Agent memory is one of those things that sounds straightforward until you actually try to build it. The filesystem metaphor makes a lot of sense as a starting point.
I've been working on a slightly different angle with MemoryLane (https://github.com/deusXmachina-dev/memorylane) - instead of giving agents a place to write their own memories, it captures the user's screen activity and makes it queryable. So the agent gets context about what the human was doing, not just what the agent itself did. It plugs in via MCP so Claude Code / Cursor can just ask it stuff.
I think there's something interesting in combining both - agent-vfs for the agent's own state, and something like MemoryLane for the human side. How do you think about that boundary between what the agent remembers vs what it knows about the user?
We need to use vector DBs just because of the amount of data. But on a different layer we want to help create file-based instrucations/skills for patterns that we detect and think can be automated.
I've been working on a slightly different angle with MemoryLane (https://github.com/deusXmachina-dev/memorylane) - instead of giving agents a place to write their own memories, it captures the user's screen activity and makes it queryable. So the agent gets context about what the human was doing, not just what the agent itself did. It plugs in via MCP so Claude Code / Cursor can just ask it stuff.
I think there's something interesting in combining both - agent-vfs for the agent's own state, and something like MemoryLane for the human side. How do you think about that boundary between what the agent remembers vs what it knows about the user?
We need to use vector DBs just because of the amount of data. But on a different layer we want to help create file-based instrucations/skills for patterns that we detect and think can be automated.