#88th of 13 in Data and scraping for AI

marker

Converts PDFs, Office files and images to markdown, JSON, HTML or chunks

Stars
40.4k
License
Apache-2.0
Last commit
Oct 2026
Last release
Jul 2026
Language
Python
Stars, last 29 weeks+7,757
25 Mar 2026: 32,688 stars1 Apr 2026: 32,911 stars8 Apr 2026: 33,206 stars15 Apr 2026: 33,625 stars22 Apr 2026: 33,932 stars29 Apr 2026: 34,243 stars6 May 2026: 34,482 stars13 May 2026: 34,788 stars20 May 2026: 35,022 stars27 May 2026: 35,231 stars3 Jun 2026: 35,486 stars10 Jun 2026: 35,747 stars17 Jun 2026: 35,967 stars24 Jun 2026: 36,170 stars1 Jul 2026: 36,769 stars8 Jul 2026: 37,123 stars15 Jul 2026: 37,355 stars22 Jul 2026: 37,605 stars29 Jul 2026: 37,858 stars5 Aug 2026: 38,297 stars12 Aug 2026: 38,584 stars19 Aug 2026: 38,771 stars26 Aug 2026: 39,208 stars2 Sept 2026: 39,402 stars9 Sept 2026: 39,551 stars16 Sept 2026: 39,729 stars23 Sept 2026: 39,878 stars30 Sept 2026: 40,084 stars7 Oct 2026: 40,225 stars14 Oct 2026: 40,445 stars

32,688 on 25 Mar 202640,445 on 14 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

Marker is a Python library and CLI that converts PDF, image, PPTX, DOCX, XLSX, HTML and EPUB files to markdown, JSON, HTML or chunks, handling tables, equations, code blocks and image extraction. Layout, OCR and table recognition run through a surya VLM served by a local vLLM (Docker, NVIDIA) or llama.cpp server that starts automatically. An optional --use_llm mode adds Gemini, Claude, OpenAI-compatible, Azure, Vertex, OpenRouter or Ollama for higher accuracy.

Who it is for: Engineers converting PDFs and documents to markdown or JSON for LLM pipelines

Strengths

  • Outputs markdown, JSON block tree with polygons, HTML or chunks, plus extracted images
  • Fast mode uses text layer and rf-detr layout on CPU; --disable_ocr starts no server
  • Batch CLI with worker pool, --skip_existing and chunk sharding across multiple machines
  • Reports 76.0% on olmocr-bench in balanced mode, ahead of MinerU and docling

Weaknesses

  • Model weights use a modified OpenRAIL-M license; commercial use above startup limits needs a paid license
  • Balanced mode needs Docker with NVIDIA Container Toolkit, or llama.cpp on CPU and Apple Silicon
  • Non-PDF formats require the marker-pdf[full] extra install
  • README says the hosted Chandra model is more accurate than Marker

What it needs

  • GPU optional
  • Needs PyTorch, Python 3.10+, vLLM via Docker with NVIDIA Container Toolkit (GPU), llama.cpp llama-server (CPU / Apple Silicon), optional LLM service for --use_llm
  • Models: surya VLM, Gemini, Claude, OpenAI-compatible, Azure

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