By combining retrieval-based and learned memory with automatic routing, UniMem lets AI agents efficiently handle evolving task streams without manual task labels or fixed memory constraints.
UniMem is a memory management system for AI agents that balances two types of memory: episodic (retrieval-based) for new or rare tasks and parametric (learned weights) for recurring patterns. Using learnable routing tokens, it automatically decides which memory type to use for each task, avoiding the need for explicit task boundaries and enabling efficient learning from continuous task streams.