Website to API

Overview

ManyPI lets you convert any website into a reliable, structured API without writing complex scraping code. Simply describe what data you want, and our AI handles the rest.

Perfect for: E-commerce monitoring, lead generation, content aggregation, market research, and any scenario where you need structured data from websites.

How it works

Create a scraper

Define what data you want to extract using natural language or a JSON schema.

AI extracts the data

Our AI intelligently navigates the page and extracts exactly what you specified.

Get structured JSON

Receive clean, validated data in your defined format via API or dashboard.

Integrate anywhere

Use the API in your applications, automation workflows, or data pipelines.

Real-world examples

Example 1: E-commerce product API

Goal: Monitor competitor prices and product availability.

1. Describe what you want
2. Generated schema
3. API response
4. Use in your app

Prompt

Extract product information from this e-commerce page:
- Product title
- Current price in USD
- Original price if on sale
- Star rating (out of 5)
- Number of reviews
- Availability status (in stock or out of stock)
- Main product image URL

Schema

{
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "Product name"
    },
    "currentPrice": {
      "type": "number",
      "description": "Current price in USD"
    },
    "originalPrice": {
      "type": "number",
      "description": "Original price before discount"
    },
    "rating": {
      "type": "number",
      "minimum": 0,
      "maximum": 5
    },
    "reviewCount": {
      "type": "integer"
    },
    "inStock": {
      "type": "boolean"
    },
    "imageUrl": {
      "type": "string",
      "format": "uri"
    }
  },
  "required": ["title", "currentPrice", "inStock"]
}

Response

{
  "success": true,
  "data": {
    "title": "Wireless Bluetooth Headphones - Noise Cancelling",
    "currentPrice": 79.99,
    "originalPrice": 129.99,
    "rating": 4.5,
    "reviewCount": 1234,
    "inStock": true,
    "imageUrl": "https://example.com/images/headphones.jpg"
  },
  "metadata": {
    "scraperId": "550e8400-e29b-41d4-a716-446655440000",
    "timestamp": "2024-01-15T10:30:00.000Z",
    "creditsCost": 1500
  }
}

Integration

// Monitor prices every hour
async function checkCompetitorPrices() {
  const products = [
    'https://competitor.com/product/123',
    'https://competitor.com/product/456',
    'https://competitor.com/product/789'
  ];
  
  for (const url of products) {
    const response = await fetch(
      'https://app.manypi.com/api/scrape/YOUR_SCRAPER_ID',
      {
        method: 'POST',
        headers: {
          'Authorization': 'Bearer YOUR_API_KEY',
          'Content-Type': 'application/json'
        },
        body: JSON.stringify({ url })
      }
    );
    
    const { data } = await response.json();
    
    // Check if price dropped
    if (data.currentPrice < data.originalPrice) {
      await notifyPriceDrop(data);
    }
    
    // Store in database
    await saveToDatabase(data);
  }
}

Example 2: Job listings API

Goal: Aggregate job postings from multiple career sites.

1. Describe what you want
2. API response
3. Aggregate multiple sources

Prompt

Extract job listing information:
- Job title
- Company name
- Location (city and state/country)
- Salary range if available
- Job type (full-time, part-time, contract, remote)
- Posted date
- Application deadline if shown
- Required skills (as an array)
- Job description summary

Response

{
  "success": true,
  "data": {
    "title": "Senior Full Stack Developer",
    "company": "Tech Innovations Inc.",
    "location": "San Francisco, CA",
    "salaryRange": "$120,000 - $180,000",
    "jobType": "Full-time, Remote",
    "postedDate": "2024-01-10",
    "applicationDeadline": "2024-02-10",
    "skills": [
      "React",
      "Node.js",
      "TypeScript",
      "PostgreSQL",
      "AWS"
    ],
    "description": "We're looking for an experienced full stack developer to join our growing team..."
  }
}

Python

import requests
from datetime import datetime

# Different scrapers for different job sites
scrapers = {
    'linkedin': 'scraper-id-1',
    'indeed': 'scraper-id-2',
    'glassdoor': 'scraper-id-3'
}

def aggregate_jobs(search_urls):
    all_jobs = []
    
    for source, scraper_id in scrapers.items():
        for url in search_urls[source]:
            response = requests.post(
                f'https://app.manypi.com/api/scrape/{scraper_id}',
                headers={
                    'Authorization': 'Bearer YOUR_API_KEY',
                    'Content-Type': 'application/json'
                },
                json={'url': url}
            )
            
            data = response.json()
            if data['success']:
                job = data['data']
                job['source'] = source
                job['scraped_at'] = datetime.now().isoformat()
                all_jobs.append(job)
    
    return all_jobs

# Use in your job board
jobs = aggregate_jobs({
    'linkedin': ['https://linkedin.com/jobs/search?q=developer'],
    'indeed': ['https://indeed.com/jobs?q=developer'],
    'glassdoor': ['https://glassdoor.com/Job/developer-jobs.htm']
})

Example 3: News article API

Goal: Create a content aggregation feed from multiple news sources.

1. Describe what you want
2. Build a content feed

Prompt

Extract article information from news websites:
- Article headline
- Author name
- Publication date and time
- Article category/section
- Full article text
- Featured image URL
- Tags or keywords
- Estimated reading time

Node.js

const express = require('express');
const app = express();

// Your custom news API endpoint
app.get('/api/news/:category', async (req, res) => {
  const { category } = req.params;
  
  // URLs for different news sources
  const sources = [
    `https://newssite1.com/${category}`,
    `https://newssite2.com/${category}`,
    `https://newssite3.com/${category}`
  ];
  
  const articles = [];
  
  for (const url of sources) {
    const response = await fetch(
      'https://app.manypi.com/api/scrape/YOUR_SCRAPER_ID',
      {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${process.env.MANYPI_API_KEY}`,
          'Content-Type': 'application/json'
        },
        body: JSON.stringify({ url })
      }
    );
    
    const { data } = await response.json();
    articles.push(data);
  }
  
  // Sort by publication date
  articles.sort((a, b) => 
    new Date(b.publicationDate) - new Date(a.publicationDate)
  );
  
  res.json({ articles });
});

app.listen(3000);

Best practices

Be specific in your prompts

Good: “Extract product title, price in USD format, star rating out of 5, and boolean availability status”Bad: “Get product info”Specific prompts lead to more accurate schemas and better extraction results.

Test with multiple pages

Test your scraper on 3-5 different pages from the same site to ensure consistency:

Different product types
Pages with missing data (out of stock, no reviews)
Pages with special formatting

This helps catch edge cases before production use.

Handle missing data gracefully

Not all pages have all fields. Make optional fields nullable in your schema:

{
  "properties": {
    "price": { "type": "number" },
    "salePrice": { 
      "type": ["number", "null"],
      "description": "Only present during sales"
    }
  }
}

Implement retry logic

Websites can be temporarily unavailable. Add retry logic to your integration:

async function scrapeWithRetry(url, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      const response = await fetch(/* ... */);
      if (response.ok) return await response.json();
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await sleep(1000 * (i + 1)); // Exponential backoff
    }
  }
}

Cache results when appropriate

For data that doesn’t change frequently, implement caching:

const cache = new Map();
const CACHE_TTL = 3600000; // 1 hour

async function getCachedData(url) {
  const cached = cache.get(url);
  if (cached && Date.now() - cached.timestamp < CACHE_TTL) {
    return cached.data;
  }
  
  const data = await scrapeUrl(url);
  cache.set(url, { data, timestamp: Date.now() });
  return data;
}

Set up monitoring

Use email notifications to stay informed:

Go to Integrations in your dashboard
Enable email notifications for your scraper
Get notified when scrapes complete or fail
Monitor credit usage and set up alerts

Common patterns

Pattern 1: Scheduled scraping

Run scrapers on a schedule using cron jobs or cloud functions:

const cron = require('node-cron');

// Run every day at 9 AM
cron.schedule('0 9 * * *', async () => {
  console.log('Starting daily scrape...');
  
  const urls = await getUrlsToScrape();
  
  for (const url of urls) {
    await scrapeAndStore(url);
  }
  
  console.log('Daily scrape complete');
});

import schedule
import time

def daily_scrape():
    print("Starting daily scrape...")
    urls = get_urls_to_scrape()
    
    for url in urls:
        scrape_and_store(url)
    
    print("Daily scrape complete")

# Run every day at 9 AM
schedule.every().day.at("09:00").do(daily_scrape)

while True:
    schedule.run_pending()
    time.sleep(60)

Pattern 2: Webhook integration

Trigger scrapes from external events:

Express.js

app.post('/webhook/new-product', async (req, res) => {
  const { productUrl } = req.body;
  
  // Scrape the new product
  const response = await fetch(
    'https://app.manypi.com/api/scrape/YOUR_SCRAPER_ID',
    {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${process.env.MANYPI_API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({ url: productUrl })
    }
  );
  
  const { data } = await response.json();
  
  // Process and store
  await processNewProduct(data);
  
  res.json({ success: true });
});

Pattern 3: Batch processing

Process multiple URLs efficiently:

Python

import asyncio
import aiohttp

async def scrape_batch(urls, scraper_id, api_key):
    async with aiohttp.ClientSession() as session:
        tasks = []
        
        for url in urls:
            task = scrape_url(session, url, scraper_id, api_key)
            tasks.append(task)
        
        results = await asyncio.gather(*tasks)
        return results

async def scrape_url(session, url, scraper_id, api_key):
    async with session.post(
        f'https://app.manypi.com/api/scrape/{scraper_id}',
        headers={
            'Authorization': f'Bearer {api_key}',
            'Content-Type': 'application/json'
        },
        json={'url': url}
    ) as response:
        return await response.json()

# Usage
urls = ['url1', 'url2', 'url3', ...]
results = asyncio.run(scrape_batch(urls, 'scraper-id', 'api-key'))

Limitations and considerations

Respect robots.txt and terms of serviceAlways check a website’s robots.txt file and terms of service before scraping. Some sites explicitly prohibit automated access.

Rate limitingBe mindful of request frequency. Excessive requests can:

Get your IP blocked
Overload target servers
Consume credits quickly

Implement reasonable delays between requests (1-2 seconds minimum).

Dynamic contentManyPi handles JavaScript-rendered content automatically. However, some sites use advanced anti-bot measures. Contact support if you encounter issues with specific sites.

Next steps

API Reference

Explore the complete API documentation

Create a scraper

Start building your first API

View examples

See more real-world use cases

Get support

Need help? We’re here for you

Getting started

Features

Overview

How it works

Real-world examples

Example 1: E-commerce product API

Example 2: Job listings API

Example 3: News article API

Best practices

Common patterns

Pattern 1: Scheduled scraping

Pattern 2: Webhook integration

Pattern 3: Batch processing

Limitations and considerations

Next steps

API Reference

Create a scraper

View examples

Get support

​Overview

​How it works

​Real-world examples

​Example 1: E-commerce product API

​Example 2: Job listings API

​Example 3: News article API

​Best practices

​Common patterns

​Pattern 1: Scheduled scraping

​Pattern 2: Webhook integration

​Pattern 3: Batch processing

​Limitations and considerations

​Next steps

API Reference

Create a scraper

View examples

Get support

Overview

How it works

Real-world examples

Example 1: E-commerce product API

Example 2: Job listings API

Example 3: News article API

Best practices

Common patterns

Pattern 1: Scheduled scraping

Pattern 2: Webhook integration

Pattern 3: Batch processing

Limitations and considerations

Next steps