MCP Persistent Memory Server

A Model Context Protocol (MCP) server that provides persistent graph memory with automatic disk storage. Perfect for building knowledge graphs, storing entity relationships, and maintaining context across sessions.

Features

Persistent Storage: Automatically saves graph data to disk
Multiple Deployment Options: Run locally or deploy to cloud
MCP Compatible: Works with Claude Desktop, Cursor, and other MCP clients
Timestamped Backups: Automatic backup rotation
Fast Indexed Lookups: Map-based indices for quick searches
Multiple Export Formats: JSON, CSV for entities and relations
Remote Access: HTTP/WebSocket server for cloud deployment
RESTful API: Direct HTTP endpoints for convenience

Quick Start

Local Installation

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

# Install dependencies
npm install

# Run in stdio mode (for MCP clients)
npm start

# Or run HTTP server
npm run start:http

Add to Cursor

Edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "persistent-memory": {
      "command": "node",
      "args": [
        "/path/to/mcp-persistent-memory/src/index.mjs"
      ],
      "env": {
        "MEMORY_STORAGE_DIR": "~/.cursor/persistent-memory"
      }
    }
  }
}

Usage

Creating Entities

// Via MCP tool call
create_entities({
  entities: [
    {
      name: "Cursor",
      entityType: "Software",
      observations: [
        "An AI-powered code editor",
        "Supports MCP protocol",
        "Built on VSCode"
      ]
    }
  ]
})

Creating Relations

create_relations({
  relations: [
    {
      from: "Cursor",
      to: "VSCode",
      relationType: "based_on"
    }
  ]
})

Searching Nodes

search_nodes({
  query: "AI editor"
})

Getting Statistics

get_stats()
// Returns: { entities: 10, relations: 15, storage: '2.5 MB' }

REST API

When running in HTTP mode:

# Create entities
curl -X POST http://localhost:8080/api/entities \
  -H "Content-Type: application/json" \
  -d '{"entities": [{"name": "Test", "entityType": "Example", "observations": ["A test entity"]}]}'

# Search
curl http://localhost:8080/api/search?q=test

# Get full graph
curl http://localhost:8080/api/graph

# Export as CSV
curl http://localhost:8080/api/export?format=csv-entities > entities.csv

Cloud Deployment

Deploy to Google Cloud

# Make deploy script executable
chmod +x deploy/gcp-deploy.sh

# Deploy (requires gcloud CLI)
./deploy/gcp-deploy.sh my-mcp-server us-central1-a

Docker Deployment

# Build image
docker build -t mcp-persistent-memory .

# Run container
docker run -d \
  -p 8080:8080 \
  -v $(pwd)/data:/data \
  --name mcp-memory \
  mcp-persistent-memory

Using Remote Server

Add to Cursor config:

{
  "persistent-memory-remote": {
    "url": "http://YOUR-SERVER-IP:8080/mcp",
    "transport": "websocket"
  }
}

Storage Structure

data/memory/
├── graph.json          # Main graph data
├── backups/            # Timestamped backups
│   ├── graph-2025-07-06T10-30-00.json
│   ├── graph-2025-07-06T11-00-00.json
│   └── ...
└── exports/            # Manual exports

Environment Variables

MEMORY_STORAGE_DIR: Directory for storing graph data (default: ./data/memory)
MAX_BACKUPS: Maximum number of backup files to keep (default: 10)
PORT: HTTP server port (default: 8080)
HOST: HTTP server host (default: 0.0.0.0)

Development

# Run with auto-reload
npm run dev

# Run tests
npm test

# Build Docker image
npm run docker:build

API Reference

MCP Tools

create_entities: Create new entities in the graph
create_relations: Create relations between entities
search_nodes: Search entities by query
read_graph: Read the entire graph
open_nodes: Get specific nodes by name
add_observations: Add observations to existing entities
delete_entities: Delete entities and their relations
delete_relations: Delete specific relations
get_stats: Get graph statistics
export_graph: Export in various formats
clear_graph: Clear all data

HTTP Endpoints

GET /health: Health check
GET /info: Server information and stats
POST /api/entities: Create entities
POST /api/relations: Create relations
GET /api/search?q=query: Search nodes
GET /api/graph: Get full graph
GET /api/stats: Get statistics
GET /api/export?format=json|csv-entities|csv-relations: Export data

License

MIT

Contributing

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Support

Issues: https://github.com/dirkenglund/mcp-persistent-memory/issues
Documentation: https://github.com/dirkenglund/mcp-persistent-memory/wiki

Built with ❤️ for the MCP ecosystem

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How are these scores calculated?

Provides persistent graph memory with automatic disk storage for building knowledge graphs, storing entity relationships, and maintaining context across sessions.

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