RAG & Knowledge
opendataloader-pdf
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
★ 29.5k starsApache-2.0
View guide →LibreAI / RAG & Knowledge / Marker
// rag · datalab-to/marker
Marker
Marker is an open-source rag & knowledge project by datalab-to (datalab-to/marker on GitHub). It has 40,648 GitHub stars and 2,945 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 22 October 2026.
Marker — converts PDFs to Markdown and JSON quickly and accurately, with layout, tables and math intact, Apache-2.0
★ 40.6k stars⑂ 2.9k forksApache-2.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
so here's a placeholder. The guide below is complete regardless.
// project facts
Project facts
- Repository
- datalab-to/marker
- GitHub stars
- 40,648
- Forks
- 2,945
- License
- Apache-2.0
- Last updated on GitHub
- 22 October 2026
- Category
- RAG & Knowledge
- Listing
- Full install guide
// how it works
How it works
- Install Marker for PDFs
The base package covers PDF conversion from PyPI.
- Add the full extras for other document types
Documents beyond PDFs need the full dependency set.
- Set up your inference backend
On an NVIDIA GPU use vLLM with Docker and the NVIDIA Container Toolkit; on CPU or Apple Silicon install the llama-server binary from llama.cpp.
// features
Why people love it
High-accuracy PDF conversion
Layout, tables and math survive as structured Markdown and JSON, not flattened text.
Two modes for two budgets
Balanced mode for maximum quality on GPU; fast mode for CPU-friendly conversion with targeted repair of bad blocks.
Surya vision models underneath
Layout detection and OCR come from dedicated vision models, with re-OCR of pages whose embedded text is unreliable.
Built for document pipelines
Output is made to feed search, RAG and dataset tooling, not just to be read once.
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// run it
Run it yourself
- Install for PDFs
The README install route from PyPI.
pip install marker-pdf - Install the full version
Adds support for documents other than PDFs.
pip install marker-pdf[full] - CPU / Apple Silicon backend
The llama-server binary Marker needs on non-GPU machines.
brew install llama.cpp
// use cases
What you can build
- Converting research papers and reports into Markdown for RAG
- Building datasets from PDF archives with tables and math intact
- Turning scanned or messy PDFs into clean structured text
- Feeding document pipelines that need JSON, not just text
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// faq
Questions, answered
Who makes Marker?
The datalab-to team, built on the Surya vision models.
Is Marker free?
Yes — Apache-2.0 licensed.
Do I need a GPU?
No — fast mode is optimised for CPU, with llama.cpp as the backend; balanced mode is best on an NVIDIA GPU with vLLM.
What does Marker output?
Markdown and JSON with layout, tables and math preserved — structured output for search and RAG, not a plain text dump.
How do I get started?
pip install marker-pdf (or marker-pdf[full] for non-PDF documents), set up the vLLM or llama.cpp backend, and convert.
// keep exploring
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// more in rag
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Get Marker
Free and open — grab the source, star the repo, and start creating.