cfa7abd23d
- Add project requirements document - Add comprehensive architecture design - Add README with quick start guide - Add .gitignore for Python/Docker/Node 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
67 lines
2.3 KiB
Markdown
67 lines
2.3 KiB
Markdown
# T6 Mem0 v2 - Project Requirements
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## Original User Request
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**Date**: 2025-10-13
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### Core Objectives
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Set up a comprehensive memory system with the following capabilities:
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- **MCP Server Integration**: Serve as an MCP server for Claude and other LLM-based systems
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- **REST API Access**: Enable memory storage and retrieval via REST API
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- **Data Storage**: Use locally running Supabase for primary data storage
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- **Graph Visualization**: Use Neo4j for storing and visualizing memory relationships
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- **LLM Integration**: Initial phase with OpenAI, future phase with local Ollama instance
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### Technology Stack
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**Phase 1 (Initial Implementation)**:
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- mem0.ai as the core memory framework
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- Supabase (local instance) for vector and structured storage
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- Neo4j for graph-based memory relationships
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- OpenAI API for embeddings and LLM capabilities
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- MCP (Model Context Protocol) server for AI agent integration
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**Phase 2 (Future)**:
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- Local Ollama integration for LLM independence
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- Additional local model support
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### Key Requirements
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1. **MCP Server**: Must function as an MCP server that can be used by Claude Code and other LLM systems
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2. **REST API**: Full REST API for CRUD operations on memories
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3. **Local Infrastructure**: All data storage must be local (Supabase, Neo4j)
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4. **Visualization**: Neo4j integration for memory graph visualization
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5. **Documentation**: Mintlify-based documentation site
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6. **Version Control**: Git repository at https://git.colsys.tech/klas/t6_mem0_v2
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### Repository Information
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- **Git Remote**: https://git.colsys.tech/klas/t6_mem0_v2
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- **Username**: klas
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- **Password**: csjXgew3In
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### Project Phases
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#### Phase 1: Foundation
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- Research and validate mem0.ai capabilities
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- Design architecture with Supabase + Neo4j + OpenAI
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- Implement core memory storage and retrieval
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- Build MCP server interface
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- Create REST API endpoints
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- Set up Mintlify documentation
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#### Phase 2: Local LLM Integration
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- Integrate Ollama for local model support
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- Add model switching capabilities
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- Performance optimization for local models
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### Success Criteria
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- Functional MCP server that Claude Code can use
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- Working REST API for memory operations
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- Memories persisted in local Supabase
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- Graph relationships visible in Neo4j
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- Complete documentation in Mintlify
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- All code versioned in git repository
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