Worked on stabilizing caching behavior in the langchain-ai/langchain-google repository by addressing a bug related to cache control preservation during the conversion of ToolMessage to tool_result. Focused on backend development and API integration using Python, the approach ensured that cache_control settings were maintained throughout the workflow, particularly for Vertex AI integrations. This fix improved the reliability and integrity of cached tool results by preventing downstream cache-related failures. The solution was validated through targeted unit testing and integration checks, and maintainability documentation was updated to support future enhancements to the caching layer, aligning the codebase with existing caching infrastructure.