Memento

Memento

iachilles

Creates persistent memory for AI conversations using a SQLite knowledge graph that stores entities, observations, and relationships with semantic search capabilities.

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.

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What it does

  • Store entities, observations, and relationships in a knowledge graph
  • Perform semantic vector search using BGE-M3 embeddings
  • Retrieve contextually relevant information across conversations
  • Search using both full-text and vector similarity
  • Switch between SQLite and PostgreSQL backends
  • Score relevance using temporal and contextual factors

Best for

AI assistants that need long-term memoryChatbots requiring context from previous conversationsKnowledge management systems with semantic searchApplications needing persistent entity relationships
Offline embedding model (no API calls)SQLite and PostgreSQL support1024-dimensional vector search

About Memento

Memento is a community-built MCP server published by iachilles that provides AI assistants with tools and capabilities via the Model Context Protocol. Memento enables persistent memory with a SQLite-based knowledge graph for intelligent context retrieval using advanced B It is categorized under ai ml.

How to install

You can install Memento in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

License

Memento is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

Memento

Some memories are best persisted.

Provides persistent memory capabilities through a SQLite-based knowledge graph that stores entities, observations, and relationships with semantic search using BGE-M3 embeddings for intelligent context retrieval across conversations.

Features

  • Semantic vector search (sqlite-vec/pgvector, 1024d)
  • Offline embedding model (bge-m3)
  • Modular repository layer with SQLite and PostgreSQL backends
  • Enhanced Relevance Scoring with temporal, popularity, contextual, and importance factors
  • Structured graph of entities, observations, and relations
  • Easy integration with Claude Desktop (via MCP)

Prerequisites

System SQLite Version Check

Memento requires SQLite 3.38+. Most macOS and Linux distros ship sqlite3 out of the box, but double-check that it's there and new enough:

sqlite3 --version       # should print a version string, e.g. 3.46.0

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:

PlatformInstall command
macOS (Homebrew)brew install sqlite
Debian / Ubuntusudo apt update && sudo apt install sqlite3

Configuration

Memento now supports pluggable storage backends. Configuration is controlled entirely through environment variables so it remains easy to embed inside MCP workflows.

VariableDescription
MEMORY_DB_DRIVEROptional selector for the database backend. Defaults to sqlite. Set to postgres to enable the PostgreSQL manager.
MEMORY_DB_PATHFilesystem path for the SQLite database file (only used when the driver is sqlite).
SQLITE_VEC_PATHOptional absolute path to a pre-built sqlite-vec extension shared library.
MEMORY_DB_DSN / DATABASE_URLPostgreSQL connection string consumed by the pg client.
PGHOST, PGPORT, PGUSER, PGPASSWORD, PGDATABASEIndividual PostgreSQL connection parameters. Used when no DSN is provided.
PGSSLMODEWhen set to require, SSL will be enabled with rejectUnauthorized: false.

PostgreSQL notes

  • The PostgreSQL manager requires the pgvector extension. It is automatically initialized with CREATE EXTENSION IF NOT EXISTS vector.

Claude Desktop:

{
  "mcpServers": {
    "memory": {
      "description": "Custom memory backed by SQLite + vec + FTS5",
      "command": "npx",
      "args": [
        "@iachilles/memento@latest"
      ],
      "env": {
        "MEMORY_DB_PATH": "/Path/To/Your/memory.db"
      },
      "options": {
        "autoStart": true,
        "restartOnCrash": true
      }
    }
  }
}

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 your node_modules directory.

If automatic vec loading fails:

# Find the Node.js-compatible vec extension
find node_modules -name "vec0.dylib"  # macOS
find node_modules -name "vec0.so"     # Linux

# Use it via environment variable
SQLITE_VEC_PATH="/full/path/to/node_modules/sqlite-vec-darwin-x64/vec0.dylib" memento

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
  • open_nodes
  • set_importance - Set importance level (critical/important/normal/temporary/deprecated)

An example of an instruction set that an LLM should know for effective memory handling (see MEMORY_PROTOCOL.md)

Embedding Model

This project uses @xenova/transformers, with a quantized version of bge-m3, running fully offline in Node.js.

License

MIT

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