# Docling

> Source: <https://github.com/docling-project/docling>
> Published: 2026-07-17 12:48:07+00:00

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](https://docling-project.github.io/docling/usage/supported_formats/)including PDF, DOCX, PPTX, XLSX, HTML, EPUB, 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](https://docling-project.github.io/docling/concepts/docling_document/)representation format - ↪️ Various
[export formats](https://docling-project.github.io/docling/usage/supported_formats/)and options, including Markdown, HTML, WebVTT, DocLang,[DocTags](https://arxiv.org/abs/2503.11576)and lossless JSON - 📜 Support for several application-specific XML schemas including
[DocLang](https://doclang.ai),[USPTO](https://www.uspto.gov/patents)patents,[JATS](https://jats.nlm.nih.gov/)articles, and[XBRL](https://www.xbrl.org/)financial reports. - 🔒 Local execution capabilities for sensitive data and air-gapped environments
- 🤖 Plug-and-play
[integrations](https://docling-project.github.io/docling/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](https://huggingface.co/ibm-granite/granite-docling-258M)) - 🎙️ Audio support with Automatic Speech Recognition (ASR) models
- 🔌 Connect to any agent using the
[MCP server](https://docling-project.github.io/docling/usage/mcp/) - 🌐 Run Docling as a service with the
[API server](https://docling-project.github.io/docling/usage/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 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 docling
```

Note: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](https://docling-project.github.io/docling/getting_started/installation/) are available in the docs.

```
docling https://arxiv.org/pdf/2206.01062
```

This generates a .md file in the current directory containing structured document content.

You can also use 🥚[GraniteDocling](https://huggingface.co/ibm-granite/granite-docling-258M) and other VLMs via Docling CLI:

```
docling --pipeline vlm --vlm-model granite_docling https://arxiv.org/pdf/2206.01062
python
from 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](https://docling-project.github.io/docling/usage/) and [configuration](https://docling-project.github.io/docling/getting_started/installation/) options.

Check out Docling's [documentation](https://docling-project.github.io/docling/) for details on
installation, usage, concepts, recipes, extensions, and more.

Go hands-on with our [examples](https://docling-project.github.io/docling/examples/),
demonstrating how to address different application use cases with Docling.

To further accelerate your AI application development, check out Docling's native
[integrations](https://docling-project.github.io/docling/integrations/) with popular frameworks
and tools.

Please feel free to connect with us using the [discussion section](https://github.com/docling-project/docling/discussions).

For more details on Docling's inner workings, check out the [Docling Technical Report](https://arxiv.org/abs/2408.09869).

Please read [Contributing to Docling](https://github.com/docling-project/docling/blob/main/CONTRIBUTING.md) 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](https://lfaidata.foundation/projects/).

The project was started by the AI for knowledge team at IBM Research Zurich.