docling-slim
Modular version of the Docling package: SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications.
Decision gist · record as of 2026-08-14
Yes, if you need modular document parsing with minimal dependencies. The package is actively maintained, permissively licensed, and has low install friction. Install the full `docling` package instead if you want all standard features and CLI tools out of the box without managing extras. Docling Slim is the right choice when you need fine-grained control over what gets installed or are optimizing for deployment size.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Optional extras (format-pdf, format-office, etc.) must be specified at install time to enable specific document format support.
- Low install friction with a pure-Python wheel and 8 runtime dependencies.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) allows unrestricted use, modification, and distribution in both open-source and commercial projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 64,771 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,699,785 downloads/mo, #2,049 on PyPI
Alternatives
Verify before relying
pip install docling-slim[format-pdf]
from docling.document_converter import DocumentConverter
converter = DocumentConverter()
result = converter.convert("document.pdf")- Whether the ~50MB base dependency footprint claim is current for version 2.120.1
- Performance characteristics (speed, memory usage) for large documents or batch processing
- Compatibility and interoperability with the full `docling` package for downstream workflows
What it is and what it does
Docling Slim is a modular, lightweight alternative to the full Docling package for developers who want fine-grained control over dependencies. It provides core document parsing and conversion capabilities with a base install of approximately 50MB, and you add only the format handlers and features you need through optional extras. The package converts PDFs, Word documents, Excel sheets, PowerPoint presentations, HTML, Markdown, LaTeX, and other formats into a unified document representation suitable for downstream processing like gen AI applications or data extraction.
The package is built on a foundation of standard Python libraries (pydantic, requests, tqdm) plus docling-core for the core parsing logic. You can optionally add PDF support (via pypdfium2 or docling-parse), Office format support, OCR engines (RapidOCR, EasyOCR, Tesseract), local or remote model inference, and CLI tools. This modular design makes it suitable for constrained environments, containerized deployments, or edge cases where you need only a subset of Docling's capabilities.
Use it for
- Extract structured data from PDFs or Word documents for downstream gen AI applications or RAG pipelines
- Build a lightweight document processing microservice with only the format handlers your application needs
- Parse HTML or Markdown content into a unified document representation for content management systems
- Add OCR capabilities to extract text from scanned documents or images on demand
- Deploy document conversion in resource-constrained environments by selecting minimal extras
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need modular document parsing with minimal dependencies.
The package is actively maintained, permissively licensed, and has low install friction. Install the full `docling` package instead if you want all standard features and CLI tools out of the box without managing extras. Docling Slim is the right choice when you need fine-grained control over what gets installed or are optimizing for deployment size.
Install
docling-slim on PyPI
Before you install
Low install friction with a pure-Python wheel and 8 runtime dependencies. Active maintenance with a recent release (0 days old) and high repository engagement (64771 stars). Supports Python 3.10 through 3.14.
Requires Python 3.10 or later. Optional extras (format-pdf, format-office, etc.) must be specified at install time to enable specific document format support.
License in practice
MIT license (permissive) allows unrestricted use, modification, and distribution in both open-source and commercial projects.
Quickstart
pip install docling-slim[format-pdf]
from docling.document_converter import DocumentConverter
converter = DocumentConverter()
result = converter.convert("document.pdf")
Verify before relying
- Whether the ~50MB base dependency footprint claim is current for version 2.120.1
- Performance characteristics (speed, memory usage) for large documents or batch processing
- Compatibility and interoperability with the full `docling` package for downstream workflows
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagescertifidocling-corefiletypepluggypydantic-settingspydanticrequeststqdm |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 5,699,785 / month, #2,049 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: docling_slim-2.120.1-py3-none-any.whl
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See also docling · docling-core · mineru · docling-ibm-models · langchain-docling · marker-pdf · docling-parse · pymupdf4llm · Plim · unoserver