CLOUDM0N
Repository intelligence

opendatalab/MinerU

GitHub

An open-source document parsing tool that converts PDFs, images, DOCX, PPTX, and XLSX files into machine-readable Markdown and JSON formats for downstream RAG and processing pipelines.

CLOUDM0N decision
REVIEW BEFORE ADOPTION
Trust REVIEW · 80/100
Good fit if

AI developers preparing high-quality, clean text datasets for pre-training large language models or building technical RAG workflows.

Watch out for

The default high-performance parsing level (effort=medium) does not support image analysis, requiring a switch to a slower mode (effort=high) if images must be parsed.

Practical intelligence

What matters before you adopt it

Problem it solves

The layout destruction, semantic breakage, and formula/table formatting loss that typically occurs when ingesting complex multi-column documents or scanned PDFs into machine-readable training data.

Best for
AI developers preparing high-quality, clean text datasets for pre-training large language models or building technical RAG workflows.
Engineers requiring high-concurrency, high-throughput document parsing systems capable of distributing workloads across multi-GPU setups.
Researchers who need to parse complex scientific literature containing dense layout formats, mathematical equations, and multi-column structures.
Main trade-offs
The default high-performance parsing level (effort=medium) does not support image analysis, requiring a switch to a slower mode (effort=high) if images must be parsed.
The key dependency 'ray' does not support Python 3.13 on Windows, restricting Windows runtime compatibility strictly to Python versions 3.10 through 3.12.
Docker-based deployment is restricted to Linux and Windows with WSL2, requiring macOS users to install directly from source or pip instead.
Why it stands out
Supports full thread-safety and multi-threaded concurrent inference, enabling seamless integration with distributed load balancers like mineru-router.
Offers a low-friction open-source license based on Apache 2.0 with reduced community and commercial adoption barriers.
Maintains wide hardware ecosystem compatibility, supporting pure CPU runtimes, standard GPU/MPS acceleration, and extensive domestic AI chips.
Trust & CVEs

Security evidence without the noise

Trust remains a decision signal; CVEs and scanner evidence explain what is driving the risk.

Security findings
2
CLOUDM0N scanner findings
Critical
0
High
0
Medium
2
Low
0
View trust evidence & security findings▼
Why this score
No trust rationale was stored for this scan.
CLOUDM0N findings
MEDIUM
Dynamic code execution pattern detected. 5 sample match(es) found.
MEDIUM
The project can spawn operating-system processes; review command construction and input handling. 5 sample match(es) found.
Architecture from code7 modules · 2 edges▼
Structural evidence

Modules and dependency edges extracted from repository code. This is code evidence, not README inference.

Code files
224
Modules
7
Dependency edges
2
Core modules
(root)
1 files
demo
1 files
mineru
217 files
Dependency flow
demo→mineru
Detected languages
Python · JavaScript
Detected frameworks
FastAPI · NumPy · OpenAI SDK · Requests
Architecture evidence details
flowchart TD
    %% mineru — high-level architecture (DRAFT, refine me)
    n0["(root) · 1 file"]
    n1["demo · 1 file"]
    n2["mineru · 217 files"]
    n3["tests · tests · 5 files"]
    n1 --> n2
    n3 --> n2
    class n3 test
    classDef test fill:#499894,color:#ffffff,stroke:#397975
Evidence, security & integrations▼
Integrations
vLLMLMDeployLangChainLlamaIndexCursorClaude DesktopWindsurfRAGFlow
Security notes
• Supports 100% offline, private air-gapped deployment configurations to prevent sensitive documents from leaving local company networks.
Still unknown
• The README does not provide concrete system resource metrics, such as minimum RAM, CPU speed, or recommended GPU VRAM capacity.
• Does not outline any licensing constraints, developer limitations, or usage costs for the referenced third-party or domestic model backends.
• Lacks architectural details about API security, rate limiting options, or multi-tenant user access controls.
Adoption guidance▼
Adopt if
+ You need to parse technical, mathematical, or scientific documents where formulas and tables must be extracted cleanly with minimal hallucination.
+ You want a fully local, thread-safe, and scalable processing pipeline that can grow to multi-GPU clustering setups easily.
+ You require an ingestion library with a flexible custom license based on Apache 2.0 that allows commercial integration.
Avoid if
– You require containerized Docker installations natively on macOS environments.
– You need detailed layout image analysis but cannot accept the parsing latency and speed reductions of the high-strength effort mode.
How it works & getting started▼
How it works
1.The user submits a file or directory via the CLI, REST API, or WebUI.
2.The ingestion pipeline automatically runs a sliding-window scan and identifies scanned pages to activate OCR.
3.MinerU removes headers, footers, and visual artifacts while reconstructing the document in human reading order.
4.The dual VLM and OCR engine processes equations and tables, converting them to LaTeX and HTML.
5.The orchestrator exports the resulting structures in NLP Markdown, multimodal Markdown, or chronological JSON.
Getting started
→ Evaluate the parsing engine layout quality using the official online web application or the login-free Gradio online demo.
→ Ensure system dependencies are resolved and run: pip install mineru[all]
→ For pure CPU processing without GPU acceleration, execute: mineru --backend pipeline -i <input_path>
Agent handoff
Use with any agent
JSON API
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