Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion

Nikolaos Livathinos, Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi +12 more
1/27/2025
cs.CLcs.CVcs.SE

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Abstract

We introduce Docling, an easy-to-use, self-contained, MIT-licensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a unified, richly structured representation. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. Docling is released as a Python package and can be used as a Python API or as a CLI tool. Docling's modular architecture and efficient document representation make it easy to implement extensions, new features, models, and customizations. Docling has been already integrated in other popular open-source frameworks (e.g., LangChain, LlamaIndex, spaCy), making it a natural fit for the processing of documents and the development of high-end applications. The open-source community has fully engaged in using, promoting, and developing for Docling, which gathered 10k stars on GitHub in less than a month and was reported as the No. 1 trending repository in GitHub worldwide in November 2024.

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Cite this paper

@article{livathinos2025docling,
  title  = {Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion},
  author = {Nikolaos Livathinos and Christoph Auer and Maksym Lysak and Ahmed Nassar and Michele Dolfi and Panos Vagenas and Cesar Berrospi Ramis and Matteo Omenetti and Kasper Dinkla and Yusik Kim and Shubham Gupta and Rafael Teixeira de Lima and Valery Weber and Lucas Morin and Ingmar Meijer and Viktor Kuropiatnyk and Peter W. J. Staar},
  year   = {2025},
  eprint = {2501.17887},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.17887v1}
}

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