Worked on the langchain-ai/langgraphjs repository, focusing on improving documentation accuracy and API consistency for Python-based notebook examples. Addressed a critical issue in the LangGraph documentation by correcting the parameter name used in the TavilySearchResults tool invocation within the langgraph_crag.ipynb example, updating it from 'query' to 'input' to match current API requirements. This targeted fix enhanced the clarity of onboarding materials and reduced potential confusion for developers integrating with the API. Leveraged skills in documentation and example maintenance to ensure that code samples accurately reflected expected usage, ultimately supporting a smoother user experience and lowering support overhead.