mcp-memory-sqlite

A personal knowledge graph and memory system for AI assistants using SQLite with optimized text 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 Text Search - Find information using flexible, relevance-ranked text search

Related MCP server: MCPMem

Features

• 100% Local & Private: All your data stays on your machine

• Easy Setup: Works out-of-the-box with Claude Desktop

• Flexible Text Search: Case-insensitive search with fuzzy matching that handles different naming conventions

• Relevance Ranking: Results prioritized by name match > type match > observation match

• Smart Deduplication: Automatically prevents duplicate relationships

• Context-Optimized: Designed specifically for LLM context efficiency - no unnecessary data bloat

• 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.

Parameters:

• entities: Array of entity objects

  • name (string): Unique entity identifier

  • entityType (string): Type/category of the entity

  • observations (string[]): Array of observation strings

Example:

{
	"entities": [
		{
			"name": "Claude",
			"entityType": "AI Assistant",
			"observations": [
				"Created by Anthropic",
				"Focuses on being helpful, harmless, and honest"
			]
		}
	]
}

search_nodes

Search for entities and their relations using text search with relevance ranking.

Parameters:

• query (string): Text to search for

• limit (number, optional): Maximum results to return (default: 10, max: 50)

Example:

{
	"query": "AI Assistant",
	"limit": 5
}

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

• Relevance ranking: Results prioritized by where match occurs (name > type > observation)

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

get_entity_with_relations

Get an entity along with all its relations and directly connected entities. Perfect for exploring the knowledge graph around a specific concept.

Parameters:

• name (string): Entity name to retrieve

Returns:

• entity: The requested entity

• relations: All relations where this entity is source or target

• relatedEntities: All entities connected to this one

Example:

{
	"name": "Claude"
}

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 pure SQLite for fast, reliable storage:

Tables

• entities: Stores entity metadata (name, type, creation time)

• observations: Stores observations linked to entities

• relations: Stores relationships between entities (with unique constraint to prevent duplicates)

All queries use optimized SQLite indexes for fast text search and relationship traversal.

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

• better-sqlite3 for Node.js integration

• Optimized text search with relevance ranking and fuzzy matching

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

• tmcp - MCP server framework