chunkr vs MinerU
Document intelligence API in Rust: layout analysis, OCR with bounding boxes, and semantic chunking that turn PDFs, PPTs and Word docs into RAG-ready structured chunks. — versus — 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.
Same job — complex documents into LLM-ready data. MinerU is the batteries-included extraction toolkit; Chunkr is an API-shaped service adding semantic chunking and bounding-box citations for RAG pipelines.
| chunkr | MinerU | |
|---|---|---|
| Stars | 4.0k | 75k |
| Forks | 263 | 6.3k |
| Language | Rust | Python |
| License | AGPL-3.0 | NOASSERTION |
| Last activity | 3 months ago | 2 days ago |
| Topics | ocr, rag | ocr, rag |
| Curated connections | 5 | 6 |
chunkr — the curator's take
The RAG-ingestion specialist: where OCR tools stop at text, Chunkr does layout analysis and SEMANTIC chunking — the chunks arrive respecting document structure, with bounding boxes for citation-grounding. Self-hosted via Docker Compose. Read the split carefully though: the AGPL open-source version runs community models while the paid cloud runs proprietary ones — the README says plainly the accuracy differs; benchmark the OSS tier on YOUR documents before committing. Also ~3 months quiet at review time, and AGPL matters if you embed.
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).