Docling vs MarkItDown
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Python library that converts PDFs and office files into structured text for AI use.
Open source · MIT
Python tool that converts files like PDF, Word, Excel and PowerPoint into Markdown.
Our verdict
Docling and MarkItDown are both free, MIT-licensed Python libraries that turn documents into text, but they aim at different jobs. Docling focuses on faithful structure (tables, reading order) and runs locally, which suits developers building retrieval pipelines over sensitive PDFs and office files. MarkItDown is a lighter converter for pulling many file types, including audio and web links, into Markdown for text analysis or LLM input, and it is less concerned with layout fidelity for human readers.
Pick Docling if you are a Python developer building a retrieval or RAG pipeline and need tables and reading order preserved, especially for sensitive documents processed locally.
Pick MarkItDown if you want a simple way to batch-convert a mix of common file types, including audio and web links, into Markdown for text analysis or LLM input.
| Key difference | Docling | MarkItDown |
|---|---|---|
| Main purpose | Structured document parsing for AI retrieval and RAG pipelines | Converting many file types into Markdown text for LLM or text analysis |
| Structure preservation | Emphasizes tables and reading order when extracting document content | Keeps headings, lists, tables and links in Markdown output |
| Local processing | Highlighted for running locally on sensitive or air-gapped documents | Cloud options like Azure are billable and need external endpoints |
| Input coverage | PDF, Word, PowerPoint, Excel, HTML and email in one library | PDFs, Office files, HTML, images, audio and YouTube links |
| Security note | No specific untrusted-input caution stated in the provided README summary | Runs with the process's file and network access, so untrusted inputs need care |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose Docling if you need…
- Python developers building retrieval or RAG pipelines over PDFs and office files
- Teams that need tables and reading order preserved when extracting document content
- Users who need local processing for sensitive or air-gapped documents
- People who want a command-line tool to convert a document to Markdown
Choose MarkItDown if you need…
- Developers building LLM or text analysis pipelines that need document text
- People who want to convert a batch of PDFs or Office files to Markdown from the command line
- Projects that need a Python library to add document conversion to existing code
Side-by-side
| Docling | MarkItDown | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 70k | 193k |
| Setup difficulty | Beginner | Beginner |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 5 days ago | 8 days ago |
What each one is
Docling
Docling parses many document formats, including PDF, Word, PowerPoint, Excel, HTML and email, and turns them into a structured representation. It exports that content to formats such as Markdown, HTML and JSON. People use it to prepare documents for generative AI and retrieval pipelines, and it can run locally for sensitive data.
MarkItDown
MarkItDown is a Python utility that turns many common file types, such as PDFs, Office documents, HTML, images, audio and YouTube links, into Markdown text. It keeps structure like headings, lists, tables and links. People use it mainly to prepare documents as plain text for large language models and text analysis pipelines.
Best for
Docling
- Python developers building retrieval or RAG pipelines over PDFs and office files
- Teams that need tables and reading order preserved when extracting document content
- Users who need local processing for sensitive or air-gapped documents
- People who want a command-line tool to convert a document to Markdown
MarkItDown
- Developers building LLM or text analysis pipelines that need document text
- People who want to convert a batch of PDFs or Office files to Markdown from the command line
- Projects that need a Python library to add document conversion to existing code
Strengths
Docling
- Supports a wide range of input formats in one library, per the README feature list
- Can run locally, which the README highlights for sensitive data and air-gapped setups
- Includes a CLI, a Python API, an MCP server and an API server option
MarkItDown
- Preserves document structure such as headings, lists, tables and links
- Supports many formats from one tool, with optional dependencies you can install selectively
- Has a Python API and command line interface, plus optional third-party plugins
Not ideal for
Docling
- Users who need a no-code desktop app, since the README centers on pip install and CLI or Python use
- Projects that need metadata extraction such as title and authors, which the README lists as coming soon
- Chemistry-heavy work, since molecular structure understanding is also listed as coming soon
MarkItDown
- Anyone needing high-fidelity layout for human reading, since the README says output is meant for text analysis tools and may not be the best for human consumption
- Untrusted environments without input sanitizing, because the tool runs with the current process's file and network access
- Users who need video conversion from the built-in converters, since the README says only the Azure Content Understanding option supports video
Trade-offs
Docling
- Advanced features such as VLM pipelines and audio or video parsing may need extra setup or models; the README does not detail requirements for each
- Some features are newer, as shown in the What's new section, so behavior may change between releases
MarkItDown
- The README says output is designed for machines and may not suit high-fidelity human reading
- Some conversions, such as cloud-based Azure options, are billable and require external endpoints
- Converting with untrusted inputs requires care because it accesses resources the process can reach
Getting started
MarkItDown
Install with pip (all optional dependencies)
pip install 'markitdown[all]'Frequently asked questions
Should I use Docling or MarkItDown?
Docling and MarkItDown are both free, MIT-licensed Python libraries that turn documents into text, but they aim at different jobs. Docling focuses on faithful structure (tables, reading order) and runs locally, which suits developers building retrieval pipelines over sensitive PDFs and office files. MarkItDown is a lighter converter for pulling many file types, including audio and web links, into Markdown for text analysis or LLM input, and it is less concerned with layout fidelity for human readers. Pick Docling if you are a Python developer building a retrieval or RAG pipeline and need tables and reading order preserved, especially for sensitive documents processed locally. Pick MarkItDown if you want a simple way to batch-convert a mix of common file types, including audio and web links, into Markdown for text analysis or LLM input.
What is the difference between Docling and MarkItDown?
Docling: Python library that converts PDFs and office files into structured text for AI use. MarkItDown: Python tool that converts files like PDF, Word, Excel and PowerPoint into Markdown. Docling suits python developers building retrieval or rag pipelines over pdfs and office files, while MarkItDown suits developers building llm or text analysis pipelines that need document text.
Are Docling and MarkItDown free?
Docling: Free and open source. MarkItDown: Free and open source.
Is Docling or MarkItDown more popular?
MarkItDown has more GitHub stars (193k) than Docling (70k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Docling and MarkItDown?
Other options people compare them with include pdf-inspector, Rich, Showdown, remark, Turndown.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.