Open-source LLM-friendly web crawler and scraper with clean Markdown output, multiple extraction strategies, MCP server integration, and crash recovery for production RAG pipelines.
An open-source web crawler built for AI — extracts clean, structured data from websites that LLMs can actually use for RAG and agent workflows.
Crawl4AI is the most-starred open-source web crawler on GitHub (50k+ stars), built specifically for turning web content into clean, LLM-ready data for RAG pipelines, AI agents, and data workflows. Where general-purpose scrapers focus on raw HTML extraction, Crawl4AI optimizes its output for AI consumption — producing clean Markdown, structured JSON, and pre-chunked text ready for embedding.
The library provides multiple extraction strategies. The LLM-based strategy uses language models to extract structured data from pages using natural language instructions — describe what data you want in plain English instead of writing CSS selectors. The CSS/XPath strategy handles traditional rule-based extraction for known page structures. JSON schema-based extraction produces typed output matching your defined schemas. For content-heavy pages, the 'Fit Markdown' mode applies heuristic filtering and BM25 content scoring to strip boilerplate and surface the most relevant content.
Crawl4AI handles the full crawling lifecycle: URL discovery, JavaScript rendering via Playwright, session management for authenticated pages, stealth mode for bypassing Cloudflare and Akamai bot detection, proxy support, and parallel async crawling with configurable concurrency. Version 0.8.x adds deep crawl crash recovery with resume-from-saved-state capability, a prefetch mode that's 5-10x faster for URL discovery by skipping Markdown generation, and Docker deployment with a real-time monitoring dashboard and browser pool management.
The chunking system is a key differentiator. Extracted content can be automatically chunked using semantic, fixed-size, regex, or sliding window strategies, with each chunk enriched with source metadata. This makes output directly usable for vector database ingestion without additional preprocessing.
Crawl4AI includes an MCP server for direct integration with AI development tools like Claude Code, enabling AI agents to crawl and extract web data as part of their tool-use workflows. The library supports adaptive crawling that learns site patterns and optimizes extraction strategies over time.
Install via pip, run as a Docker service with REST API, or integrate the MCP server into your agent toolchain. Completely free and open-source with optional sponsorship tiers for priority support.
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Converts web pages to clean Markdown preserving document structure while stripping navigation, ads, and boilerplate. Fit Markdown mode applies heuristic filtering and BM25 scoring for highest-relevance content.
Use Case:
Building a RAG knowledge base from documentation sites with clean, well-structured text that LLMs can reason over effectively.
CSS/XPath selectors for known structures, LLM-driven extraction with natural language instructions, and JSON schema-based extraction for typed output. Choose the right approach per page type.
Use Case:
Extracting product listings with CSS selectors from e-commerce sites while using LLM extraction for unstructured blog content — both in the same crawl.
Resume interrupted crawls from saved state using on_state_change callbacks. Production-ready for long-running crawls across thousands of pages.
Use Case:
Crawling a 50,000-page documentation site over multiple days with automatic resume after network interruptions or server restarts.
Undetected browser support bypasses Cloudflare, Akamai, and custom bot detection. Proxy support and session management for authenticated content.
Use Case:
Scraping competitor pricing pages protected by Cloudflare's bot detection without getting blocked.
Built-in MCP server lets AI agents like Claude Code use Crawl4AI as a tool — crawling and extracting web data as part of agent workflows.
Use Case:
An AI coding agent automatically crawls API documentation to understand a new library before generating integration code.
Production Docker deployment with real-time monitoring dashboard, browser pool management, REST API, and webhook infrastructure for job queues.
Use Case:
Running Crawl4AI as a shared service for a team, with a dashboard showing active crawls, browser pool status, and queue depth.
Free
$50
$2,000
Ready to get started with Crawl4AI?
View Pricing Options →Crawling documentation sites, knowledge bases, and content repositories to produce clean, chunked Markdown ready for embedding and vector storage in RAG pipelines.
Connecting Crawl4AI as an MCP tool to AI coding agents, enabling them to crawl and extract web data as part of their autonomous workflows.
Running long-duration crawls across thousands of pages with crash recovery, monitoring dashboards, and webhook-based job queues in Docker deployments.
Extracting product data, pricing, reviews, or other structured information from JavaScript-heavy sites using LLM or schema-based extraction strategies.
We believe in transparent reviews. Here's what Crawl4AI doesn't handle well:
Traditional scrapers extract raw HTML/text and leave processing to you. Crawl4AI is built for AI applications — it produces clean Markdown, supports LLM-driven extraction with natural language instructions, includes chunking strategies designed for RAG pipelines, and integrates directly with AI agents via MCP.
Yes. Markdown conversion, CSS/XPath extraction, and content filtering all work without any LLM. LLM-based extraction is optional — use it when you need natural language-driven scraping of unstructured pages.
Crawl4AI includes a built-in MCP server that AI tools like Claude Code can connect to. Your AI agent can then call Crawl4AI as a tool — asking it to crawl a URL and return structured data — as part of a larger workflow.
Yes. Crawl4AI uses Playwright for full JavaScript rendering, handling SPAs, dynamic loading, infinite scroll, and client-side rendered content. Stealth mode helps bypass bot detection on protected sites.
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See how Crawl4AI compares to Firecrawl and other alternatives
View Full Comparison →Search & Discovery
The Web Data API for AI that transforms websites into LLM-ready markdown and structured data, providing comprehensive web scraping, crawling, and extraction capabilities specifically designed for AI applications and agent workflows.
Search & Discovery
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