✨ Why Pangolin?

Traditional web scrapers break when websites update their HTML structure. Pangolin adapts automatically.

• ✅ Zero Maintenance — Our AI detects page changes and updates parsers automatically
• ✅ 99.9% Uptime — Enterprise-grade infrastructure with automatic retry logic
• ✅ No Proxies Needed — Built-in anti-bot bypass (CAPTCHA, rate limits, fingerprinting)
• ✅ Multi-Format Output — Get data as JSON, HTML, or Markdown — whatever your app needs
• ✅ Real-Time Data — Fresh product prices, inventory, reviews in milliseconds

Perfect for: E-commerce sellers, price monitoring tools, market research, product analytics, and dropshipping businesses.

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🎁 Get Started Free

For Open Source Community:

🆓 What You Get ✅ 200 free API calls/month ✅ Full parser template code (Go) ✅ Technical support via email ✅ No credit card required ✅ Access to all platforms

📝 How to Claim Contact us via email or WeChat Email: shiyang@pangolinfo.com WeChat: Pangolin-Scraper Mention "GitHub Free Tier" in your message Receive your API key within 24 hours

🌏 International Users: Prefer email or Telegram? Contact csm@pangolinfo.com
🇨🇳 中国用户： 添加微信 Pangolin-Scraper 备注「GitHub免费额度」

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🚀 Quick Start (3 Minutes)

Step 1: Get Your API Key

Claim your 200 free API calls

Step 2: Make Your First Request

Python:

import requests

API_KEY = "your_free_api_key_here"

response = requests.post(
    "http://scrapeapi.pangolinfo.com/api/v1/scrape",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "url": "https://www.amazon.com/dp/B0DYTF8L2W",
        "parserName": "amzProductDetail",
        "formats": ["json"]
    }
)

print(response.json())

JavaScript:

const response = await fetch("http://scrapeapi.pangolinfo.com/api/v1/scrape", {
  method: "POST",
  headers: {
    "Authorization": `Bearer YOUR_API_KEY`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    url: "https://www.amazon.com/dp/B0DYTF8L2W",
    parserName: "amzProductDetail",
    formats: ["json"]
  })
});

const data = await response.json();
console.log(data);

cURL:

curl -X POST http://scrapeapi.pangolinfo.com/api/v1/scrape \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.amazon.com/dp/B0DYTF8L2W",
    "parserName": "amzProductDetail",
    "formats": ["json"]
  }'

Step 3: Parse the Response

{
  "asin": "B0DYTF8L2W",
  "title": "Product Title Here",
  "price": 29.99,
  "rating": 4.5,
  "reviews": 1234,
  "imageUrl": "https://...",
  "inStock": true,
  ...
}

🎉 That's it! You're now scraping e-commerce data with zero infrastructure setup.

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📊 What's Included?

Feature	Open Source Parser	Free API (200 calls)	Paid API
Parser Template Code	✅ Full access (Go)	❌	❌
Monthly API Calls	∞ (self-hosted)	✅ 200 free	✅ Custom volume
Supported Platforms	Amazon search only	✅ All platforms	✅ All platforms
Auto Page Structure Updates	🔧 Manual updates	✅ Automatic	✅ Automatic
Anti-Bot Handling	🔧 DIY (proxies, CAPTCHA)	✅ Built-in	✅ Advanced
Data Formats	JSON only	JSON, HTML, Markdown	JSON, HTML, Markdown
Support	💬 Community (GitHub)	✅ Email support	✅ Priority support
Uptime SLA	N/A (self-hosted)	99%	99.9%
Use Case	Learning, testing	Small projects	Production apps

💡 Recommendation:

• Learning web scraping? → Start with open source parser
• Building a side project? → Use free API tier
• Running a business? → Upgrade to paid for reliability

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🛠️ Supported Platforms & Data

Amazon

Walmart

Shopify

eBay

Amazon Data Fields (30+ fields)

• Product Details: ASIN, title, price, images, variants
• Rankings: Best Sellers Rank, category position
• Reviews: Rating, review count, top reviews
• Seller Info: FBA/FBM, seller name, shipping
• Inventory: Stock status, availability date

Walmart, eBay, Shopify

Similar comprehensive data extraction for each platform.

📖 Full API Documentation: https://docs.pangolinfo.com/api-reference

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💼 Real-World Use Cases

🛍️ For E-commerce Sellers

# Monitor competitor prices daily
products = ["B08N5WRWNW", "B08L5VFJ2C", "B07ZPKN6YR"]

for asin in products:
    data = scrape_amazon_product(asin)
    if data['price'] < my_price:
        send_alert(f"Competitor lowered price: {data['title']}")

Result: Stay competitive by adjusting prices based on real-time market data.

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📊 For Market Researchers

// Analyze product trends in a category
const category = "Electronics > Headphones";
const results = await scrape_amazon_search(category, pages=10);

const avgPrice = results.reduce((sum, p) => sum + p.price, 0) / results.length;
const topBrands = getMostFrequent(results.map(p => p.brand));

Result: Generate market reports with pricing trends, top brands, and customer sentiment.

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🤖 For SaaS Builders

# Build a price drop notification service
def check_wishlist(user_id):
    wishlist = get_user_wishlist(user_id)
    
    for item in wishlist:
        current_price = scrape_product(item.url)['price']
        
        if current_price < item.target_price:
            send_email(user_id, f"Price drop alert: {item.name}")

Result: Create a valuable service without managing scraping infrastructure.

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📈 For Data Scientists

# Collect training data for price prediction models
import pandas as pd

data = []
for asin in asin_list:
    product = scrape_amazon_product(asin)
    data.append({
        'title': product['title'],
        'price': product['price'],
        'rating': product['rating'],
        'reviews': product['reviews'],
        'category': product['category']
    })

df = pd.DataFrame(data)
df.to_csv('amazon_products.csv')

Result: Build ML models with clean, structured e-commerce data.

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