mineru vs Crawl4AI
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.
mineru
Document parser that converts PDFs, images and Office files to Markdown and JSON
Crawl4AI
Python web crawler that converts pages to LLM-ready Markdown and JSON
Verdict
mineru ranks higher, 78 to 77. Of the 5 score parts we measure for both, mineru leads on 2, Crawl4AI on 2 and 1 is even.
- AdoptionCrawl4AI leads by 5 points, 82 to 77.
- FreshnessEven at 100.
- Maintenancemineru leads by 9 points, 99 to 90.
- Easy to runCrawl4AI leads by 17 points, 67 to 50: a wide gap.
- Agent-readymineru leads by 70 points, 70 to 0: a wide gap.
Pick mineru if you are
Engineers feeding PDFs and Office documents to LLM pipelines or agents
- Handles scanned PDFs, tables and formulas, with four quality tiers from fast preview to advanced
- Stable doc/tier/page/block locators allow paged reading and citation of long documents
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
| What we compare | mineru | Crawl4AI |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 77, popular | 82, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 99, healthy | 90, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 81.8k | 85.6k |
| License | custom license (read the license) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
mineru
MinerU parses PDFs, scanned images, Office, OpenDocument, EPUB, OFD, HTML and CSV files into Markdown, HTML, LaTeX, DOCX and structured content lists. It offers four parsing tiers (flash, basic, standard, advanced), a local document library with search, and page/block locators for citations. It ships a CLI, Python SDK, API, router and Gradio WebUI, plus an agent skill.
Who it is for: Engineers feeding PDFs and Office documents to LLM pipelines or agents
Strengths
- Handles scanned PDFs, tables and formulas, with four quality tiers from fast preview to advanced
- Stable doc/tier/page/block locators allow paged reading and citation of long documents
- Small models run on ONNX CPU; the VLM can run on llama.cpp, vLLM or LMDeploy
- Documents are not uploaded to the official service unless remote parsing is configured
Weaknesses
- Office, HTML, EPUB, OFD and CSV files only get flash-tier parsing; higher tiers are PDF and image only
- Docker deployment for non-NVIDIA devices is pending an update
- Highest throughput needs an NVIDIA GPU and mineru[full]; Windows needs torch installed separately
- License is reported as NOASSERTION, so terms need checking before use
- GPU optional
- Docker + Compose
- Needs Python >=3.10,<3.15, llama.cpp, vLLM or LMDeploy (VLM runtime), ONNX or Torch
- Models: MinerU VLM, ONNX small models, Torch small models, llama.cpp, vLLM
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
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