doobidoo/mcp-memory-service

MCP Memory Service

A universal MCP memory service providing semantic memory search, persistent storage, and autonomous memory consolidation for AI assistants and development environments. This Model Context Protocol server works with Claude Desktop, VS Code, Cursor, Continue, WindSurf, LM Studio, Zed, and 13+ AI applications, featuring vector database storage with SQLite-vec for fast semantic search and a revolutionary dream-inspired consolidation system that automatically organizes, compresses, and manages your AI conversation history over time, creating a self-evolving knowledge base for enhanced AI productivity.

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📋 Table of Contents

🚀 Getting Started

• ⚡ Quick Start
• 🎯 Claude Code Commands (v2.2.0)
• 🚀 Remote MCP Memory Service (v4.0.0)
• 📦 Installation Methods
• ⚙️ Claude MCP Configuration

🌟 Features & Capabilities

• ✨ Features Overview
• 🧠 Dream-Inspired Memory Consolidation
• 💾 Storage Backends
• 🔧 Memory Operations

🌐 Deployment & Multi-Client

• 🌐 Multi-Client Deployment
• 🔒 SSL/TLS Configuration
• 🚀 Service Installation

📖 Documentation & Support

• 📝 Usage Guide
• ⚙️ Configuration Options
• 🖥️ Hardware Compatibility
• 🧪 Testing
• ❓ FAQ
• 🛠️ Troubleshooting
• 📚 Comprehensive Documentation

👨‍💻 Development & Community

• 🏗️ Project Structure
• 📋 Development Guidelines
• 🤝 Integrations
• 💖 Sponsorship

🚀 Quick Start

Choose your preferred installation method to get started in under 5 minutes:

Option 1: Docker (Fastest - 2 minutes)

# Pull and run with default settings
docker pull doobidoo/mcp-memory-service:latest
docker run -d -p 8000:8000 -v $(pwd)/data:/app/data doobidoo/mcp-memory-service:latest

✅ Perfect for: Testing, production deployment, isolation
➡️ Complete Docker Setup

Option 2: Smithery (Simplest - 1 minute)

# Auto-install for Claude Desktop
npx -y @smithery/cli install @doobidoo/mcp-memory-service --client claude

✅ Perfect for: Claude Desktop users, zero configuration
➡️ Smithery Details

Option 3: Python Installer (Most Flexible - 5 minutes)

# Clone and install with hardware detection
git clone https://github.com/doobidoo/mcp-memory-service.git
cd mcp-memory-service && python install.py

✅ Perfect for: Developers, customization, multi-client setup
➡️ Full Installation Guide

🎯 NEW: Claude Code Commands (v2.2.0)

Get started in 2 minutes with direct memory commands!

# Install with Claude Code commands
python install.py --install-claude-commands

# Start using immediately
claude /memory-store "Important decision about architecture"
claude /memory-recall "what did we decide last week?"
claude /memory-search --tags "architecture,database"
claude /memory-health

✨ 5 conversational commands following CCPlugins pattern
🚀 Zero MCP server configuration required
🧠 Context-aware operations with automatic project detection
🎨 Professional interface with comprehensive guidance

➡️ |

🚀 NEW: Remote MCP Memory Service (v4.0.0)

Production-ready remote memory service with native MCP-over-HTTP protocol!

Remote Deployment

Deploy the memory service on any server for cross-device access:

# On your server
git clone https://github.com/doobidoo/mcp-memory-service.git
cd mcp-memory-service
python install.py
python scripts/run_http_server.py

Server Access Points:

• MCP Protocol: http://your-server:8000/mcp (for MCP clients)
• Dashboard: http://your-server:8000/ (web interface)
• API Docs: http://your-server:8000/api/docs (interactive API)

Remote API Access

Connect any MCP client or tool to your remote memory service:

# Test MCP connection
curl -X POST http://your-server:8000/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/list"
  }'

# Store memories remotely
curl -X POST http://your-server:8000/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0", 
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "store_memory",
      "arguments": {
        "content": "Your memory content",
        "tags": ["tag1", "tag2"]
      }
    }
  }'

Key Benefits:

• ✅ Cross-Device Access: Connect from any device running Claude Code
• ✅ Native MCP Protocol: Standard JSON-RPC 2.0 implementation
• ✅ No Bridge Required: Direct HTTP/HTTPS connection
• ✅ Production Ready: Proven deployment at scale

Features

🌟 Universal AI Client Compatibility

Works with 13+ AI applications and development environments via the standard Model Context Protocol (MCP):

Core Benefits:

• 🔄 Cross-Client Memory Sharing: Use memories across all your AI tools
• 🚀 Universal Setup: Single installation works everywhere
• 🔌 Standard Protocol: Full MCP compliance ensures compatibility
• 🌐 Remote Access: HTTP/HTTPS support for distributed teams

➡️ |

🧠 Intelligent Memory System

Autonomous Memory Consolidation

• Dream-inspired processing with multi-layered time horizons (daily → yearly)
• Creative association discovery finding non-obvious connections between memories
• Semantic clustering automatically organizing related memories
• Intelligent compression preserving key information while reducing storage
• Controlled forgetting with safe archival and recovery systems
• Performance optimized for processing 10k+ memories efficiently

⚡ ONNX Runtime Support (NEW!)

• PyTorch-free operation using ONNX Runtime for embeddings
• Reduced dependencies (~500MB less disk space without PyTorch)
• Faster startup with pre-optimized ONNX models
• Automatic fallback to SentenceTransformers when needed
• Compatible models with the same all-MiniLM-L6-v2 embeddings
• Enable with: export MCP_MEMORY_USE_ONNX=true

Advanced Memory Operations

• Semantic search using sentence transformers or ONNX embeddings
• 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 with OR/AND logic
• Exact match retrieval and duplicate detection
• Debug mode for similarity analysis and troubleshooting

Enhanced MCP Protocol Features (v4.1.0+)

• 📚 URI-based Resources: memory://stats, memory://tags, memory://recent/{n}, memory://search/{query}
• 📋 Guided Prompts: Interactive workflows (memory_review, memory_analysis, knowledge_export)
• 📊 Progress Tracking: Real-time notifications for long operations
• 🔄 Database Synchronization: Multi-node sync with Litestream integration
• 🎛️ Client Optimization: Auto-detection and optimization for Claude Desktop vs LM Studio

🚀 Deployment & Performance

Storage Backends

• 🪶 SQLite-vec (default): 10x faster startup, 75% less memory, zero network dependencies
• 📦 ChromaDB (legacy): Available for backward compatibility, deprecated in v6.0.0

Multi-Client Architecture

• Production FastAPI server with auto-generated SSL certificates
• mDNS Service Discovery for zero-configuration networking
• Server-Sent Events (SSE) with real-time updates
• API key authentication for secure deployments
• Cross-platform service installation (systemd, LaunchAgent, Windows Service)

Platform Support

• Cross-platform compatibility: Apple Silicon, Intel, Windows, Linux
• Hardware-aware optimizations: CUDA, MPS, DirectML, ROCm support
• Graceful fallbacks for limited hardware resources
• Container support with Docker images and Docker Compose configurations

Recent Highlights

🚀 Latest Features

• v5.0.2: ONNX Runtime support for PyTorch-free embeddings and SQLite-vec consolidation fixes
• v5.0.0: SQLite-vec is now the default backend - 10x faster startup, 75% less memory
• v4.5.0: Database synchronization for distributed memory access across multiple machines
• v4.1.0: Enhanced MCP resources, guided prompts, and progress tracking
• v3.0.0: Dream-inspired autonomous memory consolidation with exponential decay
• v2.2.0: Claude Code Commands for direct conversational memory operations

➡️ for complete version history and detailed release notes

Installation Methods

For quick setup, see the ⚡ Quick Start section above.

🚀 Intelligent Installer (Recommended)

The new unified installer automatically detects your hardware and selects the optimal configuration:

# 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 intelligent installer
python install.py

# ✨ NEW: Multi-client setup is now integrated!
# You'll be prompted to configure universal MCP client access
# for Claude Desktop, VS Code, Continue, and other MCP applications

🎯 Hardware-Specific Installation

For Intel Macs: For detailed setup instructions specific to Intel Macs, see our .

For Legacy Hardware (2013-2017 Intel Macs):

python install.py --legacy-hardware

For Server/Headless Deployment:

python install.py --server-mode

For HTTP/SSE API Development:

python install.py --enable-http-api

For Migration from ChromaDB:

python install.py --migrate-from-chromadb

For Multi-Client Setup:

# Automatic multi-client setup during installation
python install.py --setup-multi-client

# Skip the interactive multi-client prompt
python install.py --skip-multi-client-prompt

For Claude Code Commands:

# Install with Claude Code commands (prompts if CLI detected)
python install.py --install-claude-commands

# Skip the interactive Claude Code commands prompt
python install.py --skip-claude-commands-prompt

🧠 What the Installer Does

1. Hardware Detection: CPU, GPU, memory, and platform analysis
2. Intelligent Backend Selection: SQLite-vec by default, with ChromaDB as legacy option
3. Platform Optimization: macOS Intel fixes, Windows CUDA setup, Linux variations
4. Dependency Management: Compatible PyTorch and ML library versions
5. Auto-Configuration: Claude Desktop config and environment variables
6. Migration Support: Seamless ChromaDB to SQLite-vec migration

📊 Storage Backend Selection

SQLite-vec (default): 10x faster startup, zero dependencies, recommended for all users
ChromaDB (deprecated): Legacy support only, will be removed in v6.0.0

➡️ Detailed Storage Backend Comparison

To explicitly select a backend during installation:

python install.py                                 # Uses SQLite-vec by default
python install.py --storage-backend sqlite_vec    # Explicitly use SQLite-vec
python install.py --storage-backend chromadb      # Use legacy ChromaDB (not recommended)

Docker Installation

Docker Hub (Recommended)

The easiest way to run the Memory Service is using our pre-built Docker images:

# Pull the latest image
docker pull doobidoo/mcp-memory-service:latest

# Run with default settings (for MCP clients)
docker run -d -p 8000:8000 \
  -v $(pwd)/data/chroma_db:/app/chroma_db \
  -v $(pwd)/data/backups:/app/backups \
  doobidoo/mcp-memory-service:latest

# Run in standalone mode (for testing/development)
docker run -d -p 8000:8000 \
  -e MCP_STANDALONE_MODE=1 \
  -v $(pwd)/data/chroma_db:/app/chroma_db \
  -v $(pwd)/data/backups:/app/backups \
  doobidoo/mcp-memory-service:latest

Docker Compose

We provide multiple Docker Compose configurations for different scenarios:

• docker-compose.yml - Standard configuration for MCP clients
• docker-compose.standalone.yml - Standalone mode for testing/development (prevents boot loops)
• docker-compose.uv.yml - Alternative configuration using UV package manager
• docker-compose.pythonpath.yml - Configuration with explicit PYTHONPATH settings

# Using Docker Compose (recommended)
docker-compose up

# Standalone mode (prevents boot loops)
docker-compose -f docker-compose.standalone.yml up

Building from Source

If you need to build the Docker image yourself:

# Build the image
docker build -t mcp-memory-service .

# Run the container
docker run -p 8000:8000 \
  -v $(pwd)/data/chroma_db:/app/chroma_db \
  -v $(pwd)/data/backups:/app/backups \
  mcp-memory-service

uvx Installation

You can install and run the Memory Service using uvx for isolated execution:

# Install uv (which includes uvx) if not already installed
pip install uv
# Or use the installer script:
# curl -LsSf https://astral.sh/uv/install.sh | sh

# Install and run the memory service
uvx mcp-memory-service

# Or install from GitHub
uvx --from git+https://github.com/doobidoo/mcp-memory-service.git mcp-memory-service

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 .

Configuration

Basic Client Configuration

Claude Desktop Configuration

Add to your claude_desktop_config.json file:

{
  "memory": {
    "command": "uv",
    "args": ["--directory", "/path/to/mcp-memory-service", "run", "memory"],
    "env": {
      "MCP_MEMORY_STORAGE_BACKEND": "sqlite_vec",
      "MCP_MEMORY_SQLITE_PATH": "/path/to/sqlite_vec.db",
      "MCP_MEMORY_BACKUPS_PATH": "/path/to/backups"
    }
  }
}

Windows-Specific Configuration

For Windows, use the wrapper script for PyTorch compatibility:

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

➡️ Multi-Client Setup Guide for Claude Desktop + VS Code + other MCP clients

Environment Variables

Core Configuration

# Storage Backend
MCP_MEMORY_STORAGE_BACKEND=sqlite_vec          # sqlite_vec (default) or chromadb
MCP_MEMORY_SQLITE_PATH=/path/to/database.db    # SQLite database location
MCP_MEMORY_BACKUPS_PATH=/path/to/backups       # Backup directory

# Performance & Hardware
MCP_MEMORY_BATCH_SIZE=32                       # Processing batch size
MCP_MEMORY_MODEL_NAME=all-MiniLM-L6-v2        # Embedding model
PYTORCH_ENABLE_MPS_FALLBACK=1                  # Apple Silicon fallback
MCP_MEMORY_USE_ONNX=0                          # CPU-only mode
LOG_LEVEL=INFO                                 # Logging level

HTTP API & Remote Access

# Server Configuration
MCP_HTTP_ENABLED=true                          # Enable HTTP server
MCP_HTTP_HOST=0.0.0.0                         # Bind to all interfaces
MCP_HTTP_PORT=8000                            # Server port

# Security
MCP_API_KEY="your-secure-api-key"             # API authentication
MCP_HTTPS_ENABLED=true                        # Enable SSL/TLS
MCP_HTTPS_PORT=8443                           # HTTPS port

Advanced Configuration

SSL/TLS Setup

For production deployments with HTTPS:

# Enable HTTPS with custom certificates
export MCP_HTTPS_ENABLED=true
export MCP_SSL_CERT_FILE="/path/to/certificate.pem"
export MCP_SSL_KEY_FILE="/path/to/private-key.pem"

# Generate secure API key
export MCP_API_KEY="$(openssl rand -base64 32)"

Local Development with mkcert:

# Install mkcert for trusted local certificates
brew install mkcert                           # macOS
sudo apt install mkcert                       # Linux

# Generate local certificates
mkcert -install
mkcert localhost 127.0.0.1 your-domain.local

Memory Consolidation

# Enable autonomous memory consolidation
MCP_CONSOLIDATION_ENABLED=true
MCP_CONSOLIDATION_ARCHIVE_PATH=/path/to/archive

# Retention periods (days)
MCP_RETENTION_CRITICAL=365
MCP_RETENTION_REFERENCE=180
MCP_RETENTION_STANDARD=30
MCP_RETENTION_TEMPORARY=7

🌐 Multi-Client Deployment

NEW: Deploy MCP Memory Service for multiple clients sharing the same memory database!

🚀 Centralized Server Deployment (Recommended)

Perfect for distributed teams, multiple devices, or cloud deployment:

# Install and start HTTP/SSE server
python install.py --server-mode --enable-http-api
export MCP_HTTP_HOST=0.0.0.0  # Allow external connections
export MCP_API_KEY="your-secure-key"  # Optional authentication
python scripts/run_http_server.py

✅ Benefits:

• 🔄 Real-time sync across all clients via Server-Sent Events (SSE)
• 🌍 Cross-platform - works from any device with HTTP access
• 🔒 Secure with optional API key authentication
• 📈 Scalable - handles many concurrent clients
• ☁️ Cloud-ready - deploy on AWS, DigitalOcean, Docker, etc.

Access via:

• API Docs: http://your-server:8000/api/docs
• Web Dashboard: http://your-server:8000/
• REST API: All MCP operations available via HTTP

⚠️ Why NOT Cloud Storage (Dropbox/OneDrive/Google Drive)

Direct SQLite on cloud storage DOES NOT WORK for multi-client access:

❌ File locking conflicts - Cloud sync breaks SQLite's locking mechanism
❌ Data corruption - Incomplete syncs can corrupt the database
❌ Sync conflicts - Multiple clients create "conflicted copy" files
❌ Performance issues - Full database re-upload on every change

✅ Solution: Use centralized HTTP server deployment instead!

🔗 Local Multi-Client Coordination

For local development with multiple MCP clients (Claude Desktop + VS Code + Continue, etc.):

The MCP Memory Service features universal multi-client coordination for seamless concurrent access:

🚀 Integrated Setup (Recommended):

python install.py  # Automatically detects and configures all MCP clients

Key Benefits:

• ✅ Automatic Coordination: Intelligent detection of optimal access mode
• ✅ Universal Setup: Works with any MCP-compatible application
• ✅ Shared Memory: All clients access the same memory database
• ✅ No Lock Conflicts: WAL mode prevents database locking issues
• ✅ IDE-Agnostic: Switch between development tools while maintaining context

Supported Clients: Claude Desktop, Claude Code, VS Code, Continue IDE, Cursor, Cline, Zed, and more

📖 Complete Documentation

For detailed deployment guides, configuration options, and troubleshooting:

📚

Covers:

• Centralized HTTP/SSE Server setup and configuration
• Shared File Access for local networks (limited scenarios)
• Cloud Platform Deployment (AWS, DigitalOcean, Docker)
• Security & Authentication setup
• Performance Tuning for high-load environments
• Troubleshooting common multi-client issues

Usage Guide

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

• - Learn the specific keywords and phrases that trigger memory operations in Claude
• - Detailed setup instructions
• - Real-world development session showcasing advanced features

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?"

Claude Code Commands Usage

With the optional Claude Code commands installed, you can also use direct command syntax:

# Store information with context
claude /memory-store "Important architectural decision about database backend"

# Recall memories by time
claude /memory-recall "what did we decide about the database last week?"

# Search by tags or content
claude /memory-search --tags "architecture,database"

# Capture current session context
claude /memory-context --summary "Development planning session"

# Check service health
claude /memory-health

• To delete: "Please forget what I told you about my address."

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

Storage Backends

The MCP Memory Service supports multiple storage backends to suit different use cases:

SQLite-vec (Default - Recommended)

• Best for: All use cases - from personal to production deployments
• Features: Single-file database, 75% lower memory usage, zero network dependencies
• Memory usage: Minimal (~50MB for 1K memories)
• Setup: Automatically configured, works offline immediately

ChromaDB (Legacy - Deprecated)

⚠️ DEPRECATED: Will be removed in v6.0.0. Please migrate to SQLite-vec.

• Previous use cases: Large memory collections, advanced vector metrics
• Issues: Network dependencies, Hugging Face download failures, high resource usage
• Memory usage: Higher (~200MB for 1K memories)
• Migration: Run python scripts/migrate_to_sqlite_vec.py to migrate your data

Quick Setup for SQLite-vec

# Install sqlite-vec (if using installation script, this is handled automatically)
pip install sqlite-vec

# Configure the backend
export MCP_MEMORY_STORAGE_BACKEND=sqlite_vec
export MCP_MEMORY_SQLITE_PATH=/path/to/sqlite_vec.db

# Optional: For CPU-only mode without PyTorch (much lighter resource usage)
export MCP_MEMORY_USE_ONNX=1

# Restart Claude Desktop

SQLite-vec with Optional PyTorch

The SQLite-vec backend now works with or without PyTorch installed:

• With PyTorch: Full functionality including embedding generation
• Without PyTorch: Basic functionality using pre-computed embeddings and ONNX runtime
• With Homebrew PyTorch: Integration with macOS Homebrew PyTorch installation

To install optional machine learning dependencies:

# Add ML dependencies for embedding generation
pip install 'mcp-memory-service[ml]'

Homebrew PyTorch Integration

For macOS users who prefer to use Homebrew's PyTorch installation:

# Install PyTorch via Homebrew
brew install pytorch

# Run MCP Memory Service with Homebrew PyTorch integration
./run_with_homebrew.sh

This integration offers several benefits:

• Uses Homebrew's isolated Python environment for PyTorch
• Avoids dependency conflicts with Claude Desktop
• Reduces memory usage in the main process
• Provides better stability in resource-constrained environments

For detailed documentation on the Homebrew PyTorch integration:

• - Technical journey and solution architecture

Migration Between Backends

# Migrate from ChromaDB to SQLite-vec
python migrate_to_sqlite_vec.py

# Full migration with backup
python scripts/migrate_storage.py \
  --from chroma --to sqlite_vec \
  --backup --backup-path backup.json

For detailed SQLite-vec setup, migration, and troubleshooting, see the .

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

🧠 Dream-Inspired Memory Consolidation

The memory consolidation system operates autonomously in the background, inspired by how human memory works during sleep cycles. It automatically organizes, compresses, and manages your memories across multiple time horizons.

Quick Start

Enable consolidation with a single environment variable:

export MCP_CONSOLIDATION_ENABLED=true

How It Works

• Daily consolidation (light processing): Updates memory relevance and basic organization
• Weekly consolidation: Discovers creative associations between memories
• Monthly consolidation: Performs semantic clustering and intelligent compression
• Quarterly/Yearly consolidation: Deep archival and long-term memory management

New MCP Tools Available

Once enabled, you get access to powerful new consolidation tools:

• consolidate_memories - Manually trigger consolidation for any time horizon
• get_consolidation_health - Monitor system health and performance
• get_consolidation_stats - View processing statistics and insights
• schedule_consolidation - Configure autonomous scheduling
• get_memory_associations - Explore discovered memory connections
• get_memory_clusters - Browse semantic memory clusters
• get_consolidation_recommendations - Get AI-powered memory management advice

Advanced Configuration

Fine-tune the consolidation system through environment variables:

# Archive location (default: ~/.mcp_memory_archive)
export MCP_CONSOLIDATION_ARCHIVE_PATH=/path/to/archive

# Retention periods (days)
export MCP_RETENTION_CRITICAL=365  # Critical memories
export MCP_RETENTION_REFERENCE=180 # Reference materials  
export MCP_RETENTION_STANDARD=30   # Standard memories
export MCP_RETENTION_TEMPORARY=7   # Temporary memories

# Association discovery settings
export MCP_ASSOCIATION_MIN_SIMILARITY=0.3  # Sweet spot range
export MCP_ASSOCIATION_MAX_SIMILARITY=0.7  # for creative connections

# Autonomous scheduling (cron-style)
export MCP_SCHEDULE_DAILY="02:00"        # 2 AM daily
export MCP_SCHEDULE_WEEKLY="SUN 03:00"   # 3 AM on Sundays
export MCP_SCHEDULE_MONTHLY="01 04:00"   # 4 AM on 1st of month

Performance

• Designed to process 10k+ memories efficiently
• Automatic hardware optimization (CPU/GPU/MPS)
• Safe archival system - no data is ever permanently deleted
• Full recovery capabilities for all archived memories

🚀 Service Installation (NEW!)

Install MCP Memory Service as a native system service for automatic startup:

Cross-Platform Service Installer

# Install as a service (auto-detects OS)
python install_service.py

# Start the service
python install_service.py --start

# Check service status
python install_service.py --status

# Stop the service
python install_service.py --stop

# Uninstall the service
python install_service.py --uninstall

The installer provides:

• ✅ Automatic OS detection (Windows, macOS, Linux)
• ✅ Native service integration (systemd, LaunchAgent, Windows Service)
• ✅ Automatic startup on boot/login
• ✅ Service management commands
• ✅ Secure API key generation
• ✅ Platform-specific optimizations

For detailed instructions, see the .

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

FAQ

Can I use MCP Memory Service with multiple AI clients simultaneously?

Yes! The service features universal multi-client coordination for seamless concurrent access across Claude Desktop, VS Code, Continue, Cursor, and other MCP clients. See the Local Multi-Client Coordination section for details.

What's the difference between SQLite-vec and ChromaDB backends?

SQLite-vec (recommended): 10x faster startup, zero network dependencies, 75% less memory usage, single-file database
ChromaDB (deprecated): Legacy support only, requires network access for models, will be removed in v6.0.0

➡️ Detailed Backend Comparison

How do I migrate from ChromaDB to SQLite-vec?

Run the migration script to safely transfer your existing memories:

python scripts/migrate_to_sqlite_vec.py

The process preserves all memories, tags, and metadata while improving performance.

Can I deploy MCP Memory Service on a remote server?

Yes! The service supports production deployment with HTTP/HTTPS server, API authentication, SSL certificates, and Docker containers. Perfect for teams and cross-device access.

➡️ Remote Server Deployment

Why does my installation fail on Apple Silicon Macs?

Use the intelligent installer which handles Apple Silicon optimizations automatically:

python install.py

It detects MPS support, configures fallbacks, and selects compatible PyTorch versions.

How much memory and storage does the service use?

SQLite-vec: ~50MB RAM for 1K memories, single database file
ChromaDB: ~200MB RAM for 1K memories, multiple files

Storage scales linearly: ~1MB per 1000 memories with SQLite-vec.

Is my data secure and private?

Yes! All data is stored locally by default. For remote deployments, the service supports API key authentication, HTTPS encryption, and runs in user-space (not as root) for security.

Troubleshooting

See the and 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

📚 Comprehensive Documentation

Installation & Setup

• - Complete installation guide with hardware-specific paths
• ⭐ NEW - Comprehensive CLI options including multi-client setup
• ⭐ NEW - Integrated setup for any MCP application
• - Detailed comparison and selection guide
• - ChromaDB to SQLite-vec migration instructions

Platform-Specific Guides

• - Comprehensive guide for Intel Mac users
• - Optimized for 2015 MacBook Pro and older Intel Macs
• - Windows-specific installation and troubleshooting
• - Linux server installation guide

API & Integration

• - New web interface documentation
• - Configuration examples
• - Third-party tools and extensions

Advanced Topics

• ⭐ NEW - Technical implementation details
• - Using system PyTorch
• - Container-based deployment
• - Tuning for different hardware

Troubleshooting & Support

• - Common issues and solutions
• - Compatibility matrix and known issues

Quick Commands

# Get personalized setup recommendations
python install.py --help-detailed

# Generate hardware-specific setup guide
python install.py --generate-docs

# Test your installation
python scripts/test_memory_simple.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

Git Setup for Contributors

After cloning the repository, run the setup script to configure automated uv.lock conflict resolution:

./scripts/setup-git-merge-drivers.sh

This enables automatic resolution of uv.lock merge conflicts by:

1. Using the incoming version to resolve conflicts
2. Automatically running uv sync to regenerate the lock file
3. Ensuring consistent dependency resolution across all environments

The setup is required only once per clone and benefits all contributors by eliminating manual conflict resolution.

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

🎯 Why Sponsor MCP Memory Service?

🏆 In Production

• Deployed on Glama.ai
• Managing 300+ enterprise memories
• Processing queries in <1 second

Production Impact

• 319+ memories actively managed
• 828ms average query response time
• 100% cache hit ratio performance
• 20MB efficient vector storage

Developer Community

• Complete MCP protocol implementation
• Cross-platform compatibility
• React dashboard with real-time statistics
• Comprehensive documentation

Enterprise Features

• Semantic search with sentence-transformers
• Tag-based categorization system
• Automatic backup and optimization
• Health monitoring dashboard

Contact

Telegram

Integrations

The MCP Memory Service can be extended with various tools and utilities. See 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