mcp-memory

# 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 1. PostgreSQL 14+ with pgvector extension installed: ```bash # In your PostgreSQL instance: CREATE EXTENSION vector; ``` 2. Node.js 16+ ## Setup 1. Install dependencies: ```bash npm install ``` 2. Configure environment variables: Copy `.env.sample` to `.env` and adjust the values: ```bash cp .env.sample .env ``` Example `.env` configurations: ```bash # 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 ``` 3. Initialize the database: ```bash npm run prisma:migrate ``` 4. Start the server: ```bash npm start ``` For development with auto-reload: ```bash 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: ```json { "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: ```json { "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: 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** ```http 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 } ``` 2. **Search Memories** ```http GET /mcp/v1/memory/search?query=web+frameworks&type=learning&tags=nodejs ``` 3. **List Memories** ```http GET /mcp/v1/memory?type=learning&tags=nodejs,web-framework ``` ### Health Check ```http GET /mcp/v1/health ``` ### Response Format All API responses follow the standard MCP format: ```json { "status": "success", "data": { // Response data } } ``` Or for errors: ```json { "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