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memento-mcp

by gannonh·418·Score 49

Knowledge graph memory system with semantic retrieval and temporal awareness for LLMs.

knowledge-graphai-llmdeveloper-tools
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Overview

Memento MCP is a sophisticated knowledge graph memory system that provides LLMs with persistent, adaptive long-term memory. It uses Neo4j as its backend, enabling both graph storage and vector search capabilities. The system supports entities with rich metadata, relations with confidence scoring, and temporal tracking of all changes, allowing for point-in-time queries and historical analysis.

Try asking AI

After installing, here are 5 things you can ask your AI assistant:

you:Building persistent memory for AI assistants to remember user preferences and context
you:Creating knowledge bases that evolve over time with confidence tracking
you:Implementing semantic search capabilities for LLM applications
you:What makes Memento different from other memory systems?
you:Can I use it with other LLM clients besides Claude Desktop?

When to choose this

Choose Memento MCP when you need persistent knowledge graph memory with semantic retrieval capabilities for LLM applications, especially when you're already using or willing to set up Neo4j.

When NOT to choose this

Don't choose Memento MCP if you need a lightweight solution without additional database dependencies or if you require graph operations without vector search capabilities.

Tools this server exposes

12 tools extracted from the README
  • create_entities

    Create multiple new entities in the knowledge graph

  • add_observations

    Add new observations to existing entities

  • delete_entities

    Remove entities and their relations

  • delete_observations

    Remove specific observations from entities

  • create_relations

    Create new relations between entities with enhanced properties

  • get_relation

    Get a specific relation with its enhanced properties

  • update_relation

    Update an existing relation with enhanced properties

  • delete_relations

    Remove specific relations from the graph

  • read_graph

    Read the entire knowledge graph

  • search_nodes

    Search for nodes based on query

  • semantic_search

    Search for entities semantically using vector embeddings

  • get_entity_history

    Get complete version history of an entity

Comparable tools

neo4j-mcpsemantic-memory-serverlanggraph-memoryvector-storage-mcp

Installation

Installation

Prerequisites

  • Neo4j 5.13+

Setup Options

Neo4j Desktop Setup (Recommended)

  1. Download and install Neo4j Desktop
  2. Create a new project
  3. Add a new database
  4. Set password to memento_password
  5. Start the database

Docker Setup

docker-compose up -d neo4j

MCP Configuration

Add to Claude Desktop config:

{
  "mcpServers": {
    "memento": {
      "command": "npx",
      "args": ["-y", "@gannonh/memento-mcp"]
    }
  }
}

FAQ

What makes Memento different from other memory systems?
Memento uses a knowledge graph approach with Neo4j, providing temporal awareness, semantic search, and confidence decay features that traditional key-value memory systems don't offer.
Can I use it with other LLM clients besides Claude Desktop?
Yes, Memento MCP works with any LLM client that supports the Model Context Protocol, including Cursor and GitHub Copilot.

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Last updated · Auto-generated from public README + GitHub signals.