How are people handling persistent memory for AI agents?

One issue I keep running into while experimenting with local AI agents is that most systems are basically stateless. Once a conversation resets, everything the agent "learned" disappears. That means agents often end up rediscovering the same preferences, decisions, or context over and over again. I've been experimenting with different approaches to persistent memory for agents.