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:

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

Installation

If you want to use it in your own project:

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:

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:

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:

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:

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

With custom database path:

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

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