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.
Provides persistent memory capabilities through a SQLite-based knowledge graph that stores entities, observations, and relationships with full-text and semantic search using BGE-M3 embeddings for intelligent context retrieval across conversations.
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)
Prerequisites
System SQLite Version Check
Memento requires SQLite 3.38+ for FTS5 support. Most macOS and Linux distros ship sqlite3
out of the box, but double-check that it's there and new enough:
Important Note: This check is just to verify SQLite is installed on your system. Memento does NOT use the sqlite3 CLI for its operation it uses the Node.js sqlite3 module internally.
If you see "command not found" (or your version is older than 3.38), install SQLite:
Platform | Install command |
---|---|
macOS (Homebrew) | brew install sqlite |
Debian / Ubuntu | sudo apt update && sudo apt install sqlite3 |
Installation
This will automatically install all dependencies, including the platform-specific sqlite-vec extension.
Usage
Claude Desktop:
Troubleshooting
sqlite-vec Extension Issues
Important: Memento loads the sqlite-vec extension programmatically through Node.js, NOT through the sqlite3 CLI.
Common misconceptions:
- ❌ Creating shell aliases for sqlite3 CLI won't affect Memento
- ❌ Loading extensions in sqlite3 CLI won't help Memento
- ✅ Use the npm-installed sqlite-vec or set
SQLITE_VEC_PATH
environment variable if automatic detection fails. This should point to the Node.js-compatible version of the extension, typically found in yournode_modules
directory.
If automatic vec loading 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
local-only server
The server can only run on the client's local machine because it depends on local resources.
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.
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