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#  🦜🕸️LangGraph

[ ![Downloads](https://static.pepy.tech/badge/langgraph/month)
](https://pepy.tech/project/langgraph) [ ![Open
Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)
](https://github.com/langchain-ai/langgraph/issues) [
![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)
](https://discord.com/channels/1038097195422978059/1170024642245832774)

⚡ Building language agents as graphs ⚡

##  Overview  ​

[ LangGraph ](https://github.com/langchain-ai/langgraph) is a library for
building stateful, multi-actor applications with LLMs, built on top of (and
intended to be used with) [ LangChain ](https://github.com/langchain-
ai/langchain) . It extends the [ LangChain Expression Language
](https://python.langchain.com/docs/expression_language/) with the ability to
coordinate multiple chains (or actors) across multiple steps of computation in
a cyclic manner. It is inspired by [ Pregel
](https://research.google/pubs/pub37252/) and [ Apache Beam
](https://beam.apache.org/) . The current interface exposed is one inspired by
[ NetworkX ](https://networkx.org/documentation/latest/) .

The main use is for adding **cycles** to your LLM application. Crucially,
LangGraph is NOT optimized for only **DAG** workflows. If you want to build a
DAG, you should just use [ LangChain Expression Language
](https://python.langchain.com/docs/expression_language/) .

Cycles are important for agent-like behaviors, where you call an LLM in a
loop, asking it what action to take next.

##  Installation  ​

    
    
    pip install langgraph  
    

##  Quick start  ​

One of the central concepts of LangGraph