Memento▌

by iachilles
Memento enables persistent memory with a SQLite-based knowledge graph for intelligent context retrieval using advanced B
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.
best for
- / AI assistants that need long-term memory
- / Chatbots requiring context from previous conversations
- / Knowledge management systems with semantic search
- / Applications needing persistent entity relationships
capabilities
- / 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
what it does
Creates persistent memory for AI conversations using a SQLite knowledge graph that stores entities, observations, and relationships with semantic search capabilities.
about
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
MIT
Memento is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
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, andrelations - 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:
| Platform | Install command |
|---|---|
| macOS (Homebrew) | brew install sqlite |
| Debian / Ubuntu | sudo 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.
| Variable | Description |
|---|---|
MEMORY_DB_DRIVER | Optional selector for the database backend. Defaults to sqlite. Set to postgres to enable the PostgreSQL manager. |
MEMORY_DB_PATH | Filesystem path for the SQLite database file (only used when the driver is sqlite). |
SQLITE_VEC_PATH | Optional absolute path to a pre-built sqlite-vec extension shared library. |
MEMORY_DB_DSN / DATABASE_URL | PostgreSQL connection string consumed by the pg client. |
PGHOST, PGPORT, PGUSER, PGPASSWORD, PGDATABASE | Individual PostgreSQL connection parameters. Used when no DSN is provided. |
PGSSLMODE | When set to require, SSL will be enabled with rejectUnauthorized: false. |
PostgreSQL notes
- The PostgreSQL manager requires the
pgvectorextension. It is automatically initialized withCREATE 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_PATHenvironment variable if automatic detection fails. This should point to the Node.js-compatible version of the extension, typically found in yournode_modulesdirectory.
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_entitiescreate_relationsadd_observationsdelete_entitiesdelete_relationsdelete_observationsread_graphsearch_nodesopen_nodesset_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
FAQ
- What is the Memento MCP server?
- Memento is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Memento?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
Memento is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated Memento against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: Memento is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
Memento reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend Memento for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: Memento surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
Memento has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, Memento benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired Memento into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
Memento is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.