LangGraph
A Python framework for building long-running agents that keep state and can resume after failures.
- Stars
- 44.3k
- Forks
- 7.6k
- Licence
- MIT
- Language
- Python
- Last update
- 4 days ago
- Type
- Library
What it is
LangGraph is a low-level framework for building workflows and AI agents that keep track of their state over time. It is designed for tasks that run for a long stretch, can pause for human review, and need to pick up where they left off after an interruption. It is produced by LangChain Inc, but the README says it can be used without LangChain.
- Build an agent that saves progress and resumes automatically after a crash
- Add a human approval step where a person can inspect and change agent state before it continues
- Give an agent short-term working memory during a task and long-term memory across sessions
- Trace and debug agent runs visually when paired with LangSmith
Great for
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
Not ideal for
- Beginners who just want a ready-made agent: the README points to Deep Agents, a higher-level package built on LangGraph, for quicker starts
- Non-programmers, since it is a Python library that requires writing code
- JavaScript/TypeScript-only teams, who are pointed to the separate LangGraph.js project
How to get it
- 1
Install with a package manager
You'll need: Python
Install LangGraph
pip install -U langgraph - 2
Run it
- Install the package with pip install -U langgraph
- Work through the LangGraph Quickstart in the official docs
- Try the examples in the Guides section of the documentation
- 3
Get the most out of it
- Use Deep Agents if you want a higher-level starting point built on LangGraph
- Pair with LangSmith for tracing, evaluation, and visual debugging of agent runs
- Use the LangChain Academy free introductory course to learn the basics
- Use the built-in memory and human-in-the-loop features for long-running workflows
Downloads
Strengths
- Built-in durable execution that resumes workflows from where they stopped, per the README
- Supports human-in-the-loop checkpoints and both short-term and long-term memory
- Open source under the MIT license with extensive documentation and a free course
Trade-offs
- It is a low-level framework, so it has a steeper learning curve than higher-level tools
- Several of the most useful features (debugging, deployment) point to LangSmith, a separate product
- The README is truncated and does not show full API details, so the depth of specific features is hard to verify from it alone
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| langgraph (this) | 44.3k | Library | intermediate | 4 days ago | Python developers building multi-step AI agents that must survive failures or long waits |
| crewAI | 60.4k | Library | intermediate | 4 days ago | Python developers prototyping multi-agent workflows |
| langchain | 149k | Framework | intermediate | 4 days ago | Python developers building LLM-powered applications |
| Prompt-Engineering-Guide | 79.8k | Learning resource | beginner | 8 months ago | People who are new to prompting and want structured lessons with examples |
| deepagents | 30.9k | Library | intermediate | 4 days ago | Developers building AI agents that run multi-step tasks such as research and writing |
| langflow | 156k | App | intermediate | 4 days ago | Developers who want a visual way to prototype LLM workflows and then move them into code |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.