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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.

Get started

Install LangGraph:

pip install -U langgraph

Create a simple workflow:

from langgraph.graph import START, StateGraph
from typing_extensions import TypedDict

class State(TypedDict):
    text: str

def node_a(state: State) -> dict:
    return {"text": state["text"] + "a"}

def node_b(state: State) -> dict:
    return {"text": state["text"] + "b"}

graph = StateGraph(State)
graph.add_node("node_a", node_a)
graph.add_node("node_b", node_b)
graph.add_edge(START, "node_a")
graph.add_edge("node_a", "node_b")
print(graph.compile().invoke({"text": ""}))
{'text': 'ab'}

Get started with the LangGraph Quickstart.

To quickly build agents with LangChain's create_agent (built on LangGraph), see the LangChain Agents documentation.

> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.

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, automatically resuming from exactly where they left off.

Human-in-the-loop: Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.

Comprehensive memory: Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.

Debugging with LangSmith: Gain deep visibility into complex agent behavior with visuali

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