# LangGraph

> Low-level orchestration framework for building stateful, long-running AI agents

- Category: Agents & Automation / Agent Frameworks & Builders
- Language: Python
- Licence: MIT
- Repository: https://github.com/langchain-ai/langgraph
- Website: https://docs.langchain.com/oss/python/langgraph/
- Canonical page: https://ai-tldr.dev/tools/langgraph/

## In short

LangGraph is a low-level orchestration framework from the LangChain team for building, managing, and deploying long-running, stateful agents. Instead of hiding control flow, it lets you define agent workflows explicitly so you decide how steps run, how state is passed along, and where the agent can pause. It sits in the Agent Frameworks & Builders category of the Agents & Automation tools and is open-source, written in Python and licensed MIT.

## Overview

LangGraph is a low-level orchestration framework from the LangChain team for building, managing, and deploying long-running, stateful agents. Instead of hiding control flow, it lets you define agent workflows explicitly so you decide how steps run, how state is passed along, and where the agent can pause.

It is aimed at developers who need more control than a high-level agent wrapper gives them. You handle persistence, memory, and human review directly, which suits workflows that can fail, run for a long time, or need a person to check a result before continuing. Companies including Klarna, Replit, and Elastic use it in production.

As a general agent framework, LangGraph can be used on its own or paired with the wider LangChain ecosystem. It works with LangChain for model integrations, LangSmith for debugging and observability, and Deep Agents, a higher-level package built on top of it for planning and subagents.

## What it does

- Durable execution: agents persist through failures and resume from exactly where they left off
- Human-in-the-loop: inspect and modify agent state at any point during a run
- Short-term working memory plus long-term memory that persists across sessions
- Debugging and tracing through LangSmith, including execution paths and state transitions
- Production deployment for long-running, stateful workflows
- JS/TS support via LangGraph.js for the same patterns in JavaScript

## Getting started

LangGraph is a Python package installed from PyPI. Install it, then follow the official quickstart to build your first graph.

1. Install LangGraph

   Install the latest release from PyPI with pip.

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

2. Follow the quickstart

   The README does not include inline starter code. Use the official LangGraph Quickstart to build a first graph, and see the Python docs for memory, interrupts, and durable execution. For higher-level agents that plan and use subagents, see the Deep Agents package built on LangGraph.

## When to use it

- Building long-running agents that must survive failures and resume from the last completed step
- Adding human review checkpoints so a person can inspect or edit agent state before it continues
- Giving an agent both short-term reasoning memory and long-term memory across sessions
- Orchestrating multi-step agent workflows where you need explicit control over branching and state