How does anti-hallucination work?
The server verifies claims against stored knowledge using graduated confidence levels: verified, partial, weak, or unknown.

@lENADRO1910
基本信息
Persistent memory MCP for AI agents — Knowledge graph + Hebbian learning + Anti-hallucination. 12 tools, 1 dependency, zero manual setup.
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"
}
}
}
}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。
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.
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.
The server verifies claims against stored knowledge using graduated confidence levels: verified, partial, weak, or unknown.
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.).
Memories strengthen with use via Oja's rule and fade adaptively using FSRS spaced repetition, making important knowledge more retrievable over time.
All persistent data, including the knowledge graph and embeddings, is stored in a PostgreSQL database that the server auto-provisions inside Docker.
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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