- Add format_text_content override for Bedrock's {"text": ...} format
- Handle pre-formatted list content in _format_messages_for_converse
- Update Bedrock tests to use Claude 3 Haiku for on-demand availability
- Add VCR cassettes for Bedrock multimodal tests
Record VCR cassettes for OpenAI, Anthropic, Azure, and LiteLLM
multimodal tests. Gemini and Bedrock tests remain but cassettes
will be generated when credentials are available.
- Make crewai_files an optional dependency with graceful fallbacks
- Move file formatting from executor to LLM layer (_process_message_files)
- Add files field to LLMMessage type for cleaner message passing
- Add cache_control to Anthropic content blocks for prompt caching
- Clean up formatters: static methods for OpenAI/Gemini, proper error handling
- Remove unused ContentFormatter protocol
- Move test fixtures to lib/crewai-files/tests/fixtures
- Add Azure and Bedrock multimodal integration tests
- Fix mypy errors in crew_agent_executor.py
* wip restrcuturing agent executor and liteagent
* fix: handle None task in AgentExecutor to prevent errors
Added a check to ensure that if the task is None, the method returns early without attempting to access task properties. This change improves the robustness of the AgentExecutor by preventing potential errors when the task is not set.
* refactor: streamline AgentExecutor initialization by removing redundant parameters
Updated the Agent class to simplify the initialization of the AgentExecutor by removing unnecessary task and crew parameters in standalone mode. This change enhances code clarity and maintains backward compatibility by ensuring that the executor is correctly configured without redundant assignments.
* wip: clean
* ensure executors work inside a flow due to flow in flow async structure
* refactor: enhance agent kickoff preparation by separating common logic
Updated the Agent class to introduce a new private method that consolidates the common setup logic for both synchronous and asynchronous kickoff executions. This change improves code clarity and maintainability by reducing redundancy in the kickoff process, while ensuring that the agent can still execute effectively within both standalone and flow contexts.
* linting and tests
* fix test
* refactor: improve test for Agent kickoff parameters
Updated the test for the Agent class to ensure that the kickoff method correctly preserves parameters. The test now verifies the configuration of the agent after kickoff, enhancing clarity and maintainability. Additionally, the test for asynchronous kickoff within a flow context has been updated to reflect the Agent class instead of LiteAgent.
* refactor: update test task guardrail process output for improved validation
Refactored the test for task guardrail process output to enhance the validation of the output against the OpenAPI schema. The changes include a more structured request body and updated response handling to ensure compliance with the guardrail requirements. This update aims to improve the clarity and reliability of the test cases, ensuring that task outputs are correctly validated and feedback is appropriately provided.
* test fix cassette
* test fix cassette
* working
* working cassette
* refactor: streamline agent execution and enhance flow compatibility
Refactored the Agent class to simplify the execution method by removing the event loop check and clarifying the behavior when called from synchronous and asynchronous contexts. The changes ensure that the method operates seamlessly within flow methods, improving clarity in the documentation. Additionally, updated the AgentExecutor to set the response model to None, enhancing flexibility. New test cassettes were added to validate the functionality of agents within flow contexts, ensuring robust testing for both synchronous and asynchronous operations.
* fixed cassette
* Enhance Flow Execution Logic
- Introduced conditional execution for start methods in the Flow class.
- Unconditional start methods are prioritized during kickoff, while conditional starts are executed only if no unconditional starts are present.
- Improved handling of cyclic flows by allowing re-execution of conditional start methods triggered by routers.
- Added checks to continue execution chains for completed conditional starts.
These changes improve the flexibility and control of flow execution, ensuring that the correct methods are triggered based on the defined conditions.
* Enhance Agent and Flow Execution Logic
- Updated the Agent class to automatically detect the event loop and return a coroutine when called within a Flow, simplifying async handling for users.
- Modified Flow class to execute listeners sequentially, preventing race conditions on shared state during listener execution.
- Improved handling of coroutine results from synchronous methods, ensuring proper execution flow and state management.
These changes enhance the overall execution logic and user experience when working with agents and flows in CrewAI.
* Enhance Flow Listener Logic and Agent Imports
- Updated the Flow class to track fired OR listeners, ensuring that multi-source OR listeners only trigger once during execution. This prevents redundant executions and improves flow efficiency.
- Cleared fired OR listeners during cyclic flow resets to allow re-execution in new cycles.
- Modified the Agent class imports to include Coroutine from collections.abc, enhancing type handling for asynchronous operations.
These changes improve the control and performance of flow execution in CrewAI, ensuring more predictable behavior in complex scenarios.
* adjusted test due to new cassette
* ensure native tool calling works with liteagent
* ensure response model is respected
* Enhance Tool Name Handling for LLM Compatibility
- Added a new function to replace invalid characters in function names with underscores, ensuring compatibility with LLM providers.
- Updated the function to sanitize tool names before validation.
- Modified the function to use sanitized names for tool registration.
These changes improve the robustness of tool name handling, preventing potential issues with invalid characters in function names.
* ensure we dont finalize batch on just a liteagent finishing
* max tools per turn wip and ensure we drop print times
* fix sync main issues
* fix llm_call_completed event serialization issue
* drop max_tools_iterations
* for fixing model dump with state
* Add extract_tool_call_info function to handle various tool call formats
- Introduced a new utility function to extract tool call ID, name, and arguments from different provider formats (OpenAI, Gemini, Anthropic, and dictionary).
- This enhancement improves the flexibility and compatibility of tool calls across multiple LLM providers, ensuring consistent handling of tool call information.
- The function returns a tuple containing the call ID, function name, and function arguments, or None if the format is unrecognized.
* Refactor AgentExecutor to support batch execution of native tool calls
- Updated the method to process all tools from in a single batch, enhancing efficiency and reducing the number of interactions with the LLM.
- Introduced a new utility function to streamline the extraction of tool call details, improving compatibility with various tool formats.
- Removed the parameter, simplifying the initialization of the .
- Enhanced logging and message handling to provide clearer insights during tool execution.
- This refactor improves the overall performance and usability of the agent execution flow.
* Update English translations for tool usage and reasoning instructions
- Revised the `post_tool_reasoning` message to clarify the analysis process after tool usage, emphasizing the need to provide only the final answer if requirements are met.
- Updated the `format` message to simplify the instructions for deciding between using a tool or providing a final answer, enhancing clarity for users.
- These changes improve the overall user experience by providing clearer guidance on task execution and response formatting.
* fix
* fixing azure tests
* organizae imports
* dropped unused
* Remove debug print statements from AgentExecutor to clean up the code and improve readability. This change enhances the overall performance of the agent execution flow by eliminating unnecessary console output during LLM calls and iterations.
* linted
* updated cassette
* regen cassette
* revert crew agent executor
* adjust cassettes and dropped tests due to native tool implementation
* adjust
* ensure we properly fail tools and emit their events
* Enhance tool handling and delegation tracking in agent executors
- Implemented immediate return for tools with result_as_answer=True in crew_agent_executor.py.
- Added delegation tracking functionality in agent_utils.py to increment delegations when specific tools are used.
- Updated tool usage logic to handle caching more effectively in tool_usage.py.
- Enhanced test cases to validate new delegation features and tool caching behavior.
This update improves the efficiency of tool execution and enhances the delegation capabilities of agents.
* Enhance tool handling and delegation tracking in agent executors
- Implemented immediate return for tools with result_as_answer=True in crew_agent_executor.py.
- Added delegation tracking functionality in agent_utils.py to increment delegations when specific tools are used.
- Updated tool usage logic to handle caching more effectively in tool_usage.py.
- Enhanced test cases to validate new delegation features and tool caching behavior.
This update improves the efficiency of tool execution and enhances the delegation capabilities of agents.
* fix cassettes
* fix
* regen cassettes
* regen gemini
* ensure we support bedrock
* supporting bedrock
* regen azure cassettes
* Implement max usage count tracking for tools in agent executors
- Added functionality to check if a tool has reached its maximum usage count before execution in both crew_agent_executor.py and agent_executor.py.
- Enhanced error handling to return a message when a tool's usage limit is reached.
- Updated tool usage logic in tool_usage.py to increment usage counts and print current usage status.
- Introduced tests to validate max usage count behavior for native tool calling, ensuring proper enforcement and tracking.
This update improves tool management by preventing overuse and providing clear feedback when limits are reached.
* fix other test
* fix test
* drop logs
* better tests
* regen
* regen all azure cassettes
* regen again placeholder for cassette matching
* fix: unify tool name sanitization across codebase
* fix: include tool role messages in save_last_messages
* fix: update sanitize_tool_name test expectations
Align test expectations with unified sanitize_tool_name behavior
that lowercases and splits camelCase for LLM provider compatibility.
* fix: apply sanitize_tool_name consistently across codebase
Unify tool name sanitization to ensure consistency between tool names
shown to LLMs and tool name matching/lookup logic.
* regen
* fix: sanitize tool names in native tool call processing
- Update extract_tool_call_info to return sanitized tool names
- Fix delegation tool name matching to use sanitized names
- Add sanitization in crew_agent_executor tool call extraction
- Add sanitization in experimental agent_executor
- Add sanitization in LLM.call function lookup
- Update streaming utility to use sanitized names
- Update base_agent_executor_mixin delegation check
* Extract text content from parts directly to avoid warning about non-text parts
* Add test case for Gemini token usage tracking
- Introduced a new YAML cassette for tracking token usage in Gemini API responses.
- Updated the test for Gemini to validate token usage metrics and response content.
- Ensured proper integration with the Gemini model and API key handling.
---------
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
- Added functionality to check if a tool has reached its maximum usage count before execution in both crew_agent_executor.py and agent_executor.py.
- Enhanced error handling to return a message when a tool's usage limit is reached.
- Updated tool usage logic in tool_usage.py to increment usage counts and print current usage status.
- Introduced tests to validate max usage count behavior for native tool calling, ensuring proper enforcement and tracking.
This update improves tool management by preventing overuse and providing clear feedback when limits are reached.
- Implemented immediate return for tools with result_as_answer=True in crew_agent_executor.py.
- Added delegation tracking functionality in agent_utils.py to increment delegations when specific tools are used.
- Updated tool usage logic to handle caching more effectively in tool_usage.py.
- Enhanced test cases to validate new delegation features and tool caching behavior.
This update improves the efficiency of tool execution and enhances the delegation capabilities of agents.
- Add format_multimodal_content() to all LLM providers
- Support inline base64 and file reference formats
- Add FileResolver integration for upload caching
- Add module exports for files package
adds emission sequencing, parent-child event hierarchy with scope management, and integrates both into the event bus. introduces flush() for deterministic handling, resets emission counters for test isolation, and adds chain tracking via previous_event_id/triggered_by_event_id plus context variables populated during emit and listener execution. includes tracing listener typing/sorting improvements, safer tool event pairing with try/finally, additional stack checks and cache-hit formatting, context isolation fixes, cassette regen/decoding, and test updates to handle vcr race conditions and flaky behavior.
* wip restrcuturing agent executor and liteagent
* fix: handle None task in AgentExecutor to prevent errors
Added a check to ensure that if the task is None, the method returns early without attempting to access task properties. This change improves the robustness of the AgentExecutor by preventing potential errors when the task is not set.
* refactor: streamline AgentExecutor initialization by removing redundant parameters
Updated the Agent class to simplify the initialization of the AgentExecutor by removing unnecessary task and crew parameters in standalone mode. This change enhances code clarity and maintains backward compatibility by ensuring that the executor is correctly configured without redundant assignments.
* ensure executors work inside a flow due to flow in flow async structure
* refactor: enhance agent kickoff preparation by separating common logic
Updated the Agent class to introduce a new private method that consolidates the common setup logic for both synchronous and asynchronous kickoff executions. This change improves code clarity and maintainability by reducing redundancy in the kickoff process, while ensuring that the agent can still execute effectively within both standalone and flow contexts.
* linting and tests
* fix test
* refactor: improve test for Agent kickoff parameters
Updated the test for the Agent class to ensure that the kickoff method correctly preserves parameters. The test now verifies the configuration of the agent after kickoff, enhancing clarity and maintainability. Additionally, the test for asynchronous kickoff within a flow context has been updated to reflect the Agent class instead of LiteAgent.
* refactor: update test task guardrail process output for improved validation
Refactored the test for task guardrail process output to enhance the validation of the output against the OpenAPI schema. The changes include a more structured request body and updated response handling to ensure compliance with the guardrail requirements. This update aims to improve the clarity and reliability of the test cases, ensuring that task outputs are correctly validated and feedback is appropriately provided.
* test fix cassette
* test fix cassette
* working
* working cassette
* refactor: streamline agent execution and enhance flow compatibility
Refactored the Agent class to simplify the execution method by removing the event loop check and clarifying the behavior when called from synchronous and asynchronous contexts. The changes ensure that the method operates seamlessly within flow methods, improving clarity in the documentation. Additionally, updated the AgentExecutor to set the response model to None, enhancing flexibility. New test cassettes were added to validate the functionality of agents within flow contexts, ensuring robust testing for both synchronous and asynchronous operations.
* fixed cassette
* Enhance Flow Execution Logic
- Introduced conditional execution for start methods in the Flow class.
- Unconditional start methods are prioritized during kickoff, while conditional starts are executed only if no unconditional starts are present.
- Improved handling of cyclic flows by allowing re-execution of conditional start methods triggered by routers.
- Added checks to continue execution chains for completed conditional starts.
These changes improve the flexibility and control of flow execution, ensuring that the correct methods are triggered based on the defined conditions.
* Enhance Agent and Flow Execution Logic
- Updated the Agent class to automatically detect the event loop and return a coroutine when called within a Flow, simplifying async handling for users.
- Modified Flow class to execute listeners sequentially, preventing race conditions on shared state during listener execution.
- Improved handling of coroutine results from synchronous methods, ensuring proper execution flow and state management.
These changes enhance the overall execution logic and user experience when working with agents and flows in CrewAI.
* Enhance Flow Listener Logic and Agent Imports
- Updated the Flow class to track fired OR listeners, ensuring that multi-source OR listeners only trigger once during execution. This prevents redundant executions and improves flow efficiency.
- Cleared fired OR listeners during cyclic flow resets to allow re-execution in new cycles.
- Modified the Agent class imports to include Coroutine from collections.abc, enhancing type handling for asynchronous operations.
These changes improve the control and performance of flow execution in CrewAI, ensuring more predictable behavior in complex scenarios.
* adjusted test due to new cassette
* ensure we dont finalize batch on just a liteagent finishing
* feat: cancellable parallelized flow methods
* feat: allow methods to be cancelled & run parallelized
* feat: ensure state is thread safe through proxy
* fix: check for proxy state
* fix: mimic BaseModel method
* chore: update final attr checks; test
* better description
* fix test
* chore: update test assumptions
* extra
---------
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
* Adding usage info everywhere
* Changing the check
* Changing the logic
* Adding tests
* Adding casellets
* Minor change
* Fixing testcase
* remove the duplicated test case, thanks to cursor
* Adding async test cases
* Updating test case
---------
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
* supporting thinking for anthropic models
* drop comments here
* thinking and tool calling support
* fix: properly mock tool use and text block types in Anthropic tests
- Updated the test for the Anthropic tool use conversation flow to include type attributes for mocked ToolUseBlock and text blocks, ensuring accurate simulation of tool interactions during testing.
* feat: add AnthropicThinkingConfig for enhanced thinking capabilities
This update introduces the AnthropicThinkingConfig class to manage thinking parameters for the Anthropic completion model. The LLM and AnthropicCompletion classes have been updated to utilize this new configuration. Additionally, new test cassettes have been added to validate the functionality of thinking blocks across interactions.
* Add gemini-3-pro-preview
Also refactors the tool support check for better forward compatibility.
* Add cassette for Gemini 3 Pro
---------
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
* 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
