# Docling

> Convert PDFs, Office files, and more into LLM-ready Markdown or JSON

- Category: Retrieval & Knowledge (RAG) / Parsing & Ingestion
- Language: Python
- Licence: MIT
- Repository: https://github.com/docling-project/docling
- Website: https://docling-project.github.io/docling
- Canonical page: https://ai-tldr.dev/tools/docling/

## In short

Docling is an open-source document conversion library, started by IBM Research, that turns many document formats into a single structured representation. It handles PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, audio, and more, and can export the result to Markdown, HTML, JSON, and other formats. It sits in the Parsing & Ingestion category of the Retrieval & Knowledge (RAG) tools and is open-source, written in Python and licensed MIT.

## Overview

Docling is an open-source document conversion library, started by IBM Research, that turns many document formats into a single structured representation. It handles PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, audio, and more, and can export the result to Markdown, HTML, JSON, and other formats.

It is aimed at developers building retrieval and generative-AI systems who need clean, structured text out of messy source documents. Docling goes beyond plain text extraction: it reads PDF page layout, reading order, tables, code, and formulas, and includes OCR for scanned files.

Within the RAG and retrieval space, Docling sits at the parsing and ingestion stage. It produces a unified DoclingDocument that feeds into chunking and embedding, and it ships ready-made integrations with frameworks like LangChain, LlamaIndex, Crew AI, and Haystack.

## What it does

- Parses many formats including PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, audio, email (EML, MSG), LaTeX, and plain text
- Advanced PDF understanding: page layout, reading order, table structure, code, and formulas
- Unified DoclingDocument representation with export to Markdown, HTML, JSON, DocTags, and more
- OCR support for scanned PDFs and images, plus Visual Language Models such as GraniteDocling
- Local, air-gapped execution for sensitive data, with no required cloud calls
- Integrations with LangChain, LlamaIndex, Crew AI, and Haystack, plus an MCP server and a CLI

## Getting started

Install the package, then convert a document from the command line or in Python.

1. Install Docling

   Install from PyPI. Requires Python 3.10 or higher; works on macOS, Linux, and Windows (x86_64 and arm64).

```bash
pip install docling
```

2. Convert a document from the CLI

   Point the CLI at a local path or URL. This writes a Markdown file with the structured content into the current directory.

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

3. Convert a document in Python

   Use DocumentConverter to convert a file and export the result to Markdown.

```python
from docling.document_converter import DocumentConverter

source = "https://arxiv.org/pdf/2408.09869"  # document per local path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown())
```

## When to use it

- Ingest PDFs and Office files into a RAG pipeline as clean Markdown or JSON before chunking and embedding
- Extract tables, formulas, and reading order from research papers or technical reports
- Process scanned documents and images with OCR for downstream search or LLM use
- Parse sensitive documents fully on-premises in an air-gapped environment