Reflection Bank

Reflection Bank is a sophisticated semantic caching system designed to enhance LLM performance by identifying, compressing, and reusing successful reasoning patterns. It analyzes session transcripts to extract core reasoning, converts verbose explanations into concise, parameterized behavior templates, and indexes these for sub-millisecond semantic retrieval using FAISS. This creates a powerful feedback loop that reduces token usage through semantic deduplication and selective injection, ultimately improving the consistency and efficiency of AI systems. This project emerged from research into cognitive pattern caching for AI, exploring how learned reasoning can be captured and reused across sessions.

Key Features

Use Cases