A Practical Guide to Memory for Autonomous LLM Agents

Memory architecture is a critical component for autonomous LLM agents, often having a greater impact on performance than the specific model used. The process is best understood as a write-manage-read loop, where managing and curating information is essential but frequently overlooked. Memory is categorized into four temporal scopes—working, episodic, semantic, and procedural—each serving a distinct function. The discussion also explores various implementation mechanisms like retrieval-augmented stores and highlights common failure modes such as summarization drift and memory blindness.