How do I install the Langchain Docs skill?

Run npx skillmds@latest add tomevault-io/langchain-docs-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Langchain Docs safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Langchain Docs?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Langchain Docs free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Langchain Docs?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Langchain Docs by tomevault-io — Agent Skill

Name: Langchain Docs
Author: tomevault-io

Langchain Docs

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. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

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

Task	Start Here
Getting started	`references/learn.mdx`
LangChain overview	`references/langchain/overview.mdx`
Build an agent	`references/langchain/agents.mdx`
RAG implementation	`references/langchain/knowledge-base.mdx`
LangGraph intro	`references/langgraph/graph-api.mdx`
Memory & state	`references/langgraph/memory.mdx`
Python API	`references/python/`
JavaScript API	`references/javascript/`

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

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

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

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---name: langchain-docs-2description: 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. Use when this capability is needed.---# LangChain DocumentationLangChain 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| Task | Start Here ||------|------------|| Getting started | `references/learn.mdx` || LangChain overview | `references/langchain/overview.mdx` || Build an agent | `references/langchain/agents.mdx` || RAG implementation | `references/langchain/knowledge-base.mdx` || LangGraph intro | `references/langgraph/graph-api.mdx` || Memory & state | `references/langgraph/memory.mdx` || Python API | `references/python/` || JavaScript API | `references/javascript/` |## When to useUse 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 information1. **First**, read `references/STRUCTURE.md` to see all 1688 documentation files organized by directory2. Use Navigation Guide to find the section3. Check Key Entry Points for common tasks4. For API details: `references/python/` or `references/javascript/`**STRUCTURE.md contains a complete file listing - always check it first when searching for specific topics.**---> Converted and distributed by [TomeVault](https://tomevault.io/claim/jasonz-ncc42) — claim your Tome and manage your conversions.

Frequently asked questions