RuoYi AI

Enterprise-Grade AI Assistant Platform

An out-of-the-box full-stack AI platform supporting multi-agent collaboration, Supervisor mode orchestration, and multiple decision models, with advanced RAG technology and visual workflow orchestration capabilities

中文 | 📖 Documentation | 🚀 Live Demo | 🐛 Report Issues | 💡 Feature Requests

✨ Core Features

Module	Current Capabilities
Model Management	Multi-model integration (OpenAI/DeepSeek/Tongyi/Zhipu/MiniMax), multi-modal understanding, Coze/DIFY/FastGPT platform integration
Knowledge Base	Local RAG + Vector DB (Milvus/Weaviate/Qdrant) + Document parsing
Tool Management	MCP protocol integration, Skills capability + Extensible tool ecosystem
Workflow Orchestration	Visual workflow designer, drag-and-drop node orchestration, SSE streaming execution, currently supports model calls, email sending, manual review nodes
Multi-Agent	Agent framework based on Langchain4j, Supervisor mode orchestration, supports multiple decision models

Project Repositories

Module	GitHub Repository	Gitee Repository	GitCode Repository
🔧 Backend	ruoyi-ai	ruoyi-ai	ruoyi-ai
🎨 User Frontend	ruoyi-web	ruoyi-web	ruoyi-web
🛠️ Admin Panel	ruoyi-admin	ruoyi-admin	ruoyi-admin
🎬 Drama	ruoyi-drama	ruoyi-drama
🤖 Copilot	ruoyi-copilot	ruoyi-copilot	ruoyi-copilot
📱 Mini-App	ruoyi-uniapp	ruoyi-uniapp	ruoyi-uniapp

Partner Projects

Project Name	GitHub Repository	Gitee Repository
element-plus-x	element-plus-x	element-plus-x

🛠️ Technical Architecture

Core Framework

• Backend: Spring Boot 3.5.8 + Langchain4j
• Data Storage: MySQL 8.0 + Redis + Vector Databases (Milvus/Weaviate/Qdrant)
• Frontend: Vue 3 + Vben Admin + element-plus-x
• Security: Sa-Token + JWT dual-layer security
• Document Processing: PDF, Word, Excel parsing, intelligent image analysis
• Real-time Communication: WebSocket real-time communication, SSE streaming response
• System Monitoring: Comprehensive logging system, performance monitoring, service health checks

🐳 Docker Deployment

This project provides two Docker deployment methods:

Method 1: One-click Start All Services (Recommended)

Use docker-compose-all.yaml to start all services at once (including backend, admin panel, user frontend, and dependencies):

# Clone the repository
git clone https://github.com/ageerle/ruoyi-ai.git
cd ruoyi-ai

# Start all services (pull pre-built images from registry)
docker-compose -f docker-compose-all.yaml up -d

# Check service status
docker-compose -f docker-compose-all.yaml ps

# Access services
# Admin Panel: http://localhost:25666 (admin / admin123)
# User Frontend: http://localhost:25137
# Backend API: http://localhost:26039

Method 2: Step-by-step Deployment (Source Build)

If you need to build backend services from source, follow these steps:

Step 1: Deploy Backend Service

# Enter backend project directory
cd ruoyi-ai

# Start backend service (build from source)
docker-compose up -d --build

# Wait for backend service to start
docker-compose logs -f backend

Step 2: Deploy Admin Panel

# Enter admin panel project directory
cd ruoyi-admin

# Build and start admin panel
docker-compose up -d --build

# Access admin panel
# URL: http://localhost:5666

Step 3: Deploy User Frontend (Optional)

# Enter user frontend project directory
cd ruoyi-web

# Build and start user frontend
docker-compose up -d --build

# Access user frontend
# URL: http://localhost:5137

Service Ports

Service	One-click Port	Step-by-step Port	Description
Admin Panel	25666	5666	Admin backend access
User Frontend	25137	5137	User frontend access
Backend Service	26039	6039	Backend API service
MySQL	23306	23306	Database service
Redis	26379	6379	Cache service
Weaviate	28080	28080	Vector database
MinIO API	29000	9000	Object storage API
MinIO Console	29090	9090	Object storage console

Image Registry

All images are hosted on Alibaba Cloud Container Registry:

crpi-31mraxd99y2gqdgr.cn-beijing.personal.cr.aliyuncs.com/ruoyi_ai

Available images:

• mysql:v3 - MySQL database (includes initialization SQL)
• redis:6.2 - Redis cache
• weaviate:1.30.0 - Vector database
• minio:latest - Object storage
• ruoyi-ai-backend:latest - Backend service
• ruoyi-ai-admin:latest - Admin frontend
• ruoyi-ai-web:latest - User frontend

Common Commands

# Stop all services
docker-compose -f docker-compose-all.yaml down

# View service logs
docker-compose -f docker-compose-all.yaml logs -f [service-name]

# Restart a service
docker-compose -f docker-compose-all.yaml restart [service-name]

MiniMax Configuration

The built-in MiniMax provider accepts one API Host value and selects the matching protocol adapter. Use a Base URL from this table:

Region	OpenAI-compatible Base URL	Anthropic-compatible Base URL
Global	https://api.minimax.io/v1	https://api.minimax.io/anthropic
China	https://api.minimaxi.com/v1	https://api.minimaxi.com/anthropic

For Anthropic-compatible requests, configure the Base URL ending in /anthropic. Do not append /v1 or /v1/messages; the provider adapter derives the request path internally.

Model ID	Total context	Input modalities	Thinking
MiniMax-M3	1,000,000 tokens	Text, image, video	Adaptive or disabled
MiniMax-M2.7	204,800 tokens	Text	Always on

Current pay-as-you-go prices are in USD per million tokens:

Model	Service tier and input range	Input	Output	Cache read	Cache write
MiniMax-M3	Standard, up to 512,000 input tokens	$0.30	$1.20	$0.06	Not listed
MiniMax-M3	Standard, over 512,000 input tokens	$0.60	$2.40	$0.12	Not listed
MiniMax-M3	Priority, up to 512,000 input tokens	$0.45	$1.80	$0.09	Not listed
MiniMax-M3	Priority, over 512,000 input tokens	$0.90	$3.60	$0.18	Not listed
MiniMax-M2.7	Standard	$0.30	$1.20	$0.06	$0.375

See the official API overview and pay-as-you-go pricing for current details.

📚 Documentation

Want to learn more about installation, deployment, configuration, and secondary development?

👉 Complete Documentation

🤝 Contributing

We warmly welcome community contributions! Whether you are a seasoned developer or just getting started, you can contribute to the project 💪

How to Contribute

1. Fork the project to your account
2. Create a branch (git checkout -b feature/new-feature-name)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to the branch (git push origin feature/new-feature-name)
5. Create a Pull Request

💡 Tip: We recommend submitting PRs to GitHub, we will automatically sync to other code hosting platforms

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🙏 Acknowledgments

Thanks to the following excellent open-source projects for their support:

• Langchain4j - Powerful Java LLM development framework
• RuoYi-Vue-Plus - Mature enterprise-level rapid development framework
• Vben Admin - Modern Vue admin template

💎 Sponsors

Thanks to the following sponsors for supporting this project:

Visit Atlas Cloud · Coding Plan Promotion A full-modal AI inference platform that gives developers a unified AI API, supporting video generation, image generation, and LLMs. Connect once to access 300+ curated models.

Volcengine CodingPlan Developer Program Volcengine is ByteDance's cloud and AI service platform. Volcengine Ark provides API access to Doubao LLM, DeepSeek, and more — a one-stop AI development and inference platform for developers.

💬 Community Chat

Scan to add author on WeChat Join group for learning

QQ Tech Exchange Group Technical discussion

---

⭐ Star to Support • Fork to Contribute • 📚 中文 • 📖 Complete Documentation

Built with ❤️, maintained by the RuoYi AI open-source community