MCP Memory Service Documentation

Welcome to the comprehensive documentation for MCP Memory Service - a Model Context Protocol server that provides semantic memory and persistent storage capabilities for Claude Desktop and other MCP clients.

Quick Start

New Users: Start with the Installation Guide
Multi-Client Setup: See Multi-Client Integration
Docker Users: Check out Docker Deployment
Troubleshooting: Visit General Troubleshooting

Documentation Structure

📦 Installation & Setup

Master Installation Guide - Comprehensive installation instructions for all platforms
Platform-Specific Guides - Detailed setup for specific operating systems
- macOS Intel - Intel Mac setup (including legacy 2013-2017 models)
- Windows - Windows installation with CUDA/DirectML support
- Ubuntu - Ubuntu setup for desktop and server

🔗 Integration & Connectivity

Multi-Client Setup - Share memory across multiple applications
Homebrew Integration - Use system-installed PyTorch via Homebrew
Claude Desktop Integration - Connect with Claude Desktop
IDE Compatibility - VS Code, Continue, and other IDE integrations

🚀 Deployment

Docker Deployment - Containerized deployment with various configurations
Server Deployment - Production server setups
Cloud Deployment - Cloud platform deployment guides

📚 User Guides

MCP Protocol Enhancements - Resources, Prompts, and Progress Tracking (v4.1.0)
Storage Backends - ChromaDB vs SQLite-vec comparison and configuration
Migration Guide - Migrate between storage backends and versions
Scripts Reference - Available utility scripts
Invocation Guide - Different ways to run the service

🎯 Tutorials & Examples

Data Analysis Examples - Advanced data analysis with memory service
Advanced Techniques - Power user techniques and patterns
Demo Session Walkthrough - Step-by-step usage examples

🔧 Maintenance & Administration

Memory Maintenance - Database cleanup, optimization, and backup
Health Checks - Monitoring and diagnostics
Performance Tuning - Optimization techniques

📖 API Reference

Memory Metadata API - Advanced metadata operations
Tag Standardization - Tag schema and conventions
HTTP/SSE API - Web API documentation for multi-client setups

🛠️ Development & Technical

Development Guide - Contributing and development setup
Architecture Overview - System architecture and design patterns
Technical Implementation - Deep dive into technical details
- Memory Migration
- Tag Storage

🔍 Troubleshooting

General Troubleshooting - Common issues and solutions
Docker Issues - Docker-specific troubleshooting
Platform-Specific Issues - Platform-specific troubleshooting sections

Project Information

About MCP Memory Service

MCP Memory Service enables persistent, semantic memory for AI applications through the Model Context Protocol. It provides:

Semantic Search: Vector-based memory retrieval using sentence transformers
Multiple Storage Backends: ChromaDB for full features, SQLite-vec for lightweight deployments
Multi-Client Support: Shared memory across multiple applications
Cross-Platform: Support for macOS, Windows, and Linux
Flexible Deployment: Local installation, Docker containers, or cloud deployment

Key Features

✅ Semantic Memory Storage: Store and retrieve memories using natural language
✅ Multi-Client Access: Share memories across Claude Desktop, VS Code, and other MCP clients
✅ Flexible Storage: Choose between ChromaDB (full-featured) or SQLite-vec (lightweight)
✅ Cross-Platform: Native support for macOS (Intel & Apple Silicon), Windows, and Linux
✅ Docker Ready: Complete containerization support with multiple deployment options
✅ Hardware Optimized: Automatic detection and optimization for available hardware (CUDA, MPS, DirectML)
✅ Production Ready: HTTP/SSE API, authentication, monitoring, and scaling features

Recent Updates

v0.2.2+: Enhanced multi-client support with automatic MCP application detection
SQLite-vec Backend: Lightweight alternative to ChromaDB for resource-constrained systems
Homebrew Integration: Native support for Homebrew-installed PyTorch on macOS
Docker Improvements: Fixed boot loops, added multiple deployment configurations
HTTP/SSE API: Real-time multi-client communication with Server-Sent Events

Getting Help

Quick Links

Installation Issues: Check the Installation Guide and platform-specific guides
Configuration Problems: See Troubleshooting
Multi-Client Setup: Follow the Multi-Client Guide
Performance Issues: Review Performance Tuning

Support Resources

GitHub Issues: Report bugs and request features
Documentation: Comprehensive guides for all use cases
Community: Share experiences and get help from other users

Contributing

We welcome contributions! See the Development Guide for information on:

Setting up a development environment
Running tests
Submitting pull requests
Code style and conventions

Version History

Latest: Enhanced documentation organization, consolidated guides, improved navigation
v0.2.2: Multi-client improvements, SQLite-vec backend, Homebrew integration
v0.2.1: Docker deployment fixes, HTTP/SSE API enhancements
v0.2.0: Multi-client support, cross-platform compatibility improvements

Navigation Tips

📁 Folders: Click on folder names to explore sections
🔗 Links: All internal links are relative and work offline
📱 Mobile: Documentation is mobile-friendly for on-the-go reference
🔍 Search: Use your browser's search (Ctrl/Cmd+F) to find specific topics

Happy memory-ing! 🧠✨

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