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Cuba Memroys MCP Server

@lENADRO1910

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

lENADRO1910

提交者

Leandro Pérez

关于 Cuba Memroys MCP Server

Persistent memory MCP for AI agents — Knowledge graph + Hebbian learning + Anti-hallucination. 12 tools, 1 dependency, zero manual setup.

#mcp#ai-memory#knowledge-graph#graphrag#hebbian-learning#anti-hallucination#postgresql#rag#semantic-search#spaced-repetition#pagerank#python

Cuba Memroys MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "cuba-memorys": {
      "command": "python",
      "args": [
        "-m",
        "cuba_memorys"
      ],
      "env": {
        "DATABASE_URL": "postgresql://cuba:memorys2026@127.0.0.1:5488/brain"
      }
    }
  }
}

Cuba Memroys MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Cuba Memroys?

Cuba Memroys gives AI coding assistants persistent long-term memory using a knowledge graph with entities, observations, and typed relations, Hebbian learning, GraphRAG enrichment, and anti-hallucination grounding. It provides 12 tools named after Cuban culture and requires zero manual setup.

How to use Cuba Memroys?

It auto-provisions its own PostgreSQL database via Docker, so no manual configuration is needed. The server exposes 12 tools, each inspired by Cuban cultural references, that let AI agents store, retrieve, and reason over knowledge across sessions.

Key features of Cuba Memroys

  • Knowledge graph with entities, observations, and typed relations
  • Hebbian learning (Oja's rule) and FSRS spaced repetition
  • Anti-hallucination with graduated confidence scoring
  • 4-signal RRF fusion search (TF‑IDF, full-text, trigrams, pgvector HNSW)
  • GraphRAG enrichment with degree-1 neighbor context
  • REM Sleep consolidation after 15 minutes idle
  • Error memory with anti-repetition guard
  • Graph analytics: PageRank, Louvain, betweenness, Shannon entropy

Use cases of Cuba Memroys

  • AI coding assistants that remember project context across sessions
  • Agents that avoid repeating past mistakes using error memory
  • Long-term knowledge curation with autonomous consolidation
  • Graph-based retrieval-augmented generation with topological context
  • Continuous learning systems that strengthen frequently used knowledge

常见问题

1

How does anti-hallucination work?

The server verifies claims against stored knowledge using graduated confidence levels: verified, partial, weak, or unknown.

2

What runtime dependencies does it have?

It requires Docker to automatically provision a PostgreSQL database. The server is built on peer-reviewed math (FSRS, Oja's rule, RRF, PageRank, HNSW, etc.).

3

How does Hebbian learning operate?

Memories strengthen with use via Oja's rule and fade adaptively using FSRS spaced repetition, making important knowledge more retrievable over time.

4

Where is the memory data stored?

All persistent data, including the knowledge graph and embeddings, is stored in a PostgreSQL database that the server auto-provisions inside Docker.

5

Does Cuba Memroys work with any AI model?

The README describes it as giving AI coding assistants long-term memory, implying integration via the standard MCP interface, but does not specify model constraints.

评论

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