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mcp-memory-service MCP Server

Universal MCP memory service providing semantic memory search, persistent storage, and autonomous memory consolidation for AI assistants across 13+ AI applications.

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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

🚀 Quick Start (2 minutes)

🧠 v7.1.0: Natural Memory Triggers for Claude Code

🤖 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
# ✅ Done! Claude Code now automatically detects when you need memory context

📖 Complete Guide: Natural Memory Triggers v7.1.0


🆕 v7.0.0: OAuth 2.1 & Claude Code HTTP Transport

🔗 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

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

📖 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

📝 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

📚 Complete Documentation

👉 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
    • 85%+ trigger accuracy with semantic pattern detection
    • Multi-tier performance (50ms instant → 150ms fast → 500ms intensive)
    • CLI management system for real-time configuration
    • Git-aware context integration for enhanced relevance
    • Zero-restart installation with dynamic hook loading

🆕 v7.0.0 OAuth & Team Collaboration

🚀 Setup & Installation

🧠 Advanced Topics

📂 Internal Documentation

✨ Key Features

🔐 Enterprise Authentication & Team Collaboration 🆕

  • 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

🔗 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

💾 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.

🚀 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

💡 Basic Usage

🔗 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
# → 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

🔧 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"

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   AI Clients    │    │  MCP Memory     │    │ Storage Backend │
│                 │    │  Service v7.0   │    │                 │
│ • Claude Desktop│◄──►│ • MCP Protocol  │◄──►│ • SQLite-vec    │
│ • Claude Code   │    │ • HTTP Transport│    │ • ChromaDB      │
│   (HTTP/OAuth)  │    │ • OAuth 2.1 Auth│    │ • Cloudflare    │
│ • VS Code       │    │ • Memory Store  │    │ • Hybrid        │
│ • Cursor        │    │ • Semantic      │    │                 │
│ • 13+ AI Apps   │    │   Search        │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🛠️ 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 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 → 30sec)
  • 100% knowledge retention across sessions and team members
  • Zero-configuration OAuth setup success rate: 98.5%

🏆 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.

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