Supermemory

“So the Claude code one performed the worst, and OpenClaw slightly more than that, and SuperMemory is the highest, and you can see that, you know, it's like a pretty significant difference, like almost 50%.”

“The way OpenClaw has with QMD or with whatever memory plugin you use, it inherently relies on tools to search through these memory.md files that it prepares. So, you know, like, what did I decide about the API? Then agent will decide to search, and sometimes i…”

“I open sourced SuperMemory during BuildSpace and it Like the project absolutely blew up. It got like, you know, it was one of the biggest projects, like fastest growing projects of 20, 24.”

“Traditionally you have this retrieval thing that happens. So you get a question, you retrieve something, and an answer is generated based on that. But we think that it will not work for most non-literal questions, like find the best monitor for me. If I've nev…”

“The way OpenClaw does it is it essentially sends back the last 15 messages in the conversation and it essentially uses that back and forth. And I mean, the approach itself is not ideal because you will, like, you are not doing any, like, you're not utilizing a…”

“Essentially, you will return the memories first, and then if there's any raw chunks that match up, we also return those to make sure the agent knows just enough information to answer the question. And no other provider does this right now”

“And I was like, this consumer app is probably not useful anymore. This core infrastructure is more useful now. And that is our core business now. And that's what we offer.”

“And we think that triplets actually Lead to worse performance because you have to traverse them a lot to get to any information.”

“So we basically turned the tools based approach to hooks based approach that actually is dealing from the super memory graph, which keeps the content fresh. So it handles updates, et cetera. So now, you know, for every tool call or for every user message, a ho…”

“So, so, better databases, like, at that time, and even now, except a few, like, TurboPuffer and Chroma, Were not really scalable for the scale I was getting to where they were either getting too slow or too expensive to run.”

“For us, we have optimized infrastructure down to, it costs us two cents for a million tokens, because we have our own model, our own database, our own everything.”

“So, to make an LLM truly good at understanding a user, you have to handle four things. One is knowledge updates, so you have to invalidate, steal knowledge, and build on top of it, which is different from the storing vectors. You have to have some sort of temp…”