memory

Every Ditto user already has a knowledge graph. As you talk, Ditto extracts subjects (typed topics, people, projects, preferences) and links each one to the memories it touches. (That’s the dreaming pipeline at work.) We’d already seen that matching a query to subjects, rather than only to raw memories, was a powerful signal. v4 leans on it directly.

Storing memories is easy. Finding the right one from a vague question is the hard research problem. When you ask Ditto something indirect, like “what did I decide about that thing a while back?”, there’s no keyword to match on, just a fuzzy query against a haystack of everything you’ve ever told it.

We have been exploring how agents can build on that coordinate system. The files and symbols earlier agents repeatedly read, edited, cited, or returned to can become landmarks for the next agent, resolved against the code as it exists now and linked back to the conversations where that understanding was formed. By preserving the causal metadata beneath that surface representation, like which operation produced each fragment and what prior state it built on, DeltaDB gives the model a way to traverse not just the current code, but its provenance, accumulated attention, and prior reasoning.