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:
{
"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 objectsname(string): Unique entity identifierentityType(string): Type/category of the entityobservations(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 forlimit(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 objectssource(string): Source entity nametarget(string): Target entity nametype(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 nametarget(string): Target entity nametype(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 entityrelations: All relations where this entity is source or targetrelatedEntities: 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