# Docling

Get your documents ready for gen AI

Repository: https://github.com/docling-project/docling
Canonical: https://ross.abutalabs.com/products/docling
Homepage: https://docling-project.github.io/docling
Language: Python
License: MIT
License Family: permissive
Topics: ai, convert, documents, pdf, tables, document-parser, document-parsing, docx, html, markdown, pdf-converter, pdf-to-json, pdf-to-text, pptx, xlsx
Last push: 2026-08-26T16:15:06+00:00

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 56
- inputs: {"age_days": 785, "days_push": 7, "days_rel": 7, "gap_med": 3.0, "n_releases_24m": 181}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 65603, forks 4697 (observed 2026-08-28T04:12:19.317615+00:00)

## What it is
Docling is a Python library that parses and converts documents across many formats (PDF, DOCX, PPTX, XLSX, HTML, images, audio, and more) into structured representations like Markdown and JSON, with advanced PDF understanding including layout, tables, and reading order. It integrates with the generative AI ecosystem to prepare documents for RAG pipelines and LLM consumption.

## Use cases
- convert pdfs to markdown for llm ingestion
- extract tables from pdf documents
- parse docx and pptx files into structured
- prepare documents for rag pipelines
- ocr scanned pdf documents
- batch convert office documents to text
- extract document structure and reading order

## When to choose
- you need high-quality PDF parsing with layout and table understanding
- you want a unified parser for many document formats in Python
- you are building RAG or gen AI pipelines that ingest documents
- you need local, self-hosted document conversion without cloud APIs

## When to avoid
- you only need simple plain-text extraction with no structure
- you need a lightweight dependency-free converter
- your documents are already clean structured data like CSV or JSON

## Facets
- artifact type: library
- maturity: active
- function: parser, pdf, ocr, nlp, machine-learning, rag
- domain: artificial-intelligence, pdf, files, large-language-models
- platform: python, cli, cross-platform
- tags: document-parsing, document-conversion, pdf-to-markdown, genai, table-extraction, docx, pptx, xlsx, html-parsing, natural-language-processing, retrieval-augmented-generation

## Member repositories
- docling-project/docling (main) score 86
- docling-project/docling-serve (backend) score 85

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:19.317615+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:17:24.057272+00:00, confidence not recorded.
  - readme: https://github.com/docling-project/docling (fetched 2026-08-28T04:12:19.317615+00:00, sha 7e3ae5210b3b)
- Data as of 2026-08-30T08:39:29.467469+00:00.