Docling is an open-source Python package that simplifies document processing by parsing diverse formats — including advanced PDF understanding — and integrating seamlessly with the generative AI ecosystem. It supports a wide range of input types such as PDFs, DOCX, XLSX, HTML, and images, offering rich parsing capabilities including reading order, table structure, code, and formulas. Docling provides a unified and expressive DoclingDocument format, enabling easy export to Markdown, HTML, and lossless JSON. It offers plug-and-play integrations with popular frameworks like LangChain, LlamaIndex, Crew AI, and Haystack, along with strong local execution support for sensitive data and air-gapped environments. As a Python package, Docling is pip-installable and comes with a clean, intuitive API for both programmatic and CLI-based workflows, making it easy to embed into any data pipeline or AI stack. Its modular design also supports extension and customization for enterprise use cases. We also introduce SmolDocling, an ultra-compact 256M parameter vision-language model for end-to-end document conversion. SmolDocling generates a novel markup format called DocTags that captures the full content, structure, and spatial layout of a page, and offers accurate reproduction of document features such as tables, equations, charts, and code across a wide variety of formats — all while matching the performance of models up to 27× larger. about this event: https://talks.python-summit.ch/sps25/talk/QJLGCZ/