Scrapling is an adaptive Python web scraping framework designed for effortless data extraction from the modern web, capable of handling everything from single requests to full-scale, concurrent crawls. It features advanced anti-bot bypass, intelligent element relocation, and comprehensive session management with built-in proxy rotation and AI integration.

Is Scrapling open source?

Yes, licensed under BSD-3-Clause.

Scrapling

Active·★ 86.4k·BSD-3-Clause·Updated 2026-10-08

★ Most Popular★ Browser Automation★ Data Processing

Scrapling is an adaptive Python web scraping framework that scales from a single request to a full crawl. Its standout idea is adaptive parsing: elements you select can be saved and automatically relocated when a website's layout changes, so scrapers break less often. It combines fast HTTP fetching that can impersonate browser fingerprints, full browser automation for dynamic pages, and a Scrapy-like spider framework.

It suits developers and data teams collecting public web data, and AI builders who want agents to fetch and extract web content. The spider framework supports concurrency limits, per-domain throttling, automatic retry of blocked requests, pause and resume, streaming results and exports to JSON, CSV and XML, with optional robots.txt compliance. An MCP server lets AI assistants use Scrapling directly, and an interactive shell helps you develop selectors.

Getting started: requires Python 3.10+. Install with `pip install scrapling`; for browser-based fetchers install `scrapling[fetchers]` and run `scrapling install`, and for the MCP server install `scrapling[ai]`.

#Web Scraping#Python#Crawler Framework#Anti-bot Bypass#Adaptive Parsing#Proxy Rotation#Headless Browser#CLI Tool

Features

01Full-featured crawling framework with Scrapy-like API, concurrent requests, multi-session support, and pause/resume functionality.

02Advanced fetchers with anti-bot bypass, dynamic loading, persistent session management, and integrated proxy rotation.

03Adaptive scraping capabilities with smart element tracking and flexible selection methods, including AI integration via MCP server.

04High-performance and memory-efficient architecture, optimized for speed and battle-tested in production environments.

Why choose it

+Adaptive selectors that survive website redesigns reduce scraper maintenance.

+One framework covers fast HTTP requests, dynamic browser pages and large crawls.

+Production crawling features: throttling, retries, pause and resume, streaming and built-in exporters.

+MCP server for AI assistants; BSD-3-Clause licensed.

Trade-offs

!Its anti-bot bypass capabilities make it easy to scrape sites that do not want to be scraped; check each site's terms, robots.txt and applicable laws before collecting data.

!Browser-based fetchers need extra installation and much more memory and CPU than plain HTTP requests.

!The README is heavy with proxy-provider sponsorships; you do not need any of them to use the library.

Compatibility

Python

Runtime

Verified via docs

Docker

Deployment

Verified via docs

Quick start

$ pip install scrapling

Use cases

↳Performing single web requests or launching full-scale, concurrent web crawls.

↳Adapting to website design changes by automatically relocating scraped elements.

↳Bypassing advanced anti-bot systems such as Cloudflare Turnstile.

↳Scaling up to multi-session crawls with proxy rotation and checkpoint-based persistence.

↳Leveraging AI for assisted web scraping and targeted data extraction.

How it compares

≈vs Firecrawl MCP Server: Firecrawl is a hosted scraping and search API with an official MCP server; Scrapling is a library you run yourself.

≈vs crw: crw is a lightweight Rust scraper with a Firecrawl-compatible API; Scrapling is a fuller Python framework with adaptive parsing and spiders.

≈vs Playwright MCP: Playwright MCP is for agents operating websites interactively; Scrapling is built for extracting data at scale.

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