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langgraph: Build Robust Multi‑Agent AI Systems with 43k+ GitHub Stars

Launch resilient, enterprise‑grade AI agents in Python fast. Ideal for founders needing scalable, open‑source multi‑LLM workflows.

From the repo · “Build resilient agents.”

43,719 stars+414★ this week7,495 forksPythonGuide quality 9/10 · reviewed Oct 5Install verified Sep 21Updated 10/22/2026100% free · open source
What it does

Langgraph allows startup founders to build resilient agents that can understand and generate human-like language

When to use it
  • •When you need to create a conversational AI that can engage with customers
  • •When you want to build a language-based interface for your application
  • •When you need to automate tasks that involve understanding and generating human language
Ready-to-paste prompt
python -m langchain --prompt 'Create a conversational AI that can answer questions about my startup'
Heads up: You need to have a compatible Python version (3.8 or higher) and a compatible LLM (such as LLaMA or Transformers) installed to use Langgraph
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup✓ Install verified 9/21/2026

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/langgraph && curl -fsSL https://workflowstacks.com/api/skills/langgraph/claude-skill -o ~/.claude/skills/langgraph/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How langgraph: Build Robust Multi‑Agent AI Systems with 43k+ GitHub Stars works
Codeflow
Free to inspect

Langgraph: Build Robust Multi‑Agent AI Systems with 43k+ GitHub Stars is a very large Python project (~78k lines across 256 code files, plus 205 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit this month, MIT license, has a test suite.

Size
Very large codebase
~78k lines · 256 code files · days to read — use, don't read
Setup
One-command install
Installs like a normal app. Reading the code is optional.
Runs on
Python
No API keys detected
Python 100%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    libs/langgraph/README.md
    Inside libs/langgraph/ — the main logic begins here
  4. 4
    examples/README.md
    A worked example — copy this to get going
What's in each folder
libs/langgraph/Core code — the actual logic164 files
libs/cli/Command-line entry points145 files
examples/Examples you can copy39 files
docs/Documentation4 files
libs/sdk-py/Folder139 files
libs/checkpoint/Folder36 files
libs/prebuilt/Folder35 files
libs/checkpoint-postgres/Folder26 files
READMEHas testsDocumentedCI checksExamples includedMIT licenseUpdated this month
Quick Actions
Details
Creator
langchain-ai
Language
Python
Category
ai-agent
Published
8/9/2023

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Featured on WorkflowStacks
[![Featured on WorkflowStacks](https://workflowstacks.com/api/badge/langgraph.svg)](https://workflowstacks.com/skills/langgraph?utm_source=github&utm_medium=badge)
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