memento-mcp

Memento MCP Documentation# Core Concepts

Entities

Entities are the primary nodes in the knowledge graph. Each entity has:

• A unique name (identifier)
• An entity type (e.g., "person", "organization", "event")
• A list of observations
• Vector embeddings (for semantic search)
• Complete version history

Example:

{
  "name": "John_Smith",
  "entityType": "person",
  "observations": ["Speaks fluent Spanish"]
}

Relations

Relations define directed connections between entities with enhanced properties:

• Strength indicators (0.0-1.0)
• Confidence levels (0.0-1.0)
• Rich metadata (source, timestamps, tags)
• Temporal awareness with version history
• Time-based confidence decay

Example:

{
  "from": "John_Smith",
  "to": "Anthropic",
  "relationType": "works_at",
  "strength": 0.9,
  "confidence": 0.95,
  "metadata": {
    "source": "linkedin_profile",
    "last_verified": "2025-03-21"
  }
}

Storage Backend

Memento MCP uses Neo4j as its storage backend, providing a unified solution for both graph storage and vector search capabilities.

Why Neo4j?

• Unified Storage: Consolidates both graph and vector storage into a single database
• Native Graph Operations: Built specifically for graph traversal and queries
• Integrated Vector Search: Vector similarity search for embeddings built directly into Neo4j
• Scalability: Better performance with large knowledge graphs
• Simplified Architecture: Clean design with a single database for all operations

Prerequisites

• Neo4j 5.13+ (required for vector search capabilities)

Installation

To install memento-mcp for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @gannonh/memento-mcp --client claude

License

MIT