Docling
Advanced document parsing: PDFs to structured data, 69k stars.
About
Docling is the document-understanding toolkit from IBM (now under LF AI & Data): parse PDFs, DOCX, PPTX, XLSX, HTML, EPUB, images, and more into a unified structured representation — with advanced PDF understanding (page layout, reading order, table structure, formulas, code, image classification) and exports to Markdown, HTML, JSON, and DocTags. 68.8k stars, MIT, 2.9M PyPI downloads/month, paper-backed (arXiv 2408.09869), runs fully locally for sensitive data. The best quality needs its layout/table models (heavier); for RAG ingestion pipelines, it's the current default.
What it can do
- **Advanced PDF**: layout, reading order, tables, formulas
- **Many formats**: PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, audio transcripts
- **Unified model**: DoclingDocument representation
- **Multiple exports**: Markdown, HTML, JSON, DocTags
- **Local-first**: runs offline for sensitive data
- **RAG-ready**: chunking integrations
Setup
```bash
pip install docling
```
```python
from docling.document_converter import DocumentConverter
result = DocumentConverter().convert("report.pdf")
print(result.document.export_to_markdown())
```
Docs: docling-project.github.io/docling.
Prerequisites
Python 3.9+. Models download on first use (or pre-cache for air-gapped).
Use cases
Works with
Trust score — 9/10
Scored 9/10 (verified tier, metrics-v1). Evidence: 68,771 GitHub stars (log-scaled vs catalog peers); blended with ~2,909,751 monthly package downloads; last push 0d ago; 98 releases/12mo; 1045 open issues. Manual review: IBM/LF document understanding (advanced PDF, 68.8k stars, 2.9M PyPI/mo), paper-backed, local-first; best quality needs heavy layout models. License: MIT.
★ 68,771 GitHub stars
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 9.1/10
- Maintenance · recency, releases, issue load
- 9.4/10
- Openness · license + docs
- 10.0/10
- Manual review · sanity read + red-flag check
- 9.0/10
- · 68,771 GitHub stars (log-scaled vs catalog peers)
- · blended with ~2,909,751 monthly package downloads
- · last push 0d ago; 98 releases/12mo; 1045 open issues
- · manual review: IBM/LF document understanding (advanced PDF, 68.8k stars, 2.9M PyPI/mo), paper-backed, local-first; best quality needs heavy layout models
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.