The task planner was blindly zipping tasks with plans returned by the LLM,
assuming they were in the same order. However, the LLM sometimes returns
plans in the wrong order (e.g., starting with Task 21 instead of Task 1),
causing plans to be attached to the wrong tasks.
This fix:
- Extracts the task number from each plan's task field using regex
- Creates a mapping of task number to plan
- Applies plans to tasks based on the correct task number match
- Adds warning logs when task numbers can't be extracted or plans are missing
Added comprehensive test that reproduces the bug by mocking an LLM response
with plans in the wrong order and verifies the fix correctly matches plans
to their corresponding tasks.
Co-Authored-By: João <joao@crewai.com>
- 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.
---------
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
- 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
* Implement improvements on QdrantVectorSearchTool
- Allow search filters to be set at the constructor level
- Fix issue that prevented multiple records from being returned
* Implement improvements on QdrantVectorSearchTool
- Allow search filters to be set at the constructor level
- Fix issue that prevented multiple records from being returned
---------
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
* chore: update codeql config paths to new folders
* tests: use threading.Condition for event check
---------
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
