Cuba Memroys

Created By
Leandro Pérez G3 months ago
Persistent memory MCP for AI agents — Knowledge graph + Hebbian learning + Anti-hallucination. 12 tools, 1 dependency, zero manual setup.
Overview

Persistent memory for AI agents — 12 tools with Cuban soul.

Cuba-Memorys gives AI coding assistants long-term memory with a knowledge graph, Hebbian learning, GraphRAG enrichment, and anti-hallucination grounding.

What makes it different:

Knowledge graph — Entities, observations, and typed relations that persist across sessions Hebbian learning — Memories strengthen with use (Oja's rule) and fade adaptively (FSRS spaced repetition) Anti-hallucination — Verify claims against stored knowledge with graduated confidence scoring (verified/partial/weak/unknown) 4-signal RRF fusion search — TF-IDF + full-text + trigrams + optional pgvector HNSW GraphRAG — Top results enriched with degree-1 graph neighbors for topological context REM Sleep — Autonomous background consolidation after 15min idle Error memory — Never repeat the same mistake twice with anti-repetition guard Graph analytics — Personalized PageRank, Louvain communities, betweenness centrality, Shannon entropy Built on peer-reviewed math: Wozniak/Ye (FSRS), Oja (Hebbian), Cormack (RRF), Brin & Page (PageRank), Collins & Loftus (spreading activation), Malkov & Yashunin (HNSW).

Zero manual setup. Auto-provisions its own PostgreSQL via Docker. 12 tools, all named after Cuban culture.

Server Config

{
  "mcpServers": {
    "cuba-memorys": {
      "command": "python",
      "args": [
        "-m",
        "cuba_memorys"
      ],
      "env": {
        "DATABASE_URL": "postgresql://cuba:memorys2026@127.0.0.1:5488/brain"
      }
    }
  }
}
Project Info
Created At
3 months ago
Updated At
3 months ago
Author Name
Leandro Pérez G
Star
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