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Langgraph Docs

Official
langchain-ai·vlatest·agents·MIT

Langgraph Docs provides interactive documentation and examples for Langgraph, enabling developers to quickly learn and utilize its powerful graph-based AI toolchain.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add langchain-ai/deepagents --skill langgraph-docs --full-depth -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

langchain-ai

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned langchain-ai/deepagents at f2a98d2bb4.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
f2a98d2bb4
Manifest
libs/code/examples/skills/langgraph-docs/SKILL.md
Scanned
1 file, 29 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The langgraph-docs skill provides access to documentation and resources related to LangGraph, a framework for building stateful, modular AI applications. It enables users to explore how to design, implement, and manage complex workflows using graph-based structures.

When to use it

  • You're developing an application that requires managing multi-step processes or state transitions.
  • You need guidance on integrating LangGraph with other tools in the LangChain ecosystem.
  • You want to understand best practices for building modular AI agents with persistent state.

Key capabilities

  • Access to detailed documentation about LangGraph's architecture and features.
  • Examples of how to define nodes, edges, and state management within a graph.
  • Integration support with other LangChain tools and libraries.

Example prompts

  • "How do I create a stateful workflow using LangGraph?"
  • "Can you show me an example of defining nodes in a LangGraph application?"
  • "What are the best practices for integrating LangGraph with LangChain agents?"

Tips & gotchas

  • Ensure you have a foundational understanding of LangChain before diving into LangGraph.
  • The documentation assumes familiarity with concepts like state management and modular design in AI workflows.