LangGraph - Firecrawl Docs

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。

安装与配置#

npm install @langchain/langgraph @langchain/openai firecrawl

创建一个 .env 文件：

FIRECRAWL_API_KEY=your_firecrawl_keyOPENAI_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'。

基本工作流#

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。

import { Firecrawl } from 'firecrawl';import { ChatOpenAI } from '@langchain/openai';import { StateGraph, MessagesAnnotation, START, END } from '@langchain/langgraph';// Initialize Firecrawlconst firecrawl = new Firecrawl({ apiKey: process.env.FIRECRAWL_API_KEY });// Initialize LLMconst llm = new ChatOpenAI({    model: "gpt-5-nano",    apiKey: process.env.OPENAI_API_KEY});// Define the scrape nodeasync 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 nodeasync function analyzeNode(state: typeof MessagesAnnotation.State) {    console.log('Analyzing...');    const response = await llm.invoke(state.messages);    return { messages: [response] };}// Build the graphconst graph = new StateGraph(MessagesAnnotation)    .addNode("scrape", scrapeNode)    .addNode("analyze", analyzeNode)    .addEdge(START, "scrape")    .addEdge("scrape", "analyze")    .addEdge("analyze", END);// Compile the graphconst app = graph.compile();// Run the workflowconst result = await app.invoke({    messages: [{ role: "user", content: "Summarize the website" }]});console.log(JSON.stringify(result, null, 2));

多步骤工作流#

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。

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 URLasync 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。