T6 Mem0 v2 - Memory System for LLM Applications

Comprehensive memory system based on mem0.ai featuring MCP server integration, REST API, hybrid storage architecture, and AI-powered memory management.

Features

• MCP Server: Model Context Protocol integration for Claude Code and other AI tools
• REST API: Full HTTP API for memory operations (CRUD)
• Hybrid Storage: Supabase (pgvector) + Neo4j (graph relationships)
• AI-Powered: OpenAI embeddings and LLM processing
• Multi-Agent Support: User and agent-specific memory isolation
• Graph Visualization: Neo4j Browser for relationship exploration
• Docker-Native: Fully containerized with Docker Compose

Architecture

Clients (Claude, N8N, Apps)
    ↓
MCP Server (8765) + REST API (8080)
    ↓
Mem0 Core Library
    ↓
Supabase (Vector) + Neo4j (Graph) + OpenAI (LLM)

Quick Start

Prerequisites

• Docker and Docker Compose
• Existing Supabase instance (PostgreSQL with pgvector)
• OpenAI API key
• Python 3.11+ (for development)

Installation

# Clone repository
git clone https://git.colsys.tech/klas/t6_mem0_v2
cd t6_mem0_v2

# Configure environment
cp .env.example .env
# Edit .env with your credentials

# Start services
docker compose up -d

# Verify health
curl http://localhost:8080/v1/health

Configuration

Create .env file:

# OpenAI
OPENAI_API_KEY=sk-...

# Supabase
SUPABASE_CONNECTION_STRING=postgresql://user:pass@172.21.0.12:5432/postgres

# Neo4j
NEO4J_URI=neo4j://neo4j:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your-password

# API
API_KEY=your-secure-api-key

Usage

REST API

# Add memory
curl -X POST http://localhost:8080/v1/memories/ \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"I love pizza"}],"user_id":"alice"}'

# Search memories
curl -X GET "http://localhost:8080/v1/memories/search?query=food&user_id=alice" \
  -H "Authorization: Bearer YOUR_API_KEY"

MCP Server (Claude Code)

Add to Claude Code configuration:

{
  "mcpServers": {
    "t6-mem0": {
      "url": "http://localhost:8765/mcp/claude/sse/user-123"
    }
  }
}

Documentation

Full documentation available at: docs/ (Mintlify)

• Architecture
• Project Requirements
• API Reference
• Deployment Guide

Project Structure

t6_mem0_v2/
├── api/              # REST API (FastAPI)
├── mcp-server/       # MCP server implementation
├── migrations/       # Database migrations
├── docker/           # Docker configurations
├── docs/             # Mintlify documentation
├── tests/            # Test suites
└── docker-compose.yml

Technology Stack

• Core: mem0ai library
• Vector DB: Supabase with pgvector
• Graph DB: Neo4j 5.x
• LLM: OpenAI API (Phase 1), Ollama (Phase 2)
• REST API: FastAPI
• MCP: Python MCP SDK
• Container: Docker & Docker Compose

Roadmap

Phase 1: Foundation (Current)

• ✅ Architecture design
• ⏳ REST API implementation
• ⏳ MCP server implementation
• ⏳ Supabase integration
• ⏳ Neo4j integration
• ⏳ Documentation site

Phase 2: Local LLM

• Local Ollama integration
• Model switching capabilities
• Performance optimization

Phase 3: Advanced Features

• Memory versioning
• Advanced graph queries
• Multi-modal memory support
• Analytics dashboard

Development

# Install dependencies
pip install -r requirements.txt

# Run tests
pytest tests/

# Format code
black .
ruff check .

# Run locally (development)
python -m api.main

Contributing

This is a private project. For issues or suggestions, contact the maintainer.

License

Proprietary - All rights reserved

Support

• Repository: https://git.colsys.tech/klas/t6_mem0_v2
• Documentation: See docs/ directory
• Issues: Contact maintainer

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Status: In Development Version: 0.1.0 Last Updated: 2025-10-13