📁 Persistent AI Memory System - Project Structure
=====================================================

persistent-ai-memory/
├── 📄 README.md                    # Comprehensive project documentation
├── 📄 LICENSE                      # MIT License
├── 📄 .gitignore                   # Git ignore patterns
├── 📄 requirements.txt             # Python dependencies
├── 📄 setup.py                     # Package setup configuration
├── 📄 GITHUB_GUIDE.md             # Step-by-step GitHub publishing guide
├── 
├── 🧠 Core System Files
├── 📄 ai_memory_core.py            # Main memory system with all database managers
├── 📄 mcp_server.py                # MCP server with tool call logging
├── 📄 test_tool_logging.py         # Test script for tool call functionality
├── 
└── 📁 memory_data/                 # Created automatically
    ├── 💾 conversations.db         # Chat messages with auto-threading
    ├── 💾 ai_memories.db           # Curated memories and insights  
    ├── 💾 mcp_tool_calls.db        # 🔧 NEW: Tool call logging & reflection
    ├── 💾 schedule.db              # Appointments and reminders
    └── 💾 vscode_project.db        # Development sessions & insights

🎯 Key Features Implemented:
========================== 

✅ Multi-Database Architecture
   - 5 specialized SQLite databases
   - Foreign key constraints
   - Auto-creation of sessions/conversations

✅ Real-Time Conversation Capture  
   - VS Code chat session monitoring
   - LM Studio conversation import
   - Cross-platform file path detection
   - Hash-based duplicate prevention

✅ MCP Server Integration
   - Standardized tool interface
   - Async tool execution
   - JSON schema validation
   - Clean error handling

✅ 🔧 NEW: Tool Call Logging & Reflection
   - Every MCP tool call logged with timing
   - Daily usage statistics tracking
   - Self-reflection capabilities for AI assistants
   - Usage pattern analysis and recommendations

✅ Semantic Search (Framework)
   - LM Studio embedding service integration
   - Vector similarity search structure
   - Embedding storage in databases

✅ Health Monitoring
   - Comprehensive system diagnostics
   - Database statistics
   - Component status tracking

✅ Cross-Platform Support
   - Windows, Linux, macOS compatibility
   - Automatic conversation path detection
   - Platform-specific configurations

🚀 Ready for GitHub Publication!
==============================

The system is now:
- ✅ Fully modularized and reusable
- ✅ Well-documented with comprehensive README
- ✅ Tool call logging implemented and tested
- ✅ MIT licensed for open source sharing
- ✅ Package-ready with setup.py
- ✅ GitHub-ready with proper .gitignore

Next Steps:
1. Follow GITHUB_GUIDE.md to publish
2. Share with the AI community
3. Watch it become the standard for AI memory! 🌟
