* Refactor EventListener and TraceCollectionListener for improved event handling
- Removed unused threading and method branches from EventListener to simplify the code.
- Updated event handling methods in EventListener to use new formatter methods for better clarity and consistency.
- Refactored TraceCollectionListener to eliminate unnecessary parameters in formatter calls, enhancing readability.
- Simplified ConsoleFormatter by removing outdated tree management methods and focusing on panel-based output for status updates.
- Enhanced ToolUsage to track run attempts for better tool usage metrics.
* clearer for knowledge retrieval and dropped some reduancies
* Refactor EventListener and ConsoleFormatter for improved clarity and consistency
- Removed the MCPToolExecutionCompletedEvent handler from EventListener to streamline event processing.
- Updated ConsoleFormatter to enhance output formatting by adding line breaks for better readability in status content.
- Renamed status messages for MCP Tool execution to provide clearer context during tool operations.
* fix run attempt incrementation
* task name consistency
* memory events consistency
* ensure hitl works
* linting
* WIP gh pr refactor: update agent executor handling and introduce flow-based executor
* wip
* refactor: clean up comments and improve code clarity in agent executor flow
- Removed outdated comments and unnecessary explanations in and classes to enhance code readability.
- Simplified parameter updates in the agent executor to avoid confusion regarding executor recreation.
- Improved clarity in the method to ensure proper handling of non-final answers without raising errors.
* bumping pytest-randomly numpy
* also bump versions of anthropic sdk
* ensure flow logs are not passed if its on executor
* revert anthropic bump
* fix
* refactor: update dependency markers in uv.lock for platform compatibility
- Enhanced dependency markers for , , , and others to ensure compatibility across different platforms (Linux, Darwin, and architecture-specific conditions).
- Removed unnecessary event emission in the class during kickoff.
- Cleaned up commented-out code in the class for better readability and maintainability.
* drop dupllicate
* test: enhance agent executor creation and stop word assertions
- Added calls to create_agent_executor in multiple test cases to ensure proper agent execution setup.
- Updated assertions for stop words in the agent tests to remove unnecessary checks and improve clarity.
- Ensured consistency in task handling by invoking create_agent_executor with the appropriate task parameter.
* refactor: reorganize agent executor imports and introduce CrewAgentExecutorFlow
- Removed the old import of CrewAgentExecutorFlow and replaced it with the new import from the experimental module.
- Updated relevant references in the codebase to ensure compatibility with the new structure.
- Enhanced the organization of imports in core.py and base_agent.py for better clarity and maintainability.
* updating name
* dropped usage of printer here for rich console and dropped non-added value logging
* address i18n
* Enhance concurrency control in CrewAgentExecutorFlow by introducing a threading lock to prevent concurrent executions. This change ensures that the executor instance cannot be invoked while already running, improving stability and reliability during flow execution.
* string literal returns
* string literal returns
* Enhance CrewAgentExecutor initialization by allowing optional i18n parameter for improved internationalization support. This change ensures that the executor can utilize a provided i18n instance or fallback to the default, enhancing flexibility in multilingual contexts.
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Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
* feat: introduce human feedback events and decorator for flow methods
- Added HumanFeedbackRequestedEvent and HumanFeedbackReceivedEvent classes to handle human feedback interactions within flows.
- Implemented the @human_feedback decorator to facilitate human-in-the-loop workflows, allowing for feedback collection and routing based on responses.
- Enhanced Flow class to store human feedback history and manage feedback outcomes.
- Updated flow wrappers to preserve attributes from methods decorated with @human_feedback.
- Added integration and unit tests for the new human feedback functionality, ensuring proper validation and routing behavior.
* adding deployment docs
* New docs
* fix printer
* wrong change
* Adding Async Support
feat: enhance human feedback support in flows
- Updated the @human_feedback decorator to use 'message' parameter instead of 'request' for clarity.
- Introduced new FlowPausedEvent and MethodExecutionPausedEvent to handle flow and method pauses during human feedback.
- Added ConsoleProvider for synchronous feedback collection and integrated async feedback capabilities.
- Implemented SQLite persistence for managing pending feedback context.
- Expanded documentation to include examples of async human feedback usage and best practices.
* linter
* fix
* migrating off printer
* updating docs
* new tests
* doc update
* fix: use CREWAI_PLUS_URL env var in precedence over PlusAPI configured value
* feat: bypass TLS certificate verification when calling platform
* test: fix test
- Replace Python representation with JsonSchema for tool arguments
- Remove deprecated PydanticSchemaParser in favor of direct schema generation
- Add handling for VAR_POSITIONAL and VAR_KEYWORD parameters
- Improve tool argument schema collection
* fix: gracefully terminate the future when executing a task async
* core: add unit test
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Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.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.
