Git for your agent's beliefs.

A model forgets between sessions — but the deeper issue is what it keeps: beliefs with no source, no expiry, no way to tell if they still hold or to correct the wrong ones.

An agent that acts on a stale or wrong belief doesn't feel broken — it feels confident. And the more it runs on its own, the more that confidence costs you.

Every belief is a typed engram — a claim, a decision, a status — with a source, a lifecycle, and a scope. New facts supersede old ones instead of piling up.

That makes the whole memory legible: you can search what your agent knows, trace where each belief came from, and the relevant piece surfaces right when it's needed. Strong recall is the floor Engram stands on — staying current and correct is what sets it apart.

Beliefs get checked against what actually happened, build a track-record, and stale or disproven ones are superseded automatically — so the memory gets more accurate over time instead of drifting.

A self-closing loop nobody else ships. Not “where did this come from” — but “was it right, and is it still true?”

When several people and agents work off one brain, they need shared state, not just shared storage — who claimed what, whose belief wins on a conflict, what's still current.

Engram is the typed-state layer under the team-AI stack: shared memory with scope, provenance, and supersession built in. Git for agent beliefs, not Dropbox for agent content.

Long-lived, acting agents where a wrong belief has real cost — the ones you act on, not the ones you demo once.

As models get more capable and autonomous, ungrounded memory gets more costly, not less. Engram fills the gap every bigger model makes more important.