- Deleted the `__init__.py` file from the tests/hooks directory as it contained no tests or functionality. This cleanup helps maintain a tidy test structure.
- add trust_remote_completion_status flag to A2AConfig, Adds configuration flag to control whether to trust A2A agent completion status. Resolves#3899
- update docs
* feat: implement LLM call hooks and enhance agent execution context
- Introduced LLM call hooks to allow modification of messages and responses during LLM interactions.
- Added support for before and after hooks in the CrewAgentExecutor, enabling dynamic adjustments to the execution flow.
- Created LLMCallHookContext for comprehensive access to the executor state, facilitating in-place modifications.
- Added validation for hook callables to ensure proper functionality.
- Enhanced tests for LLM hooks and tool hooks to verify their behavior and error handling capabilities.
- Updated LiteAgent and CrewAgentExecutor to accommodate the new crew context in their execution processes.
* feat: implement LLM call hooks and enhance agent execution context
- Introduced LLM call hooks to allow modification of messages and responses during LLM interactions.
- Added support for before and after hooks in the CrewAgentExecutor, enabling dynamic adjustments to the execution flow.
- Created LLMCallHookContext for comprehensive access to the executor state, facilitating in-place modifications.
- Added validation for hook callables to ensure proper functionality.
- Enhanced tests for LLM hooks and tool hooks to verify their behavior and error handling capabilities.
- Updated LiteAgent and CrewAgentExecutor to accommodate the new crew context in their execution processes.
* fix verbose
* feat: introduce crew-scoped hook decorators and refactor hook registration
- Added decorators for before and after LLM and tool calls to enhance flexibility in modifying execution behavior.
- Implemented a centralized hook registration mechanism within CrewBase to automatically register crew-scoped hooks.
- Removed the obsolete base.py file as its functionality has been integrated into the new decorators and registration system.
- Enhanced tests for the new hook decorators to ensure proper registration and execution flow.
- Updated existing hook handling to accommodate the new decorator-based approach, improving code organization and maintainability.
* feat: enhance hook management with clear and unregister functions
- Introduced functions to unregister specific before and after hooks for both LLM and tool calls, improving flexibility in hook management.
- Added clear functions to remove all registered hooks of each type, facilitating easier state management and cleanup.
- Implemented a convenience function to clear all global hooks in one call, streamlining the process for testing and execution context resets.
- Enhanced tests to verify the functionality of unregistering and clearing hooks, ensuring robust behavior in various scenarios.
* refactor: enhance hook type management for LLM and tool hooks
- Updated hook type definitions to use generic protocols for better type safety and flexibility.
- Replaced Callable type annotations with specific BeforeLLMCallHookType and AfterLLMCallHookType for clarity.
- Improved the registration and retrieval functions for before and after hooks to align with the new type definitions.
- Enhanced the setup functions to handle hook execution results, allowing for blocking of LLM calls based on hook logic.
- Updated related tests to ensure proper functionality and type adherence across the hook management system.
* feat: add execution and tool hooks documentation
- Introduced new documentation for execution hooks, LLM call hooks, and tool call hooks to provide comprehensive guidance on their usage and implementation in CrewAI.
- Updated existing documentation to include references to the new hooks, enhancing the learning resources available for users.
- Ensured consistency across multiple languages (English, Portuguese, Korean) for the new documentation, improving accessibility for a wider audience.
- Added examples and troubleshooting sections to assist users in effectively utilizing hooks for agent operations.
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Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
- Added support for before and after LLM call hooks to allow modification of messages and responses during LLM interactions.
- Introduced LLMCallHookContext to provide hooks with access to the executor state, enabling in-place modifications of messages.
- Updated get_llm_response function to utilize the new hooks, ensuring that modifications persist across iterations.
- Enhanced tests to verify the functionality of the hooks and their error handling capabilities, ensuring robust execution flow.
* feat: add messages to task and agent outputs
- Introduced a new field in and to capture messages from the last task execution.
- Updated the class to store the last messages and provide a property for easy access.
- Enhanced the and classes to include messages in their outputs.
- Added tests to ensure that messages are correctly included in task outputs and agent outputs during execution.
* using typing_extensions for 3.10 compatability
* feat: add last_messages attribute to agent for improved task tracking
- Introduced a new `last_messages` attribute in the agent class to store messages from the last task execution.
- Updated the `Crew` class to handle the new messages attribute in task outputs.
- Enhanced existing tests to ensure that the `last_messages` attribute is correctly initialized and utilized across various guardrail scenarios.
* fix: add messages field to TaskOutput in tests for consistency
- Updated multiple test cases to include the new `messages` field in the `TaskOutput` instances.
- Ensured that all relevant tests reflect the latest changes in the TaskOutput structure, maintaining consistency across the test suite.
- This change aligns with the recent addition of the `last_messages` attribute in the agent class for improved task tracking.
* feat: preserve messages in task outputs during replay
- Added functionality to the Crew class to store and retrieve messages in task outputs.
- Enhanced the replay mechanism to ensure that messages from stored task outputs are preserved and accessible.
- Introduced a new test case to verify that messages are correctly stored and replayed, ensuring consistency in task execution and output handling.
- This change improves the overall tracking and context retention of task interactions within the CrewAI framework.
* fix original test, prev was debugging
- Added section on LLM-based guardrails, explaining their usage and requirements.
- Updated examples to demonstrate the implementation of multiple guardrails, including both function-based and LLM-based approaches.
- Clarified the distinction between single and multiple guardrails in task configurations.
- Improved explanations of guardrail functionality to ensure better understanding of validation processes.
- Enhanced the MCP tool execution in both synchronous and asynchronous contexts by utilizing for better event loop management.
- Updated error handling to provide clearer messages for connection issues and task cancellations.
- Added tests to validate MCP tool execution in both sync and async scenarios, ensuring robust functionality across different contexts.
* WIP transport support mcp
* refactor: streamline MCP tool loading and error handling
* linted
* Self type from typing with typing_extensions in MCP transport modules
* added tests for mcp setup
* added tests for mcp setup
* docs: enhance MCP overview with detailed integration examples and structured configurations
* feat: implement MCP event handling and logging in event listener and client
- Added MCP event types and handlers for connection and tool execution events.
- Enhanced MCPClient to emit events on connection status and tool execution.
- Updated ConsoleFormatter to handle MCP event logging.
- Introduced new MCP event types for better integration and monitoring.
* 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
fix: refine nested flow conditionals and ensure router methods and routes are fully parsed
fix: improve docstrings, typing, and logging coverage across all events
feat: update flow.plot feature with new UI enhancements
chore: apply Ruff linting, reorganize imports, and remove deprecated utilities/files
chore: split constants and utils, clean JS comments, and add typing for linters
tests: strengthen test coverage for flow execution paths and router logic
* fix: update default LLM model and improve error logging in LLM utilities
* Updated the default LLM model from "gpt-4o-mini" to "gpt-4.1-mini" for better performance.
* Enhanced error logging in the LLM utilities to use logger.error instead of logger.debug, ensuring that errors are properly reported and raised.
* Added tests to verify behavior when OpenAI API key is missing and when Anthropic dependency is not available, improving robustness and error handling in LLM creation.
* fix: update test for default LLM model usage
* Refactored the test_create_llm_with_none_uses_default_model to use the imported DEFAULT_LLM_MODEL constant instead of a hardcoded string.
* Ensured that the test correctly asserts the model used is the current default, improving maintainability and consistency across tests.
* change default model to gpt-4.1-mini
* change default model use defualt
* feat: enhance InternalInstructor to support multiple LLM providers
- Updated InternalInstructor to conditionally create an instructor client based on the LLM provider.
- Introduced a new method _create_instructor_client to handle client creation using the modern from_provider pattern.
- Added functionality to extract the provider from the LLM model name.
- Implemented tests for InternalInstructor with various LLM providers including OpenAI, Anthropic, Gemini, and Azure, ensuring robust integration and error handling.
This update improves flexibility and extensibility for different LLM integrations.
* fix test
