Super AI Agent - Intelligent Conversational Assistant Platform

📸 Screenshots

Home Page

Home Page - Choose Your AI Assistant

Highlights:

• ✅ Clean and modern UI design
• ✅ Two AI applications to choose from
• ✅ Quick access to Love Master and Super Agent
• ✅ Responsive layout for all devices

AI Love Master

AI Love Master - Relationship Counseling & Plain Text Chat

Highlights:

• ✅ Natural plain text chat without Markdown formatting
• ✅ Three conversation modes: Basic, Smart (recommended), RAG Q&A
• ✅ Feature enhancements: Love report generation, tool invocation
• ✅ Session management: Create, rename, delete conversations
• ✅ Real-time streaming output with typewriter effect

AI Super Agent (Manus)

Manus Super Agent - Deep Thinking & Tool Invocation

Highlights:

• ✅ Gemini-style thinking process display (collapsible)
• ✅ Real-time thinking steps and duration
• ✅ 14+ automatic tool calls (search, files, email, PDF, etc.)
• ✅ MCP protocol integration (Amap 15 tools)
• ✅ Smart question classification (simple → direct answer, complex → deep thinking)

📖 About

Super AI Agent is a production-grade AI conversational platform that demonstrates how to build a complete intelligent agent application using Spring AI.

🎭 Two Core Applications

🌟 Why This Project?

✨ Features

AI Love Master

• 💬 Smart Chat: Three conversation modes (Basic/Smart/RAG)
• 📊 Love Reports: Auto-generate structured relationship analysis reports
• 📥 Report Download: Download and copy report content
• 🎯 RAG Knowledge: Professional answers based on relationship knowledge base
• 🔄 Smart Fallback: Auto-switch to regular chat when RAG fails

AI Super Agent (Manus)

• 🧠 Deep Thinking: Display complete thinking process (collapsible)
• 🔧 Tool Invocation: 14+ tools (search, files, email, PDF generation, etc.)
• 🌐 MCP Integration: Amap 15 tools (POI search, route planning, etc.)
• 💭 Thinking Visualization: Gemini-style thinking process display
• ⚡ Streaming Output: Real-time AI response and thinking steps
• 🎨 Smart Classification: Auto-detect simple/complex questions for selective thinking

Core Capabilities

🏗️ Tech Architecture

Backend Stack

Frontend Stack

AI Capabilities

Architecture Design

┌─────────────────────────────────────────────────────────┐
│                    Frontend Layer (Vue 3)                 │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │  Love Master  │  │  Super Agent  │  │   Home Page   │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
└─────────────────────────────────────────────────────────┘
                            │
                            │ HTTP/SSE
                            ▼
┌─────────────────────────────────────────────────────────┐
│                  Controller Layer (Spring MVC)            │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │ LoveApp      │  │ Manus        │  │ ChatHistory  │  │
│  │ Controller   │  │ Controller   │  │ Controller   │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│                     Agent Layer                           │
│  ┌──────────────────────────────────────────────────┐  │
│  │              MonuoManus (Super Agent)              │  │
│  │  ┌────────────┐  ┌────────────┐  ┌────────────┐ │  │
│  │  │ Thinking   │  │ ToolCall   │  │ Database   │ │  │
│  │  │ Agent      │  │ Agent      │  │ Memory     │ │  │
│  │  └────────────┘  └────────────┘  └────────────┘ │  │
│  └──────────────────────────────────────────────────┘  │
│  ┌──────────────────────────────────────────────────┐  │
│  │                LoveApp (Love Master)               │  │
│  │  ┌────────────┐  ┌────────────┐  ┌────────────┐ │  │
│  │  │ RAG        │  │ Fallback   │  │ Report     │ │  │
│  │  │ Advisor    │  │ Strategy   │  │ Generator  │ │  │
│  │  └────────────┘  └────────────┘  └────────────┘ │  │
│  └──────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│                    Tools Layer                            │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐  │
│  │ Web      │ │ File     │ │ Mail     │ │ PDF      │  │
│  │ Search   │ │ Operation│ │ Send     │ │ Generate │  │
│  └──────────┘ └──────────┘ └──────────┘ └──────────┘  │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐  │
│  │ Terminal │ │ Download │ │ Scraping │ │ Document │  │
│  │ Operation│ │ Resource │ │ Web      │ │ Reader   │  │
│  └──────────┘ └──────────┘ └──────────┘ └──────────┘  │
│  ┌──────────────────────────────────────────────────┐  │
│  │         MCP Tools (Amap 15 tools)                 │  │
│  └──────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│                    Data Layer                             │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │ MySQL        │  │ PostgreSQL   │  │ File System  │  │
│  │ (Chat History)│  │ (Vector Store)│ │ (Docs/Cache) │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
└─────────────────────────────────────────────────────────┘

🚀 Quick Start

Option 1: Docker Compose (Recommended)

If you have Docker installed, this is the easiest way:

# 1. Set environment variables
export DASHSCOPE_API_KEY=your_api_key
export MYSQL_PASSWORD=your_password
export POSTGRESQL_PASSWORD=your_password

# 2. Start all services (App + MySQL + PostgreSQL)
docker-compose -f docker-compose.local.yml up --build

# 3. Wait for startup, then access:
# Backend Swagger UI: http://localhost:8123/api/swagger-ui.html
# Frontend: http://localhost:5173

Option 2: Local Manual Setup

1. Prerequisites

• ✅ Java 21+
• ✅ Node.js 18+
• ✅ Maven 3.8+
• ✅ MySQL 8.0+
• ✅ PostgreSQL 14+ (with PGVector extension)
• ✅ Alibaba Cloud DashScope API Key

2. Clone the Repository

git clone https://github.com/muonuo/Super-ai-agent.git
cd Super-ai-agent

3. Configure Databases

MySQL Setup:

-- Create database
CREATE DATABASE super_ai_agent CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;

-- Tables are auto-created, no manual SQL needed

PostgreSQL + PGVector Setup:

-- Create database
CREATE DATABASE super_ai_agent;

-- Install PGVector extension (Spring AI will auto-initialize vector tables)
CREATE EXTENSION IF NOT EXISTS vector;

4. Configure Environment Variables

Edit src/main/resources/application.yml:

# MySQL config
spring:
  datasource:
    url: jdbc:mysql://localhost:3306/super_ai_agent?useUnicode=true&characterEncoding=utf-8&useSSL=false&serverTimezone=Asia/Shanghai
    username: root
    password: your_mysql_password

  # AI config (required)
  ai:
    dashscope:
      api-key: your_dashscope_api_key  # Get it at https://dashscope.console.aliyun.com/

# PostgreSQL config
pgvector:
  datasource:
    url: jdbc:postgresql://localhost:5432/super_ai_agent
    username: postgres
    password: your_postgresql_password

# Optional config
search-api:
  tavily-api-key: your_tavily_api_key  # For web search
qq-email:
  from: your_qq_email
  auth-code: your_qq_email_auth_code   # For sending love reports

💡 Getting a DashScope API Key:

1. Visit https://dashscope.console.aliyun.com/
2. Register/login to Alibaba Cloud
3. Enable DashScope service
4. Create an API Key
5. New users get free credits

5. Start Backend

# Option A: Using Maven (recommended)
cd Super-ai-agent
mvn clean package -DskipTests
java -jar target/Super-ai-agent-0.0.1-SNAPSHOT.jar

# Option B: Using IDE
# Run src/main/java/com/monuo/superaiagent/SuperAiAgentApplication.java directly

Backend will start at http://localhost:8123/api

6. Start Frontend

cd super-ai-agent-web
npm install
npm run dev

Frontend will start at http://localhost:5173

7. Access the Application

Open your browser and visit:

• Frontend: http://localhost:5173
• Backend Swagger UI: http://localhost:8123/api/swagger-ui.html
• Backend API Docs: http://localhost:8123/api/v3/api-docs

❓ FAQ

Q1: Port Already in Use

Change the port in src/main/resources/application.yml:

server:
  port: 8123  # Change to another port, e.g. 8124

Q2: Database Connection Failed

Make sure MySQL and PostgreSQL services are running:

# Windows
net start MySQL80
net start postgresql-x64-14

# Linux/Mac
sudo systemctl start mysql
sudo systemctl start postgresql

Q3: PGVector Extension Not Installed

PostgreSQL requires the PGVector extension:

# Ubuntu/Debian
sudo apt install postgresql-14-pgvector

# macOS (Homebrew)
brew install pgvector

# Windows
# Download from https://github.com/pgvector/pgvector-windows/releases

Q4: Maven Build Failed

Make sure you're using Java 21 and Maven 3.8+:

java -version
mvn -version

Q5: DashScope API Key Not Found

1. Visit https://dashscope.console.aliyun.com/
2. Register/login to Alibaba Cloud
3. Enable DashScope service
4. Create an API Key
5. New users get free credits

🛠️ Development Guide

Project Structure

Super-ai-agent/
├── src/main/java/com/monuo/superaiagent/
│   ├── agent/              # Agent core
│   │   ├── BaseAgent.java
│   │   ├── ThinkingAgent.java
│   │   ├── ToolCallAgent.java
│   │   └── MonuoManus.java
│   ├── app/                # Application layer
│   │   └── LoveApp.java
│   ├── tools/              # Tools
│   ├── rag/                # RAG related
│   ├── controller/         # Controllers
│   ├── service/            # Services
│   └── config/             # Configuration
├── super-ai-agent-web/     # Frontend project
│   ├── src/
│   │   ├── views/          # Pages
│   │   ├── components/     # Components
│   │   ├── api/            # API interfaces
│   │   └── router/         # Routes
│   └── package.json
├── docs/                   # Documentation
└── docker-compose.yaml     # Docker config

Adding New Tools

1. Create a tool class:

@Component
public class MyTool {

    @Tool(description = "Tool description")
    public String myFunction(
        @ToolParam(description = "Parameter description") String param) {
        // Tool logic
        return "result";
    }
}

1. Register the tool:

@Configuration
public class ToolRegistration {

    @Bean
    public List<ToolCallback> myTools(MyTool myTool) {
        return ToolCallback.from(myTool);
    }
}

Adding New RAG Documents

Place Markdown documents in the src/main/resources/ directory. The system will auto-load them.

🎯 Usage Examples

AI Love Master

User: My girlfriend and I have been together for 3 years. She seems a bit cold lately. What should I do?

AI: [Smart Mode + Love Report]
1. Analyze the conversation in detail
2. Auto-generate a relationship analysis report
3. Provide 3-5 specific, actionable suggestions
4. Support downloading and copying the report

AI Super Agent

User: Search for today's AI news

AI: [Showing thinking process]
💭 Thinking...
├─ Question type: Complex
├─ User wants today's AI news
├─ Need to use webSearch tool
└─ Thinking time: 1.2s

[Calling tool: webSearch]
[Returning search results...]

🤝 Contributing

Issues and Pull Requests are welcome!

1. Fork this repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Submit a Pull Request

📄 License

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

🙏 Acknowledgments

• Spring AI - AI integration framework
• Alibaba Cloud Bailian - AI model service
• LangChain4j - AI orchestration framework
• PGVector - PostgreSQL vector extension

📞 Contact

• GitHub: @muonuo
• Repository: Super-ai-agent
• Issues: Report a bug