---
title: "langgraph"
type: "tool"
slug: "langchain-ai-langgraph"
canonical_url: "https://www.graphcanon.com/tools/langchain-ai-langgraph"
github_url: "https://github.com/langchain-ai/langgraph"
homepage_url: "https://docs.langchain.com/oss/python/langgraph/"
stars: 36718
forks: 6161
primary_language: "Python"
license: "MIT"
categories: ["ai-agents", "llm-frameworks", "inference-serving"]
tags: ["agents", "ai", "gemini", "deepagents", "chatgpt", "framework", "ai-agents", "enterprise"]
updated_at: "2026-07-07T17:31:44.536059+00:00"
---

# langgraph

> Build resilient agents.

Build resilient agents.

## Facts

- Repository: https://github.com/langchain-ai/langgraph
- Homepage: https://docs.langchain.com/oss/python/langgraph/
- Stars: 36,718 · Forks: 6,161 · Open issues: 602 · Watchers: 165
- Primary language: Python
- License: MIT
- Last pushed: 2026-07-06T20:40:30+00:00

## Categories

- [AI Agents](/categories/ai-agents.md)
- [LLM Frameworks](/categories/llm-frameworks.md)
- [Inference & Serving](/categories/inference-serving.md)

## Tags

agents, ai, gemini, deepagents, chatgpt, framework, ai-agents, enterprise

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## README (excerpt)

```text
<div align="center">
  <a href="https://www.langchain.com/langgraph">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
      <source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
      <img alt="LangGraph Logo" src=".github/images/logo-dark.svg" width="50%">
    </picture>
  </a>
</div>

<div align="center">
  <h3>Low-level orchestration framework for building stateful agents.</h3>
</div>

<div align="center">
  <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/pypi/l/langgraph" alt="PyPI - License"></a>
  <a href="https://pypistats.org/packages/langgraph" target="_blank"><img src="https://img.shields.io/pepy/dt/langgraph" alt="PyPI - Downloads"></a>
  <a href="https://pypi.org/project/langgraph/" target="_blank"><img src="https://img.shields.io/pypi/v/langgraph.svg?label=%20" alt="Version"></a>
  <a href="https://x.com/langchain_oss" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
</div>

<br>

Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.

```bash
pip install -U langgraph
```

> [!TIP]
> If you're looking to quickly build agents, check out **[Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview)** — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.

For an equivalent JS/TS library, check out [LangGraph.js](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).

## Why use LangGraph?

LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent:

- **[Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution)** — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- **[Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts)** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- **[Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory)** — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- **[Debugging with LangSmith](https://www.langchain.com/langsmith)** — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- **[Production-ready deployment](https://docs.langchain.com/langsmith/deployments)** — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.

> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).

## LangGraph ecosystem

While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.

To improve your LLM application development, pair LangGraph with:

- [Deep Agents](https://docs.langchain.com/oss/python/deepagents/overview) – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
- [LangSmith](https://www.langchain.com/langsmith) – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate age
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/langchain-ai-langgraph`](/api/graphcanon/tools/langchain-ai-langgraph)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_