Crawl4AI vs markitdown
Two of the top projects in Data and scraping for AI, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Crawl4AI
Python web crawler that converts pages to LLM-ready Markdown and JSON
markitdown
Python tool that converts PDF, Office, HTML and audio files to Markdown for LLMs
Verdict
Crawl4AI ranks higher, 77 to 71. Of the 5 score parts we measure for both, Crawl4AI leads on 1, markitdown on 1 and 3 are even.
- Adoptionmarkitdown leads by 13 points, 95 to 82.
- FreshnessEven at 100.
- MaintenanceEven at 90.
- Easy to runCrawl4AI leads by 34 points, 67 to 33: a wide gap.
- Agent-readyEven at 0.
Pick Crawl4AI if you are
Developers feeding web content into RAG, agents and data pipelines
- Schema-based CSS/XPath extraction runs without any LLM call
- Fit Markdown filters (pruning, BM25, LLM) strip menus and boilerplate
Pick markitdown if you are
Developers feeding documents into LLM and RAG pipelines
- Covers many formats through one CLI and Python API (convert, convert_local, convert_stream)
- Built-in converters run offline; install only needed formats via extras like [pdf,docx,pptx]
| What we compare | Crawl4AI | markitdown |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 82, widely used | 95, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 90, healthy | 90, healthy |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 85.6k | 190.3k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Crawl4AI
Crawl4AI is a Python library, CLI (`crwl`) and Docker REST server that drives Playwright browsers (Chromium, Firefox, WebKit) to turn web pages into clean or filtered Markdown. It extracts structured data with CSS/XPath/regex schemas without an LLM, or through any LiteLLM-supported provider into a typed JSON schema. It also supports deep crawls, adaptive crawling and batch fetching. A hosted Crawl4AI Cloud with search and answer endpoints is sold separately.
Who it is for: Developers feeding web content into RAG, agents and data pipelines
Strengths
- Schema-based CSS/XPath extraction runs without any LLM call
- Fit Markdown filters (pruning, BM25, LLM) strip menus and boilerplate
- Persistent profiles, CDP remote browsers, rotating proxies and stealth mode
- Docker server exposes /md, /crawl, /screenshot, /pdf, plus MCP, dashboard and playground
Weaknesses
- Search, SERP and answer endpoints exist only in the paid hosted cloud
- Bot-wall handling when self-hosted depends on your own proxies and settings
- Needs a Playwright browser install via crawl4ai-setup
- README asks for attribution when you use it
- no GPU
- Docker + Compose
- Needs Playwright browsers (Chromium), Docker (optional), LLM provider API key (optional)
- Models: LiteLLM providers, OpenAI, Ollama
- port 11235
markitdown
MarkItDown is a Python library and CLI that converts PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV/JSON/XML, ZIP, EPUB and YouTube URLs into Markdown, keeping headings, lists, tables and links. Optional backends add LLM image descriptions, Azure Document Intelligence, Azure Content Understanding, and OCR via the markitdown-ocr plugin. Output targets LLM pipelines rather than high-fidelity human-facing documents.
Who it is for: Developers feeding documents into LLM and RAG pipelines
Strengths
- Covers many formats through one CLI and Python API (convert, convert_local, convert_stream)
- Built-in converters run offline; install only needed formats via extras like [pdf,docx,pptx]
- Third-party plugin system, plus an LLM-vision OCR plugin using any OpenAI-compatible client
- MIT license, active releases, Python 3.10 through 3.14
Weaknesses
- Not meant for high-fidelity conversion; output is tuned for text analysis tools
- Video and structured field extraction need billable Azure Content Understanding
- convert() accepts URIs and files with process privileges; untrusted input must be sanitized
- LLM image descriptions currently limited to pptx and image files; OCR needs a plugin
- no GPU
- Docker
- Needs Python 3.10-3.14, Azure Document Intelligence (optional), Azure Content Understanding (optional), OpenAI-compatible client (optional)
- Models: OpenAI-compatible vision models (e.g. gpt-4o)
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