Crawl4AI vs docling
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
docling
Python library and CLI that converts PDFs, Office files and more into structured text
Verdict
Crawl4AI ranks higher, 72 to 70. Of the 5 score parts we measure for both, Crawl4AI leads on 2, docling on 1 and 2 are even.
- AdoptionCrawl4AI leads by 16 points, 68 to 52: a wide gap.
- FreshnessEven at 100.
- MaintenanceEven at 81.
- Easy to runCrawl4AI leads by 17 points, 67 to 50: a wide gap.
- Agent-readydocling leads by 85 points, 85 to 0: a wide gap.
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 docling if you are
Developers preparing documents for RAG and LLM pipelines
- Wide input coverage: PDF, Office, ODF, EPUB, email, Apple iWork, XBRL, audio, video
- Local execution suits sensitive data and air-gapped environments
| What we compare | Crawl4AI | docling |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 68, popular | 52, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 81, healthy | 81, healthy |
| Easy to run | 67, easy | 50, easy |
| Agent-ready | 0, none | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 85.4k | 68.8k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
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
docling
Docling parses PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, audio and other formats into a unified DoclingDocument, with PDF layout, reading order, table structure, code and formula recognition. It exports to Markdown, HTML, DocLang, DocTags and lossless JSON. It runs locally via a CLI or Python API, with OCR, VLM support (GraniteDocling), an MCP server and a docling-serve API server.
Who it is for: Developers preparing documents for RAG and LLM pipelines
Strengths
- Wide input coverage: PDF, Office, ODF, EPUB, email, Apple iWork, XBRL, audio, video
- Local execution suits sensitive data and air-gapped environments
- Integrations with LangChain, LlamaIndex, Crew AI and Haystack
- Ships a CLI, Python API, MCP server and docling-serve HTTP server
Weaknesses
- Hardware requirements (RAM, GPU) are not stated in the README
- Individual models carry their own licenses, separate from the MIT codebase
- Metadata extraction and chemistry understanding are listed as coming soon
- Requires Python 3.10 or higher; 3.9 support was dropped in 2.70.0
- GPU optional
- Docker
- Models: GraniteDocling, VLMs, OCR engines, ASR models
More in Data and scraping for AI
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.