docling vs opendataloader-pdf
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.
docling
Python library and CLI that converts PDFs, Office files and more into structured text
opendataloader-pdf
PDF parser that outputs Markdown, JSON with bounding boxes, and Tagged PDF
Verdict
docling ranks higher, 91 to 87. Of the 4 score parts we measure for both, docling leads on 1, opendataloader-pdf on 1 and 2 are even.
- Adoptiondocling leads by 18 points, 86 to 68: a wide gap.
- FreshnessEven at 100.
- Maintenanceopendataloader-pdf leads by 6 points, 94 to 88.
- Agent-readyEven at 85.
Pick docling if you are
Developers preparing documents for RAG and LLM pipelines
- Wide input coverage: PDF, Office, ODF, EPUB, email, Apple iWork, XBRL, audio, video
- Local execution suits sensitive data and air-gapped environments
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
| What we compare | docling | opendataloader-pdf |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 86, widely used | 68, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 88, healthy | 94, healthy |
| Agent-ready | 85, ready | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 69.9k | 30.1k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Java |
| Docker | Yes | No |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
docling
Docling parses PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, audio and other formats into a unified DoclingDocument, with PDF layout, reading order, table structure, code and formula recognition. It exports to Markdown, HTML, DocLang, DocTags and lossless JSON. It runs locally via a CLI or Python API, with OCR, VLM support (GraniteDocling), an MCP server and a docling-serve API server.
Who it is for: Developers preparing documents for RAG and LLM pipelines
Strengths
- Wide input coverage: PDF, Office, ODF, EPUB, email, Apple iWork, XBRL, audio, video
- Local execution suits sensitive data and air-gapped environments
- Integrations with LangChain, LlamaIndex, Crew AI and Haystack
- Ships a CLI, Python API, MCP server and docling-serve HTTP server
Weaknesses
- Individual models carry their own licenses, separate from the MIT codebase
- Metadata extraction and chemistry understanding are listed as coming soon
- Requires Python 3.10 or higher; 3.9 support was dropped in 2.70.0
- GPU optional
- Docker
- Models: GraniteDocling, VLMs, OCR engines, ASR models
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
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