Introduce the A2UI extension for declarative UI generation, including
support for both v0.8 and v0.9 protocol specs. Add A2UI content type
integration in A2A utils, along with schema definitions, catalog models,
and client extension improvements.
Enhance models with explicit defaults, field descriptions, and ConfigDict,
and improve typing and instance state handling across the extension.
Add schema conformance tests and align test structure.
Add and register A2UI documentation, including extension guide and
navigation updates.
* better DevEx
* Refactor: Update supported native providers and enhance memory handling
- Removed "groq" and "meta" from the list of supported native providers in `llm.py`.
- Added a safeguard in `flow.py` to ensure all background memory saves complete before returning.
- Improved error handling in `unified_memory.py` to prevent exceptions during shutdown, ensuring smoother memory operations and event bus interactions.
* Enhance Memory System with Consolidation and Learning Features
- Introduced memory consolidation mechanisms to prevent duplicate records during content saving, utilizing similarity checks and LLM decision-making.
- Implemented non-blocking save operations in the memory system, allowing agents to continue tasks while memory is being saved.
- Added support for learning from human feedback, enabling the system to distill lessons from past corrections and improve future outputs.
- Updated documentation to reflect new features and usage examples for memory consolidation and HITL learning.
* Enhance cyclic flow handling for or_() listeners
- Updated the Flow class to ensure that all fired or_() listeners are cleared between cycle iterations, allowing them to fire again in subsequent cycles. This change addresses a bug where listeners remained suppressed across iterations.
- Added regression tests to verify that or_() listeners fire correctly on every iteration in cyclic flows, ensuring expected behavior in complex routing scenarios.
* 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
- 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>
* docs(cli): document device-code login and config reset guidance; renumber sections
* docs(cli): fix duplicate numbering (renumber Login/API Keys/Configuration sections)
* docs: Fix webhook documentation to include meta dict in all webhook payloads
- Add note explaining that meta objects from kickoff requests are included in all webhook payloads
- Update webhook examples to show proper payload structure including meta field
- Fix webhook examples to match actual API implementation
- Apply changes to English, Korean, and Portuguese documentation
Resolves the documentation gap where meta dict passing to webhooks was not documented despite being implemented in the API.
* WIP: CrewAI docs theme, changelog, GEO, localization
* docs(cli): fix merge markers; ensure mode: "wide"; convert ASCII tables to Markdown (en/pt-BR/ko)
* docs: add group icons across locales; split Automation/Integrations; update tools overviews and links
* Adding Nebius to docs
Submitting this PR on behalf of Nebius AI Studio to add Nebius models to the CrewAI documentation.
I tested with the latest CrewAI + Nebius setup to ensure compatibility.
cc @tonykipkemboi
* updated LiteLLM page
---------
Co-authored-by: Lucas Gomide <lucaslg200@gmail.com>
* docs: add pt-br translations
Powered by a CrewAI Flow https://github.com/danielfsbarreto/docs_translator
* Update mcp/overview.mdx brazilian docs
Its en-US counterpart was updated after I did a pass,
so now it includes the new section about @CrewBase
