Scrapling — AI Agent Framework: Live Stats & TrendScore

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: Jun 17, 2026 • Sentiment updated: Jun 26, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As seen on GitHub, 'Scrapling fetch pipeline (Fetcher → StealthyFetcher fallback)' is a highly discussed topic, with one user stating 'Build the website-fetch layer: try fast `Fetcher` (HTTP) first, escalate to `StealthyFetcher` (Camoufox) on block/JS-needed'

Pros & Cons

What People Love

Scrapling's ease of use on GitHub, Dev.to users praise Scrapling's flexibility

Common Complaints

Scrapling issues, Complexity of integration

Biggest Positive: Scrapling useful

Biggest Negative: Scrapling issues

Why Scrapling Stands Out

Scrapling stands out from alternative web scraping frameworks due to its adaptive parser, which learns from website changes and automatically relocates elements. This feature, combined with its stealthy fetchers that bypass anti-bot systems like Cloudflare Turnstile, makes it an invaluable tool for web scraping. Additionally, Scrapling's support for concurrent, multi-session crawls and real-time stats provides a significant advantage over other frameworks. Its ability to scale up to full crawls and handle large amounts of data also sets it apart from other solutions.

Built With

Build a scalable e-commerce product scraper — Scrapling's adaptive parser and stealthy fetchers enable it to bypass anti-bot systems and extract data from dynamic websites, Build a real-time web monitoring system — Scrapling's ability to handle full-scale crawls and provide real-time stats allows for immediate alerts and actions, Build a research data extraction pipeline — Scrapling's spider framework and support for concurrent, multi-session crawls make it ideal for large-scale data collection, Build a custom web data integration service — Scrapling's flexible architecture and support for various fetchers and parsers enable it to be tailored to specific use cases, Build an automated website change detection system — Scrapling's parser learns from website changes and automatically relocates elements, making it perfect for monitoring website updates

Getting Started

  1. Install Scrapling using pip: `pip install scrapling`
  2. Import the StealthyFetcher class and set its adaptive property to True: `from scrapling.fetchers import StealthyFetcher; StealthyFetcher.adaptive = True`
  3. Use the StealthyFetcher to fetch a website: `p = StealthyFetcher.fetch('https://example.com', headless=True, network_idle=True)`
  4. Configure the parser to extract specific data: `products = p.css('.product', auto_save=True)`
  5. Try scaling up to a full crawl using the Spider class to verify it works: `MySpider().start()`

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

Competitive market with emerging technologies