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memento logo

memento

gannonh/memento-mcp

Scalable, high performance knowledge graph memory system with semantic retrieval, contextual recall, and temporal awareness. Provides any LLM client that supports the model context protocol (e.g., Claude Desktop, Cursor, Github Copilot) with resilient, adaptive, and persistent long-term ontological memory.

View on GitHub48

Install

npx -y @gannonh/memento-mcp

Client configuration

{
  "mcpServers": {
    "memento": {
      "command": "npx",
      "args": [
        "-y",
        "@gannonh/memento-mcp"
      ],
      "env": {
        "MEMORY_STORAGE_TYPE": "<MEMORY_STORAGE_TYPE>",
        "NEO4J_URI": "<NEO4J_URI>",
        "NEO4J_USERNAME": "<NEO4J_USERNAME>",
        "NEO4J_PASSWORD": "<NEO4J_PASSWORD>",
        "NEO4J_DATABASE": "<NEO4J_DATABASE>",
        "NEO4J_VECTOR_INDEX": "<NEO4J_VECTOR_INDEX>",
        "NEO4J_VECTOR_DIMENSIONS": "<NEO4J_VECTOR_DIMENSIONS>",
        "NEO4J_SIMILARITY_FUNCTION": "<NEO4J_SIMILARITY_FUNCTION>",
        "OPENAI_API_KEY": "<OPENAI_API_KEY>",
        "OPENAI_EMBEDDING_MODEL": "<OPENAI_EMBEDDING_MODEL>",
        "DEBUG": "<DEBUG>"
      }
    }
  }
}

Environment variables

MEMORY_STORAGE_TYPENEO4J_URINEO4J_USERNAMENEO4J_PASSWORDNEO4J_DATABASENEO4J_VECTOR_INDEXNEO4J_VECTOR_DIMENSIONSNEO4J_SIMILARITY_FUNCTIONOPENAI_API_KEYOPENAI_EMBEDDING_MODEL
GitHub stars
48
Author
@gannonh
Category
Knowledge Management, Developer Tools
License
MIT
Updated
Oct 6, 2026

Features

  • Vector Embeddings: Entities are automatically encoded into high-dimensional vector space using OpenAI's embedding models

  • Cosine Similarity: Find related concepts even when they use different terminology

  • Configurable Thresholds: Set minimum similarity scores to control result relevance

  • Cross-Modal Search: Query with text to find relevant entities regardless of how they were described

  • Multi-Model Support: Compatible with multiple embedding models (OpenAI text-embedding-3-small/large)

  • Contextual Retrieval: Retrieve information based on semantic meaning rather than exact keyword matches

  • Optimized Defaults: Tuned parameters for balance between precision and recall (0.6 similarity threshold, hybrid search enabled)

  • Hybrid Search: Combines semantic and keyword search for more comprehensive results

  • Adaptive Search: System intelligently chooses between vector-only, keyword-only, or hybrid search based on query characteristics and available data

  • Performance Optimization: Prioritizes vector search for semantic understanding while maintaining fallback mechanisms for resilience

Tools (17)

  • create_entities

  • add_observations

  • delete_entities

  • delete_observations

  • create_relations

  • get_relation

  • update_relation

  • delete_relations

  • read_graph

  • search_nodes

  • open_nodes

  • semantic_search

  • get_entity_embedding

  • get_entity_history

  • get_relation_history

  • get_graph_at_time

  • get_decayed_graph

Details on this page are taken from the project's README. Open README

Supported clients

Clients mentioned in this server's README:

View all
Claude Desktop logo

Claude Desktop

Desktop · Freemium · Proprietary

Anthropic's official Claude AI desktop application. Supports MCP servers to extend functionality.

WindowsMacOS
Cursor logo

Cursor

Desktop · Freemium · Proprietary

The first agentic IDE. The Cursor editor truly merges how developers and AI work together, delivering a magical coding experience.

WindowsMacOSLinux

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