---
title: Introduction
description: 'Welcome to T6 Mem0 v2 - Memory System for LLM Applications'
---

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  src="/images/hero-light.svg"
  alt="Hero Light"
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## What is T6 Mem0 v2?

T6 Mem0 v2 is a comprehensive memory system for LLM applications built on **mem0.ai**, featuring:

- 🔌 **MCP Server Integration** - Native Model Context Protocol support for Claude Code and AI tools
- 🌐 **REST API** - Full HTTP API for memory operations
- 🗄️ **Hybrid Storage** - Supabase (vector) + Neo4j (graph) for optimal performance
- 🤖 **AI-Powered** - OpenAI embeddings with 26% accuracy improvement
- 📊 **Graph Visualization** - Explore memory relationships in Neo4j Browser
- 🐳 **Docker-Native** - Fully containerized deployment

## Key Features

<CardGroup cols={2}>
  <Card
    title="Semantic Memory Search"
    icon="magnifying-glass"
    href="/api-reference/memories/search"
  >
    Find relevant memories using AI-powered semantic similarity
  </Card>
  <Card
    title="MCP Integration"
    icon="plug"
    href="/mcp/introduction"
  >
    Use as MCP server with Claude Code, Cursor, and other AI tools
  </Card>
  <Card
    title="Graph Relationships"
    icon="diagram-project"
    href="/setup/neo4j"
  >
    Visualize and explore memory connections with Neo4j
  </Card>
  <Card
    title="Multi-Agent Support"
    icon="users"
    href="/api-reference/introduction"
  >
    Isolate memories by user, agent, or run identifiers
  </Card>
</CardGroup>

## Architecture

T6 Mem0 v2 uses a **hybrid storage architecture** for optimal performance:

```
┌──────────────────────────────────┐
│  Clients (Claude, N8N, Apps)    │
└──────────────┬───────────────────┘
               │
┌──────────────┴───────────────────┐
│  MCP Server (8765) + REST (8080) │
└──────────────┬───────────────────┘
               │
┌──────────────┴───────────────────┐
│      Mem0 Core Library           │
└──────────────┬───────────────────┘
               │
    ┌──────────┴──────────┐
    │                     │
┌───┴──────┐      ┌──────┴─────┐
│ Supabase │      │   Neo4j    │
│ (Vector) │      │   (Graph)  │
└──────────┘      └────────────┘
```

### Storage Layers

- **Vector Store (Supabase)**: Semantic similarity search with pgvector
- **Graph Store (Neo4j)**: Relationship modeling between memories
- **Key-Value Store (PostgreSQL JSONB)**: Flexible metadata storage

## Performance

Based on mem0.ai research:

- **26% higher accuracy** compared to baseline OpenAI
- **91% lower latency** than full-context approaches
- **90% token cost savings** through selective retrieval

## Use Cases

<AccordionGroup>
  <Accordion icon="comment" title="Conversational AI">
    Maintain context across conversations, remember user preferences, and provide personalized responses
  </Accordion>
  <Accordion icon="robot" title="AI Agents">
    Give agents long-term memory, enable learning from past interactions, and improve decision-making
  </Accordion>
  <Accordion icon="headset" title="Customer Support">
    Remember customer history, track issues across sessions, and provide consistent support
  </Accordion>
  <Accordion icon="graduation-cap" title="Educational Tools">
    Track learning progress, adapt to user knowledge level, and personalize content delivery
  </Accordion>
</AccordionGroup>

## Quick Links

<CardGroup cols={2}>
  <Card
    title="Quickstart"
    icon="rocket"
    href="/quickstart"
  >
    Get up and running in 5 minutes
  </Card>
  <Card
    title="Architecture Deep Dive"
    icon="sitemap"
    href="/architecture"
  >
    Understand the system design
  </Card>
  <Card
    title="API Reference"
    icon="code"
    href="/api-reference/introduction"
  >
    Explore the REST API
  </Card>
  <Card
    title="MCP Integration"
    icon="link"
    href="/mcp/introduction"
  >
    Connect with Claude Code
  </Card>
</CardGroup>

## 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 + Pydantic
- **MCP**: Python MCP SDK
- **Container**: Docker & Docker Compose

## Support & Community

- **Repository**: [git.colsys.tech/klas/t6_mem0_v2](https://git.colsys.tech/klas/t6_mem0_v2)
- **mem0.ai**: [Official mem0 website](https://mem0.ai)
- **Issues**: Contact maintainer

---

Ready to get started? Continue to the [Quickstart Guide](/quickstart).
