RAG Brain Memory Layer

Example Use Cases

• 1

  Persistent memory across sessions to retain context and preferences.

• 2

  Semantic search and quality-filtered recall of past decisions, bug fixes, and lessons learned.

• 3

  Continuous improvement of memory relevance through feedback and retraining.

Description

RAG Brain provides a persistent memory system for AI agents, allowing them to write and read memories with quality gating and semantic search. It continuously improves memory usefulness by retraining a quality model every 500 memories, promoting helpful memories and quarantining low-quality ones. The system integrates with Claude Code and supports Docker deployment, offering a robust solution to maintain context and improve agent performance over time.