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:
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:
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
MinerU is a high-accuracy document parsing engine that converts PDF, DOCX, PPTX, XLSX, images and web pages into structured Markdown or JSON, built for LLM, RAG and agent workflows.