- Updated the Stream method to include options for handling message history and context more effectively, ensuring original messages are preserved for autoSearch and delegation.
- Introduced a new buildContextMessage function to consolidate conversation context, filtering out system messages and limiting to the last five user messages for efficiency.
- Enhanced content processing by adding a convertToContentParts function to handle different content formats, improving compatibility with historical data.
- Improved logging and error handling in the executeLLMStream method to ensure clarity in LLM request tracing and response handling.
- Added new utility functions for extracting text content and building context messages, enhancing overall code clarity and maintainability.
- Added a new method to set store settings during assistant initialization, allowing for configuration of storage parameters such as MaxSize and TTL.
- Updated context creation methods to streamline the setup process, ensuring that essential fields are populated consistently across various test contexts.
- Revised tests to validate the new initialization behavior and context management, ensuring proper handling of assistant settings and context properties.
- Updated the Stream, BuildContent, and LLM execution methods to accept an Options parameter, allowing for more flexible context handling.
- Removed debug print statements to clean up the code and improve readability.
- Enhanced locale handling in the loadMap function to automatically inject assistant name and description into all locales, ensuring better localization support.
- Introduced output skipping functionality in context options to manage internal A2A calls more effectively.
- Improved output writer resolution logic to prioritize context settings, enhancing output management during agent calls.
- Implemented traceLLMFail method to mark LLM requests as failed in the trace context.
- Updated executeLLMStream and executeLLMForToolRetry methods to call traceLLMFail on errors, enhancing error tracking and debugging capabilities.
- Improved overall error handling in LLM execution flows, ensuring failures are properly logged in the trace.
- Added detailed debug logging throughout the Stream method to trace execution flow and tool call results, improving visibility during runtime.
- Implemented error tracing with the new traceAgentFail method to capture failures in agent nodes, enhancing error handling.
- Updated MCP tools debugging to provide insights into tool application and validation processes, aiding in troubleshooting.
- Refactored output handling in traceAgentOutput for better clarity and consistency in response management.
- Enhanced the Stream method to include a retry mechanism for tool calls, allowing for up to three attempts in case of errors.
- Introduced detailed logging for each retry attempt, improving traceability and debugging capabilities.
- Added a new method, buildToolRetryMessages, to construct messages for LLM retries, ensuring proper context is maintained.
- Updated the executeLLMForToolRetry method to handle streaming output during retries, providing real-time feedback to users.
- Improved error handling to differentiate between retryable and non-retryable errors, enhancing the robustness of tool call executions.
- Updated MCP server configuration to support advanced formats, including tools and resources.
- Refactored the Assistant's Stream method for improved readability and maintainability, adding clear section comments.
- Enhanced error handling and logging for better traceability during streaming operations.
- Removed deprecated methods and streamlined MCP server handling in tests to ensure robust functionality and validation.
- Simplified the Stream method by extracting capability initialization into a separate function, improving readability and maintainability.
- Enhanced error handling by centralizing trace logging for failures and history tracking.
- Removed the MCP hook implementation as it was no longer needed, streamlining the codebase.
- Updated agent trace handling to ensure consistent logging of agent output and errors.
