> ## 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.

# LangChain

> Use o Firecrawl com o LangChain para web scraping e fluxos de IA

Integre o Firecrawl ao LangChain para criar aplicativos de IA alimentados por dados da web.

<div id="setup">
  ## Configuração
</div>

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

Crie o arquivo `.env`:

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

> **Observação:** Se estiver usando Node \< 20, instale `dotenv` e adicione `import 'dotenv/config'` ao seu código.

<div id="scrape-chat">
  ## Scrape + Chat
</div>

Este exemplo demonstra um fluxo de trabalho simples: fazer scraping de um site e processar o conteúdo com o LangChain.

```typescript theme={null}
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);
```

<div id="chains">
  ## Chains
</div>

Este exemplo mostra como criar uma chain no LangChain para processar e analisar o conteúdo extraído.

```typescript theme={null}
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);

// Criar chain de processamento
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());

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

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

<div id="tool-calling">
  ## Chamada de Ferramentas
</div>

Este exemplo demonstra como usar o recurso de chamada de ferramentas do LangChain para permitir que o modelo decida quando fazer scraping de sites.

```typescript theme={null}
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 });

// Cria a ferramenta de scraping
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);
```

<div id="structured-data-extraction">
  ## Extração de Dados Estruturados
</div>

Este exemplo mostra como extrair dados estruturados usando a funcionalidade de saída estruturada do LangChain.

```typescript theme={null}
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('Tamanho do conteúdo extraído:', 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: 'Extraia informações da empresa do conteúdo do site.'
    },
    {
        role: 'user',
        content: `Extraia os dados: ${scrapeResult.markdown}`
    }
]);

console.log('Informações da empresa extraídas:', companyInfo);
```

Para mais exemplos, consulte a [documentação do LangChain](https://js.langchain.com/docs).