What is Mcp Memory Service

MCP Memory Service is an MCP server that provides semantic memory and persistent storage capabilities for Claude Desktop. It utilizes ChromaDB and sentence transformers to enable long-term memory storage and semantic search functionalities, ensuring context is maintained across conversations and instances.

Use cases

Use cases for the MCP Memory Service include maintaining context in conversations, managing long-term memory for applications, facilitating personalized user experiences, and supporting data retrieval in various scenarios where semantic understanding is crucial.

How to use

To use the MCP Memory Service, clone the repository, create a virtual environment, and run the installation script. Configure the service in the Claude Desktop configuration file, and then you can run the memory server directly or use the provided memory operations for storing and retrieving information.

Key features

Key features include semantic search using sentence transformers, natural language time-based recall, tag-based memory retrieval, persistent storage with ChromaDB, automatic database backups, memory optimization tools, exact match retrieval, and cross-platform compatibility.

Where to use

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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”)
• Enhanced tag deletion system with flexible multi-tag support
• 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

Recent Enhancements

• ✅ API Consistency: Enhanced delete_by_tag to support both single and multiple tags
• ✅ New Delete Methods: Added delete_by_tags (OR logic) and delete_by_all_tags (AND logic)
• ✅ Backward Compatibility: All existing code continues to work unchanged
• ✅ Dashboard Integration: Enhanced UI with multiple tag selection capabilities

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:

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

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:

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:

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:

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 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

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

7. create_backup - Create database backup
8. get_stats - Get memory statistics
9. optimize_db - Optimize database performance
10. check_database_health - Get database health metrics
11. check_embedding_model - Verify model status

Memory Management

12. delete_memory - Delete specific memory by hash
13. delete_by_tag - Enhanced: Delete memories with specific tag(s) - supports both single tags and multiple tags
14. delete_by_tags - New: Explicitly delete memories containing any of the specified tags (OR logic)
15. delete_by_all_tags - New: Delete memories containing all specified tags (AND logic)
16. cleanup_duplicates - Remove duplicate entries

API Consistency Improvements

Issue 5 Resolution: Enhanced tag deletion functionality for consistent API design.

• Before: search_by_tag accepted arrays, delete_by_tag only accepted single strings
• After: Both operations now support flexible tag handling

// Single tag deletion (backward compatible)
delete_by_tag("temporary")

// Multiple tag deletion (new!)
delete_by_tag(["temporary", "outdated", "test"])  // OR logic

// Explicit methods for clarity
delete_by_tags(["tag1", "tag2"])                  // OR logic  
delete_by_all_tags(["urgent", "important"])       // AND logic

Example Usage

// Store memories with tags
store_memory("Project deadline is May 15th", {tags: ["work", "deadlines", "important"]})
store_memory("Grocery list: milk, eggs, bread", {tags: ["personal", "shopping"]})
store_memory("Meeting notes from sprint planning", {tags: ["work", "meetings", "important"]})

// Search by multiple tags (existing functionality)
search_by_tag(["work", "important"])  // Returns memories with either tag

// Enhanced deletion options (new!)
delete_by_tag("temporary")                    // Delete single tag (backward compatible)
delete_by_tag(["temporary", "outdated"])     // Delete memories with any of these tags
delete_by_tags(["personal", "shopping"])     // Explicit multi-tag deletion
delete_by_all_tags(["work", "important"])    // Delete only memories with BOTH tags

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

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

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