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Memento MCP Server

@gannonh

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stdio

作者

gannonh

投稿者

Gannon Hall

Memento MCP Server について

概要はまだありません

#knowledge-graph#memory-system#semantic-search

Memento MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "memento": {
      "command": "npx",
      "args": [
        "-y",
        "@gannonh/memento-mcp"
      ],
      "env": {
        "MEMORY_STORAGE_TYPE": "neo4j",
        "NEO4J_URI": "bolt://127.0.0.1:7687",
        "NEO4J_USERNAME": "neo4j",
        "NEO4J_PASSWORD": "memento_password",
        "NEO4J_DATABASE": "neo4j",
        "NEO4J_VECTOR_INDEX": "entity_embeddings",
        "NEO4J_VECTOR_DIMENSIONS": "1536",
        "NEO4J_SIMILARITY_FUNCTION": "cosine",
        "OPENAI_API_KEY": "your-openai-api-key",
        "OPENAI_EMBEDDING_MODEL": "text-embedding-3-small"
      }
    }
  }
}

Memento MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is Memento?

Memento is a knowledge graph memory system for large language models (LLMs) that provides persistent ontological memory via the Model Context Protocol (MCP). It uses Neo4j as its storage backend to manage entities, relations, semantic search, temporal awareness, and confidence decay. It is designed for any MCP-compatible client such as Claude Desktop, Cursor, and GitHub Copilot.

How to use Memento?

Set up a Neo4j database (version 5.13 or later) using Neo4j Desktop or Docker Compose. Configure the connection via environment variables or the provided CLI utilities (e.g., npm run neo4j:test). The schema initializes automatically on first connection. MCP tools are then available to the LLM client for creating, reading, updating, and deleting entities and relations.

Key features of Memento

  • Entity management with unique names, types, observations, and vector embeddings.
  • Relation management with strength, confidence, metadata, and temporal version history.
  • Semantic search using vector embeddings and cosine similarity with configurable thresholds.
  • Temporal awareness preserving full version history and point-in-time queries.
  • Confidence decay of relations over time based on a configurable half-life.
  • Rich metadata support including source tracking, custom tags, and structured data.

Use cases of Memento

  • Providing LLM clients with persistent, updatable memory across conversations.
  • Building knowledge graphs that track evolving relationships and facts over time.
  • Enabling semantic retrieval of related concepts even when terminology differs.
  • Maintaining audit trails of how knowledge was acquired and modified.
  • Supporting point-in-time analysis of historical graph states.

よくある質問

1

What problem does Memento solve?

Memento gives LLMs a scalable, high-performance knowledge graph memory so they can remember entities, relationships, and observations persistently across sessions, without retraining.

2

What are the runtime requirements?

Neo4j 5.13 or later is required. The server can be run locally via Neo4j Desktop or Docker Compose. Node.js is needed to run the MCP server itself.

3

Where is data stored?

All data (entities, relations, embeddings, version history) is stored in a Neo4j database. When using Docker, data persists in the mapped volumes (`./neo4j-data`, `./neo4j-logs`, `./neo4j-import`).

4

How can I reset the database?

Stop the Neo4j container, remove the data directory contents (`rm -rf ./neo4j-data/*`), restart the container, and reinitialize the schema with `npm run neo4j:init`.

5

What transports and authentication are supported?

Memento uses the Model Context Protocol (MCP) for transport. Neo4j authentication is required (default credentials: username `neo4j`, password `memento_password`).

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