Gemini - Firecrawl Docs

Integrate Firecrawl with Google's Gemini for AI applications powered by web data.

Setup#

npm install firecrawl @google/genai

Create .env file:

FIRECRAWL_API_KEY=your_firecrawl_keyGEMINI_API_KEY=your_gemini_key

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

Scrape + Summarize#

This example demonstrates a simple workflow: scrape a website and summarize the content using Gemini.

import { Firecrawl } from 'firecrawl';import { GoogleGenAI } from '@google/genai';const firecrawl = new Firecrawl({ apiKey: process.env.FIRECRAWL_API_KEY });const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });const scrapeResult = await firecrawl.scrape('https://firecrawl.dev', {    formats: ['markdown']});console.log('Scraped content length:', scrapeResult.markdown?.length);const response = await ai.models.generateContent({    model: 'gemini-2.5-flash',    contents: `Summarize: ${scrapeResult.markdown}`,});console.log('Summary:', response.text);

Content Analysis#

This example shows how to analyze website content using Gemini's multi-turn conversation capabilities.

import { Firecrawl } from 'firecrawl';import { GoogleGenAI } from '@google/genai';const firecrawl = new Firecrawl({ apiKey: process.env.FIRECRAWL_API_KEY });const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });const scrapeResult = await firecrawl.scrape('https://news.ycombinator.com/', {    formats: ['markdown']});console.log('Scraped content length:', scrapeResult.markdown?.length);const chat = ai.chats.create({    model: 'gemini-2.5-flash'});// Ask for the top 3 stories on Hacker Newsconst result1 = await chat.sendMessage({    message: `Based on this website content from Hacker News, what are the top 3 stories right now?\n\n${scrapeResult.markdown}`});console.log('Top 3 Stories:', result1.text);// Ask for the 4th and 5th stories on Hacker Newsconst result2 = await chat.sendMessage({    message: `Now, what are the 4th and 5th top stories on Hacker News from the same content?`});console.log('4th and 5th Stories:', result2.text);

Structured Extraction#

This example demonstrates how to extract structured data using Gemini's JSON mode from scraped website content.

import { Firecrawl } from 'firecrawl';import { GoogleGenAI, Type } from '@google/genai';const firecrawl = new Firecrawl({ apiKey: process.env.FIRECRAWL_API_KEY });const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });const scrapeResult = await firecrawl.scrape('https://stripe.com', {    formats: ['markdown']});console.log('Scraped content length:', scrapeResult.markdown?.length);const response = await ai.models.generateContent({    model: 'gemini-2.5-flash',    contents: `Extract company information: ${scrapeResult.markdown}`,    config: {        responseMimeType: 'application/json',        responseSchema: {            type: Type.OBJECT,            properties: {                name: { type: Type.STRING },                industry: { type: Type.STRING },                description: { type: Type.STRING },                products: {                    type: Type.ARRAY,                    items: { type: Type.STRING }                }            },            propertyOrdering: ['name', 'industry', 'description', 'products']        }    }});console.log('Extracted company info:', response?.text);

For more examples, check the Gemini documentation.