LangChain

Use Firecrawl with LangChain for web scraping + AI workflows

Integrate Firecrawl with LangChain to build AI applications powered by web data.

Setup

npm install @langchain/openai @mendable/firecrawl-js 

Create .env file:

FIRECRAWL_API_KEY=your_firecrawl_key
OPENAI_API_KEY=your_openai_key

Note: If using Node < 20, install dotenv and add import 'dotenv/config' to your code.

Scrape + Chat

This example demonstrates a simple workflow: scrape a website and process the content using LangChain.

import FirecrawlApp from '@mendable/firecrawl-js';
import { ChatOpenAI } from '@langchain/openai';
import { HumanMessage } from '@langchain/core/messages';

const firecrawl = new FirecrawlApp({ 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

This example shows how to build a LangChain chain to process and analyze scraped content.

import FirecrawlApp from '@mendable/firecrawl-js';
import { ChatOpenAI } from '@langchain/openai';
import { ChatPromptTemplate } from '@langchain/core/prompts';
import { StringOutputParser } from '@langchain/core/output_parsers';

const firecrawl = new FirecrawlApp({ 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);

// Create processing chain
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());

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

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

Tool Calling

This example demonstrates how to use LangChain’s tool calling feature to let the model decide when to scrape websites.

import FirecrawlApp from '@mendable/firecrawl-js';
import { ChatOpenAI } from '@langchain/openai';
import { DynamicStructuredTool } from '@langchain/core/tools';
import { z } from 'zod';

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

// Create the scraping tool
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);

Structured Data Extraction

This example shows how to extract structured data using LangChain’s structured output feature.

import FirecrawlApp from '@mendable/firecrawl-js';
import { ChatOpenAI } from '@langchain/openai';
import { z } from 'zod';

const firecrawl = new FirecrawlApp({ 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);

For more examples, check the LangChain documentation.