Official agent skill

Langgraph

Build stateful, durable agent workflows with LangGraph. An agent skill from langchain-ai/docs.

OfficialMITAuto-check passedAI & LLM Engineering

Install Langgraph

skills CLI

$ npx skills add langchain-ai/docs --skill langgraph -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install langchain-ai/docs langgraph --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/langchain-ai/docs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/.mintlify/skills/langgraph .claude/skills/langgraph && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "langgraph" agent skill from https://github.com/langchain-ai/docs/tree/main/src/.mintlify/skills/langgraph into .claude/skills/langgraph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/langchain-ai/docs/tree/main/src/.mintlify/skills/langgraph

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add langchain-ai/docs --skill langgraph -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install langchain-ai/docs langgraph --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/langchain-ai/docs.git skills-src && mkdir -p .agents/skills && cp -r skills-src/src/.mintlify/skills/langgraph .agents/skills/langgraph && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "langgraph" agent skill from https://github.com/langchain-ai/docs/tree/main/src/.mintlify/skills/langgraph into .agents/skills/langgraph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add langchain-ai/docs --skill langgraph -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install langchain-ai/docs langgraph --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/langchain-ai/docs.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/src/.mintlify/skills/langgraph .cursor/skills/langgraph && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "langgraph" agent skill from https://github.com/langchain-ai/docs/tree/main/src/.mintlify/skills/langgraph into .cursor/skills/langgraph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/langchain-ai/docs.git --path src/.mintlify/skills/langgraph

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add langchain-ai/docs --skill langgraph -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install langchain-ai/docs langgraph --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/langchain-ai/docs.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/src/.mintlify/skills/langgraph .gemini/skills/langgraph && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "langgraph" agent skill from https://github.com/langchain-ai/docs/tree/main/src/.mintlify/skills/langgraph into .gemini/skills/langgraph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install langchain-ai/docs langgraph

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add langchain-ai/docs --skill langgraph -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/langchain-ai/docs.git skills-src && mkdir -p .github/skills && cp -r skills-src/src/.mintlify/skills/langgraph .github/skills/langgraph && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "langgraph" agent skill from https://github.com/langchain-ai/docs/tree/main/src/.mintlify/skills/langgraph into .github/skills/langgraph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add langchain-ai/docs --skill langgraph -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install langchain-ai/docs langgraph --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/langchain-ai/docs.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/src/.mintlify/skills/langgraph .opencode/skills/langgraph && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "langgraph" agent skill from https://github.com/langchain-ai/docs/tree/main/src/.mintlify/skills/langgraph into .opencode/skills/langgraph/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langgraph", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: langgraph
GitHub stars: 425
Token cost: ~1.1k tokens
SKILL.md length: 285 words
Files: 1
Skills in repo: 17
Repo updated: Oct 8, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Build stateful, durable agent workflows with LangGraph. An agent skill from langchain-ai/docs.

You need custom graph-based control flow
SKILL.md covers When to use, When NOT to use, Install and Quick reference, plus 4 more sections
Calls pip and npm; reaches reference.langchain.com
Human-in-the-loop

What it does

Langgraph is an agent skill from langchain-ai/docs, published by the product's own GitHub organization. Build stateful, durable agent workflows with LangGraph. Use when you need custom graph-based control flow, human-in-the-loop, persistence, or multi-agent orchestration.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Python 3.10+, Node.js 22+

It sits in AI & LLM Engineering, covering Building AI agents and Human-in-the-loop approvals. It works with LangGraph, LangChain and LangSmith. The repository describes itself as: Unified LangChain documentation. The licence is MIT.

When your agent uses it

You need custom graph-based control flow
Human-in-the-loop
Multi-agent orchestration

Example prompts

“/langgraph”

Requirements

Python 3
Node.js
Compatibility (from SKILL.md): Python 3.10+, Node.js 22+

What it can do on your machine

Read from SKILL.md and the folder at commit dcfc8b4. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- pip
- npm
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- reference.langchain.com
Also links to:
- docs.langchain.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Python 3.10+, Node.js 22+
From compatibility in the SKILL.md frontmatter.

Context cost

Langgraph loads about 1.1k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 285 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~45
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from langchain-ai/docs at commit dcfc8b4, republished under its MIT licence (© langchain-ai). 285 words, ~1,060 tokens.

Download SKILL.mdSave it as .claude/skills/langgraph/SKILL.md (or your agent's skills folder).

name: langgraph
description: Build stateful, durable agent workflows with LangGraph. Use when you need custom graph-based control flow, human-in-the-loop, persistence, or multi-agent orchestration.
compatibility: Python 3.10+, Node.js 22+
license: MIT
metadata.author: langchain-ai
metadata.version: 1.0

LangGraph

LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. It provides durable execution, streaming, human-in-the-loop interactions, and time-travel debugging.

When to use

Use LangGraph when you need to:

Design custom agent workflows with explicit graph-based control flow
Add durable execution so agents survive failures and restarts
Implement human-in-the-loop with interrupts and approval steps
Build multi-agent systems with state shared across agents
Stream intermediate results from long-running agent tasks
Time-travel debug by replaying agent execution from any checkpoint

When NOT to use

For a simple tool-calling agent, use LangChain agents instead—less boilerplate for common patterns
For a batteries-included agent with planning and subagents, use Deep Agents instead
LangGraph is the orchestration layer—use it when you need fine-grained control over agent behavior

Install

bash

# Python
pip install -U langgraph

# JavaScript/TypeScript
npm install @langchain/langgraph @langchain/core

Quick reference

Graph API (recommended for most use cases)

python

from langgraph.graph import StateGraph, MessagesState, START, END

def my_node(state: MessagesState):
    return {"messages": [{"role": "ai", "content": "hello world"}]}

graph = StateGraph(MessagesState)
graph.add_node(my_node)
graph.add_edge(START, "my_node")
graph.add_edge("my_node", END)
graph = graph.compile()

result = graph.invoke(
    {"messages": [{"role": "user", "content": "Hello!"}]}
)

Functional API (for simple pipelines)

python

from langgraph.func import entrypoint, task

@task
def step_one(input: str) -> str:
    return f"processed: {input}"

@entrypoint()
def pipeline(input: str) -> str:
    return step_one(input).result()

Add human-in-the-loop

python

from langgraph.types import interrupt

def human_approval(state: MessagesState):
    answer = interrupt({"question": "Approve this action?"})
    return {"messages": [{"role": "user", "content": answer}]}

Key concepts

Concept	Description
`StateGraph`	Define nodes and edges that form your agent's control flow
`MessagesState`	Built-in state schema for chat-based agents
`compile()`	Compile a graph builder into an executable graph
`interrupt()`	Pause execution and wait for human input
Checkpointer	Persist state for durable execution and time-travel
Graph API vs Functional API	Graph API for complex workflows; Functional API for linear pipelines

Key documentation

Overview—What LangGraph is and when to use it
Quickstart—Build your first graph
Persistence—Add memory and durable execution
Interrupts—Human-in-the-loop patterns
Streaming—Stream intermediate results
Graph API—Define nodes, edges, and state
Deploy—Deploy to production with LangSmith

API reference

For SDK class and method details, use the LangChain API Reference site:

Browse: https://reference.langchain.com/python/langgraph
MCP server: https://reference.langchain.com/mcp

langchain—Core building blocks for models, tools, and simple agents
deep-agents—High-level agent harness built on LangGraph
langsmith—Trace, evaluate, and deploy your LangGraph agents

Files

Just SKILL.md in src/.mintlify/skills/langgraph of langchain-ai/docs.

Open the folder on GitHubat commit dcfc8b4

Compare with similar skills

Langgraph next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Langgraph compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Langgraph this skilllangchain-ai/docs	425	—	~1.1k	Automated safety check: Pass	MIT	today
Deep Agents Corelangchain-ai/langchain-skills	1.3k	1 repos	~3.1k	Automated safety check: Pass	MIT	2 days ago
LangSmith Trace DebuggingComposioHQ/awesome-claude-skills	77k	9 repos	~2.7k	Automated safety check: Pass	None	20 days ago
Dive Into LangGraphluochang212/dive-into-langgraph	457	—	~837	Automated safety check: Notes	Custom licence	27 days ago
LangGraph Decision Modelslangchain-ai/langchain-skills	1.3k	—	~2.3k	Automated safety check: Pass	MIT	2 days ago
Langchain Dependencieslangchain-ai/langchain-skills	1.3k	1 repos	~3.6k	Automated safety check: Pass	MIT	2 days ago

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More from langchain-ai/docs

All 17 skills in this repo

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Docs Code Samples
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Docs Edit
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Edit a docs page that already has an open pull request, or revise a page in place.
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Docs Restructure
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Restructure documentation that spans several pages. An agent skill from langchain-ai/docs.
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Docs Team Voice
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Write or revise documentation prose so it reads like the rest of this site, in the docs team's shared voice.
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Works with

Questions about Langgraph

What does Langgraph do?

Build stateful, durable agent workflows with LangGraph. An agent skill from langchain-ai/docs. Langgraph is an agent skill from langchain-ai/docs, published by the product's own GitHub organization. Build stateful, durable agent workflows with LangGraph.

When should I use Langgraph?

Langgraph fits situations like: you need custom graph-based control flow; human-in-the-loop; multi-agent orchestration.

How do I install Langgraph in Claude Code?

Run `npx skills add langchain-ai/docs --skill langgraph -a claude-code`. Or copy the skill folder (src/.mintlify/skills/langgraph in langchain-ai/docs) into .claude/skills/langgraph in your project. Claude Code loads it when a task matches its description.

How do I install Langgraph in Codex?

Run `npx skills add langchain-ai/docs --skill langgraph -a codex`. Or copy the skill folder (src/.mintlify/skills/langgraph in langchain-ai/docs) into .agents/skills/langgraph in your project. Codex loads it when a task matches its description.

Can I use Langgraph in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add langchain-ai/docs --skill langgraph -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langgraph, .gemini/skills/langgraph, .github/skills/langgraph and .opencode/skills/langgraph in your project.

What does Langgraph need to run?

Going by SKILL.md and its folder, Langgraph needs the command-line tools its instructions call (pip and npm). Our summary lists: Python 3; Node.js. Compatibility (from SKILL.md): Python 3.10+, Node.js 22+.

Does Langgraph access the network?

SKILL.md names 2 domains. In commands or code: reference.langchain.com; the agent is likely to contact it when it follows the instructions. As links in the text: docs.langchain.com. This is read from the text; nothing was executed.

Is Langgraph safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Langgraph use?

Langgraph is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Langgraph use?

About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Langgraph?

Skills that share tags, products or a category with Langgraph: Deep Agents Core (langchain-ai/langchain-skills, 1.3k stars), LangSmith Trace Debugging (ComposioHQ/awesome-claude-skills, 77k stars), Dive Into LangGraph (luochang212/dive-into-langgraph, 457 stars) and LangGraph Decision Models (langchain-ai/langchain-skills, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Langgraph?

langchain-ai (a GitHub organization, an official publisher) maintains it in langchain-ai/docs, which has 425 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 8, 2026.

Source: langchain-ai/docs on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Langgraph

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Compatibility

Context cost

Safety

SKILL.md

LangGraph

When to use

When NOT to use

Install

Quick reference

Graph API (recommended for most use cases)

Functional API (for simple pipelines)

Add human-in-the-loop

Key concepts

Key documentation

API reference

Related skills

Files

Compare with similar skills

Similar skills

More from langchain-ai/docs

Related

Questions about Langgraph