Agentic AI System MCP Server
Enables deployment of autonomous AI agents with memory and tool execution capabilities through a WebSocket-based MCP protocol. Provides production-ready infrastructure with REST API access, persistent state management, and extensible function registry for building self-hosted AI systems.
README
MCP-Based Agentic AI System
Production-ready, self-hosted AI infrastructure with Model Context Protocol.
Quick Start
- Install Dependencies
pip install -r requirements.txt
- Configure Environment
cp .env.example .env
Edit .env with your configuration
- Initialize Database
python scripts/init_db.py
- Start Services
Start MCP Server
python -m server.mcp_server
Start API Server (in new terminal)
python -m api.main
- Test the System
pytest tests/
Architecture
- MCP Server: WebSocket-based protocol server
- REST API: FastAPI application for HTTP access
- Agent System: Autonomous AI agents with memory
- Tool Registry: Extensible function execution
- State Management: Redis + PostgreSQL persistence
API Documentation
Once running, visit: http://localhost:8000/docs
Configuration
All settings managed through environment variables:
- Database: PostgreSQL connection
- Redis: Caching and sessions
- LLM: Model provider and settings
- Security: JWT tokens and CORS
Deployment
Docker
docker-compose up -d
Kubernetes
kubectl apply -f kubernetes/
Monitoring
Prometheus metrics available at :9090/metrics
Support
For issues and questions, see docs/ directory.
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