opendataloader-pdf vs markitdown
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.
opendataloader-pdf
PDF parser that outputs Markdown, JSON with bounding boxes, and Tagged PDF
markitdown
Python library and CLI that converts office files, PDFs and audio to Markdown
Verdict
opendataloader-pdf ranks higher, 87 to 86. Of the 4 score parts we measure for both, opendataloader-pdf leads on 2, markitdown on 1 and 1 is even.
- Adoptionmarkitdown leads by 30 points, 98 to 68: a wide gap.
- FreshnessEven at 100.
- Maintenanceopendataloader-pdf leads by 5 points, 94 to 89.
- Agent-readyopendataloader-pdf leads by 85 points, 85 to 0: a wide gap.
Pick opendataloader-pdf if you are
Engineers feeding PDFs into RAG pipelines or automating PDF tagging
- Bounding boxes and element types for every extracted item, usable for source citations
- Local mode needs no GPU; README reports 0.015 s/page
Pick markitdown if you are
Developers feeding documents into LLM and text-analysis pipelines
- Covers many formats (PDF, DOCX, PPTX, XLSX, EPub, ZIP, YouTube) through one convert call
- Built-in converters run offline; install only needed formats via extras like [pdf,docx,pptx]
| What we compare | opendataloader-pdf | markitdown |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 68, popular | 98, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 94, healthy | 89, healthy |
| Agent-ready | 85, ready | 0, none |
| Facts from GitHub and the README | ||
| Stars | 30.1k | 193.3k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Java | Python |
| Docker | No | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
opendataloader-pdf
Java-based PDF parser with Python, Node.js and Java SDKs. It extracts Markdown, JSON with per-element bounding boxes, and HTML, using XY-Cut++ reading order in a deterministic local mode. An optional hybrid mode sends complex pages to a Docling-based backend for OCR, borderless tables, LaTeX formulas and chart descriptions. It also auto-tags untagged PDFs into Tagged PDF.
Who it is for: Engineers feeding PDFs into RAG pipelines or automating PDF tagging
Strengths
- Bounding boxes and element types for every extracted item, usable for source citations
- Local mode needs no GPU; README reports 0.015 s/page
- Hybrid mode adds OCR, LaTeX formulas and chart descriptions; OCR engine is selectable
- Reports 0.907 overall on its own 200-PDF benchmark, ahead of docling and marker
Weaknesses
- Requires Java 11+ on the host, even when using the Python package
- Each convert() call spawns a JVM, so calls must be batched
- PDF/UA export and accessibility studio are enterprise-only
- Does not process Word, Excel or PowerPoint; benchmark is run by the project itself
- no GPU
- Needs Java 11+, Python 3.10+ (Python SDK), Docling backend (hybrid mode)
- Models: Docling, EasyOCR, RapidOCR, Tesseract, SmolVLM (256M)
- port 5002
markitdown
MarkItDown converts PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV/JSON/XML, ZIP, EPub and YouTube URLs into Markdown that keeps headings, lists, tables and links. It runs as a Python library or the `markitdown` CLI. It can optionally call an OpenAI-compatible LLM for image descriptions, or Azure Document Intelligence and Content Understanding for higher-quality extraction. Output targets LLM pipelines, not human-facing fidelity.
Who it is for: Developers feeding documents into LLM and text-analysis pipelines
Strengths
- Covers many formats (PDF, DOCX, PPTX, XLSX, EPub, ZIP, YouTube) through one convert call
- Built-in converters run offline; install only needed formats via extras like [pdf,docx,pptx]
- Plugin system, plus a markitdown-ocr plugin that uses an LLM vision client for embedded images
- Optional Azure Document Intelligence and Content Understanding backends with YAML front matter fields
Weaknesses
- Not meant for high-fidelity conversion for human reading, per the README
- Runs with the calling process's privileges; untrusted input must be sanitized by the caller
- Azure backends are billable; Content Understanding is the only route for video
- Repo accepts no web server, REST API or GUI; those must be separate projects
- no GPU
- Docker
- Needs Python 3.10-3.14, Azure Document Intelligence (optional), Azure Content Understanding (optional), OpenAI-compatible client (optional)
- Models: OpenAI-compatible vision models (gpt-4o in examples)
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