CarrotAI

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<h3>AI Agent with Multi-Server Streaming & Multi-Language Support</h3>
<p>Flutter Frontend + FastAPI Backend</p>
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🚀 <a href="https://chat.jintongshu.com/">Experience online now</a> | <a href="https://jintongshu.com/download/">SaaS Client download</a>
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🇨🇳 <a href="README_zh.md">阅读中文文档</a>
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🥕 Introduction

CarrotAI is a cutting-edge AI agent application that delivers real-time streaming chat via Server-Sent Events (SSE) and streamable HTTP with built-in Model Control Protocol (MCP) integration. It supports concurrent connections to multiple SSE MCP servers and provides user interfaces in English, Chinese, and Japanese.

🚀 Features

- AI Agent: Real-time chat powered by SSE and MCP adapters for a seamless conversational experience.
- Multi-Server Support: Connect to and call multiple SSE MCP servers simultaneously to aggregate intelligent responses.
- Multi-Language: Full localization in English, 中文 (Chinese), and 日本語 (Japanese).
- Deep Thinking Mode: Advanced analysis for complex or multi-step queries.
- Authentication: Secure login/register flow using JWT tokens.
- Responsive UI: Adaptive design for mobile, desktop, and web platforms.
- Theme Customization: Light/dark mode, custom seed colors, and dynamic Material 3 theming via dynamic_color.
- File Upload: Attach and parse files within conversations for richer context.

🤖 Supported Model APIs

- DeepSeek: Advanced language model with strong reasoning capabilities

🛠️ Tech Stack

Frontend

Backend

📋 Prerequisites

- Flutter SDK ^3.7.2
- Python >=3.12
- PostgreSQL

⚡ Quick Start

> Please make sure that it has been installed. uv

# Clone repository
git clone https://github.com/Xingsandesu/CarrotAI.git && cd CarrotAI
Deal with environment variables
mv backend/.env.example backend/.env && mv .env.example .env
Edit environment variables
vim .env
vim backend/.env
Temporarily start PostgreSQL
docker-compose -f docker-compose.yml -f docker-compose.override.yml up -d postgres
Backend setup
uv run backend/scripts/startup.py --user --email <email> --username <name> --password <password>
Stop PostgreSQL
docker-compose -f docker-compose.yml -f docker-compose.override.yml down
Deal with Config
vim config/
Run Backend
docker compose up -d

🔧 Installation

Backend Setup

1. Navigate to the backend directory:

   cd backend

   

   uv sync

   

   cp .env.example .env

   

   uv run scripts/init_db.py && uv run scripts/init_config.py

   

   python main.py       # Development mode

   python main.py prod  # Production mode with Gunicorn

   

Frontend Setup

1. Return to the project root:

   cd ..

   

   flutter pub get

   

   flutter gen-l10n

   

   flutter run

   

   flutter build web --wasm

   

🌐 Configuration

- Frontend: Edit lib/core/config/app_config.dart for API endpoints and theming defaults.
- Backend: Configure .env and backend/app/core/config.py for database, and MCP servers.

Backend Configuration Files

The backend uses JSON files located in backend/config/ to define models, MCP servers, and custom adapters. Below is the default folder structure:

backend/config/
├── model_configs.json       # LLM model definitions and metadata
├── mcp_servers.json         # SSE MCP server endpoints and env settings
└── app/                     # Custom adapter definitions
    └── duckduckgo-search.json

model_configs.json

Defines available LLM models for CarrotAI. Each entry includes:
- id (string): Unique model identifier.
- icon (string): Icon name for display.
- translations (object): Localized names and descriptions (zh, en, ja).
- exclusiveRules (object): Toggles and exclusion rules for features.

Example:

[

  {

    "id": "deepseek",

    "icon": "smart_toy_outlined",

    "translations": {

      "zh": { "name": "DeepSeek", "description": "专注于深度思考和复杂推理的满血模型" },

      "en": { "name": "DeepSeek", "description": "Powerful Chinese large model focused on deep thinking and complex reasoning" },

      "ja": { "name": "DeepSeek", "description": "深い思考と複雑な推論に特化した強力な中国語大規模モデル" }

    },

    "exclusiveRules": {

      "deepThinking": { "enabled": true, "excludes": ["mcpServices"] },

      "mcpServices": { "enabled": true, "excludes": ["deepThinking"] }

    }

  }

]

mcp_servers.json

Specifies SSE Model Control Protocol (MCP) endpoints. Format:
- Keys: service names.
- url (string): SSE endpoint URL.
- env (object): Environment variables for the adapter.

Example:

{

  "serviceA": {

    "url": "http://localhost:10000/sse",

    "env": {

      "API_KEY": "your_api_key"

    }

  }

}

Custom Adapters (app/.json)

{

  "id": "duckduckgo-search",

  "icon": "🔍",

  "mcpServer": {

    "url": "http://localhost:10000/duckduckgo-search",

    "env": {}

  },

  "transportType": "sse",

  "translations": {

    "en": { "name": "DuckDuckGo Search", "type": "Search Tool", "description": "Use DuckDuckGo search engine for secure and private web searches" },

    "zh": { "name": "DuckDuckGo搜索", "type": "搜索工具", "description": "使用DuckDuckGo搜索引擎进行安全、私密的网络搜索" },

    "ja": { "name": "DuckDuckGo検索", "type": "検索ツール", "description": "DuckDuckGo検索エンジンを使用して安全でプライベートなウェブ検索を行います" }

  }

}

   uv run scripts/init_config.py

   

| Key | Description | Default |
|----------------------|----------------------------------------|-----------|
| DATABASE_URL | PostgreSQL connection URL | required|
| BACKEND_CORS_ORIGINS | Allowed CORS origins (comma-separated) | [] |
| MCP_SERVERS | JSON list of SSE MCP server endpoints | required|
| SECRET_KEY | JWT secret key | required*|

Frontend (lib/core/config/app_config.dart)

static String get baseUrl => "http://127.0.0.1:8000";

💡 Usage

1. Launch backend and frontend as shown in Quick Start.
2. Open the app in your browser or mobile emulator.
3. Register or login to obtain a JWT token.
4. Use deep thinking mode or default chat mode to interact with the AI agent.
5. Switch between MCP servers or add new endpoints under Settings.

🔗 API Reference

Access the interactive Swagger UI at:

http://127.0.0.1:8000/docs

🛣️ Roadmap

- [x] SSE multi-server support
- [x] Multi-language (EN, 中文, 日本語)
- [x] Docker Compose setup
- [x] streamable HTTP support
- [ ] Local Stdio multi-server support
- [ ] Local OCR support
- [ ] Support for more formats of the upload interface
- [ ] Frontend custom prompts
- [ ] More model support
- [ ] More language support

🛡️ Security

- Authentication: All backend endpoints secured with JWT; tokens stored securely in encrypted storage.
- Data Protection: Use HTTPS in production; configure allowed CORS origins via BACKEND_CORS_ORIGINS in .env.
- Secret Management: Define SECRET_KEY in .env; ensure no secrets are committed to source control.

🔍 Monitoring & Logging

- Server Logs: Configured in gunicorn.conf.py; access and error logs in logs/.
- Application Logs: Uses Loguru for structured logging; frontend disables debugPrint in release mode.

🚀 Performance & Optimization

- Caching: Frontend caches static assets; backend uses async connection pooling for PostgreSQL.
- Bundle Size: Web artifact built with --wasm for optimized delivery.

🗂️ Changelog

> All notable changes are documented in CHANGELOG.md.

📱 Screenshots

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🤝 Contributing

Contributions are welcome! Please open a Pull Request with your suggestions.

📄 License

This project is licensed under the CarrotAI Open Source License. See the LICENSE file for details.