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MCP Memory Service

by doobidoo
GitHub
Python
MIT License
53
  • Linux
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local-only server

The server can only run on the client’s local machine because it depends on local resources.

Integrations

  • Mentioned as a potential cloud storage option where users should ensure sync is complete before accessing from another device.

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.

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

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:

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

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:

  1. Detecting CUDA availability and version
  2. Installing the appropriate PyTorch version from the correct index URL
  3. Installing other dependencies without conflicting with PyTorch
  4. 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" } } }

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:

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

Usage

To run the memory server directly (for testing):

# Quick run script (recommended for testing) python scripts/run_memory_server.py # For isolated testing of methods python src/chroma_test_isolated.py

Memory Operations

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

Core Memory Operations

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

Database Management

  1. create_backup - Create database backup
  2. get_stats - Get memory statistics
  3. optimize_db - Optimize database performance
  4. check_database_health - Get database health metrics
  5. check_embedding_model - Verify model status

Memory Management

  1. delete_memory - Delete specific memory by hash
  2. delete_by_tag - Delete all memories with specific tag
  3. 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

PlatformArchitectureAcceleratorStatus
macOSApple Silicon (M1/M2/M3)MPS✅ Fully supported
macOSApple Silicon under Rosetta 2CPU✅ Supported with fallbacks
macOSIntelCPU✅ Fully supported
Windowsx86_64CUDA✅ Fully supported
Windowsx86_64DirectML✅ Supported
Windowsx86_64CPU✅ Supported with fallbacks
Linuxx86_64CUDA✅ Fully supported
Linuxx86_64ROCm✅ Supported
Linuxx86_64CPU✅ Supported with fallbacks
LinuxARM64CPU✅ 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

Telegram

This server cannot be installed

A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval capabilities.

  1. Features
    1. Installation
      1. Quick Start (Recommended)
        1. Windows Installation (Special Case)
          1. Installing via Smithery
            1. Detailed Installation Guide
            2. Claude MCP Configuration
              1. Standard Configuration
                1. Windows-Specific Configuration (Recommended)
                2. Usage
                  1. Memory Operations
                    1. Core Memory Operations
                      1. Database Management
                        1. Memory Management
                        2. Configuration Options
                          1. Hardware Compatibility
                            1. Testing
                              1. Troubleshooting
                                1. Quick Troubleshooting Tips
                                2. Project Structure
                                  1. Development Guidelines
                                    1. License
                                      1. Acknowledgments
                                        1. Contact

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