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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。

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安装与配置

npm install @langchain/langgraph @langchain/openai @mendable/firecrawl-js
创建一个 .env 文件： 
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'。

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基本工作流

本示例展示了一个基本的 LangGraph 工作流，用于抓取网站并分析其内容。 
import FirecrawlApp from '@mendable/firecrawl-js';
import { ChatOpenAI } from '@langchain/openai';
import { StateGraph, MessagesAnnotation, START, END } from '@langchain/langgraph';

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

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

// 定义爬取节点
async function scrapeNode(state: typeof MessagesAnnotation.State) {
    console.log('爬取中...');
    const result = await firecrawl.scrape('https://firecrawl.dev', { formats: ['markdown'] });
    return {
        messages: [{
            role: "system",
            content: `爬取内容: ${result.markdown}`
        }]
    };
}

// 定义分析节点
async function analyzeNode(state: typeof MessagesAnnotation.State) {
    console.log('分析中...');
    const response = await llm.invoke(state.messages);
    return { messages: [response] };
}

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

// 编译图
const app = graph.compile();

// 运行工作流
const result = await app.invoke({
    messages: [{ role: "user", content: "总结网站内容" }]
});

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

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多步工作流

本示例展示了一个更复杂的工作流，会抓取多个 URL 并对其进行处理。 
import FirecrawlApp from '@mendable/firecrawl-js';
import { ChatOpenAI } from '@langchain/openai';
import { StateGraph, Annotation, START, END } from '@langchain/langgraph';

const firecrawl = new FirecrawlApp({ 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 };
}

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

    const response = await llm.invoke([
        { role: "user", content: `汇总这些网站：\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);
更多示例请参见 LangGraph 文档。