TL;DR the 30-second version
A static graph tells you what’s true now. An episodic graph tells you what was true at a point in time — which is what agents actually need to reason about change, audits, and their own past decisions.
In part one we argued that relationships beat raw similarity. But real knowledge isn’t static — facts get superseded, preferences change, decisions get revised.
The problem with overwriting
If your memory layer just upserts the latest value, you lose history. An agent asked “why did we switch databases?” can’t answer, because the previous state is gone.
Bi-temporal in practice
Graphiti tracks both when something happened and when the system learned about it. That bi-temporal model is what lets an agent reconstruct the world as it was — essential for audits, debugging, and explaining its own past decisions.
The cost is more edges and more careful queries. The payoff is memory that reflects reality instead of flattening it.