Use contextvars.copy_context() to capture the calling thread's context
and run the worker thread target via ctx.run() so that ContextVar values
(used by OpenTelemetry, Langfuse, and other tracing libraries) are
preserved inside async task execution threads.
Previously, threading.Thread() was used without copying context, causing
all ContextVar values to silently reset to defaults in worker threads.
Fixes#4822
Co-Authored-By: João <joao@crewai.com>
* 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
* Implement user input handling in Flow class
- Introduced `FlowInputRequestedEvent` and `FlowInputReceivedEvent` to manage user input requests and responses during flow execution.
- Added `InputProvider` protocol and `InputResponse` dataclass for customizable input handling.
- Enhanced `Flow` class with `ask()` method to request user input, including timeout handling and state checkpointing.
- Updated `FlowConfig` to support custom input providers.
- Created `input_provider.py` for default input provider implementations, including a console-based provider.
- Added comprehensive tests for `ask()` functionality, covering basic usage, timeout behavior, and integration with flow machinery.
* Potential fix for pull request finding 'Unused import'
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
* Refactor test_flow_ask.py to streamline flow kickoff calls
- Removed unnecessary variable assignments for the result of `flow.kickoff()` in two test cases, improving code clarity.
- Updated assertions to ensure the expected execution log entries are present after the flow kickoff, enhancing test reliability.
* Add current_flow_method_name context variable for flow method tracking
- Introduced a new context variable, `current_flow_method_name`, to store the name of the currently executing flow method, defaulting to "unknown".
- Updated the Flow class to set and reset this context variable during method execution, enhancing the ability to track method calls without stack inspection.
- Removed the obsolete `_resolve_calling_method_name` method, streamlining the code and improving clarity.
* Enhance input history management in Flow class
- Introduced a new `InputHistoryEntry` TypedDict to structure user input history for the `ask()` method, capturing details such as the question, user response, method name, timestamp, and associated metadata.
- Updated the `_input_history` attribute in the Flow class to utilize the new `InputHistoryEntry` type, improving type safety and clarity in input history management.
* Enhance timeout handling in Flow class input requests
- Updated the `ask()` method to improve timeout management by manually managing the `ThreadPoolExecutor`, preventing potential deadlocks when the provider call exceeds the timeout duration.
- Added clarifications in the documentation regarding the behavior of the timeout and the underlying request handling, ensuring better understanding for users.
* Enhance memory reset functionality in CLI commands
- Introduced flow memory reset capabilities in the `reset_memories_command`, allowing for both crew and flow memory resets.
- Added a new utility function `_reset_flow_memory` to handle memory resets for individual flow instances, improving modularity and clarity.
- Updated the `get_flows` utility to discover flow instances from project files, enhancing the CLI's ability to manage flow states.
- Expanded test coverage to validate the new flow memory reset features, ensuring robust functionality and error handling.
* LINTER
* Fix resumption flag logic in Flow class and add regression test for cyclic flow persistence
- Updated the logic for setting the `_is_execution_resuming` flag to ensure it only activates when there are completed methods to replay, preventing incorrect suppression of cyclic re-execution during state reloads.
- Added a regression test to validate that cyclic router flows complete all iterations when persistence is enabled and an 'id' is passed in inputs, ensuring robust handling of flow execution in these scenarios.
---------
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
