Agentience Tribal MCP Server

🚀 Tribal Project Documentation

The "Tribal" is a tool designed for managing local AI model servers. It offers a variety of functional modules, including data storage, task queues, and API services. Users can easily deploy and utilize this tool via the command line or Docker containers.

🚀 Quick Start

The Tribal tool simplifies the management of local AI model servers. With multiple functional modules and easy - to - follow deployment methods, it provides users with a seamless experience.

✨ Features

Data Storage Service

Utilize ChromaDB or AWS S3 for data storage and management.

Task Queue System

Support for Redis Queue or AWS SQS.

📦 Installation

Install Dependencies

pip install tribal

Configure Environment Variables

Edit the ~/.env file:

DB_TYPE=sqlite
DB_PATH=/path/to/db.sqlite
API_PORT=8080

💻 Usage Examples

Create an API Service

from fastapi import FastAPI
from tribal.app import TribalAPI

app = FastAPI()
tribal_api = TribalAPI(app)

@tribal_api.route('/hello')
async def hello():
    return {'message': 'Hello, World!'}

Start the Command - Line Tool

tribal --help

ChromaDB Integration

from tribal.services.chroma_storage import ChromaStorage

storage = ChromaStorage()

Redis Queue Configuration

from tribal.services.redis_queue import RedisQueue

queue = RedisQueue(host='localhost', port=6379)

📚 Documentation

Submitting Code

1. Create a feature branch: git checkout -b feature/<your-feature>
2. Push the code: git push origin feature/<your-feature>
3. Submit a Pull Request to the main branch

Project Structure

tribal/
├── src/
│   ├── mcp_server_tribal/      # Core package
│   │   ├── api/                # FastAPI endpoints
│   │   ├── cli/                # Command - line interface
│   │   ├── models/             # Pydantic models
│   │   ├── services/           # Service layer
│   │   │   ├── aws/            # AWS integration
│   │   │   └── chroma_storage.py # ChromaDB implementation
│   │   └── utils/              # Utility functions
│   └── examples/               # Example code
├── tests/                      # pytest test suite
├── docker-compose.yml          # Docker production settings
├── pyproject.toml              # Project configuration
├── VERSIONING.md               # Versioning strategy document
├── CHANGELOG.md                # Version history
├── .bumpversion.cfg            # Version increment configuration
└── README.md                   # Project documentation

📄 License

MIT License