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.

This server cannot be installed

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security - not tested

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license - not tested

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quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

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.

Agentic AI System MCP Server