MCP Memory Service

An MCP server providing semantic memory and persistent storage capabilities for Claude Desktop using ChromaDB and sentence transformers. This service enables long-term memory storage with semantic search capabilities, making it ideal for maintaining context across conversations and instances.

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Features

Semantic search using sentence transformers
Natural language time-based recall (e.g., "last week", "yesterday morning")
Tag-based memory retrieval system
Persistent storage using ChromaDB
Automatic database backups
Memory optimization tools
Exact match retrieval
Debug mode for similarity analysis
Database health monitoring
Duplicate detection and cleanup
Customizable embedding model
Cross-platform compatibility (Apple Silicon, Intel, Windows, Linux)
Hardware-aware optimizations for different environments
Graceful fallbacks for limited hardware resources

Installation

Quick Start (Recommended)

The enhanced installation script automatically detects your system and installs the appropriate dependencies:

# Clone the repository
git clone https://github.com/doobidoo/mcp-memory-service.git
cd mcp-memory-service

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Run the installation script
python install.py

The install.py script will:

Detect your system architecture and available hardware accelerators
Install the appropriate dependencies for your platform
Configure the optimal settings for your environment
Verify the installation and provide diagnostics if needed

Docker Installation

You can run the Memory Service using Docker:

# Using Docker Compose (recommended)
docker-compose up

# Using Docker directly
docker build -t mcp-memory-service .
docker run -p 8000:8000 -v /path/to/data:/app/chroma_db -v /path/to/backups:/app/backups mcp-memory-service

We provide multiple Docker Compose configurations for different scenarios:

docker-compose.yml - Standard configuration using pip install
docker-compose.uv.yml - Alternative configuration using UV package manager
docker-compose.pythonpath.yml - Configuration with explicit PYTHONPATH settings

To use an alternative configuration:

docker-compose -f docker-compose.uv.yml up

Windows Installation (Special Case)

Windows users may encounter PyTorch installation issues due to platform-specific wheel availability. Use our Windows-specific installation script:

# After activating your virtual environment
python scripts/install_windows.py

This script handles:

Detecting CUDA availability and version
Installing the appropriate PyTorch version from the correct index URL
Installing other dependencies without conflicting with PyTorch
Verifying the installation

Installing via Smithery

To install Memory Service for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @doobidoo/mcp-memory-service --client claude

Detailed Installation Guide

For comprehensive installation instructions and troubleshooting, see the Installation Guide.

Claude MCP Configuration

Standard Configuration

Add the following to your claude_desktop_config.json file:

{
  "memory": {
    "command": "uv",
    "args": [
      "--directory",
      "your_mcp_memory_service_directory",  // e.g., "C:\\REPOSITORIES\\mcp-memory-service"
      "run",
      "memory"
    ],
    "env": {
      "MCP_MEMORY_CHROMA_PATH": "your_chroma_db_path",  // e.g., "C:\\Users\\John.Doe\\AppData\\Local\\mcp-memory\\chroma_db"
      "MCP_MEMORY_BACKUPS_PATH": "your_backups_path"  // e.g., "C:\\Users\\John.Doe\\AppData\\Local\\mcp-memory\\backups"
    }
  }
}

Windows-Specific Configuration (Recommended)

For Windows users, we recommend using the wrapper script to ensure PyTorch is properly installed:

{
  "memory": {
    "command": "python",
    "args": [
      "C:\\path\\to\\mcp-memory-service\\memory_wrapper.py"
    ],
    "env": {
      "MCP_MEMORY_CHROMA_PATH": "C:\\Users\\YourUsername\\AppData\\Local\\mcp-memory\\chroma_db",
      "MCP_MEMORY_BACKUPS_PATH": "C:\\Users\\YourUsername\\AppData\\Local\\mcp-memory\\backups"
    }
  }
}

The wrapper script will:

Check if PyTorch is installed and properly configured
Install PyTorch with the correct index URL if needed
Run the memory server with the appropriate configuration

Usage Guide

For detailed instructions on how to interact with the memory service in Claude Desktop:

Invocation Guide - Learn the specific keywords and phrases that trigger memory operations in Claude
Installation Guide - Detailed setup instructions

The memory service is invoked through natural language commands in your conversations with Claude. For example:

To store: "Please remember that my project deadline is May 15th."
To retrieve: "Do you remember what I told you about my project deadline?"
To delete: "Please forget what I told you about my address."

See the Invocation Guide for a complete list of commands and detailed usage examples.

Memory Operations

The memory service provides the following operations through the MCP server:

Core Memory Operations

store_memory - Store new information with optional tags
retrieve_memory - Perform semantic search for relevant memories
recall_memory - Retrieve memories using natural language time expressions
search_by_tag - Find memories using specific tags
exact_match_retrieve - Find memories with exact content match
debug_retrieve - Retrieve memories with similarity scores

Database Management

create_backup - Create database backup
get_stats - Get memory statistics
optimize_db - Optimize database performance
check_database_health - Get database health metrics
check_embedding_model - Verify model status

Memory Management

delete_memory - Delete specific memory by hash
delete_by_tag - Delete all memories with specific tag
cleanup_duplicates - Remove duplicate entries

Configuration Options

Configure through environment variables:

CHROMA_DB_PATH: Path to ChromaDB storage
BACKUP_PATH: Path for backups
AUTO_BACKUP_INTERVAL: Backup interval in hours (default: 24)
MAX_MEMORIES_BEFORE_OPTIMIZE: Threshold for auto-optimization (default: 10000)
SIMILARITY_THRESHOLD: Default similarity threshold (default: 0.7)
MAX_RESULTS_PER_QUERY: Maximum results per query (default: 10)
BACKUP_RETENTION_DAYS: Number of days to keep backups (default: 7)
LOG_LEVEL: Logging level (default: INFO)

# Hardware-specific environment variables
PYTORCH_ENABLE_MPS_FALLBACK: Enable MPS fallback for Apple Silicon (default: 1)
MCP_MEMORY_USE_ONNX: Use ONNX Runtime for CPU-only deployments (default: 0)
MCP_MEMORY_USE_DIRECTML: Use DirectML for Windows acceleration (default: 0)
MCP_MEMORY_MODEL_NAME: Override the default embedding model
MCP_MEMORY_BATCH_SIZE: Override the default batch size

Hardware Compatibility

Platform	Architecture	Accelerator	Status
macOS	Apple Silicon (M1/M2/M3)	MPS	✅ Fully supported
macOS	Apple Silicon under Rosetta 2	CPU	✅ Supported with fallbacks
macOS	Intel	CPU	✅ Fully supported
Windows	x86_64	CUDA	✅ Fully supported
Windows	x86_64	DirectML	✅ Supported
Windows	x86_64	CPU	✅ Supported with fallbacks
Linux	x86_64	CUDA	✅ Fully supported
Linux	x86_64	ROCm	✅ Supported
Linux	x86_64	CPU	✅ Supported with fallbacks
Linux	ARM64	CPU	✅ Supported with fallbacks

Testing

# Install test dependencies
pip install pytest pytest-asyncio

# Run all tests
pytest tests/

# Run specific test categories
pytest tests/test_memory_ops.py
pytest tests/test_semantic_search.py
pytest tests/test_database.py

# Verify environment compatibility
python scripts/verify_environment_enhanced.py

# Verify PyTorch installation on Windows
python scripts/verify_pytorch_windows.py

# Perform comprehensive installation verification
python scripts/test_installation.py

Troubleshooting

See the Installation Guide for detailed troubleshooting steps.

Quick Troubleshooting Tips

Windows PyTorch errors: Use python scripts/install_windows.py
macOS Intel dependency conflicts: Use python install.py --force-compatible-deps
Recursion errors: Run python scripts/fix_sitecustomize.py
Environment verification: Run python scripts/verify_environment_enhanced.py
Memory issues: Set MCP_MEMORY_BATCH_SIZE=4 and try a smaller model
Apple Silicon: Ensure Python 3.10+ built for ARM64, set PYTORCH_ENABLE_MPS_FALLBACK=1
Installation testing: Run python scripts/test_installation.py

Project Structure

mcp-memory-service/
├── src/mcp_memory_service/      # Core package code
│   ├── __init__.py
│   ├── config.py                # Configuration utilities
│   ├── models/                  # Data models
│   ├── storage/                 # Storage implementations
│   ├── utils/                   # Utility functions
│   └── server.py                # Main MCP server
├── scripts/                     # Helper scripts
├── memory_wrapper.py            # Windows wrapper script
├── install.py                   # Enhanced installation script
└── tests/                       # Test suite

Development Guidelines

Python 3.10+ with type hints
Use dataclasses for models
Triple-quoted docstrings for modules and functions
Async/await pattern for all I/O operations
Follow PEP 8 style guidelines
Include tests for new features

License

MIT License - See LICENSE file for details

Acknowledgments

ChromaDB team for the vector database
Sentence Transformers project for embedding models
MCP project for the protocol specification

Contact

Integrations

The MCP Memory Service can be extended with various tools and utilities. See Integrations for a list of available options, including:

MCP Memory Dashboard - Web UI for browsing and managing memories
Claude Memory Context - Inject memory context into Claude project instructions

MCP Memory Service

Integrations