docling vs markitdown
Document conversion tools compared
docling and markitdown convert documents into structured text for applications and AI workflows, and both provide Python libraries and command-line interfaces. docling emphasizes layout-aware analysis and multiple structured export formats, while markitdown focuses on readable Markdown for text analysis.
docling: Convert Documents into Structured Content
Docling parses documents such as PDFs, Word files, spreadsheets, and images into structured content for applications and generative AI workflows. It offers local processing, a Python library, and a command-line interface.
markitdown: Convert Documents and Files to Markdown
MarkItDown converts documents and other files into Markdown for LLM and text-analysis workflows. It offers a Python library and command-line interface, with optional format-specific dependencies and plugins.
| docling | markitdown | |
|---|---|---|
| Language | Python | Python |
| License | MIT | MIT |
| Stars | 68.7k | 188k |
| Forks | 5k | 13.9k |
| Last analyzed | Oct 12, 2026 | Oct 4, 2026 |
Key differences
- docling analyzes PDF layout, reading order, tables, code, formulas, and images; markitdown preserves useful text structure such as headings, lists, tables, and links in Markdown.
- docling exports to Markdown, HTML, WebVTT, DocTags, and JSON among other formats; markitdown is centered on Markdown output.
- docling supports local processing and can be exposed through an API or MCP server; markitdown offers optional Azure-based extraction integrations that require an endpoint and make billable API calls.
- docling lists integrations with LangChain, LlamaIndex, Crew AI, and Haystack; markitdown supports third-party plugins and integrations with Azure Document Intelligence and Azure Content Understanding.
- Both projects are Python tools under the MIT license. The provided repository figures list 68.7k stars and 5k forks for docling, and 188k stars and 13.9k forks for markitdown.
Choose docling if you…
- need layout-aware PDF analysis, including reading order and tables.
- want exports beyond Markdown, such as HTML, WebVTT, DocTags, or JSON.
- need local processing options or integrations for document and agent workflows.
Choose markitdown if you…
- want Markdown focused on readable text structure for LLM or text-analysis pipelines.
- prefer optional dependencies for selected formats or explicitly enabled third-party plugins.
- want to evaluate Azure-based extraction or LLM-assisted image descriptions for supported conversions.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.