klas/t6_mem0_v2
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t6_mem0_v2/PROJECT_REQUIREMENTS.md
Claude Code cfa7abd23d Initial commit: Project foundation and architecture 
- Add project requirements document
- Add comprehensive architecture design
- Add README with quick start guide
- Add .gitignore for Python/Docker/Node

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-13 15:01:50 +02:00

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T6 Mem0 v2 - Project Requirements

Original User Request

Date: 2025-10-13

Core Objectives

Set up a comprehensive memory system with the following capabilities:

  • MCP Server Integration: Serve as an MCP server for Claude and other LLM-based systems
  • REST API Access: Enable memory storage and retrieval via REST API
  • Data Storage: Use locally running Supabase for primary data storage
  • Graph Visualization: Use Neo4j for storing and visualizing memory relationships
  • LLM Integration: Initial phase with OpenAI, future phase with local Ollama instance

Technology Stack

Phase 1 (Initial Implementation):

  • mem0.ai as the core memory framework
  • Supabase (local instance) for vector and structured storage
  • Neo4j for graph-based memory relationships
  • OpenAI API for embeddings and LLM capabilities
  • MCP (Model Context Protocol) server for AI agent integration

Phase 2 (Future):

  • Local Ollama integration for LLM independence
  • Additional local model support

Key Requirements

  1. MCP Server: Must function as an MCP server that can be used by Claude Code and other LLM systems
  2. REST API: Full REST API for CRUD operations on memories
  3. Local Infrastructure: All data storage must be local (Supabase, Neo4j)
  4. Visualization: Neo4j integration for memory graph visualization
  5. Documentation: Mintlify-based documentation site
  6. Version Control: Git repository at https://git.colsys.tech/klas/t6_mem0_v2

Repository Information

Project Phases

Phase 1: Foundation

  • Research and validate mem0.ai capabilities
  • Design architecture with Supabase + Neo4j + OpenAI
  • Implement core memory storage and retrieval
  • Build MCP server interface
  • Create REST API endpoints
  • Set up Mintlify documentation

Phase 2: Local LLM Integration

  • Integrate Ollama for local model support
  • Add model switching capabilities
  • Performance optimization for local models

Success Criteria

  • Functional MCP server that Claude Code can use
  • Working REST API for memory operations
  • Memories persisted in local Supabase
  • Graph relationships visible in Neo4j
  • Complete documentation in Mintlify
  • All code versioned in git repository