langchain▌

Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.

Framework for building LLM applications with agents, chains, RAG, and 500+ integrations. \n \n Supports multiple LLM providers (OpenAI, Anthropic, Google) with unified interface and easy provider swapping \n Implements ReAct agents with tool calling, structured outputs, and parallel tool execution for autonomous reasoning \n Includes RAG pipelines with document loaders, text splitters, vector stores (Chroma, Pinecone, FAISS), and retrieval chains \n Provides conversation memory management, strea

Build production LangChain agents with create_agent(), tools, and middleware patterns. \n \n Use create_agent() with model, tools list, and system prompt; configure state persistence with checkpointer and thread_id for conversation memory across invocations \n Define tools via @tool decorator (Python) or tool() function (TypeScript) with clear descriptions so agents know when to call them \n Add middleware like HumanInTheLoopMiddleware for approval workflows, custom error handling, and human-in-

Build sophisticated LLM applications with LangChain 1.x and LangGraph for agents, memory, and tool integration. \n \n LangGraph provides the standard agent framework with StateGraph for explicit state management, durable execution, human-in-the-loop inspection, and checkpointing across sessions \n Supports ReAct agents, plan-and-execute workflows, multi-agent supervision, and structured tool invocation with Pydantic schemas \n Memory systems include ConversationBufferMemory, ConversationSummaryM

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

Complete RAG pipeline for document ingestion, embedding, retrieval, and LLM-powered response generation. \n \n Supports multiple document loaders (PDF, web pages, directories) and persistent vector stores (Chroma, FAISS, Pinecone) with configurable chunk size and overlap for optimal context preservation \n Includes similarity search, MMR (Maximal Marginal Relevance) retrieval, and metadata filtering to balance relevance and diversity in results \n Works with OpenAI embeddings and integrates seam

Human-in-the-loop approval, custom middleware, and structured output patterns for LangChain agents. \n \n HumanInTheLoopMiddleware pauses execution before dangerous tool calls, allowing humans to approve, edit arguments, or reject with feedback \n Per-tool interrupt policies let you configure different approval rules based on risk level; requires a checkpointer and thread_id for state persistence \n Command resume pattern continues execution after human decisions, with support for editing tool a

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