# LangGraph

> A low-level library for building stateful, controllable agents as graphs, with checkpointing and human-in-the-loop.

LangGraph models an agent as an explicit state graph of nodes and edges, trading some abstraction for control. Its built-in persistence (checkpointing), human-in-the-loop interrupts, and streaming make it a common choice for production agents that need to be debuggable and resumable.

Website: https://www.langchain.com/langgraph

LangGraph is a low-level orchestration library for building agents as **explicit state graphs**: you define nodes (steps), edges (transitions), and a shared state object, and the agent's control flow becomes something you can see, test, and resume. It trades the one-line convenience of higher-level frameworks for control — which is exactly what production agents tend to need once they outgrow a demo.

It is aimed at engineers building durable, multi-step or multi-agent systems where you care about persistence, branching logic, and being able to pause for human input. Despite the name, LangGraph does not require the rest of LangChain.

## Highlights

- **Graph-based control flow** — model loops, branches, and multi-agent handoffs as explicit nodes and edges instead of opaque prompt chains.
- **Persistence / checkpointing** — save and restore agent state, so runs are resumable and crash-safe.
- **Human-in-the-loop** — interrupt the graph for approval or input, then resume from the exact point.
- **Streaming** — stream tokens and intermediate steps for responsive UIs and debugging.
- **Deployable** — pairs with LangGraph Platform for hosted deployment, plus [LangSmith](/tools/langsmith) for tracing.

## In an AI-assisted workflow

```python
from langgraph.graph import StateGraph, START, END

g = StateGraph(State)
g.add_node("retrieve", retrieve)
g.add_node("generate", generate)
g.add_edge(START, "retrieve"); g.add_edge("retrieve", "generate"); g.add_edge("generate", END)
app = g.compile(checkpointer=checkpointer)  # resumable, interruptible
```

> [!TIP]
> Reach for LangGraph when you need control and durability (checkpoints, HITL, branching). For quick role-based multi-agent setups, a higher-level framework like [CrewAI](/tools/crewai) is faster to start — see [the framework comparison](/guides/concepts/agent-frameworks-2026).

## Good to know

LangGraph is open source (MIT) and free to self-host; the optional LangGraph Platform (hosted deployment) and LangSmith (observability) are commercial. It's lower-level than role-based frameworks, so expect to write more wiring in exchange for more control.

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_Source: https://agentscamp.com/tools/langgraph — Tool on AgentsCamp._