MemorySAFE

This server implements long-term memory capabilities for AI assistants using mem0 principles, powered by PostgreSQL with pgvector for efficient vector similarity search.

Features

• PostgreSQL with pgvector for vector similarity search
• Automatic embedding generation using BERT
• RESTful API for memory operations
• Semantic search capabilities
• Support for different types of memories (learnings, experiences, etc.)
• Tag-based memory retrieval
• Confidence scoring for memories
• Server-Sent Events (SSE) for real-time updates
• Cursor MCP protocol compatible

Prerequisites

1. PostgreSQL 14+ with pgvector extension installed:

# In your PostgreSQL instance:
CREATE EXTENSION vector;

1. Node.js 16+

Setup

1. Install dependencies:

npm install

1. Configure environment variables:

Copy .env.sample to .env and adjust the values:

cp .env.sample .env

Example .env configurations:

# With username/password
DATABASE_URL="postgresql://username:password@localhost:5432/mcp_memory"
PORT=3333

# Local development with peer authentication
DATABASE_URL="postgresql:///mcp_memory"
PORT=3333

1. Initialize the database:

npm run prisma:migrate

1. Start the server:

npm start

For development with auto-reload:

npm run dev

Using with Cursor

Adding the MCP Server in Cursor

To add the memory server to Cursor, you need to modify your MCP configuration file located at ~/.cursor/mcp.json. Add the following configuration to the mcpServers object:

{
"mcpServers": {
"memory": {
"command": "node",
"args": [
"/path/to/your/memory/src/server.js"
]
}
}
}

Replace /path/to/your/memory with the actual path to your memory server installation.

For example, if you cloned the repository to /Users/username/workspace/memory, your configuration would look like:

{
"mcpServers": {
"memory": {
"command":