I’ve been researching how chatbots handle memory and the current state is pretty underwhelming. Most implementations just dump your past messages into a vector database and retrieve whatever looks “similar.” That’s not memory — that’s search.
Think about what actual memory does for a human conversation:
You remember facts about the person — they’re a developer, they prefer Python, they have a dog named Max.
You remember what happened — last time I suggested X, they said it didn’t work for their use case. That recommendation was a miss.
You remember what works — this person responds better to direct answers, not long explanations. When I gave step-by-step last time, they actually followed through.
Most chatbots only do the first one, and even that poorly. The second and third are where conversations start feeling genuinely personalized instead of “I looked up your name in a database.”
I’ve been working on this problem myself — building an open-source memory API that separates these three memory types instead of dumping everything into one vector store. Early stage but the approach is showing promise: github.com/alibaizhanov/mengram
Curious what experiences people here have had — has anyone found a chatbot that actually gets memory right?
submitted by /u/No_Advertising2536
[link] [comments]