> ## Documentation Index
> Fetch the complete documentation index at: https://kindling.birklid.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Scrapling

> Python web scraping framework with adaptive element tracking, anti-bot bypass, concurrent crawling, and MCP server for AI agent integration.

<div style={{display: "flex", gap: "8px", marginBottom: "1.5rem", flexWrap: "wrap"}}>
  <Badge>Open Source</Badge>
  <Badge color="#F97316">Applications Layer</Badge>
</div>

# Scrapling

**Adaptive Python scraping that survives website redesigns — selectors relocate themselves after layout changes, with stealth mode for anti-bot resistance.**

<Frame>
  <img src="https://mintcdn.com/tumbleweedlabs/QT0SlrwbzlJBSMcS/images/og-scrapling.png?fit=max&auto=format&n=QT0SlrwbzlJBSMcS&q=85&s=0e82a956bb0fa6d52ad228b038bf98e0" alt="Scrapling GitHub" width="1200" height="600" data-path="images/og-scrapling.png" />
</Frame>

<CardGroup cols={4}>
  <Card title="Type" icon="code-branch">Open Source (BSD-3)</Card>
  <Card title="Stack Layer" icon="browsers">Applications</Card>
  <Card title="Language" icon="code">Python</Card>
  <Card title="Stars" icon="star">48k+</Card>
</CardGroup>

## What it is

Scrapling is a Python web scraping framework that handles the full spectrum from simple HTTP requests to anti-bot-resistant browser automation. Its headline feature is adaptive element tracking: scraped selectors automatically relocate themselves when a website redesigns, dramatically reducing maintenance overhead for long-running scraping projects. It matches Parsel and Scrapy for performance on text extraction while adding stealth browsing modes and Cloudflare bypass for modern dynamic sites.

Concurrent crawling with pause/resume is built in, and Scrapling ships an MCP server that lets AI agents call it directly as a tool — making it a natural fit for autonomous research agents that need structured web data. Benchmarks show strong performance relative to Selenium and Playwright alternatives for data-extraction tasks.

<Tip>
  **Use this when** you're building scrapers that need to outlive website changes, or when you need anti-bot-resistant data extraction for AI pipelines — the MCP integration makes it directly usable inside agent workflows.
</Tip>

## Get started

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  <Card title="GitHub ↗" icon="github" href="https://github.com/D4Vinci/Scrapling">
    Source, benchmarks, and MCP server documentation.
  </Card>
</CardGroup>

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