Built a tool for myself and now I'm not sure what category it belongs in
I've been using KeepDB internally for months and recently started opening it up to other people.
It started because I had customer feedback from my apps, waitlist signups, notes, research, prompts, docs, and agent memory scattered across different places. Some of it belonged in a database, some in documents, and some was just context I wanted my agents to remember.
So I ended up building something that sits somewhere in the middle. Agents can save and search memories, but I can also use the same data through an API like a simple database. Everything is organized into folders so feedback doesn't get mixed with research, docs, prompts, or agent memory.
The funny thing is that most of the work wasn't storage. It was retrieval. Saving information is easy. Finding the right thing months later is hard.
I'm still trying to figure out where this fits. It's not trying to replace Postgres. It's not a traditional knowledge base. It's also not just memory for a single AI agent.
Curious what you're using today for feedback, notes, prompts, docs, and agent memory if you're building AI products.
Genuine question: for someone not yet building multi-agent systems, how does this compare to just using Notion with an API connector?
I get the value for agents that need to read/write programmatically, but I'm trying to understand where the line is between "Notion is enough" and "you actually need KeepDB".
What stood out to me isn't where KeepDB fits today.
It's whether the category question is even the right question to solve first.
The tricky part is that the same product can look like several different things depending on which usage pattern ends up mattering most.
That's what makes this kind of decision difficult early on.
Not because the product is unclear.
Because several interpretations can feel equally valid before enough evidence exists to separate them.