Cuba Memroys
@lENADRO1910
Cuba Memroys について
Persistent memory MCP for AI agents — Knowledge graph + Hebbian learning + Anti-hallucination. 12 tools, 1 dependency, zero manual setup.
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"cuba-memorys": {
"command": "python",
"args": [
"-m",
"cuba_memorys"
],
"env": {
"DATABASE_URL": "postgresql://cuba:[email protected]:5488/brain"
}
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
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
FAQ from Cuba Memroys
How does anti-hallucination work?
The server verifies claims against stored knowledge using graduated confidence levels: verified, partial, weak, or unknown.
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.).
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.
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.
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.
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