> ## Documentation Index
> Fetch the complete documentation index at: https://docs.firecrawl.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# LangGraph

> 将 Firecrawl 与 LangGraph 集成以构建智能体工作流

本指南介绍如何将 Firecrawl 与 LangGraph 集成，以构建可抓取和处理网页内容的 AI 智能体工作流。

<div id="setup">
  ## 安装与配置
</div>

```bash theme={null}
npm install @langchain/langgraph @langchain/openai firecrawl
```

创建一个 `.env` 文件：

```bash theme={null}
FIRECRAWL_API_KEY=your_firecrawl_key
OPENAI_API_KEY=your_openai_key
```

> **注意：** 如果使用 Node 版本低于 20，请安装 `dotenv` 并在代码中添加 `import 'dotenv/config'`。

<div id="basic-workflow">
  ## 基本工作流
</div>

此示例演示了一个基本的 LangGraph 工作流，用于抓取网站并分析其内容。

```typescript theme={null}
import { Firecrawl } from 'firecrawl';
import { ChatOpenAI } from '@langchain/openai';
import { StateGraph, MessagesAnnotation, START, END } from '@langchain/langgraph';

// Initialize Firecrawl
const firecrawl = new Firecrawl({ apiKey: process.env.FIRECRAWL_API_KEY });

// Initialize LLM
const llm = new ChatOpenAI({
    model: "gpt-5-nano",
    apiKey: process.env.OPENAI_API_KEY
});

// Define the scrape node
async function scrapeNode(state: typeof MessagesAnnotation.State) {
    console.log('Scraping...');
    const result = await firecrawl.scrape('https://firecrawl.dev', { formats: ['markdown'] });
    return {
        messages: [{
            role: "system",
            content: `Scraped content: ${result.markdown}`
        }]
    };
}

// Define the analyze node
async function analyzeNode(state: typeof MessagesAnnotation.State) {
    console.log('Analyzing...');
    const response = await llm.invoke(state.messages);
    return { messages: [response] };
}

// Build the graph
const graph = new StateGraph(MessagesAnnotation)
    .addNode("scrape", scrapeNode)
    .addNode("analyze", analyzeNode)
    .addEdge(START, "scrape")
    .addEdge("scrape", "analyze")
    .addEdge("analyze", END);

// Compile the graph
const app = graph.compile();

// Run the workflow
const result = await app.invoke({
    messages: [{ role: "user", content: "Summarize the website" }]
});

console.log(JSON.stringify(result, null, 2));
```

<div id="multi-step-workflow">
  ## 多步骤工作流
</div>

此示例演示了一个更复杂的工作流，它会抓取多个 URL 并对其进行处理。

```typescript theme={null}
import { Firecrawl } from 'firecrawl';
import { ChatOpenAI } from '@langchain/openai';
import { StateGraph, Annotation, START, END } from '@langchain/langgraph';

const firecrawl = new Firecrawl({ apiKey: process.env.FIRECRAWL_API_KEY });
const llm = new ChatOpenAI({ model: "gpt-5-nano", apiKey: process.env.OPENAI_API_KEY });

// 定义自定义状态
const WorkflowState = Annotation.Root({
    urls: Annotation<string[]>(),
    scrapedData: Annotation<Array<{ url: string; content: string }>>(),
    summary: Annotation<string>()
});

// 抓取多个 URL
async function scrapeMultiple(state: typeof WorkflowState.State) {
    const scrapedData = [];
    for (const url of state.urls) {
        const result = await firecrawl.scrape(url, { formats: ['markdown'] });
        scrapedData.push({ url, content: result.markdown || '' });
    }
    return { scrapedData };
}

// 汇总所有已抓取的内容
async function summarizeAll(state: typeof WorkflowState.State) {
    const combinedContent = state.scrapedData
        .map(item => `Content from ${item.url}:\n${item.content}`)
        .join('\n\n');

    const response = await llm.invoke([
        { role: "user", content: `Summarize these websites:\n${combinedContent}` }
    ]);

    return { summary: response.content as string };
}

// 构建工作流图
const workflow = new StateGraph(WorkflowState)
    .addNode("scrape", scrapeMultiple)
    .addNode("summarize", summarizeAll)
    .addEdge(START, "scrape")
    .addEdge("scrape", "summarize")
    .addEdge("summarize", END);

const app = workflow.compile();

// 执行工作流
const result = await app.invoke({
    urls: ["https://firecrawl.dev", "https://firecrawl.dev/pricing"]
});

console.log(result.summary);
```

更多示例，请查阅 [LangGraph 文档](https://langchain-ai.github.io/langgraphjs/)。