What per-tool memory does well
Per-tool memory is genuinely useful for the individual. ChatGPT, Claude, and Cursor each remember your own history within that tool, so your next chat or session is a little more personalized. For a solo user staying inside one tool, that is often enough.
Where per-tool memory stops
Per-tool memory stops at two boundaries: the tool and the person. It does not carry context from Claude Code into Cursor, and it does not share your teammate's session with you. So the moment a team uses more than one AI tool, or more than one person, the picture fragments into private, partial silos.
A team is not one user in one tool. It is many people across many AI tools, all generating context that, by default, never reaches each other.
What a shared team memory layer adds
A shared team memory layer adds the dimension per-tool memory lacks: it sits above the tools and across the team. It captures decisions and context from each session, meeting, and document, and recalls them in any tool, for any teammate, so the team works from one shared brain instead of many disconnected memories.
- -Agent-agnostic: context lives above the tools, not locked to one editor (Claude Code today, more agents on the roadmap).
- -Cross-team: one engineer's session is context for the next engineer's.
- -Beyond code: meetings and documents feed the same memory.
Which do you need?
If you are a solo user inside one tool, built-in memory may be all you need. If you are an AI-native team running many sessions across several tools, you need a shared memory layer; built-in memory cannot span tools or people. The two are complementary: the layer works alongside each tool's own memory.