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

arXiv: https://arxiv.org/abs/2501.17887 · HTML: https://mlsanity.com/paper/2501.17887/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2025.

## Built on by (most cited first, 30 of 63)

- [SmolVLM: Redefining small and efficient multimodal models](https://mlsanity.com/paper/2504.05299/) (2025)
- [MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm](https://mlsanity.com/paper/2506.05218/) (2025)
- [Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers](https://mlsanity.com/paper/2505.21497/) (2025)
- [SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion](https://mlsanity.com/paper/2503.11576/) (2025)
- [dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model](https://mlsanity.com/paper/2512.02498/) (2025)
- [MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale](https://mlsanity.com/paper/2604.04771/) (2026)
- [Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence](https://mlsanity.com/paper/2502.09927/) (2025)
- [Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing](https://mlsanity.com/paper/2510.15349/) (2025)
- [BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents](https://mlsanity.com/paper/2512.03413/) (2025)
- [No One Knows the State of the Art in Geospatial Foundation Models](https://mlsanity.com/paper/2605.12678/) (2026)
- [ParseBench: A Document Parsing Benchmark for AI Agents](https://mlsanity.com/paper/2604.08538/) (2026)
- [Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding](https://mlsanity.com/paper/2601.20430/) (2026)
- [The Hidden Threat in Plain Text: Attacking RAG Data Loaders](https://mlsanity.com/paper/2507.05093/) (2025)
- [ChartGen: Scaling Chart Understanding Via Code-Guided Synthetic Chart Generation](https://mlsanity.com/paper/2507.19492/) (2025)
- [MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding](https://mlsanity.com/paper/2603.22458/) (2026)
- [ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding](https://mlsanity.com/paper/2603.27064/) (2026)
- [Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding](https://mlsanity.com/paper/2506.16035/) (2025)
- [HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding](https://mlsanity.com/paper/2602.12957/) (2026)
- [Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?](https://mlsanity.com/paper/2508.03644/) (2025)
- [MDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios](https://mlsanity.com/paper/2603.28130/) (2026)
- [Narrative-Driven Paper-to-Slide Generation via ArcDeck](https://mlsanity.com/paper/2604.11969/) (2026)
- [Any2Poster: Any-Source Poster Generation Across Modalities and Domains](https://mlsanity.com/paper/2606.02915/) (2026)
- [Exploring LLMs for Scientific Information Extraction Using The SciEx Framework](https://mlsanity.com/paper/2512.10004/) (2025)
- [VAREX: A Benchmark for Multi-Modal Structured Extraction from Documents](https://mlsanity.com/paper/2603.15118/) (2026)
- [Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing](https://mlsanity.com/paper/2506.03197/) (2025)
- [VideoAgent: Personalized Synthesis of Scientific Videos](https://mlsanity.com/paper/2509.11253/) (2025)
- [Uni-Parser Technical Report](https://mlsanity.com/paper/2512.15098/) (2025)
- [ABot-OCR Technical Report](https://mlsanity.com/paper/2605.27978/) (2026)
- [DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents](https://mlsanity.com/paper/2606.26122/) (2026)
- [CardAIc-Agents: A Multimodal Framework with Hierarchical Adaptation for Cardiac Care Support](https://mlsanity.com/paper/2508.13256/) (2025)

## Abstract (arXiv)

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.