Leverages Node.js for running the embedding model offline via @xenova/transformers, enabling semantic search capabilities without requiring an internet connection.
Provides a persistent memory storage backend using SQLite with FTS5 for fast keyword search and sqlite-vec for semantic vector search, supporting a structured graph of entities, observations, and relations.
Memento
Some memories are best persisted.
A local, fully-offline MCP memory server using SQLite + FTS5 + sqlite-vec
with embedding support via @xenova/transformers.
Features
- Fast keyword search (FTS5)
- Semantic vector search (sqlite-vec, 1024d)
- Offline embedding model (
bge-m3
) - Structured graph of
entities
,observations
, andrelations
- Easy integration with Claude Desktop (via MCP)
Prerequisite: sqlite3
CLI
Most macOS and Linux distros ship sqlite3
out of the box, but double-check that it’s there and new enough (≥ 3.38 for proper FTS5).
If you see “command not found” (or your version is older than 3.38), install the CLI:
Platform | Install command |
---|---|
macOS (Homebrew) | brew install sqlite |
Debian / Ubuntu | sudo apt update && sudo apt install sqlite3 |
Installation
Make sure the platform-specific sqlite-vec
subpackage is installed automatically (e.g. sqlite-vec-darwin-x64
). You can verify or force install via:
Usage
Claude Desktop:
Optional:
Use SQLITE_VEC_PATH=/full/path/to/vec0.dylib
if automatic detection fails.
API Overview
This server exposes the following MCP tools:
create_entities
create_relations
add_observations
delete_entities
delete_relations
delete_observations
read_graph
search_nodes
(mode:keyword
,semantic
)open_nodes
An example of an instruction set that an LLM should know for effective memory handling.
This is just an example of instructions, you can define your own rules for the model.
Embedding Model
This project uses @xenova/transformers, with a quantized version of bge-m3
, running fully offline in Node.js.
License
MIT
This server cannot be installed
A local, fully-offline MCP memory server that enables persistent storage and retrieval of information using SQLite with both keyword and semantic vector search capabilities.
Related MCP Servers
- -securityFlicense-qualityAn MCP server that provides safe, read-only access to SQLite databases through MCP. This server is built with the FastMCP framework, which enables LLMs to explore and query SQLite databases with built-in safety features and query validation.Last updated -49Python
- AsecurityAlicenseAqualityA high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.Last updated -67956TypeScriptMIT License
- -securityFlicense-qualityAn MCP server that allows Claude and other LLMs to manage persistent memories across conversations through text file storage, enabling commands to add, search, delete and list memory entries.Last updated -2TypeScript
- AsecurityAlicenseAqualityA high-performance, persistent memory system for the Model Context Protocol (MCP) providing vector search capabilities and efficient knowledge storage using libSQL as the backing store.Last updated -6791TypeScriptMIT License