Software Development & Delivery · Public

marker

Convert PDF to markdown + JSON quickly with high accuracy

Best fitUsers who want source-backed project understanding before installing it.

Check whether this project matches your task before installing it.

What it can doprompt, recipe, host_instruction, eval, preflight

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot36k stars

2.5k forks · 29 contributors

Doramagic.ai Last verification date: 2026-07-07 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-07-07

What is marker?

Convert PDF to markdown + JSON quickly with high accuracy
Best fit: Users who want source-backed project understanding before installing it.
Not for: Not for users who want to skip sandbox verification or cannot accept configuration, permission, or maintenance overhead.
Capability added to an AI workflow: prompt, recipe, host_instruction, eval, preflight
First safe verification step: Verify the smallest path in an isolated environment and keep a rollback path.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/datalab-to/marker, https://github.com/datalab-to/marker#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forUsers who want source-backed project understanding before installing it.

Match the project to your task before installing it.

Capabilityprompt, recipe, host_instruction, eval, preflight

Convert PDF to markdown + JSON quickly with high accuracy

Repositorydatalab-to/marker

36k stars · 2.5k forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Overview and Getting Started

Related topics: Pipeline Architecture and Extensibility, Output Formats, Deployment, and Operational Pitfalls

Source: https://github.com/datalab-to/marker / Human Manual

Pipeline Architecture and Extensibility

Related topics: Overview and Getting Started, LLM Integration and Hybrid Mode, Output Formats, Deployment, and Operational Pitfalls

Source: https://github.com/datalab-to/marker / Human Manual

LLM Integration and Hybrid Mode

Related topics: Overview and Getting Started, Pipeline Architecture and Extensibility, Output Formats, Deployment, and Operational Pitfalls

Source: https://github.com/datalab-to/marker / Human Manual

Output Formats, Deployment, and Operational Pitfalls

Related topics: Overview and Getting Started, Pipeline Architecture and Extensibility, LLM Integration and Hybrid Mode

Source: https://github.com/datalab-to/marker / Human Manual

Community Discussion Evidence

These external discussion links are review inputs, not standalone proof that the project is production-ready.

Source: Project Pack community evidence and pitfall evidence

Sources: https://github.com/datalab-to/marker, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars36k stars

Forks2.5k forks

Contributors29 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using marker with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
Unauthenticated arbitrary local file read via `filepath` in POST /marker
github / github_issue
02
[BUG: Output] ListItem html logic strips all internal dashes/hyphens fro
github / github_issue
03
[BUG: Breaking] missing dependency: psutil
github / github_issue
04
[Performance] RTX 3090 extremely slow (0.014 pages/sec) despite 19GB fre
github / github_issue
05
OCR_ENGINE=None Doesn't work
github / github_issue
06
Converting speed is farely slow with marker_single
github / github_issue
07
marker_single defaults output_dir to site packages
github / github_issue
08
marker_single always emits json which is not documented as a feature
github / github_issue
09
incorrect logging about Markdown format for html or json output; [INFO]
github / github_issue
10
Flawed CUDA / NVidia GPU detection logic
github / github_issue
11
Model weights are not loaded when marker-single is first run after insta
github / github_issue
12
[BUG: Breaking] Marker is 20x+ slower since v1.9.0+ in Mac
github / github_issue

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install marker-pdf

Official start command · https://github.com/datalab-to/marker#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

marker Manual

Convert PDF to markdown + JSON quickly with high accuracy

Open the full manual

https://github.com/datalab-to/marker Project Manual
Table of Contents
Overview and Getting Started
Related Pages
Purpose and Scope
Installation and Entry Points
Core Conversion Workflow
Basic Usage Patterns

Overview and Getting Started

Related topics: Pipeline Architecture and Extensibility, Output Formats, Deployment, and Operational Pitfalls

Source: https://github.com/datalab-to/marker / Human Manual

Pipeline Architecture and Extensibility

Related topics: Overview and Getting Started, LLM Integration and Hybrid Mode, Output Formats, Deployment, and Operational Pitfalls

Source: https://github.com/datalab-to/marker / Human Manual

LLM Integration and Hybrid Mode

Related topics: Overview and Getting Started, Pipeline Architecture and Extensibility, Output Formats, Deployment, and Operational Pitfalls

Source: https://github.com/datalab-to/marker / Human Manual

Output Formats, Deployment, and Operational Pitfalls

Related topics: Overview and Getting Started, Pipeline Architecture and Extensibility, LLM Integration and Hybrid Mode

Source: https://github.com/datalab-to/marker / Human Manual

Community Discussion Evidence

These external discussion links are review inputs, not standalone proof that the project is production-ready.

Source: Project Pack community evidence and pitfall evidence

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.