crewai/tests/utilities/test_converter.py @@ -1,10 +1,9 @@ +# Tests for enums +from enum import Enum import json -from typing import Dict, List, Optional +import os from unittest.mock import MagicMock, Mock, patch -import pytest -from pydantic import BaseModel - from crewai.llm import LLM from crewai.utilities.converter import ( Converter, @@ -18,14 +17,14 @@ from crewai.utilities.converter import ( validate_model, ) from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser -# Tests for enums -from enum import Enum +from pydantic import BaseModel +import pytest @pytest.fixture(scope="module") def vcr_config(request) -> dict: return { - "cassette_library_dir": "tests/utilities/cassettes", + "cassette_library_dir": os.path.join(os.path.dirname(__file__), "cassettes"), } @@ -253,7 +252,7 @@ def test_supports_function_calling_true(): def test_supports_function_calling_false(): - llm = LLM(model="non-existent-model") + llm = LLM(model="non-existent-model", is_litellm=True) assert llm.supports_function_calling() is False @@ -310,17 +309,17 @@ def test_generate_model_description_nested_model(): def test_generate_model_description_optional_field(): class ModelWithOptionalField(BaseModel): - name: Optional[str] - age: int + name: str + age: int | None description = generate_model_description(ModelWithOptionalField) - expected_description = '{\n "name": Optional[str],\n "age": int\n}' + expected_description = '{\n "name": str,\n "age": int | None\n}' assert description == expected_description def test_generate_model_description_list_field(): class ModelWithListField(BaseModel): - items: List[int] + items: list[int] description = generate_model_description(ModelWithListField) expected_description = '{\n "items": List[int]\n}' @@ -329,7 +328,7 @@ def test_generate_model_description_list_field(): def test_generate_model_description_dict_field(): class ModelWithDictField(BaseModel): - attributes: Dict[str, int] + attributes: dict[str, int] description = generate_model_description(ModelWithDictField) expected_description = '{\n "attributes": Dict[str, int]\n}' @@ -469,7 +468,7 @@ def test_converter_retry_logic(): def test_converter_with_optional_fields(): class OptionalModel(BaseModel): name: str - age: Optional[int] + age: int | None llm = Mock(spec=LLM) llm.supports_function_calling.return_value = False @@ -495,7 +494,7 @@ def test_converter_with_optional_fields(): # Tests for list fields def test_converter_with_list_field(): class ListModel(BaseModel): - items: List[int] + items: list[int] llm = Mock(spec=LLM) llm.supports_function_calling.return_value = False diff --git a/tests/utilities/test_events.py b/lib/crewai/tests/utilities/test_events.py similarity index 60% rename from tests/utilities/test_events.py rename to lib/crewai/tests/utilities/test_events.py index 505504c8e..1eeba199a 100644 --- a/tests/utilities/test_events.py +++ b/lib/crewai/tests/utilities/test_events.py @@ -1,16 +1,13 @@ +import threading from datetime import datetime +import os from unittest.mock import Mock, patch -import pytest -from pydantic import Field - from crewai.agent import Agent from crewai.agents.crew_agent_executor import CrewAgentExecutor from crewai.crew import Crew -from crewai.flow.flow import Flow, listen, start -from crewai.llm import LLM -from crewai.task import Task -from crewai.tools.base_tool import BaseTool +from crewai.events.event_bus import crewai_event_bus +from crewai.events.event_listener import EventListener from crewai.events.types.agent_events import ( AgentExecutionCompletedEvent, AgentExecutionErrorEvent, @@ -24,9 +21,6 @@ from crewai.events.types.crew_events import ( CrewTestResultEvent, CrewTestStartedEvent, ) -from crewai.events.event_bus import crewai_event_bus -from crewai.events.event_listener import EventListener -from crewai.events.types.tool_usage_events import ToolUsageFinishedEvent from crewai.events.types.flow_events import ( FlowCreatedEvent, FlowFinishedEvent, @@ -47,13 +41,22 @@ from crewai.events.types.task_events import ( ) from crewai.events.types.tool_usage_events import ( ToolUsageErrorEvent, + ToolUsageFinishedEvent, ) +from crewai.flow.flow import Flow, listen, start +from crewai.llm import LLM +from crewai.task import Task +from crewai.tools.base_tool import BaseTool +from pydantic import Field +import pytest + +from ..utils import wait_for_event_handlers @pytest.fixture(scope="module") def vcr_config(request) -> dict: return { - "cassette_library_dir": "tests/utilities/cassettes", + "cassette_library_dir": os.path.join(os.path.dirname(__file__), "cassettes"), } @@ -118,6 +121,7 @@ def test_crew_emits_start_kickoff_event( # Now when Crew creates EventListener, it will use our mocked telemetry crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") crew.kickoff() + wait_for_event_handlers() mock_telemetry.crew_execution_span.assert_called_once_with(crew, None) mock_telemetry.end_crew.assert_called_once_with(crew, "hi") @@ -131,15 +135,20 @@ def test_crew_emits_start_kickoff_event( @pytest.mark.vcr(filter_headers=["authorization"]) def test_crew_emits_end_kickoff_event(base_agent, base_task): received_events = [] + event_received = threading.Event() @crewai_event_bus.on(CrewKickoffCompletedEvent) def handle_crew_end(source, event): received_events.append(event) + event_received.set() crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") crew.kickoff() + assert event_received.wait(timeout=5), ( + "Timeout waiting for crew kickoff completed event" + ) assert len(received_events) == 1 assert received_events[0].crew_name == "TestCrew" assert isinstance(received_events[0].timestamp, datetime) @@ -165,6 +174,7 @@ def test_crew_emits_test_kickoff_type_event(base_agent, base_task): eval_llm = LLM(model="gpt-4o-mini") crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") crew.test(n_iterations=1, eval_llm=eval_llm) + wait_for_event_handlers() assert len(received_events) == 3 assert received_events[0].crew_name == "TestCrew" @@ -181,40 +191,44 @@ def test_crew_emits_test_kickoff_type_event(base_agent, base_task): @pytest.mark.vcr(filter_headers=["authorization"]) def test_crew_emits_kickoff_failed_event(base_agent, base_task): received_events = [] + event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(CrewKickoffFailedEvent) + def handle_crew_failed(source, event): + received_events.append(event) + event_received.set() - @crewai_event_bus.on(CrewKickoffFailedEvent) - def handle_crew_failed(source, event): - received_events.append(event) + crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") - crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") + with patch.object(Crew, "_execute_tasks") as mock_execute: + error_message = "Simulated crew kickoff failure" + mock_execute.side_effect = Exception(error_message) - with patch.object(Crew, "_execute_tasks") as mock_execute: - error_message = "Simulated crew kickoff failure" - mock_execute.side_effect = Exception(error_message) + with pytest.raises(Exception): # noqa: B017 + crew.kickoff() - with pytest.raises(Exception): - crew.kickoff() - - assert len(received_events) == 1 - assert received_events[0].error == error_message - assert isinstance(received_events[0].timestamp, datetime) - assert received_events[0].type == "crew_kickoff_failed" + assert event_received.wait(timeout=5), "Timeout waiting for failed event" + assert len(received_events) == 1 + assert received_events[0].error == error_message + assert isinstance(received_events[0].timestamp, datetime) + assert received_events[0].type == "crew_kickoff_failed" @pytest.mark.vcr(filter_headers=["authorization"]) def test_crew_emits_start_task_event(base_agent, base_task): received_events = [] + event_received = threading.Event() @crewai_event_bus.on(TaskStartedEvent) def handle_task_start(source, event): received_events.append(event) + event_received.set() crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") crew.kickoff() + assert event_received.wait(timeout=5), "Timeout waiting for task started event" assert len(received_events) == 1 assert isinstance(received_events[0].timestamp, datetime) assert received_events[0].type == "task_started" @@ -225,10 +239,12 @@ def test_crew_emits_end_task_event( base_agent, base_task, reset_event_listener_singleton ): received_events = [] + event_received = threading.Event() @crewai_event_bus.on(TaskCompletedEvent) def handle_task_end(source, event): received_events.append(event) + event_received.set() mock_span = Mock() @@ -246,6 +262,7 @@ def test_crew_emits_end_task_event( mock_telemetry.task_started.assert_called_once_with(crew=crew, task=base_task) mock_telemetry.task_ended.assert_called_once_with(mock_span, base_task, crew) + assert event_received.wait(timeout=5), "Timeout waiting for task completed event" assert len(received_events) == 1 assert isinstance(received_events[0].timestamp, datetime) assert received_events[0].type == "task_completed" @@ -255,11 +272,13 @@ def test_crew_emits_end_task_event( def test_task_emits_failed_event_on_execution_error(base_agent, base_task): received_events = [] received_sources = [] + event_received = threading.Event() @crewai_event_bus.on(TaskFailedEvent) def handle_task_failed(source, event): received_events.append(event) received_sources.append(source) + event_received.set() with patch.object( Task, @@ -278,9 +297,12 @@ def test_task_emits_failed_event_on_execution_error(base_agent, base_task): agent=agent, ) - with pytest.raises(Exception): + with pytest.raises(Exception): # noqa: B017 agent.execute_task(task=task) + assert event_received.wait(timeout=5), ( + "Timeout waiting for task failed event" + ) assert len(received_events) == 1 assert received_sources[0] == task assert received_events[0].error == error_message @@ -291,17 +313,27 @@ def test_task_emits_failed_event_on_execution_error(base_agent, base_task): @pytest.mark.vcr(filter_headers=["authorization"]) def test_agent_emits_execution_started_and_completed_events(base_agent, base_task): received_events = [] + lock = threading.Lock() + all_events_received = threading.Event() @crewai_event_bus.on(AgentExecutionStartedEvent) def handle_agent_start(source, event): - received_events.append(event) + with lock: + received_events.append(event) @crewai_event_bus.on(AgentExecutionCompletedEvent) def handle_agent_completed(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) >= 2: + all_events_received.set() crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") crew.kickoff() + + assert all_events_received.wait(timeout=5), ( + "Timeout waiting for agent execution events" + ) assert len(received_events) == 2 assert received_events[0].agent == base_agent assert received_events[0].task == base_task @@ -320,10 +352,12 @@ def test_agent_emits_execution_started_and_completed_events(base_agent, base_tas @pytest.mark.vcr(filter_headers=["authorization"]) def test_agent_emits_execution_error_event(base_agent, base_task): received_events = [] + event_received = threading.Event() @crewai_event_bus.on(AgentExecutionErrorEvent) def handle_agent_start(source, event): received_events.append(event) + event_received.set() error_message = "Error happening while sending prompt to model." base_agent.max_retry_limit = 0 @@ -332,11 +366,14 @@ def test_agent_emits_execution_error_event(base_agent, base_task): ) as invoke_mock: invoke_mock.side_effect = Exception(error_message) - with pytest.raises(Exception): + with pytest.raises(Exception): # noqa: B017 base_agent.execute_task( task=base_task, ) + assert event_received.wait(timeout=5), ( + "Timeout waiting for agent execution error event" + ) assert len(received_events) == 1 assert received_events[0].agent == base_agent assert received_events[0].task == base_task @@ -358,10 +395,12 @@ class SayHiTool(BaseTool): @pytest.mark.vcr(filter_headers=["authorization"]) def test_tools_emits_finished_events(): received_events = [] + event_received = threading.Event() @crewai_event_bus.on(ToolUsageFinishedEvent) def handle_tool_end(source, event): received_events.append(event) + event_received.set() agent = Agent( role="base_agent", @@ -377,6 +416,10 @@ def test_tools_emits_finished_events(): ) crew = Crew(agents=[agent], tasks=[task], name="TestCrew") crew.kickoff() + + assert event_received.wait(timeout=5), ( + "Timeout waiting for tool usage finished event" + ) assert len(received_events) == 1 assert received_events[0].agent_key == agent.key assert received_events[0].agent_role == agent.role @@ -389,10 +432,15 @@ def test_tools_emits_finished_events(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_tools_emits_error_events(): received_events = [] + lock = threading.Lock() + all_events_received = threading.Event() @crewai_event_bus.on(ToolUsageErrorEvent) def handle_tool_end(source, event): - received_events.append(event) + with lock: + received_events.append(event) + if len(received_events) >= 48: + all_events_received.set() class ErrorTool(BaseTool): name: str = Field( @@ -423,6 +471,9 @@ def test_tools_emits_error_events(): crew = Crew(agents=[agent], tasks=[task], name="TestCrew") crew.kickoff() + assert all_events_received.wait(timeout=5), ( + "Timeout waiting for tool usage error events" + ) assert len(received_events) == 48 assert received_events[0].agent_key == agent.key assert received_events[0].agent_role == agent.role @@ -435,11 +486,13 @@ def test_tools_emits_error_events(): def test_flow_emits_start_event(reset_event_listener_singleton): received_events = [] + event_received = threading.Event() mock_span = Mock() @crewai_event_bus.on(FlowStartedEvent) def handle_flow_start(source, event): received_events.append(event) + event_received.set() class TestFlow(Flow[dict]): @start() @@ -458,6 +511,7 @@ def test_flow_emits_start_event(reset_event_listener_singleton): flow = TestFlow() flow.kickoff() + assert event_received.wait(timeout=5), "Timeout waiting for flow started event" mock_telemetry.flow_execution_span.assert_called_once_with("TestFlow", ["begin"]) assert len(received_events) == 1 assert received_events[0].flow_name == "TestFlow" @@ -466,6 +520,7 @@ def test_flow_emits_start_event(reset_event_listener_singleton): def test_flow_name_emitted_to_event_bus(): received_events = [] + event_received = threading.Event() class MyFlowClass(Flow): name = "PRODUCTION_FLOW" @@ -477,119 +532,133 @@ def test_flow_name_emitted_to_event_bus(): @crewai_event_bus.on(FlowStartedEvent) def handle_flow_start(source, event): received_events.append(event) + event_received.set() flow = MyFlowClass() flow.kickoff() + assert event_received.wait(timeout=5), "Timeout waiting for flow started event" assert len(received_events) == 1 assert received_events[0].flow_name == "PRODUCTION_FLOW" def test_flow_emits_finish_event(): received_events = [] + event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(FlowFinishedEvent) + def handle_flow_finish(source, event): + received_events.append(event) + event_received.set() - @crewai_event_bus.on(FlowFinishedEvent) - def handle_flow_finish(source, event): - received_events.append(event) + class TestFlow(Flow[dict]): + @start() + def begin(self): + return "completed" - class TestFlow(Flow[dict]): - @start() - def begin(self): - return "completed" + flow = TestFlow() + result = flow.kickoff() - flow = TestFlow() - result = flow.kickoff() - - assert len(received_events) == 1 - assert received_events[0].flow_name == "TestFlow" - assert received_events[0].type == "flow_finished" - assert received_events[0].result == "completed" - assert result == "completed" + assert event_received.wait(timeout=5), "Timeout waiting for finish event" + assert len(received_events) == 1 + assert received_events[0].flow_name == "TestFlow" + assert received_events[0].type == "flow_finished" + assert received_events[0].result == "completed" + assert result == "completed" def test_flow_emits_method_execution_started_event(): received_events = [] + lock = threading.Lock() + second_event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): - - @crewai_event_bus.on(MethodExecutionStartedEvent) - def handle_method_start(source, event): - print("event in method name", event.method_name) + @crewai_event_bus.on(MethodExecutionStartedEvent) + async def handle_method_start(source, event): + with lock: received_events.append(event) + if event.method_name == "second_method": + second_event_received.set() - class TestFlow(Flow[dict]): - @start() - def begin(self): - return "started" + class TestFlow(Flow[dict]): + @start() + def begin(self): + return "started" - @listen("begin") - def second_method(self): - return "executed" + @listen("begin") + def second_method(self): + return "executed" - flow = TestFlow() - flow.kickoff() + flow = TestFlow() + flow.kickoff() - assert len(received_events) == 2 + assert second_event_received.wait(timeout=5), ( + "Timeout waiting for second_method event" + ) + assert len(received_events) == 2 - assert received_events[0].method_name == "begin" - assert received_events[0].flow_name == "TestFlow" - assert received_events[0].type == "method_execution_started" + # Events may arrive in any order due to async handlers, so check both are present + method_names = {event.method_name for event in received_events} + assert method_names == {"begin", "second_method"} - assert received_events[1].method_name == "second_method" - assert received_events[1].flow_name == "TestFlow" - assert received_events[1].type == "method_execution_started" + for event in received_events: + assert event.flow_name == "TestFlow" + assert event.type == "method_execution_started" @pytest.mark.vcr(filter_headers=["authorization"]) def test_register_handler_adds_new_handler(base_agent, base_task): received_events = [] + event_received = threading.Event() def custom_handler(source, event): received_events.append(event) + event_received.set() - with crewai_event_bus.scoped_handlers(): - crewai_event_bus.register_handler(CrewKickoffStartedEvent, custom_handler) + crewai_event_bus.register_handler(CrewKickoffStartedEvent, custom_handler) - crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") - crew.kickoff() + crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") + crew.kickoff() - assert len(received_events) == 1 - assert isinstance(received_events[0].timestamp, datetime) - assert received_events[0].type == "crew_kickoff_started" + assert event_received.wait(timeout=5), "Timeout waiting for handler event" + assert len(received_events) == 1 + assert isinstance(received_events[0].timestamp, datetime) + assert received_events[0].type == "crew_kickoff_started" @pytest.mark.vcr(filter_headers=["authorization"]) def test_multiple_handlers_for_same_event(base_agent, base_task): received_events_1 = [] received_events_2 = [] + event_received = threading.Event() def handler_1(source, event): received_events_1.append(event) def handler_2(source, event): received_events_2.append(event) + event_received.set() - with crewai_event_bus.scoped_handlers(): - crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_1) - crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_2) + crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_1) + crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_2) - crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") - crew.kickoff() + crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew") + crew.kickoff() - assert len(received_events_1) == 1 - assert len(received_events_2) == 1 - assert received_events_1[0].type == "crew_kickoff_started" - assert received_events_2[0].type == "crew_kickoff_started" + assert event_received.wait(timeout=5), "Timeout waiting for handler events" + assert len(received_events_1) == 1 + assert len(received_events_2) == 1 + assert received_events_1[0].type == "crew_kickoff_started" + assert received_events_2[0].type == "crew_kickoff_started" def test_flow_emits_created_event(): received_events = [] + event_received = threading.Event() @crewai_event_bus.on(FlowCreatedEvent) def handle_flow_created(source, event): received_events.append(event) + event_received.set() class TestFlow(Flow[dict]): @start() @@ -599,6 +668,7 @@ def test_flow_emits_created_event(): flow = TestFlow() flow.kickoff() + assert event_received.wait(timeout=5), "Timeout waiting for flow created event" assert len(received_events) == 1 assert received_events[0].flow_name == "TestFlow" assert received_events[0].type == "flow_created" @@ -606,11 +676,13 @@ def test_flow_emits_created_event(): def test_flow_emits_method_execution_failed_event(): received_events = [] + event_received = threading.Event() error = Exception("Simulated method failure") @crewai_event_bus.on(MethodExecutionFailedEvent) def handle_method_failed(source, event): received_events.append(event) + event_received.set() class TestFlow(Flow[dict]): @start() @@ -618,9 +690,12 @@ def test_flow_emits_method_execution_failed_event(): raise error flow = TestFlow() - with pytest.raises(Exception): + with pytest.raises(Exception): # noqa: B017 flow.kickoff() + assert event_received.wait(timeout=5), ( + "Timeout waiting for method execution failed event" + ) assert len(received_events) == 1 assert received_events[0].method_name == "begin" assert received_events[0].flow_name == "TestFlow" @@ -642,6 +717,7 @@ def test_llm_emits_call_started_event(): llm = LLM(model="gpt-4o-mini") llm.call("Hello, how are you?") + wait_for_event_handlers() assert len(received_events) == 2 assert received_events[0].type == "llm_call_started" @@ -654,20 +730,29 @@ def test_llm_emits_call_started_event(): @pytest.mark.vcr(filter_headers=["authorization"]) +@pytest.mark.isolated def test_llm_emits_call_failed_event(): received_events = [] + event_received = threading.Event() @crewai_event_bus.on(LLMCallFailedEvent) def handle_llm_call_failed(source, event): received_events.append(event) + event_received.set() + + error_message = "OpenAI API call failed: Simulated API failure" + + with patch( + "crewai.llms.providers.openai.completion.OpenAICompletion._handle_completion" + ) as mock_handle_completion: + mock_handle_completion.side_effect = Exception("Simulated API failure") - error_message = "Simulated LLM call failure" - with patch("crewai.llm.litellm.completion", side_effect=Exception(error_message)): llm = LLM(model="gpt-4o-mini") with pytest.raises(Exception) as exc_info: llm.call("Hello, how are you?") - assert str(exc_info.value) == error_message + assert str(exc_info.value) == "Simulated API failure" + assert event_received.wait(timeout=5), "Timeout waiting for failed event" assert len(received_events) == 1 assert received_events[0].type == "llm_call_failed" assert received_events[0].error == error_message @@ -681,24 +766,28 @@ def test_llm_emits_call_failed_event(): def test_llm_emits_stream_chunk_events(): """Test that LLM emits stream chunk events when streaming is enabled.""" received_chunks = [] + event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(LLMStreamChunkEvent) + def handle_stream_chunk(source, event): + received_chunks.append(event.chunk) + if len(received_chunks) >= 1: + event_received.set() - @crewai_event_bus.on(LLMStreamChunkEvent) - def handle_stream_chunk(source, event): - received_chunks.append(event.chunk) + # Create an LLM with streaming enabled + llm = LLM(model="gpt-4o", stream=True) - # Create an LLM with streaming enabled - llm = LLM(model="gpt-4o", stream=True) + # Call the LLM with a simple message + response = llm.call("Tell me a short joke") - # Call the LLM with a simple message - response = llm.call("Tell me a short joke") + # Wait for at least one chunk + assert event_received.wait(timeout=5), "Timeout waiting for stream chunks" - # Verify that we received chunks - assert len(received_chunks) > 0 + # Verify that we received chunks + assert len(received_chunks) > 0 - # Verify that concatenating all chunks equals the final response - assert "".join(received_chunks) == response + # Verify that concatenating all chunks equals the final response + assert "".join(received_chunks) == response @pytest.mark.vcr(filter_headers=["authorization"]) @@ -706,23 +795,21 @@ def test_llm_no_stream_chunks_when_streaming_disabled(): """Test that LLM doesn't emit stream chunk events when streaming is disabled.""" received_chunks = [] - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(LLMStreamChunkEvent) + def handle_stream_chunk(source, event): + received_chunks.append(event.chunk) - @crewai_event_bus.on(LLMStreamChunkEvent) - def handle_stream_chunk(source, event): - received_chunks.append(event.chunk) + # Create an LLM with streaming disabled + llm = LLM(model="gpt-4o", stream=False) - # Create an LLM with streaming disabled - llm = LLM(model="gpt-4o", stream=False) + # Call the LLM with a simple message + response = llm.call("Tell me a short joke") - # Call the LLM with a simple message - response = llm.call("Tell me a short joke") + # Verify that we didn't receive any chunks + assert len(received_chunks) == 0 - # Verify that we didn't receive any chunks - assert len(received_chunks) == 0 - - # Verify we got a response - assert response and isinstance(response, str) + # Verify we got a response + assert response and isinstance(response, str) @pytest.mark.vcr(filter_headers=["authorization"]) @@ -730,98 +817,105 @@ def test_streaming_fallback_to_non_streaming(): """Test that streaming falls back to non-streaming when there's an error.""" received_chunks = [] fallback_called = False + event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(LLMStreamChunkEvent) + def handle_stream_chunk(source, event): + received_chunks.append(event.chunk) + if len(received_chunks) >= 2: + event_received.set() - @crewai_event_bus.on(LLMStreamChunkEvent) - def handle_stream_chunk(source, event): - received_chunks.append(event.chunk) + # Create an LLM with streaming enabled + llm = LLM(model="gpt-4o", stream=True) - # Create an LLM with streaming enabled - llm = LLM(model="gpt-4o", stream=True) + # Store original methods + original_call = llm.call - # Store original methods - original_call = llm.call + # Create a mock call method that handles the streaming error + def mock_call(messages, tools=None, callbacks=None, available_functions=None): + nonlocal fallback_called + # Emit a couple of chunks to simulate partial streaming + crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 1")) + crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2")) - # Create a mock call method that handles the streaming error - def mock_call(messages, tools=None, callbacks=None, available_functions=None): - nonlocal fallback_called - # Emit a couple of chunks to simulate partial streaming - crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 1")) - crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2")) + # Mark that fallback would be called + fallback_called = True - # Mark that fallback would be called - fallback_called = True + # Return a response as if fallback succeeded + return "Fallback response after streaming error" - # Return a response as if fallback succeeded - return "Fallback response after streaming error" + # Replace the call method with our mock + llm.call = mock_call - # Replace the call method with our mock - llm.call = mock_call + try: + # Call the LLM + response = llm.call("Tell me a short joke") + wait_for_event_handlers() - try: - # Call the LLM - response = llm.call("Tell me a short joke") + assert event_received.wait(timeout=5), "Timeout waiting for stream chunks" - # Verify that we received some chunks - assert len(received_chunks) == 2 - assert received_chunks[0] == "Test chunk 1" - assert received_chunks[1] == "Test chunk 2" + # Verify that we received some chunks + assert len(received_chunks) == 2 + assert received_chunks[0] == "Test chunk 1" + assert received_chunks[1] == "Test chunk 2" - # Verify fallback was triggered - assert fallback_called + # Verify fallback was triggered + assert fallback_called - # Verify we got the fallback response - assert response == "Fallback response after streaming error" + # Verify we got the fallback response + assert response == "Fallback response after streaming error" - finally: - # Restore the original method - llm.call = original_call + finally: + # Restore the original method + llm.call = original_call @pytest.mark.vcr(filter_headers=["authorization"]) def test_streaming_empty_response_handling(): """Test that streaming handles empty responses correctly.""" received_chunks = [] + event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(LLMStreamChunkEvent) + def handle_stream_chunk(source, event): + received_chunks.append(event.chunk) + if len(received_chunks) >= 3: + event_received.set() - @crewai_event_bus.on(LLMStreamChunkEvent) - def handle_stream_chunk(source, event): - received_chunks.append(event.chunk) + # Create an LLM with streaming enabled + llm = LLM(model="gpt-3.5-turbo", stream=True) - # Create an LLM with streaming enabled - llm = LLM(model="gpt-3.5-turbo", stream=True) + # Store original methods + original_call = llm.call - # Store original methods - original_call = llm.call + # Create a mock call method that simulates empty chunks + def mock_call(messages, tools=None, callbacks=None, available_functions=None): + # Emit a few empty chunks + for _ in range(3): + crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="")) - # Create a mock call method that simulates empty chunks - def mock_call(messages, tools=None, callbacks=None, available_functions=None): - # Emit a few empty chunks - for _ in range(3): - crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="")) + # Return the default message for empty responses + return "I apologize, but I couldn't generate a proper response. Please try again or rephrase your request." - # Return the default message for empty responses - return "I apologize, but I couldn't generate a proper response. Please try again or rephrase your request." + # Replace the call method with our mock + llm.call = mock_call - # Replace the call method with our mock - llm.call = mock_call + try: + # Call the LLM - this should handle empty response + response = llm.call("Tell me a short joke") - try: - # Call the LLM - this should handle empty response - response = llm.call("Tell me a short joke") + assert event_received.wait(timeout=5), "Timeout waiting for empty chunks" - # Verify that we received empty chunks - assert len(received_chunks) == 3 - assert all(chunk == "" for chunk in received_chunks) + # Verify that we received empty chunks + assert len(received_chunks) == 3 + assert all(chunk == "" for chunk in received_chunks) - # Verify the response is the default message for empty responses - assert "I apologize" in response and "couldn't generate" in response + # Verify the response is the default message for empty responses + assert "I apologize" in response and "couldn't generate" in response - finally: - # Restore the original method - llm.call = original_call + finally: + # Restore the original method + llm.call = original_call @pytest.mark.vcr(filter_headers=["authorization"]) @@ -830,41 +924,49 @@ def test_stream_llm_emits_event_with_task_and_agent_info(): failed_event = [] started_event = [] stream_event = [] + event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(LLMCallFailedEvent) + def handle_llm_failed(source, event): + failed_event.append(event) - @crewai_event_bus.on(LLMCallFailedEvent) - def handle_llm_failed(source, event): - failed_event.append(event) + @crewai_event_bus.on(LLMCallStartedEvent) + def handle_llm_started(source, event): + started_event.append(event) - @crewai_event_bus.on(LLMCallStartedEvent) - def handle_llm_started(source, event): - started_event.append(event) + @crewai_event_bus.on(LLMCallCompletedEvent) + def handle_llm_completed(source, event): + completed_event.append(event) + if len(started_event) >= 1 and len(stream_event) >= 12: + event_received.set() - @crewai_event_bus.on(LLMCallCompletedEvent) - def handle_llm_completed(source, event): - completed_event.append(event) + @crewai_event_bus.on(LLMStreamChunkEvent) + def handle_llm_stream_chunk(source, event): + stream_event.append(event) + if ( + len(completed_event) >= 1 + and len(started_event) >= 1 + and len(stream_event) >= 12 + ): + event_received.set() - @crewai_event_bus.on(LLMStreamChunkEvent) - def handle_llm_stream_chunk(source, event): - stream_event.append(event) + agent = Agent( + role="TestAgent", + llm=LLM(model="gpt-4o-mini", stream=True), + goal="Just say hi", + backstory="You are a helpful assistant that just says hi", + ) + task = Task( + description="Just say hi", + expected_output="hi", + llm=LLM(model="gpt-4o-mini", stream=True), + agent=agent, + ) - agent = Agent( - role="TestAgent", - llm=LLM(model="gpt-4o-mini", stream=True), - goal="Just say hi", - backstory="You are a helpful assistant that just says hi", - ) - task = Task( - description="Just say hi", - expected_output="hi", - llm=LLM(model="gpt-4o-mini", stream=True), - agent=agent, - ) - - crew = Crew(agents=[agent], tasks=[task]) - crew.kickoff() + crew = Crew(agents=[agent], tasks=[task]) + crew.kickoff() + assert event_received.wait(timeout=10), "Timeout waiting for LLM events" assert len(completed_event) == 1 assert len(failed_event) == 0 assert len(started_event) == 1 @@ -883,8 +985,8 @@ def test_stream_llm_emits_event_with_task_and_agent_info(): assert len(all_task_name) == 14 assert set(all_agent_roles) == {agent.role} - assert set(all_agent_id) == {agent.id} - assert set(all_task_id) == {task.id} + assert set(all_agent_id) == {str(agent.id)} + assert set(all_task_id) == {str(task.id)} assert set(all_task_name) == {task.name or task.description} @@ -894,28 +996,30 @@ def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task): failed_event = [] started_event = [] stream_event = [] + event_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(LLMCallFailedEvent) + def handle_llm_failed(source, event): + failed_event.append(event) - @crewai_event_bus.on(LLMCallFailedEvent) - def handle_llm_failed(source, event): - failed_event.append(event) + @crewai_event_bus.on(LLMCallStartedEvent) + def handle_llm_started(source, event): + started_event.append(event) - @crewai_event_bus.on(LLMCallStartedEvent) - def handle_llm_started(source, event): - started_event.append(event) + @crewai_event_bus.on(LLMCallCompletedEvent) + def handle_llm_completed(source, event): + completed_event.append(event) + if len(started_event) >= 1: + event_received.set() - @crewai_event_bus.on(LLMCallCompletedEvent) - def handle_llm_completed(source, event): - completed_event.append(event) + @crewai_event_bus.on(LLMStreamChunkEvent) + def handle_llm_stream_chunk(source, event): + stream_event.append(event) - @crewai_event_bus.on(LLMStreamChunkEvent) - def handle_llm_stream_chunk(source, event): - stream_event.append(event) - - crew = Crew(agents=[base_agent], tasks=[base_task]) - crew.kickoff() + crew = Crew(agents=[base_agent], tasks=[base_task]) + crew.kickoff() + assert event_received.wait(timeout=10), "Timeout waiting for LLM events" assert len(completed_event) == 1 assert len(failed_event) == 0 assert len(started_event) == 1 @@ -934,8 +1038,8 @@ def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task): assert len(all_task_name) == 2 assert set(all_agent_roles) == {base_agent.role} - assert set(all_agent_id) == {base_agent.id} - assert set(all_task_id) == {base_task.id} + assert set(all_agent_id) == {str(base_agent.id)} + assert set(all_task_id) == {str(base_task.id)} assert set(all_task_name) == {base_task.name or base_task.description} @@ -945,32 +1049,41 @@ def test_llm_emits_event_with_lite_agent(): failed_event = [] started_event = [] stream_event = [] + all_events_received = threading.Event() - with crewai_event_bus.scoped_handlers(): + @crewai_event_bus.on(LLMCallFailedEvent) + def handle_llm_failed(source, event): + failed_event.append(event) - @crewai_event_bus.on(LLMCallFailedEvent) - def handle_llm_failed(source, event): - failed_event.append(event) + @crewai_event_bus.on(LLMCallStartedEvent) + def handle_llm_started(source, event): + started_event.append(event) - @crewai_event_bus.on(LLMCallStartedEvent) - def handle_llm_started(source, event): - started_event.append(event) + @crewai_event_bus.on(LLMCallCompletedEvent) + def handle_llm_completed(source, event): + completed_event.append(event) + if len(started_event) >= 1 and len(stream_event) >= 15: + all_events_received.set() - @crewai_event_bus.on(LLMCallCompletedEvent) - def handle_llm_completed(source, event): - completed_event.append(event) + @crewai_event_bus.on(LLMStreamChunkEvent) + def handle_llm_stream_chunk(source, event): + stream_event.append(event) + if ( + len(completed_event) >= 1 + and len(started_event) >= 1 + and len(stream_event) >= 15 + ): + all_events_received.set() - @crewai_event_bus.on(LLMStreamChunkEvent) - def handle_llm_stream_chunk(source, event): - stream_event.append(event) + agent = Agent( + role="Speaker", + llm=LLM(model="gpt-4o-mini", stream=True), + goal="Just say hi", + backstory="You are a helpful assistant that just says hi", + ) + agent.kickoff(messages=[{"role": "user", "content": "say hi!"}]) - agent = Agent( - role="Speaker", - llm=LLM(model="gpt-4o-mini", stream=True), - goal="Just say hi", - backstory="You are a helpful assistant that just says hi", - ) - agent.kickoff(messages=[{"role": "user", "content": "say hi!"}]) + assert all_events_received.wait(timeout=10), "Timeout waiting for all events" assert len(completed_event) == 1 assert len(failed_event) == 0 @@ -990,4 +1103,4 @@ def test_llm_emits_event_with_lite_agent(): assert len(all_task_name) == 0 assert set(all_agent_roles) == {agent.role} - assert set(all_agent_id) == {agent.id} + assert set(all_agent_id) == {str(agent.id)} diff --git a/tests/utilities/test_file_handler.py b/lib/crewai/tests/utilities/test_file_handler.py similarity index 99% rename from tests/utilities/test_file_handler.py rename to lib/crewai/tests/utilities/test_file_handler.py index 7d3073a03..1e1cbfba8 100644 --- a/tests/utilities/test_file_handler.py +++ b/lib/crewai/tests/utilities/test_file_handler.py @@ -3,7 +3,6 @@ import unittest import uuid import pytest - from crewai.utilities.file_handler import PickleHandler diff --git a/tests/utilities/test_i18n.py b/lib/crewai/tests/utilities/test_i18n.py similarity index 99% rename from tests/utilities/test_i18n.py rename to lib/crewai/tests/utilities/test_i18n.py index 8627b0bec..10c403edf 100644 --- a/tests/utilities/test_i18n.py +++ b/lib/crewai/tests/utilities/test_i18n.py @@ -1,5 +1,4 @@ import pytest - from crewai.utilities.i18n import I18N diff --git a/tests/utilities/test_import_utils.py b/lib/crewai/tests/utilities/test_import_utils.py similarity index 99% rename from tests/utilities/test_import_utils.py rename to lib/crewai/tests/utilities/test_import_utils.py index 29738172c..c156a61bd 100644 --- a/tests/utilities/test_import_utils.py +++ b/lib/crewai/tests/utilities/test_import_utils.py @@ -4,7 +4,6 @@ import sys from unittest.mock import MagicMock, patch import pytest - from crewai.utilities.import_utils import ( OptionalDependencyError, import_and_validate_definition, diff --git a/tests/utilities/test_knowledge_planning.py b/lib/crewai/tests/utilities/test_knowledge_planning.py similarity index 99% rename from tests/utilities/test_knowledge_planning.py rename to lib/crewai/tests/utilities/test_knowledge_planning.py index 9ff29c573..2b3874529 100644 --- a/tests/utilities/test_knowledge_planning.py +++ b/lib/crewai/tests/utilities/test_knowledge_planning.py @@ -6,7 +6,6 @@ This module ensures that agent knowledge is properly included during task planni from unittest.mock import patch import pytest - from crewai.agent import Agent from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource from crewai.task import Task diff --git a/tests/utilities/test_llm_utils.py b/lib/crewai/tests/utilities/test_llm_utils.py similarity index 62% rename from tests/utilities/test_llm_utils.py rename to lib/crewai/tests/utilities/test_llm_utils.py index 5aa4f1a1a..d20e0b528 100644 --- a/tests/utilities/test_llm_utils.py +++ b/lib/crewai/tests/utilities/test_llm_utils.py @@ -1,11 +1,16 @@ import os from unittest.mock import patch -import pytest -from litellm.exceptions import BadRequestError - from crewai.llm import LLM +from crewai.llms.base_llm import BaseLLM from crewai.utilities.llm_utils import create_llm +import pytest + + +try: + from litellm.exceptions import BadRequestError +except ImportError: + BadRequestError = Exception def test_create_llm_with_llm_instance(): @@ -16,13 +21,19 @@ def test_create_llm_with_llm_instance(): def test_create_llm_with_valid_model_string(): llm = create_llm(llm_value="gpt-4o") - assert isinstance(llm, LLM) + assert isinstance(llm, BaseLLM) assert llm.model == "gpt-4o" def test_create_llm_with_invalid_model_string(): - with pytest.raises(BadRequestError, match="LLM Provider NOT provided"): - llm = create_llm(llm_value="invalid-model") + # For invalid model strings, create_llm succeeds but call() fails with API error + llm = create_llm(llm_value="invalid-model") + assert llm is not None + assert isinstance(llm, BaseLLM) + + # The error should occur when making the actual API call + # We expect some kind of API error (NotFoundError, etc.) + with pytest.raises(Exception): # noqa: B017 llm.call(messages=[{"role": "user", "content": "Hello, world!"}]) @@ -33,16 +44,16 @@ def test_create_llm_with_unknown_object_missing_attributes(): unknown_obj = UnknownObject() llm = create_llm(llm_value=unknown_obj) - # Attempt to call the LLM and expect it to raise an error due to missing attributes - with pytest.raises(BadRequestError, match="LLM Provider NOT provided"): - llm.call(messages=[{"role": "user", "content": "Hello, world!"}]) + # Should succeed because str(unknown_obj) provides a model name + assert llm is not None + assert isinstance(llm, BaseLLM) def test_create_llm_with_none_uses_default_model(): - with patch.dict(os.environ, {}, clear=True): - with patch("crewai.cli.constants.DEFAULT_LLM_MODEL", "gpt-4o"): + with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True): + with patch("crewai.utilities.llm_utils.DEFAULT_LLM_MODEL", "gpt-4o-mini"): llm = create_llm(llm_value=None) - assert isinstance(llm, LLM) + assert isinstance(llm, BaseLLM) assert llm.model == "gpt-4o-mini" @@ -54,7 +65,7 @@ def test_create_llm_with_unknown_object(): unknown_obj = UnknownObject() llm = create_llm(llm_value=unknown_obj) - assert isinstance(llm, LLM) + assert isinstance(llm, BaseLLM) assert llm.model == "gpt-4o" assert llm.temperature == 0.7 assert llm.max_tokens == 1500 @@ -65,13 +76,14 @@ def test_create_llm_from_env_with_unaccepted_attributes(): os.environ, { "OPENAI_MODEL_NAME": "gpt-3.5-turbo", + "OPENAI_API_KEY": "fake-key", "AWS_ACCESS_KEY_ID": "fake-access-key", "AWS_SECRET_ACCESS_KEY": "fake-secret-key", "AWS_REGION_NAME": "us-west-2", }, ): llm = create_llm(llm_value=None) - assert isinstance(llm, LLM) + assert isinstance(llm, BaseLLM) assert llm.model == "gpt-3.5-turbo" assert not hasattr(llm, "AWS_ACCESS_KEY_ID") assert not hasattr(llm, "AWS_SECRET_ACCESS_KEY") @@ -85,12 +97,18 @@ def test_create_llm_with_partial_attributes(): obj = PartialAttributes() llm = create_llm(llm_value=obj) - assert isinstance(llm, LLM) + assert isinstance(llm, BaseLLM) assert llm.model == "gpt-4o" assert llm.temperature is None # Should handle missing attributes gracefully def test_create_llm_with_invalid_type(): - with pytest.raises(BadRequestError, match="LLM Provider NOT provided"): - llm = create_llm(llm_value=42) + # For integers, create_llm succeeds because str(42) becomes "42" + llm = create_llm(llm_value=42) + assert llm is not None + assert isinstance(llm, BaseLLM) + assert llm.model == "42" + + # The error should occur when making the actual API call + with pytest.raises(Exception): # noqa: B017 llm.call(messages=[{"role": "user", "content": "Hello, world!"}]) diff --git a/tests/utilities/test_planning_handler.py b/lib/crewai/tests/utilities/test_planning_handler.py similarity index 97% rename from tests/utilities/test_planning_handler.py rename to lib/crewai/tests/utilities/test_planning_handler.py index e1c27c341..6e75e3626 100644 --- a/tests/utilities/test_planning_handler.py +++ b/lib/crewai/tests/utilities/test_planning_handler.py @@ -1,9 +1,6 @@ -from typing import Optional -from unittest.mock import MagicMock, patch +from unittest.mock import patch import pytest -from pydantic import BaseModel - from crewai.agent import Agent from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource from crewai.task import Task @@ -100,7 +97,7 @@ class InternalCrewPlanner: # Knowledge field should not be present when empty assert '"agent_knowledge"' not in tasks_summary - @patch('crewai.knowledge.storage.knowledge_storage.chromadb') + @patch("crewai.knowledge.storage.knowledge_storage.chromadb") def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma): """Test task summary generation with both knowledge and tools present.""" # Mock ChromaDB collection @@ -146,8 +143,8 @@ class InternalCrewPlanner: tools=[tool1, tool2], knowledge_sources=[ StringKnowledgeSource(content="Test knowledge content") - ] - ) + ], + ), ) # Create planner with the new task diff --git a/tests/utilities/test_pydantic_schema_parser.py b/lib/crewai/tests/utilities/test_pydantic_schema_parser.py similarity index 100% rename from tests/utilities/test_pydantic_schema_parser.py rename to lib/crewai/tests/utilities/test_pydantic_schema_parser.py diff --git a/tests/utilities/test_serialization.py b/lib/crewai/tests/utilities/test_serialization.py similarity index 99% rename from tests/utilities/test_serialization.py rename to lib/crewai/tests/utilities/test_serialization.py index b1e042639..e786554cb 100644 --- a/tests/utilities/test_serialization.py +++ b/lib/crewai/tests/utilities/test_serialization.py @@ -1,11 +1,9 @@ from datetime import date, datetime from typing import List -from unittest.mock import Mock import pytest -from pydantic import BaseModel - from crewai.utilities.serialization import to_serializable, to_string +from pydantic import BaseModel class Address(BaseModel): diff --git a/tests/utilities/test_string_utils.py b/lib/crewai/tests/utilities/test_string_utils.py similarity index 99% rename from tests/utilities/test_string_utils.py rename to lib/crewai/tests/utilities/test_string_utils.py index 441aae8c0..074beda77 100644 --- a/tests/utilities/test_string_utils.py +++ b/lib/crewai/tests/utilities/test_string_utils.py @@ -1,7 +1,6 @@ from typing import Any, Dict, List, Union import pytest - from crewai.utilities.string_utils import interpolate_only diff --git a/tests/utilities/test_training_converter.py b/lib/crewai/tests/utilities/test_training_converter.py similarity index 92% rename from tests/utilities/test_training_converter.py rename to lib/crewai/tests/utilities/test_training_converter.py index 7eb21ae81..65c73ac38 100644 --- a/tests/utilities/test_training_converter.py +++ b/lib/crewai/tests/utilities/test_training_converter.py @@ -1,10 +1,9 @@ -from unittest.mock import MagicMock, patch - -from pydantic import BaseModel, Field from typing import List +from unittest.mock import MagicMock, patch from crewai.utilities.converter import ConverterError from crewai.utilities.training_converter import TrainingConverter +from pydantic import BaseModel, Field class TestModel(BaseModel): @@ -14,7 +13,6 @@ class TestModel(BaseModel): class TestTrainingConverter: - def setup_method(self): self.llm_mock = MagicMock() self.test_text = "Sample text for evaluation" @@ -23,26 +21,28 @@ class TestTrainingConverter: llm=self.llm_mock, text=self.test_text, model=TestModel, - instructions=self.test_instructions + instructions=self.test_instructions, ) @patch("crewai.utilities.converter.Converter.to_pydantic") def test_fallback_to_field_by_field(self, parent_to_pydantic_mock): - parent_to_pydantic_mock.side_effect = ConverterError("Failed to convert directly") + parent_to_pydantic_mock.side_effect = ConverterError( + "Failed to convert directly" + ) llm_responses = { "string_field": "test string value", "list_field": "- item1\n- item2\n- item3", - "number_field": "8.5" + "number_field": "8.5", } def llm_side_effect(messages): prompt = messages[1]["content"] if "string_field" in prompt: return llm_responses["string_field"] - elif "list_field" in prompt: + if "list_field" in prompt: return llm_responses["list_field"] - elif "number_field" in prompt: + if "number_field" in prompt: return llm_responses["number_field"] return "unknown field" diff --git a/tests/utilities/test_training_handler.py b/lib/crewai/tests/utilities/test_training_handler.py similarity index 100% rename from tests/utilities/test_training_handler.py rename to lib/crewai/tests/utilities/test_training_handler.py diff --git a/lib/crewai/tests/utils.py b/lib/crewai/tests/utils.py new file mode 100644 index 000000000..a514634ae --- /dev/null +++ b/lib/crewai/tests/utils.py @@ -0,0 +1,39 @@ +"""Test utilities for CrewAI tests.""" + +import asyncio +from concurrent.futures import ThreadPoolExecutor + + +def wait_for_event_handlers(timeout: float = 5.0) -> None: + """Wait for all pending event handlers to complete. + + This helper ensures all sync and async handlers finish processing before + proceeding. Useful in tests to make assertions deterministic. + + Args: + timeout: Maximum time to wait in seconds. + """ + from crewai.events.event_bus import crewai_event_bus + + loop = getattr(crewai_event_bus, "_loop", None) + + if loop and not loop.is_closed(): + + async def _wait_for_async_tasks() -> None: + tasks = { + t for t in asyncio.all_tasks(loop) if t is not asyncio.current_task() + } + if tasks: + await asyncio.gather(*tasks, return_exceptions=True) + + future = asyncio.run_coroutine_threadsafe(_wait_for_async_tasks(), loop) + try: + future.result(timeout=timeout) + except Exception: # noqa: S110 + pass + + crewai_event_bus._sync_executor.shutdown(wait=True) + crewai_event_bus._sync_executor = ThreadPoolExecutor( + max_workers=10, + thread_name_prefix="CrewAISyncHandler", + ) diff --git a/lib/devtools/README.md b/lib/devtools/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/lib/devtools/pyproject.toml b/lib/devtools/pyproject.toml new file mode 100644 index 000000000..632ef8c4d --- /dev/null +++ b/lib/devtools/pyproject.toml @@ -0,0 +1,32 @@ +[project] +name = "crewai-devtools" +dynamic = ["version"] +description = "Development tools for version bumping and git automation" +readme = "README.md" +authors = [ + { name = "Greyson R. LaLonde", email = "greyson@crewai.com" }, +] +requires-python = ">=3.10, <3.14" +classifiers = ["Private :: Do Not Upload"] +private = true +dependencies = [ + "click>=8.3.0", + "toml>=0.10.2", + "openai>=1.0.0", + "python-dotenv>=1.1.1", + "pygithub>=1.59.1", + "rich>=13.9.4", +] + +[project.scripts] +bump-version = "crewai_devtools.cli:bump" +tag = "crewai_devtools.cli:tag" +devtools = "crewai_devtools.cli:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.version] +path = "src/crewai_devtools/__init__.py" + diff --git a/lib/devtools/src/crewai_devtools/__init__.py b/lib/devtools/src/crewai_devtools/__init__.py new file mode 100644 index 000000000..f5be6f40b --- /dev/null +++ b/lib/devtools/src/crewai_devtools/__init__.py @@ -0,0 +1,3 @@ +"""CrewAI development tools.""" + +__version__ = "1.0.0b3" diff --git a/lib/devtools/src/crewai_devtools/cli.py b/lib/devtools/src/crewai_devtools/cli.py new file mode 100644 index 000000000..f2a117468 --- /dev/null +++ b/lib/devtools/src/crewai_devtools/cli.py @@ -0,0 +1,706 @@ +"""Development tools for version bumping and git automation.""" + +import os +from pathlib import Path +import subprocess +import sys + +import click +from dotenv import load_dotenv +from github import Github +from openai import OpenAI +from rich.console import Console +from rich.markdown import Markdown +from rich.panel import Panel +from rich.prompt import Confirm + +from crewai_devtools.prompts import RELEASE_NOTES_PROMPT + + +load_dotenv() + +console = Console() + + +def run_command(cmd: list[str], cwd: Path | None = None) -> str: + """Run a shell command and return output. + + Args: + cmd: Command to run as list of strings. + cwd: Working directory for command. + + Returns: + Command output as string. + + Raises: + subprocess.CalledProcessError: If command fails. + """ + result = subprocess.run( # noqa: S603 + cmd, + cwd=cwd, + capture_output=True, + text=True, + check=True, + ) + return result.stdout.strip() + + +def check_gh_installed() -> None: + """Check if GitHub CLI is installed and offer to install it. + + Raises: + SystemExit: If gh is not installed and user declines installation. + """ + try: + run_command(["gh", "--version"]) + except (subprocess.CalledProcessError, FileNotFoundError): + console.print("[yellow]Warning:[/yellow] GitHub CLI (gh) is not installed") + import platform + + if platform.system() == "Darwin": + try: + run_command(["brew", "--version"]) + from rich.prompt import Confirm + + if Confirm.ask( + "\n[bold]Would you like to install GitHub CLI via Homebrew?[/bold]", + default=True, + ): + try: + console.print("\nInstalling GitHub CLI...") + subprocess.run( + ["brew", "install", "gh"], # noqa: S607 + check=True, + ) + console.print( + "[green]✓[/green] GitHub CLI installed successfully" + ) + console.print("\nAuthenticating with GitHub...") + subprocess.run( + ["gh", "auth", "login"], # noqa: S607 + check=True, + ) + console.print("[green]✓[/green] GitHub authentication complete") + return + except subprocess.CalledProcessError as e: + console.print( + f"[red]Error:[/red] Failed to install or authenticate gh: {e}" + ) + console.print( + "\nYou can try running [bold]gh auth login[/bold] manually" + ) + except (subprocess.CalledProcessError, FileNotFoundError): + pass + + console.print("\nPlease install GitHub CLI from: https://cli.github.com/") + console.print("\nInstallation instructions:") + console.print(" macOS: brew install gh") + console.print( + " Linux: https://github.com/cli/cli/blob/trunk/docs/install_linux.md" + ) + console.print(" Windows: winget install --id GitHub.cli") + sys.exit(1) + + +def check_git_clean() -> None: + """Check if git working directory is clean. + + Raises: + SystemExit: If there are uncommitted changes. + """ + try: + status = run_command(["git", "status", "--porcelain"]) + if status: + console.print( + "[red]Error:[/red] You have uncommitted changes. Please commit or stash them first." + ) + sys.exit(1) + except subprocess.CalledProcessError as e: + console.print(f"[red]Error checking git status:[/red] {e}") + sys.exit(1) + + +def update_version_in_file(file_path: Path, new_version: str) -> bool: + """Update __version__ attribute in a Python file. + + Args: + file_path: Path to Python file. + new_version: New version string. + + Returns: + True if version was updated, False otherwise. + """ + if not file_path.exists(): + return False + + content = file_path.read_text() + lines = content.splitlines() + updated = False + + for i, line in enumerate(lines): + if line.strip().startswith("__version__"): + lines[i] = f'__version__ = "{new_version}"' + updated = True + break + + if updated: + file_path.write_text("\n".join(lines) + "\n") + return True + + return False + + +def update_pyproject_dependencies(file_path: Path, new_version: str) -> bool: + """Update workspace dependency versions in pyproject.toml. + + Args: + file_path: Path to pyproject.toml file. + new_version: New version string. + + Returns: + True if any dependencies were updated, False otherwise. + """ + if not file_path.exists(): + return False + + content = file_path.read_text() + lines = content.splitlines() + updated = False + + workspace_packages = ["crewai", "crewai-tools", "crewai-devtools"] + + for i, line in enumerate(lines): + for pkg in workspace_packages: + if f"{pkg}==" in line: + stripped = line.lstrip() + indent = line[: len(line) - len(stripped)] + + if '"' in line: + lines[i] = f'{indent}"{pkg}=={new_version}",' + elif "'" in line: + lines[i] = f"{indent}'{pkg}=={new_version}'," + else: + lines[i] = f"{indent}{pkg}=={new_version}," + + updated = True + + if updated: + file_path.write_text("\n".join(lines) + "\n") + return True + + return False + + +def find_version_files(base_path: Path) -> list[Path]: + """Find all __init__.py files that contain __version__. + + Args: + base_path: Base directory to search in. + + Returns: + List of paths to files containing __version__. + """ + return [ + init_file + for init_file in base_path.rglob("__init__.py") + if "__version__" in init_file.read_text() + ] + + +def get_packages(lib_dir: Path) -> list[Path]: + """Get all packages from lib/ directory. + + Args: + lib_dir: Path to lib/ directory. + + Returns: + List of package directory paths. + + Raises: + SystemExit: If lib/ doesn't exist or no packages found. + """ + if not lib_dir.exists(): + console.print("[red]Error:[/red] lib/ directory not found") + sys.exit(1) + + packages = [p for p in lib_dir.iterdir() if p.is_dir()] + + if not packages: + console.print("[red]Error:[/red] No packages found in lib/") + sys.exit(1) + + return packages + + +def get_commits_from_last_tag(tag_name: str, version: str) -> tuple[str, str]: + """Get commits from the last tag, excluding current version. + + Args: + tag_name: Current tag name (e.g., "v1.0.0"). + version: Current version (e.g., "1.0.0"). + + Returns: + Tuple of (commit_range, commits) where commits is newline-separated. + """ + try: + all_tags = run_command(["git", "tag", "--sort=-version:refname"]).split("\n") + prev_tags = [t for t in all_tags if t and t != tag_name and t != f"v{version}"] + + if prev_tags: + last_tag = prev_tags[0] + commit_range = f"{last_tag}..HEAD" + commits = run_command(["git", "log", commit_range, "--pretty=format:%s"]) + else: + commit_range = "HEAD" + commits = run_command(["git", "log", "--pretty=format:%s"]) + except subprocess.CalledProcessError: + commit_range = "HEAD" + commits = run_command(["git", "log", "--pretty=format:%s"]) + + return commit_range, commits + + +def get_github_contributors(commit_range: str) -> list[str]: + """Get GitHub usernames from commit range using GitHub API. + + Args: + commit_range: Git commit range (e.g., "abc123..HEAD"). + + Returns: + List of GitHub usernames sorted alphabetically. + """ + try: + # Get GitHub token from gh CLI + try: + gh_token = run_command(["gh", "auth", "token"]) + except subprocess.CalledProcessError: + gh_token = None + + g = Github(login_or_token=gh_token) if gh_token else Github() + github_repo = g.get_repo("crewAIInc/crewAI") + + commit_shas = run_command( + ["git", "log", commit_range, "--pretty=format:%H"] + ).split("\n") + + contributors = set() + for sha in commit_shas: + if not sha: + continue + try: + commit = github_repo.get_commit(sha) + if commit.author and commit.author.login: + contributors.add(commit.author.login) + + if commit.commit.message: + for line in commit.commit.message.split("\n"): + if line.strip().startswith("Co-authored-by:"): + if "<" in line and ">" in line: + email_part = line.split("<")[1].split(">")[0] + if "@users.noreply.github.com" in email_part: + username = email_part.split("+")[-1].split("@")[0] + contributors.add(username) + except Exception: # noqa: S112 + continue + + return sorted(list(contributors)) + + except Exception as e: + console.print( + f"[yellow]Warning:[/yellow] Could not fetch GitHub contributors: {e}" + ) + return [] + + +@click.group() +def cli() -> None: + """Development tools for version bumping and git automation.""" + + +@click.command() +@click.argument("version") +@click.option( + "--dry-run", is_flag=True, help="Show what would be done without making changes" +) +@click.option("--no-push", is_flag=True, help="Don't push changes to remote") +def bump(version: str, dry_run: bool, no_push: bool) -> None: + """Bump version across all packages in lib/. + + Args: + version: New version to set (e.g., 1.0.0, 1.0.0a1). + dry_run: Show what would be done without making changes. + no_push: Don't push changes to remote. + """ + try: + # Check prerequisites + check_gh_installed() + + cwd = Path.cwd() + lib_dir = cwd / "lib" + + if not dry_run: + console.print("Checking git status...") + check_git_clean() + console.print("[green]✓[/green] Working directory is clean") + else: + console.print("[dim][DRY RUN][/dim] Would check git status") + + packages = get_packages(lib_dir) + + console.print(f"\nFound {len(packages)} package(s) to update:") + for pkg in packages: + console.print(f" - {pkg.name}") + + console.print(f"\nUpdating version to {version}...") + updated_files = [] + + for pkg in packages: + version_files = find_version_files(pkg) + for vfile in version_files: + if dry_run: + console.print( + f"[dim][DRY RUN][/dim] Would update: {vfile.relative_to(cwd)}" + ) + else: + if update_version_in_file(vfile, version): + console.print( + f"[green]✓[/green] Updated: {vfile.relative_to(cwd)}" + ) + updated_files.append(vfile) + else: + console.print( + f"[red]✗[/red] Failed to update: {vfile.relative_to(cwd)}" + ) + + pyproject = pkg / "pyproject.toml" + if pyproject.exists(): + if dry_run: + console.print( + f"[dim][DRY RUN][/dim] Would update dependencies in: {pyproject.relative_to(cwd)}" + ) + else: + if update_pyproject_dependencies(pyproject, version): + console.print( + f"[green]✓[/green] Updated dependencies in: {pyproject.relative_to(cwd)}" + ) + updated_files.append(pyproject) + + if not updated_files and not dry_run: + console.print( + "[yellow]Warning:[/yellow] No __version__ attributes found to update" + ) + + if not dry_run: + console.print("\nSyncing workspace...") + run_command(["uv", "sync"]) + console.print("[green]✓[/green] Workspace synced") + else: + console.print("[dim][DRY RUN][/dim] Would run: uv sync") + + branch_name = f"feat/bump-version-{version}" + if not dry_run: + console.print(f"\nCreating branch {branch_name}...") + run_command(["git", "checkout", "-b", branch_name]) + console.print("[green]✓[/green] Branch created") + + console.print("\nCommitting changes...") + run_command(["git", "add", "."]) + run_command(["git", "commit", "-m", f"feat: bump versions to {version}"]) + console.print("[green]✓[/green] Changes committed") + + if not no_push: + console.print("\nPushing branch...") + run_command(["git", "push", "-u", "origin", branch_name]) + console.print("[green]✓[/green] Branch pushed") + else: + console.print(f"[dim][DRY RUN][/dim] Would create branch: {branch_name}") + console.print( + f"[dim][DRY RUN][/dim] Would commit: feat: bump versions to {version}" + ) + if not no_push: + console.print(f"[dim][DRY RUN][/dim] Would push branch: {branch_name}") + + if not dry_run and not no_push: + console.print("\nCreating pull request...") + run_command( + [ + "gh", + "pr", + "create", + "--base", + "release/v1.0.0", + "--title", + f"feat: bump versions to {version}", + "--body", + "", + ] + ) + console.print("[green]✓[/green] Pull request created") + elif dry_run: + console.print( + f"[dim][DRY RUN][/dim] Would create PR: feat: bump versions to {version}" + ) + else: + console.print("\nSkipping PR creation (--no-push flag set)") + + console.print(f"\n[green]✓[/green] Version bump to {version} complete!") + + except subprocess.CalledProcessError as e: + console.print(f"[red]Error running command:[/red] {e}") + if e.stderr: + console.print(e.stderr) + sys.exit(1) + except Exception as e: + console.print(f"[red]Error:[/red] {e}") + sys.exit(1) + + +@click.command() +@click.option( + "--dry-run", is_flag=True, help="Show what would be done without making changes" +) +@click.option("--no-edit", is_flag=True, help="Skip editing release notes") +def tag(dry_run: bool, no_edit: bool) -> None: + """Create and push a version tag on main branch. + + Run this after the version bump PR has been merged. + Automatically detects version from __version__ in packages. + + Args: + dry_run: Show what would be done without making changes. + no_edit: Skip editing release notes. + """ + try: + cwd = Path.cwd() + lib_dir = cwd / "lib" + + packages = get_packages(lib_dir) + + with console.status("[cyan]Validating package versions..."): + versions = {} + for pkg in packages: + version_files = find_version_files(pkg) + for vfile in version_files: + content = vfile.read_text() + for line in content.splitlines(): + if line.strip().startswith("__version__"): + ver = line.split("=")[1].strip().strip('"').strip("'") + versions[vfile.relative_to(cwd)] = ver + break + + if not versions: + console.print( + "[red]✗[/red] Validated package versions: Could not find __version__ in any package" + ) + sys.exit(1) + + unique_versions = set(versions.values()) + if len(unique_versions) > 1: + console.print( + "[red]✗[/red] Validated package versions: Version mismatch detected" + ) + for file, ver in versions.items(): + console.print(f" {file}: {ver}") + sys.exit(1) + + version = unique_versions.pop() + console.print(f"[green]✓[/green] Validated packages @ [bold]{version}[/bold]") + tag_name = version + + if not dry_run: + with console.status("[cyan]Checking out release/v1.0.0 branch..."): + try: + run_command(["git", "checkout", "release/v1.0.0"]) + except subprocess.CalledProcessError as e: + console.print( + f"[red]✗[/red] Checked out release/v1.0.0 branch: {e}" + ) + sys.exit(1) + console.print("[green]✓[/green] On release/v1.0.0 branch") + + with console.status("[cyan]Pulling latest changes..."): + try: + run_command(["git", "pull"]) + except subprocess.CalledProcessError as e: + console.print(f"[red]✗[/red] Pulled latest changes: {e}") + sys.exit(1) + console.print("[green]✓[/green] release/v1.0.0 branch up to date") + + release_notes = f"Release {version}" + commits = "" + + with console.status("[cyan]Generating release notes..."): + try: + prev_bump_commit = run_command( + [ + "git", + "log", + "--grep=^feat: bump versions to", + "--format=%H", + "-n", + "2", + ] + ) + commits_list = prev_bump_commit.strip().split("\n") + + if len(commits_list) > 1: + prev_commit = commits_list[1] + commit_range = f"{prev_commit}..HEAD" + commits = run_command( + ["git", "log", commit_range, "--pretty=format:%s"] + ) + + commit_lines = [ + line + for line in commits.split("\n") + if not line.startswith("feat: bump versions to") + ] + commits = "\n".join(commit_lines) + else: + commit_range, commits = get_commits_from_last_tag(tag_name, version) + + except subprocess.CalledProcessError: + commit_range, commits = get_commits_from_last_tag(tag_name, version) + + github_contributors = get_github_contributors(commit_range) + + if commits.strip(): + client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + + contributors_section = "" + if github_contributors: + contributors_section = f"\n\n## Contributors\n\n{', '.join([f'@{u}' for u in github_contributors])}" + + prompt = RELEASE_NOTES_PROMPT.substitute( + version=version, + commits=commits, + contributors_section=contributors_section, + ) + + response = client.chat.completions.create( + model="gpt-4o-mini", + messages=[ + { + "role": "system", + "content": "You are a helpful assistant that generates clear, concise release notes.", + }, + {"role": "user", "content": prompt}, + ], + temperature=0.7, + ) + + release_notes = ( + response.choices[0].message.content or f"Release {version}" + ) + + console.print("[green]✓[/green] Generated release notes") + + if commits.strip(): + try: + console.print() + md = Markdown(release_notes, justify="left") + console.print( + Panel( + md, + title="[bold cyan]Generated Release Notes[/bold cyan]", + border_style="cyan", + padding=(1, 2), + ) + ) + except Exception as e: + console.print( + f"[yellow]Warning:[/yellow] Could not generate release notes with OpenAI: {e}" + ) + console.print("Using default release notes") + + if not no_edit: + if Confirm.ask( + "\n[bold]Would you like to edit the release notes?[/bold]", default=True + ): + edited_notes = click.edit(release_notes) + if edited_notes is not None: + release_notes = edited_notes.strip() + console.print("\n[green]✓[/green] Release notes updated") + else: + console.print("\n[green]✓[/green] Using original release notes") + else: + console.print( + "\n[green]✓[/green] Using generated release notes without editing" + ) + else: + console.print( + "\n[green]✓[/green] Using generated release notes without editing" + ) + + if not dry_run: + with console.status(f"[cyan]Creating tag {tag_name}..."): + try: + run_command(["git", "tag", "-a", tag_name, "-m", release_notes]) + except subprocess.CalledProcessError as e: + console.print(f"[red]✗[/red] Created tag {tag_name}: {e}") + sys.exit(1) + console.print(f"[green]✓[/green] Created tag {tag_name}") + + with console.status(f"[cyan]Pushing tag {tag_name}..."): + try: + run_command(["git", "push", "origin", tag_name]) + except subprocess.CalledProcessError as e: + console.print(f"[red]✗[/red] Pushed tag {tag_name}: {e}") + sys.exit(1) + console.print(f"[green]✓[/green] Pushed tag {tag_name}") + + is_prerelease = any( + indicator in version.lower() + for indicator in ["a", "b", "rc", "alpha", "beta", "dev"] + ) + + with console.status("[cyan]Creating GitHub Release..."): + try: + gh_cmd = [ + "gh", + "release", + "create", + tag_name, + "--title", + tag_name, + "--notes", + release_notes, + ] + if is_prerelease: + gh_cmd.append("--prerelease") + + run_command(gh_cmd) + except subprocess.CalledProcessError as e: + console.print(f"[red]✗[/red] Created GitHub Release: {e}") + sys.exit(1) + + release_type = "prerelease" if is_prerelease else "release" + console.print( + f"[green]✓[/green] Created GitHub {release_type} for {tag_name}" + ) + + console.print( + f"\n[green]✓[/green] Packages @ [bold]{version}[/bold] tagged successfully!" + ) + + except subprocess.CalledProcessError as e: + console.print(f"[red]Error running command:[/red] {e}") + if e.stderr: + console.print(e.stderr) + sys.exit(1) + except Exception as e: + console.print(f"[red]Error:[/red] {e}") + sys.exit(1) + + +cli.add_command(bump) +cli.add_command(tag) + + +def main() -> None: + """Entry point for the CLI.""" + cli() + + +if __name__ == "__main__": + main() diff --git a/lib/devtools/src/crewai_devtools/prompts.py b/lib/devtools/src/crewai_devtools/prompts.py new file mode 100644 index 000000000..1e96f03f4 --- /dev/null +++ b/lib/devtools/src/crewai_devtools/prompts.py @@ -0,0 +1,45 @@ +"""Prompt templates for AI-generated content.""" + +from string import Template + + +RELEASE_NOTES_PROMPT = Template( + """Generate concise release notes for version $version based on these commits: + +$commits + +The commits follow the Conventional Commits standard (feat:, fix:, chore:, etc.). + +Use this exact template format: + +## What's Changed + +### Features +- [List feat: commits here, using imperative mood like "Add X", "Implement Y"] + +### Bug Fixes +- [List fix: commits here, using imperative mood like "Fix X", "Resolve Y"] + +### Documentation +- [List docs: commits here, using imperative mood like "Update X", "Add Y"] + +### Performance +- [List perf: commits here, using imperative mood like "Improve X", "Optimize Y"] + +### Refactoring +- [List refactor: commits here, using imperative mood like "Refactor X", "Simplify Y"] + +### Breaking Changes +- [List commits with BREAKING CHANGE in footer or ! after type, using imperative mood]$contributors_section + +Instructions: +- Parse conventional commit format (type: description or type(scope): description) +- Only include sections that have relevant changes from the commits +- Skip chore:, ci:, test:, and style: commits unless significant +- Convert commit messages to imperative mood if needed (e.g., "adds" → "Add") +- Be concise but informative +- Focus on user-facing changes +- Use the exact Contributors list provided above, do not modify it + +Keep it professional and clear.""" +) diff --git a/pyproject.toml b/pyproject.toml index c0909bbd9..2b0e5445d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,87 +1,10 @@ -[project] -name = "crewai" -dynamic = ["version"] +name = "crewai-workspace" description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks." readme = "README.md" requires-python = ">=3.10,<3.14" authors = [ { name = "Joao Moura", email = "joao@crewai.com" } ] -dependencies = [ - # Core Dependencies - "pydantic>=2.11.9", - "openai>=1.13.3", - "litellm==1.74.9", - "instructor>=1.3.3", - # Text Processing - "pdfplumber>=0.11.4", - "regex>=2024.9.11", - # Telemetry and Monitoring - "opentelemetry-api>=1.30.0", - "opentelemetry-sdk>=1.30.0", - "opentelemetry-exporter-otlp-proto-http>=1.30.0", - # Data Handling - "chromadb~=1.1.0", - "tokenizers>=0.20.3", - "openpyxl>=3.1.5", - "pyvis>=0.3.2", - # Authentication and Security - "python-dotenv>=1.1.1", - "pyjwt>=2.9.0", - # Configuration and Utils - "click>=8.1.7", - "appdirs>=1.4.4", - "jsonref>=1.1.0", - "json-repair==0.25.2", - "uv>=0.4.25", - "tomli-w>=1.1.0", - "tomli>=2.0.2", - "blinker>=1.9.0", - "json5>=0.10.0", - "portalocker==2.7.0", - "pydantic-settings>=2.10.1", -] - -[project.urls] -Homepage = "https://crewai.com" -Documentation = "https://docs.crewai.com" -Repository = "https://github.com/crewAIInc/crewAI" - -[project.optional-dependencies] -tools = [ - "crewai-tools>=0.76.0", -] -embeddings = [ - "tiktoken~=0.8.0" -] -pdfplumber = [ - "pdfplumber>=0.11.4", -] -pandas = [ - "pandas>=2.2.3", -] -openpyxl = [ - "openpyxl>=3.1.5", -] -mem0 = ["mem0ai>=0.1.94"] -docling = [ - "docling>=2.12.0", -] -aisuite = [ - "aisuite>=0.1.10", -] -qdrant = [ - "qdrant-client[fastembed]>=1.14.3", -] -aws = [ - "boto3>=1.40.38", -] -watson = [ - "ibm-watsonx-ai>=1.3.39", -] -voyageai = [ - "voyageai>=0.3.5", -] [dependency-groups] dev = [ @@ -92,28 +15,41 @@ dev = [ "pytest>=8.4.2", "pytest-asyncio>=1.2.0", "pytest-subprocess>=1.5.3", + "vcrpy==7.0.0", # pinned, less versions break pytest-recording "pytest-recording>=0.13.4", "pytest-randomly>=4.0.1", "pytest-timeout>=2.4.0", "pytest-xdist>=3.8.0", "pytest-split>=0.10.0", - "types-requests==2.32.*", + "types-requests~=2.31.0.6", "types-pyyaml==6.0.*", "types-regex==2024.11.6.*", "types-appdirs==1.4.*", + "boto3-stubs[bedrock-runtime]>=1.40.54", + "types-psycopg2>=2.9.21.20251012", + "types-pymysql>=1.1.0.20250916", ] -[project.scripts] -crewai = "crewai.cli.cli:crewai" [tool.ruff] -exclude = [ - "src/crewai/cli/templates", +src = ["lib/*"] +extend-exclude = [ + "lib/crewai/src/crewai/experimental/a2a", + "lib/crewai/src/crewai/cli/templates", + "lib/crewai/tests/", + "lib/crewai-tools/tests/", ] +respect-gitignore = true +force-exclude = true fix = true +target-version = "py310" + +[tool.ruff.format] +docstring-code-format = true [tool.ruff.lint] -select = [ +future-annotations = true +extend-select = [ "E", # pycodestyle errors (style issues) "F", # Pyflakes (code errors) "B", # flake8-bugbear (bug prevention) @@ -121,8 +57,12 @@ select = [ "RUF", # ruff-specific rules "N", # pep8-naming (naming conventions) "W", # pycodestyle warnings + "I", # isort (import formatting) + "T", # flake8-print (print statements) +# "D", # pydocstyle (docstring conventions) disabled until "PERF", # performance issues "PIE", # flake8-pie (unnecessary code) + "TID", # flake8-tidy-imports (import best practices) "ASYNC", # async/await best practices "RET", # flake8-return (return improvements) "UP006", # use collections.abc @@ -141,58 +81,81 @@ select = [ ] ignore = ["E501"] # ignore line too long globally +[tool.ruff.lint.flake8-tidy-imports] +ban-relative-imports = "all" + +[tool.ruff.lint.flake8-type-checking] +runtime-evaluated-base-classes = ["pydantic.BaseModel"] + +[tool.ruff.lint.isort] +no-sections = false +case-sensitive = true +combine-as-imports = true +force-single-line = false +force-sort-within-sections = true +known-first-party = [] +section-order = ["future", "standard-library", "third-party", "first-party", "local-folder"] +lines-after-imports = 2 +split-on-trailing-comma = true + +[tool.ruff.lint.pydocstyle] +convention = "google" +ignore-decorators = ["typing.overload"] + [tool.ruff.lint.per-file-ignores] -"tests/**/*.py" = ["S101", "RET504"] # Allow assert statements and unnecessary assignments before return in tests +"lib/crewai/tests/**/*.py" = ["S101", "RET504", "S105", "S106"] # Allow assert statements, unnecessary assignments, and hardcoded passwords in tests +"lib/crewai-tools/tests/**/*.py" = ["S101", "RET504", "S105", "S106", "RUF012", "N818", "E402", "RUF043", "S110", "B017"] # Allow various test-specific patterns + [tool.mypy] -exclude = ["src/crewai/cli/templates", "tests/"] +strict = true +disallow_untyped_defs = true +disallow_any_unimported = true +no_implicit_optional = true +check_untyped_defs = true +warn_return_any = true +show_error_codes = true +warn_unused_ignores = true +python_version = "3.12" +exclude = [ + "lib/crewai/src/crewai/cli/templates", + "lib/crewai/tests/", + # crewai-tools + "lib/crewai-tools/tests/", + "lib/crewai/src/crewai/experimental/a2a" +] plugins = ["pydantic.mypy"] [tool.bandit] -exclude_dirs = ["src/crewai/cli/templates"] +exclude_dirs = ["lib/crewai/src/crewai/cli/templates"] + [tool.pytest.ini_options] markers = [ "telemetry: mark test as a telemetry test (don't mock telemetry)", ] +testpaths = [ + "lib/crewai/tests", + "lib/crewai-tools/tests", +] +asyncio_mode = "strict" +asyncio_default_fixture_loop_scope = "function" +addopts = "--tb=short" +python_files = "test_*.py" +python_classes = "Test*" +python_functions = "test_*" -# PyTorch index configuration, since torch 2.5.0 is not compatible with python 3.13 -[[tool.uv.index]] -name = "pytorch-nightly" -url = "https://download.pytorch.org/whl/nightly/cpu" -explicit = true -[[tool.uv.index]] -name = "pytorch" -url = "https://download.pytorch.org/whl/cpu" -explicit = true +[tool.uv.workspace] +members = [ + "lib/crewai", + "lib/crewai-tools", + "lib/devtools", +] + [tool.uv.sources] -torch = [ - { index = "pytorch-nightly", marker = "python_version >= '3.13'" }, - { index = "pytorch", marker = "python_version < '3.13'" }, -] -torchvision = [ - { index = "pytorch-nightly", marker = "python_version >= '3.13'" }, - { index = "pytorch", marker = "python_version < '3.13'" }, -] - -[tool.hatch.version] -path = "src/crewai/__init__.py" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.wheel] -exclude = [ -"docs/**", -"docs/", -] - -[tool.hatch.build.targets.sdist] -exclude = [ -"docs/**", -"docs/", -] +crewai = { workspace = true } +crewai-tools = { workspace = true } +crewai-devtools = { workspace = true } diff --git a/src/crewai/agents/agent_adapters/base_converter_adapter.py b/src/crewai/agents/agent_adapters/base_converter_adapter.py deleted file mode 100644 index 18654df61..000000000 --- a/src/crewai/agents/agent_adapters/base_converter_adapter.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Base converter adapter for structured output conversion.""" - -from __future__ import annotations - -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter - from crewai.task import Task - - -class BaseConverterAdapter(ABC): - """Abstract base class for converter adapters in CrewAI. - - Defines the common interface for converting agent outputs to structured formats. - All converter adapters must implement the methods defined here. - """ - - def __init__(self, agent_adapter: BaseAgentAdapter) -> None: - """Initialize the converter adapter. - - Args: - agent_adapter: The agent adapter to configure for structured output. - """ - self.agent_adapter = agent_adapter - - @abstractmethod - def configure_structured_output(self, task: Task) -> None: - """Configure agents to return structured output. - - Must support both JSON and Pydantic output formats. - - Args: - task: The task requiring structured output. - """ - - @abstractmethod - def enhance_system_prompt(self, base_prompt: str) -> str: - """Enhance the system prompt with structured output instructions. - - Args: - base_prompt: The original system prompt. - - Returns: - Enhanced prompt with structured output guidance. - """ - - @abstractmethod - def post_process_result(self, result: str) -> str: - """Post-process the result to ensure proper string format. - - Args: - result: The raw result from agent execution. - - Returns: - Processed result as a string. - """ diff --git a/src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py b/src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py deleted file mode 100644 index b7bb5d1d7..000000000 --- a/src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py +++ /dev/null @@ -1,134 +0,0 @@ -"""OpenAI structured output converter for CrewAI task integration. - -This module contains the OpenAIConverterAdapter class that handles structured -output conversion for OpenAI agents, supporting JSON and Pydantic model formats. -""" - -import json -import re -from typing import Any, Literal - -from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter -from crewai.utilities.converter import generate_model_description -from crewai.utilities.i18n import I18N - - -class OpenAIConverterAdapter(BaseConverterAdapter): - """Adapter for handling structured output conversion in OpenAI agents. - - This adapter enhances the OpenAI agent to handle structured output formats - and post-processes the results when needed. - - Attributes: - _output_format: The expected output format (json, pydantic, or None) - _schema: The schema description for the expected output - _output_model: The Pydantic model for the output - """ - - def __init__(self, agent_adapter: Any) -> None: - """Initialize the converter adapter with a reference to the agent adapter. - - Args: - agent_adapter: The OpenAI agent adapter instance. - """ - super().__init__(agent_adapter=agent_adapter) - self.agent_adapter: Any = agent_adapter - self._output_format: Literal["json", "pydantic"] | None = None - self._schema: str | None = None - self._output_model: Any = None - - def configure_structured_output(self, task: Any) -> None: - """Configure the structured output for OpenAI agent based on task requirements. - - Args: - task: The task containing output format requirements. - """ - # Reset configuration - self._output_format = None - self._schema = None - self._output_model = None - - # If no structured output is required, return early - if not (task.output_json or task.output_pydantic): - return - - # Configure based on task output format - if task.output_json: - self._output_format = "json" - self._schema = generate_model_description(task.output_json) - self.agent_adapter._openai_agent.output_type = task.output_json - self._output_model = task.output_json - elif task.output_pydantic: - self._output_format = "pydantic" - self._schema = generate_model_description(task.output_pydantic) - self.agent_adapter._openai_agent.output_type = task.output_pydantic - self._output_model = task.output_pydantic - - def enhance_system_prompt(self, base_prompt: str) -> str: - """Enhance the base system prompt with structured output requirements if needed. - - Args: - base_prompt: The original system prompt. - - Returns: - Enhanced system prompt with output format instructions if needed. - """ - if not self._output_format: - return base_prompt - - output_schema: str = ( - I18N() - .slice("formatted_task_instructions") - .format(output_format=self._schema) - ) - - return f"{base_prompt}\n\n{output_schema}" - - def post_process_result(self, result: str) -> str: - """Post-process the result to ensure it matches the expected format. - - This method attempts to extract valid JSON from the result if necessary. - - Args: - result: The raw result from the agent. - - Returns: - Processed result conforming to the expected output format. - """ - if not self._output_format: - return result - # Try to extract valid JSON if it's wrapped in code blocks or other text - if isinstance(result, str) and self._output_format in ["json", "pydantic"]: - # First, try to parse as is - try: - json.loads(result) - return result - except json.JSONDecodeError: - # Try to extract JSON from markdown code blocks - code_block_pattern: str = r"```(?:json)?\s*([\s\S]*?)```" - code_blocks: list[str] = re.findall(code_block_pattern, result) - - for block in code_blocks: - stripped_block = block.strip() - try: - json.loads(stripped_block) - return stripped_block - except json.JSONDecodeError: - pass - - # Try to extract any JSON-like structure - json_pattern: str = r"(\{[\s\S]*\})" - json_matches: list[str] = re.findall(json_pattern, result, re.DOTALL) - - for match in json_matches: - is_valid = True - try: - json.loads(match) - except json.JSONDecodeError: - is_valid = False - - if is_valid: - return match - - # If all extraction attempts fail, return the original - return str(result) diff --git a/src/crewai/agents/cache/__init__.py b/src/crewai/agents/cache/__init__.py deleted file mode 100644 index 6b4d20081..000000000 --- a/src/crewai/agents/cache/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .cache_handler import CacheHandler - -__all__ = ["CacheHandler"] diff --git a/src/crewai/cli/authentication/__init__.py b/src/crewai/cli/authentication/__init__.py deleted file mode 100644 index 484453771..000000000 --- a/src/crewai/cli/authentication/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .main import AuthenticationCommand - -__all__ = ["AuthenticationCommand"] diff --git a/src/crewai/cli/organization/main.py b/src/crewai/cli/organization/main.py deleted file mode 100644 index 8bf23d531..000000000 --- a/src/crewai/cli/organization/main.py +++ /dev/null @@ -1,76 +0,0 @@ -from rich.console import Console -from rich.table import Table - -from requests import HTTPError -from crewai.cli.command import BaseCommand, PlusAPIMixin -from crewai.cli.config import Settings - -console = Console() - -class OrganizationCommand(BaseCommand, PlusAPIMixin): - def __init__(self): - BaseCommand.__init__(self) - PlusAPIMixin.__init__(self, telemetry=self._telemetry) - - def list(self): - try: - response = self.plus_api_client.get_organizations() - response.raise_for_status() - orgs = response.json() - - if not orgs: - console.print("You don't belong to any organizations yet.", style="yellow") - return - - table = Table(title="Your Organizations") - table.add_column("Name", style="cyan") - table.add_column("ID", style="green") - for org in orgs: - table.add_row(org["name"], org["uuid"]) - - console.print(table) - except HTTPError as e: - if e.response.status_code == 401: - console.print("You are not logged in to any organization. Use 'crewai login' to login.", style="bold red") - return - console.print(f"Failed to retrieve organization list: {str(e)}", style="bold red") - raise SystemExit(1) - except Exception as e: - console.print(f"Failed to retrieve organization list: {str(e)}", style="bold red") - raise SystemExit(1) - - def switch(self, org_id): - try: - response = self.plus_api_client.get_organizations() - response.raise_for_status() - orgs = response.json() - - org = next((o for o in orgs if o["uuid"] == org_id), None) - if not org: - console.print(f"Organization with id '{org_id}' not found.", style="bold red") - return - - settings = Settings() - settings.org_name = org["name"] - settings.org_uuid = org["uuid"] - settings.dump() - - console.print(f"Successfully switched to {org['name']} ({org['uuid']})", style="bold green") - except HTTPError as e: - if e.response.status_code == 401: - console.print("You are not logged in to any organization. Use 'crewai login' to login.", style="bold red") - return - console.print(f"Failed to retrieve organization list: {str(e)}", style="bold red") - raise SystemExit(1) - except Exception as e: - console.print(f"Failed to switch organization: {str(e)}", style="bold red") - raise SystemExit(1) - - def current(self): - settings = Settings() - if settings.org_uuid: - console.print(f"Currently logged in to organization {settings.org_name} ({settings.org_uuid})", style="bold green") - else: - console.print("You're not currently logged in to any organization.", style="yellow") - console.print("Use 'crewai org list' to see available organizations.", style="yellow") - console.print("Use 'crewai org switch ' to switch to an organization.", style="yellow") diff --git a/src/crewai/cli/templates/flow/main.py b/src/crewai/cli/templates/flow/main.py deleted file mode 100644 index 920b56c04..000000000 --- a/src/crewai/cli/templates/flow/main.py +++ /dev/null @@ -1,53 +0,0 @@ -#!/usr/bin/env python -from random import randint - -from pydantic import BaseModel - -from crewai.flow import Flow, listen, start - -from {{folder_name}}.crews.poem_crew.poem_crew import PoemCrew - - -class PoemState(BaseModel): - sentence_count: int = 1 - poem: str = "" - - -class PoemFlow(Flow[PoemState]): - - @start() - def generate_sentence_count(self): - print("Generating sentence count") - self.state.sentence_count = randint(1, 5) - - @listen(generate_sentence_count) - def generate_poem(self): - print("Generating poem") - result = ( - PoemCrew() - .crew() - .kickoff(inputs={"sentence_count": self.state.sentence_count}) - ) - - print("Poem generated", result.raw) - self.state.poem = result.raw - - @listen(generate_poem) - def save_poem(self): - print("Saving poem") - with open("poem.txt", "w") as f: - f.write(self.state.poem) - - -def kickoff(): - poem_flow = PoemFlow() - poem_flow.kickoff() - - -def plot(): - poem_flow = PoemFlow() - poem_flow.plot() - - -if __name__ == "__main__": - kickoff() diff --git a/src/crewai/context.py b/src/crewai/context.py deleted file mode 100644 index 1701c279c..000000000 --- a/src/crewai/context.py +++ /dev/null @@ -1,25 +0,0 @@ -import os -import contextvars -from typing import Optional -from contextlib import contextmanager - -_platform_integration_token: contextvars.ContextVar[Optional[str]] = contextvars.ContextVar( - "platform_integration_token", default=None -) - -def set_platform_integration_token(integration_token: str) -> None: - _platform_integration_token.set(integration_token) - -def get_platform_integration_token() -> Optional[str]: - token = _platform_integration_token.get() - if token is None: - token = os.getenv("CREWAI_PLATFORM_INTEGRATION_TOKEN") - return token - -@contextmanager -def platform_context(integration_token: str): - token = _platform_integration_token.set(integration_token) - try: - yield - finally: - _platform_integration_token.reset(token) diff --git a/src/crewai/crews/__init__.py b/src/crewai/crews/__init__.py deleted file mode 100644 index 92f297d9f..000000000 --- a/src/crewai/crews/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .crew_output import CrewOutput - -__all__ = ["CrewOutput"] diff --git a/src/crewai/events/event_bus.py b/src/crewai/events/event_bus.py deleted file mode 100644 index d5d322121..000000000 --- a/src/crewai/events/event_bus.py +++ /dev/null @@ -1,125 +0,0 @@ -from __future__ import annotations - -import threading -from collections.abc import Callable -from contextlib import contextmanager -from typing import Any, TypeVar, cast - -from blinker import Signal - -from crewai.events.base_events import BaseEvent -from crewai.events.event_types import EventTypes - -EventT = TypeVar("EventT", bound=BaseEvent) - - -class CrewAIEventsBus: - """ - A singleton event bus that uses blinker signals for event handling. - Allows both internal (Flow/Crew) and external event handling. - """ - - _instance = None - _lock = threading.Lock() - - def __new__(cls): - if cls._instance is None: - with cls._lock: - if cls._instance is None: # prevent race condition - cls._instance = super().__new__(cls) - cls._instance._initialize() - return cls._instance - - def _initialize(self) -> None: - """Initialize the event bus internal state""" - self._signal = Signal("crewai_event_bus") - self._handlers: dict[type[BaseEvent], list[Callable]] = {} - - def on( - self, event_type: type[EventT] - ) -> Callable[[Callable[[Any, EventT], None]], Callable[[Any, EventT], None]]: - """ - Decorator to register an event handler for a specific event type. - - Usage: - @crewai_event_bus.on(AgentExecutionCompletedEvent) - def on_agent_execution_completed( - source: Any, event: AgentExecutionCompletedEvent - ): - print(f"👍 Agent '{event.agent}' completed task") - print(f" Output: {event.output}") - """ - - def decorator( - handler: Callable[[Any, EventT], None], - ) -> Callable[[Any, EventT], None]: - if event_type not in self._handlers: - self._handlers[event_type] = [] - self._handlers[event_type].append( - cast(Callable[[Any, EventT], None], handler) - ) - return handler - - return decorator - - @staticmethod - def _call_handler( - handler: Callable, source: Any, event: BaseEvent, event_type: type - ) -> None: - """Call a single handler with error handling.""" - try: - handler(source, event) - except Exception as e: - print( - f"[EventBus Error] Handler '{handler.__name__}' failed for event '{event_type.__name__}': {e}" - ) - - def emit(self, source: Any, event: BaseEvent) -> None: - """ - Emit an event to all registered handlers - - Args: - source: The object emitting the event - event: The event instance to emit - """ - for event_type, handlers in self._handlers.items(): - if isinstance(event, event_type): - for handler in handlers: - self._call_handler(handler, source, event, event_type) - - self._signal.send(source, event=event) - - def register_handler( - self, event_type: type[EventTypes], handler: Callable[[Any, EventTypes], None] - ) -> None: - """Register an event handler for a specific event type""" - if event_type not in self._handlers: - self._handlers[event_type] = [] - self._handlers[event_type].append( - cast(Callable[[Any, EventTypes], None], handler) - ) - - @contextmanager - def scoped_handlers(self): - """ - Context manager for temporary event handling scope. - Useful for testing or temporary event handling. - - Usage: - with crewai_event_bus.scoped_handlers(): - @crewai_event_bus.on(CrewKickoffStarted) - def temp_handler(source, event): - print("Temporary handler") - # Do stuff... - # Handlers are cleared after the context - """ - previous_handlers = self._handlers.copy() - self._handlers.clear() - try: - yield - finally: - self._handlers = previous_handlers - - -# Global instance -crewai_event_bus = CrewAIEventsBus() diff --git a/src/crewai/flow/config.py b/src/crewai/flow/config.py deleted file mode 100644 index b04d5d0c2..000000000 --- a/src/crewai/flow/config.py +++ /dev/null @@ -1,59 +0,0 @@ -DARK_GRAY = "#333333" -CREWAI_ORANGE = "#FF5A50" -GRAY = "#666666" -WHITE = "#FFFFFF" -BLACK = "#000000" - -COLORS = { - "bg": WHITE, - "start": CREWAI_ORANGE, - "method": DARK_GRAY, - "router": DARK_GRAY, - "router_border": CREWAI_ORANGE, - "edge": GRAY, - "router_edge": CREWAI_ORANGE, - "text": WHITE, -} - -NODE_STYLES = { - "start": { - "color": CREWAI_ORANGE, - "shape": "box", - "font": {"color": WHITE}, - "margin": {"top": 10, "bottom": 8, "left": 10, "right": 10}, - }, - "method": { - "color": DARK_GRAY, - "shape": "box", - "font": {"color": WHITE}, - "margin": {"top": 10, "bottom": 8, "left": 10, "right": 10}, - }, - "router": { - "color": { - "background": DARK_GRAY, - "border": CREWAI_ORANGE, - "highlight": { - "border": CREWAI_ORANGE, - "background": DARK_GRAY, - }, - }, - "shape": "box", - "font": {"color": WHITE}, - "borderWidth": 3, - "borderWidthSelected": 4, - "shapeProperties": {"borderDashes": [5, 5]}, - "margin": {"top": 10, "bottom": 8, "left": 10, "right": 10}, - }, - "crew": { - "color": { - "background": WHITE, - "border": CREWAI_ORANGE, - }, - "shape": "box", - "font": {"color": BLACK}, - "borderWidth": 3, - "borderWidthSelected": 4, - "shapeProperties": {"borderDashes": False}, - "margin": {"top": 10, "bottom": 8, "left": 10, "right": 10}, - }, -} diff --git a/src/crewai/llms/base_llm.py b/src/crewai/llms/base_llm.py deleted file mode 100644 index 0cd95c347..000000000 --- a/src/crewai/llms/base_llm.py +++ /dev/null @@ -1,100 +0,0 @@ -"""Base LLM abstract class for CrewAI. - -This module provides the abstract base class for all LLM implementations -in CrewAI. -""" - -from abc import ABC, abstractmethod -from typing import Any, Final - -DEFAULT_CONTEXT_WINDOW_SIZE: Final[int] = 4096 -DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True - - -class BaseLLM(ABC): - """Abstract base class for LLM implementations. - - This class defines the interface that all LLM implementations must follow. - Users can extend this class to create custom LLM implementations that don't - rely on litellm's authentication mechanism. - - Custom LLM implementations should handle error cases gracefully, including - timeouts, authentication failures, and malformed responses. They should also - implement proper validation for input parameters and provide clear error - messages when things go wrong. - - Attributes: - model: The model identifier/name. - temperature: Optional temperature setting for response generation. - stop: A list of stop sequences that the LLM should use to stop generation. - """ - - def __init__( - self, - model: str, - temperature: float | None = None, - stop: list[str] | None = None, - ) -> None: - """Initialize the BaseLLM with default attributes. - - Args: - model: The model identifier/name. - temperature: Optional temperature setting for response generation. - stop: Optional list of stop sequences for generation. - """ - self.model = model - self.temperature = temperature - self.stop: list[str] = stop or [] - - @abstractmethod - def call( - self, - messages: str | list[dict[str, str]], - tools: list[dict] | None = None, - callbacks: list[Any] | None = None, - available_functions: dict[str, Any] | None = None, - from_task: Any | None = None, - from_agent: Any | None = None, - ) -> str | Any: - """Call the LLM with the given messages. - - Args: - messages: Input messages for the LLM. - Can be a string or list of message dictionaries. - If string, it will be converted to a single user message. - If list, each dict must have 'role' and 'content' keys. - tools: Optional list of tool schemas for function calling. - Each tool should define its name, description, and parameters. - callbacks: Optional list of callback functions to be executed - during and after the LLM call. - available_functions: Optional dict mapping function names to callables - that can be invoked by the LLM. - from_task: Optional task caller to be used for the LLM call. - from_agent: Optional agent caller to be used for the LLM call. - - Returns: - Either a text response from the LLM (str) or - the result of a tool function call (Any). - - Raises: - ValueError: If the messages format is invalid. - TimeoutError: If the LLM request times out. - RuntimeError: If the LLM request fails for other reasons. - """ - - def supports_stop_words(self) -> bool: - """Check if the LLM supports stop words. - - Returns: - True if the LLM supports stop words, False otherwise. - """ - return DEFAULT_SUPPORTS_STOP_WORDS - - def get_context_window_size(self) -> int: - """Get the context window size for the LLM. - - Returns: - The number of tokens/characters the model can handle. - """ - # Default implementation - subclasses should override with model-specific values - return DEFAULT_CONTEXT_WINDOW_SIZE diff --git a/src/crewai/memory/__init__.py b/src/crewai/memory/__init__.py deleted file mode 100644 index 0c8aacdde..000000000 --- a/src/crewai/memory/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -from .entity.entity_memory import EntityMemory -from .external.external_memory import ExternalMemory -from .long_term.long_term_memory import LongTermMemory -from .short_term.short_term_memory import ShortTermMemory - -__all__ = [ - "EntityMemory", - "ExternalMemory", - "LongTermMemory", - "ShortTermMemory", -] diff --git a/src/crewai/project/annotations.py b/src/crewai/project/annotations.py deleted file mode 100644 index d7c636ccf..000000000 --- a/src/crewai/project/annotations.py +++ /dev/null @@ -1,127 +0,0 @@ -from functools import wraps -from typing import Callable - -from crewai import Crew -from crewai.project.utils import memoize - -"""Decorators for defining crew components and their behaviors.""" - - -def before_kickoff(func): - """Marks a method to execute before crew kickoff.""" - func.is_before_kickoff = True - return func - - -def after_kickoff(func): - """Marks a method to execute after crew kickoff.""" - func.is_after_kickoff = True - return func - - -def task(func): - """Marks a method as a crew task.""" - func.is_task = True - - @wraps(func) - def wrapper(*args, **kwargs): - result = func(*args, **kwargs) - if not result.name: - result.name = func.__name__ - return result - - return memoize(wrapper) - - -def agent(func): - """Marks a method as a crew agent.""" - func.is_agent = True - func = memoize(func) - return func - - -def llm(func): - """Marks a method as an LLM provider.""" - func.is_llm = True - func = memoize(func) - return func - - -def output_json(cls): - """Marks a class as JSON output format.""" - cls.is_output_json = True - return cls - - -def output_pydantic(cls): - """Marks a class as Pydantic output format.""" - cls.is_output_pydantic = True - return cls - - -def tool(func): - """Marks a method as a crew tool.""" - func.is_tool = True - return memoize(func) - - -def callback(func): - """Marks a method as a crew callback.""" - func.is_callback = True - return memoize(func) - - -def cache_handler(func): - """Marks a method as a cache handler.""" - func.is_cache_handler = True - return memoize(func) - - -def crew(func) -> Callable[..., Crew]: - """Marks a method as the main crew execution point.""" - - @wraps(func) - def wrapper(self, *args, **kwargs) -> Crew: - instantiated_tasks = [] - instantiated_agents = [] - agent_roles = set() - - # Use the preserved task and agent information - tasks = self._original_tasks.items() - agents = self._original_agents.items() - - # Instantiate tasks in order - for task_name, task_method in tasks: - task_instance = task_method(self) - instantiated_tasks.append(task_instance) - agent_instance = getattr(task_instance, "agent", None) - if agent_instance and agent_instance.role not in agent_roles: - instantiated_agents.append(agent_instance) - agent_roles.add(agent_instance.role) - - # Instantiate agents not included by tasks - for agent_name, agent_method in agents: - agent_instance = agent_method(self) - if agent_instance.role not in agent_roles: - instantiated_agents.append(agent_instance) - agent_roles.add(agent_instance.role) - - self.agents = instantiated_agents - self.tasks = instantiated_tasks - - crew = func(self, *args, **kwargs) - - def callback_wrapper(callback, instance): - def wrapper(*args, **kwargs): - return callback(instance, *args, **kwargs) - - return wrapper - - for _, callback in self._before_kickoff.items(): - crew.before_kickoff_callbacks.append(callback_wrapper(callback, self)) - for _, callback in self._after_kickoff.items(): - crew.after_kickoff_callbacks.append(callback_wrapper(callback, self)) - - return crew - - return memoize(wrapper) diff --git a/src/crewai/project/crew_base.py b/src/crewai/project/crew_base.py deleted file mode 100644 index 44871f6a0..000000000 --- a/src/crewai/project/crew_base.py +++ /dev/null @@ -1,298 +0,0 @@ -import inspect -import logging -from collections.abc import Callable -from pathlib import Path -from typing import Any, TypeVar, cast - -import yaml -from dotenv import load_dotenv - -from crewai.tools import BaseTool - -load_dotenv() - -T = TypeVar("T", bound=type) - -"""Base decorator for creating crew classes with configuration and function management.""" - - -def CrewBase(cls: T) -> T: # noqa: N802 - """Wraps a class with crew functionality and configuration management.""" - - class WrappedClass(cls): # type: ignore - is_crew_class: bool = True # type: ignore - - # Get the directory of the class being decorated - base_directory = Path(inspect.getfile(cls)).parent - - original_agents_config_path = getattr( - cls, "agents_config", "config/agents.yaml" - ) - original_tasks_config_path = getattr(cls, "tasks_config", "config/tasks.yaml") - - mcp_server_params: Any = getattr(cls, "mcp_server_params", None) - mcp_connect_timeout: int = getattr(cls, "mcp_connect_timeout", 30) - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.load_configurations() - self.map_all_agent_variables() - self.map_all_task_variables() - # Preserve all decorated functions - self._original_functions = { - name: method - for name, method in cls.__dict__.items() - if any( - hasattr(method, attr) - for attr in [ - "is_task", - "is_agent", - "is_before_kickoff", - "is_after_kickoff", - "is_kickoff", - ] - ) - } - # Store specific function types - self._original_tasks = self._filter_functions( - self._original_functions, "is_task" - ) - self._original_agents = self._filter_functions( - self._original_functions, "is_agent" - ) - self._before_kickoff = self._filter_functions( - self._original_functions, "is_before_kickoff" - ) - self._after_kickoff = self._filter_functions( - self._original_functions, "is_after_kickoff" - ) - self._kickoff = self._filter_functions( - self._original_functions, "is_kickoff" - ) - - # Add close mcp server method to after kickoff - bound_method = self._create_close_mcp_server_method() - self._after_kickoff['_close_mcp_server'] = bound_method - - def _create_close_mcp_server_method(self): - def _close_mcp_server(self, instance, outputs): - adapter = getattr(self, '_mcp_server_adapter', None) - if adapter is not None: - try: - adapter.stop() - except Exception as e: - logging.warning(f"Error stopping MCP server: {e}") - return outputs - - _close_mcp_server.is_after_kickoff = True - - import types - return types.MethodType(_close_mcp_server, self) - - def get_mcp_tools(self, *tool_names: list[str]) -> list[BaseTool]: - if not self.mcp_server_params: - return [] - - from crewai_tools import MCPServerAdapter # type: ignore[import-untyped] - - adapter = getattr(self, '_mcp_server_adapter', None) - if not adapter: - self._mcp_server_adapter = MCPServerAdapter( - self.mcp_server_params, - connect_timeout=self.mcp_connect_timeout - ) - - return self._mcp_server_adapter.tools.filter_by_names(tool_names or None) - - - def load_configurations(self): - """Load agent and task configurations from YAML files.""" - if isinstance(self.original_agents_config_path, str): - agents_config_path = ( - self.base_directory / self.original_agents_config_path - ) - try: - self.agents_config = self.load_yaml(agents_config_path) - except FileNotFoundError: - logging.warning( - f"Agent config file not found at {agents_config_path}. " - "Proceeding with empty agent configurations." - ) - self.agents_config = {} - else: - logging.warning( - "No agent configuration path provided. Proceeding with empty agent configurations." - ) - self.agents_config = {} - - if isinstance(self.original_tasks_config_path, str): - tasks_config_path = ( - self.base_directory / self.original_tasks_config_path - ) - try: - self.tasks_config = self.load_yaml(tasks_config_path) - except FileNotFoundError: - logging.warning( - f"Task config file not found at {tasks_config_path}. " - "Proceeding with empty task configurations." - ) - self.tasks_config = {} - else: - logging.warning( - "No task configuration path provided. Proceeding with empty task configurations." - ) - self.tasks_config = {} - - @staticmethod - def load_yaml(config_path: Path): - try: - with open(config_path, "r", encoding="utf-8") as file: - return yaml.safe_load(file) - except FileNotFoundError: - print(f"File not found: {config_path}") - raise - - def _get_all_functions(self): - return { - name: getattr(self, name) - for name in dir(self) - if callable(getattr(self, name)) - } - - def _filter_functions( - self, functions: dict[str, Callable], attribute: str - ) -> dict[str, Callable]: - return { - name: func - for name, func in functions.items() - if hasattr(func, attribute) - } - - def map_all_agent_variables(self) -> None: - all_functions = self._get_all_functions() - llms = self._filter_functions(all_functions, "is_llm") - tool_functions = self._filter_functions(all_functions, "is_tool") - cache_handler_functions = self._filter_functions( - all_functions, "is_cache_handler" - ) - callbacks = self._filter_functions(all_functions, "is_callback") - - for agent_name, agent_info in self.agents_config.items(): - self._map_agent_variables( - agent_name, - agent_info, - llms, - tool_functions, - cache_handler_functions, - callbacks, - ) - - def _map_agent_variables( - self, - agent_name: str, - agent_info: dict[str, Any], - llms: dict[str, Callable], - tool_functions: dict[str, Callable], - cache_handler_functions: dict[str, Callable], - callbacks: dict[str, Callable], - ) -> None: - if llm := agent_info.get("llm"): - try: - self.agents_config[agent_name]["llm"] = llms[llm]() - except KeyError: - self.agents_config[agent_name]["llm"] = llm - - if tools := agent_info.get("tools"): - self.agents_config[agent_name]["tools"] = [ - tool_functions[tool]() for tool in tools - ] - - if function_calling_llm := agent_info.get("function_calling_llm"): - try: - self.agents_config[agent_name]["function_calling_llm"] = llms[function_calling_llm]() - except KeyError: - self.agents_config[agent_name]["function_calling_llm"] = function_calling_llm - - if step_callback := agent_info.get("step_callback"): - self.agents_config[agent_name]["step_callback"] = callbacks[ - step_callback - ]() - - if cache_handler := agent_info.get("cache_handler"): - self.agents_config[agent_name]["cache_handler"] = ( - cache_handler_functions[cache_handler]() - ) - - def map_all_task_variables(self) -> None: - all_functions = self._get_all_functions() - agents = self._filter_functions(all_functions, "is_agent") - tasks = self._filter_functions(all_functions, "is_task") - output_json_functions = self._filter_functions( - all_functions, "is_output_json" - ) - tool_functions = self._filter_functions(all_functions, "is_tool") - callback_functions = self._filter_functions(all_functions, "is_callback") - output_pydantic_functions = self._filter_functions( - all_functions, "is_output_pydantic" - ) - - for task_name, task_info in self.tasks_config.items(): - self._map_task_variables( - task_name, - task_info, - agents, - tasks, - output_json_functions, - tool_functions, - callback_functions, - output_pydantic_functions, - ) - - def _map_task_variables( - self, - task_name: str, - task_info: dict[str, Any], - agents: dict[str, Callable], - tasks: dict[str, Callable], - output_json_functions: dict[str, Callable], - tool_functions: dict[str, Callable], - callback_functions: dict[str, Callable], - output_pydantic_functions: dict[str, Callable], - ) -> None: - if context_list := task_info.get("context"): - self.tasks_config[task_name]["context"] = [ - tasks[context_task_name]() for context_task_name in context_list - ] - - if tools := task_info.get("tools"): - self.tasks_config[task_name]["tools"] = [ - tool_functions[tool]() for tool in tools - ] - - if agent_name := task_info.get("agent"): - self.tasks_config[task_name]["agent"] = agents[agent_name]() - - if output_json := task_info.get("output_json"): - self.tasks_config[task_name]["output_json"] = output_json_functions[ - output_json - ] - - if output_pydantic := task_info.get("output_pydantic"): - self.tasks_config[task_name]["output_pydantic"] = ( - output_pydantic_functions[output_pydantic] - ) - - if callbacks := task_info.get("callbacks"): - self.tasks_config[task_name]["callbacks"] = [ - callback_functions[callback]() for callback in callbacks - ] - - if guardrail := task_info.get("guardrail"): - self.tasks_config[task_name]["guardrail"] = guardrail - - # Include base class (qual)name in the wrapper class (qual)name. - WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")" - WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")" - WrappedClass._crew_name = cls.__name__ - - return cast(T, WrappedClass) diff --git a/src/crewai/project/utils.py b/src/crewai/project/utils.py deleted file mode 100644 index e8876d941..000000000 --- a/src/crewai/project/utils.py +++ /dev/null @@ -1,14 +0,0 @@ -from functools import wraps - - -def memoize(func): - cache = {} - - @wraps(func) - def memoized_func(*args, **kwargs): - key = (args, tuple(kwargs.items())) - if key not in cache: - cache[key] = func(*args, **kwargs) - return cache[key] - - return memoized_func diff --git a/src/crewai/telemetry/__init__.py b/src/crewai/telemetry/__init__.py deleted file mode 100644 index 1556f4fa5..000000000 --- a/src/crewai/telemetry/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .telemetry import Telemetry - -__all__ = ["Telemetry"] diff --git a/src/crewai/tools/__init__.py b/src/crewai/tools/__init__.py deleted file mode 100644 index 51c363e1a..000000000 --- a/src/crewai/tools/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .base_tool import BaseTool, EnvVar, tool - -__all__ = [ - "BaseTool", - "EnvVar", - "tool", -] diff --git a/src/crewai/utilities/events/__init__.py b/src/crewai/utilities/events/__init__.py deleted file mode 100644 index 2b484d125..000000000 --- a/src/crewai/utilities/events/__init__.py +++ /dev/null @@ -1,142 +0,0 @@ -"""Backwards compatibility - this module has moved to crewai.events.""" - -import warnings -from abc import ABC -from collections.abc import Callable -from typing import Any, TypeVar - -from typing_extensions import deprecated - -import crewai.events as new_events -from crewai.events.base_events import BaseEvent -from crewai.events.event_types import EventTypes - -EventT = TypeVar("EventT", bound=BaseEvent) - - -warnings.warn( - "Importing from 'crewai.utilities.events' is deprecated and will be removed in v1.0.0. " - "Please use 'crewai.events' instead.", - DeprecationWarning, - stacklevel=2, -) - - -@deprecated("Use 'from crewai.events import BaseEventListener' instead") -class BaseEventListener(new_events.BaseEventListener, ABC): - """Deprecated: Use crewai.events.BaseEventListener instead.""" - - -@deprecated("Use 'from crewai.events import crewai_event_bus' instead") -class crewai_event_bus: # noqa: N801 - """Deprecated: Use crewai.events.crewai_event_bus instead.""" - - @classmethod - def on( - cls, event_type: type[EventT] - ) -> Callable[[Callable[[Any, EventT], None]], Callable[[Any, EventT], None]]: - """Delegate to the actual event bus instance.""" - return new_events.crewai_event_bus.on(event_type) - - @classmethod - def emit(cls, source: Any, event: BaseEvent) -> None: - """Delegate to the actual event bus instance.""" - return new_events.crewai_event_bus.emit(source, event) - - @classmethod - def register_handler( - cls, event_type: type[EventTypes], handler: Callable[[Any, EventTypes], None] - ) -> None: - """Delegate to the actual event bus instance.""" - return new_events.crewai_event_bus.register_handler(event_type, handler) - - @classmethod - def scoped_handlers(cls) -> Any: - """Delegate to the actual event bus instance.""" - return new_events.crewai_event_bus.scoped_handlers() - - -@deprecated("Use 'from crewai.events import CrewKickoffStartedEvent' instead") -class CrewKickoffStartedEvent(new_events.CrewKickoffStartedEvent): - """Deprecated: Use crewai.events.CrewKickoffStartedEvent instead.""" - - -@deprecated("Use 'from crewai.events import CrewKickoffCompletedEvent' instead") -class CrewKickoffCompletedEvent(new_events.CrewKickoffCompletedEvent): - """Deprecated: Use crewai.events.CrewKickoffCompletedEvent instead.""" - - -@deprecated("Use 'from crewai.events import AgentExecutionCompletedEvent' instead") -class AgentExecutionCompletedEvent(new_events.AgentExecutionCompletedEvent): - """Deprecated: Use crewai.events.AgentExecutionCompletedEvent instead.""" - - -@deprecated("Use 'from crewai.events import MemoryQueryCompletedEvent' instead") -class MemoryQueryCompletedEvent(new_events.MemoryQueryCompletedEvent): - """Deprecated: Use crewai.events.MemoryQueryCompletedEvent instead.""" - - -@deprecated("Use 'from crewai.events import MemorySaveCompletedEvent' instead") -class MemorySaveCompletedEvent(new_events.MemorySaveCompletedEvent): - """Deprecated: Use crewai.events.MemorySaveCompletedEvent instead.""" - - -@deprecated("Use 'from crewai.events import MemorySaveStartedEvent' instead") -class MemorySaveStartedEvent(new_events.MemorySaveStartedEvent): - """Deprecated: Use crewai.events.MemorySaveStartedEvent instead.""" - - -@deprecated("Use 'from crewai.events import MemoryQueryStartedEvent' instead") -class MemoryQueryStartedEvent(new_events.MemoryQueryStartedEvent): - """Deprecated: Use crewai.events.MemoryQueryStartedEvent instead.""" - - -@deprecated("Use 'from crewai.events import MemoryRetrievalCompletedEvent' instead") -class MemoryRetrievalCompletedEvent(new_events.MemoryRetrievalCompletedEvent): - """Deprecated: Use crewai.events.MemoryRetrievalCompletedEvent instead.""" - - -@deprecated("Use 'from crewai.events import MemorySaveFailedEvent' instead") -class MemorySaveFailedEvent(new_events.MemorySaveFailedEvent): - """Deprecated: Use crewai.events.MemorySaveFailedEvent instead.""" - - -@deprecated("Use 'from crewai.events import MemoryQueryFailedEvent' instead") -class MemoryQueryFailedEvent(new_events.MemoryQueryFailedEvent): - """Deprecated: Use crewai.events.MemoryQueryFailedEvent instead.""" - - -@deprecated("Use 'from crewai.events import KnowledgeRetrievalStartedEvent' instead") -class KnowledgeRetrievalStartedEvent(new_events.KnowledgeRetrievalStartedEvent): - """Deprecated: Use crewai.events.KnowledgeRetrievalStartedEvent instead.""" - - -@deprecated("Use 'from crewai.events import KnowledgeRetrievalCompletedEvent' instead") -class KnowledgeRetrievalCompletedEvent(new_events.KnowledgeRetrievalCompletedEvent): - """Deprecated: Use crewai.events.KnowledgeRetrievalCompletedEvent instead.""" - - -@deprecated("Use 'from crewai.events import LLMStreamChunkEvent' instead") -class LLMStreamChunkEvent(new_events.LLMStreamChunkEvent): - """Deprecated: Use crewai.events.LLMStreamChunkEvent instead.""" - - -__all__ = [ - "AgentExecutionCompletedEvent", - "BaseEventListener", - "CrewKickoffCompletedEvent", - "CrewKickoffStartedEvent", - "KnowledgeRetrievalCompletedEvent", - "KnowledgeRetrievalStartedEvent", - "LLMStreamChunkEvent", - "MemoryQueryCompletedEvent", - "MemoryQueryFailedEvent", - "MemoryQueryStartedEvent", - "MemoryRetrievalCompletedEvent", - "MemorySaveCompletedEvent", - "MemorySaveFailedEvent", - "MemorySaveStartedEvent", - "crewai_event_bus", -] - -__deprecated__ = "Use 'crewai.events' instead of 'crewai.utilities.events'" diff --git a/src/crewai/utilities/events/base_event_listener.py b/src/crewai/utilities/events/base_event_listener.py deleted file mode 100644 index a4fd8330b..000000000 --- a/src/crewai/utilities/events/base_event_listener.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Backwards compatibility stub for crewai.utilities.events.base_event_listener.""" - -import warnings - -from crewai.events import BaseEventListener - -warnings.warn( - "Importing from 'crewai.utilities.events.base_event_listener' is deprecated and will be removed in v1.0.0. " - "Please use 'from crewai.events import BaseEventListener' instead.", - DeprecationWarning, - stacklevel=2, -) - -__all__ = ["BaseEventListener"] diff --git a/src/crewai/utilities/events/crewai_event_bus.py b/src/crewai/utilities/events/crewai_event_bus.py deleted file mode 100644 index 959dedb6f..000000000 --- a/src/crewai/utilities/events/crewai_event_bus.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Backwards compatibility stub for crewai.utilities.events.crewai_event_bus.""" - -import warnings - -from crewai.events import crewai_event_bus - -warnings.warn( - "Importing from 'crewai.utilities.events.crewai_event_bus' is deprecated and will be removed in v1.0.0. 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No trademark licenses are granted under this Agreement, and in connection - with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark - owned by or associated with the other or any of its affiliates, \\nexcept as - required for reasonable and customary use in describing and redistributing the - Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants - you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required - \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s - brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). - All goodwill arising out of your use of the Mark \\nwill inure to the benefit - of Meta.\\n\\n b. 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This restriction does not apply to end users - of a product or service that incorporates any such multimodal models.\\n\\nPlease - report any violation of this Policy, software \u201Cbug,\u201D or other problems - that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* - Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* - Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* - Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama - 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama - show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# - FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE - \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting - Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- - if .Tools }}When you receive a tool call response, use the output to format - an answer to the orginal user question.\\n\\nYou are a helpful assistant with - tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, - $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- - if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- - if and $.Tools $last }}\\n\\nGiven the following functions, please respond with - a JSON for a function call with its proper arguments that best answers the given - prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": - dictionary of argument name and its value}. 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(if you are located outside of the - EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using - or distributing any portion or element of the Llama Materials,\\nyou agree to - be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n - \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable - and royalty-free limited license under Meta\u2019s intellectual property or - other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, - distribute, copy, create derivative works \\nof, and make modifications to the - Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If - you distribute or make available the Llama Materials (or any derivative works - thereof), \\nor a product or service (including another AI model) that contains - any of them, you shall (A) provide\\na copy of this Agreement with any such - Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non - a related website, user interface, blogpost, about page, or product documentation. - If you use the\\nLlama Materials or any outputs or results of the Llama Materials - to create, train, fine tune, or\\notherwise improve an AI model, which is distributed - or made available, you shall also include \u201CLlama\u201D\\nat the beginning - of any such AI model name.\\n\\n ii. If you receive Llama Materials, - or any derivative works thereof, from a Licensee as part\\nof an integrated - end user product, then Section 2 of this Agreement will not apply to you. \\n\\n - \ iii. You must retain in all copies of the Llama Materials that you distribute - the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed - as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 - Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n - \ iv. Your use of the Llama Materials must comply with applicable laws - and regulations\\n(including trade compliance laws and regulations) and adhere - to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), - which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. - Additional Commercial Terms. If, on the Llama 3.2 version release date, the - monthly active users\\nof the products or services made available by or for - Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly - active users in the preceding calendar month, you must request \\na license - from Meta, which Meta may grant to you in its sole discretion, and you are not - authorized to\\nexercise any of the rights under this Agreement unless or until - Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. - UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS - THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF - ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND - IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, - MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR - DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS - AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY - OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR - ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, - TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, - \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, - EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED - OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n - \ a. No trademark licenses are granted under this Agreement, and in connection - with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark - owned by or associated with the other or any of its affiliates, \\nexcept as - required for reasonable and customary use in describing and redistributing the - Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants - you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required - \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s - brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). - All goodwill arising out of your use of the Mark \\nwill inure to the benefit - of Meta.\\n\\n b. 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Term and Termination. - The term of this Agreement will commence upon your acceptance of this Agreement - or access\\nto the Llama Materials and will continue in full force and effect - until terminated in accordance with the terms\\nand conditions herein. Meta - may terminate this Agreement if you are in breach of any term or condition of - this\\nAgreement. Upon termination of this Agreement, you shall delete and cease - use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination - of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will - be governed and construed under the laws of the State of \\nCalifornia without - regard to choice of law principles, and the UN Convention on Contracts for the - International\\nSale of Goods does not apply to this Agreement. 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Engage in any action, or facilitate any action, to intentionally circumvent - or remove usage restrictions or other safety measures, or to enable functionality - disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the - planning or development of activities that present a risk of death or bodily - harm to individuals, including use of Llama 3.2 related to the following:\\n - \ 8. Military, warfare, nuclear industries or applications, espionage, use - for materials or activities that are subject to the International Traffic Arms - Regulations (ITAR) maintained by the United States Department of State or to - the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons - Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including - weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n - \ 11. Operation of critical infrastructure, transportation technologies, or - heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting, - and eating disorders\\n 13. Any content intended to incite or promote violence, - abuse, or any infliction of bodily harm to an individual\\n3. Intentionally - deceive or mislead others, including use of Llama 3.2 related to the following:\\n - \ 14. Generating, promoting, or furthering fraud or the creation or promotion - of disinformation\\n 15. Generating, promoting, or furthering defamatory - content, including the creation of defamatory statements, images, or other content\\n - \ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating - another individual without consent, authorization, or legal right\\n 18. - Representing that the use of Llama 3.2 or outputs are human-generated\\n 19. - Generating or facilitating false online engagement, including fake reviews and - other means of fake online engagement\\n4. Fail to appropriately disclose to - end users any known dangers of your AI system\\n5. Interact with third party - tools, models, or software designed to generate unlawful content or engage in - unlawful or harmful conduct and/or represent that the outputs of such tools, - models, or software are associated with Meta or Llama 3.2\\n\\nWith respect - to any multimodal models included in Llama 3.2, the rights granted under Section - 1(a) of the Llama 3.2 Community License Agreement are not being granted to you - if you are an individual domiciled in, or a company with a principal place of - business in, the European Union. 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You - are a seasoned manager with a knack for getting the best out of your team.\nYou - are also known for your ability to delegate work to the right people, and to - ask the right questions to get the best out of your team.\nEven though you don''t - perform tasks by yourself, you have a lot of experience in the field, which - allows you to properly evaluate the work of your team members.\nYour personal - goal is: Manage the team to complete the task in the best way possible.\nYou - ONLY have access to the following tools, and should NEVER make up tools that - are not listed here:\n\nTool Name: Delegate work to coworker\nTool Arguments: - {''task'': {''description'': ''The task to delegate'', ''type'': ''str''}, ''context'': - {''description'': ''The context for the task'', ''type'': ''str''}, ''coworker'': - {''description'': ''The role/name of the coworker to delegate to'', ''type'': - ''str''}}\nTool Description: Delegate a specific task to one of the following - coworkers: First Agent\nThe input to this tool should be the coworker, the task - you want them to do, and ALL necessary context to execute the task, they know - nothing about the task, so share absolutely everything you know, don''t reference - things but instead explain them.\nTool Name: Ask question to coworker\nTool - Arguments: {''question'': {''description'': ''The question to ask'', ''type'': - ''str''}, ''context'': {''description'': ''The context for the question'', ''type'': - ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to - ask'', ''type'': ''str''}}\nTool Description: Ask a specific question to one - of the following coworkers: First Agent\nThe input to this tool should be the - coworker, the question you have for them, and ALL necessary context to ask the - question properly, they know nothing about the question, so share absolutely - everything you know, don''t reference things but instead explain them.\n\nIMPORTANT: - Use the following format in your response:\n\n```\nThought: you should always - think about what to do\nAction: the action to take, only one name of [Delegate - work to coworker, Ask question to coworker], just the name, exactly as it''s - written.\nAction Input: the input to the action, just a simple JSON object, - enclosed in curly braces, using \" to wrap keys and values.\nObservation: the - result of the action\n```\n\nOnce all necessary information is gathered, return - the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: - the final answer to the original input question\n```"}, {"role": "user", "content": - "\nCurrent Task: Process initial data\n\nThis is the expected criteria for your - final answer: Initial analysis\nyou MUST return the actual complete content - as the final answer, not a summary.\n\nBegin! 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You must retain in all copies of the Llama Materials that you distribute - the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed - as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 - Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n - \ iv. Your use of the Llama Materials must comply with applicable laws - and regulations\\n(including trade compliance laws and regulations) and adhere - to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), - which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. - Additional Commercial Terms. If, on the Llama 3.2 version release date, the - monthly active users\\nof the products or services made available by or for - Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly - active users in the preceding calendar month, you must request \\na license - from Meta, which Meta may grant to you in its sole discretion, and you are not - authorized to\\nexercise any of the rights under this Agreement unless or until - Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. - UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS - THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF - ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND - IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, - MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR - DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS - AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY - OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR - ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, - TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, - \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, - EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED - OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n - \ a. No trademark licenses are granted under this Agreement, and in connection - with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark - owned by or associated with the other or any of its affiliates, \\nexcept as - required for reasonable and customary use in describing and redistributing the - Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants - you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required - \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s - brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). - All goodwill arising out of your use of the Mark \\nwill inure to the benefit - of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and - derivatives made by or for Meta, with respect to any\\n derivative works - and modifications of the Llama Materials that are made by you, as between you - and Meta,\\n you are and will be the owner of such derivative works and modifications.\\n\\n - \ c. If you institute litigation or other proceedings against Meta or any - entity (including a cross-claim or\\n counterclaim in a lawsuit) alleging - that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n - \ of any of the foregoing, constitutes infringement of intellectual property - or other rights owned or licensable\\n by you, then any licenses granted - to you under this Agreement shall terminate as of the date such litigation or\\n - \ claim is filed or instituted. You will indemnify and hold harmless Meta - from and against any claim by any third\\n party arising out of or related - to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination. - The term of this Agreement will commence upon your acceptance of this Agreement - or access\\nto the Llama Materials and will continue in full force and effect - until terminated in accordance with the terms\\nand conditions herein. Meta - may terminate this Agreement if you are in breach of any term or condition of - this\\nAgreement. Upon termination of this Agreement, you shall delete and cease - use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination - of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will - be governed and construed under the laws of the State of \\nCalifornia without - regard to choice of law principles, and the UN Convention on Contracts for the - International\\nSale of Goods does not apply to this Agreement. The courts of - California shall have exclusive jurisdiction of\\nany dispute arising out of - this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed - to promoting safe and fair use of its tools and features, including Llama 3.2. - If you access or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). - The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited - Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree - you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate - the law or others\u2019 rights, including to:\\n 1. Engage in, promote, generate, - contribute to, encourage, plan, incite, or further illegal or unlawful activity - or content, such as:\\n 1. Violence or terrorism\\n 2. Exploitation - or harm to children, including the solicitation, creation, acquisition, or dissemination - of child exploitative content or failure to report Child Sexual Abuse Material\\n - \ 3. Human trafficking, exploitation, and sexual violence\\n 4. - The illegal distribution of information or materials to minors, including obscene - materials, or failure to employ legally required age-gating in connection with - such information or materials.\\n 5. Sexual solicitation\\n 6. - Any other criminal activity\\n 1. Engage in, promote, incite, or facilitate - the harassment, abuse, threatening, or bullying of individuals or groups of - individuals\\n 2. Engage in, promote, incite, or facilitate discrimination - or other unlawful or harmful conduct in the provision of employment, employment - benefits, credit, housing, other economic benefits, or other essential goods - and services\\n 3. Engage in the unauthorized or unlicensed practice of any - profession including, but not limited to, financial, legal, medical/health, - or related professional practices\\n 4. Collect, process, disclose, generate, - or infer private or sensitive information about individuals, including information - about individuals\u2019 identity, health, or demographic information, unless - you have obtained the right to do so in accordance with applicable law\\n 5. - Engage in or facilitate any action or generate any content that infringes, misappropriates, - or otherwise violates any third-party rights, including the outputs or results - of any products or services using the Llama Materials\\n 6. Create, generate, - or facilitate the creation of malicious code, malware, computer viruses or do - anything else that could disable, overburden, interfere with or impair the proper - working, integrity, operation or appearance of a website or computer system\\n - \ 7. Engage in any action, or facilitate any action, to intentionally circumvent - or remove usage restrictions or other safety measures, or to enable functionality - disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the - planning or development of activities that present a risk of death or bodily - harm to individuals, including use of Llama 3.2 related to the following:\\n - \ 8. Military, warfare, nuclear industries or applications, espionage, use - for materials or activities that are subject to the International Traffic Arms - Regulations (ITAR) maintained by the United States Department of State or to - the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons - Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including - weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n - \ 11. Operation of critical infrastructure, transportation technologies, or - heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting, - and eating disorders\\n 13. Any content intended to incite or promote violence, - abuse, or any infliction of bodily harm to an individual\\n3. Intentionally - deceive or mislead others, including use of Llama 3.2 related to the following:\\n - \ 14. Generating, promoting, or furthering fraud or the creation or promotion - of disinformation\\n 15. Generating, promoting, or furthering defamatory - content, including the creation of defamatory statements, images, or other content\\n - \ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating - another individual without consent, authorization, or legal right\\n 18. - Representing that the use of Llama 3.2 or outputs are human-generated\\n 19. - Generating or facilitating false online engagement, including fake reviews and - other means of fake online engagement\\n4. Fail to appropriately disclose to - end users any known dangers of your AI system\\n5. Interact with third party - tools, models, or software designed to generate unlawful content or engage in - unlawful or harmful conduct and/or represent that the outputs of such tools, - models, or software are associated with Meta or Llama 3.2\\n\\nWith respect - to any multimodal models included in Llama 3.2, the rights granted under Section - 1(a) of the Llama 3.2 Community License Agreement are not being granted to you - if you are an individual domiciled in, or a company with a principal place of - business in, the European Union. This restriction does not apply to end users - of a product or service that incorporates any such multimodal models.\\n\\nPlease - report any violation of this Policy, software \u201Cbug,\u201D or other problems - that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* - Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* - Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* - Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama - 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama - show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# - FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE - \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting - Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- - if .Tools }}When you receive a tool call response, use the output to format - an answer to the orginal user question.\\n\\nYou are a helpful assistant with - tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, - $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- - if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- - if and $.Tools $last }}\\n\\nGiven the following functions, please respond with - a JSON for a function call with its proper arguments that best answers the given - prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": - dictionary of argument name and its value}. Do not use variables.\\n\\n{{ range - $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- - else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ - end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{- - if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name - }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else }}\\n\\n{{ - .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ end }}\\n{{- - else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{ - .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{ - end }}\\n{{- end }}\\n{{- end }}\\\"\\\"\\\"\\nPARAMETER stop \\u003c|start_header_id|\\u003e\\nPARAMETER - stop \\u003c|end_header_id|\\u003e\\nPARAMETER stop \\u003c|eot_id|\\u003e\\nLICENSE - \\\"LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\\nLlama 3.2 Version Release Date: - September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions - for use, reproduction, distribution \\nand modification of the Llama Materials - set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications, manuals - and documentation accompanying Llama 3.2\\ndistributed by Meta at https://llama.meta.com/doc/overview.\\n\\n\u201CLicensee\u201D - or \u201Cyou\u201D means you, or your employer or any other person or entity - (if you are \\nentering into this Agreement on such person or entity\u2019s - behalf), of the age required under\\napplicable laws, rules or regulations to - provide legal consent and that has legal authority\\nto bind your employer or - such other person or entity if you are entering in this Agreement\\non their - behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language models - and software and algorithms, including\\nmachine-learning model code, trained - model weights, inference-enabling code, training-enabling code,\\nfine-tuning - enabling code and other elements of the foregoing distributed by Meta at \\nhttps://www.llama.com/llama-downloads.\\n\\n\u201CLlama - Materials\u201D means, collectively, Meta\u2019s proprietary Llama 3.2 and Documentation - (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D - or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in - or, \\nif you are an entity, your principal place of business is in the EEA - or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the - EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using - or distributing any portion or element of the Llama Materials,\\nyou agree to - be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n - \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable - and royalty-free limited license under Meta\u2019s intellectual property or - other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, - distribute, copy, create derivative works \\nof, and make modifications to the - Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If - you distribute or make available the Llama Materials (or any derivative works - thereof), \\nor a product or service (including another AI model) that contains - any of them, you shall (A) provide\\na copy of this Agreement with any such - Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non - a related website, user interface, blogpost, about page, or product documentation. - If you use the\\nLlama Materials or any outputs or results of the Llama Materials - to create, train, fine tune, or\\notherwise improve an AI model, which is distributed - or made available, you shall also include \u201CLlama\u201D\\nat the beginning - of any such AI model name.\\n\\n ii. If you receive Llama Materials, - or any derivative works thereof, from a Licensee as part\\nof an integrated - end user product, then Section 2 of this Agreement will not apply to you. \\n\\n - \ iii. You must retain in all copies of the Llama Materials that you distribute - the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed - as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 - Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n - \ iv. Your use of the Llama Materials must comply with applicable laws - and regulations\\n(including trade compliance laws and regulations) and adhere - to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), - which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. - Additional Commercial Terms. If, on the Llama 3.2 version release date, the - monthly active users\\nof the products or services made available by or for - Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly - active users in the preceding calendar month, you must request \\na license - from Meta, which Meta may grant to you in its sole discretion, and you are not - authorized to\\nexercise any of the rights under this Agreement unless or until - Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. - UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS - THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF - ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND - IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, - MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR - DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS - AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY - OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR - ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, - TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, - \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, - EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED - OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n - \ a. No trademark licenses are granted under this Agreement, and in connection - with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark - owned by or associated with the other or any of its affiliates, \\nexcept as - required for reasonable and customary use in describing and redistributing the - Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants - you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required - \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s - brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). - All goodwill arising out of your use of the Mark \\nwill inure to the benefit - of Meta.\\n\\n b. Subject to Meta\u2019s ownership of Llama Materials and - derivatives made by or for Meta, with respect to any\\n derivative works - and modifications of the Llama Materials that are made by you, as between you - and Meta,\\n you are and will be the owner of such derivative works and modifications.\\n\\n - \ c. If you institute litigation or other proceedings against Meta or any - entity (including a cross-claim or\\n counterclaim in a lawsuit) alleging - that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n - \ of any of the foregoing, constitutes infringement of intellectual property - or other rights owned or licensable\\n by you, then any licenses granted - to you under this Agreement shall terminate as of the date such litigation or\\n - \ claim is filed or instituted. You will indemnify and hold harmless Meta - from and against any claim by any third\\n party arising out of or related - to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination. - The term of this Agreement will commence upon your acceptance of this Agreement - or access\\nto the Llama Materials and will continue in full force and effect - until terminated in accordance with the terms\\nand conditions herein. Meta - may terminate this Agreement if you are in breach of any term or condition of - this\\nAgreement. Upon termination of this Agreement, you shall delete and cease - use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination - of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will - be governed and construed under the laws of the State of \\nCalifornia without - regard to choice of law principles, and the UN Convention on Contracts for the - International\\nSale of Goods does not apply to this Agreement. The courts of - California shall have exclusive jurisdiction of\\nany dispute arising out of - this Agreement.\\\"\\nLICENSE \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta - is committed to promoting safe and fair use of its tools and features, including - Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use - Policy (\u201C**Policy**\u201D). The most recent copy of this policy can be - found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited - Uses**\\n\\nWe want everyone to use Llama 3.2 safely and responsibly. You agree - you will not use, or allow others to use, Llama 3.2 to:\\n\\n\\n\\n1. Violate - the law or others\u2019 rights, including to:\\n 1. Engage in, promote, generate, - contribute to, encourage, plan, incite, or further illegal or unlawful activity - or content, such as:\\n 1. Violence or terrorism\\n 2. Exploitation - or harm to children, including the solicitation, creation, acquisition, or dissemination - of child exploitative content or failure to report Child Sexual Abuse Material\\n - \ 3. Human trafficking, exploitation, and sexual violence\\n 4. - The illegal distribution of information or materials to minors, including obscene - materials, or failure to employ legally required age-gating in connection with - such information or materials.\\n 5. Sexual solicitation\\n 6. - Any other criminal activity\\n 1. Engage in, promote, incite, or facilitate - the harassment, abuse, threatening, or bullying of individuals or groups of - individuals\\n 2. Engage in, promote, incite, or facilitate discrimination - or other unlawful or harmful conduct in the provision of employment, employment - benefits, credit, housing, other economic benefits, or other essential goods - and services\\n 3. Engage in the unauthorized or unlicensed practice of any - profession including, but not limited to, financial, legal, medical/health, - or related professional practices\\n 4. Collect, process, disclose, generate, - or infer private or sensitive information about individuals, including information - about individuals\u2019 identity, health, or demographic information, unless - you have obtained the right to do so in accordance with applicable law\\n 5. - Engage in or facilitate any action or generate any content that infringes, misappropriates, - or otherwise violates any third-party rights, including the outputs or results - of any products or services using the Llama Materials\\n 6. Create, generate, - or facilitate the creation of malicious code, malware, computer viruses or do - anything else that could disable, overburden, interfere with or impair the proper - working, integrity, operation or appearance of a website or computer system\\n - \ 7. Engage in any action, or facilitate any action, to intentionally circumvent - or remove usage restrictions or other safety measures, or to enable functionality - disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the - planning or development of activities that present a risk of death or bodily - harm to individuals, including use of Llama 3.2 related to the following:\\n - \ 8. Military, warfare, nuclear industries or applications, espionage, use - for materials or activities that are subject to the International Traffic Arms - Regulations (ITAR) maintained by the United States Department of State or to - the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons - Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including - weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n - \ 11. Operation of critical infrastructure, transportation technologies, or - heavy machinery\\n 12. Self-harm or harm to others, including suicide, cutting, - and eating disorders\\n 13. Any content intended to incite or promote violence, - abuse, or any infliction of bodily harm to an individual\\n3. Intentionally - deceive or mislead others, including use of Llama 3.2 related to the following:\\n - \ 14. Generating, promoting, or furthering fraud or the creation or promotion - of disinformation\\n 15. Generating, promoting, or furthering defamatory - content, including the creation of defamatory statements, images, or other content\\n - \ 16. Generating, promoting, or further distributing spam\\n 17. Impersonating - another individual without consent, authorization, or legal right\\n 18. - Representing that the use of Llama 3.2 or outputs are human-generated\\n 19. - Generating or facilitating false online engagement, including fake reviews and - other means of fake online engagement\\n4. Fail to appropriately disclose to - end users any known dangers of your AI system\\n5. Interact with third party - tools, models, or software designed to generate unlawful content or engage in - unlawful or harmful conduct and/or represent that the outputs of such tools, - models, or software are associated with Meta or Llama 3.2\\n\\nWith respect - to any multimodal models included in Llama 3.2, the rights granted under Section - 1(a) of the Llama 3.2 Community License Agreement are not being granted to you - if you are an individual domiciled in, or a company with a principal place of - business in, the European Union. This restriction does not apply to end users - of a product or service that incorporates any such multimodal models.\\n\\nPlease - report any violation of this Policy, software \u201Cbug,\u201D or other problems - that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* - Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* - Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* - Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama - 3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop - \ \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting - Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- - if .Tools }}When you receive a tool call response, use the output to format - an answer to the orginal user question.\\n\\nYou are a helpful assistant with - tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, - $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- - if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- - if and $.Tools $last }}\\n\\nGiven the following functions, please respond with - a JSON for a function call with its proper arguments that best answers the given - prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": - dictionary of argument name and its value}. 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(if you are located outside of the - EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using - or distributing any portion or element of the Llama Materials,\\nyou agree to - be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n - \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable - and royalty-free limited license under Meta\u2019s intellectual property or - other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, - distribute, copy, create derivative works \\nof, and make modifications to the - Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If - you distribute or make available the Llama Materials (or any derivative works - thereof), \\nor a product or service (including another AI model) that contains - any of them, you shall (A) provide\\na copy of this Agreement with any such - Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non - a related website, user interface, blogpost, about page, or product documentation. - If you use the\\nLlama Materials or any outputs or results of the Llama Materials - to create, train, fine tune, or\\notherwise improve an AI model, which is distributed - or made available, you shall also include \u201CLlama\u201D\\nat the beginning - of any such AI model name.\\n\\n ii. If you receive Llama Materials, - or any derivative works thereof, from a Licensee as part\\nof an integrated - end user product, then Section 2 of this Agreement will not apply to you. \\n\\n - \ iii. 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If, on the Llama 3.2 version release date, the - monthly active users\\nof the products or services made available by or for - Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly - active users in the preceding calendar month, you must request \\na license - from Meta, which Meta may grant to you in its sole discretion, and you are not - authorized to\\nexercise any of the rights under this Agreement unless or until - Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. - UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS - THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF - ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND - IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, - MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR - DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS - AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY - OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR - ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, - TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, - \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, - EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED - OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n - \ a. No trademark licenses are granted under this Agreement, and in connection - with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark - owned by or associated with the other or any of its affiliates, \\nexcept as - required for reasonable and customary use in describing and redistributing the - Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants - you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required - \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s - brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). - All goodwill arising out of your use of the Mark \\nwill inure to the benefit - of Meta.\\n\\n b. 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Term and Termination. - The term of this Agreement will commence upon your acceptance of this Agreement - or access\\nto the Llama Materials and will continue in full force and effect - until terminated in accordance with the terms\\nand conditions herein. Meta - may terminate this Agreement if you are in breach of any term or condition of - this\\nAgreement. Upon termination of this Agreement, you shall delete and cease - use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination - of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will - be governed and construed under the laws of the State of \\nCalifornia without - regard to choice of law principles, and the UN Convention on Contracts for the - International\\nSale of Goods does not apply to this Agreement. 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Engage in the unauthorized or unlicensed practice of any - profession including, but not limited to, financial, legal, medical/health, - or related professional practices\\n 4. Collect, process, disclose, generate, - or infer private or sensitive information about individuals, including information - about individuals\u2019 identity, health, or demographic information, unless - you have obtained the right to do so in accordance with applicable law\\n 5. - Engage in or facilitate any action or generate any content that infringes, misappropriates, - or otherwise violates any third-party rights, including the outputs or results - of any products or services using the Llama Materials\\n 6. Create, generate, - or facilitate the creation of malicious code, malware, computer viruses or do - anything else that could disable, overburden, interfere with or impair the proper - working, integrity, operation or appearance of a website or computer system\\n - \ 7. Engage in any action, or facilitate any action, to intentionally circumvent - or remove usage restrictions or other safety measures, or to enable functionality - disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in the - planning or development of activities that present a risk of death or bodily - harm to individuals, including use of Llama 3.2 related to the following:\\n - \ 8. Military, warfare, nuclear industries or applications, espionage, use - for materials or activities that are subject to the International Traffic Arms - Regulations (ITAR) maintained by the United States Department of State or to - the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons - Convention Implementation Act of 1997\\n 9. Guns and illegal weapons (including - weapon development)\\n 10. Illegal drugs and regulated/controlled substances\\n - \ 11. Operation of critical infrastructure, transportation technologies, or - heavy machinery\\n 12. 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This restriction does not apply to end users - of a product or service that incorporates any such multimodal models.\\n\\nPlease - report any violation of this Policy, software \u201Cbug,\u201D or other problems - that could lead to a violation of this Policy through one of the following means:\\n\\n\\n\\n* - Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n* - Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n* - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n* - Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama - 3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama - show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# - FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE - \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting - Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{- - if .Tools }}When you receive a tool call response, use the output to format - an answer to the orginal user question.\\n\\nYou are a helpful assistant with - tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range $i, - $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- - if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{- - if and $.Tools $last }}\\n\\nGiven the following functions, please respond with - a JSON for a function call with its proper arguments that best answers the given - prompt.\\n\\nRespond in the format {\\\"name\\\": function name, \\\"parameters\\\": - dictionary of argument name and its value}. 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(if you are located outside of the - EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using - or distributing any portion or element of the Llama Materials,\\nyou agree to - be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n - \ a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable - and royalty-free limited license under Meta\u2019s intellectual property or - other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce, - distribute, copy, create derivative works \\nof, and make modifications to the - Llama Materials. \\n\\n b. Redistribution and Use. \\n\\n i. If - you distribute or make available the Llama Materials (or any derivative works - thereof), \\nor a product or service (including another AI model) that contains - any of them, you shall (A) provide\\na copy of this Agreement with any such - Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non - a related website, user interface, blogpost, about page, or product documentation. - If you use the\\nLlama Materials or any outputs or results of the Llama Materials - to create, train, fine tune, or\\notherwise improve an AI model, which is distributed - or made available, you shall also include \u201CLlama\u201D\\nat the beginning - of any such AI model name.\\n\\n ii. If you receive Llama Materials, - or any derivative works thereof, from a Licensee as part\\nof an integrated - end user product, then Section 2 of this Agreement will not apply to you. \\n\\n - \ iii. You must retain in all copies of the Llama Materials that you distribute - the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed - as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2 - Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n - \ iv. Your use of the Llama Materials must comply with applicable laws - and regulations\\n(including trade compliance laws and regulations) and adhere - to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy), - which is hereby \\nincorporated by reference into this Agreement.\\n \\n2. - Additional Commercial Terms. If, on the Llama 3.2 version release date, the - monthly active users\\nof the products or services made available by or for - Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly - active users in the preceding calendar month, you must request \\na license - from Meta, which Meta may grant to you in its sole discretion, and you are not - authorized to\\nexercise any of the rights under this Agreement unless or until - Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. - UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS - THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF - ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND - IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT, - MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR - DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS - AND ASSUME ANY RISKS ASSOCIATED\\nWITH YOUR USE OF THE LLAMA MATERIALS AND ANY - OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR - ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, - TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, - \\nFOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, - EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED - OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n - \ a. No trademark licenses are granted under this Agreement, and in connection - with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark - owned by or associated with the other or any of its affiliates, \\nexcept as - required for reasonable and customary use in describing and redistributing the - Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants - you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required - \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s - brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/). - All goodwill arising out of your use of the Mark \\nwill inure to the benefit - of Meta.\\n\\n b. 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Term and Termination. - The term of this Agreement will commence upon your acceptance of this Agreement - or access\\nto the Llama Materials and will continue in full force and effect - until terminated in accordance with the terms\\nand conditions herein. Meta - may terminate this Agreement if you are in breach of any term or condition of - this\\nAgreement. Upon termination of this Agreement, you shall delete and cease - use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination - of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will - be governed and construed under the laws of the State of \\nCalifornia without - regard to choice of law principles, and the UN Convention on Contracts for the - International\\nSale of Goods does not apply to this Agreement. 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-from crewai.agent import Agent -from crewai.task import Task -from crewai.crew import Crew -from crewai.experimental.evaluation.agent_evaluator import AgentEvaluator -from crewai.experimental.evaluation.base_evaluator import AgentEvaluationResult -from crewai.experimental.evaluation import ( - GoalAlignmentEvaluator, - SemanticQualityEvaluator, - ToolSelectionEvaluator, - ParameterExtractionEvaluator, - ToolInvocationEvaluator, - ReasoningEfficiencyEvaluator, - MetricCategory, - EvaluationScore, -) - -from crewai.events.types.agent_events import ( - AgentEvaluationStartedEvent, - AgentEvaluationCompletedEvent, - AgentEvaluationFailedEvent, -) -from crewai.events.event_bus import crewai_event_bus -from crewai.experimental.evaluation import create_default_evaluator - - -class TestAgentEvaluator: - @pytest.fixture - def mock_crew(self): - agent = Agent( - role="Test Agent", - goal="Complete test tasks successfully", - backstory="An agent created for testing purposes", - allow_delegation=False, - verbose=False, - ) - - task = Task( - description="Test task description", - agent=agent, - expected_output="Expected test output", - ) - - crew = Crew(agents=[agent], tasks=[task]) - return crew - - def test_set_iteration(self): - agent_evaluator = AgentEvaluator(agents=[]) - - agent_evaluator.set_iteration(3) - assert agent_evaluator._execution_state.iteration == 3 - - @pytest.mark.vcr(filter_headers=["authorization"]) - def test_evaluate_current_iteration(self, mock_crew): - agent_evaluator = AgentEvaluator( - agents=mock_crew.agents, evaluators=[GoalAlignmentEvaluator()] - ) - - mock_crew.kickoff() - - results = agent_evaluator.get_evaluation_results() - - assert isinstance(results, dict) - - (agent,) = mock_crew.agents - (task,) = mock_crew.tasks - - assert len(mock_crew.agents) == 1 - assert agent.role in results - assert len(results[agent.role]) == 1 - - (result,) = results[agent.role] - assert isinstance(result, AgentEvaluationResult) - - assert result.agent_id == str(agent.id) - assert result.task_id == str(task.id) - - (goal_alignment,) = result.metrics.values() - assert goal_alignment.score == 5.0 - - expected_feedback = "The agent's output demonstrates an understanding of the need for a comprehensive document outlining task" - assert expected_feedback in goal_alignment.feedback - - assert goal_alignment.raw_response is not None - assert '"score": 5' in goal_alignment.raw_response - - def test_create_default_evaluator(self, mock_crew): - agent_evaluator = create_default_evaluator(agents=mock_crew.agents) - assert isinstance(agent_evaluator, AgentEvaluator) - assert agent_evaluator.agents == mock_crew.agents - - expected_types = [ - GoalAlignmentEvaluator, - SemanticQualityEvaluator, - ToolSelectionEvaluator, - ParameterExtractionEvaluator, - ToolInvocationEvaluator, - ReasoningEfficiencyEvaluator, - ] - - assert len(agent_evaluator.evaluators) == len(expected_types) - for evaluator, expected_type in zip(agent_evaluator.evaluators, expected_types): - assert isinstance(evaluator, expected_type) - - @pytest.mark.vcr(filter_headers=["authorization"]) - def test_eval_lite_agent(self): - agent = Agent( - role="Test Agent", - goal="Complete test tasks successfully", - backstory="An agent created for testing purposes", - ) - - with crewai_event_bus.scoped_handlers(): - events = {} - - @crewai_event_bus.on(AgentEvaluationStartedEvent) - def capture_started(source, event): - events["started"] = event - - @crewai_event_bus.on(AgentEvaluationCompletedEvent) - def capture_completed(source, event): - events["completed"] = event - - @crewai_event_bus.on(AgentEvaluationFailedEvent) - def capture_failed(source, event): - events["failed"] = event - - agent_evaluator = AgentEvaluator( - agents=[agent], evaluators=[GoalAlignmentEvaluator()] - ) - - agent.kickoff(messages="Complete this task successfully") - - assert events.keys() == {"started", "completed"} - assert events["started"].agent_id == str(agent.id) - assert events["started"].agent_role == agent.role - assert events["started"].task_id is None - assert events["started"].iteration == 1 - - assert events["completed"].agent_id == str(agent.id) - assert events["completed"].agent_role == agent.role - assert events["completed"].task_id is None - assert events["completed"].iteration == 1 - assert events["completed"].metric_category == MetricCategory.GOAL_ALIGNMENT - assert isinstance(events["completed"].score, EvaluationScore) - assert events["completed"].score.score == 2.0 - - results = agent_evaluator.get_evaluation_results() - - assert isinstance(results, dict) - - (result,) = results[agent.role] - assert isinstance(result, AgentEvaluationResult) - - assert result.agent_id == str(agent.id) - assert result.task_id == "lite_task" - - (goal_alignment,) = result.metrics.values() - assert goal_alignment.score == 2.0 - - expected_feedback = "The agent did not demonstrate a clear understanding of the task goal, which is to complete test tasks successfully" - assert expected_feedback in goal_alignment.feedback - - assert goal_alignment.raw_response is not None - assert '"score": 2' in goal_alignment.raw_response - - @pytest.mark.vcr(filter_headers=["authorization"]) - def test_eval_specific_agents_from_crew(self, mock_crew): - agent = Agent( - role="Test Agent Eval", - goal="Complete test tasks successfully", - backstory="An agent created for testing purposes", - ) - task = Task( - description="Test task description", - agent=agent, - expected_output="Expected test output", - ) - mock_crew.agents.append(agent) - mock_crew.tasks.append(task) - - with crewai_event_bus.scoped_handlers(): - events = {} - - @crewai_event_bus.on(AgentEvaluationStartedEvent) - def capture_started(source, event): - events["started"] = event - - @crewai_event_bus.on(AgentEvaluationCompletedEvent) - def capture_completed(source, event): - events["completed"] = event - - @crewai_event_bus.on(AgentEvaluationFailedEvent) - def capture_failed(source, event): - events["failed"] = event - - agent_evaluator = AgentEvaluator( - agents=[agent], evaluators=[GoalAlignmentEvaluator()] - ) - mock_crew.kickoff() - - assert events.keys() == {"started", "completed"} - assert events["started"].agent_id == str(agent.id) - assert events["started"].agent_role == agent.role - assert events["started"].task_id == str(task.id) - assert events["started"].iteration == 1 - - assert events["completed"].agent_id == str(agent.id) - assert events["completed"].agent_role == agent.role - assert events["completed"].task_id == str(task.id) - assert events["completed"].iteration == 1 - assert events["completed"].metric_category == MetricCategory.GOAL_ALIGNMENT - assert isinstance(events["completed"].score, EvaluationScore) - assert events["completed"].score.score == 5.0 - - results = agent_evaluator.get_evaluation_results() - - assert isinstance(results, dict) - assert len(results.keys()) == 1 - (result,) = results[agent.role] - assert isinstance(result, AgentEvaluationResult) - - assert result.agent_id == str(agent.id) - assert result.task_id == str(task.id) - - (goal_alignment,) = result.metrics.values() - assert goal_alignment.score == 5.0 - - expected_feedback = "The agent provided a thorough guide on how to conduct a test task but failed to produce specific expected output" - assert expected_feedback in goal_alignment.feedback - - assert goal_alignment.raw_response is not None - assert '"score": 5' in goal_alignment.raw_response - - @pytest.mark.vcr(filter_headers=["authorization"]) - def test_failed_evaluation(self, mock_crew): - (agent,) = mock_crew.agents - (task,) = mock_crew.tasks - - with crewai_event_bus.scoped_handlers(): - events = {} - - @crewai_event_bus.on(AgentEvaluationStartedEvent) - def capture_started(source, event): - events["started"] = event - - @crewai_event_bus.on(AgentEvaluationCompletedEvent) - def capture_completed(source, event): - events["completed"] = event - - @crewai_event_bus.on(AgentEvaluationFailedEvent) - def capture_failed(source, event): - events["failed"] = event - - # Create a mock evaluator that will raise an exception - from crewai.experimental.evaluation.base_evaluator import BaseEvaluator - from crewai.experimental.evaluation import MetricCategory - - class FailingEvaluator(BaseEvaluator): - metric_category = MetricCategory.GOAL_ALIGNMENT - - def evaluate(self, agent, task, execution_trace, final_output): - raise ValueError("Forced evaluation failure") - - agent_evaluator = AgentEvaluator( - agents=[agent], evaluators=[FailingEvaluator()] - ) - mock_crew.kickoff() - - assert events.keys() == {"started", "failed"} - assert events["started"].agent_id == str(agent.id) - assert events["started"].agent_role == agent.role - assert events["started"].task_id == str(task.id) - assert events["started"].iteration == 1 - - assert events["failed"].agent_id == str(agent.id) - assert events["failed"].agent_role == agent.role - assert events["failed"].task_id == str(task.id) - assert events["failed"].iteration == 1 - assert events["failed"].error == "Forced evaluation failure" - - results = agent_evaluator.get_evaluation_results() - (result,) = results[agent.role] - assert isinstance(result, AgentEvaluationResult) - - assert result.agent_id == str(agent.id) - assert result.task_id == str(task.id) - - assert result.metrics == {} diff --git a/tests/test_task_guardrails.py b/tests/test_task_guardrails.py deleted file mode 100644 index b4f9f71e2..000000000 --- a/tests/test_task_guardrails.py +++ /dev/null @@ -1,306 +0,0 @@ -from unittest.mock import Mock, patch - -import pytest - -from crewai import Agent, Task -from crewai.events.event_bus import crewai_event_bus -from crewai.events.event_types import ( - LLMGuardrailCompletedEvent, - LLMGuardrailStartedEvent, -) -from crewai.llm import LLM -from crewai.tasks.hallucination_guardrail import HallucinationGuardrail -from crewai.tasks.llm_guardrail import LLMGuardrail -from crewai.tasks.task_output import TaskOutput - - -def test_task_without_guardrail(): - """Test that tasks work normally without guardrails (backward compatibility).""" - agent = Mock() - agent.role = "test_agent" - agent.execute_task.return_value = "test result" - agent.crew = None - - task = Task(description="Test task", expected_output="Output") - - result = task.execute_sync(agent=agent) - assert isinstance(result, TaskOutput) - assert result.raw == "test result" - - -def test_task_with_successful_guardrail_func(): - """Test that successful guardrail validation passes transformed result.""" - - def guardrail(result: TaskOutput): - return (True, result.raw.upper()) - - agent = Mock() - agent.role = "test_agent" - agent.execute_task.return_value = "test result" - agent.crew = None - - task = Task(description="Test task", expected_output="Output", guardrail=guardrail) - - result = task.execute_sync(agent=agent) - assert isinstance(result, TaskOutput) - assert result.raw == "TEST RESULT" - - -def test_task_with_failing_guardrail(): - """Test that failing guardrail triggers retry with error context.""" - - def guardrail(result: TaskOutput): - return (False, "Invalid format") - - agent = Mock() - agent.role = "test_agent" - agent.execute_task.side_effect = ["bad result", "good result"] - agent.crew = None - - task = Task( - description="Test task", - expected_output="Output", - guardrail=guardrail, - guardrail_max_retries=1, - ) - - # First execution fails guardrail, second succeeds - agent.execute_task.side_effect = ["bad result", "good result"] - with pytest.raises(Exception) as exc_info: - task.execute_sync(agent=agent) - - assert "Task failed guardrail validation" in str(exc_info.value) - assert task.retry_count == 1 - - -def test_task_with_guardrail_retries(): - """Test that guardrail respects max_retries configuration.""" - - def guardrail(result: TaskOutput): - return (False, "Invalid format") - - agent = Mock() - agent.role = "test_agent" - agent.execute_task.return_value = "bad result" - agent.crew = None - - task = Task( - description="Test task", - expected_output="Output", - guardrail=guardrail, - guardrail_max_retries=2, - ) - - with pytest.raises(Exception) as exc_info: - task.execute_sync(agent=agent) - - assert task.retry_count == 2 - assert "Task failed guardrail validation after 2 retries" in str(exc_info.value) - assert "Invalid format" in str(exc_info.value) - - -def test_guardrail_error_in_context(): - """Test that guardrail error is passed in context for retry.""" - - def guardrail(result: TaskOutput): - return (False, "Expected JSON, got string") - - agent = Mock() - agent.role = "test_agent" - agent.crew = None - - task = Task( - description="Test task", - expected_output="Output", - guardrail=guardrail, - guardrail_max_retries=1, - ) - - # Mock execute_task to succeed on second attempt - first_call = True - - def execute_task(task, context, tools): - nonlocal first_call - if first_call: - first_call = False - return "invalid" - return '{"valid": "json"}' - - agent.execute_task.side_effect = execute_task - - with pytest.raises(Exception) as exc_info: - task.execute_sync(agent=agent) - - assert "Task failed guardrail validation" in str(exc_info.value) - assert "Expected JSON, got string" in str(exc_info.value) - - -@pytest.fixture -def sample_agent(): - return Agent(role="Test Agent", goal="Test Goal", backstory="Test Backstory") - - -@pytest.fixture -def task_output(): - return TaskOutput( - raw=""" - Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever - """, - description="Test task", - expected_output="Output", - agent="Test Agent", - ) - - -@pytest.mark.vcr(filter_headers=["authorization"]) -def test_task_guardrail_process_output(task_output): - guardrail = LLMGuardrail( - description="Ensure the result has less than 10 words", llm=LLM(model="gpt-4o") - ) - - result = guardrail(task_output) - assert result[0] is False - - assert "exceeding the guardrail limit of fewer than" in result[1].lower() - - guardrail = LLMGuardrail( - description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o") - ) - - result = guardrail(task_output) - assert result[0] is True - assert result[1] == task_output.raw - - -@pytest.mark.vcr(filter_headers=["authorization"]) -def test_guardrail_emits_events(sample_agent): - started_guardrail = [] - completed_guardrail = [] - - task = Task( - description="Gather information about available books on the First World War", - agent=sample_agent, - expected_output="A list of available books on the First World War", - guardrail="Ensure the authors are from Italy", - ) - - with crewai_event_bus.scoped_handlers(): - - @crewai_event_bus.on(LLMGuardrailStartedEvent) - def handle_guardrail_started(source, event): - assert source == task - started_guardrail.append( - {"guardrail": event.guardrail, "retry_count": event.retry_count} - ) - - @crewai_event_bus.on(LLMGuardrailCompletedEvent) - def handle_guardrail_completed(source, event): - assert source == task - completed_guardrail.append( - { - "success": event.success, - "result": event.result, - "error": event.error, - "retry_count": event.retry_count, - } - ) - - result = task.execute_sync(agent=sample_agent) - - def custom_guardrail(result: TaskOutput): - return (True, "good result from callable function") - - task = Task( - description="Test task", - expected_output="Output", - guardrail=custom_guardrail, - ) - - task.execute_sync(agent=sample_agent) - - expected_started_events = [ - {"guardrail": "Ensure the authors are from Italy", "retry_count": 0}, - {"guardrail": "Ensure the authors are from Italy", "retry_count": 1}, - { - "guardrail": """def custom_guardrail(result: TaskOutput): - return (True, "good result from callable function")""", - "retry_count": 0, - }, - ] - - expected_completed_events = [ - { - "success": False, - "result": None, - "error": "The task result does not comply with the guardrail because none of " - "the listed authors are from Italy. All authors mentioned are from " - "different countries, including Germany, the UK, the USA, and others, " - "which violates the requirement that authors must be Italian.", - "retry_count": 0, - }, - {"success": True, "result": result.raw, "error": None, "retry_count": 1}, - { - "success": True, - "result": "good result from callable function", - "error": None, - "retry_count": 0, - }, - ] - assert started_guardrail == expected_started_events - assert completed_guardrail == expected_completed_events - - -@pytest.mark.vcr(filter_headers=["authorization"]) -def test_guardrail_when_an_error_occurs(sample_agent, task_output): - with ( - patch( - "crewai.Agent.kickoff", - side_effect=Exception("Unexpected error"), - ), - pytest.raises( - Exception, - match="Error while validating the task output: Unexpected error", - ), - ): - task = Task( - description="Gather information about available books on the First World War", - agent=sample_agent, - expected_output="A list of available books on the First World War", - guardrail="Ensure the authors are from Italy", - guardrail_max_retries=0, - ) - task.execute_sync(agent=sample_agent) - - -def test_hallucination_guardrail_integration(): - """Test that HallucinationGuardrail integrates properly with the task system.""" - agent = Mock() - agent.role = "test_agent" - agent.execute_task.return_value = "test result" - agent.crew = None - - mock_llm = Mock(spec=LLM) - guardrail = HallucinationGuardrail( - context="Test reference context for validation", llm=mock_llm, threshold=8.0 - ) - - task = Task( - description="Test task with hallucination guardrail", - expected_output="Valid output", - guardrail=guardrail, - ) - - result = task.execute_sync(agent=agent) - assert isinstance(result, TaskOutput) - assert result.raw == "test result" - - -def test_hallucination_guardrail_description_in_events(): - """Test that HallucinationGuardrail description appears correctly in events.""" - mock_llm = Mock(spec=LLM) - guardrail = HallucinationGuardrail(context="Test context", llm=mock_llm) - - assert guardrail.description == "HallucinationGuardrail (no-op)" - - event = LLMGuardrailStartedEvent(guardrail=guardrail, retry_count=0) - assert event.guardrail == "HallucinationGuardrail (no-op)" diff --git a/tests/utilities/events/test_crewai_event_bus.py b/tests/utilities/events/test_crewai_event_bus.py deleted file mode 100644 index f034de595..000000000 --- a/tests/utilities/events/test_crewai_event_bus.py +++ /dev/null @@ -1,47 +0,0 @@ -from unittest.mock import Mock - -from crewai.events.base_events import BaseEvent -from crewai.events.event_bus import crewai_event_bus - - -class TestEvent(BaseEvent): - pass - - -def test_specific_event_handler(): - mock_handler = Mock() - - @crewai_event_bus.on(TestEvent) - def handler(source, event): - mock_handler(source, event) - - event = TestEvent(type="test_event") - crewai_event_bus.emit("source_object", event) - - mock_handler.assert_called_once_with("source_object", event) - - -def test_wildcard_event_handler(): - mock_handler = Mock() - - @crewai_event_bus.on(BaseEvent) - def handler(source, event): - mock_handler(source, event) - - event = TestEvent(type="test_event") - crewai_event_bus.emit("source_object", event) - - mock_handler.assert_called_once_with("source_object", event) - - -def test_event_bus_error_handling(capfd): - @crewai_event_bus.on(BaseEvent) - def broken_handler(source, event): - raise ValueError("Simulated handler failure") - - event = TestEvent(type="test_event") - crewai_event_bus.emit("source_object", event) - - out, err = capfd.readouterr() - assert "Simulated handler failure" in out - assert "Handler 'broken_handler' failed" in out diff --git a/uv.lock b/uv.lock index f0f457949..0e18f38ef 100644 --- a/uv.lock +++ b/uv.lock @@ -2,49 +2,89 @@ version = 1 revision = 3 requires-python = ">=3.10, <3.14" resolution-markers = [ - "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 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and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", ] +[manifest] +members = [ + "crewai", + "crewai-devtools", + "crewai-tools", +] + +[manifest.dependency-groups] +dev = [ + { name = "bandit", specifier = ">=1.8.6" }, + { name = "boto3-stubs", extras = ["bedrock-runtime"], 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