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e47ebb7169a17dc81fdfd3486c19efa45b3d4f70
30 Commits
| Author | SHA1 | Message | Date | |
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Greyson LaLonde
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8bfdb188f7 | feat: bump versions to 1.10.1a1 | ||
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João Moura
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1bdb9496a3 |
refactor: update step callback methods to support asynchronous invocation (#4633)
* refactor: update step callback methods to support asynchronous invocation - Replaced synchronous step callback invocations with asynchronous counterparts in the CrewAgentExecutor class. - Introduced a new async method _ainvoke_step_callback to handle step callbacks in an async context, improving responsiveness and performance in asynchronous workflows. * chore: bump version to 1.10.1b1 across multiple files - Updated version strings from 1.10.1b to 1.10.1b1 in various project files including pyproject.toml and __init__.py files. - Adjusted dependency specifications to reflect the new version in relevant templates and modules. |
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Joao Moura
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979aa26c3d | bump new alpha version | ||
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Greyson LaLonde
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df2778f08b |
fix: make branch for release notes
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Greyson LaLonde
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d8fea2518d |
feat: bump versions to 1.10.0
* feat: bump versions to 1.10.0 * chore: update tool specifications --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> |
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Greyson LaLonde
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ee374d01de | chore: add versioning logic for devtools | ||
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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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Lorenze Jay
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63a508f601 |
feat: bump versions to 1.9.3 (#4316)
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* feat: bump versions to 1.9.3 * bump bump |
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Lorenze Jay
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e291a97bdd |
chore: update version to 1.9.2 across all relevant files (#4299)
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Lorenze Jay
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381ad3a9a8 |
chore: update version to 1.9.1
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Lorenze Jay
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6b926b90d0 |
chore: update version to 1.9.0 across all relevant files (#4284)
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- Bumped the version number to 1.9.0 in pyproject.toml files and __init__.py files across the CrewAI library and its tools. - Updated dependencies to use the new version of crewai-tools (1.9.0) for improved functionality and compatibility. - Ensured consistency in versioning across the codebase to reflect the latest updates. |
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Lorenze Jay
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8f022be106 |
feat: bump versions to 1.8.1 (#4242)
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* feat: bump versions to 1.8.1 * bump bump |
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Lorenze Jay
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9a212b8e29 |
feat: bump versions to 1.8.0 (#4199)
* feat: bump versions to 1.8.0 * bump 1.8.0 |
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Greyson LaLonde
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0c359f4df8 | feat: bump versions to 1.7.2 | ||
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Lorenze Jay
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88d3c0fa97 |
feat: bump versions to 1.7.1 (#4092)
* feat: bump versions to 1.7.1 * bump projects |
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Matt Aitchison
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75ff7dce0c |
feat: add --no-commit flag to bump command (#4087)
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Allows updating version files without creating a commit, branch, or PR. |
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Lorenze Jay
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3e3b9df761 |
feat: bump versions to 1.7.0 (#4051)
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* feat: bump versions to 1.7.0 * bump |
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Greyson LaLonde
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3ce019b07b |
chore: pin dependencies in crewai, crewai-tools, devtools
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Lorenze Jay
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bc4e6a3127 |
feat: bump versions to 1.6.1 (#3993)
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* feat: bump versions to 1.6.1 * chore: update crewAI dependency version to 1.6.1 in project templates |
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Lorenze Jay
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52444ad390 |
feat: bump versions to 1.6.0 (#3974)
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* feat: bump versions to 1.6.0 * bump project templates |
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Lorenze Jay
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9fcf55198f |
feat: bump versions to 1.5.0 (#3924)
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* feat: bump versions to 1.5.0 * chore: update crewAI tools dependency to version 1.5.0 in project templates |
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Lorenze Jay
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0f1c173d02 |
feat: bump versions to 1.4.1 (#3862)
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* feat: bump versions to 1.4.1 * chore: update crewAI tools dependency to version 1.4.1 in project templates |
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Lorenze Jay
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d29867bbb6 |
chore: update version numbers to 1.4.0
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Lorenze Jay
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40932af3fa |
feat: bump versions to 1.3.0 (#3820)
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* feat: bump versions to 1.3.0 * chore: update crew and flow templates to use crewai[tools] version 1.3.0 |
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Lorenze Jay
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5d6b4c922b |
feat: bump versions to 1.2.1 (#3800)
* feat: bump versions to 1.2.1 * updated templates too |
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Lorenze Jay
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a83c57a2f2 |
feat: bump versions to 1.2.0 (#3787)
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* feat: bump versions to 1.2.0 * also include projects |
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Lorenze Jay
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d28daa26cd |
feat: bump versions to 1.1.0 (#3770)
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* feat: bump versions to 1.1.0 * chore: bump template versions --------- Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com> |
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Lorenze Jay
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cc83c1ead5 |
feat: bump versions to 1.0.0
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Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com> |
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Greyson LaLonde
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7578901f6d | chore: use main for devtools branch | ||
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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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