Scrapling — AI Agent Review & Live Stats

Live GitHub stats, community sentiment, and trend data for Scrapling. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 1, 2026 • Sentiment updated: Mar 17, 2026

GitHub Statistics

Community Sentiment

Community Buzz: Scrapling has gained significant attention in the web scraping community for its innovative use of Playwright and MCP server for efficient and stealthy data extraction. Users praise its ease of use and high performance. The project's documentation is also well-regarded.

Why Scrapling Stands Out

Scrapling is different from alternatives because of its 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 elements, making it a unique solution for web scraping. The project's use of Playwright and MCP server for efficient and stealthy data extraction is also a key differentiator. Additionally, Scrapling's ease of use and high performance make it a valuable tool for web scrapers.

Built With

Build a stealthy web scraper that bypasses anti-bot systems — Scrapling's fetchers bypass Cloudflare Turnstile out of the box, Build a concurrent, multi-session crawl with pause/resume and automatic proxy rotation — Scrapling's spider framework enables this in a few lines of Python, Build a research agent that extracts data from websites with changing structures — Scrapling's parser learns from website changes and relocates elements automatically, Build a real-time web monitoring system with streaming stats — Scrapling's fetchers and spiders provide blazing fast crawls with real-time stats, Build a custom proxy service for your business — Scrapling's integration with DataImpulse enables this

Getting Started

  1. Install Scrapling using pip: `pip install scrapling`
  2. Import the necessary modules: `from scrapling.fetchers import Fetcher, AsyncFetcher, StealthyFetcher, DynamicFetcher`
  3. Configure the fetcher: `StealthyFetcher.adaptive = True`
  4. Fetch a website: `p = StealthyFetcher.fetch('https://example.com', headless=True, network_idle=True)`
  5. Try scraping data with adaptive parsing to verify it works: `products = p.css('.product', adaptive=True)`

About

🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!

Official site: https://scrapling.readthedocs.io/en/latest/

Category & Tags

Category: data

Tags: ai, ai-scraping, automation, crawler, crawling, crawling-python, data, data-extraction, mcp, mcp-server, playwright, python, scraping, selectors, stealth, web-scraper, web-scraping, web-scraping-python, webscraping, xpath

Market Context

Scrapling is a popular web scraping library in the Python ecosystem, offering a robust and efficient solution for data extraction and crawling tasks.