61a4050a8e
Implementation Summary:
- REST API with FastAPI (complete CRUD operations)
- MCP Server with Python MCP SDK (7 tools)
- Supabase migrations (pgvector setup)
- Docker Compose orchestration
- Mintlify documentation site
- Environment configuration
- Shared config module
REST API Features:
- POST /v1/memories/ - Add memory
- GET /v1/memories/search - Semantic search
- GET /v1/memories/{id} - Get memory
- GET /v1/memories/user/{user_id} - User memories
- PATCH /v1/memories/{id} - Update memory
- DELETE /v1/memories/{id} - Delete memory
- GET /v1/health - Health check
- GET /v1/stats - Statistics
- Bearer token authentication
- OpenAPI documentation
MCP Server Tools:
- add_memory - Add from messages
- search_memories - Semantic search
- get_memory - Retrieve by ID
- get_all_memories - List all
- update_memory - Update content
- delete_memory - Delete by ID
- delete_all_memories - Bulk delete
Infrastructure:
- Neo4j 5.26 with APOC/GDS
- Supabase pgvector integration
- Docker network: localai
- Health checks and monitoring
- Structured logging
Documentation:
- Introduction page
- Quickstart guide
- Architecture deep dive
- Mintlify configuration
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
169 lines
5.0 KiB
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169 lines
5.0 KiB
Plaintext
---
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title: Introduction
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description: 'Welcome to T6 Mem0 v2 - Memory System for LLM Applications'
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---
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<img
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className="block dark:hidden"
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src="/images/hero-light.svg"
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alt="Hero Light"
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/>
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<img
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className="hidden dark:block"
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src="/images/hero-dark.svg"
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alt="Hero Dark"
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/>
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## What is T6 Mem0 v2?
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T6 Mem0 v2 is a comprehensive memory system for LLM applications built on **mem0.ai**, featuring:
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- 🔌 **MCP Server Integration** - Native Model Context Protocol support for Claude Code and AI tools
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- 🌐 **REST API** - Full HTTP API for memory operations
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- 🗄️ **Hybrid Storage** - Supabase (vector) + Neo4j (graph) for optimal performance
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- 🤖 **AI-Powered** - OpenAI embeddings with 26% accuracy improvement
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- 📊 **Graph Visualization** - Explore memory relationships in Neo4j Browser
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- 🐳 **Docker-Native** - Fully containerized deployment
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## Key Features
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<CardGroup cols={2}>
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<Card
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title="Semantic Memory Search"
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icon="magnifying-glass"
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href="/api-reference/memories/search"
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>
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Find relevant memories using AI-powered semantic similarity
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</Card>
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<Card
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title="MCP Integration"
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icon="plug"
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href="/mcp/introduction"
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>
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Use as MCP server with Claude Code, Cursor, and other AI tools
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</Card>
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<Card
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title="Graph Relationships"
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icon="diagram-project"
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href="/setup/neo4j"
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>
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Visualize and explore memory connections with Neo4j
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</Card>
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<Card
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title="Multi-Agent Support"
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icon="users"
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href="/api-reference/introduction"
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>
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Isolate memories by user, agent, or run identifiers
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</Card>
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</CardGroup>
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## Architecture
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T6 Mem0 v2 uses a **hybrid storage architecture** for optimal performance:
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```
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┌──────────────────────────────────┐
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│ Clients (Claude, N8N, Apps) │
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└──────────────┬───────────────────┘
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│
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┌──────────────┴───────────────────┐
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│ MCP Server (8765) + REST (8080) │
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└──────────────┬───────────────────┘
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│
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┌──────────────┴───────────────────┐
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│ Mem0 Core Library │
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└──────────────┬───────────────────┘
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│
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┌──────────┴──────────┐
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│ │
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┌───┴──────┐ ┌──────┴─────┐
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│ Supabase │ │ Neo4j │
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│ (Vector) │ │ (Graph) │
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└──────────┘ └────────────┘
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```
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### Storage Layers
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- **Vector Store (Supabase)**: Semantic similarity search with pgvector
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- **Graph Store (Neo4j)**: Relationship modeling between memories
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- **Key-Value Store (PostgreSQL JSONB)**: Flexible metadata storage
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## Performance
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Based on mem0.ai research:
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- **26% higher accuracy** compared to baseline OpenAI
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- **91% lower latency** than full-context approaches
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- **90% token cost savings** through selective retrieval
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## Use Cases
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<AccordionGroup>
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<Accordion icon="comment" title="Conversational AI">
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Maintain context across conversations, remember user preferences, and provide personalized responses
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</Accordion>
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<Accordion icon="robot" title="AI Agents">
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Give agents long-term memory, enable learning from past interactions, and improve decision-making
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</Accordion>
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<Accordion icon="headset" title="Customer Support">
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Remember customer history, track issues across sessions, and provide consistent support
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</Accordion>
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<Accordion icon="graduation-cap" title="Educational Tools">
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Track learning progress, adapt to user knowledge level, and personalize content delivery
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</Accordion>
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</AccordionGroup>
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## Quick Links
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<CardGroup cols={2}>
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<Card
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title="Quickstart"
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icon="rocket"
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href="/quickstart"
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>
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Get up and running in 5 minutes
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</Card>
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<Card
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title="Architecture Deep Dive"
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icon="sitemap"
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href="/architecture"
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>
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Understand the system design
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</Card>
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<Card
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title="API Reference"
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icon="code"
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href="/api-reference/introduction"
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>
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Explore the REST API
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</Card>
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<Card
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title="MCP Integration"
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icon="link"
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href="/mcp/introduction"
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>
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Connect with Claude Code
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</Card>
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</CardGroup>
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## Technology Stack
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- **Core**: mem0ai library
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- **Vector DB**: Supabase with pgvector
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- **Graph DB**: Neo4j 5.x
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- **LLM**: OpenAI API (Phase 1), Ollama (Phase 2)
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- **REST API**: FastAPI + Pydantic
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- **MCP**: Python MCP SDK
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- **Container**: Docker & Docker Compose
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## Support & Community
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- **Repository**: [git.colsys.tech/klas/t6_mem0_v2](https://git.colsys.tech/klas/t6_mem0_v2)
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- **mem0.ai**: [Official mem0 website](https://mem0.ai)
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- **Issues**: Contact maintainer
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---
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Ready to get started? Continue to the [Quickstart Guide](/quickstart).
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