Minimalism is my design philosophy :-)

Good question. Since it is just an LLM reading files, it depends entirely on how fast it can call tools, so it depends on the token/s of the model.

Haven't done a formal benchmark, but from the vibes, it feels like a few seconds for GPT-5.4-high per query.

There is an implicit "caching" mechanism, so the more you use it, the smoother it will feel.

What is the memory system you are referring to? I've been trying Memori with OpenClaw. Haven't had a ton of time to really kick the tires on it, so the jury's still out.

The armchair ML engineer in me says our current context management approach is the issue. With a proper memory management system wired up to it’s own LLM-driven orchestrator, memories should be pulled in and pushed out between prompts, and ideally, in the middle of a “thinking” cycle. You can enhance this to be performant using vector databases and such but the core principle remains the same and is oft repeated by parents across the world: “Clean up your toys before you pull a new one out!”

Also since I thought for another 30 seconds, the “too many memories!” Problem imo is the same problem as context management and compaction and requires the same approach: more AI telling AI what AI should be thinking about. De-rank “memories” in the context manager as irrelevant and don’t pass them to the outer context. If a memory is de-ranked often and not used enough it gets purged.

Context bloat is real, but the architecture has the potential to solve it.

You need clever naming for the filesystem and exploration policy in AGENTS.md. (not trivial!)

The benchmark is definitely the core bottleneck. I don't know any good benchmark for this, probably an open research question in itself.

This assumes that the model's behavior and memories are faithful to their english/human language representation, and don't stray into (even subtle) "neuralese".

Is there anything (besides plumbing) that prevents both? i.e. when the file is edited, all the representations are updated

The editability is surely an underrated advantage, both for the program itself and the memories it generated.

I think in terms of noise, it is less problematic here because not everything is being retrieved. The agent can selectively explore subsets of the tree (plus you can edit the exploration policy by yourself).

Since there is no context bloat, it is quite forgivable to just write things down.

Yep. Markdown is the future :-)