MinerU vs olmocr
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 — Open toolkit that linearizes messy PDFs — scans, tables, equations, handwriting — into clean ordered Markdown with a self-hosted vision-language model. Built for LLM training data and RAG ingestion.
Same job — self-hosted document→markdown for LLM ingestion: olmocr is a focused VLM PDF-linearizer; MinerU is the broader pipeline (PDF + Office, layout analysis) with far more traction.
| MinerU | olmocr | |
|---|---|---|
| Stars | 82k | 20k |
| Forks | 6.8k | 1.6k |
| Language | Python | Python |
| License | LicenseRef-MinerU-Open-Source-License | Apache-2.0 |
| Last activity | yesterday | 6 months ago |
| Topics | ocr, rag | rag, ocr |
| Curated connections | 14 | 12 |
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. Licence is Apache-2.0 plus MinerU's own terms: commercial use is fine until you pass 100M MAU or US$20M monthly revenue, and online services must attribute.
olmocr — the curator's take
Reach for olmocr when you have lots of messy PDFs to convert into high-quality text for a training corpus or RAG index and you have GPU to run the VLM. It is the parsing FRONT-END of a pipeline, not a RAG system itself — pair it with an indexer/retriever (LlamaIndex) and a vector store (Chroma). NOT worth it for clean, digital-native PDFs where a cheap text extractor does the job — running a vision-language model for those is overkill.