Web Scraping with Scrapling: 2026 Tutorial

What is Scrapling?

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Scrapling is an open-source Python library designed for efficient web scraping with a unique adaptive capability. Unlike older libraries like BeautifulSoup, Scrapling can automatically adapt to minor HTML changes using built-in similarity algorithms. It features its own rapid parsing engine that outperforms most Python scraping libraries.

Scrapling supports scraping dynamic content using Playwright for JavaScript rendering through multiple fetcher classes, each optimized for different scenarios. It includes advanced stealth mode features using a modified Firefox browser to bypass sophisticated anti-bot protections.

Key Features of Scrapling

• Adaptive Selectors: Automatically relocate elements when HTML structures change using intelligent similarity algorithms
• Multiple Fetcher Classes: Choose between Fetcher (HTTP), DynamicFetcher (Playwright Chromium), and StealthyFetcher (modified Firefox with advanced stealth)
• Selector Chaining: Chain CSS and XPath selectors without looping
• Session Management: Persistent sessions with FetcherSession, DynamicSession, and StealthySession classes
• Async Support: Complete async support across all fetchers with dedicated async session classes
• Regex Integration: Built-in regex support with re() and re_first() methods
• CLI Utility: Command-line interface with interactive shell for rapid development
• High Performance: Optimized performance with benchmarks showing up to 698x faster than BeautifulSoup in some operations

How to Do Web Scraping with Scrapling?

Ready to master modern web scraping? Learn how to use Scrapling to extract data easily, adapt to site changes, and stay undetected.

Prerequisites

• Python 3.10 or newer
• Scrapling installed

Installation

Starting with v0.3.2, Scrapling uses a modular installation approach:

Base installation (parser only, no fetchers):

pip install scrapling

Install with fetchers and browser dependencies:

pip install "scrapling[fetchers]"
scrapling install

The scrapling install command downloads all browsers with their system dependencies and fingerprint manipulation tools.

Install everything (fetchers, AI features, CLI tools):

pip install "scrapling[all]"
scrapling install

Step 1: Fetch HTML from a Web Page

Start by testing if Scrapling can access your target site. Use the Fetcher class for HTTP requests:

from scrapling.fetchers import Fetcher
page = Fetcher.get("https://www.scrapingcourse.com/ecommerce/")
print(page.status)  # Should print: 200
print(page.html_content)

If successful, you’ll see the HTTP status code 200 and the full HTML of the webpage.

Step 2: Scrape Product Data Using CSS Selectors

Scrapling can auto-adjust to minor layout changes using adaptive selectors, but this feature is disabled by default.

First, inspect the product elements using DevTools. For this site:

• Product names are in h2.woocommerce-loop-product__title
• Prices in .price
• Images in .woocommerce-LoopProduct-link img

from scrapling.fetchers import Fetcher
page = Fetcher.get("https://www.scrapingcourse.com/ecommerce/")
# Extract product data
names = page.css("h2.woocommerce-loop-product__title")
prices = page.css(".price")
images = page.css(".woocommerce-LoopProduct-link img")
product_data = []
for name, price, image in zip(names, prices, images):
    # Use regex to extract just the price number
    price_value = price.re_first(r'[d.,] ')
    
    data = {
        "name": name.text,
        "price": f"${price_value}",
        "image": image.attrib["src"],
    }
    product_data.append(data)
print(product_data)

To enable adaptive selectors (tracks elements even after site updates):

# Enable adaptive globally
Fetcher.adaptive = True
page = Fetcher.get("https://www.scrapingcourse.com/ecommerce/")
# Save element properties for future adaptation
names = page.css(".product-name", auto_save=True)
# Later, when the site structure changes, use adaptive mode
names = page.css(".product-name", adaptive=True)  # Scrapling finds them even if CSS changed!

Step 3: Enable Stealth Mode

Many websites use anti-bot tools like Cloudflare to block scrapers. Scrapling’s StealthyFetcher uses a modified Firefox browser with advanced fingerprint spoofing.

Use StealthySession or StealthyFetcher with the headless=True flag:

from scrapling.fetchers import StealthyFetcher
# One-off request (opens and closes browser)
page = StealthyFetcher.fetch(
    "https://www.scrapingcourse.com/cloudflare-challenge/",
    headless=True
)
print(page.status)

For multiple requests, use session to keep browser open:

from scrapling.fetchers import StealthySession
# Keep browser open for multiple requests
with StealthySession(headless=True) as session:
    page = session.fetch("https://www.scrapingcourse.com/cloudflare-challenge/")
    print(page.html_content)

Note: The solve_cloudflare parameter is available but should be used carefully based on the specific Cloudflare protection type.

Real-World Example: Scraping E-commerce Data

Here’s a complete scraper with error handling:

from scrapling.fetchers import Fetcher
page = Fetcher.get("https://www.scrapingcourse.com/ecommerce/")
if page.status != 200:
    print(f"Failed to fetch page: {page.status}")
    exit()
names = page.css("h2.woocommerce-loop-product__title")
prices = page.css(".price")
images = page.css(".woocommerce-LoopProduct-link img")
product_data = []
for name, price, image in zip(names, prices, images):
    price_value = price.re_first(r'[d.,] ')
    
    data = {
        "name": name.text,
        "price": f"${price_value}",
        "image": image.attrib["src"],
    }
    product_data.append(data)
for product in product_data:
    print(product)

Output structure:

[
    {"name": "Abominable Hoodie", "price": "$69.00", "image": "https://...jpg"},
    {"name": "Artemis Running Short", "price": "$45.00", "image": "https://...jpg"}
]

Limitations of Scrapling

Scrapling works well for small- to medium-scale scraping, but it hits some walls:

• No Built-in Proxy Rotation: No native support for rotating proxies or automatic geo-targeting
• Resource Heavy for Browser-Based Scraping: DynamicFetcher and StealthyFetcher use browser instances, which consume significant memory
• Adaptive Feature Requires Manual Enable: The adaptive feature is disabled by default and requires explicit configuration
• No Scaling Infrastructure: You need to manage concurrency, retries, and distributed scraping yourself
• First Element Only for Adaptive: When saving adaptive data, only the first element’s properties are saved

Scaling Tip: Use Bright Data for Proxy Management

To overcome Scrapling’s proxy limitation, integrate it with a service like Bright Data or Oxylabs. Such providers usually offer residential proxies, rotating IPs, and geo-targeting. I am NOT affiliated with any of these brands!

Example with FetcherSession:

from scrapling.fetchers import FetcherSession
proxies = {
    "http": "http://username:[email protected]:22225",
    "https": "http://username:[email protected]:22225",
}
with FetcherSession() as session:
    page = session.get(
        "https://www.scrapingcourse.com/ecommerce/",
        proxies=proxies
    )
    print(page.html_content)

You’ll need an account with proper credentials. This integration helps you avoid IP bans and scrape at higher volumes.

Best Practices for Scrapling

Enable Adaptive Selectors When Needed: Use auto_save=True for critical selectors that might break with site updates

Choose the Right Fetcher:

• Fetcher for static sites (fastest)
• DynamicFetcher for JavaScript-heavy sites with basic protections
• StealthyFetcher for sites with advanced anti-bot systems

Use Sessions for Multiple Requests: Reuse browser instances with session classes to reduce overhead

Rotate Proxies: Prevent IP blocks on large-scale operations

Add Rate Limiting: Use time.sleep() or queue systems to throttle requests

Leverage Built-in Regex: Use .re() and .re_first() methods for precise data extraction

Handle Errors Gracefully: Always check response status and handle exceptions

Use Async for Concurrency: Leverage AsyncFetcher and async sessions for parallel requests

Conclusion

Scrapling gives you a cleaner and more resilient way to build scrapers in 2025. Its adaptive selectors and multiple fetcher options make it ideal for sites ranging from simple static pages to those with sophisticated anti-bot protections. However, it’s not bulletproof — large-scale operations still require external infrastructure like proxy rotation and distributed systems.

Pair Scrapling with Bright Data or any other top-quality rotating proxies, and you’ll have a powerful setup that can handle most scraping challenges without constant maintenance. The library’s modular design (since v0.3.2) means you only install what you need, and its 92% test coverage ensures reliability.

Got questions for me? Let me know in the comments!