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Agent memory as a file format

calpaterson.com

1–10 of 114 posts

Re: Agent memory as a file format

#4
I've come to a similar lofi solution for my agent fleet. Markdown wiki with simple querying is decently effective as a memory system. Setting up a skill that can effectively reduce a session into useful long term lessons is the easiest unlock for these systems.

Re: Agent memory as a file format

#5
This was a compelling writeup to me. I read through the spec and found it easy to understand and make sense of.

I wonder how much my system needs something like this. Between the invisible system memory of my random chats with Gippity, my Matt Pocock skills saving terminology and plans, and whatever else Cursor and Codex do, I don't think I feel a need for more agent memory. I do like how it's exposed and searchable, and not invisible. But I honestly just send my questions/tasks away to my magic agent and eventually it gets it right anyway; do I need more discrete memory my team has to maintain? (That's an earnest question, not disregard for this)

Re: Agent memory as a file format

#7
Agent memory is to computer memory is what Mongo DB is to relational database.

Incredible to watch things come full circle. Next thing you know, someone is going to figure out a binary encoding.

Re: Agent memory as a file format

#8
post #3

I'm not convinced an unstructured collection of memory files is the way to go at all.

If you look at how agents navigate source code, they do not look at directory names, and drill down into the ones with plausible names, instead the grep the whole repo for plausible keywords.

Of course, ideally your data would be structured, but the agents will mostly be grepping anyway, and maybe look at sibbling files.

Re: Agent memory as a file format

#10
I see a lot of claims in this article without ... any proof?

Both can be true: - It's useful to anthropomorphize agents when predicting behavior and - we have to use specific language to specify what we mean.

What does the author mean by "confuse the models" ? Are they talking about not picking right information? Picking the wrong information? Losing their previous context / task?

Part of setting up a proper eval is also deciding what we actually mean ourself. What are we actually optimizing for? It's not, e.g. % confusion, %rubbish, etc.

The article does point to it: retrieval latency, accuracy, etc.

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