23 Daily — According to the project's description, Scrapling is a comprehensive Python web scraping framework that appeared on GitHub Trending on November 2, 2026, with over 92,674 stars and an additional 858 stars that day. It integrates fast HTTP requests, stealth browsers, powerful parsing, and concurrent crawling into a single library designed for everything from single-page requests to large-scale web crawling.
Scrapling supports scraping static pages, rendering JavaScript, interacting with websites, and extracting data from API calls. It includes reusable sessions, proxy rotation, automatic retries, pause/resume crawling, and multiple data export formats. Users can operate it through Python API, terminal commands, or via AI agent integration such as MCP server and Agent Skill.
Robust and Flexible Scraping Workflow
Scrapling performs speedy HTTP requests and uses stealthy browsers to handle protections such as Cloudflare Turnstile, maintaining persistent sessions and capturing background API requests. It offers various parsing techniques including CSS selectors, XPath, regex, adaptive element tracking, and outputs clean Markdown data.
Its crawling features include concurrent spiders, multi-session requests, proxy rotation, AutoThrottle for dynamic rate limiting, and the ability to pause and resume crawls. Export options cover JSON, JSONL, CSV, and XML formats, enabling developers to tailor their scraping strategy efficiently and at scale.
Enterprise-Level Anti-Bot Solutions and Proxy Integration
The framework supports generating valid anti-bot tokens for protections like Akamai, DataDome, Kasada, and Incapsula without relying on browser automation. It also integrates with various residential and datacenter proxy providers and automated CAPTCHA solving services to overcome barriers such as reCAPTCHA and Cloudflare security measures, improving the success and stability of large-scale scraping operations.
Editor's take: Scrapling as a Powerful Tool for Startup Data Acquisition
Scrapling covers the entire scraping workflow from simple HTTP requests to fully featured crawling frameworks, which is critical for startups aiming to quickly gather valuable web data. Its combination of proxy management and anti-bot measures lowers development barriers and enhances operational efficiency.
To validate its effectiveness, startups can begin with official examples using terminal or Python APIs to test scraping on static and JavaScript-rendered pages, then advance to testing proxy rotation and automatic retries on target sites. Integrating Scrapling into data acquisition processes allows evaluation of its impact on business intelligence and data sourcing capabilities.