How to Structure AI Agent Memory in TypeScript Using Three Distinct Layers

A software developer has outlined a practical TypeScript architecture for managing AI agent memory by separating it into three distinct types: working memory, long-term memory, and episodic memory. Working memory handles the active message window for the current session, while long-term memory stores durable facts about users or domains across conversations. Episodic memory logs records of previous runs for audit and operational analysis rather than direct model use. The approach addresses a common failure where all memory is stored in a single growing message array, causing important facts to be silently dropped when context limits are hit. The design makes each memory type explicit through separate interfaces, with long-term facts written only when the agent deliberately calls a dedicated tool and retrieved selectively at the start of each run.