mirror of
https://github.com/crewAIInc/crewAI.git
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126b91eab3
* 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.
122 lines
2.6 KiB
TOML
122 lines
2.6 KiB
TOML
[project]
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name = "crewai"
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dynamic = ["version"]
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description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
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readme = "README.md"
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authors = [
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{ name = "Joao Moura", email = "joao@crewai.com" }
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]
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requires-python = ">=3.10, <3.14"
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dependencies = [
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# Core Dependencies
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"pydantic>=2.11.9",
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"openai>=1.13.3",
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"instructor>=1.3.3",
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# Text Processing
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"pdfplumber>=0.11.4",
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"regex>=2024.9.11",
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# Telemetry and Monitoring
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"opentelemetry-api>=1.30.0",
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"opentelemetry-sdk>=1.30.0",
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"opentelemetry-exporter-otlp-proto-http>=1.30.0",
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# Data Handling
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"chromadb~=1.1.0",
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"tokenizers>=0.20.3",
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"openpyxl>=3.1.5",
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"pyvis>=0.3.2",
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# Authentication and Security
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"python-dotenv>=1.1.1",
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"pyjwt>=2.9.0",
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# Configuration and Utils
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"click>=8.1.7",
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"appdirs>=1.4.4",
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"jsonref>=1.1.0",
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"json-repair==0.25.2",
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"uv>=0.4.25",
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"tomli-w>=1.1.0",
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"tomli>=2.0.2",
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"blinker>=1.9.0",
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"json5>=0.10.0",
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"portalocker==2.7.0",
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"pydantic-settings>=2.10.1",
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]
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[project.urls]
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Homepage = "https://crewai.com"
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Documentation = "https://docs.crewai.com"
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Repository = "https://github.com/crewAIInc/crewAI"
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[project.optional-dependencies]
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tools = [
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"crewai-tools==1.0.0a1",
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]
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embeddings = [
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"tiktoken~=0.8.0"
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]
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pdfplumber = [
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"pdfplumber>=0.11.4",
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]
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pandas = [
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"pandas>=2.2.3",
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]
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openpyxl = [
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"openpyxl>=3.1.5",
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]
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mem0 = ["mem0ai>=0.1.94"]
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docling = [
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"docling>=2.12.0",
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]
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aisuite = [
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"aisuite>=0.1.10",
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]
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qdrant = [
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"qdrant-client[fastembed]>=1.14.3",
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]
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aws = [
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"boto3>=1.40.38",
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]
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watson = [
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"ibm-watsonx-ai>=1.3.39",
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]
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voyageai = [
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"voyageai>=0.3.5",
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]
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litellm = [
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"litellm>=1.74.9",
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]
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[project.scripts]
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crewai = "crewai.cli.cli:crewai"
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# PyTorch index configuration, since torch 2.5.0 is not compatible with python 3.13
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[[tool.uv.index]]
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name = "pytorch-nightly"
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url = "https://download.pytorch.org/whl/nightly/cpu"
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explicit = true
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[[tool.uv.index]]
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name = "pytorch"
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url = "https://download.pytorch.org/whl/cpu"
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explicit = true
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[tool.uv.sources]
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torch = [
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{ index = "pytorch-nightly", marker = "python_version >= '3.13'" },
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{ index = "pytorch", marker = "python_version < '3.13'" },
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]
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torchvision = [
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{ index = "pytorch-nightly", marker = "python_version >= '3.13'" },
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{ index = "pytorch", marker = "python_version < '3.13'" },
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]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.version]
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path = "src/crewai/__init__.py"
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