The memory system that leaks like an old bucket

Every AI memory system I've built so far has eventually failed me, and it turns out the ways it can fail are countable. I recently came across research from Verging Labs that compared different AI memory systems against exactly three major modes of failure: the agent never saved the information in the first place, the agent never used its memory at all, or the agent failed to retrieve the information from memory when it mattered.

For me, the most emotional moment, by far, is when I've spent time building a balanced system and then realise, once real use starts, that it leaks like an old bucket. It either has no idea where to look for its instructions or completely ignores the context provided to it and supplies hallucinated responses instead. I can't explain why it feels so frustrating, but I've been through this more times than I can admit.

So I'm genuinely glad to see plain-text wikis showing good results against the more complex systems. That should probably be expected, at least until speed becomes an actual bottleneck, which takes tens of thousands of nodes. I do wonder whether they tried anything like Optmem.

Frankly, I had no idea OpenClaw had any memory management, but maybe I could try utilising it: I'm now close to a somewhat stable (!) release of my entirely OpenClaw-based agentic network. Verging Labs' next round is set to cover Hermes and OpenClaw, and I'll be reading that one closely. If OpenClaw's built-in memory turns out to be any good, I may finally own a bucket that holds water.

Yours, without a second brain,
Zhenia