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What You'll Build

An AI-powered data extraction agent that pulls product data from e-commerce sites and stores it in MongoDB with automatic schema validation and data analysis.

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Before you start

Ensure you have these requirements ready:

Node.js 16+

Required runtime environment

MongoDB

Local install or MongoDB Atlas

API Access

Browserbase API key

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Step 1: Project setup

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Clone and install

# Clone the integration template
npx degit browserbase/integrations/examples/integrations/mongodb/typescript browserbase-mongodb
cd browserbase-mongodb

# Install all dependencies
npm install

# Install browser binaries
npx playwright install
Core packages:
  • @browserbasehq/stagehand - AI-powered data extraction
  • mongodb - MongoDB driver for data storage
  • zod - Schema validation for type safety
Utilities:
  • chalk & boxen - Terminal styling and output formatting
  • playwright - Browser automation engine

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Step 2: Start MongoDB

Required: MongoDB must be running on your system before proceeding. Start it with mongod if installed locally, or ensure your MongoDB Atlas connection is ready.
If using local MongoDB, ensure it’s running: 
# Start MongoDB (if installed locally)
mongod
MongoDB Atlas users can skip this step as the database is already hosted in the cloud.

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Step 3: Configuration

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Environment variables

Create your .env file with the required configuration: 
# Browserbase Configuration (Recommended)
BROWSERBASE_API_KEY=your_browserbase_api_key

# MongoDB Configuration
MONGO_URI=mongodb://localhost:27017
DB_NAME=scraper_db

# Stagehand Configuration
ANTHROPIC_API_KEY=your_anthropic_api_key_here

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Step 4: Configure Stagehand

The integration uses Browserbase cloud browsers:
stagehand.config.ts
const StagehandConfig: V3Options = {
  verbose: 1,
  domSettleTimeout: 30_000,
  
  // LLM Configuration  
  model: "anthropic/claude-sonnet-4-6",
  
  // Run in Browserbase cloud (Recommended)
  env: "BROWSERBASE",
  apiKey: process.env.BROWSERBASE_API_KEY,
  browserbaseSessionCreateParams: {
    browserSettings: {
      blockAds: true,
      viewport: { width: 1024, height: 768 },
    },
  },
};

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Step 5: Run your first extraction

What happens when you run the agent:
  1. Connects to MongoDB and creates necessary collections
  2. Navigates to Amazon laptop category
  3. Extracts product listings with AI-powered data extraction
  4. Extracts detailed information for the first 3 products
  5. Stores all data in MongoDB with schema validation
  6. Runs analysis queries and displays results

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Execute the extraction

npm start

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Customization options

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Extend data schema

Add custom fields to capture more product information: 
const ProductSchema = z.object({
  // Existing fields...
  title: z.string(),
  price: z.string().optional(),
  rating: z.string().optional(),
  
  // Add your custom fields
  brand: z.string().optional(),
  availability: z.string().optional(),
  shippingInfo: z.string().optional(),
  specifications: z.array(z.string()).optional(),
  customerReviews: z.number().optional(),
});

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Custom extraction instructions

Modify the AI extraction to capture specific data: 
const data = await stagehand.extract(
  `Extract comprehensive product information including:
    - Brand and model details
    - Detailed specifications
    - Availability and shipping information
    - Customer ratings and review counts`,
  ProductSchema,
);

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What’s next?

Now that you have a working MongoDB + Stagehand integration:

Scale your extraction

Learn how to scale your data extraction across multiple sites and handle larger datasets.

Deploy to production

Deploy your extraction pipeline to production with Browserbase.
Need help? Join the Stagehand Slack community for support and to share your projects!