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

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:

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.

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:

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:

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:

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:

Usage with Claude Desktop

Add to your Claude Desktop configuration:

Minimal configuration (uses default

With custom database path:

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

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