I built Nemp Memory because I think there is an important distinction in AI memory:
memory inside one AI tool is not the same as memory for your project.
A lot of tools are adding their own memory now, which is useful. But if each tool has its own memory, project knowledge gets fragmented. When you switch tools, you end up repeating the stack, architecture decisions, auth setup, API patterns, and debugging lessons.
The question I’m interested in is not just:
which AI tool has memory?
It is:
can project memory survive tool switching?
Nemp is an experiment in local, portable project memory. The goal is to keep memory readable, local-first, and reusable across tools rather than trapped inside one assistant.
Would especially love feedback on:
1. whether “tool memory vs project memory” resonates
2. how people here are handling memory across multiple AI tools today
