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MCP Memory Service

License: Apache 2.0 GitHub stars Production Ready

Works with Claude Works with Cursor MCP Protocol Multi-Client

Universal MCP memory service providing semantic memory search and persistent storage for AI assistants. Works with Claude Desktop, VS Code, Cursor, Continue, and 13+ AI applications with SQLite-vec for fast local search and Cloudflare for global distribution.

MCP Memory Service

🚀 Quick Start (2 minutes)

Universal Installer (Recommended)

# Clone and install with automatic platform detection git clone https://github.com/doobidoo/mcp-memory-service.git cd mcp-memory-service python install.py

Docker (Fastest)

# For MCP protocol (Claude Desktop) docker-compose up -d # For HTTP API (Web Dashboard) docker-compose -f docker-compose.http.yml up -d

Smithery (Claude Desktop)

# Auto-install for Claude Desktop npx -y @smithery/cli install @doobidoo/mcp-memory-service --client claude

⚠️ First-Time Setup Expectations

On your first run, you'll see some warnings that are completely normal:

  • "WARNING: Failed to load from cache: No snapshots directory" - The service is checking for cached models (first-time setup)
  • "WARNING: Using TRANSFORMERS_CACHE is deprecated" - Informational warning, doesn't affect functionality
  • Model download in progress - The service automatically downloads a ~25MB embedding model (takes 1-2 minutes)

These warnings disappear after the first successful run. The service is working correctly! For details, see our First-Time Setup Guide.

🐍 Python 3.13 Compatibility Note

sqlite-vec may not have pre-built wheels for Python 3.13 yet. If installation fails:

  • The installer will automatically try multiple installation methods
  • Consider using Python 3.12 for the smoothest experience: brew install [email protected]
  • Alternative: Use ChromaDB backend with --storage-backend chromadb
  • See Troubleshooting Guide for details

📚 Complete Documentation

👉 Visit our comprehensive Wiki for detailed guides:

🚀 Setup & Installation

🧠 Advanced Topics

🔧 Help & Reference

✨ Key Features

🧠 Intelligent Memory Management

  • Semantic search with vector embeddings
  • Natural language time queries ("yesterday", "last week")
  • Tag-based organization with smart categorization
  • Memory consolidation with dream-inspired algorithms

🔗 Universal Compatibility

  • Claude Desktop - Native MCP integration
  • Claude Code - Memory-aware development with hooks
  • VS Code, Cursor, Continue - IDE extensions
  • 13+ AI applications - REST API compatibility

💾 Flexible Storage

  • SQLite-vec - Fast local storage (recommended)
  • ChromaDB - Multi-client collaboration
  • Cloudflare - Global edge distribution
  • Automatic backups and synchronization

🚀 Production Ready

  • Cross-platform - Windows, macOS, Linux
  • Service installation - Auto-start background operation
  • HTTPS/SSL - Secure connections
  • Docker support - Easy deployment

💡 Basic Usage

# Store a memory uv run memory store "Fixed race condition in authentication by adding mutex locks" # Search for relevant memories uv run memory recall "authentication race condition" # Search by tags uv run memory search --tags python debugging # Check system health uv run memory health

🔧 Configuration

Claude Desktop Integration

Add to your Claude Desktop config (~/.claude/config.json):

{ "mcpServers": { "memory": { "command": "uv", "args": ["--directory", "/path/to/mcp-memory-service", "run", "memory", "server"], "env": { "MCP_MEMORY_STORAGE_BACKEND": "sqlite_vec" } } } }

Environment Variables

# Storage backend (sqlite_vec recommended) export MCP_MEMORY_STORAGE_BACKEND=sqlite_vec # Enable HTTP API export MCP_HTTP_ENABLED=true export MCP_HTTP_PORT=8000 # Security export MCP_API_KEY="your-secure-key"

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   AI Clients    │    │  MCP Protocol   │    │ Storage Backend │
│                 │    │                 │    │                 │
│ • Claude Desktop│◄──►│ • Memory Store  │◄──►│ • SQLite-vec    │
│ • Claude Code   │    │ • Semantic      │    │ • ChromaDB      │
│ • VS Code       │    │   Search        │    │ • Cloudflare    │
│ • Cursor        │    │ • Tag System    │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🛠️ Development

Project Structure

mcp-memory-service/
├── src/mcp_memory_service/    # Core application
│   ├── models/                # Data models
│   ├── storage/               # Storage backends
│   ├── web/                   # HTTP API & dashboard
│   └── server.py              # MCP server
├── scripts/                   # Utilities & installation
├── tests/                     # Test suite
└── tools/docker/              # Docker configuration

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes with tests
  4. Submit a pull request

See CONTRIBUTING.md for detailed guidelines.

🆘 Support

📊 In Production

Real-world metrics from active deployments:

  • 750+ memories stored and actively used
  • <500ms response time for semantic search
  • 65% token reduction in Claude Code sessions
  • 96.7% faster context setup (15min → 30sec)
  • 100% knowledge retention across sessions

🏆 Recognition

  • Smithery Verified MCP Server
  • Glama AI Featured AI Tool
  • Production-tested across 13+ AI applications
  • Community-driven with real-world feedback and improvements

📄 License

Apache License 2.0 - see LICENSE for details.


Ready to supercharge your AI workflow? 🚀

👉 Start with our Installation Guide or explore the Wiki for comprehensive documentation.

Transform your AI conversations into persistent, searchable knowledge that grows with you.

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