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Reflex-based memory system for AI agents - retrieval through activation, not search

Project description

🐶 PugBrain: Reflex-based Neural Memory

Reflex-based memory system for AI agents — retrieval through activation, not search.

PugBrain is a hybrid neural-vector memory system designed for long-term agent continuity. It combines graph relationships with vector embeddings to simulate "reflexive" recall.

🚀 Quick Start

pip install "pug-brain[server]"

🛠 Features

  • Hybrid Retrieval: Neural graph + Vector similarity.
  • Built-in Dashboard: Visualize memory clusters and agent habits.
  • MCP Support: Ready for Claude Desktop and OpenClaw.
  • Pluggable Backends: SQLite (default), Neo4j, FalkorDB, ChromaDB.

🖥 Dashboard

Serve the memory system with a built-in UI:

pugbrain serve --port 18790

Visit: http://localhost:18790/ui

🧠 Core Philosophy

Memory shouldn't be a database query; it should be an activation signal. PugBrain treats entities as neurons that "fire" based on context.


Created by PugBrain Contributors. Woof! 🐶

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