MinerU README

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MinerU — High-accuracy document parsing engine for LLM · RAG · Agent workflows

Converts PDF · DOCX · PPTX · XLSX · Images · Web pages into structured Markdown / JSON · VLM+OCR dual engine · 109 languages
MCP Server · LangChain / Dify / FastGPT native integration · 10+ domestic AI chip support

🔍 Core Parsing Capabilities

Native support for DOCX, PPTX, and XLSX parsing
Formulas → LaTeX · Tables → HTML, accurate layout reconstruction
Supports scanned docs, handwriting, multi-column layouts, cross-page table merging
Output follows human reading order with automatic header/footer removal
VLM + OCR dual engine, 109-language OCR recognition

🔌 Integration

Use Case	Solution
AI Coding Tools	MCP Server — Cursor · Claude Desktop · Windsurf
RAG Frameworks	LangChain · LlamaIndex · RAGFlow · RAG-Anything · Flowise · Dify · FastGPT
Development	Python / Go / TypeScript SDK · CLI · REST API · Docker
No-Code	mineru.net online · Gradio WebUI · Desktop client

🖥️ Deployment (Private · Fully Offline)

Inference Backend	Best For
pipeline	Fast & stable, no hallucination, runs on CPU or GPU
vlm-engine	High accuracy, supports vLLM / LMDeploy / mlx ecosystem
hybrid-engine	High accuracy, native text extraction, low hallucination

Domestic AI chips: Ascend · Cambricon · Enflame · MetaX · Moore Threads · Kunlunxin · Iluvatar · Hygon · Biren · T-Head

Changelog

2026/06/18 3.4 Released

This release focuses on OCR capability upgrades for the pipeline backend, OCR processing pipeline optimization, and model download experience improvements. The main updates include:
- OCR model upgrade and processing acceleration
  - The OCR model for the pipeline backend has been upgraded to PP-OCRv6, improving OCR accuracy by about 11% on OmniDocBench v1.6.
  - Removed Japanese, Traditional Chinese, English, and Latin options from OCR language selection. These scenarios are now routed to the ch OCR model, simplifying model configuration and language selection.
  - Optimized the OCR inference and processing pipeline, increasing OCR processing speed by about 100% and significantly improving parsing efficiency for batch documents and OCR-intensive documents.
- Model download logic optimization
  - Added automatic model source selection, allowing first-time installations to choose a better model source based on the current network environment.
  - Before downloading models, MinerU now prioritizes checking locally downloaded model cache files. Cache hits can be reused directly, reducing repeated downloads and unnecessary remote requests.
  - For more details about model source configuration, automatic source selection, and local model usage, see the Model Source Documentation.
With the 3.4 release, MinerU further improves the parsing accuracy and processing efficiency of the pipeline backend in OCR scenarios. It also optimizes model downloads, cache reuse, and local configuration write-back, making first-time installation, model updates, and multi-environment deployment more stable and automated.
2026/06/11 3.3 Released

This release focuses on Hybrid parsing performance optimization and VLM model capability upgrades. The main updates include:
- New effort parsing-strength parameter for the Hybrid backend
  - Added two parsing-strength levels, medium and high, allowing users to balance parsing speed, parsing accuracy, and feature requirements.
  - On OmniDocBench v1.6, medium reduces overall accuracy by only 0.13 points compared with high, while delivering 35% ~ 220% parsing speed improvements across different devices and scenarios:
    - Linux: about 80% faster for text PDF scenarios and about 35% faster for OCR scenarios
    - Windows: about 90% faster for text PDF scenarios and about 45% faster for OCR scenarios
    - macOS: about 220% faster for text PDF scenarios and about 50% faster for OCR scenarios
  - The default Hybrid backend now uses effort=medium, significantly improving overall parsing efficiency while maintaining high parsing accuracy.
  - The medium level does not support image analysis; for maximum parsing accuracy or image analysis support, switch to the high-strength parsing mode with effort=high, which may have an impact on parsing speed.
- VLM model upgraded to MinerU2.5-Pro-2605-1.2B
  - Fixed multiple model issues found in the 2604 version, further improving parsing stability on complex documents.
  - Added native multilingual OCR support, reducing the need for extra language-parameter configuration and improving out-of-the-box usability for multilingual documents.
With the 3.3 release, MinerU further improves Hybrid backend efficiency across platforms and scenarios while maintaining high-accuracy parsing. The default medium effort level is better suited for most day-to-day document processing tasks, while high is designed for scenarios that require maximum parsing accuracy or image analysis capabilities.
2026/04/18 3.1.0 Released

This release focuses on licensing openness, parsing accuracy, and full-format native support. The main updates include:
- License upgrade
  - MinerU has officially moved from AGPLv3 to the MinerU Open Source License, a custom license based on Apache 2.0.
  - This change significantly reduces adoption friction for both community users and commercial deployments, making MinerU easier to integrate into real-world workflows.
- VLM main model upgrade
  - The primary VLM model has been upgraded to MinerU2.5-Pro-2604-1.2B, bringing overall parsing accuracy to a state-of-the-art level.
  - The new model now supports image and chart parsing, truncated paragraph merging, cross-page table merging, and image recognition inside tables, further strengthening performance on complex document layouts.
- Full-format native parsing support
  - Native parsing support has now been extended to PPTX and XLSX.
  - MinerU now fully supports parsing across images, PDF, DOCX, PPTX, and XLSX, providing a more complete multi-format document understanding workflow.
With the 3.1.0 release, MinerU becomes more open, more accurate, and easier to adopt in production. The new license lowers the barrier for both community and commercial use, MinerU2.5-Pro-2604-1.2B improves parsing quality on complex content, and native PPTX / XLSX support completes end-to-end coverage of mainstream document formats.
2026/03/29 3.0.0 Released

This release delivers a systematic upgrade centered on parsing capability, system architecture, and engineering usability. The main updates include:
- Native DOCX parsing
  - Official support for native DOCX parsing, delivering high-precision results without hallucinations.
  - Compared with the traditional workflow of first converting DOCX to PDF and then parsing it, end-to-end speed is improved by tens of times, making it better suited for scenarios with high requirements for both accuracy and throughput.
- pipeline backend upgrade
  - The pipeline backend achieves a score of 86.2 on OmniDocBench (v1.5), surpassing the accuracy of the previous-generation mainstream VLM MinerU2.0-2505-0.9B.
  - Added support for parsing images/formulas inside tables, seal text recognition, vertical text support, and interline formula numbering recognition, continuously improving parsing quality for complex document scenarios.
  - While maintaining high accuracy, it keeps resource usage extremely low and continues to support inference in pure CPU environments.
- API / CLI / Router orchestration upgrade
  - mineru now runs as an orchestration client based on mineru-api; when --api-url is not provided, it will automatically start a local temporary service.
  - mineru-api adds a new asynchronous task endpoint POST /tasks, supporting task submission, status querying, and result retrieval; meanwhile, it retains the synchronous parsing endpoint POST /file_parse for compatibility with legacy plugins.
  - Added mineru-router, designed for unified entry deployment and task routing across multiple services and multiple GPUs; its interfaces are fully compatible with mineru-api and support automatic task load balancing.
- Deployment and usability improvements
  - Resolved compatibility issues with torch >= 2.8; the base image has been upgraded to vllm0.11.2 + torch2.9.0, unifying installation paths across different Compute Capabilities.
  - Optimized the parsing pipeline with a sliding-window mechanism, significantly reducing peak memory usage in long-document scenarios, so documents with tens of thousands of pages no longer need to be split manually.
  - Batch inference in pipeline now supports streaming writes to disk, allowing completed parsing results to be written out in time and further improving the experience for long-running tasks.
  - Completed thread-safety optimization and now fully supports multi-threaded concurrent inference; together with mineru-router, this enables one-click multi-GPU deployment and makes it easy to build high-concurrency, high-throughput parsing systems.
  - Completely removed the use of two AGPLv3 models (doclayoutyolo and mfd_yolov8) and one CC-BY-NC-SA 4.0 model (layoutreader).
This update is not just a set of feature enhancements, but a key leap forward in MinerU's overall system capabilities. We specifically addressed the peak memory usage issue in long-document parsing. Through optimizations such as sliding windows and streaming writes to disk, ultra-long document parsing has moved from “requiring manual splitting and careful handling” to being “stable, scalable, and ready for production workloads.” At the same time, we completed thread-safety optimization and fully enabled multi-threaded concurrent inference, further improving single-machine resource utilization and runtime stability under high-concurrency workloads. On top of this, with mineru-router and the new API / CLI orchestration framework, MinerU now supports one-click multi-GPU deployment, unified access across multiple services, and automatic task load balancing, significantly reducing the difficulty of large-scale deployment. As a result, MinerU is evolving from a standalone data production tool into a large-scale document parsing foundation for high-concurrency and high-throughput scenarios, providing enterprise-grade document data processing with in