5 Commits

Author	SHA1	Message	Date
João Moura	bb477f8a91	JSON first crews (#6131) ... * feat(cli): introduce JSON crew project support and TUI enhancements - Added support for creating and running JSON-defined crew projects, allowing users to scaffold projects with a new `create_json_crew.py` file. - Implemented a full-screen Textual TUI for crew execution in `crew_run_tui.py`, enhancing user interaction with a two-column layout. - Updated `run_crew.py` to prioritize JSON crew projects and added daemon mode for running without TUI. - Introduced interactive pickers in `tui_picker.py` for improved CLI prompts. - Enhanced validation for JSON crew files in `validate.py` to ensure proper structure and agent definitions. - Updated `.gitignore` to exclude demo and crewai directories. * feat: update LLM model references to gpt-5.4-mini - Changed default LLM model from gpt-4o-mini to gpt-5.4-mini across various files, including CLI options, JSON crew configurations, and agent definitions. - Enhanced benchmark and human feedback functionalities to utilize the new model. - Improved user interface elements in the TUI for better interaction and feedback during execution. - Added support for new skills directory in JSON crew project creation. * feat(benchmark): add crew-level benchmarking functionality - Introduced a new `benchmark` command in the CLI for crew-level benchmarking, allowing users to specify agents, models, and timeout settings. - Implemented `CrewBenchmarkCase` to handle crew-level benchmark cases with inputs and criteria. - Enhanced the benchmark runner to support progress tracking and detailed reporting of results for multiple models. - Added tests for loading crew benchmark cases and validating their structure. - Updated existing benchmark functions to accommodate the new crew-level execution model. * feat(cli): enhance JSON crew project functionality and TUI improvements - Added optional agent-level guardrails and advanced options in JSON crew configurations to improve output validation and flexibility. - Updated the TUI to better handle plan step statuses, including visual indicators for task completion and failure. - Introduced methods for parsing and managing step observation events, ensuring accurate updates to task statuses during execution. - Enhanced validation for JSON crew projects, ensuring proper structure and error handling for agent and task definitions. - Added comprehensive tests for new features and validation logic, ensuring robustness in JSON crew project handling. * refactor(cli): streamline JSON crew project handling and improve validation - Refactored JSON crew project loading and validation logic to enhance clarity and maintainability. - Introduced utility functions for finding JSON crew files, improving code reuse across modules. - Removed deprecated benchmark functionality and associated tests to simplify the codebase. - Updated CLI commands to utilize the new JSON project structure, ensuring compatibility with recent changes. - Enhanced test coverage for JSON crew project features, ensuring robust validation and error handling. * feat(cli): enhance activity log navigation and focus management - Added functionality to focus on the activity log when navigating through log entries. - Implemented refresh logic for the log panel to ensure updates are displayed correctly during navigation. - Improved keyboard navigation for log entries, allowing users to expand and scroll through logs seamlessly. - Added tests to verify the correct behavior of log navigation and focus management in the TUI. * feat(cli): enhance JSON crew project interaction and input handling - Introduced a new function to enable prompt line editing for better user experience during input prompts. - Updated the JSON crew project wizards to show interpolation hints for dynamic values, improving user guidance. - Enhanced the handling of missing input placeholders by prompting users for required values during crew setup. - Refactored the crew run logic to ensure proper loading and preparation of JSON-defined crews, including runtime input management. - Added tests to verify the correct behavior of new input handling features and JSON crew project interactions. * feat(cli): improve crew project input prompts and event handling - Enhanced the `_prompt_text` function to allow for configurable spacing before prompts, improving user experience during input collection. - Updated the wizards for agent and task creation to utilize the new prompt configuration, ensuring a more compact and streamlined interaction. - Introduced new plan step lifecycle events (`PlanStepStartedEvent`, `PlanStepCompletedEvent`) to better track the execution status of plan steps. - Refactored the step executor to emit these events during the execution of tasks, improving observability and debugging capabilities. - Added tests to verify the correct behavior of new prompt handling and event emissions during crew project execution. * fix: refine json-first crew interactions * fix: prioritize common json crew tools * fix: make json crew more tools expandable * fix: show json crew tools by category * feat(memory): update default embedder to OpenAI text-embedding-3-large and enhance memory compatibility - Changed the default embedding model for Memory to OpenAI text-embedding-3-large, which uses 3072-dimensional vectors. - Added warnings regarding compatibility issues with existing local memory stores created with 1536-dimensional embeddings. - Updated documentation to reflect the new default embedder and its configuration options. - Enhanced the CLI and codebase to support the new embedding model across various components, ensuring a seamless transition for users. * fix: address PR review feedback for JSON-first crews Review blockers: - Forward trained_agents_file to JSON crews: crewai run -f now exports CREWAI_TRAINED_AGENTS_FILE for the in-process JSON crew path - Wizard agent picker: Esc/cancel now reprompts instead of silently assigning the first agent - JSON tool resolution hard-fails: unknown tool names, missing custom tool files, and invalid custom tool modules raise JSONProjectError with actionable messages instead of warn-and-continue - Embedding dimension mismatch: LanceDB and Qdrant Edge storages raise EmbeddingDimensionMismatchError with reset/pin guidance instead of silently zero-filling vectors or returning empty search results - Custom tool code execution documented in loader docstring and the scaffolded project README CI fixes: - ruff format across lib/ - All 133 PR-introduced mypy errors fixed (llm.py lazy-litellm and cli.py lazy command shims now use TYPE_CHECKING imports; textual is_mounted misuse fixed; pick_many overloads; misc annotations) Bot review comments: - Empty except blocks now have explanatory comments or debug logging - Removed unused _C_BG/_C_PANEL/_C_BORDER globals and redundant import re; tests use a single import style for create_json_crew Tests: trained-agents propagation, wizard cancel, tool resolution failures, and dimension mismatch guidance. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix: address second round of PR review comments Cursor Bugbot: - Wizard agent slugs: strip to [a-z0-9_] and fall back to agent_<n> so symbol-only roles can't produce an empty agents/.jsonc filename - Wizard task names: dedupe against prior task names and fall back to task_<n> for symbol-only descriptions CodeRabbit: - Agent.message(): import Task explicitly at runtime instead of relying on the namespace injection done by crewai/__init__ - Async executor: move the native-tools-unsupported fallback from _ainvoke_loop_react (self-recursion) to _ainvoke_loop_native_tools, mirroring the sync implementation - StepExecutor downgrade: keep the in-step conversation and append the text-tooling instructions instead of rebuilding messages, so completed native tool calls are not re-executed - crewai-files: extension-based MIME lookup now runs before byte sniffing so csv/xml types are not degraded to text/plain - Memory storages: validate every record in a save() batch against a consistent embedding dimension (LanceDB previously checked only the first record); added mixed-batch tests - _print_post_tui_summary now typed against CrewRunApp - Docs: Azure OpenAI default embedder change called out in the memory migration warning and provider table Code quality bots: - Removed unused _C_YELLOW/_C_CYAN (crew_run_tui) and _GREEN (tui_picker) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(cli): accordion tool picker in JSON crew wizard The flat tool list had grown to ~90 rows. The picker now shows: - Common tools always visible at the top - Every other category as a single expandable row with tool and selection counts (e.g. "Search & Research (27 tools, 2 selected)") - Expanding a category collapses the previously expanded one - Selections persist across expand/collapse via new preselected support in pick_many; cursor follows the toggled category row tui_picker gains preselected + initial_cursor options on pick_many, and Esc in multi-select now confirms the current selection instead of discarding it (required so collapsing can't silently drop choices). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * refactor(cli): remove --daemon flag from crewai run The flag only affected JSON crew projects — classic and flow projects ignored it entirely, which made the behavior inconsistent. Removed the option, the daemon code path (_run_json_crew_daemon), and its helper (_load_json_crew_with_inputs). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * test: update run command tests after --daemon removal lib/crewai/tests/cli/test_run_crew.py still asserted the old run_crew(trained_agents_file=..., daemon=False) call signature. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix(cli): exit codes, mid-run quit, async statuses, hyphen placeholders Addresses the latest Bugbot review round: - Failed JSON crew runs now exit non-zero (SystemExit(1)) so scripts and CI don't treat failures as success, mirroring the classic path - Quitting the TUI mid-run now ends the process (os._exit(130)); kickoff runs in a thread worker that cannot be force-cancelled, so letting the CLI return would leave LLM/tool work burning tokens in the background - Sidebar task statuses are now async-safe: completion/failure events resolve the task's own row via identity instead of assuming the most recently started task, and starting a task no longer blanket-marks earlier active rows as done - The runtime-input prompt regex now accepts hyphenated placeholder names ({my-topic}), matching kickoff's interpolation pattern Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix: validation safety, custom tool sandboxing, TUI log integrity, memory error surfacing - Deploy validation no longer executes project code: validation mode checks tool declarations structurally (well-formed entries, custom tool file exists) without importing or instantiating anything. custom:<name> resolution only happens on the actual run path. - custom:<name> is constrained to [A-Za-z_][A-Za-z0-9_]* and the resolved path must stay inside the project's tools/ directory, so custom:../foo or absolute-path names cannot execute code outside it. Tool paths resolve relative to the crew project root, not cwd. - TUI task logs are built from per-task state captured at task start (idx, description, agent, start time); an out-of-order completion takes its output from the event and no longer steals or resets the current task's streamed steps/output. - EmbeddingDimensionMismatchError now inherits ValueError instead of RuntimeError so background saves surface it through MemorySaveFailedEvent instead of silently dropping the save; the shutdown catch in _background_encode_batch is narrowed to the "cannot schedule new futures" case. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix(cli): declared project type wins over crew.json presence A flow project that also contains a crew.json(c) file now runs and validates as the flow it declares in pyproject.toml instead of being hijacked by the JSON crew path. Both crewai run (_has_json_crew) and deploy validation (_is_json_crew) check tool.crewai.type; a missing or unreadable pyproject still means a bare JSON crew project. Also documents why StepObservationFailedEvent intentionally marks the plan step "done": the event signals an observer failure, not a step failure, and the executor continues past it. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix(cli): type the declared_type locals so mypy stays clean Comparing an Any-typed .get() chain returns Any, which tripped no-any-return on the previous commit. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com>	2026-06-14 04:19:48 -03:00
Greyson LaLonde	fd10c64148	chore(crewai): drop self-explanatory comments	2026-05-26 10:23:33 -07:00
Greyson LaLonde	c925d2d519	chore: restructure test env, cassettes, and conftest; fix flaky tests ... Consolidates pytest config, standardizes env handling, reorganizes cassette layout, removes outdated VCR configs, improves sync with threading.Condition, updates event-waiting logic, ensures cleanup, regenerates Gemini cassettes, and reverts unintended test changes.	2025-11-29 16:55:24 -05:00
Lorenze Jay	fc521839e4	Lorenze/fix duplicating doc ids for knowledge (#3840) ... * fix: update document ID handling in ChromaDB utility functions to use SHA-256 hashing and include index for uniqueness * test: add tests for hash-based ID generation in ChromaDB utility functions * drop idx for preventing dups, upsert should handle dups * fix: update document ID extraction logic in ChromaDB utility functions to check for doc_id at the top level of the document * fix: enhance document ID generation in ChromaDB utility functions to deduplicate documents and ensure unique hash-based IDs without suffixes * fix: improve error handling and document ID generation in ChromaDB utility functions to ensure robust processing and uniqueness	2025-11-06 10:59:52 -08:00
Lorenze Jay	d1343b96ed	Release/v1.0.0 (#3618) ... * 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 <greyson.r.lalonde@gmail.com> * fix: handle properly anyOf oneOf allOf schema's props Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com> * 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 <greyson.r.lalonde@gmail.com> * 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 <greyson.r.lalonde@gmail.com> * 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 <greyson.r.lalonde@gmail.com> * 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 0bbe6dee90. * 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 <lorenzejaytech@gmail.com> Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com> * feat: a2a experimental experimental a2a support --------- Co-authored-by: Lucas Gomide <lucaslg200@gmail.com> Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com> Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com> Co-authored-by: Mike Plachta <mplachta@users.noreply.github.com> Co-authored-by: João Moura <joaomdmoura@gmail.com>	2025-10-20 14:10:19 -07:00