# LangGraph — Stateful AI Agent Graphs by LangChain

> Framework for building stateful, multi-actor AI agent applications as directed graphs. Supports cycles, branching, persistence, and human-in-the-loop patterns. By LangChain. 8,000+ stars.

## Install

Save as a script file and run:

## Quick Use

```bash
pip install langgraph
```

```python
from langgraph.graph import StateGraph, END
from typing import TypedDict

class State(TypedDict):
    messages: list
    next_step: str

def research(state):
    # Agent researches the topic
    return {"messages": state["messages"] + ["Research complete"]}

def write(state):
    # Agent writes based on research
    return {"messages": state["messages"] + ["Article written"]}

graph = StateGraph(State)
graph.add_node("research", research)
graph.add_node("write", write)
graph.add_edge("research", "write")
graph.add_edge("write", END)
graph.set_entry_point("research")

app = graph.compile()
result = app.invoke({"messages": ["Write about AI agents"], "next_step": ""})
```

---

## Intro

LangGraph is a framework by LangChain for building stateful, multi-actor AI agent applications as directed graphs with 8,000+ GitHub stars. Unlike chain-based approaches, LangGraph supports cycles (agent loops), conditional branching, persistent state, and human-in-the-loop workflows. Define your agent logic as nodes and edges in a graph, and LangGraph handles state management, checkpointing, and error recovery. Best for building complex agent systems with non-linear workflows. Works with: Claude, GPT-4, any LangChain-supported model. Setup time: under 3 minutes.

---

## Core Concepts

### State Graph
Define your workflow as a directed graph:

```python
graph = StateGraph(AgentState)
graph.add_node("plan", plan_step)
graph.add_node("execute", execute_step)
graph.add_node("review", review_step)

# Conditional routing
graph.add_conditional_edges("review", decide_next, {
    "revise": "execute",   # Loop back if needs revision
    "approve": END          # Finish if approved
})
```

### Cycles (Agent Loops)
LangGraph supports loops — agents can iterate until satisfied:

```
plan → execute → review → execute → review → approve → END
                  ↑___________|
                  (revision loop)
```

### Human-in-the-Loop
Pause execution for human approval:

```python
from langgraph.checkpoint import MemorySaver

graph.add_node("human_review", interrupt_before=True)

# Agent pauses here, human reviews, then continues
app = graph.compile(checkpointer=MemorySaver())
```

### Persistence
Checkpoint state for resuming later:

```python
from langgraph.checkpoint.postgres import PostgresSaver

checkpointer = PostgresSaver(connection_string="...")
app = graph.compile(checkpointer=checkpointer)

# Resume from any checkpoint
result = app.invoke(None, config={"thread_id": "task-123"})
```

### Multi-Agent Patterns

```python
# Supervisor pattern: one agent delegates to specialists
supervisor = create_supervisor(
    agents=[researcher, coder, reviewer],
    model="claude-sonnet-4-20250514"
)

# Each agent is a subgraph node
graph.add_node("supervisor", supervisor)
graph.add_node("researcher", researcher_graph)
graph.add_node("coder", coder_graph)
```

### Key Stats
- 8,000+ GitHub stars
- By LangChain team
- Cycles and conditional branching
- Persistent checkpointing
- Human-in-the-loop support

### FAQ

**Q: What is LangGraph?**
A: LangGraph is a framework for building stateful AI agent applications as directed graphs with support for cycles, branching, persistence, and human-in-the-loop workflows.

**Q: Is LangGraph free?**
A: Yes, open-source under MIT license. LangGraph Platform (managed hosting) is paid.

**Q: How is LangGraph different from CrewAI?**
A: LangGraph uses graph-based control flow with explicit state management. CrewAI uses role-based agents with implicit orchestration. LangGraph offers finer-grained control.

---

## Source & Thanks

> Created by [LangChain](https://github.com/langchain-ai). Licensed under MIT.
>
> [langgraph](https://github.com/langchain-ai/langgraph) — ⭐ 8,000+

Thanks to the LangChain team for making complex agent workflows manageable.

---

<!-- ZH -->

## 快速使用

```bash
pip install langgraph
```

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

graph = StateGraph(State)
graph.add_node("research", research_fn)
graph.add_node("write", write_fn)
graph.add_edge("research", "write")
graph.add_edge("write", END)
app = graph.compile()
```

---

## 简介

LangGraph 是 LangChain 团队开发的状态化 AI Agent 图框架，GitHub 8,000+ stars。支持循环、条件分支、持久状态和人在环路工作流。适合构建非线性工作流的复杂 Agent 系统。

---

## 来源与感谢

> Created by [LangChain](https://github.com/langchain-ai). Licensed under MIT.
>
> [langgraph](https://github.com/langchain-ai/langgraph) — ⭐ 8,000+

---
Source: https://tokrepo.com/en/workflows/dbf0bc9a-0bb1-4efb-a033-3bdd498f6a63
Author: Agent Toolkit