* feat: introduce PlanningConfig for enhanced agent planning capabilities
This update adds a new PlanningConfig class to manage agent planning configurations, allowing for customizable planning behavior before task execution. The existing reasoning parameter is deprecated in favor of this new configuration, ensuring backward compatibility while enhancing the planning process. Additionally, the Agent class has been updated to utilize this new configuration, and relevant utility functions have been adjusted accordingly. Tests have been added to validate the new planning functionality and ensure proper integration with existing agent workflows.
* dropping redundancy
* fix test
* revert handle_reasoning here
* refactor: update reasoning handling in Agent class
This commit modifies the Agent class to conditionally call the handle_reasoning function based on the executor class being used. The legacy CrewAgentExecutor will continue to utilize handle_reasoning, while the new AgentExecutor will manage planning internally. Additionally, the PlanningConfig class has been referenced in the documentation to clarify its role in enabling or disabling planning. Tests have been updated to reflect these changes and ensure proper functionality.
* improve planning prompts
* matching
* refactor: remove default enabled flag from PlanningConfig in Agent class
* more cassettes
* fix test
* feat: enhance agent planning with structured todo management
This commit introduces a new planning system within the AgentExecutor class, allowing for the creation of structured todo items from planning steps. The TodoList and TodoItem models have been added to facilitate tracking of plan execution. The reasoning plan now includes a list of steps, improving the clarity and organization of agent tasks. Additionally, tests have been added to validate the new planning functionality and ensure proper integration with existing workflows.
* refactor: update planning prompt and remove deprecated methods in reasoning handler
* improve planning prompt
* improve handler
* execute todos and be able to track them
* feat: introduce PlannerObserver and StepExecutor for enhanced plan execution
This commit adds the PlannerObserver and StepExecutor classes to the CrewAI framework, implementing the observation phase of the Plan-and-Execute architecture. The PlannerObserver analyzes step execution results, determines plan validity, and suggests refinements, while the StepExecutor executes individual todo items in isolation. These additions improve the overall planning and execution process, allowing for more dynamic and responsive agent behavior. Additionally, new observation events have been defined to facilitate monitoring and logging of the planning process.
* refactor: enhance final answer synthesis in AgentExecutor
This commit improves the synthesis of final answers in the AgentExecutor class by implementing a more coherent approach to combining results from multiple todo items. The method now utilizes a single LLM call to generate a polished response, falling back to concatenation if the synthesis fails. Additionally, the test cases have been updated to reflect the changes in planning and execution, ensuring that the results are properly validated and that the plan-and-execute architecture is functioning as intended.
* refactor: enhance final answer synthesis in AgentExecutor
This commit improves the synthesis of final answers in the AgentExecutor class by implementing a more coherent approach to combining results from multiple todo items. The method now utilizes a single LLM call to generate a polished response, falling back to concatenation if the synthesis fails. Additionally, the test cases have been updated to reflect the changes in planning and execution, ensuring that the results are properly validated and that the plan-and-execute architecture is functioning as intended.
* refactor: implement structured output handling in final answer synthesis
This commit enhances the final answer synthesis process in the AgentExecutor class by introducing support for structured outputs when a response model is specified. The synthesis method now utilizes the response model to produce outputs that conform to the expected schema, while still falling back to concatenation in case of synthesis failures. This change ensures that intermediate steps yield free-text results, but the final output can be structured, improving the overall coherence and usability of the synthesized answers.
* regen tests
* linted
* fix
* Enhance PlanningConfig and AgentExecutor with Reasoning Effort Levels
This update introduces a new attribute in the class, allowing users to customize the observation and replanning behavior during task execution. The class has been modified to utilize this new attribute, routing step observations based on the specified reasoning effort level: low, medium, or high.
Additionally, tests have been added to validate the functionality of the reasoning effort levels, ensuring that the agent behaves as expected under different configurations. This enhancement improves the adaptability and efficiency of the planning process in agent execution.
* regen cassettes for test and fix test
* cassette regen
* fixing tests
* dry
* Refactor PlannerObserver and StepExecutor to Utilize I18N for Prompts
This update enhances the PlannerObserver and StepExecutor classes by integrating the I18N utility for managing prompts and messages. The system and user prompts are now retrieved from the I18N module, allowing for better localization and maintainability. Additionally, the code has been cleaned up to remove hardcoded strings, improving readability and consistency across the planning and execution processes.
* Refactor PlannerObserver and StepExecutor to Utilize I18N for Prompts
This update enhances the PlannerObserver and StepExecutor classes by integrating the I18N utility for managing prompts and messages. The system and user prompts are now retrieved from the I18N module, allowing for better localization and maintainability. Additionally, the code has been cleaned up to remove hardcoded strings, improving readability and consistency across the planning and execution processes.
* consolidate agent logic
* fix datetime
* improving step executor
* refactor: streamline observation and refinement process in PlannerObserver
- Updated the PlannerObserver to apply structured refinements directly from observations without requiring a second LLM call.
- Renamed method to for clarity.
- Enhanced documentation to reflect changes in how refinements are handled.
- Removed unnecessary LLM message building and parsing logic, simplifying the refinement process.
- Updated event emissions to include summaries of refinements instead of raw data.
* enhance step executor with tool usage events and validation
- Added event emissions for tool usage, including started and finished events, to track tool execution.
- Implemented validation to ensure expected tools are called during step execution, raising errors when not.
- Refactored the method to handle tool execution with event logging.
- Introduced a new method for parsing tool input into a structured format.
- Updated tests to cover new functionality and ensure correct behavior of tool usage events.
* refactor: enhance final answer synthesis logic in AgentExecutor
- Updated the finalization process to conditionally skip synthesis when the last todo result is sufficient as a complete answer.
- Introduced a new method to determine if the last todo result can be used directly, improving efficiency.
- Added tests to verify the new behavior, ensuring synthesis is skipped when appropriate and maintained when a response model is set.
* fix: update observation handling in PlannerObserver for LLM errors
- Modified the error handling in the PlannerObserver to default to a conservative replan when an LLM call fails.
- Updated the return values to indicate that the step was not completed successfully and that a full replan is needed.
- Added a new test to verify the behavior of the observer when an LLM error occurs, ensuring the correct replan logic is triggered.
* refactor: enhance planning and execution flow in agents
- Updated the PlannerObserver to accept a kickoff input for standalone task execution, improving flexibility in task handling.
- Refined the step execution process in StepExecutor to support multi-turn action loops, allowing for iterative tool execution and observation.
- Introduced a method to extract relevant task sections from descriptions, ensuring clarity in task requirements.
- Enhanced the AgentExecutor to manage step failures more effectively, triggering replans only when necessary and preserving completed task history.
- Updated translations to reflect changes in planning principles and execution prompts, emphasizing concrete and executable steps.
* refactor: update setup_native_tools to include tool_name_mapping
- Modified the setup_native_tools function to return an additional mapping of tool names.
- Updated StepExecutor and AgentExecutor classes to accommodate the new return value from setup_native_tools.
* fix tests
* linted
* linted
* feat: enhance image block handling in Anthropic provider and update AgentExecutor logic
- Added a method to convert OpenAI-style image_url blocks to Anthropic's required format.
- Updated AgentExecutor to handle cases where no todos are ready, introducing a needs_replan return state.
- Improved fallback answer generation in AgentExecutor to prevent RuntimeErrors when no final output is produced.
* lint
* lint
* 1. Added failed to TodoStatus (planning_types.py)
- TodoStatus now includes failed as a valid state: Literal[pending, running, completed, failed]
- Added mark_failed(step_number, result) method to TodoList
- Added get_failed_todos() method to TodoList
- Updated is_complete to treat both completed and failed as terminal states
- Updated replace_pending_todos docstring to mention failed items are preserved
2. Mark running todos as failed before replan (agent_executor.py)
All three effort-level handlers now call mark_failed() on the current todo before routing to replan_now:
- Low effort (handle_step_observed_low): hard-failure branch
- Medium effort (handle_step_observed_medium): needs_full_replan branch
- High effort (decide_next_action): both needs_full_replan and step_completed_successfully=False branches
3. Updated _should_replan to use get_failed_todos()
Previously filtered on todo.status == failed which was dead code. Now uses the proper accessor method that will actually find failed items.
What this fixes: Before these changes, a step that triggered a replan would stay in running status permanently, causing is_complete to never
return True and next_pending to skip it — leading to stuck execution states. Now failed steps are properly tracked, replanning context correctly
reports them, and LiteAgentOutput.failed_todos will actually return results.
* fix test
* imp on failed states
* fix(bedrock): group parallel tool results in single user message
When an AWS Bedrock model makes multiple tool calls in a single
response, the Converse API requires all corresponding tool results
to be sent back in a single user message. Previously, each tool
result was emitted as a separate user message, causing:
ValidationException: Expected toolResult blocks at messages.2.content
Fix: When processing consecutive tool messages, append the toolResult
block to the preceding user message (if it already contains
toolResult blocks) instead of creating a new message. This groups
all parallel tool results together while keeping tool results from
different assistant turns separate.
Fixes#4749
Signed-off-by: Giulio Leone <6887247+giulio-leone@users.noreply.github.com>
* Update lib/crewai/tests/llms/bedrock/test_bedrock.py
* fix: group bedrock tool results
Co-authored-by: João Moura <joaomdmoura@gmail.com>
---------
Signed-off-by: Giulio Leone <6887247+giulio-leone@users.noreply.github.com>
Co-authored-by: Giulio Leone <6887247+giulio-leone@users.noreply.github.com>
Co-authored-by: João Moura <joaomdmoura@gmail.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* fix: allow hyphenated tool names in MCP references like notion#get-page
The _SLUG_RE regex on BaseAgent rejected MCP tool references containing
hyphens (e.g. "notion#get-page") because the fragment pattern only
matched \w (word chars)
* fix: create fresh MCP client per tool invocation to prevent parallel call races
When the LLM dispatches parallel calls to MCP tools on the same server, the executor runs them concurrently via ThreadPoolExecutor. Previously, all tools from a server shared a single MCPClient instance, and even the same tool called twice would reuse one client. Since each thread creates its own asyncio event loop via asyncio.run(), concurrent connect/disconnect calls on the shared client caused anyio cancel-scope errors ("Attempted to exit cancel scope in a different task than it was entered in").
The fix introduces a client_factory pattern: MCPNativeTool now receives a zero-arg callable that produces a fresh MCPClient + transport on every
_run_async() invocation. This eliminates all shared mutable connection state between concurrent calls, whether to the same tool or different tools from the same server.
* test: ensure we can filter hyphenated MCP tool
* feat: add dedicated Brave Search tools for web, news, image, video, local POIs, and Brave's newest LLM Context endpoint
* fix: normalize transformed response shape
* revert legacy tool name
* fix: schema change prevented property resolution
* Update tool.specs.json
* fix: add fallback for search_langugage
* simplify exports
* makes rate-limiting logic per-instance
* fix(brave-tools): correct _refine_response return type annotations
The abstract method and subclasses annotated _refine_response as returning
dict[str, Any] but most implementations actually return list[dict[str, Any]].
Updated base to return Any, and each subclass to match its actual return type.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Joao Moura <joaomdmoura@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* refactor(memory): convert Memory, MemoryScope, and MemorySlice to BaseModel
* fix(test): update mock memory attribute from _read_only to read_only
* fix: handle re-validation in wrap validators and patch BaseModel class in tests
ThreadPoolExecutor threads do not inherit the calling thread's contextvars
context, causing _event_id_stack and _current_celery_task_id to be empty
in worker threads. This broke OTel span parenting for parallel tool calls
(missing parent_event_id) and lost the Celery task ID in the enterprise
tracking layer ([Task ID: no-task]).
Fix by capturing an independent context copy per submission via
contextvars.copy_context().run in CrewAgentExecutor._handle_native_tool_calls,
so each worker thread starts with the correct inherited context without
sharing mutable state across threads.
* feat: enhance memory recall limits and update documentation
- Increased the memory recall limit in the Agent class from 5 to 15.
- Updated the RecallMemoryTool to allow a recall limit of 20.
- Expanded the documentation for the recall_memory feature to emphasize the importance of multiple queries for comprehensive results.
* feat: increase memory recall limit and enhance memory context documentation
- Increased the memory recall limit in the Agent class from 15 to 20.
- Updated the memory context message to clarify the nature of the memories presented and the importance of using the Search memory tool for comprehensive results.
* refactor: remove inferred_categories from RecallState and update category merging logic
- Removed the inferred_categories field from RecallState to simplify state management.
- Updated the _merged_categories method to only merge caller-supplied categories, enhancing clarity in category handling.
* refactor: simplify category handling in RecallFlow
- Updated the _merged_categories method to return only caller-supplied categories, removing the previous merging logic for inferred categories. This change enhances clarity and maintains consistency in category management.
* fix(gemini): group parallel function_response parts in a single Content object
When Gemini makes N parallel tool calls, the API requires all N function_response parts in one Content object. Previously each tool result created a separate Content, causing 400 INVALID_ARGUMENT errors. Merge consecutive function_response parts into the existing Content instead of appending new ones.
* Address change requested
- function_response is a declared field on the types.Part Pydantic model so hasattr can be replaced with p.function_response is not None
* fix(telemetry): skip signal handler registration in non-main threads
When CrewAI is initialized from a non-main thread (e.g. Streamlit, Flask,
Django, Jupyter), the telemetry module attempted to register signal handlers
which only work in the main thread. This caused multiple noisy ValueError
tracebacks to be printed to stderr, confusing users even though the errors
were caught and non-fatal.
Check `threading.current_thread() is not threading.main_thread()` before
attempting signal registration, and skip silently with a debug-level log
message instead of printing full tracebacks.
Fixes crewAIInc/crewAI#4289
* fix(test): move Telemetry() inside signal.signal mock context
Refs: #4649
* fix(telemetry): move signal.signal mock inside thread to wrap Telemetry() construction
The patch context now activates inside init_in_thread so the mock
is guaranteed to be active before and during Telemetry.__init__,
addressing the Copilot review feedback.
Refs: #4289
* fix(test): mock logger.debug instead of capsys for deterministic assertion
Replace signal.signal-only mock with combined logger + signal mock.
Assert logger.debug was called with the skip message and signal.signal
was never invoked from the non-main thread.
Refs: #4289
pypdf <6.7.4 has multiple DoS vulnerabilities via crafted PDF streams
(FlateDecode, LZWDecode, RunLengthDecode, XFA, TreeObject, outlines).
Only basic PdfReader/PdfWriter APIs are used in crewai-files, none of
which changed in the 5.0 or 6.0 breaking releases.
* refactor: update step callback methods to support asynchronous invocation
- Replaced synchronous step callback invocations with asynchronous counterparts in the CrewAgentExecutor class.
- Introduced a new async method _ainvoke_step_callback to handle step callbacks in an async context, improving responsiveness and performance in asynchronous workflows.
* chore: bump version to 1.10.1b1 across multiple files
- Updated version strings from 1.10.1b to 1.10.1b1 in various project files including pyproject.toml and __init__.py files.
- Adjusted dependency specifications to reflect the new version in relevant templates and modules.
- Introduced lazy imports for the Memory and EncodingFlow classes to optimize import time and reduce initial load, particularly beneficial for deployment scenarios like Celery pre-fork.
- Updated the Memory class to include new configuration options for aggregation queries, enhancing its functionality.
- Adjusted the __getattr__ method in both the crewai and memory modules to support lazy loading of specified attributes.
* chore(deps): update lancedb version and add lance-namespace packages
- Updated lancedb dependency version from 0.4.0 to 0.29.2 in multiple files.
- Added new packages: lance-namespace and lance-namespace-urllib3-client with version 0.5.2, including their dependencies and installation details.
- Enhanced MemoryTUI to display a limit on entries and improved the LanceDBStorage class with automatic background compaction and index creation for better performance.
* linter
* refactor: update memory recall limit and formatting in Agent class
- Reduced the memory recall limit from 10 to 5 in multiple locations within the Agent class.
- Updated the memory formatting to use a new `format` method in the MemoryMatch class for improved readability and metadata inclusion.
* refactor: enhance memory handling with read-only support
- Updated memory-related classes and methods to support read-only functionality, allowing for silent no-ops when attempting to remember data in read-only mode.
- Modified the LiteAgent and CrewAgentExecutorMixin classes to check for read-only status before saving memories.
- Adjusted MemorySlice and Memory classes to reflect changes in behavior when read-only is enabled.
- Updated tests to verify that memory operations behave correctly under read-only conditions.
* test: set mock memory to read-write in unit tests
- Updated unit tests in test_unified_memory.py to set mock_memory._read_only to False, ensuring that memory operations can be tested in a writable state.
* fix test
* fix: preserve falsy metadata values and fix remember() return type
---------
Co-authored-by: lorenzejay <lorenzejaytech@gmail.com>
Co-authored-by: Greyson LaLonde <greyson@crewai.com>
* fix: preserve null types in tool parameter schemas for LLM
Tool parameter schemas were stripping null from optional fields via
generate_model_description, forcing the LLM to provide non-null values
for fields.
Adds strip_null_types parameter to generate_model_description and passes False when generating tool
schemas, so optional fields keep anyOf: [{type: T}, {type: null}]
* Update lib/crewai/src/crewai/utilities/pydantic_schema_utils.py
Co-authored-by: Gabe Milani <gabriel@crewai.com>
---------
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
Co-authored-by: Gabe Milani <gabriel@crewai.com>
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
* fix: map output_pydantic/output_json to native structured output
* test: add crew+tools+structured output integration test for Gemini
* fix: re-record stale cassette for test_crew_testing_function
* fix: re-record remaining stale cassettes for native structured output
* fix: enable native structured output for lite agent and fix mypy errors
* feat: enhance JSON argument parsing and validation in CrewAgentExecutor and BaseTool
- Added error handling for malformed JSON tool arguments in CrewAgentExecutor, providing descriptive error messages.
- Implemented schema validation for tool arguments in BaseTool, ensuring that invalid arguments raise appropriate exceptions.
- Introduced tests to verify correct behavior for both valid and invalid JSON inputs, enhancing robustness of tool execution.
* refactor: improve argument validation in BaseTool
- Introduced a new private method to handle argument validation for tools, enhancing code clarity and reusability.
- Updated the method to utilize the new validation method, ensuring consistent error handling for invalid arguments.
- Enhanced exception handling to specifically catch , providing clearer error messages for tool argument validation failures.
* feat: introduce parse_tool_call_args for improved argument parsing
- Added a new utility function, parse_tool_call_args, to handle parsing of tool call arguments from JSON strings or dictionaries, enhancing error handling for malformed JSON inputs.
- Updated CrewAgentExecutor and AgentExecutor to utilize the new parsing function, streamlining argument validation and improving clarity in error reporting.
- Introduced unit tests for parse_tool_call_args to ensure robust functionality and correct handling of various input scenarios.
* feat: add keyword argument validation in BaseTool and Tool classes
- Introduced a new method `_validate_kwargs` in BaseTool to validate keyword arguments against the defined schema, ensuring proper argument handling.
- Updated the `run` and `arun` methods in both BaseTool and Tool classes to utilize the new validation method, improving error handling and robustness.
- Added comprehensive tests for asynchronous execution in `TestBaseToolArunValidation` to verify correct behavior for valid and invalid keyword arguments.
* Potential fix for pull request finding 'Syntax error'
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
---------
Co-authored-by: lorenzejay <lorenzejaytech@gmail.com>
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
* fix: bedrock region was always set to "us-east-1" not respecting the env
var.
code had AWS_REGION_NAME referenced, but not used, unified to
AWS_DEFAULT_REGION as per documentation
* DRY code improvement and fix caught by tests.
* Supporting litellm configuration
