mirror of
https://github.com/crewAIInc/crewAI.git
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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>
This commit is contained in:
Lorenze Jay
@@ -110,19 +110,49 @@ CrewAI AMP 트리거는 팀이 이미 사용하고 있는 도구의 실시간
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- `allow_crewai_trigger_context` 옵션으로 컨텍스트 자동 주입 여부를 결정했나요?
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- 웹훅 로그, CrewAI 실행 기록, 외부 알림 등 모니터링을 준비했나요?
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### Payload & Crew 예제 저장소
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### CLI로 로컬에서 트리거 테스트
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| 통합 | 동작 시점 | Payload 예제 | Crew 예제 |
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| :-- | :-- | :-- | :-- |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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CrewAI CLI는 프로덕션에 배포하기 전에 트리거 기반 자동화를 개발하고 테스트할 수 있는 강력한 명령을 제공합니다.
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예제 payload를 참고해 파싱 로직을 검증하고, 제공되는 crew를 복사해 실제 데이터로 교체하세요.
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#### 사용 가능한 트리거 목록 보기
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연결된 통합에 사용 가능한 모든 트리거를 확인하세요:
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```bash
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crewai triggers list
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```
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이 명령은 연결된 통합을 기반으로 사용 가능한 모든 트리거를 표시합니다:
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- 통합 이름 및 연결 상태
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- 사용 가능한 트리거 유형
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- 트리거 이름 및 설명
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#### 트리거 실행 시뮬레이션
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배포 전에 실제 트리거 payload로 크루를 테스트하세요:
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```bash
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crewai triggers run <트리거_이름>
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```
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예시:
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```bash
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crewai triggers run microsoft_onedrive/file_changed
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```
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||||
|
||||
이 명령은:
|
||||
- 로컬에서 크루를 실행합니다
|
||||
- 완전하고 실제적인 트리거 payload를 전달합니다
|
||||
- 프로덕션에서 크루가 호출되는 방식을 정확히 시뮬레이션합니다
|
||||
|
||||
<Warning>
|
||||
**중요한 개발 노트:**
|
||||
- 개발 중 트리거 실행을 시뮬레이션하려면 `crewai triggers run <trigger>`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
|
||||
</Warning>
|
||||
|
||||
### 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 <trigger>`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.
|
||||
|
||||
<Callout icon="github" title="예제 저장소">
|
||||
<a href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples">
|
||||
CrewAI AMP Trigger Examples
|
||||
</a>
|
||||
</Callout>
|
||||
|
||||
@@ -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.
|
||||
|
||||
<Warning>
|
||||
개발 중에는 `crewai triggers run gmail/new_email`을 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
|
||||
</Warning>
|
||||
|
||||
## Monitoring Executions
|
||||
|
||||
@@ -70,16 +79,10 @@ Track history and performance of triggered runs:
|
||||
<img src="/images/enterprise/list-executions.png" alt="List of executions triggered by automation" />
|
||||
</Frame>
|
||||
|
||||
## Payload Reference
|
||||
|
||||
See the sample payloads and field descriptions:
|
||||
|
||||
<Card title="Gmail samples in Trigger Examples Repo" href="https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail" icon="envelopes-bulk">
|
||||
Gmail samples in Trigger Examples Repo
|
||||
</Card>
|
||||
|
||||
## 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)
|
||||
|
||||
@@ -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.
|
||||
|
||||
<Warning>
|
||||
개발 중에는 `crewai triggers run google_calendar/event_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
|
||||
</Warning>
|
||||
|
||||
## 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)
|
||||
|
||||
@@ -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.
|
||||
|
||||
<Warning>
|
||||
개발 중에는 `crewai triggers run google_drive/file_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
|
||||
</Warning>
|
||||
|
||||
## 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)
|
||||
|
||||
@@ -49,6 +49,4 @@ mode: "wide"
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
## 추가 자료
|
||||
|
||||
사용 가능한 작업과 사용자 지정 옵션에 대한 자세한 정보는 [HubSpot 워크플로우 문서](https://knowledge.hubspot.com/workflows/create-workflows)를 참고하세요.
|
||||
|
||||
@@ -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.
|
||||
|
||||
<Warning>
|
||||
개발 중에는 `crewai triggers run microsoft_teams/teams_message_created`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
|
||||
</Warning>
|
||||
|
||||
## 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)
|
||||
|
||||
@@ -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.
|
||||
|
||||
<Warning>
|
||||
개발 중에는 `crewai triggers run microsoft_onedrive/file_changed`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
|
||||
</Warning>
|
||||
|
||||
## 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)
|
||||
|
||||
@@ -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.
|
||||
|
||||
<Warning>
|
||||
개발 중에는 `crewai triggers run microsoft_outlook/email_received`를 사용하세요 (`crewai run`이 아님). 배포 후에는 크루가 자동으로 트리거 payload를 받습니다.
|
||||
</Warning>
|
||||
|
||||
## 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)
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Reference in New Issue
Block a user