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MinerU-Ecosystem

The official ecosystem toolkit for MinerU Open API

Empowering developers and AI agents with seamless document parsing capabilities — PDF · Word · PPT · Images · Web pages → Markdown / JSON · VLM+OCR dual engine · 109 languages · MCP Server · LangChain / RAGFlow / Dify / FastGPT native integration.

  

中文文档

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📖 Overview

MinerU-Ecosystem provides a full suite of tools, SDKs, and integrations built on top of the MinerU Open API. Whether you're building production pipelines, integrating with LangChain for RAG, or enabling AI agents to parse documents on the fly — this repository has you covered.

MinerU is an open-source, high-accuracy document parsing engine that converts unstructured documents (PDFs, images, Office files, etc.) into machine-readable Markdown and JSON, purpose-built for LLM pre-training, RAG, and agentic workflows.

Core capabilities:

• Formulas → LaTeX · Tables → HTML, accurate complex 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

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🏗️ Repository Structure

MinerU-Ecosystem/
├── cli/                  # Command-line tool for document parsing
├── sdk/                  # Multi-language SDKs
│   ├── python/           #   Python SDK
│   ├── go/               #   Go SDK
│   └── typescript/       #   TypeScript SDK
├── langchain_mineru/     # LangChain document loader integration
├── llama-index-readers-mineru/     # LlamaIndex document reader integration
├── mcp/                  # Model Context Protocol server (Python)
└── skills/               # AI agent skills (Claude Code, OpenClaw, etc.)

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🔑 Supported APIs

All components support both API modes:

| Comparison | 🎯 Precision Extract API | ⚡ Quick Parse API (Agent-Oriented) | | --------------- | ----------------------------------------------------------- | ----------------------------------- | | Auth | ✅ Token required | ❌ Not required (IP rate-limited) | | Model Versions | pipeline (default) / vlm (recommended) / MinerU-HTML | Fixed lightweight pipeline model | | File Size Limit | ≤ 200 MB | ≤ 10 MB | | Page Limit | ≤ 200 pages | ≤ 20 pages | | Batch Support | ✅ Supported (≤ 200 files) | ❌ Single file only | | Output Formats | Markdown, JSON, Zip; optional export to DOCX / HTML / LaTeX | Markdown only |

🧭 Choose Your Integration Path

Not sure where to start? Pick the path that matches your use case:

I want to...
│
├── 🌐 Try it instantly, with no install and no code
│   └── Web App → https://mineru.net/OpenSourceTools/Extractor
│
├── 💻 Parse documents from the terminal
│   └── CLI → cli/
│       flash-extract: no token, best for quick previews
│       extract: full features, better for production workflows
│
├── 🐍 Integrate it into my Python / Go / TypeScript project
│   └── SDK → sdk/python/ | sdk/go/ | sdk/typescript/
│
├── 🤖 Enable my AI agent to parse documents
│   ├── Call the CLI directly → cli/
│   ├── Use natural-language skills (OpenClaw, ZeroClaw, etc.) → skills/
│   └── Use MCP protocol (Cursor, Claude Desktop, Windsurf, etc.) → mcp/
│
├── 📚 Build a RAG pipeline / knowledge base
│   ├── LangChain Loader → langchain_mineru/
│   └── LlamaIndex Reader → llama-index-readers-mineru/
│       flash mode: zero-token quick start
│       precision mode: OCR, tables, formulas, and higher fidelity

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🚀 Quick Start

💻 CLI (cli/)

A fast command-line tool for parsing documents directly from your terminal.

Installation

# Linux / macOS
curl -fsSL https://cdn-mineru.openxlab.org.cn/open-api-cli/install.sh | sh

# Windows (PowerShell)
irm https://cdn-mineru.openxlab.org.cn/open-api-cli/install.ps1 | iex

Flash Extract (no login)

mineru-open-api flash-extract report.pdf

Precision Extract (login required)

# First-time setup
mineru-open-api auth

# Extract to stdout
mineru-open-api extract paper.pdf

# Save all resources (images/tables) to directory
mineru-open-api extract report.pdf -o ./output/

# Export to multiple formats
mineru-open-api extract report.pdf -f docx,latex,html -o ./results/

Web Crawl

mineru-open-api crawl https://www.example.com

Batch Processing

# All PDFs in current directory
mineru-open-api extract *.pdf -o ./results/

# From a file list
mineru-open-api extract --list filelist.txt -o ./results/

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🐍 Python SDK

Installation

pip install mineru-open-sdk

Flash Extract (no token)

from mineru import MinerU

client = MinerU()
result = client.flash_extract("https://cdn-mineru.openxlab.org.cn/demo/example.pdf")
print(result.markdown)

Precision Extract (token required)

from mineru import MinerU

client = MinerU("your-api-token")
result = client.extract("https://cdn-mineru.openxlab.org.cn/demo/example.pdf")
print(result.markdown)
print(result.images)  # extracted image list

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🐹 Go SDK

Installation

go get github.com/opendatalab/MinerU-Ecosystem/sdk/go@latest

Flash Extract

package main

import (
    "context"
    "fmt"
    mineru "github.com/opendatalab/MinerU-Ecosystem/sdk/go"
)

func main() {
    client := mineru.NewFlash()
    result, err := client.FlashExtract(
        context.Background(),
        "https://cdn-mineru.openxlab.org.cn/demo/example.pdf",
    )
    if err != nil {
        panic(err)
    }
    fmt.Println(result.Markdown)
}

Precision Extract

client, err := mineru.New("your-api-token")
if err != nil {
    panic(err)
}
result, err := client.Extract(
    context.Background(),
    "https://cdn-mineru.openxlab.org.cn/demo/example.pdf",
)
if err != nil {
    panic(err)
}
fmt.Println(result.Markdown)

Precision Extract with options

result, err := client.Extract(ctx, "./paper.pdf",
    mineru.WithModel("vlm"),
    mineru.WithLanguage("en"),
    mineru.WithPages("1-20"),
    mineru.WithExtraFormats("docx"),
    mineru.WithPollTimeout(10*time.Minute),
)
if err != nil {
    panic(err)
}
if err := result.SaveAll("./output"); err != nil {
    panic(err)
}

Batch Processing

ch, err := client.ExtractBatch(ctx, []string{"a.pdf", "b.pdf"})
if err != nil {
    panic(err)
}
for result := range ch {
    fmt.Printf("%s: %s\n", result.Filename, result.State)
}

Web Crawling

result, err := client.Crawl(ctx, "https://www.example.com")
if err != nil {
    panic(err)
}
fmt.Println(result.Markdown)

---

🟦 TypeScript / JavaScript SDK

Installation

npm install mineru-open-sdk

Flash Extract

import { MinerU } from "mineru-open-sdk";

const client = new MinerU();
const result = await client.flashExtract(
  "https://cdn-mineru.openxlab.org.cn/demo/example.pdf"
);
console.log(result.markdown);

Precision Extract

import { MinerU } from "mineru-open-sdk";

const client = new MinerU("your-api-token");
const result = await client.extract(
  "https://cdn-mineru.openxlab.org.cn/demo/example.pdf"
);
console.log(result.markdown);
console.log(result.images);

Precision Extract with options

import { MinerU, saveAll } from "mineru-open-sdk";

const client = new MinerU("your-api-token");
const result = await client.extract("./paper.pdf", {
  model: "vlm",       // "vlm" | "pipeline" | "html"
  language: "en",
  pages: "1-20",
  extraFormats: ["docx"],
  timeout: 600,
});
await saveAll(result, "./output");

Batch Processing

for await (const result of client.extractBatch(["a.pdf", "b.pdf"])) {
  console.log(`${result.filename}: ${result.state}`);
}

Web Crawling

const result = await client.crawl("https://www.example.com");
console.log(result.markdown);

---

🤖 Use with Claude / Cursor (MCP Server)

MinerU provides an official MCP Server allowing Claude Desktop, Cursor, Windsurf, and any MCP-compatible AI client to parse documents as a native tool.

No API key needed — Flash mode works out of the box, free, up to 20 pages / 10 MB per file.

Configure: claude_desktop_config.json / .cursor/mcp.json

{
  "mcpServers": {
    "mineru": {
      "command": "uvx",
      "args": ["mineru-open-mcp"],
      "env": {
        "MINERU_API_TOKEN": "your_key_here"
      }
    }
  }
}

Streamable HTTP mode (web-based MCP clients)

MINERU_API_TOKEN=your_key mineru-open-mcp --transport streamable-http --port 8001

{
  "mcpServers": {
    "mineru": {
      "type": "streamableHttp",
      "url": "http://127.0.0.1:8001/mcp"
    }
  }
}

Tools exposed via MCP:

| Tool | Description | |---|---| | parse_documents | Convert PDF, DOCX, PPTX, images, HTML to Markdown | | get_ocr_languages | List all 109 supported OCR languages | | clean_logs | Delete old server log files (when ENABLE_LOG=true) |

Environment Variables:

| Variable | Description | Default | |---|---|---| | MINERU_API_TOKEN | MinerU cloud API token | — | | OUTPUT_DIR | Directory for saved output | ~/mineru-downloads | | ENABLE_LOG | Set true to write log files | disabled | | MINERU_LOG_DIR | Override log file directory | ~/.mineru-open-mcp/logs/ |

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🦜 Use in RAG with LangChain

langchain-mineru is an official LangChain Document Loader — parse any document into LangChain Document objects with one line of code.

Installation

pip install langchain-mineru

Minimal example (no token)

1. Basic usage (flash mode by default, no token required)

from langchain_mineru import MinerULoader

loader = MinerULoader(source="demo.pdf")   # flash mode, no token needed
docs = loader.load()
print(docs[0].page_content[:500])
print(docs[0].metadata)

Default is mode="flash", which is ideal for quick previews and lightweight integrations.

2. Precision mode (token required)

Best for long documents, larger files, and workflows that need higher-fidelity extraction or standard API outputs. Flash mode also supports OCR, table, and formula switches within flash API limits.

from langchain_mineru import MinerULoader

loader = MinerULoader(
    source="/path/to/manual.pdf",
    mode="precision",
    token="your-api-token",  # or set MINERU_TOKEN
    split_pages=True,
    pages="1-5",
)

docs = loader.load()
for doc in docs:
    print(doc.metadata.get("page"), doc.page_content[:200])

3. Use it in a LangChain RAG pipeline

from langchain_mineru import MinerULoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

loader = MinerULoader(source="demo.pdf", split_pages=True)
docs = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=1200, chunk_overlap=200)
chunks = splitter.split_documents(docs)

vs = FAISS.from_documents(chunks, OpenAIEmbeddings())
results = vs.similarity_search("What are the key conclusions in this document?", k=3)

for r in results:
    print(r.page_content[:200])

Default is mode="flash" (no API token required). Switch to mode="precision" for higher fidelity with token auth. For RAG use cases, split_pages=True is usually a better default for PDFs because it gives you page-level Document granularity.

## Use in RAG with LlamaIndex

A document reader for LlamaIndex that parses PDFs, Word files, PPTs, images, and Excel files through MinerU and returns LlamaIndex-compatible Document objects for indexing and retrieval.

Installation

pip install llama-index-readers-mineru

Usage

1. Flash mode (default, no token required)

Good for quick setup and lightweight parsing. Output is returned as Markdown.

from llama_index.readers.mineru import MinerUReader

reader = MinerUReader()
documents = reader.load_data("https://cdn-mineru.openxlab.org.cn/demo/example.pdf")

print(documents[0].text[:500])
print(documents[0].metadata)

2. Precision mode (token required)

Best for longer documents, larger files, and use cases that need higher-fidelity extraction or standard API outputs. Flash mode also supports OCR, formula, and table switches within flash API limits.

from llama_index.readers.mineru import MinerUReader

reader = MinerUReader(
    mode="precision",
    token="your-api-token",  # or set MINERU_TOKEN
    pages="1-20",
)
documents = reader.load_data("/path/to/paper.pdf")

3. Use it in a LlamaIndex pipeline

from llama_index.core import VectorStoreIndex
from llama_index.readers.mineru import MinerUReader

reader = MinerUReader(split_pages=True)
documents = reader.load_data("/path/to/paper.pdf")

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("Summarize the main findings of this document")
print(response)

Default is mode="flash" with no token required. Switch to mode="precision" when you need higher parsing fidelity. For PDF-based RAG pipelines, split_pages=True is recommended so each page becomes a separate Document.

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🤖 AI Agent Skills (skills/)

Pre-built skills for AI coding agents, wrapping the mineru-open-api CLI for use in agent workflows.

• OpenClaw / ClawHub — View skill details
• One-click download — Skill package
• Compatible with Claude Code, OpenClaw, ZeroClaw, and other skill-interface agents

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🔗 All Integrations

| Framework / Tool | Status | Notes | |---|---|---| | LangChain | ✅ Official | pip install langchain-mineru | | LlamaIndex | ✅ Community | See MinerU-Ecosystem | | RAGFlow | ✅ Supported | Document loader integration | | RAG-Anything | ✅ Supported | Multi-modal RAG pipeline | | Flowise | ✅ Supported | Node-based RAG builder | | Dify | ✅ Native Plugin | Built-in document loader | | FastGPT | ✅ Native Plugin | Integration guide | | Claude Desktop | ✅ MCP | uvx mineru-open-mcp | | Cursor | ✅ MCP | .cursor/mcp.json config | | Windsurf | ✅ MCP | stdio / streamable-http | | OpenClaw / ZeroClaw | ✅ Agent Skill | ClawHub | | Go SDK | ✅ Official | go get .../sdk/go@latest | | TypeScript SDK | ✅ Official | npm install mineru-open-sdk | | Python SDK | ✅ Official | pip install mineru-open-sdk |

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📚 Documentation

| Resource | Link | |---|---| | MinerU Open API Docs | mineru.net/apiManage/docs | | MinerU Online Demo | mineru.net/OpenSourceTools/Extractor | | MinerU Open Source | github.com/opendatalab/MinerU |

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📄 License

This project is licensed under the Apache License 2.0.