Langchain Architecture

This is a comprehensive reference for building LLM applications with LangChain's core primitives: agents, chains, memory systems, and document processing pipelines. You get concrete patterns for RAG implementations, custom tool integration, and multi-step workflows, plus practical guidance on choosing between memory types like ConversationBufferMemory for short chats versus ConversationSummaryMemory for longer sessions. The callback system examples and testing strategies are genuinely useful. Most valuable when you're architecting production LLM apps and need to understand how these components fit together, though the code samples assume you already know Python basics. It won't teach you LangChain from scratch, but it's solid for reference when implementing specific patterns.