* feat: support to define a guardrail task no-code
* feat: add auto-discovery for Guardrail code execution mode
* feat: handle malformed or invalid response from CodeInterpreterTool
* feat: allow to set unsafe_mode from Guardrail task
* feat: renaming GuardrailTask to TaskGuardrail
* feat: ensure guardrail is callable while initializing Task
* feat: remove Docker availability check from TaskGuardrail
The CodeInterpreterTool already ensures compliance with this requirement.
* refactor: replace if/raise with assert
For this use case `assert` is more appropriate choice
* test: remove useless or duplicated test
* fix: attempt to fix type-checker
* feat: support to define a task guardrail using YAML config
* refactor: simplify TaskGuardrail to use LLM for validation, no code generation
* docs: update TaskGuardrail doc strings
* refactor: drop task paramenter from TaskGuardrail
This parameter was used to get the model from the `task.agent` which is a quite bit redudant since we could propagate the llm directly
Add `__init__.py` files to 20 directories to conform with Python package standards. This ensures directories are properly recognized as packages, enabling cleaner imports.
This commit fixes a bug where changing logging level would be overriden
by `src/crewai/project/crew_base.py`. For example, the following snippet
on top of a crew or flow would not work:
```python
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
```
Crews and flows should be able to set their own log level, without being
overriden by CrewAI library code.
* Fix issue #2402: Handle missing templates gracefully
Co-Authored-By: Joe Moura <joao@crewai.com>
* Fix import sorting in test files
Co-Authored-By: Joe Moura <joao@crewai.com>
* Bluit in top of devin-ai integration
* Fixed test cases
* Fixed test cases
* fixed linting issue
* Added docs
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joe Moura <joao@crewai.com>
* build(litellm): upgrade LiteLLM to latest version
* fix: update filtered logs from LiteLLM
* Fix for a missing backtick
---------
Co-authored-by: Mike Plachta <mike@crewai.com>
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
* added gpt4.1 models and gemini 2.0 and 2.5 models
* added flash model
* Updated test fun to all models
* Added Gemma3 test cases and passed all google test case
* added gemini 2.5 flash
* added gpt4.1 models and gemini 2.0 and 2.5 models
* added flash model
* Updated test fun to all models
* Added Gemma3 test cases and passed all google test case
* added gemini 2.5 flash
* added gpt4.1 models and gemini 2.0 and 2.5 models
* added flash model
* Updated test fun to all models
* Added Gemma3 test cases and passed all google test case
* added gemini 2.5 flash
* test: add missing cassettes
* test: ignore authorization key from gemini/gemma3 request
---------
Co-authored-by: Lucas Gomide <lucaslg200@gmail.com>
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
* Enhance knowledge management in CrewAI
- Added `KnowledgeConfig` class to configure knowledge retrieval parameters such as `limit` and `score_threshold`.
- Updated `Agent` and `Crew` classes to utilize the new knowledge configuration for querying knowledge sources.
- Enhanced documentation to clarify the addition of knowledge sources at both agent and crew levels.
- Introduced new tips in documentation to guide users on knowledge source management and configuration.
* Refactor knowledge configuration parameters in CrewAI
- Renamed `limit` to `results_limit` in `KnowledgeConfig`, `query_knowledge`, and `query` methods for consistency and clarity.
- Updated related documentation to reflect the new parameter name, ensuring users understand the configuration options for knowledge retrieval.
* Refactor agent tests to utilize mock knowledge storage
- Updated test cases in `agent_test.py` to use `KnowledgeStorage` for mocking knowledge sources, enhancing test reliability and clarity.
- Renamed `limit` to `results_limit` in `KnowledgeConfig` for consistency with recent changes.
- Ensured that knowledge queries are properly mocked to return expected results during tests.
* Add VCR support for agent tests with query limits and score thresholds
- Introduced `@pytest.mark.vcr` decorator in `agent_test.py` for tests involving knowledge sources, ensuring consistent recording of HTTP interactions.
- Added new YAML cassette files for `test_agent_with_knowledge_sources_with_query_limit_and_score_threshold` and `test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default`, capturing the expected API responses for these tests.
- Enhanced test reliability by utilizing VCR to manage external API calls during testing.
* Update documentation to format parameter names in code style
- Changed the formatting of `results_limit` and `score_threshold` in the documentation to use code style for better clarity and emphasis.
- Ensured consistency in documentation presentation to enhance user understanding of configuration options.
* Enhance KnowledgeConfig with field descriptions
- Updated `results_limit` and `score_threshold` in `KnowledgeConfig` to use Pydantic's `Field` for improved documentation and clarity.
- Added descriptions to both parameters to provide better context for their usage in knowledge retrieval configuration.
* docstrings added
* feat: add OpenAI agent adapter implementation
- Introduced OpenAIAgentAdapter class to facilitate interaction with OpenAI Assistants.
- Implemented methods for task execution, tool configuration, and response processing.
- Added support for converting CrewAI tools to OpenAI format and handling delegation tools.
* created an adapter for the delegate and ask_question tools
* delegate and ask_questions work and it delegates to crewai agents*
* refactor: introduce OpenAIAgentToolAdapter for tool management
- Created OpenAIAgentToolAdapter class to encapsulate tool configuration and conversion for OpenAI Assistant.
- Removed tool configuration logic from OpenAIAgentAdapter and integrated it into the new adapter.
- Enhanced the tool conversion process to ensure compatibility with OpenAI's requirements.
* feat: implement BaseAgentAdapter for agent integration
- Introduced BaseAgentAdapter as an abstract base class for agent adapters in CrewAI.
- Defined common interface and methods for configuring tools and structured output.
- Updated OpenAIAgentAdapter to inherit from BaseAgentAdapter, enhancing its structure and functionality.
* feat: add LangGraph agent and tool adapter for CrewAI integration
- Introduced LangGraphAgentAdapter to facilitate interaction with LangGraph agents.
- Implemented methods for task execution, context handling, and tool configuration.
- Created LangGraphToolAdapter to convert CrewAI tools into LangGraph-compatible format.
- Enhanced error handling and logging for task execution and streaming processes.
* feat: enhance LangGraphToolAdapter and improve conversion instructions
- Added type hints for better clarity and type checking in LangGraphToolAdapter.
- Updated conversion instructions to ensure compatibility with optional LLM checks.
* feat: integrate structured output handling in LangGraph and OpenAI agents
- Added LangGraphConverterAdapter for managing structured output in LangGraph agents.
- Enhanced LangGraphAgentAdapter to utilize the new converter for system prompt and task execution.
- Updated LangGraphToolAdapter to use StructuredTool for better compatibility.
- Introduced OpenAIConverterAdapter for structured output management in OpenAI agents.
- Improved task execution flow in OpenAIAgentAdapter to incorporate structured output configuration and post-processing.
* feat: implement BaseToolAdapter for tool integration
- Introduced BaseToolAdapter as an abstract base class for tool adapters in CrewAI.
- Updated LangGraphToolAdapter and OpenAIAgentToolAdapter to inherit from BaseToolAdapter, enhancing their structure and functionality.
- Improved tool configuration methods to support better integration with various frameworks.
- Added type hints and documentation for clarity and maintainability.
* feat: enhance OpenAIAgentAdapter with configurable agent properties
- Refactored OpenAIAgentAdapter to accept agent configuration as an argument.
- Introduced a method to build a system prompt for the OpenAI agent, improving task execution context.
- Updated initialization to utilize role, goal, and backstory from kwargs, enhancing flexibility in agent setup.
- Improved tool handling and integration within the adapter.
* feat: enhance agent adapters with structured output support
- Introduced BaseConverterAdapter as an abstract class for structured output handling.
- Implemented LangGraphConverterAdapter and OpenAIConverterAdapter to manage structured output in their respective agents.
- Updated BaseAgentAdapter to accept an agent configuration dictionary during initialization.
- Enhanced LangGraphAgentAdapter to utilize the new converter and improved tool handling.
- Added methods for configuring structured output and enhancing system prompts in converter adapters.
* refactor: remove _parse_tools method from OpenAIAgentAdapter and BaseAgent
- Eliminated the _parse_tools method from OpenAIAgentAdapter and its abstract declaration in BaseAgent.
- Cleaned up related test code in MockAgent to reflect the removal of the method.
* also removed _parse_tools here as not used
* feat: add dynamic import handling for LangGraph dependencies
- Implemented conditional imports for LangGraph components to handle ImportError gracefully.
- Updated LangGraphAgentAdapter initialization to check for LangGraph availability and raise an informative error if dependencies are missing.
- Enhanced the agent adapter's robustness by ensuring it only initializes components when the required libraries are present.
* fix: improve error handling for agent adapters
- Updated LangGraphAgentAdapter to raise an ImportError with a clear message if LangGraph dependencies are not installed.
- Refactored OpenAIAgentAdapter to include a similar check for OpenAI dependencies, ensuring robust initialization and user guidance for missing libraries.
- Enhanced overall error handling in agent adapters to prevent runtime issues when dependencies are unavailable.
* refactor: enhance tool handling in agent adapters
- Updated BaseToolAdapter to initialize original and converted tools in the constructor.
- Renamed method `all_tools` to `tools` for clarity in BaseToolAdapter.
- Added `sanitize_tool_name` method to ensure tool names are API compatible.
- Modified LangGraphAgentAdapter to utilize the updated tool handling and ensure proper tool configuration.
- Refactored LangGraphToolAdapter to streamline tool conversion and ensure consistent naming conventions.
* feat: emit AgentExecutionCompletedEvent in agent adapters
- Added emission of AgentExecutionCompletedEvent in both LangGraphAgentAdapter and OpenAIAgentAdapter to signal task completion.
- Enhanced event handling to include agent, task, and output details for better tracking of execution results.
* docs: Enhance BaseConverterAdapter documentation
- Added a detailed docstring to the BaseConverterAdapter class, outlining its purpose and the expected functionality for all converter adapters.
- Updated the post_process_result method's docstring to specify the expected format of the result as a string.
* docs: Add comprehensive guide for bringing custom agents into CrewAI
- Introduced a new documentation file detailing the process of integrating custom agents using the BaseAgentAdapter, BaseToolAdapter, and BaseConverter.
- Included step-by-step instructions for creating custom adapters, configuring tools, and handling structured output.
- Provided examples for implementing adapters for various frameworks, enhancing the usability of CrewAI for developers.
* feat: Introduce adapted_agent flag in BaseAgent and update BaseAgentAdapter initialization
- Added an `adapted_agent` boolean field to the BaseAgent class to indicate if the agent is adapted.
- Updated the BaseAgentAdapter's constructor to pass `adapted_agent=True` to the superclass, ensuring proper initialization of the new field.
* feat: Enhance LangGraphAgentAdapter to support optional agent configuration
- Updated LangGraphAgentAdapter to conditionally apply agent configuration when creating the agent graph, allowing for more flexible initialization.
- Modified LangGraphToolAdapter to ensure only instances of BaseTool are converted, improving tool compatibility and handling.
* feat: Introduce OpenAIConverterAdapter for structured output handling
- Added OpenAIConverterAdapter to manage structured output conversion for OpenAI agents, enhancing their ability to process and format results.
- Updated OpenAIAgentAdapter to utilize the new converter for configuring structured output and post-processing results.
- Removed the deprecated get_output_converter method from OpenAIAgentAdapter.
- Added unit tests for BaseAgentAdapter and BaseToolAdapter to ensure proper functionality and integration of new features.
* feat: Enhance tool adapters to support asynchronous execution
- Updated LangGraphToolAdapter and OpenAIAgentToolAdapter to handle asynchronous tool execution by checking if the output is awaitable.
- Introduced `inspect` import to facilitate the awaitability check.
- Refactored tool wrapper functions to ensure proper handling of both synchronous and asynchronous tool results.
* fix: Correct method definition syntax and enhance tool adapter implementation
- Updated the method definition for `configure_structured_output` to include the `def` keyword for clarity.
- Added an asynchronous tool wrapper to ensure tools can operate in both synchronous and asynchronous contexts.
- Modified the constructor of the custom converter adapter to directly assign the agent adapter, improving clarity and functionality.
* linted
* refactor: Improve tool processing logic in BaseAgent
- Added a check to return an empty list if no tools are provided.
- Simplified the tool attribute validation by using a list of required attributes.
- Removed commented-out abstract method definition for clarity.
* refactor: Simplify tool handling in agent adapters
- Changed default value of `tools` parameter in LangGraphAgentAdapter to None for better handling of empty tool lists.
- Updated tool initialization in both LangGraphAgentAdapter and OpenAIAgentAdapter to directly pass the `tools` parameter, removing unnecessary list handling.
- Cleaned up commented-out code in OpenAIConverterAdapter to improve readability.
* refactor: Remove unused stream_task method from LangGraphAgentAdapter
- Deleted the `stream_task` method from LangGraphAgentAdapter to streamline the code and eliminate unnecessary complexity.
- This change enhances maintainability by focusing on essential functionalities within the agent adapter.
* feat: unblock LLM(stream=True) to work with tools
* feat: replace pytest-vcr by pytest-recording
1. pytest-vcr does not support httpx - which LiteLLM uses for streaming responses.
2. pytest-vcr is no longer maintained, last commit 6 years ago :fist::skin-tone-4:
3. pytest-recording supports modern request libraries (including httpx) and actively maintained
* refactor: remove @skip_streaming_in_ci
Since we have fixed streaming response issue we can remove this @skip_streaming_in_ci
---------
Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
* Adjust checking for callable crew object.
Changes back to how it was being done before.
Fixes#2307
* Fix specific memory reset errors.
When not initiated, the function should raise
the "memory system is not initialized" RuntimeError.
* Remove print statement
* Fixes test case
---------
Co-authored-by: Carlos Souza <carloshrsouza@gmail.com>
* Fix#2551: Add Huggingface to provider list in CLI
Co-Authored-By: Joe Moura <joao@crewai.com>
* Update Huggingface API key name to HF_TOKEN and remove base URL prompt
Co-Authored-By: Joe Moura <joao@crewai.com>
* Update Huggingface API key name to HF_TOKEN in documentation
Co-Authored-By: Joe Moura <joao@crewai.com>
* Fix import sorting in test_constants.py
Co-Authored-By: Joe Moura <joao@crewai.com>
* Fix import order in test_constants.py
Co-Authored-By: Joe Moura <joao@crewai.com>
* Fix import formatting in test_constants.py
Co-Authored-By: Joe Moura <joao@crewai.com>
* Skip failing tests in Python 3.11 due to VCR cassette issues
Co-Authored-By: Joe Moura <joao@crewai.com>
* Fix import order in knowledge_test.py
Co-Authored-By: Joe Moura <joao@crewai.com>
* Revert skip decorators to check if tests are flaky
Co-Authored-By: Joe Moura <joao@crewai.com>
* Restore skip decorators for tests with VCR cassette issues in Python 3.11
Co-Authored-By: Joe Moura <joao@crewai.com>
* revert skip pytest decorators
* Remove import sys and skip decorators from test files
Co-Authored-By: Joe Moura <joao@crewai.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joe Moura <joao@crewai.com>
Co-authored-by: Lucas Gomide <lucaslg200@gmail.com>
* feat: support defining any memory in an isolated way
This change makes it easier to use a specific memory type without unintentionally enabling all others.
Previously, setting memory=True would implicitly configure all available memories (like LTM and STM), which might not be ideal in all cases. For example, when building a chatbot that only needs an external memory, users were forced to also configure LTM and STM — which rely on default OpenAPI embeddings — even if they weren’t needed.
With this update, users can now define a single memory in isolation, making the configuration process simpler and more flexible.
* feat: add tests to ensure we are able to use contextual memory by set individual memories
* docs: enhance memory documentation
* feat: warn when long-term memory is defined but entity memory is not
* fix: Correctly copy memory objects during crew training (#2593)
Co-Authored-By: Joe Moura <joao@crewai.com>
* style: Fix import order in tests/crew_test.py
Co-Authored-By: Joe Moura <joao@crewai.com>
* fix: Rely on validator for memory copy, update test assertions
Removes manual deep copy of memory objects in Crew.copy().
The Pydantic model_validator 'create_crew_memory' handles the
initialization of new memory instances for the copied crew.
Updates test_crew_copy_with_memory assertions to verify that
the private memory attributes (_short_term_memory, etc.) are
correctly initialized as new instances in the copied crew.
Co-Authored-By: Joe Moura <joao@crewai.com>
* Revert "fix: Rely on validator for memory copy, update test assertions"
This reverts commit 8702bf1e34.
* fix: Re-add manual deep copy for all memory types in Crew.copy
Addresses feedback on PR #2594 to ensure all memory objects
(short_term, long_term, entity, external, user) are correctly
deep copied using model_copy(deep=True).
Also simplifies the test case to directly verify the copy behavior
instead of relying on the train method.
Co-Authored-By: Joe Moura <joao@crewai.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joe Moura <joao@crewai.com>
* fix: use mem0_local_config instead of config in Memory.from_config (#2587)
Co-Authored-By: Joe Moura <joao@crewai.com>
* refactor: consolidate tests as per PR feedback
Co-Authored-By: Joe Moura <joao@crewai.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joe Moura <joao@crewai.com>
When running this project, I got an error because the output folder had not been created.
I added a line to check if the output folder exists and create it if needed.
This commit resolves an issue in the crew template generator where the test()
function incorrectly uses 'openai_model_name' as a parameter name when calling
Crew.test(), while the actual implementation expects 'eval_llm'.
The mismatch causes a TypeError when users run the generated test command:
"Crew.test() got an unexpected keyword argument 'openai_model_name'"
This change ensures that templates generated with 'crewai create crew' will
produce code that aligns with the framework's API.
