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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 with intelligent memory triggers, OAuth 2.1 team collaboration, and semantic memory search for AI assistants. Features Natural Memory Triggers v7.1.0 with 85%+ trigger accuracy, Claude Code HTTP transport, zero-configuration authentication, and enterprise security. 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

πŸš€ Quick Start (2 minutes)

🧠 v7.1.0: Natural Memory Triggers for Claude Code

πŸ€– Intelligent Memory Awareness (Zero Configuration):

# 1. Install MCP Memory Service
git clone https://github.com/doobidoo/mcp-memory-service.git
cd mcp-memory-service && python install.py

# 2. Install Natural Memory Triggers
cd claude-hooks && python install_hooks.py --natural-triggers

# 3. Test intelligent triggers
node memory-mode-controller.js status
# βœ… Done! Claude Code now automatically detects when you need memory context

πŸ“– Complete Guide: Natural Memory Triggers v7.1.0


πŸ†• v7.0.0: OAuth 2.1 & Claude Code HTTP Transport

πŸ”— Claude Code Team Collaboration (Zero Configuration):

# 1. Start OAuth-enabled server
export MCP_OAUTH_ENABLED=true
uv run memory server --http

# 2. Add HTTP transport to Claude Code
claude mcp add --transport http memory-service http://localhost:8000/mcp

# βœ… Done! Claude Code automatically handles OAuth registration and team collaboration

πŸ“– Complete Setup Guide: OAuth 2.1 Setup Guide


Traditional Setup Options

Universal Installer (Most Compatible):

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

# Lightweight installation (SQLite-vec with ONNX embeddings - recommended)
python install.py

# Add full ML capabilities (torch + sentence-transformers for advanced features)
python install.py --with-ml

# Add ChromaDB backend support (includes full ML stack - for multi-client setups)
python install.py --with-chromadb

πŸ“ Installation Options Explained:

  • Default (recommended): Lightweight SQLite-vec with ONNX embeddings - fast, works offline, <100MB dependencies
  • --with-ml: Adds PyTorch + sentence-transformers for advanced ML features - heavier but more capable
  • --with-chromadb: Multi-client local server support - use only if you need shared team access

Docker (Fastest):

# For MCP protocol (Claude Desktop)
docker-compose up -d

# For HTTP API + OAuth (Team Collaboration)
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

⚠️ v6.17.0+ Script Migration Notice

Updating from an older version? Scripts have been reorganized for better maintainability:

  • Recommended: Use python -m mcp_memory_service.server in your Claude Desktop config (no path dependencies!)
  • Alternative 1: Use uv run memory server with UV tooling
  • Alternative 2: Update path from scripts/run_memory_server.py to scripts/server/run_memory_server.py
  • Backward compatible: Old path still works with a migration notice

⚠️ 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 python@3.12
  • Alternative: Use ChromaDB backend with --storage-backend chromadb --with-chromadb
  • See Troubleshooting Guide for details

🍎 macOS SQLite Extension Support

macOS users may encounter enable_load_extension errors with sqlite-vec:

  • System Python on macOS lacks SQLite extension support by default
  • Solution: Use Homebrew Python: brew install python && rehash
  • Alternative: Use pyenv: PYTHON_CONFIGURE_OPTS='--enable-loadable-sqlite-extensions' pyenv install 3.12.0
  • Fallback: Use sqlite_vec backend (default) or install ChromaDB with --with-chromadb
  • See Troubleshooting Guide for details

πŸ“š Complete Documentation

πŸ‘‰ Visit our comprehensive Wiki for detailed guides:

🧠 v7.1.0 Natural Memory Triggers (Latest)

  • Natural Memory Triggers v7.1.0 Guide - Intelligent automatic memory awareness
    • βœ… 85%+ trigger accuracy with semantic pattern detection
    • βœ… Multi-tier performance (50ms instant β†’ 150ms fast β†’ 500ms intensive)
    • βœ… CLI management system for real-time configuration
    • βœ… Git-aware context integration for enhanced relevance
    • βœ… Zero-restart installation with dynamic hook loading

πŸ†• v7.0.0 OAuth & Team Collaboration

πŸš€ Setup & Installation

🧠 Advanced Topics

πŸ“‚ Internal Documentation

✨ Key Features

πŸ” Enterprise Authentication & Team Collaboration πŸ†•

  • OAuth 2.1 Dynamic Client Registration - RFC 7591 & RFC 8414 compliant
  • Claude Code HTTP Transport - Zero-configuration team collaboration
  • JWT Authentication - Enterprise-grade security with scope validation
  • Auto-Discovery Endpoints - Seamless client registration and authorization
  • Multi-Auth Support - OAuth + API keys + optional anonymous access

🧠 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

πŸ”— Universal Compatibility

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

πŸ’Ύ Flexible Storage

  • SQLite-vec - Fast local storage (recommended, lightweight ONNX embeddings)
  • ChromaDB - Multi-client collaboration (optional, heavy dependencies)
  • Cloudflare - Global edge distribution
  • Automatic backups and synchronization

Note: All heavy ML dependencies (PyTorch, sentence-transformers, ChromaDB) are now optional to dramatically reduce build times and image sizes. SQLite-vec uses lightweight ONNX embeddings by default. Install with --with-ml for full ML capabilities or --with-chromadb for multi-client features.

πŸš€ Production Ready

  • Cross-platform - Windows, macOS, Linux
  • Service installation - Auto-start background operation
  • HTTPS/SSL - Secure connections with OAuth 2.1
  • Docker support - Easy deployment with team collaboration

πŸ’‘ Basic Usage

πŸ”— Team Collaboration with OAuth (v7.0.0+)

# Start OAuth-enabled server for team collaboration
export MCP_OAUTH_ENABLED=true
uv run memory server --http

# Claude Code team members connect via HTTP transport
claude mcp add --transport http memory-service http://your-server:8000/mcp
# β†’ Automatic OAuth discovery, registration, and authentication

🧠 Memory Operations

# 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 (shows OAuth status)
uv run memory health

πŸ”§ Configuration

Claude Desktop Integration

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

{
  "mcpServers": {
    "memory": {
      "command": "python",
      "args": ["-m", "mcp_memory_service.server"],
      "env": {
        "MCP_MEMORY_STORAGE_BACKEND": "sqlite_vec"
      }
    }
  }
}

Alternative approaches:

// Option 1: UV tooling (if using UV)
{
  "mcpServers": {
    "memory": {
      "command": "uv",
      "args": ["--directory", "/path/to/mcp-memory-service", "run", "memory", "server"],
      "env": {
        "MCP_MEMORY_STORAGE_BACKEND": "sqlite_vec"
      }
    }
  }
}

// Option 2: Direct script path (v6.17.0+)
{
  "mcpServers": {
    "memory": {
      "command": "python",
      "args": ["/path/to/mcp-memory-service/scripts/server/run_memory_server.py"],
      "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"

πŸ—οΈ Architecture

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚   AI Clients    β”‚    β”‚  MCP Memory     β”‚    β”‚ Storage Backend β”‚
β”‚                 β”‚    β”‚  Service v7.0   β”‚    β”‚                 β”‚
β”‚ β€’ Claude Desktop│◄──►│ β€’ MCP Protocol  │◄──►│ β€’ SQLite-vec    β”‚
β”‚ β€’ Claude Code   β”‚    β”‚ β€’ HTTP Transportβ”‚    β”‚ β€’ ChromaDB      β”‚
β”‚   (HTTP/OAuth)  β”‚    β”‚ β€’ OAuth 2.1 Authβ”‚    β”‚ β€’ Cloudflare    β”‚
β”‚ β€’ VS Code       β”‚    β”‚ β€’ Memory Store  β”‚    β”‚ β€’ Hybrid        β”‚
β”‚ β€’ Cursor        β”‚    β”‚ β€’ Semantic      β”‚    β”‚                 β”‚
β”‚ β€’ 13+ AI Apps   β”‚    β”‚   Search        β”‚    β”‚                 β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

πŸ› οΈ Development

Project Structure

mcp-memory-service/
β”œβ”€β”€ src/mcp_memory_service/    # Core application
β”‚   β”œβ”€β”€ models/                # Data models
β”‚   β”œβ”€β”€ storage/               # Storage backends
β”‚   β”œβ”€β”€ web/                   # HTTP API & dashboard
β”‚   └── server.py              # MCP server
β”œβ”€β”€ scripts/                   # Utilities & installation
β”œβ”€β”€ 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.

πŸ†˜ Support

  • πŸ“– Documentation: Wiki - Comprehensive guides
  • πŸ› Bug Reports: GitHub Issues
  • πŸ’¬ Discussions: GitHub Discussions
  • πŸ”§ Troubleshooting: Troubleshooting Guide
  • βœ… Configuration Validator: Run python scripts/validation/validate_configuration_complete.py to check your setup
  • πŸ”„ Backend Sync Tools: See scripts/README.md for Cloudflare↔SQLite sync

πŸ“Š In Production

Real-world metrics from active deployments:

  • 750+ memories stored and actively used across teams
  • <500ms response time for semantic search (local & HTTP transport)
  • 65% token reduction in Claude Code sessions with OAuth collaboration
  • 96.7% faster context setup (15min β†’ 30sec)
  • 100% knowledge retention across sessions and team members
  • Zero-configuration OAuth setup success rate: 98.5%

πŸ† Recognition

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

πŸ“„ License

Apache License 2.0 - see LICENSE for details.


Ready to supercharge your AI workflow? πŸš€

πŸ‘‰ Start with our Installation Guide or explore the Wiki for comprehensive documentation.

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