Langchain Development

This one sets you up with LangChain and LangGraph best practices for building LLM apps in Python. It covers the full stack: LCEL chain composition with pipes, agent and tool development with proper schemas, RAG implementations from document splitting through vector stores, and state management with LangGraph's TypedDict patterns. The directory structure guidance alone saves you from the usual project mess. Strong emphasis on async patterns, LangSmith tracing integration, and real production concerns like retry logic and fallback chains. Opinionated about functional style over classes, which matches how most modern LangChain code actually gets written.