The thing: A prosthetic hippocampus for LLM's providing human like memory over the last year. You can check out a demo here: https://kijito.ai/demo and I'd love feedback. It at times has beaten competition in leading benchmarks and generally consumes ~20x less tokens to do so.
About a year ago I was thinking about LLMs a lot, their strengths and their limitations, mostly daydreaming on bike rides. It hit me that an LLM isn't so different from a person with a fully functional frontal lobe but only a partially functional hippocampus. High executive function, high intelligence, very little ability to gather experience in any meaningful way. It's like someone who has to rely almost entirely on notebooks for any memory formed after the day their hippocampus stopped working, endlessly filling notebook after notebook as they get more lossy and harder to manage.
From there I started daydreaming about what a prosthetic hippocampus would have to look like, and what it would have to do to support an LLM. I've been an engineer for more than a decade, but my background was originally in bio, so I pulled every bit of research I could find on how the human hippocampus actually works.
Then I started building it. That was nearly a year ago, and it's been a tremendous amount of work.
I named it Kijito, Swahili for small stream. It's a descendant of the original name, Tributary, because I first pictured it as memories flowing in and out of a network of streams.
It's an associative recall and storage engine modeled on how the hippocampus works. When you remember something, other memories come with it, some more strongly associated than others. When you look back, you remember when you were wrong, how you found out and what you learned. You can reflect on your memories and pull out bigger lessons from the patterns. In a word, you can grow.
Because recall is associative instead of exhaustive, an agent gets back a handful of related memories instead of rereading whole notebooks, and that saves a lot of context. In Claude Code it adds about 1.5k tokens at session start. It's let me run many more agents on a single Max 20 subscription than I could before, I believe. I've benchmarked it privately on the public memory benchmarks and it holds its own, soon I'll be publishing all of that.
For anyone curious about what's underneath: an embedded graph/vector database I've extended for this specific job, a quantized embedding model to encode memories, and the layer agents use to talk to their prosthetic hippocampus.
Your memories are encrypted at rest with a key derived from your password. If you lose your password and your recovery codes, the data can't be recovered. So if you try it, please back those codes up.
It's a beta, but I've been careful about stability - I have a decent number of users who depend on it now. Updates only go out in a window in the middle of the night, a restart holds requests for a few seconds instead of dropping them, and everything is backed up nightly and encrypted, with restores tested.
I've put together a demo where you can act as an agent querying, kijito.ai/demo is a staged example on a live, read-only account. Ask it questions and watch a corrected decision stay out of recall.
