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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