For those asking how this is different from a simple text based memory archive, I think that is answered here:
--- Unlike most memory systems—which act like basic sticky notes, only showing what’s true right now. C.O.R.E is built as a dynamic, living temporal knowledge graph:
Every fact is a first-class “Statement” with full history, not just a static edge between entities. Each statement includes what was said, who said it, when it happened, and why it matters. You get full transparency: you can always trace the source, see what changed, and explore why the system “believes” something. ---
I built this with simple text-based memory archive too. What you said is simply adding git to the equation. I tried many approaches and, to my surprise, Markdown + Git + plain-old UNIX tooling is powerful.
I've noticed that anchoring the tool on well thought out standards correlates with good performance.
Concretely: using Markdown, JSON, RFC 822 MESSAGE ID for identifying emails, or using self-contained binaries (or simply executable files with UNIX shebangs) are all instances of where I've converged after many attempts at using more complex techniques. Examples of those techniques are PostgreSQL, XML, trying to recreate what's essentially Git (for the time component), and even embeddings in some cases.
I think this is an instance of worse-is-better.
I'm not sure the graph offers any clear advantage in the demonstrated use case.
It's overhead in coding.
The source is the doc. Raw text is as much of a fact as an abstracted data structure derived from that text (which is done by an external LLM - provenance seems to break here btw, what other context is used to support that transcription, why is it more reliable than a doc within the actual codebase?).
Hey - i agree that the demonstrated use can be solved with simple plan.md file in the codebase itself.
With use-case we wanted to showcase the shareable aspect of CORE more. The main problem statement we wanted to address was "take your memory to every AI" and not repeating yourself again and again anymore.
The relational graph based aspect of CORE architecture is an overkill for simple fact recalling. But if you want an intelligent memory layer about you that can answer What, When, Why and also is accessible in all the major AI tools that you use, then CORE would make more sense.