Many Retrieval-Augmented Generation (RAG) systems struggle because they view memory as a static entity, akin to a filing cabinet. This leads to clutter, as outdated bug fixes and obsolete rules accumulate, making it difficult for the system to function efficiently. The proposed solution takes a different approach by applying the principles of the Ebbinghaus forgetting curve, viewing memory as a dynamic and evolving resource. In this model, each memory is given a "strength" score that increases with each successful recall, effectively reducing its decay rate. This innovative method aims to streamline memory management, alleviating token costs and improving the reasoning capabilities of AI agents.