mcp-memory-sqlite

A personal knowledge graph and memory system for AI assistants using SQLite and vector search. Perfect for giving Claude (or any MCP-compatible AI) persistent memory across conversations!

Why Use This?

Give your AI assistant a memory! This tool lets Claude (or other AI assistants) remember entities, concepts, and their relationships across conversations. Perfect for:

• 📚 Personal Knowledge Management - Build your own knowledge graph
• 🤖 AI Assistant Memory - Help Claude remember important information about your projects, preferences, and context
• 🔗 Relationship Tracking - Connect ideas, people, projects, and concepts
• 🔍 Smart Search - Find information using text search or semantic similarity

Features

• 100% Local & Private: All your data stays on your machine
• Easy Setup: Works out-of-the-box with Claude Desktop
• Flexible Search: Case-insensitive text search that handles different naming conventions
• Vector Search: Semantic similarity using OpenAI-compatible embeddings (1536 dimensions)
• Smart Deduplication: Automatically prevents duplicate relationships
• Simple API: Intuitive tools for creating, searching, and managing your knowledge graph

Quick Start

For Claude Desktop users (recommended):

Add this to your Claude Desktop config:

{
	"mcpServers": {
		"memory": {
			"command": "npx",
			"args": ["-y", "mcp-memory-sqlite"]
		}
	}
}

That's it! Claude can now remember things across conversations.

Installation

If you want to use it in your own project:

npm install mcp-memory-sqlite
# or
pnpm add mcp-memory-sqlite

Configuration

Optional: Customize the database location with an environment variable:

• SQLITE_DB_PATH: Where to store your data (default: ./sqlite-memory.db)

MCP Tools

create_entities

Create or update entities with observations and optional embeddings.

Parameters:

• entities: Array of entity objects
  • name (string): Unique entity identifier
  • entityType (string): Type/category of the entity
  • observations (string[]): Array of observation strings
  • embedding (number[], optional): 1536-dimensional vector for semantic search

Example:

{
  "entities": [
    {
      "name": "Claude",
      "entityType": "AI Assistant",
      "observations": [
        "Created by Anthropic",
        "Focuses on being helpful, harmless, and honest"
      ],
      "embedding": [0.1, 0.2, ...] // 1536 dimensions
    }
  ]
}

search_nodes

Search for entities and their relations using text or vector similarity.

Parameters:

• query: String for text search OR array of numbers for vector similarity search

Text Search Example:

{
	"query": "AI Assistant"
}

Text Search Features:

• Case-insensitive: Searches ignore case differences
• Flexible matching: Automatically handles variations in spacing, underscores, and hyphens
  • "JavaScript framework" will match "javascript_framework"
  • "web-development" will match "web_development" or "web development"
• Searches across: Entity names, entity types, and all observations

Vector Search Example:

{
  "query": [0.1, 0.2, 0.3, ...] // 1536 dimensions
}

read_graph

Get recent entities and their relations (returns last 10 entities by default).

Parameters: None

create_relations

Create relationships between entities. Duplicate relations (same source, target, and type) are automatically ignored.

Parameters:

• relations: Array of relation objects
  • source (string): Source entity name
  • target (string): Target entity name
  • type (string): Relationship type

Example:

{
	"relations": [
		{
			"source": "Claude",
			"target": "Anthropic",
			"type": "created_by"
		}
	]
}

Note: If you attempt to create the same relation multiple times, only the first one will be stored. This prevents duplicate relationships in your knowledge graph.

delete_entity

Delete an entity and all associated data (observations and relations).

Parameters:

• name (string): Entity name to delete

delete_relation

Delete a specific relation between entities.

Parameters:

• source (string): Source entity name
• target (string): Target entity name
• type (string): Relationship type

Usage with Claude Desktop

Add to your Claude Desktop configuration:

Minimal configuration (uses default ./sqlite-memory.db):

{
	"mcpServers": {
		"memory": {
			"command": "npx",
			"args": ["-y", "mcp-memory-sqlite"]
		}
	}
}

With custom database path:

{
	"mcpServers": {
		"memory": {
			"command": "npx",
			"args": ["-y", "mcp-memory-sqlite"],
			"env": {
				"SQLITE_DB_PATH": "/path/to/your/memory.db"
			}
		}
	}
}

Database Schema

The tool uses SQLite with the sqlite-vec extension for vector operations:

Regular Tables

• entities: Stores entity metadata (name, type, creation time)
• observations: Stores observations linked to entities
• relations: Stores relationships between entities

Virtual Table

• entities_vec: Virtual table using vec0 for 1536-dimensional vector embeddings

Vector Embeddings

The tool expects 1536-dimensional float vectors, compatible with:

• OpenAI text-embedding-ada-002
• OpenAI text-embedding-3-small
• Other models producing 1536-dimensional embeddings

To generate embeddings, you can use:

• OpenAI Embeddings API
• Local embedding models like sentence-transformers
• Other embedding services that produce 1536-dim vectors

Development

# Install dependencies
pnpm install

# Build
pnpm run build

# Run in development mode
pnpm run dev

# Run tests
pnpm test

How It Works

Under the hood, this uses:

• SQLite for fast, reliable local storage
• sqlite-vec for vector similarity search
• better-sqlite3 for Node.js integration

Your data is stored in a single .db file on your computer - no cloud, no external services, completely private.

License

MIT

Credits

Built with:

• better-sqlite3 - Fast SQLite driver
• sqlite-vec - Vector search extension
• tmcp - MCP server framework