mirror of
https://github.com/crewAIInc/crewAI.git
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* 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>
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---
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title: Notion 연동
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description: "CrewAI를 위한 Notion 연동을 통한 페이지 및 데이터베이스 관리."
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icon: "book"
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mode: "wide"
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---
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## 개요
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에이전트가 Notion을 통해 페이지, 데이터베이스, 콘텐츠를 관리할 수 있도록 지원합니다. 페이지 생성 및 업데이트, 콘텐츠 블록 관리, 지식 베이스 구성, AI 기반 자동화를 통해 문서화 작업 흐름을 효율화할 수 있습니다.
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## 필수 조건
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Notion 통합을 사용하기 전에 다음을 확인하세요:
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- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정
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- 적절한 워크스페이스 권한이 있는 Notion 계정
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- [통합 페이지](https://app.crewai.com/crewai_plus/connectors)를 통해 Notion 계정을 연결함
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## Notion 연동 설정
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### 1. Notion 계정 연결하기
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1. [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)로 이동합니다.
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2. 인증 통합(Auhtentication Integrations) 섹션에서 **Notion**을(를) 찾습니다.
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3. **Connect**를 클릭하고 OAuth 플로우를 완료합니다.
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4. 페이지 및 데이터베이스 관리를 위한 필요한 권한을 부여합니다.
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5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다.
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### 2. 필수 패키지 설치
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```bash
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uv add crewai-tools
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```
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## 사용 가능한 액션
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<AccordionGroup>
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<Accordion title="notion/create_page">
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**설명:** Notion에서 페이지를 생성합니다.
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**파라미터:**
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- `parent` (object, 필수): 상위 - 새 페이지가 삽입될 상위 페이지 또는 데이터베이스를 나타내는 JSON 객체로, page_id 또는 database_id 키를 포함합니다.
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```json
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{
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"database_id": "DATABASE_ID"
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}
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```
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- `properties` (object, 필수): 속성 - 페이지 속성의 값입니다. 상위가 데이터베이스인 경우, 스키마는 상위 데이터베이스의 속성과 일치해야 합니다.
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```json
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{
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"title": [
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{
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"text": {
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"content": "My Page"
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}
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}
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]
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}
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```
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- `icon` (object, 필수): 아이콘 - 페이지 아이콘입니다.
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```json
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{
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"emoji": "🥬"
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}
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```
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- `children` (object, 선택): 자식 - 페이지에 추가할 콘텐츠 블록입니다.
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```json
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[
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{
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"object": "block",
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"type": "heading_2",
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"heading_2": {
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"rich_text": [
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{
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"type": "text",
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"text": {
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"content": "Lacinato kale"
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}
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}
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]
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}
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}
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]
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```
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- `cover` (object, 선택): 표지 - 페이지 표지 이미지입니다.
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```json
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{
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"external": {
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"url": "https://upload.wikimedia.org/wikipedia/commons/6/62/Tuscankale.jpg"
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}
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}
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```
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</Accordion>
|
|
|
|
<Accordion title="notion/update_page">
|
|
**설명:** Notion에서 페이지를 업데이트합니다.
|
|
|
|
**파라미터:**
|
|
- `pageId` (string, 필수): 페이지 ID - 업데이트할 페이지의 ID를 지정합니다. (예: "59833787-2cf9-4fdf-8782-e53db20768a5").
|
|
- `icon` (object, 필수): 아이콘 - 페이지 아이콘입니다.
|
|
```json
|
|
{
|
|
"emoji": "🥬"
|
|
}
|
|
```
|
|
- `archived` (boolean, 선택): 보관됨 - 페이지가 보관(삭제)되었는지 여부입니다. true로 설정하면 페이지를 보관합니다. false로 설정하면 보관 해제(복원)합니다.
|
|
- `properties` (object, 선택): 속성 - 페이지에서 업데이트할 속성 값입니다.
|
|
```json
|
|
{
|
|
"title": [
|
|
{
|
|
"text": {
|
|
"content": "My Updated Page"
|
|
}
|
|
}
|
|
]
|
|
}
|
|
```
|
|
- `cover` (object, 선택): 표지 - 페이지 표지 이미지입니다.
|
|
```json
|
|
{
|
|
"external": {
|
|
"url": "https://upload.wikimedia.org/wikipedia/commons/6/62/Tuscankale.jpg"
|
|
}
|
|
}
|
|
```
|
|
</Accordion>
|
|
|
|
<Accordion title="notion/get_page_by_id">
|
|
**설명:** Notion에서 ID로 페이지를 가져옵니다.
|
|
|
|
**파라미터:**
|
|
- `pageId` (string, 필수): 페이지 ID - 가져올 페이지의 ID를 지정합니다. (예: "59833787-2cf9-4fdf-8782-e53db20768a5").
|
|
</Accordion>
|
|
|
|
<Accordion title="notion/archive_page">
|
|
**설명:** Notion에서 페이지를 보관합니다.
|
|
|
|
**파라미터:**
|
|
- `pageId` (string, 필수): 페이지 ID - 보관할 페이지의 ID를 지정합니다. (예: "59833787-2cf9-4fdf-8782-e53db20768a5").
|
|
</Accordion>
|
|
|
|
<Accordion title="notion/search_pages">
|
|
**설명:** 필터를 사용하여 Notion에서 페이지를 검색합니다.
|
|
|
|
**파라미터:**
|
|
- `searchByTitleFilterSearch` (object, 선택): 불리언 정규형(OR 조건 그룹의 AND 그룹) 형태의 필터입니다.
|
|
```json
|
|
{
|
|
"operator": "OR",
|
|
"conditions": [
|
|
{
|
|
"operator": "AND",
|
|
"conditions": [
|
|
{
|
|
"field": "query",
|
|
"operator": "$stringExactlyMatches",
|
|
"value": "meeting notes"
|
|
}
|
|
]
|
|
}
|
|
]
|
|
}
|
|
```
|
|
사용 가능한 필드: `query`, `filter.value`, `direction`, `page_size`
|
|
</Accordion>
|
|
|
|
<Accordion title="notion/get_page_content">
|
|
**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").
|
|
</Accordion>
|
|
|
|
<Accordion title="notion/update_block">
|
|
**설명:** Notion에서 블록을 업데이트합니다.
|
|
|
|
**파라미터:**
|
|
- `blockId` (string, 필수): 블록 ID - 업데이트할 블록의 ID를 지정합니다. (예: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6").
|
|
- `archived` (boolean, 선택): 보관됨 - true로 설정하면 블록을 보관(삭제)합니다. false로 설정하면 보관 해제(복원)합니다.
|
|
- `paragraph` (object, 선택): 단락 콘텐츠.
|
|
```json
|
|
{
|
|
"rich_text": [
|
|
{
|
|
"type": "text",
|
|
"text": {
|
|
"content": "Lacinato kale",
|
|
"link": null
|
|
}
|
|
}
|
|
],
|
|
"color": "default"
|
|
}
|
|
```
|
|
- `image` (object, 선택): 이미지 블록.
|
|
```json
|
|
{
|
|
"type": "external",
|
|
"external": {
|
|
"url": "https://website.domain/images/image.png"
|
|
}
|
|
}
|
|
```
|
|
- `bookmark` (object, 선택): 북마크 블록.
|
|
```json
|
|
{
|
|
"caption": [],
|
|
"url": "https://companywebsite.com"
|
|
}
|
|
```
|
|
- `code` (object, 선택): 코드 블록.
|
|
```json
|
|
{
|
|
"rich_text": [
|
|
{
|
|
"type": "text",
|
|
"text": {
|
|
"content": "const a = 3"
|
|
}
|
|
}
|
|
],
|
|
"language": "javascript"
|
|
}
|
|
```
|
|
- `pdf` (object, 선택): PDF 블록.
|
|
```json
|
|
{
|
|
"type": "external",
|
|
"external": {
|
|
"url": "https://website.domain/files/doc.pdf"
|
|
}
|
|
}
|
|
```
|
|
- `table` (object, 선택): 테이블 블록.
|
|
```json
|
|
{
|
|
"table_width": 2,
|
|
"has_column_header": false,
|
|
"has_row_header": false
|
|
}
|
|
```
|
|
- `tableOfContent` (object, 선택): 목차 블록.
|
|
```json
|
|
{
|
|
"color": "default"
|
|
}
|
|
```
|
|
- `additionalFields` (object, 선택): 추가 블록 유형.
|
|
```json
|
|
{
|
|
"child_page": {
|
|
"title": "Lacinato kale"
|
|
},
|
|
"child_database": {
|
|
"title": "My database"
|
|
}
|
|
}
|
|
```
|
|
</Accordion>
|
|
|
|
<Accordion title="notion/get_block_by_id">
|
|
**설명:** Notion에서 ID로 블록을 가져옵니다.
|
|
|
|
**파라미터:**
|
|
- `blockId` (string, 필수): 블록 ID - 가져올 블록의 ID를 지정합니다. (예: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6").
|
|
</Accordion>
|
|
|
|
<Accordion title="notion/delete_block">
|
|
**설명:** Notion에서 블록을 삭제합니다.
|
|
|
|
**파라미터:**
|
|
- `blockId` (string, 필수): 블록 ID - 삭제할 블록의 ID를 지정합니다. (예: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6").
|
|
</Accordion>
|
|
</AccordionGroup>
|
|
|
|
## 사용 예시
|
|
|
|
### 기본 Notion Agent 설정
|
|
|
|
```python
|
|
from crewai import Agent, Task, Crew
|
|
|
|
# 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.",
|
|
apps=['notion']
|
|
)
|
|
|
|
# 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",
|
|
agent=notion_agent,
|
|
expected_output="Meeting notes page created successfully with structured content"
|
|
)
|
|
|
|
# Run the task
|
|
crew = Crew(
|
|
agents=[notion_agent],
|
|
tasks=[create_notes_task]
|
|
)
|
|
|
|
crew.kickoff()
|
|
```
|
|
|
|
### 특정 Notion 도구 필터링
|
|
|
|
```python
|
|
|
|
content_manager = Agent(
|
|
role="Content Manager",
|
|
goal="Create and manage content pages efficiently",
|
|
backstory="An AI assistant that focuses on content creation and management.",
|
|
apps=['notion']
|
|
)
|
|
|
|
# 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"
|
|
)
|
|
|
|
crew = Crew(
|
|
agents=[content_manager],
|
|
tasks=[content_workflow]
|
|
)
|
|
|
|
crew.kickoff()
|
|
```
|
|
|
|
### 지식 베이스 관리
|
|
|
|
```python
|
|
from crewai import Agent, Task, Crew
|
|
|
|
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.",
|
|
apps=['notion']
|
|
)
|
|
|
|
# Task to curate knowledge base
|
|
curation_task = Task(
|
|
description="""
|
|
1. 새로운 제품 기능과 관련된 기존 문서 페이지를 검색합니다.
|
|
2. 적절한 구조로 포괄적인 기능 문서 페이지를 생성합니다.
|
|
3. 코드 예제, 이미지 및 관련 리소스에 대한 링크를 추가합니다.
|
|
4. 기존 페이지를 업데이트하여 새 문서에 대한 교차 참조를 추가합니다.
|
|
""",
|
|
agent=knowledge_curator,
|
|
expected_output="Feature documentation created and integrated with existing knowledge base"
|
|
)
|
|
|
|
crew = Crew(
|
|
agents=[knowledge_curator],
|
|
tasks=[curation_task]
|
|
)
|
|
|
|
crew.kickoff()
|
|
```
|
|
|
|
### 콘텐츠 구조 및 구성
|
|
|
|
```python
|
|
from crewai import Agent, Task, Crew
|
|
|
|
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.",
|
|
apps=['notion']
|
|
)
|
|
|
|
# Task to organize content structure
|
|
organization_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
|
|
""",
|
|
agent=content_organizer,
|
|
expected_output="Content reorganized with improved structure and navigation"
|
|
)
|
|
|
|
crew = Crew(
|
|
agents=[content_organizer],
|
|
tasks=[organization_task]
|
|
)
|
|
|
|
crew.kickoff()
|
|
```
|
|
|
|
### 자동화된 문서화 워크플로우
|
|
|
|
```python
|
|
from crewai import Agent, Task, Crew
|
|
|
|
doc_automator = Agent(
|
|
role="Documentation Automator",
|
|
goal="Automate documentation workflows and maintenance",
|
|
backstory="An AI assistant that automates repetitive documentation tasks.",
|
|
apps=['notion']
|
|
)
|
|
|
|
# Complex documentation automation task
|
|
automation_task = Task(
|
|
description="""
|
|
1. 최근 30일 이내에 업데이트되지 않은 페이지 검색
|
|
2. 오래된 콘텐츠 블록 검토 및 업데이트
|
|
3. 일관된 포맷으로 주간 팀 업데이트 페이지 생성
|
|
4. 프로젝트 페이지에 상태 표시기 및 진행 상황 추적 추가
|
|
5. 월간 문서 헬스 리포트 생성
|
|
6. 완료된 프로젝트 페이지를 아카이브 섹션에 정리 및 보관
|
|
""",
|
|
agent=doc_automator,
|
|
expected_output="업데이트된 콘텐츠, 주간 리포트, 정리된 아카이브로 문서화 자동화 완료"
|
|
)
|
|
|
|
crew = Crew(
|
|
agents=[doc_automator],
|
|
tasks=[automation_task]
|
|
)
|
|
|
|
crew.kickoff()
|
|
```
|
|
|
|
## 문제 해결
|
|
|
|
### 일반적인 문제
|
|
|
|
**권한 오류**
|
|
- Notion 계정이 대상 워크스페이스에 대한 편집 권한이 있는지 확인하세요
|
|
- OAuth 연결에 Notion API에 필요한 범위가 포함되어 있는지 확인하세요
|
|
- 페이지와 데이터베이스가 인증된 통합에 공유되어 있는지 확인하세요
|
|
|
|
**잘못된 페이지 및 블록 ID**
|
|
- 페이지 ID 및 블록 ID가 올바른 UUID 형식인지 다시 확인하세요
|
|
- 참조되는 페이지와 블록이 존재하고 접근 가능한지 확인하세요
|
|
- 새 페이지를 생성할 때 상위 페이지 또는 데이터베이스 ID가 유효한지 검증하세요
|
|
|
|
**속성 스키마 문제**
|
|
- 데이터베이스에 페이지를 생성할 때 페이지 속성이 데이터베이스 스키마와 일치하는지 확인하세요
|
|
- 대상 데이터베이스에 대해 속성 이름과 타입이 올바른지 확인하세요
|
|
- 페이지를 생성하거나 업데이트할 때 필수 속성이 포함되어 있는지 확인하세요
|
|
|
|
**콘텐츠 블록 구조**
|
|
- 블록 콘텐츠가 Notion의 리치 텍스트 형식 사양을 따르는지 확인하세요
|
|
- 중첩된 블록 구조가 올바르게 포맷되어 있는지 확인하세요
|
|
- 미디어 URL이 접근 가능하며 올바른 형식인지 확인하세요
|
|
|
|
**검색 및 필터 문제**
|
|
- 검색 쿼리가 올바르게 포맷되어 있고 비어 있지 않은지 확인하세요
|
|
- 필터 공식에서 유효한 필드명을 사용하세요: `query`, `filter.value`, `direction`, `page_size`
|
|
- 복잡한 필터 조건을 만들기 전에 간단한 검색을 테스트하세요
|
|
|
|
**상위-하위 관계**
|
|
- 하위 페이지를 생성하기 전에 상위 페이지 또는 데이터베이스가 존재하는지 확인하세요
|
|
- 상위 컨테이너에 대한 적절한 권한이 있는지 확인하세요
|
|
- 데이터베이스 스키마가 설정하려는 속성을 허용하는지 확인하세요
|
|
|
|
**리치 텍스트 및 미디어 콘텐츠**
|
|
- 외부 이미지, PDF, 북마크의 URL이 접근 가능한지 확인하세요
|
|
- 리치 텍스트 포매팅이 Notion의 API 사양을 따르는지 확인하세요
|
|
- 코드 블록의 언어 타입이 Notion에서 지원되는지 확인하세요
|
|
|
|
**아카이브 및 삭제 작업**
|
|
- 아카이브(복구 가능)와 삭제(영구적)의 차이를 이해하세요
|
|
- 대상 콘텐츠를 아카이브 또는 삭제할 수 있는 권한이 있는지 확인하세요
|
|
- 여러 페이지 또는 블록에 영향을 줄 수 있는 대량 작업은 신중히 진행하세요
|
|
|
|
### 도움 받기
|
|
|
|
<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
|
|
Notion 연동 설정 또는 문제 해결에 대해 지원팀에 문의해 주세요.
|
|
</Card>
|