for one, the mcp-server architecture makes it usable from claude.ai and other surfaces where you have mcp but no filesystem. there are claude-specific workarounds (workspaces) but you lose portability across systems.

[delayed]

Please excuse my noob-ish, naïve question, but to what extent is the business of getting the LLM to actually consult memory a model-dependent thing? Do you have to introduce the tool and guide models with different language for different model families?

Looking at your tool descriptions (as wit the ones on the original post) I wonder if this something perhaps only current frontier models will do, but the systems themselves seem like they'd be even more useful for open weights models with shorter working contexts.

Since you built something on OKF, how would you contrast it with knowledge graph implementations? How do you manage the ontology of what to keep knowledge about? Any cases where traversal would have helped?

I have seen the value of recording past sessions but I am more skeptical of the value in recording facts, which may soon become stale, about a constantly changing code base. Got benchmarks?

OKF is basically md files with front-matter for meta data

that's a good idea. how might you test this? could also include a codex memory test.

I'm guessing having a portable memory that's comparable with first party memory is the goal.