> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt
> Use this file to discover all available pages before exploring further.

# 概述

> 将 LangGraph 智能体渲染至前端

构建能够实时可视化 LangGraph 流水线的前端界面。这些模式展示了如何渲染多步骤图执行过程，包括每个节点的状态以及从自定义 `StateGraph` 工作流中流式传输内容。

## 架构

LangGraph 图由通过边连接的命名节点组成。每个节点执行一个步骤（分类、研究、分析、合成）并将输出写入特定的状态键。在前端，`useStream` 提供了对节点输出、流式令牌和图元数据的响应式访问，从而可以将每个节点映射到一个 UI 卡片。

```mermaid theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
%%{
  init: {
    "fontFamily": "monospace",
    "flowchart": {
      "curve": "curve"
    }
  }
}%%
graph LR
  FRONTEND["useStream()"]
  GRAPH["StateGraph"]
  N1["节点 A"]
  N2["节点 B"]
  N3["节点 C"]

  GRAPH --"流式传输"--> FRONTEND
  FRONTEND --"提交"--> GRAPH
  GRAPH --> N1
  N1 --> N2
  N2 --> N3

  classDef blueHighlight fill:#DBEAFE,stroke:#2563EB,color:#1E3A8A;
  classDef greenHighlight fill:#DCFCE7,stroke:#16A34A,color:#14532D;
  classDef orangeHighlight fill:#FEF3C7,stroke:#D97706,color:#92400E;
  class FRONTEND blueHighlight;
  class GRAPH greenHighlight;
  class N1,N2,N3 orangeHighlight;
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langgraph.graph import StateGraph, MessagesState, START, END

class State(MessagesState):
    classification: str
    research: str
    analysis: str

graph = StateGraph(State)
graph.add_node("classify", classify_node)
graph.add_node("research", research_node)
graph.add_node("analyze", analyze_node)
graph.add_edge(START, "classify")
graph.add_edge("classify", "research")
graph.add_edge("research", "analyze")
graph.add_edge("analyze", END)

app = graph.compile()
```

在前端，`useStream` 暴露了 `stream.values` 用于获取已完成的节点输出，以及 `getMessagesMetadata` 用于识别每个流式令牌是由哪个节点产生的。

```ts theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { useStream } from "@langchain/react";

function Pipeline() {
  const stream = useStream<typeof graph>({
    apiUrl: "http://localhost:2024",
    assistantId: "pipeline",
  });

  const classification = stream.values?.classification;
  const research = stream.values?.research;
  const analysis = stream.values?.analysis;
}
```

## 模式

<CardGroup cols={2}>
  <Card title="图执行" icon="chart-dots" href="/oss/python/langgraph/frontend/graph-execution">
    可视化多步骤图流水线，包括每个节点的状态和流式内容。
  </Card>
</CardGroup>

## 相关模式

[LangChain 前端模式](/oss/python/langchain/frontend/overview)——如 Markdown 消息、工具调用、乐观更新等——可与任何 LangGraph 图配合使用。无论您使用 `createAgent`、`createDeepAgent` 还是自定义的 `StateGraph`，`useStream` 钩子都提供相同的核心 API。

***

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