Is this a significant problem? It seems like there are 15 solutions to every AI “text format” problem, but all are merely structure over feeding the AI informal English, structure which I believe the “transformer” model is specifically able to strip to a common form (which is how training it on the entire internet worked).

AI does benefit from learning to use skills, and being instructed with good structures (like “save [specific important kind of detail] in memories” and “plan before implementing anything”). But none of these memory solutions seem better than any other, they’re basically just different folders. Your CLAUDE.md can say “see AGENTS.md” and AGENTS.md can say “write memories to .agent/memories/“, and I’d be interested in a measurement, but I assume it performs almost the exact same as native CLAUDE.md and .claude/memories.

One thing I’ve noticed AI does much better than me, is not have OCD over the “perfect” solution.

Entire blog post reeks of LLM. If there was an adequately reliable way of detecting LLM text i'd build a browser extension that would just filter it out altogether.

Hacker News needs its slop-rejection moment.

On the general premise, I agree. But I don't think that moment is today, not yet anyway. I don't think we understand the right way to approach context and memory yet or how long they should last for, never mind standardizing on an abstraction prematurely that ends up being like SOAP or SGML.

Do we really need this..? What LLMs do well is interpret natural language. So it's a sponge for whatever format is used in a specific project: a predefined format is not really needed.

Besides, do we really want memory of user A's agents to co-exist with memory of user B? Not really. So the argument for collaboration and sharing is quite slim, at least for coding agents. The repository specific context can be encoded into a docs/ or architecture/ directory, just like it's been done pre-agent times as well.

For monitoring deployed agents: maybe. For this I built (yet another) memory management tool slivingdoc.dev, with this it's possible to pull down the durable context of all agents. But here the problem is a distributed system one, not format of the memories.

The need for a unified format or spec is greatly reduced in the case of Agent memory because by definition it is read by agents, and agents are already instructed / programmed to read memory files of different formats. Even if such instructions did not exist, agents are extremely good at traversing files to get the necessary context it needs.

I am curious about what people do these days and how do they tackle this problem. At repo level you can alter by with AGENTS.md and perhaps a curated set of additional docs with the project history, but as soon as you get out of it and want to have additional context the solutions available are too many - Hindsight, Graphiti, and many others; or simply roll out your own? I would like something I can spin up with Postgres or with an S3 bucket and that writes me .md files while retaining some sort of relationship among different entities - however it looks like there is no “simple” solution out there yet, with many of the existing implementations being way too elaborate and/or requiring exotic DB solutions. Would be very interesting to hear if someone knows of a bare bones solution I can plug into any agent.

What about .. reference identity.. currently memories and recall are referencing local things, that are specific to the environment they were made. and in ways that are as context eficient as possible like @message100Line10-50 or sessionsabc@100-300 is ./yourfiles/thisfile.mb ..

but is were going to make the memorys portable.. we need to make the things they are pointing to portable also..

I have been running openviking self-hosted for 6+ months now and it has worked well for me. It is open source, self-hostable, and you can bring your own LLM and embedding model.

The bytedance team makes it. I often wonder if it does not get attention here in America b/c it is Chinese?

Should we even be storing 'memories' in text files at all?

Imagine how humans would function if we carried our memories around in overflowing notepads and had to consult them every time we wanted to know a fact or remember a person we had met, it would be exhausting and inefficient.

Well, coding agents are smart, they can use all kinds of memory. A simple skill on how and when to access it is enough...

How does this handle parallel sessions writing the same memory? From a quick look at the repo, it seems that one session can silently overwrite another. Anthropic's recent memory post shows how severe this can be (https://www.anthropic.com/research/components-of-agent-memor...). Should the spec cover this?

Working with LLM agents comes with a hard pill to swallow: they are already remarkably good at figuring out what to do on their own. In fact, the more rigid structures we impose on a system, the less efficient these agents become.

While there have been many attempts to structure how agents operate, a recent Anthropic article showed that when they didn't tell Claude how to use memory, it actually used its memory better. Ultimately, we have to come to terms with how little is required from us when LLMs are this intelligent. often doing things better in ways that we sometimes cannot comprehend.

I agree this is very much needed but I doubt the other ai companies would a-comply with this as they need users and mainly paid users otherwise they'll lose themselves. What I think is that with 20$ subs of 2-3 ai can give you more usage compared to their 100$ subs if you do it that way. I don't know will these companies work together to form a standardized memory system. I highly doubt that.

I'm ready for the conversation to just assume a linux file system with mostly markdown. And you schedule some time every day for an agent to make it not suck as much as a living archive.

What are some real issues now? Auth? You want to retroactively implement boundaries? Is version control the problem?

I think It needs to be as diverse as people .. If agents are to become our constant "muse" like companions, I want the wiring of the brain of mine to be as wired as possible.. Why not juggling chain saws in orbit.. https://www.youtube.com/watch?v=JcJSW7Rprio&t=17s

This seems like local memory for one user. The need for corporate agent memory that works across users and company processes is acute.

Do you mean another overcomplicated enteprise standard teeming with constant CVEs to increase surveillance and control?

at this point i'm pretty convinced agent memory is strictly harmful. it remembers the wrong things, and then turns them into an inane focus for each task. i disable it universally on every harness. if i want memory, i specifically instruct the agent to record findings into a file.

So we need another complicated format to store information so that the machines which read unstructured text can work with it?

Some times when I see ai generated text like this, it feels possible to see how sophisticated their AI skills are if they couldn’t even get the text to render better, and how that might transfer to the code itself.

This looked interesting, unfortunately it feels far too long for humans to spend time reading.

People might really be starting to write like LLMs, it’s why reading books is the single biggest anti-slopping protection for the human brain.

The “shape” of the article was a word generator performatively generating larger words where simpler ones would do. “That holds”

Maybe I’m going to get downvoted for this but premature-standardisation is a problem.

We only have OpenTelemetry because of years of people working out what metrics and logging look like.

It’s too early in my opinion to standardise agent memory.

Not trying to be disingenuous but what's wrong with just a normal filesystem?

The best version of memory I've experienced is when I give my agent a memory directory and just let it do what it wants in there, with minimal rules on my end.

it's called a markdown file

This is slop. If you can’t be bothered to even edit your AI slop, I won’t be bothered to read it.

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I decided long ago to not wait for you all to figure this out.

Index it all. Embed it all. Give your agents the ability to query it all. Eval different configurations to figure out what works best for you.