Docling

🩺 Vitals

• 📦 Version: v2.70.0 (Released 2026-01-23)
• 🚀 Velocity: Active (Last commit 2026-01-28)
• 🌟 Community: 51.6k Stars · 3.5k Forks
• 🐞 Backlog: 817 Open Issues

🏗️ Profile

• Official: docling-project.github.io/docling
• Source: github.com/docling-project/docling
• License: MIT
• Deployment: Python Library / CLI / Container
• Data Model: Unstructured -> Structured (JSON/MD)
• Jurisdiction: USA 🇺🇸
• Compliance: Not specified (Open source library)
• Complexity: Low (2/5) - Python Pip Install
• Maintenance: Low (2/5) - Stateless Utility
• Enterprise Ready: High (4/5) - LF AI & Data Foundation

1. The Executive Summary

What is it? Docling is an advanced document parsing engine born out of IBM Research and now governed by the LF AI & Data Foundation. It solves the "Garbage In, Garbage Out" problem for AI by converting messy, unstructured documents (PDFs, Word files, HTML) into clean, semantic representations (Markdown, JSON) that preserve layout, tables, and reading order.

The Strategic Verdict:

• 🔴 For Simple OCR: Caution. Overkill if you just need raw text extraction without structure.
• 🟢 For RAG Pipelines: Strong Buy. Essential infrastructure for any enterprise building "Chat with your Data" applications. It bridges the gap between human documents and machine understanding.

2. The "Hidden" Costs (TCO Analysis)

3. The "Day 2" Reality Check

🚀 Deployment & Operations

• Integration: Runs as a standard Python library (pip install docling). Can be easily wrapped in aDocker container or Lambda function.
• Scalability: Stateless architecture scales horizontally. Performance depends on CPU/GPU resources for the underlying vision models.

🛡️ Security & Governance

• Compliance: Not specified (Open source library)
• Governance: Backed by the Linux Foundation (LF AI & Data), ensuring long-term neutrality and support.

4. Market Landscape

🏢 Proprietary Incumbents

• Amazon Textract
• Azure Text Analytics

🤝 Open Source Ecosystem

• Unstructured.io