MCP Memory Server

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

PostgreSQL 14+ with pgvector extension installed:

# In your PostgreSQL instance:
CREATE EXTENSION vector;

Node.js 16+

Setup

Install dependencies:

npm install

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

Initialize the database:

npm run prisma:migrate

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": "node",
      "args": [
        "/Users/username/workspace/memory/src/server.js"
      ]
    }
  }
}

The server will be automatically started by Cursor when needed. You can verify it's working by:

Opening Cursor
The memory server will be started automatically when Cursor launches
You can check the server status by visiting http://localhost:3333/mcp/v1/health

Available MCP Endpoints

SSE Connection

Endpoint: GET /mcp/v1/sse
Query Parameters:
- subscribe: Comma-separated list of events to subscribe to (optional)
Events:
- connected: Sent on initial connection
- memory.created: Sent when new memories are created
- memory.updated: Sent when existing memories are updated

Memory Operations

Create Memory

POST /mcp/v1/memory
Content-Type: application/json

{
  "type": "learning",
  "content": {
    "topic": "Express.js",
    "details": "Express.js is a web application framework for Node.js"
  },
  "source": "documentation",
  "tags": ["nodejs", "web-framework"],
  "confidence": 0.95
}

Search Memories

GET /mcp/v1/memory/search?query=web+frameworks&type=learning&tags=nodejs

List Memories

GET /mcp/v1/memory?type=learning&tags=nodejs,web-framework

Health Check

GET /mcp/v1/health

Response Format

All API responses follow the standard MCP format:

{
  "status": "success",
  "data": {
    // Response data
  }
}

Or for errors:

{
  "status": "error",
  "error": "Error message"
}

Memory Schema

id: Unique identifier
type: Type of memory (learning, experience, etc.)
content: Actual memory content (JSON)
source: Where the memory came from
embedding: Vector representation of the content (384 dimensions)
tags: Array of relevant tags
confidence: Confidence score (0-1)
createdAt: When the memory was created
updatedAt: When the memory was last updated

Integrations