mcp-memory-libsql

A high-performance, persistent memory system for the Model Context Protocol (MCP) powered by libSQL. This server provides vector search capabilities and efficient knowledge storage using libSQL as the backing store.

Features

• 🚀 High-performance vector search using libSQL
• 💾 Persistent storage of entities and relations
• 🔍 Semantic search capabilities
• 🔄 Knowledge graph management
• 🌐 Compatible with local and remote libSQL databases
• 🔒 Secure token-based authentication for remote databases

Configuration

This server is designed to be used as part of an MCP configuration. Here are examples for different environments:

Cline Configuration

Add this to your Cline MCP settings:

{
	"mcpServers": {
		"mcp-memory-libsql": {
			"command": "npx",
			"args": ["-y", "mcp-memory-libsql"],
			"env": {
				"LIBSQL_URL": "file:/path/to/your/database.db"
			}
		}
	}
}

Claude Desktop with WSL Configuration

For a detailed guide on setting up this server with Claude Desktop in WSL, see Getting MCP Server Working with Claude Desktop in WSL.

Add this to your Claude Desktop configuration for WSL environments:

{
	"mcpServers": {
		"mcp-memory-libsql": {
			"command": "wsl.exe",
			"args": [
				"bash",
				"-c",
				"source ~/.nvm/nvm.sh && LIBSQL_URL=file:/path/to/database.db /home/username/.nvm/versions/node/v20.12.1/bin/npx mcp-memory-libsql"
			]
		}
	}
}

Database Configuration

The server supports both local SQLite and remote libSQL databases through the LIBSQL_URL environment variable:

For local SQLite databases:

{
	"env": {
		"LIBSQL_URL": "file:/path/to/database.db"
	}
}

For remote libSQL databases (e.g., Turso):

{
	"env": {
		"LIBSQL_URL": "libsql://your-database.turso.io",
		"LIBSQL_AUTH_TOKEN": "your-auth-token"
	}
}

Note: When using WSL, ensure the database path uses the Linux filesystem format (e.g., /home/username/...) rather than Windows format.

By default, if no URL is provided, it will use file:/memory-tool.db in the current directory.

API

The server implements the standard MCP memory interface with additional vector search capabilities:

• Entity Management
  • Create/Update entities with embeddings
  • Delete entities
  • Search entities by similarity
• Relation Management
  • Create relations between entities
  • Delete relations
  • Query related entities

Architecture

The server uses a libSQL database with the following schema:

• Entities table: Stores entity information and embeddings
• Relations table: Stores relationships between entities
• Vector search capabilities implemented using libSQL’s built-in vector operations

Development

Publishing