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Last Updated: 3/7/2026

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LangGraph APIs

LangGraph overview

Gain control with LangGraph to design agents that reliably handle complex tasks

Trusted by companies shaping the future of agents— including Klarna, Replit, Elastic, and more— LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. LangGraph is very low-level, and focused entirely on agent orchestration. Before using LangGraph, we recommend you familiarize yourself with some of the components used to build agents, starting with models and tools. We will commonly use LangChain components throughout the documentation to integrate models and tools, but you don’t need to use LangChain to use LangGraph. If you are just getting started with agents or want a higher-level abstraction, we recommend you use LangChain’s agents that provide pre-built architectures for common LLM and tool-calling loops. LangGraph is focused on the underlying capabilities important for agent orchestration: durable execution, streaming, human-in-the-loop, and more.

​ Install

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pip install -U langgraph pip  install -U  langgraph

Then, create a simple hello world example:

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from langgraph.graph import StateGraph, MessagesState, START, END from langgraph.graph import StateGraph, MessagesState, START, END def mock_llm(state: MessagesState): def  mock_llm(state: MessagesState): return {"messages": [{"role": "ai", "content": "hello world"}]}  return {"messages": [{"role": "ai", "content": "hello world"}]} graph = StateGraph(MessagesState) graph = StateGraph(MessagesState)graph.add_node(mock_llm)graph.add_node(mock_llm)graph.add_edge(START, "mock_llm")graph.add_edge(START, "mock_llm")graph.add_edge("mock_llm", END)graph.add_edge("mock_llm", END)graph = graph.compile() graph = graph.compile() graph.invoke({"messages": [{"role": "user", "content": "hi!"}]})graph.invoke({"messages": [{"role": "user", "content": "hi!"}]})

​ Core benefits

LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:

  • Durable execution: Build agents that persist through failures and can run for extended periods, resuming from where they left off.
  • Human-in-the-loop: Incorporate human oversight by inspecting and modifying agent state at any point.
  • Comprehensive memory: Create stateful agents with both short-term working memory for ongoing reasoning and long-term memory across sessions.
  • Debugging with 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: Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.

​ 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:

[## LangSmith

Trace requests, evaluate outputs, and monitor deployments in one place. Prototype locally with LangGraph, then move to production with integrated observability and evaluation to build more reliable agent systems.](http://www.langchain.com/langsmith)[##  LangSmith Agent Server

Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in Studio.](/langsmith/agent-server)[## LangChain

Provides integrations and composable components to streamline LLM application development. Contains agent abstractions built on top of LangGraph.](/oss/python/langchain/overview)

​ Acknowledgements

LangGraph is inspired by Pregel  and Apache Beam . The public interface draws inspiration from NetworkX . LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.

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Install LangGraph

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