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name: langchain-docs description: Local LangChain AI documentation reference. Use when asked about LangChain, LangGraph, agents, chains, prompts, memory, tools, retrieval, RAG, vector stores, document loaders, or building LLM applications.

LangChain Documentation

LangChain is a framework for building LLM-powered applications. It provides components for chains, agents, RAG, memory, and tools. LangGraph extends this with stateful multi-agent orchestration.

Navigation Guide

LangChain: references/langchain/ - Core framework docs (51 files)

• Agents, chains, RAG, memory, tools, models, guardrails
• Key files: overview.mdx, agents.mdx, knowledge-base.mdx

LangGraph: references/langgraph/ - Multi-agent orchestration (35 files)

• Graph API, memory, interrupts, durable execution
• Key files: graph-api.mdx, memory.mdx, agentic-rag.mdx

Python SDK: references/python/ - Python API reference (1283 files)

• Complete API docs for langchain-core, langchain, langgraph

JavaScript SDK: references/javascript/ - JS/TS API reference (299 files)

• Complete API docs for @langchain/core, langchain, langgraph

Concepts: references/concepts/ - Foundational concepts (2 files)

Deep Agents: references/deepagents/ - Advanced agent patterns (10 files)

Key Entry Points

When to use

Use this skill when the user asks about:

• LangChain chains, agents, or components
• LangGraph multi-agent orchestration
• RAG (Retrieval Augmented Generation)
• Memory and conversation history
• Tools and tool calling
• Vector stores and embeddings
• Building LLM applications

How to find information

1. First, read references/STRUCTURE.md to see all 1688 documentation files organized by directory
2. Use Navigation Guide to find the section
3. Check Key Entry Points for common tasks
4. For API details: references/python/ or references/javascript/

STRUCTURE.md contains a complete file listing - always check it first when searching for specific topics.