- 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.
- 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.
- OpenAI: Use Uploads API for files > 512MB with chunked streaming
- Gemini: Pass file path directly to SDK for FilePath sources
- Bedrock: Use upload_fileobj with TransferConfig for automatic multipart
- Add tests for file processing constraints and validators
- Add tests for FileProcessor and FileResolver
- Add tests for resolved file types
- Add tests for file store operations
- Add unit tests for multimodal LLM support
- Add input_files parameter to Task for file attachments
- Add file_handling mode to Crew for processing behavior
- Integrate file injection in CrewAgentExecutor
- Update prepare_kickoff to handle KickoffInputs type
- Add format_multimodal_content() to all LLM providers
- Support inline base64 and file reference formats
- Add FileResolver integration for upload caching
- Add module exports for files package
- 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.
- 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.
- 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.
