8 Best Docling Alternatives in 2026 (Open Source)
Docling — Get your documents ready for gen AI. Unlike LlamaParse (cloud-only, paid) or PyMuPDF (basic extraction), Docling runs fully locally, handles 20+ formats including audio and XML schemas, and produces a unified DoclingDocument representation with advanced PDF layout understanding backed by IBM Research.
Short answer
- Closest match to Docling: unstructured.
- Most actively developed: MinerU (905 commits in the last 90 days).
- Fastest growing: Skills (+26,013 GitHub stars in the last 30 days).
- No commit in 6+ months: MegaParse, LLM Sherpa, olmocr and text-extract-api.
These 8 open-source tools do the same job. They are ordered by how closely they match Docling, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| Docling(original) | 68.3k | +1,855 | 2026-10-02 |
| unstructured | 15.5k | +188 | 2026-10-02 |
| MinerU | 81.0k | +3,746 | 2026-09-29 |
| MegaParse | 7.4k | +11 | 2025-02-21 |
| LLM Sherpa | 1.8k | +1 | 2024-10-18 |
| olmocr | 19.7k | +416 | 2026-03-25 |
| MarkItDown | 188.0k | +15,124 | 2026-10-02 |
| text-extract-api | 3.2k | +17 | 2025-12-08 |
| Skills | 179.4k | +26,013 | 2026-09-29 |
1. unstructured
Open-source ETL for converting documents into structured data for language models
What sets it apart: vs LlamaParse: broader format support (20+ types) with open-source core; vs Apache Tika: ML-enhanced extraction with table detection and LLM-optimized output
Best for: RAG pipelines needing document ingestion; Enterprise document processing for AI applications; Converting unstructured documents to structured data for LLMs
2. MinerU
Transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows.
What sets it apart: Unlike PyPDF (text-only extraction) or cloud OCR services, MinerU preserves document layout including tables with embedded formulas and achieves 86.2 on OmniDocBench — purpose-built for AI/RAG document pipelines
Best for: RAG pipelines needing high-fidelity PDF/DOCX extraction with tables, formulas, and layouts preserved; Academic and research teams processing scientific papers with complex mathematical notation
3. MegaParse
File Parser optimised for LLM Ingestion with no loss 🧠 Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.
What sets it apart: vs Unstructured / LLMSherpa / PyPDF: vision-powered multimodal parsing using GPT-4o/Claude for complex layouts — handles tables, images, and visual formatting that rule-based parsers miss
Best for: Complex document digitization preserving layout and structure; RAG pipelines needing high-fidelity document parsing; Mixed-format data extraction and content migration
4. LLM Sherpa
Developer APIs to Accelerate LLM Projects
What sets it apart: vs PyPDF/unstructured/pdfplumber: preserves document hierarchy (sections, subsections, tables-in-context) that other parsers discard — enables semantically optimal chunks for RAG instead of arbitrary line-break splits
Best for: RAG applications needing structure-aware PDF chunking; Table extraction with section context preservation; Document analysis where layout semantics matter for LLM accuracy
5. olmocr
Toolkit for linearizing PDFs for LLM datasets/training
What sets it apart: Open-source VLM-based OCR achieving 82+ on olmOCR-Bench, rivaling commercial solutions like Mistral OCR — vs traditional OCR tools (Tesseract) that struggle with complex layouts
Best for: Batch PDF-to-text conversion at scale with high accuracy; Academic and research document digitization; Building RAG pipelines that need clean text from PDFs
6. MarkItDown
Python tool for converting files and office documents to Markdown.
What sets it apart: Microsoft's official document-to-Markdown converter for LLMs — built by the AutoGen team with MCP server support, unlike textract which predates the LLM era
Best for: Converting documents to Markdown for LLM consumption in RAG pipelines; Batch document processing for AI text analysis
7. text-extract-api
Local FastAPI for OCR extraction and PII removal from images, PDFs and Office files to Markdown or JSON
What sets it apart: vs cloud OCR services: fully on-premise with pluggable OCR strategies (4 engines), built-in PII removal, and distributed Celery scaling — no vendor lock-in
Best for: High-volume document digitization pipelines; Extracting structured data from invoices, reports, and forms with PII removal
8. Skills
Public repository for Agent Skills
What sets it apart: Official Anthropic skill system for Claude with production-grade document skills (PDF/DOCX/PPTX/XLSX) and a standardized Agent Skills specification
Best for: Claude users wanting to extend capabilities with domain expertise; Teams building repeatable workflows in Claude; Developers creating Claude Code plugins
FAQ
- What are the best alternatives to Docling?
- The closest open-source alternatives to Docling are unstructured, MinerU and MegaParse, followed by LLM Sherpa, olmocr and MarkItDown. They are ranked by how closely they match what Docling does.
- Which Docling alternative is the most popular?
- MarkItDown has the most GitHub stars among Docling alternatives, with 187,968 stars.
- Which Docling alternative is the most actively maintained?
- By recent activity, MinerU (905 commits in the last 90 days) is the most actively developed alternative.