Github · GitHub Repository Radar
docling-project docling
Get your documents ready for gen AI
Stars
65,951
Forks
4,742
Watchers: 65,951
Language
License: MIT License
Repository Radar Score
65 / 100
Growth
- 7d
- +0
- 30d
- +0
- %
- 0.0%
Not enough metric snapshots yet to chart growth for this repository.
Score breakdown
- popularity 88
- growth 0
- activity 100
- freshness 100
- community 90
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arXiv
Docs
PyPI version
PyPI - Python Version
uv
Ruff
Pydantic v2
prek
License MIT
PyPI Downloads
Docling Actor
Chat with Dosu
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OpenSSF Best Practices
LF AI & Data
Docling simplifies document processing by parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the generative AI ecosystem.
- 🗂️ Parsing of multiple document formats including PDF, DOCX, PPTX, XLSX, HTML, EPUB, Apple Pages, WAV, MP3, WebVTT, Box Notes, email formats (EML, MSG), images (PNG, TIFF, JPEG, ...), LaTeX, DocLang, plain text, and more
- 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
- 🧬 A unified, expressive DoclingDocument representation format
- ↪️ Various export formats and options, including Markdown, HTML, WebVTT, DocLang, DocTags and lossless JSON
- 📜 Support for several application-specific XML schemas including DocLang, USPTO patents, JATS articles, and XBRL financial reports.
- 🔒 Local execution capabilities for sensitive data and air-gapped environments
- 🤖 Plug-and-play integrations incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
- 🔍 Extensive OCR support for scanned PDFs and images
- 👓 Support for several Visual Language Models, such as (GraniteDocling)
- 🎙️ Audio support with Automatic Speech Recognition (ASR) models
- 🔌 Connect to any agent using the MCP server
- 🌐 Run Docling as a service with the API server (docling-serve)
- 💻 Simple and convenient CLI
- 🎬 Parsing of video files (MP4, AVI, MOV, MKV, and WebM) with an ASR transcript and representative keyframes
- 📄 Parsing of ODF (OpenDocument Format) files for text documents (
.odt), spreadsheets (.ods), and presentations (.odp) - 💼 Parsing of XBRL (eXtensible Business Reporting Language) documents for financial reports
- 📧 Parsing of email files (
.eml,.msg) - 📚 Parsing of EPUB (Electronic Publication) files for e-books
- 🍎 Parsing of Apple Pages (
.pages) documents, both the modern IWA container and iWork '09 - 📝 Parsing of plain-text files (
.txt,.text) and Markdown supersets (.qmd,.Rmd) - 📊 Chart understanding (Barchart, Piechart, LinePlot): convert them into tables or code and add detailed descriptions
- 📝 Metadata extraction, including title, authors, references & language
- 📝 Complex chemistry understanding (Molecular structures)
pip install doclingNote: Python 3.9 support was dropped in docling version 2.70.0. Please use Python 3.10 or higher.
Works on macOS, Linux and Windows environments for both x86_64 and arm64 architectures.
More detailed installation instructions are available in the docs.
docling https://arxiv.org/pdf/2206.01062This generates a .md file in the current directory containing structured document content.
You can also use 🥚GraniteDocling and other VLMs via Docling CLI:
docling --pipeline vlm --vlm-model granite_docling https://arxiv.org/pdf/2206.01062from docling.document_converter import DocumentConverter
source = "https://arxiv.org/pdf/2408.09869" # a document via a local path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown()) # output: "## Docling Technical Report[...]"More advanced usage and configuration options.
Check out Docling's documentation for details on installation, usage, concepts, recipes, extensions, and more.
Go hands-on with our examples, demonstrating how to address different application use cases with Docling.
To further accelerate your AI application development, check out Docling's native integrations with popular frameworks and tools.
Please feel free to connect with us using the discussion section.
For more details on Docling's inner workings, check out the Docling Technical Report.
Please read Contributing to Docling for details.
If you use Docling in your projects, please consider citing the following:
@techreport{Docling,
author = {Deep Search Team},
month = {8},
title = {Docling Technical Report},
url = {https://arxiv.org/abs/2408.09869},
eprint = {2408.09869},
doi = {10.48550/arXiv.2408.09869},
version = {1.0.0},
year = {2024}
}The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.
Docling is hosted as a project in the LF AI & Data Foundation.
The project was started by the AI for knowledge team at IBM Research Zurich.
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- High absolute popularity (65,951 stars) signals broad adoption.
- Maintained recently (last push 2 weeks ago).
Who should use it
- Developers working primarily with Python
- Teams exploring AI tooling, agents, or ML infrastructure
Potential use cases
- Reference or evaluate Python open-source approaches in this domain
- Prototype AI/agent workflows or study reference architectures
Strengths
- Recent repository activity
- README present in our index
- Declared license: MIT License
- Substantial fork count (4,742) suggests reuse and contribution interest
Limitations / considerations
- Insights are derived from public metadata and our observations — not a substitute for code review
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Strong signals: Strong community interest · Active maintenance
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.