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

Related MCP server: MCP Memory Server

Prerequisites

1. PostgreSQL 14+ with pgvector extension installed:

2. Node.js 16+

Setup

1. Install dependencies:

2. Configure environment variables: Copy .env.sample to .env and adjust the values:

Example .env configurations:

3. Initialize the database:

4. Start the server:

For development with auto-reload:

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:

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:

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

1. Opening Cursor

2. The memory server will be started automatically when Cursor launches

3. 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

1. Create Memory

2. Search Memories

3. List Memories

Health Check

Response Format

All API responses follow the standard MCP format:

Or for errors:

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