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38 Commits
| Author | SHA1 | Message | Date | |
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Lucas Gomide
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d259150d8d |
Enhance MCP tool resolution and related events (#4580)
* feat: enhance MCP tool resolution * feat: emit event when MCP configuration fails * feat: emit event when MCP tool execution has failed * style: resolve linter issues * refactor: use clear and natural mcp tool name resolution * test: fix broken tests * fix: resolve MCP connection leaks, slug validation, duplicate connections, and httpx exception handling --------- Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com> Co-authored-by: Greyson LaLonde <greyson@crewai.com> |
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João Moura
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86d3ee022d |
feat: update lancedb version and add lance-namespace packages
* 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> |
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João Moura
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8102d0a6ca |
feat: enhance JSON argument parsing and validation in CrewAgentExecutor and BaseTool
* 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> |
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Greyson LaLonde
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71b4f8402a |
fix: ensure callbacks are ran/awaited if promise
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Lorenze Jay
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d09656664d |
supporting parallel tool use (#4513)
* supporting parallel tool use * ensure we respect max_usage_count * ensure result_as_answer, hooks, and cache parodity * improve crew agent executor * address test comments |
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João Moura
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18d266c8e7 |
New Unified Memory System (#4420)
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* chore: update memory management and dependencies - Enhance the memory system by introducing a unified memory API that consolidates short-term, long-term, entity, and external memory functionalities. - Update the `.gitignore` to exclude new memory-related files and blog directories. - Modify `conftest.py` to handle missing imports for vcr stubs more gracefully. - Add new development dependencies in `pyproject.toml` for testing and memory management. - Refactor the `Crew` class to utilize the new unified memory system, replacing deprecated memory attributes. - Implement memory context injection in `LiteAgent` to improve memory recall during agent execution. - Update documentation to reflect changes in memory usage and configuration. * feat: introduce Memory TUI for enhanced memory management - Add a new command to the CLI for launching a Textual User Interface (TUI) to browse and recall memories. - Implement the MemoryTUI class to facilitate user interaction with memory scopes and records. - Enhance the unified memory API by adding a method to list records within a specified scope. - Update `pyproject.toml` to include the `textual` dependency for TUI functionality. - Ensure proper error handling for missing dependencies when accessing the TUI. * feat: implement consolidation flow for memory management - Introduce the ConsolidationFlow class to handle the decision-making process for inserting, updating, or deleting memory records based on new content. - Add new data models: ConsolidationAction and ConsolidationPlan to structure the actions taken during consolidation. - Enhance the memory types with new fields for consolidation thresholds and limits. - Update the unified memory API to utilize the new consolidation flow for managing memory records. - Implement embedding functionality for new content to facilitate similarity checks. - Refactor existing memory analysis methods to integrate with the consolidation process. - Update translations to include prompts for consolidation actions and user interactions. * feat: enhance Memory TUI with Rich markup and improved UI elements - Update the MemoryTUI class to utilize Rich markup for better visual representation of memory scope information. - Introduce a color palette for consistent branding across the TUI interface. - Refactor the CSS styles to improve the layout and aesthetics of the memory browsing experience. - Enhance the display of memory entries, including better formatting for records and importance ratings. - Implement loading indicators and error messages with Rich styling for improved user feedback during recall operations. - Update the action bindings and navigation prompts for a more intuitive user experience. * feat: enhance Crew class memory management and configuration - Update the Crew class to allow for more flexible memory configurations by accepting Memory, MemoryScope, or MemorySlice instances. - Refactor memory initialization logic to support custom memory configurations while maintaining backward compatibility. - Improve documentation for memory-related fields to clarify usage and expectations. - Introduce a recall oversample factor to optimize memory recall processes. - Update related memory types and configurations to ensure consistency across the memory management system. * chore: update dependency overrides and enhance memory management - Added an override for the 'rich' dependency to allow compatibility with 'textual' requirements. - Updated the 'pyproject.toml' and 'uv.lock' files to reflect the new dependency specifications. - Refactored the Crew class to simplify memory configuration handling by allowing any type for the memory attribute. - Improved error messages in the CLI for missing 'textual' dependency to guide users on installation. - Introduced new packages and dependencies in the project to enhance functionality and maintain compatibility. * refactor: enhance thread safety in flow management - Updated LockedListProxy and LockedDictProxy to subclass list and dict respectively, ensuring compatibility with libraries requiring strict type checks. - Improved documentation to clarify the purpose of these proxies and their thread-safe operations. - Ensured that all mutations are protected by locks while reads delegate to the underlying data structures, enhancing concurrency safety. * chore: update dependency versions and improve Python compatibility - Downgraded 'vcrpy' dependency to version 7.0.0 for compatibility. - Enhanced 'uv.lock' to include more granular resolution markers for Python versions and implementations, ensuring better compatibility across different environments. - Updated 'urllib3' and 'selenium' dependencies to specify versions based on Python implementation, improving stability and performance. - Removed deprecated resolution markers for 'fastembed' and streamlined its dependencies for better clarity. * fix linter * chore: update uv.lock for improved dependency management and memory management enhancements - Incremented revision number in uv.lock to reflect changes. - Added a new development dependency group in uv.lock, specifying versions for tools like pytest, mypy, and pre-commit to streamline development workflows. - Enhanced error handling in CLI memory functions to provide clearer feedback on missing dependencies. - Refactored memory management classes to improve type hints and maintainability, ensuring better compatibility with future updates. * fix tests * refactor: remove obsolete RAGStorage tests and clean up error handling - Deleted outdated tests for RAGStorage that were no longer relevant, including tests for client failures, save operation failures, and reset failures. - Cleaned up the test suite to focus on current functionality and improve maintainability. - Ensured that remaining tests continue to validate the expected behavior of knowledge storage components. * fix test * fix texts * fix tests * forcing new commit * fix: add location parameter to Google Vertex embedder configuration for memory integration tests * debugging CI * adding debugging for CI * refactor: remove unnecessary logging for memory checks in agent execution - Eliminated redundant logging statements related to memory checks in the Agent and CrewAgentExecutor classes. - Simplified the memory retrieval logic by directly checking for available memory without logging intermediate states. - Improved code readability and maintainability by reducing clutter in the logging output. * udpating desp * feat: enhance thread safety in LockedListProxy and LockedDictProxy - Added equality comparison methods (__eq__ and __ne__) to LockedListProxy and LockedDictProxy to allow for safe comparison of their contents. - Implemented consistent locking mechanisms to prevent deadlocks during comparisons. - Improved the overall robustness of these proxy classes in multi-threaded environments. * feat: enhance memory functionality in Flows documentation and memory system - Added a new section on memory usage within Flows, detailing built-in methods for storing and recalling memories. - Included an example of a Research and Analyze Flow demonstrating the integration of memory for accumulating knowledge over time. - Updated the Memory documentation to clarify the unified memory system and its capabilities, including adaptive-depth recall and composite scoring. - Introduced a new configuration parameter, `recall_oversample_factor`, to improve the effectiveness of memory retrieval processes. * update docs * refactor: improve memory record handling and pagination in unified memory system - Simplified the `get_record` method in the Memory class by directly accessing the storage's `get_record` method. - Enhanced the `list_records` method to include an `offset` parameter for pagination, allowing users to skip a specified number of records. - Updated documentation for both methods to clarify their functionality and parameters, improving overall code clarity and usability. * test: update memory scope assertions in unified memory tests - Modified assertions in `test_lancedb_list_scopes_get_scope_info` and `test_memory_list_scopes_info_tree` to check for the presence of the "/team" scope instead of the root scope. - Clarified comments to indicate that `list_scopes` returns child scopes rather than the root itself, enhancing test clarity and accuracy. * feat: integrate memory tools for agents and crews - Added functionality to inject memory tools into agents during initialization, enhancing their ability to recall and remember information mid-task. - Implemented a new `_add_memory_tools` method in the Crew class to facilitate the addition of memory tools when memory is available. - Introduced `RecallMemoryTool` and `RememberTool` classes in a new `memory_tools.py` file, providing agents with active recall and memory storage capabilities. - Updated English translations to include descriptions for the new memory tools, improving user guidance on their usage. * refactor: streamline memory recall functionality across agents and tools - Removed the 'depth' parameter from memory recall calls in LiteAgent and Agent classes, simplifying the recall process. - Updated the MemoryTUI to use 'deep' depth by default for more comprehensive memory retrieval. - Enhanced the MemoryScope and MemorySlice classes to default to 'deep' depth, improving recall accuracy. - Introduced a new 'recall_queries' field in QueryAnalysis to optimize semantic vector searches with targeted phrases. - Updated documentation and comments to reflect changes in memory recall behavior and parameters. * refactor: optimize memory management in flow classes - Enhanced memory auto-creation logic in Flow class to prevent unnecessary Memory instance creation for internal flows (RecallFlow, ConsolidationFlow) by introducing a _skip_auto_memory flag. - Removed the deprecated time_hints field from QueryAnalysis and replaced it with a more flexible time_filter field to better handle time-based queries. - Updated documentation and comments to reflect changes in memory handling and query analysis structure, improving clarity and usability. * updates tests * feat: introduce EncodingFlow for enhanced memory encoding pipeline - Added a new EncodingFlow class to orchestrate the encoding process for memory, integrating LLM analysis and embedding. - Updated the Memory class to utilize EncodingFlow for saving content, improving the overall memory management and conflict resolution. - Enhanced the unified memory module to include the new EncodingFlow in its public API, facilitating better memory handling. - Updated tests to ensure proper functionality of the new encoding flow and its integration with existing memory features. * refactor: optimize memory tool integration and recall flow - Streamlined the addition of memory tools in the Agent class by using list comprehension for cleaner code. - Enhanced the RecallFlow class to build task lists more efficiently with list comprehensions, improving readability and performance. - Updated the RecallMemoryTool to utilize list comprehensions for formatting memory results, simplifying the code structure. - Adjusted test assertions in LiteAgent to reflect the default behavior of memory recall depth, ensuring clarity in expected outcomes. * Potential fix for pull request finding 'Empty except' Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> * chore: gen missing cassette * fix * test: enhance memory extraction test by mocking recall to prevent LLM calls Updated the test for memory extraction to include a mock for the recall method, ensuring that the test focuses on the save path without invoking external LLM calls. This improves test reliability and clarity. * refactor: enhance memory handling by adding agent role parameter Updated memory storage methods across multiple classes to include an optional `agent_role` parameter, improving the context of stored memories. Additionally, modified the initialization of several flow classes to suppress flow events, enhancing performance and reducing unnecessary event triggers. * feat: enhance agent memory functionality with recall and save mechanisms Implemented memory context injection during agent kickoff, allowing for memory recall before execution and passive saving of results afterward. Added new methods to handle memory saving and retrieval, including error handling for memory operations. Updated the BaseAgent class to support dynamic memory resolution and improved memory record structure with source and privacy attributes for better provenance tracking. * test * feat: add utility method to simplify tools field in console formatter Introduced a new static method `_simplify_tools_field` in the console formatter to transform the 'tools' field from full tool objects to a comma-separated string of tool names. This enhancement improves the readability of tool information in the output. * refactor: improve lazy initialization of LLM and embedder in Memory class Refactored the Memory class to implement lazy initialization for the LLM and embedder, ensuring they are only created when first accessed. This change enhances the robustness of the Memory class by preventing initialization failures when constructed without an API key. Additionally, updated error handling to provide clearer guidance for users on resolving initialization issues. * refactor: consolidate memory saving methods for improved efficiency Refactored memory handling across multiple classes to replace individual memory saving calls with a batch method, `remember_many`, enhancing performance and reducing redundancy. Updated related tools and schemas to support single and multiple item memory operations, ensuring a more streamlined interface for memory interactions. Additionally, improved documentation and test coverage for the new functionality. * feat: enhance MemoryTUI with improved layout and entry handling Updated the MemoryTUI class to incorporate a new vertical layout, adding an OptionList for displaying entries and enhancing the detail view for selected records. Introduced methods for populating entry and recall lists, improving user interaction and data presentation. Additionally, refined CSS styles for better visual organization and focus handling. * fix test * feat: inject memory tools into LiteAgent for enhanced functionality Added logic to the LiteAgent class to inject memory tools if memory is configured, ensuring that memory tools are only added if they are not already present. This change improves the agent's capability to utilize memory effectively during execution. * feat: add synchronous execution method to ConsolidationFlow for improved integration Introduced a new `run_sync()` method in the ConsolidationFlow class to facilitate procedural execution of the consolidation pipeline without relying on asynchronous event loops. Updated the EncodingFlow class to utilize this method for conflict resolution, ensuring compatibility within its async context. This change enhances the flow's ability to manage memory records effectively during nested executions. * refactor: update ConsolidationFlow and EncodingFlow for improved async handling Removed the synchronous `run_sync()` method from ConsolidationFlow and refactored the consolidate method in EncodingFlow to be asynchronous. This change allows for direct awaiting of the ConsolidationFlow's kickoff method, enhancing compatibility within the async event loop and preventing nested asyncio.run() issues. Additionally, updated the execution plan to listen for multiple paths, streamlining the consolidation process. * fix: update flow documentation and remove unused ConsolidationFlow Corrected the comment in Flow class regarding internal flows, replacing "ConsolidationFlow" with "EncodingFlow". Removed the ConsolidationFlow class as it is no longer needed, streamlining the memory handling process. Updated related imports and ensured that the memory module reflects these changes, enhancing clarity and maintainability. * feat: enhance memory handling with background saving and query analysis optimization Implemented a background saving mechanism in the Memory class to allow non-blocking memory operations, improving performance during high-load scenarios. Added a query analysis threshold to skip LLM calls for short queries, optimizing recall efficiency. Updated related methods and documentation to reflect these changes, ensuring a more responsive and efficient memory management system. * fix test * fix test * fix: handle synchronous fallback for save operations in Memory class Updated the Memory class to implement a synchronous fallback mechanism for save operations when the background thread pool is shut down. This change ensures that late save requests still succeed, improving reliability in memory management during shutdown scenarios. * feat: implement HITL learning features in human feedback decorator Added support for learning from human feedback in the human feedback decorator. Introduced parameters to enable lesson distillation and pre-review of outputs based on past feedback. Updated related tests to ensure proper functionality of the learning mechanism, including memory interactions and default LLM usage. --------- Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com> |
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Greyson LaLonde
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7d498b29be |
fix: event ordering; flow state locks, routing
* fix: add current task id context and flow updates introduce a context var for the current task id in `crewai.context` to track task scope. update `Flow._execute_single_listener` to return `(result, event_id)` and adjust callers to unpack it and append `FlowMethodName(str(result))` to `router_results`. set/reset the current task id at the start/end of task execution (async + sync) with minor import and call-site tweaks. * fix: await event futures and flush event bus call `crewai_event_bus.flush()` after crew kickoff. in `Flow`, await event handler futures instead of just collecting them: await pending `_event_futures` before finishing, await emitted futures immediately with try/except to log failures, then clear `_event_futures`. ensures handlers complete and errors surface. * fix: continue iteration on tool completion events expand the loop bridge listener to also trigger on tool completion events (`tool_completed` and `native_tool_completed`) so agent iteration resumes after tools finish. add a `requests.post` mock and response fixture in the liteagent test to simulate platform tool execution. refresh and sanitize vcr cassettes (updated model responses, timestamps, and header placeholders) to reflect tool-call flows and new recordings. * fix: thread-safe state proxies & native routing add thread-safe state proxies and refactor native tool routing. * introduce `LockedListProxy` and `LockedDictProxy` in `flow.py` and update `StateProxy` to return them for list/dict attrs so mutations are protected by the flow lock. * update `AgentExecutor` to use `StateProxy` on flow init, guard the messages setter with the state lock, and return a `StateProxy` from the temp state accessor. * convert `call_llm_native_tools` into a listener (no direct routing return) and add `route_native_tool_result` to route based on state (pending tool calls, final answer, or context error). * minor cleanup in `continue_iteration` to drop orphan listeners on init. * update test cassettes for new native tool call responses, timestamps, and ids. improves concurrency safety for shared state and makes native tool routing explicit. * chore: regen cassettes * chore: regen cassettes, remove duplicate listener call path |
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Greyson LaLonde
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6bb1b178a1 |
chore: extension points
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Introduce ContextVar-backed hooks and small API/behavior changes to improve extensibility and testability. Changes include: - agents: mark configure_structured_output as abstract and change its parameter to task to reflect use of task metadata. - tracing: convert _first_time_trace_hook to a ContextVar and call .get() to safely retrieve the hook. - console formatter: add _disable_version_check ContextVar and skip version checks when set (avoids noisy checks in certain contexts). - flow: use current_triggering_event_id variable when scheduling listener tasks to keep naming consistent. - hallucination guardrail: make context optional, add _validate_output_hook to allow custom validation hooks, update examples and return contract to allow hooks to override behavior. - agent utilities: add _create_plus_client_hook for injecting a Plus client (used in tests/alternate flows), ensure structured tools have current_usage_count initialized and propagate to original tool, and fall back to creating PlusAPI client when no hook is provided. |
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Greyson LaLonde
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6bfc98e960 |
refactor: extract hitl to provider pattern
* refactor: extract hitl to provider pattern - add humaninputprovider protocol with setup_messages and handle_feedback - move sync hitl logic from executor to synchuman inputprovider - add _passthrough_exceptions extension point in agent/core.py - create crewai.core.providers module for extensible components - remove _ask_human_input from base_agent_executor_mixin |
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Greyson LaLonde
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5fb602dff2 | fix: replace timing-based concurrency test with state tracking | ||
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Greyson LaLonde
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9d7f45376a | fix: use contextvars for flow execution context | ||
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Greyson LaLonde
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102b6ae855 |
feat: add a2a liteagent, auth, transport negotiation, and file support
* feat: add server-side auth schemes and protocol extensions - add server auth scheme base class and implementations (api key, bearer token, basic/digest auth, mtls) - add server-side extension system for a2a protocol extensions - add extensions middleware for x-a2a-extensions header management - add extension validation and registry utilities - enhance auth utilities with server-side support - add async intercept method to match client call interceptor protocol - fix type_checking import to resolve mypy errors with a2aconfig * feat: add transport negotiation and content type handling - add transport negotiation logic with fallback support - add content type parser and encoder utilities - add transport configuration models (client and server) - add transport types and enums - enhance config with transport settings - add negotiation events for transport and content type * feat: add a2a delegation support to LiteAgent * feat: add file input support to a2a delegation and tasks Introduces handling of file inputs in A2A delegation flows by converting file dictionaries to protocol-compatible parts and propagating them through delegation and task execution functions. Updates include utility functions for file conversion, changes to message construction, and passing input_files through relevant APIs. * feat: liteagent a2a delegation support to kickoff methods |
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Lorenze Jay
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19ce56032c |
fix: improve output handling and response model integration in agents (#4307)
* fix: improve output handling and response model integration in agents - Refactored output handling in the Agent class to ensure proper conversion and formatting of outputs, including support for BaseModel instances. - Enhanced the AgentExecutor class to correctly utilize response models during execution, improving the handling of structured outputs. - Updated the Gemini and Anthropic completion providers to ensure compatibility with new response model handling, including the addition of strict mode for function definitions. - Improved the OpenAI completion provider to enforce strict adherence to function schemas. - Adjusted translations to clarify instructions regarding output formatting and schema adherence. * drop what was a print that didnt get deleted properly * fixes gemini * azure working * bedrock works * added tests * adjust test * fix tests and regen * fix tests and regen * refactor: ensure stop words are applied correctly in Azure, Gemini, and OpenAI completions; add tests to validate behavior with structured outputs * linting |
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Greyson LaLonde
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1e27cf3f0f | fix: ensure verbosity flag is applied | ||
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Greyson LaLonde
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c4c9208229 |
feat: native multimodal file handling; openai responses api
- add input_files parameter to Crew.kickoff(), Flow.kickoff(), Task, and Agent.kickoff() - add provider-specific file uploaders for OpenAI, Anthropic, Gemini, and Bedrock - add file type detection, constraint validation, and automatic format conversion - add URL file source support for multimodal content - add streaming uploads for large files - add prompt caching support for Anthropic - add OpenAI Responses API support |
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Lorenze Jay
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bd4d039f63 |
Lorenze/imp/native tool calling (#4258)
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* 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> |
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Lorenze Jay
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741bf12bf4 |
Lorenze/enh decouple executor from crew (#4209)
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* 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> |
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Greyson LaLonde
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ceef062426 |
feat: add additional a2a events and enrich event metadata
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Lorenze Jay
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b9dd166a6b |
Lorenze/agent executor flow pattern (#3975)
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* 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. --------- Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com> |
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Greyson LaLonde
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f2f994612c |
fix: ensure otel span is closed
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Greyson LaLonde
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9b8f31fa07 |
feat: async task support (#4024)
* feat: add async support for tools, add async tool tests * chore: improve tool decorator typing * fix: ensure _run backward compat * chore: update docs * chore: make docstrings a little more readable * feat: add async execution support to agent executor * chore: add tests * feat: add aiosqlite dep; regenerate lockfile * feat: add async ops to memory feat; create tests * feat: async knowledge support; add tests * feat: add async task support * chore: dry out duplicate logic |
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Greyson LaLonde
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633e279b51 |
feat: add async support for tools and agent executor; improve typing and docs
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Introduces async tool support with new tests, adds async execution to the agent executor, improves tool decorator typing, ensures _run backward compatibility, updates docs and docstrings, and adds additional tests. |
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Greyson LaLonde
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c925d2d519 |
chore: restructure test env, cassettes, and conftest; fix flaky tests
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Consolidates pytest config, standardizes env handling, reorganizes cassette layout, removes outdated VCR configs, improves sync with threading.Condition, updates event-waiting logic, ensures cleanup, regenerates Gemini cassettes, and reverts unintended test changes. |
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Greyson LaLonde
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a559cedbd1 |
chore: ensure proper cassettes for agent tests
* chore: ensure proper cassettes for agent tests * chore: tweak eval test to avoid race condition |
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Greyson LaLonde
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b546982690 | fix: ensure instrumentation flags | ||
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Greyson LaLonde
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d7bdac12a2 |
feat: a2a trust remote completion status flag
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- add trust_remote_completion_status flag to A2AConfig, Adds configuration flag to control whether to trust A2A agent completion status. Resolves #3899 - update docs |
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Lorenze Jay
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528d812263 |
Lorenze/feat hooks (#3902)
* 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> |
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Lorenze Jay
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c205d2e8de |
feat: implement before and after LLM call hooks in CrewAgentExecutor (#3893)
- 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. |
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Lorenze Jay
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6b52587c67 |
feat: expose messages to TaskOutput and LiteAgentOutputs (#3880)
* 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 |
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Greyson LaLonde
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19c5b9a35e |
fix: properly handle agent max iterations
fixes #3847 |
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Greyson LaLonde
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9e5906c52f | feat: add pydantic validation dunder to BaseInterceptor | ||
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Greyson LaLonde
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7e6171d5bc |
fix: ensure lite agents course-correct on validation errors
* fix: ensure lite agents course-correct on validation errors * chore: update cassettes and test expectations * fix: ensure multiple guardrails propogate |
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Greyson LaLonde
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e134e5305b |
Gl/feat/a2a refactor (#3793)
* feat: agent metaclass, refactor a2a to wrappers * feat: a2a schemas and utils * chore: move agent class, update imports * refactor: organize imports to avoid circularity, add a2a to console * feat: pass response_model through call chain * feat: add standard openapi spec serialization to tools and structured output * feat: a2a events * chore: add a2a to pyproject * docs: minimal base for learn docs * fix: adjust a2a conversation flow, allow llm to decide exit until max_retries * fix: inject agent skills into initial prompt * fix: format agent card as json in prompt * refactor: simplify A2A agent prompt formatting and improve skill display * chore: wide cleanup * chore: cleanup logic, add auth cache, use json for messages in prompt * chore: update docs * fix: doc snippets formatting * feat: optimize A2A agent card fetching and improve error reporting * chore: move imports to top of file * chore: refactor hasattr check * chore: add httpx-auth, update lockfile * feat: create base public api * chore: cleanup modules, add docstrings, types * fix: exclude extra fields in prompt * chore: update docs * tests: update to correct import * chore: lint for ruff, add missing import * fix: tweak openai streaming logic for response model * tests: add reimport for test * tests: add reimport for test * fix: don't set a2a attr if not set * fix: don't set a2a attr if not set * chore: update cassettes * tests: fix tests * fix: use instructor and dont pass response_format for litellm * chore: consolidate event listeners, add typing * fix: address race condition in test, update cassettes * tests: add correct mocks, rerun cassette for json * tests: update cassette * chore: regenerate cassette after new run * fix: make token manager access-safe * fix: make token manager access-safe * merge * chore: update test and cassete for output pydantic * fix: tweak to disallow deadlock * chore: linter * fix: adjust event ordering for threading * fix: use conditional for batch check * tests: tweak for emission * tests: simplify api + event check * fix: ensure non-function calling llms see json formatted string * tests: tweak message comparison * fix: use internal instructor for litellm structure responses --------- Co-authored-by: Mike Plachta <mike@crewai.com> |
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Lorenze Jay
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494ed7e671 |
liteagent supports apps and mcps (#3794)
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* liteagent supports apps and mcps * generated cassettes for these |
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Lorenze Jay
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08e15ab267 |
fix: update default LLM model and improve error logging in LLM utilities (#3785)
* fix: update default LLM model and improve error logging in LLM utilities * Updated the default LLM model from "gpt-4o-mini" to "gpt-4.1-mini" for better performance. * Enhanced error logging in the LLM utilities to use logger.error instead of logger.debug, ensuring that errors are properly reported and raised. * Added tests to verify behavior when OpenAI API key is missing and when Anthropic dependency is not available, improving robustness and error handling in LLM creation. * fix: update test for default LLM model usage * Refactored the test_create_llm_with_none_uses_default_model to use the imported DEFAULT_LLM_MODEL constant instead of a hardcoded string. * Ensured that the test correctly asserts the model used is the current default, improving maintainability and consistency across tests. * change default model to gpt-4.1-mini * change default model use defualt |
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Lorenze Jay
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a850813f2b |
feat: enhance InternalInstructor to support multiple LLM providers (#3767)
* feat: enhance InternalInstructor to support multiple LLM providers - Updated InternalInstructor to conditionally create an instructor client based on the LLM provider. - Introduced a new method _create_instructor_client to handle client creation using the modern from_provider pattern. - Added functionality to extract the provider from the LLM model name. - Implemented tests for InternalInstructor with various LLM providers including OpenAI, Anthropic, Gemini, and Azure, ensuring robust integration and error handling. This update improves flexibility and extensibility for different LLM integrations. * fix test |
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Greyson LaLonde
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f6e13eb890 |
chore: update codeql config paths to new folders
* chore: update codeql config paths to new folders * tests: use threading.Condition for event check --------- Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> |
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Lorenze Jay
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d1343b96ed |
Release/v1.0.0 (#3618)
* feat: add `apps` & `actions` attributes to Agent (#3504)
* feat: add app attributes to Agent
* feat: add actions attribute to Agent
* chore: resolve linter issues
* refactor: merge the apps and actions parameters into a single one
* fix: remove unnecessary print
* feat: logging error when CrewaiPlatformTools fails
* chore: export CrewaiPlatformTools directly from crewai_tools
* style: resolver linter issues
* test: fix broken tests
* style: solve linter issues
* fix: fix broken test
* feat: monorepo restructure and test/ci updates
- Add crewai workspace member
- Fix vcr cassette paths and restore test dirs
- Resolve ci failures and update linter/pytest rules
* chore: update python version to 3.13 and package metadata
* feat: add crewai-tools workspace and fix tests/dependencies
* feat: add crewai-tools workspace structure
* Squashed 'temp-crewai-tools/' content from commit 9bae5633
git-subtree-dir: temp-crewai-tools
git-subtree-split: 9bae56339096cb70f03873e600192bd2cd207ac9
* feat: configure crewai-tools workspace package with dependencies
* fix: apply ruff auto-formatting to crewai-tools code
* chore: update lockfile
* fix: don't allow tool tests yet
* fix: comment out extra pytest flags for now
* fix: remove conflicting conftest.py from crewai-tools tests
* fix: resolve dependency conflicts and test issues
- Pin vcrpy to 7.0.0 to fix pytest-recording compatibility
- Comment out types-requests to resolve urllib3 conflict
- Update requests requirement in crewai-tools to >=2.32.0
* chore: update CI workflows and docs for monorepo structure
* chore: update CI workflows and docs for monorepo structure
* fix: actions syntax
* chore: ci publish and pin versions
* fix: add permission to action
* chore: bump version to 1.0.0a1 across all packages
- Updated version to 1.0.0a1 in pyproject.toml for crewai and crewai-tools
- Adjusted version in __init__.py files for consistency
* WIP: v1 docs (#3626)
(cherry picked from commit d46e20fa09bcd2f5916282f5553ddeb7183bd92c)
* docs: parity for all translations
* docs: full name of acronym AMP
* docs: fix lingering unused code
* docs: expand contextual options in docs.json
* docs: add contextual action to request feature on GitHub (#3635)
* chore: apply linting fixes to crewai-tools
* feat: add required env var validation for brightdata
Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com>
* fix: handle properly anyOf oneOf allOf schema's props
Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com>
* feat: bump version to 1.0.0a2
* Lorenze/native inference sdks (#3619)
* ruff linted
* using native sdks with litellm fallback
* drop exa
* drop print on completion
* Refactor LLM and utility functions for type consistency
- Updated `max_tokens` parameter in `LLM` class to accept `float` in addition to `int`.
- Modified `create_llm` function to ensure consistent type hints and return types, now returning `LLM | BaseLLM | None`.
- Adjusted type hints for various parameters in `create_llm` and `_llm_via_environment_or_fallback` functions for improved clarity and type safety.
- Enhanced test cases to reflect changes in type handling and ensure proper instantiation of LLM instances.
* fix agent_tests
* fix litellm tests and usagemetrics fix
* drop print
* Refactor LLM event handling and improve test coverage
- Removed commented-out event emission for LLM call failures in `llm.py`.
- Added `from_agent` parameter to `CrewAgentExecutor` for better context in LLM responses.
- Enhanced test for LLM call failure to simulate OpenAI API failure and updated assertions for clarity.
- Updated agent and task ID assertions in tests to ensure they are consistently treated as strings.
* fix test_converter
* fixed tests/agents/test_agent.py
* Refactor LLM context length exception handling and improve provider integration
- Renamed `LLMContextLengthExceededException` to `LLMContextLengthExceededExceptionError` for clarity and consistency.
- Updated LLM class to pass the provider parameter correctly during initialization.
- Enhanced error handling in various LLM provider implementations to raise the new exception type.
- Adjusted tests to reflect the updated exception name and ensure proper error handling in context length scenarios.
* Enhance LLM context window handling across providers
- Introduced CONTEXT_WINDOW_USAGE_RATIO to adjust context window sizes dynamically for Anthropic, Azure, Gemini, and OpenAI LLMs.
- Added validation for context window sizes in Azure and Gemini providers to ensure they fall within acceptable limits.
- Updated context window size calculations to use the new ratio, improving consistency and adaptability across different models.
- Removed hardcoded context window sizes in favor of ratio-based calculations for better flexibility.
* fix test agent again
* fix test agent
* feat: add native LLM providers for Anthropic, Azure, and Gemini
- Introduced new completion implementations for Anthropic, Azure, and Gemini, integrating their respective SDKs.
- Added utility functions for tool validation and extraction to support function calling across LLM providers.
- Enhanced context window management and token usage extraction for each provider.
- Created a common utility module for shared functionality among LLM providers.
* chore: update dependencies and improve context management
- Removed direct dependency on `litellm` from the main dependencies and added it under extras for better modularity.
- Updated the `litellm` dependency specification to allow for greater flexibility in versioning.
- Refactored context length exception handling across various LLM providers to use a consistent error class.
- Enhanced platform-specific dependency markers for NVIDIA packages to ensure compatibility across different systems.
* refactor(tests): update LLM instantiation to include is_litellm flag in test cases
- Modified multiple test cases in test_llm.py to set the is_litellm parameter to True when instantiating the LLM class.
- This change ensures that the tests are aligned with the latest LLM configuration requirements and improves consistency across test scenarios.
- Adjusted relevant assertions and comments to reflect the updated LLM behavior.
* linter
* linted
* revert constants
* fix(tests): correct type hint in expected model description
- Updated the expected description in the test_generate_model_description_dict_field function to use 'Dict' instead of 'dict' for consistency with type hinting conventions.
- This change ensures that the test accurately reflects the expected output format for model descriptions.
* refactor(llm): enhance LLM instantiation and error handling
- Updated the LLM class to include validation for the model parameter, ensuring it is a non-empty string.
- Improved error handling by logging warnings when the native SDK fails, allowing for a fallback to LiteLLM.
- Adjusted the instantiation of LLM in test cases to consistently include the is_litellm flag, aligning with recent changes in LLM configuration.
- Modified relevant tests to reflect these updates, ensuring better coverage and accuracy in testing scenarios.
* fixed test
* refactor(llm): enhance token usage tracking and add copy methods
- Updated the LLM class to track token usage and log callbacks in streaming mode, improving monitoring capabilities.
- Introduced shallow and deep copy methods for the LLM instance, allowing for better management of LLM configurations and parameters.
- Adjusted test cases to instantiate LLM with the is_litellm flag, ensuring alignment with recent changes in LLM configuration.
* refactor(tests): reorganize imports and enhance error messages in test cases
- Cleaned up import statements in test_crew.py for better organization and readability.
- Enhanced error messages in test cases to use `re.escape` for improved regex matching, ensuring more robust error handling.
- Adjusted comments for clarity and consistency across test scenarios.
- Ensured that all necessary modules are imported correctly to avoid potential runtime issues.
* feat: add base devtooling
* fix: ensure dep refs are updated for devtools
* fix: allow pre-release
* feat: allow release after tag
* feat: bump versions to 1.0.0a3
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
* fix: match tag and release title, ignore devtools build for pypi
* fix: allow failed pypi publish
* feat: introduce trigger listing and execution commands for local development (#3643)
* chore: exclude tests from ruff linting
* chore: exclude tests from GitHub Actions linter
* fix: replace print statements with logger in agent and memory handling
* chore: add noqa for intentional print in printer utility
* fix: resolve linting errors across codebase
* feat: update docs with new approach to consume Platform Actions (#3675)
* fix: remove duplicate line and add explicit env var
* feat: bump versions to 1.0.0a4 (#3686)
* Update triggers docs (#3678)
* docs: introduce triggers list & triggers run command
* docs: add KO triggers docs
* docs: ensure CREWAI_PLATFORM_INTEGRATION_TOKEN is mentioned on docs (#3687)
* Lorenze/bedrock llm (#3693)
* feat: add AWS Bedrock support and update dependencies
- Introduced BedrockCompletion class for AWS Bedrock integration in LLM.
- Added boto3 as a new dependency in both pyproject.toml and uv.lock.
- Updated LLM class to support Bedrock provider.
- Created new files for Bedrock provider implementation.
* using converse api
* converse
* linted
* refactor: update BedrockCompletion class to improve parameter handling
- Changed max_tokens from a fixed integer to an optional integer.
- Simplified model ID assignment by removing the inference profile mapping method.
- Cleaned up comments and unnecessary code related to tool specifications and model-specific parameters.
* feat: improve event bus thread safety and async support
Add thread-safe, async-compatible event bus with read–write locking and
handler dependency ordering. Remove blinker dependency and implement
direct dispatch. Improve type safety, error handling, and deterministic
event synchronization.
Refactor tests to auto-wait for async handlers, ensure clean teardown,
and add comprehensive concurrency coverage. Replace thread-local state
in AgentEvaluator with instance-based locking for correct cross-thread
access. Enhance tracing reliability and event finalization.
* feat: enhance OpenAICompletion class with additional client parameters (#3701)
* feat: enhance OpenAICompletion class with additional client parameters
- Added support for default_headers, default_query, and client_params in the OpenAICompletion class.
- Refactored client initialization to use a dedicated method for client parameter retrieval.
- Introduced new test cases to validate the correct usage of OpenAICompletion with various parameters.
* fix: correct test case for unsupported OpenAI model
- Updated the test_openai.py to ensure that the LLM instance is created before calling the method, maintaining proper error handling for unsupported models.
- This change ensures that the test accurately checks for the NotFoundError when an invalid model is specified.
* fix: enhance error handling in OpenAICompletion class
- Added specific exception handling for NotFoundError and APIConnectionError in the OpenAICompletion class to provide clearer error messages and improve logging.
- Updated the test case for unsupported models to ensure it raises a ValueError with the appropriate message when a non-existent model is specified.
- This change improves the robustness of the OpenAI API integration and enhances the clarity of error reporting.
* fix: improve test for unsupported OpenAI model handling
- Refactored the test case in test_openai.py to create the LLM instance after mocking the OpenAI client, ensuring proper error handling for unsupported models.
- This change enhances the clarity of the test by accurately checking for ValueError when a non-existent model is specified, aligning with recent improvements in error handling for the OpenAICompletion class.
* feat: bump versions to 1.0.0b1 (#3706)
* Lorenze/tools drop litellm (#3710)
* completely drop litellm and correctly pass config for qdrant
* feat: add support for additional embedding models in EmbeddingService
- Expanded the list of supported embedding models to include Google Vertex, Hugging Face, Jina, Ollama, OpenAI, Roboflow, Watson X, custom embeddings, Sentence Transformers, Text2Vec, OpenClip, and Instructor.
- This enhancement improves the versatility of the EmbeddingService by allowing integration with a wider range of embedding providers.
* fix: update collection parameter handling in CrewAIRagAdapter
- Changed the condition for setting vectors_config in the CrewAIRagAdapter to check for QdrantConfig instance instead of using hasattr. This improves type safety and ensures proper configuration handling for Qdrant integration.
* moved stagehand as optional dep (#3712)
* feat: bump versions to 1.0.0b2 (#3713)
* feat: enhance AnthropicCompletion class with additional client parame… (#3707)
* feat: enhance AnthropicCompletion class with additional client parameters and tool handling
- Added support for client_params in the AnthropicCompletion class to allow for additional client configuration.
- Refactored client initialization to use a dedicated method for retrieving client parameters.
- Implemented a new method to handle tool use conversation flow, ensuring proper execution and response handling.
- Introduced comprehensive test cases to validate the functionality of the AnthropicCompletion class, including tool use scenarios and parameter handling.
* drop print statements
* test: add fixture to mock ANTHROPIC_API_KEY for tests
- Introduced a pytest fixture to automatically mock the ANTHROPIC_API_KEY environment variable for all tests in the test_anthropic.py module.
- This change ensures that tests can run without requiring a real API key, improving test isolation and reliability.
* refactor: streamline streaming message handling in AnthropicCompletion class
- Removed the 'stream' parameter from the API call as it is set internally by the SDK.
- Simplified the handling of tool use events and response construction by extracting token usage from the final message.
- Enhanced the flow for managing tool use conversation, ensuring proper integration with the streaming API response.
* fix streaming here too
* fix: improve error handling in tool conversion for AnthropicCompletion class
- Enhanced exception handling during tool conversion by catching KeyError and ValueError.
- Added logging for conversion errors to aid in debugging and maintain robustness in tool integration.
* feat: enhance GeminiCompletion class with client parameter support (#3717)
* feat: enhance GeminiCompletion class with client parameter support
- Added support for client_params in the GeminiCompletion class to allow for additional client configuration.
- Refactored client initialization into a dedicated method for improved parameter handling.
- Introduced a new method to retrieve client parameters, ensuring compatibility with the base class.
- Enhanced error handling during client initialization to provide clearer messages for missing configuration.
- Updated documentation to reflect the changes in client parameter usage.
* add optional dependancies
* refactor: update test fixture to mock GOOGLE_API_KEY
- Renamed the fixture from `mock_anthropic_api_key` to `mock_google_api_key` to reflect the change in the environment variable being mocked.
- This update ensures that all tests in the module can run with a mocked GOOGLE_API_KEY, improving test isolation and reliability.
* fix tests
* feat: enhance BedrockCompletion class with advanced features
* feat: enhance BedrockCompletion class with advanced features and error handling
- Added support for guardrail configuration, additional model request fields, and custom response field paths in the BedrockCompletion class.
- Improved error handling for AWS exceptions and added token usage tracking with stop reason logging.
- Enhanced streaming response handling with comprehensive event management, including tool use and content block processing.
- Updated documentation to reflect new features and initialization parameters.
- Introduced a new test suite for BedrockCompletion to validate functionality and ensure robust integration with AWS Bedrock APIs.
* chore: add boto typing
* fix: use typing_extensions.Required for Python 3.10 compatibility
---------
Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com>
* feat: azure native tests
* feat: add Azure AI Inference support and related tests
- Introduced the `azure-ai-inference` package with version `1.0.0b9` and its dependencies in `uv.lock` and `pyproject.toml`.
- Added new test files for Azure LLM functionality, including tests for Azure completion and tool handling.
- Implemented comprehensive test cases to validate Azure-specific behavior and integration with the CrewAI framework.
- Enhanced the testing framework to mock Azure credentials and ensure proper isolation during tests.
* feat: enhance AzureCompletion class with Azure OpenAI support
- Added support for the Azure OpenAI endpoint in the AzureCompletion class, allowing for flexible endpoint configurations.
- Implemented endpoint validation and correction to ensure proper URL formats for Azure OpenAI deployments.
- Enhanced error handling to provide clearer messages for common HTTP errors, including authentication and rate limit issues.
- Updated tests to validate the new endpoint handling and error messaging, ensuring robust integration with Azure AI Inference.
- Refactored parameter preparation to conditionally include the model parameter based on the endpoint type.
* refactor: convert project module to metaclass with full typing
* Lorenze/OpenAI base url backwards support (#3723)
* fix: enhance OpenAICompletion class base URL handling
- Updated the base URL assignment in the OpenAICompletion class to prioritize the new `api_base` attribute and fallback to the environment variable `OPENAI_BASE_URL` if both are not set.
- Added `api_base` to the list of parameters in the OpenAICompletion class to ensure proper configuration and flexibility in API endpoint management.
* feat: enhance OpenAICompletion class with api_base support
- Added the `api_base` parameter to the OpenAICompletion class to allow for flexible API endpoint configuration.
- Updated the `_get_client_params` method to prioritize `base_url` over `api_base`, ensuring correct URL handling.
- Introduced comprehensive tests to validate the behavior of `api_base` and `base_url` in various scenarios, including environment variable fallback.
- Enhanced test coverage for client parameter retrieval, ensuring robust integration with the OpenAI API.
* fix: improve OpenAICompletion class configuration handling
- Added a debug print statement to log the client configuration parameters during initialization for better traceability.
- Updated the base URL assignment logic to ensure it defaults to None if no valid base URL is provided, enhancing robustness in API endpoint configuration.
- Refined the retrieval of the `api_base` environment variable to streamline the configuration process.
* drop print
* feat: improvements on import native sdk support (#3725)
* feat: add support for Anthropic provider and enhance logging
- Introduced the `anthropic` package with version `0.69.0` in `pyproject.toml` and `uv.lock`, allowing for integration with the Anthropic API.
- Updated logging in the LLM class to provide clearer error messages when importing native providers, enhancing debugging capabilities.
- Improved error handling in the AnthropicCompletion class to guide users on installation via the updated error message format.
- Refactored import error handling in other provider classes to maintain consistency in error messaging and installation instructions.
* feat: enhance LLM support with Bedrock provider and update dependencies
- Added support for the `bedrock` provider in the LLM class, allowing integration with AWS Bedrock APIs.
- Updated `uv.lock` to replace `boto3` with `bedrock` in the dependencies, reflecting the new provider structure.
- Introduced `SUPPORTED_NATIVE_PROVIDERS` to include `bedrock` and ensure proper error handling when instantiating native providers.
- Enhanced error handling in the LLM class to raise informative errors when native provider instantiation fails.
- Added tests to validate the behavior of the new Bedrock provider and ensure fallback mechanisms work correctly for unsupported providers.
* test: update native provider fallback tests to expect ImportError
* adjust the test with the expected bevaior - raising ImportError
* this is exoecting the litellm format, all gemini native tests are in test_google.py
---------
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
* fix: remove stdout prints, improve test determinism, and update trace handling
Removed `print` statements from the `LLMStreamChunkEvent` handler to prevent
LLM response chunks from being written directly to stdout. The listener now
only tracks chunks internally.
Fixes #3715
Added explicit return statements for trace-related tests.
Updated cassette for `test_failed_evaluation` to reflect new behavior where
an empty trace dict is used instead of returning early.
Ensured deterministic cleanup order in test fixtures by making
`clear_event_bus_handlers` depend on `setup_test_environment`. This guarantees
event bus shutdown and file handle cleanup occur before temporary directory
deletion, resolving intermittent “Directory not empty” errors in CI.
* chore: remove lib/crewai exclusion from pre-commit hooks
* feat: enhance task guardrail functionality and validation
* feat: enhance task guardrail functionality and validation
- Introduced support for multiple guardrails in the Task class, allowing for sequential processing of guardrails.
- Added a new `guardrails` field to the Task model to accept a list of callable guardrails or string descriptions.
- Implemented validation to ensure guardrails are processed correctly, including handling of retries and error messages.
- Enhanced the `_invoke_guardrail_function` method to manage guardrail execution and integrate with existing task output processing.
- Updated tests to cover various scenarios involving multiple guardrails, including success, failure, and retry mechanisms.
This update improves the flexibility and robustness of task execution by allowing for more complex validation scenarios.
* refactor: enhance guardrail type handling in Task model
- Updated the Task class to improve guardrail type definitions, introducing GuardrailType and GuardrailsType for better clarity and type safety.
- Simplified the validation logic for guardrails, ensuring that both single and multiple guardrails are processed correctly.
- Enhanced error messages for guardrail validation to provide clearer feedback when incorrect types are provided.
- This refactor improves the maintainability and robustness of task execution by standardizing guardrail handling.
* feat: implement per-guardrail retry tracking in Task model
- Introduced a new private attribute `_guardrail_retry_counts` to the Task class for tracking retry attempts on a per-guardrail basis.
- Updated the guardrail processing logic to utilize the new retry tracking, allowing for independent retry counts for each guardrail.
- Enhanced error handling to provide clearer feedback when guardrails fail validation after exceeding retry limits.
- Modified existing tests to validate the new retry tracking behavior, ensuring accurate assertions on guardrail retries.
This update improves the robustness and flexibility of task execution by allowing for more granular control over guardrail validation and retry mechanisms.
* chore: 1.0.0b3 bump (#3734)
* chore: full ruff and mypy
improved linting, pre-commit setup, and internal architecture. Configured Ruff to respect .gitignore, added stricter rules, and introduced a lock pre-commit hook with virtualenv activation. Fixed type shadowing in EXASearchTool using a type_ alias to avoid PEP 563 conflicts and resolved circular imports in agent executor and guardrail modules. Removed agent-ops attributes, deprecated watson alias, and dropped crewai-enterprise tools with corresponding test updates. Refactored cache and memoization for thread safety and cleaned up structured output adapters and related logic.
* New MCL DSL (#3738)
* Adding MCP implementation
* New tests for MCP implementation
* fix tests
* update docs
* Revert "New tests for MCP implementation"
This reverts commit
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