跳转到主要内容
将 Firecrawl 与 LangChain 集成，构建由网页数据驱动的 AI 应用。

​
安装与设置

npm install @langchain/openai firecrawl
创建 .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'。

​
抓取 + 对话

本示例展示一个简单的工作流：抓取网站，并使用 LangChain 处理抓取到的内容。 
import { Firecrawl } from 'firecrawl';
import { ChatOpenAI } from '@langchain/openai';
import { HumanMessage } from '@langchain/core/messages';

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

const scrapeResult = await firecrawl.scrape('https://firecrawl.dev', {
    formats: ['markdown']
});

console.log('Scraped content length:', scrapeResult.markdown?.length);

const response = await chat.invoke([
    new HumanMessage(`Summarize: ${scrapeResult.markdown}`)
]);

console.log('Summary:', response.content);

​
Chains

本示例演示如何构建一个 LangChain 链，用于处理和分析抓取到的内容。 
import { Firecrawl } from 'firecrawl';
import { ChatOpenAI } from '@langchain/openai';
import { ChatPromptTemplate } from '@langchain/core/prompts';
import { StringOutputParser } from '@langchain/core/output_parsers';

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

const scrapeResult = await firecrawl.scrape('https://stripe.com', {
    formats: ['markdown']
});

console.log('Scraped content length:', scrapeResult.markdown?.length);

// 创建处理链
const prompt = ChatPromptTemplate.fromMessages([
    ['system', 'You are an expert at analyzing company websites.'],
    ['user', 'Extract the company name and main products from: {content}']
]);

const chain = prompt.pipe(model).pipe(new StringOutputParser());

// 执行链
const result = await chain.invoke({
    content: scrapeResult.markdown
});

console.log('Chain result:', result);

​
工具调用

此示例演示如何使用 LangChain 的工具调用功能，让模型自动决定何时抓取网站。 
import { Firecrawl } from 'firecrawl';
import { ChatOpenAI } from '@langchain/openai';
import { DynamicStructuredTool } from '@langchain/core/tools';
import { z } from 'zod';

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

// 创建抓取工具
const scrapeWebsiteTool = new DynamicStructuredTool({
    name: 'scrape_website',
    description: 'Scrape content from any website URL',
    schema: z.object({
        url: z.string().url().describe('The URL to scrape')
    }),
    func: async ({ url }) => {
        console.log('Scraping:', url);
        const result = await firecrawl.scrape(url, {
            formats: ['markdown']
        });
        console.log('Scraped content preview:', result.markdown?.substring(0, 200) + '...');
        return result.markdown || 'No content scraped';
    }
});

const model = new ChatOpenAI({
    model: 'gpt-5-nano',
    apiKey: process.env.OPENAI_API_KEY
}).bindTools([scrapeWebsiteTool]);

const response = await model.invoke('What is Firecrawl? Visit firecrawl.dev and tell me about it.');

console.log('Response:', response.content);
console.log('Tool calls:', response.tool_calls);

​
结构化数据提取

此示例演示如何使用 LangChain 的结构化输出功能来提取结构化数据。 
import { Firecrawl } from 'firecrawl';
import { ChatOpenAI } from '@langchain/openai';
import { z } from 'zod';

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

const scrapeResult = await firecrawl.scrape('https://stripe.com', {
    formats: ['markdown']
});

console.log('Scraped content length:', scrapeResult.markdown?.length);

const CompanyInfoSchema = z.object({
    name: z.string(),
    industry: z.string(),
    description: z.string(),
    products: z.array(z.string())
});

const model = new ChatOpenAI({
    model: 'gpt-5-nano',
    apiKey: process.env.OPENAI_API_KEY
}).withStructuredOutput(CompanyInfoSchema);

const companyInfo = await model.invoke([
    {
        role: 'system',
        content: 'Extract company information from website content.'
    },
    {
        role: 'user',
        content: `Extract data: ${scrapeResult.markdown}`
    }
]);

console.log('Extracted company info:', companyInfo);
更多示例，请参阅 LangChain 文档。