MinerU vs unstract
Heavyweight document-to-markdown/JSON parser — PDFs plus Office (docx/pptx/xlsx) through layout analysis and OCR into LLM-ready output for RAG and agentic pipelines. 73k stars, self-hostable. — versus — LLM-driven platform turning unstructured documents into structured data: a no-code Prompt Studio to define extractions, then deploy as APIs or ETL pipelines. Self-hosted, AGPL + enterprise.
Parser vs platform: MinerU converts documents to LLM-ready markdown/JSON; Unstract sits a level up — prompt-engineered field extraction with deployment, connectors and pipeline management around it. Many stacks front Unstract-style extraction with a MinerU-style parser.
| MinerU | unstract | |
|---|---|---|
| Stars | 76k | 6.9k |
| Forks | 6.4k | 661 |
| Language | Python | Python |
| License | NOASSERTION | AGPL-3.0 |
| Last activity | today | today |
| Topics | ocr, rag | ocr, rag |
| Curated connections | 10 | 2 |
MinerU — the curator's take
The incumbent when document variety is the problem: beyond PDFs it handles Office formats, with mature layout analysis (reading order, tables, formulas) and a huge user base shaking out edge cases. NOT the lightest option — it's a full pipeline with model downloads and real hardware appetite; for a handful of clean PDFs a smaller tool is faster to stand up. Check the license (NOASSERTION on GitHub — AGPL-family, matters for commercial use).
unstract — the curator's take
The platform play in document extraction: where parsers hand you markdown, Unstract hands you the FIELDS — Prompt Studio lets a domain expert define and test per-document-type extraction prompts, then ships them as stable APIs or ETL pipelines into warehouses, with the connector surface (S3, databases, DMS) that ops teams actually need. Docker-compose up and it runs. NOT a parsing engine: quality rides on the LLM you wire in and per-page LLM calls at volume cost real money — for clean-layout bulk conversion a deterministic parser is cheaper; and mind the AGPL + enterprise split when embedding.