{
  "id": "art_VuYFuGdgNbjF",
  "slug": "langgraph-stategraph-introduction-building-stateful-ai-agents",
  "author": "goumang",
  "title": "LangGraph 状态图入门：构建有状态的 AI Agent",
  "summary": "本文介绍 LangGraph 的核心概念 State/Node/Edge 以及如何构建第一个状态图。通过实际代码示例展示如何使用 StateGraph 定义节点、添加边、配置入口点，以及编译和执行图的完整流程。适合希望构建复杂多步 Agent 工作流的开发者。",
  "content": "# 概述\n\nLangGraph 是 LangChain 生态中用于构建有状态、多步 Agent 工作流的核心库。与传统的 Chain 不同，LangGraph 使用图（Graph）的概念来建模 Agent 行为，其中节点（Node）表示计算步骤，边（Edge）表示控制流，状态（State）在节点间传递。\n\n## 前置条件\n\n- Python 3.10+\n- LangGraph >= 0.0.20\n- 安装命令：`pip install langgraph`\n\n## 核心概念\n\n### 1. State（状态）\n\nState 是图的共享数据结构，包含整个工作流的当前信息：\n\n```python\nfrom typing import TypedDict\nfrom typing_extensions import Annotated\nfrom operator import add\n\nclass AgentState(TypedDict):\n    # 基本字段：直接覆盖更新\n    messages: list[str]\n    \n    # 带 Reducer 的字段：累加更新\n    history: Annotated[list[str], add]\n```\n\n### 2. Node（节点）\n\n节点是执行特定逻辑的 Python 函数：\n\n```python\ndef process_node(state: AgentState) -> AgentState:\n    \"\"\"处理节点：接收当前状态，返回更新后的状态\"\"\"\n    current_msg = state[\"messages\"][-1]\n    return {\n        \"messages\": [f\"Processed: {current_msg}\"]\n    }\n```\n\n### 3. Edge（边）\n\n边决定下一个执行哪个节点：\n\n```python\n# 普通边：从 node_a 直接到 node_b\nbuilder.add_edge(\"node_a\", \"node_b\")\n\n# 条件边：根据状态决定路由\ndef routing_func(state: AgentState) -> str:\n    if len(state[\"messages\"]) > 5:\n        return \"end_node\"\n    return \"continue_node\"\n\nbuilder.add_conditional_edges(\"router\", routing_func)\n```\n\n## 完整示例：简单的对话 Agent\n\n```python\nfrom typing import TypedDict\nfrom langgraph.graph import StateGraph, START, END\n\n# 1. 定义状态\nclass ChatState(TypedDict):\n    messages: list[str]\n    turn_count: int\n\n# 2. 创建图构建器\nbuilder = StateGraph(ChatState)\n\n# 3. 定义节点\ndef should_continue(state: ChatState) -> str:\n    \"\"\"判断是否继续对话\"\"\"\n    if state[\"turn_count\"] >= 3:\n        return \"end\"\n    return \"continue\"\n\ndef agent_node(state: ChatState) -> ChatState:\n    \"\"\"Agent 核心逻辑\"\"\"\n    last_msg = state[\"messages\"][-1]\n    response = f\"我收到了: {last_msg} (第 {state['turn_count'] + 1} 轮)\"\n    return {\n        \"messages\": state[\"messages\"] + [response],\n        \"turn_count\": state[\"turn_count\"] + 1\n    }\n\n# 4. 添加节点和边\nbuilder.add_node(\"agent\", agent_node)\nbuilder.add_edge(START, \"agent\")\nbuilder.add_conditional_edges(\n    \"agent\",\n    should_continue,\n    {\"continue\": \"agent\", \"end\": END}\n)\n\n# 5. 编译图\ngraph = builder.compile()\n\n# 6. 执行\nresult = graph.invoke({\n    \"messages\": [\"你好\"],\n    \"turn_count\": 0\n})\n\nprint(result[\"messages\"])\n# ['你好', '我收到了: 你好 (第 1 轮)', '我收到了: 我收到了: 你好 (第 1 轮) (第 2 轮)', '我收到了: ... (第 3 轮)']\n```\n\n## 与 LangChain Agent 的区别\n\n| 特性 | LangChain Chain | LangGraph |\n|------|----------------|-----------|\n| 工作流 | 线性/固定 | 图结构/可条件分支 |\n| 状态管理 | 无内置状态 | 内置 State 共享 |\n| 循环支持 | 不支持 | 支持任意循环 |\n| 调试难度 | 较低 | 需要理解图执行模型 |\n\n## 常见问题\n\n**Q1: State 的 Reducer 有什么用？**\n- 默认 Reducer 直接覆盖字段值\n- 使用 Annotated[list, add] 可以实现列表累加\n- 自定义 Reducer 可以实现复杂的合并逻辑\n\n**Q2: 如何处理多轮对话的记忆？**\n- 在 State 中定义 messages 列表存储对话历史\n- 使用 add Reducer 保留所有历史消息\n- 定期使用 LLM 总结历史信息以节省 Token\n\n**Q3: 条件边的返回值类型有什么要求？**\n- 返回值必须是字符串，对应目标节点的名称\n- 可以返回 END 表示结束执行\n- 可以返回 START 表示重新开始\n\n## 参考资料\n\n- [LangGraph 官方文档](https://langchain-ai.github.io/langgraph/)\n- [Graph API 概述](https://docs.langchain.com/oss/python/langgraph/graph-api)\n- [LangGraph GitHub 仓库](https://github.com/langchain-ai/langgraph)\n",
  "lang": "zh",
  "domain": "foundation",
  "tags": [
    "langgraph",
    "state-graph",
    "agent-workflow",
    "state-management",
    "node",
    "edge",
    "multi-step-agent"
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  "keywords": [
    "LangGraph",
    "StateGraph",
    "stateful agent",
    "workflow orchestration",
    "conditional routing",
    "graph execution"
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