Scrapling is an adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl.
Its parser learns from website changes and automatically relocates your elements when pages update. Its fetchers bypass anti-bot systems like Cloudflare Turnstile out of the box. And its spider framework lets you scale up to concurrent, multi-session crawls with pause/resume and automatic proxy rotation - all in a few lines of Python. One library, zero compromises.
Blazing fast crawls with real-time stats and streaming. Built by Web Scrapers for Web Scrapers and regular users, there's something for everyone.
from scrapling.fetchers import Fetcher, AsyncFetcher, StealthyFetcher, DynamicFetcher
StealthyFetcher.adaptive = True
p = StealthyFetcher.fetch('https://example.com', headless=True, network_idle=True) # Fetch website under the radar!
products = p.css('.product', auto_save=True) # Scrape data that survives website design changes!
products = p.css('.product', adaptive=True) # Later, if the website structure changes, pass `adaptive=True` to find them!
Or scale up to full crawls
from scrapling.spiders import Spider, Response
class MySpider(Spider):
name = "demo"
start_urls = ["https://example.com/"]
async def parse(self, response: Response):
for item in response.css('.product'):
yield {"title": item.css('h2::text').get()}
MySpider().start()
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Key Features
Optional Dependencies
1. If you are going to use any of the extra features below, the fetchers, or their classes, you will need to install fetchers' dependencies and their browser dependencies as follows:
pip install "scrapling[fetchers]"
scrapling install # normal install
scrapling install --force # force reinstall
This downloads all browsers, along with their system dependencies and fingerprint manipulation dependencies.
Or you can install them from the code instead of running a command like this:
from scrapling.cli import install
install([], standalone_mode=False) # normal install
install(["--force"], standalone_mode=False) # force reinstall
2. Extra features: - Install the MCP server feature:
pip install "scrapling[ai]"
- Install shell features (Web Scraping shell and the extract command):
pip install "scrapling[shell]"
- Install everything:
pip install "scrapling[all]"
Remember that you need to install the browser dependencies with scrapling install after any of these extras (if you didn't already)
Getting Started
Let's give you a quick glimpse of what Scrapling can do without deep diving.
Installation
Scrapling requires Python 3.10 or higher:
pip install scrapling
This installation only includes the parser engine and its dependencies, without any fetchers or commandline dependencies.
Docker
You can also install a Docker image with all extras and browsers with the following command from DockerHub:
docker pull pyd4vinci/scrapling
Or download it from the GitHub registry:
docker pull ghcr.io/d4vinci/scrapling:latest
This image is automatically built and pushed using GitHub Actions and the repository's main branch.
Basic Usage
HTTP requests with session support ```python from scrapling.fetchers import Fetcher, FetcherSession
with FetcherSession(impersonate='chrome') as session: # Use latest version of Chrome's TLS fingerprint page = session.get('https://quotes.toscrape.com/', stealthy_headers=True) quotes = page.css('.quote .text::text').getall()
Async Session Management Examples
```python import asyncio from scrapling.fetchers import FetcherSession, AsyncStealthySession, AsyncDynamicSession
async with FetcherSession(http3=True) as session: # FetcherSession is context-aware and can work in both sync/async patterns page1 = session.get('https://quotes.toscrape.com/') page2 = session.get('https://quotes.toscrape.com/', impersonate='firefox135')
Async session usage
async with AsyncStealthySession(max_pages=2) as session: tasks = [] urls = ['https://example.com/page1', 'https://example.com/page2'] for url in urls: task = session.fetch(url) tasks.append(task) print(session.get_pool_stats()) # Optional - The status of the browser tabs pool (busy/free/error) results = await asyncio.gather(*tasks) print(session.get_pool_stats()) ```
Adaptive Scraping & AI Integration
🔄 Smart Element Tracking: Relocate elements after website changes using intelligent similarity algorithms.
🎯 Smart Flexible Selection: CSS selectors, XPath selectors, filter-based search, text search, regex search, and more.
🔍 Find Similar Elements: Automatically locate elements similar to found elements.
🤖 MCP Server to be used with AI: Built-in MCP server for AI-assisted Web Scraping and data extraction. The MCP server features powerful, custom capabilities that leverage Scrapling to extract targeted content before passing it to the AI (Claude/Cursor/etc), thereby speeding up operations and reducing costs by minimizing token usage. (demo video)
CLI & Interactive Shell
Scrapling includes a powerful command-line interface:
Launch the interactive Web Scraping shell
scrapling shell
Extract pages to a file directly without programming (Extracts the content inside the body tag by default). If the output file ends with .txt, then the text content of the target will be extracted. If it ends in .md, it will be a Markdown representation of the HTML content; if it ends in .html, it will be the HTML content itself.
scrapling extract get 'https://example.com' content.md
scrapling extract get 'https://example.com' content.txt --css-selector '#fromSkipToProducts' --impersonate 'chrome' # All elements matching the CSS selector '#fromSkipToProducts'
scrapling extract fetch 'https://example.com' content.md --css-selector '#fromSkipToProducts' --no-headless
scrapling extract stealthy-fetch 'https://nopecha.com/demo/cloudflare' captchas.html --css-selector '#padded_content a' --solve-cloudflare
[!NOTE] There are many additional features, but we want to keep this page concise, including the MCP server and the interactive Web Scraping Shell. Check out the full documentation here
