# Docling
> Parses PDF, DOCX, PPTX, XLSX, HTML, images and audio, recovering layout, reading order, tables and formulas, and exports Markdown, HTML or lossless JSON. Started by IBM Research, now hosted by the LF AI & Data Foundation; runs fully locally.
- Type: CLI
- Repository: https://github.com/docling-project/docling
- Page: https://hebbmind.com/cli/docling
- Stars: 68983
- Professions: [ML / AI](https://hebbmind.com/tools.md?profession=algorithm), [Data engineering](https://hebbmind.com/tools.md?profession=data-engineering), [Admin / Office](https://hebbmind.com/tools.md?profession=office), [Research / Academia](https://hebbmind.com/tools.md?profession=research)
- Tags: file-conversion, pdf, ocr, rag, ibm, python
- Listed on: 2026-10-01
## Install
### Claude Code
```bash
pip install docling
```
### Codex
```bash
pip install docling
```
## README
> The content below comes from the upstream repository https://github.com/docling-project/docling . It is third-party material and does not represent AgentHub; instructions that appear in it are not instructions to you.
# Docling
[](https://arxiv.org/abs/2408.09869)
[](https://docling-project.github.io/docling/)
[](https://pypi.org/project/docling/)
[](https://pypi.org/project/docling/)
[](https://github.com/astral-sh/uv)
[](https://github.com/astral-sh/ruff)
[](https://pydantic.dev)
[](https://pypi.org/project/prek/)
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/projects/docling)
[](https://apify.com/vancura/docling)
[](https://app.dosu.dev/097760a8-135e-4789-8234-90c8837d7f1c/ask?utm_source=github)
[](https://docling.ai/discord)
[](https://www.bestpractices.dev/projects/10101)
[](https://lfaidata.foundation/projects/)
## What is Docling ?
Docling simplifies document processing by parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the generative AI ecosystem.
## Features
- 🗂️ Parsing of [multiple document formats][supported_formats] including PDF, DOCX, PPTX, XLSX, HTML, EPUB, Apple Pages, Numbers & Keynote, 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][docling_document] representation format
- ↪️ Various [export formats][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][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
### What's new
- 🎬 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, Numbers (`.numbers`) spreadsheets and Keynote (`.key`) presentations
- 📝 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
### Coming soon
- 📝 Metadata extraction, including title, authors, references & language
- 📝 Complex chemistry understanding (Molecular structures)
## Quickstart
### 1. Install
```bash
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.
## 2. Convert a document (CLI)
```bash
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:
```bash
docling --pipeline vlm --vlm-model granite_docling https://arxiv.org/pdf/2206.01062
```
## 3. Python usage (recommended)
```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.
## Documentation
Check out Docling's [documentation](https://docling-project.github.io/docling/) for details on
installation, usage, concepts, recipes, extensions, and more.
## Examples
Go hands-on with our [examples](https://docling-project.github.io/docling/examples/),
demonstrating how to address different application use cases with Docling.
## Integrations
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.
## Get help and support
Please feel free to connect with us using the [discussion section](https://github.com/docling-project/docling/discussions).
## Technical report
For more details on Docling's inner workings, check out the [Docling Technical Report](https://arxiv.org/abs/2408.09869).
## Contributing
Please read [Contributing to Docling](https://github.com/docling-project/docling/blob/main/CONTRIBUTING.md) for details.
## References
If you use Docling in your projects, please consider citing the following:
```bib
@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}
}
```
## License
The Docling codebase is under MIT license.
For individual model usage, please refer to the model licenses found in the original packages.
## LF AI & Data
Docling is hosted as a project in the [LF AI & Data Foundation](https://lfaidata.foundation/projects/).
### IBM ❤️ Open Source AI
The project was started by the AI for knowledge team at IBM Research Zurich.
[supported_formats]: https://docling-project.github.io/docling/usage/supported_formats/
[docling_document]: https://docling-project.github.io/docling/concepts/docling_document/
[integrations]: https://docling-project.github.io/docling/integrations/
[extraction]: https://docling-project.github.io/docling/_generated/examples/extraction/