# 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**: HTTP/SSE and stdio transports for universal AI integration
  - ✅ n8n AI Agent workflows
  - ✅ Claude Code integration
  - ✅ 7 memory tools (add, search, get, update, delete)
- **REST API**: Full HTTP API for memory operations (CRUD)
- **Hybrid Storage**: Supabase (pgvector) + Neo4j (graph relationships)
- **Synchronized Operations**: Automatic sync across vector and graph stores
- **Flexible LLM Support**:
  - ✅ OpenAI (GPT-4, GPT-3.5)
  - ✅ Ollama (Llama 3.1, Mistral, local models)
  - ✅ Switchable via environment variables
- **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 (n8n, Claude Code, Custom Apps)
    ↓
┌─────────────────┬───────────────────┐
│ MCP Server      │ REST API          │
│ Port 8765       │ Port 8080         │
│ HTTP/SSE+stdio  │ FastAPI           │
└─────────────────┴───────────────────┘
    ↓
Mem0 Core Library (v0.1.118)
    ↓
┌─────────────────┬───────────────────┬───────────────────┐
│ Supabase        │ Neo4j             │ OpenAI            │
│ Vector Store    │ Graph Store       │ Embeddings+LLM    │
│ pgvector        │ Cypher Queries    │ text-embedding-3  │
└─────────────────┴───────────────────┴───────────────────┘
```

## Quick Start

### Prerequisites

- Docker and Docker Compose
- Existing Supabase instance (PostgreSQL with pgvector)
- **Choose one:**
  - OpenAI API key (for cloud LLM)
  - Ollama installed (for local LLM) - [Setup Guide](docs/setup/ollama.mdx)
- Python 3.11+ (for development)

### Installation

```bash
# 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
curl http://localhost:8765/health
```

### Configuration

Create `.env` file:

**Option 1: OpenAI (Default)**

```bash
# LLM Configuration
LLM_PROVIDER=openai
EMBEDDER_PROVIDER=openai
OPENAI_API_KEY=sk-your-key-here

# 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

# REST API
API_KEY=your-secure-api-key

# MCP Server
MCP_HOST=0.0.0.0
MCP_PORT=8765

# Mem0 Configuration
MEM0_COLLECTION_NAME=t6_memories
MEM0_EMBEDDING_DIMS=1536  # OpenAI embeddings
MEM0_VERSION=v1.1
```

**Option 2: Ollama (Local LLM)**

```bash
# LLM Configuration
LLM_PROVIDER=ollama
EMBEDDER_PROVIDER=ollama
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_LLM_MODEL=llama3.1:8b
OLLAMA_EMBEDDING_MODEL=nomic-embed-text

# 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

# REST API
API_KEY=your-secure-api-key

# MCP Server
MCP_HOST=0.0.0.0
MCP_PORT=8765

# Mem0 Configuration
MEM0_COLLECTION_NAME=t6_memories
MEM0_EMBEDDING_DIMS=768  # Ollama nomic-embed-text
MEM0_VERSION=v1.1
```

See [Ollama Setup Guide](docs/setup/ollama.mdx) for detailed configuration.

## Usage

### REST API

```bash
# 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

**HTTP/SSE Transport (for n8n, web clients):**

```bash
# MCP endpoint
http://localhost:8765/mcp

# Test tools/list
curl -X POST http://localhost:8765/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'
```

**stdio Transport (for Claude Code, local tools):**

Add to `~/.config/claude/mcp.json`:

```json
{
  "mcpServers": {
    "t6-mem0": {
      "command": "python",
      "args": ["-m", "mcp_server.main"],
      "cwd": "/path/to/t6_mem0_v2",
      "env": {
        "OPENAI_API_KEY": "${OPENAI_API_KEY}",
        "SUPABASE_CONNECTION_STRING": "${SUPABASE_CONNECTION_STRING}",
        "NEO4J_URI": "neo4j://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "${NEO4J_PASSWORD}"
      }
    }
  }
}
```

**n8n Integration:**

Use the MCP Client Tool node in n8n AI Agent workflows:

```javascript
{
  "endpointUrl": "http://172.21.0.14:8765/mcp",  // Use Docker network IP
  "serverTransport": "httpStreamable",
  "authentication": "none",
  "include": "all"
}
```

See [n8n integration guide](docs/examples/n8n.mdx) for complete workflow examples.

## Documentation

Full documentation available at: `docs/` (Mintlify)

### MCP Server
- [MCP Server Introduction](docs/mcp/introduction.mdx)
- [MCP Installation Guide](docs/mcp/installation.mdx)
- [MCP Tool Reference](docs/mcp/tools.mdx)

### Integration Guides
- [n8n Integration Guide](docs/examples/n8n.mdx)
- [Claude Code Integration](docs/examples/claude-code.mdx)

### Setup
- [Ollama Setup (Local LLM)](docs/setup/ollama.mdx)

### Architecture
- [Architecture Overview](ARCHITECTURE.md)
- [Project Requirements](PROJECT_REQUIREMENTS.md)

## Project Structure

```
t6_mem0_v2/
├── api/                    # REST API (FastAPI)
│   ├── main.py            # API entry point
│   ├── memory_service.py  # Memory operations
│   └── routes.py          # API endpoints
├── mcp_server/            # MCP server implementation
│   ├── main.py           # stdio transport (Claude Code)
│   ├── http_server.py    # HTTP/SSE transport (n8n, web)
│   ├── tools.py          # MCP tool definitions
│   └── server.py         # Core MCP server logic
├── docker/                # Docker configurations
│   ├── Dockerfile.api    # REST API container
│   └── Dockerfile.mcp    # MCP server container
├── docs/                  # Mintlify documentation
│   ├── mcp/              # MCP server docs
│   └── examples/         # Integration examples
├── tests/                 # Test suites
├── config.py             # Configuration management
├── requirements.txt      # Python dependencies
└── docker-compose.yml    # Service orchestration
```

## Technology Stack

- **Core**: mem0ai library (v0.1.118+)
- **Vector DB**: Supabase with pgvector
- **Graph DB**: Neo4j 5.x
- **LLM Options**:
  - OpenAI API (GPT-4o-mini, text-embedding-3-small)
  - Ollama (Llama 3.1, Mistral, nomic-embed-text)
- **REST API**: FastAPI
- **MCP**: Python MCP SDK
- **Container**: Docker & Docker Compose

## Roadmap

### Phase 1: Foundation ✅ COMPLETED
- ✅ Architecture design
- ✅ REST API implementation (FastAPI with Bearer auth)
- ✅ MCP server implementation (HTTP/SSE + stdio transports)
- ✅ Supabase integration (pgvector for embeddings)
- ✅ Neo4j integration (graph relationships)
- ✅ Documentation site (Mintlify)
- ✅ n8n AI Agent integration
- ✅ Claude Code integration
- ✅ Docker deployment with health checks

### Phase 2: Local LLM ✅ COMPLETED
- ✅ Local Ollama integration
- ✅ Model switching capabilities (OpenAI ↔ Ollama)
- ✅ Embedding model selection
- ✅ Environment-based provider configuration

### Phase 3: Advanced Features
- ⏳ Memory versioning and history
- ⏳ Advanced graph queries and analytics
- ⏳ Multi-modal memory support (images, audio)
- ⏳ Analytics dashboard
- ⏳ Memory export/import
- ⏳ Custom embedding models

## Development

```bash
# 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

---

**Status**: Phase 2 Complete - Production Ready with Ollama Support
**Version**: 1.1.0
**Last Updated**: 2025-10-15

## Recent Updates

- **2025-10-15**: ✅ Ollama integration complete - local LLM support
- **2025-10-15**: ✅ Flexible provider switching (OpenAI ↔ Ollama)
- **2025-10-15**: ✅ Support for multiple embedding models
- **2025-10-15**: MCP HTTP/SSE server implementation complete
- **2025-10-15**: n8n AI Agent integration tested and documented
- **2025-10-15**: Complete Mintlify documentation site
- **2025-10-15**: Synchronized delete operations across stores
- **2025-10-13**: Initial project setup and architecture