New compression technique enables AI agents to retain more context efficiently while reducing memory overhead

Steno introduces a memory compression method designed specifically for AI agents using Retrieval-Augmented Generation (RAG)

The approach addresses the challenge of managing large context windows and memory constraints in AI agent systems

Available as an open-source project on GitHub for developers to integrate into their AI agent implementations

Offers a potential solution for making AI agents more efficient without sacrificing access to historical information