Was ready to write something snarky because this is essentially RAG, but I think the author is getting at some subtle details which are seemingly important.
- memory systems are a specific type of knowledge base where you generate all the documents. You might as well generate them to be less than your embedding token limit to obviate the need for chunking.
- embedding models are getting better and are no longer just semantic averaging.
- small models are getting dirt cheap, making parallel reads cost manageable
What they describe is sort of the simplest architecture that takes advantage of these observations. I believe them when they say it works well.
I do suspect though that things like keyword lookup will completely fail if every memory is just a vector. Hence why something like Typesense hybrid search can still be useful.
I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database structure and it has been implemented, that information should not be contained in any text document -- it is noise, will drift, and be impossible to debug why the agent keeps producing undesired behavior.
I have a ~/Projects folder. For example, I use Playwright with Chrome DevTools Protocol in order to do performance testing and leak detection. There is a script that handles this. My prompt is "Search ~/Projects for perf testing with CDP and Playwright and implement here". Point being, if I need anything I point to a resource or ask to search a resource and it will find it quick and, most importantly, tends to improve it every iteration.
If I was in an institution, I would have a repository and would rather just point the resource and say use that than have memory of it locally.
What I propose is only a very slightly more formal version of what you describe.
Just to start with: memoryfields are possible to use in a server/client system. That was a key aim and I do already use them over Amazon S3 (though not always).
I started, like you did, with a personal library of prompts. But the issue is that as your library of little pieces of prompts increases a) you get tired of constantly editing them yourself b) you have no easy way to export and share them with others c) it's frustrating that the agent doesn't "automatically" find your little bit of prompt on X even when clearly it is relevant - hence sem search.
I think a lot of people are still using the "personal library of bits of prompt" model. It is ok. But I wanted to propose an minimal, interchangeable standard for sharing them. So the idea of being an institution and having a shared memoryfield: that's something I want as well!
I keep it on for my web chats, but I think I dislike it more than it helps. I'll ask a question about something and it'll find a way to tie it back to something from three months ago thinking it was a deep project I was working on, instead of what it really was: an inane question I was curious about.
I turn it off for local agents because I bounce around a few and I really don't like the mostly implicit nature of it. I want to write my instructions in version control if I have anything to say consistently to an agent.
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)
It’s good that a lot of people are trying a lot of things when it comes to agentic memory. Sadly none of it represents a complete solution at this time. But we need the experimentation.
I still dont understand what "agentic memory" is . agents can already call sql / rag and grep through files or whatever. why is "agentic memory" a special thing.
Memory isn’t the same thing as rag it’s usually just a text based index of past events and the llm reads it and decides what’s important rather than querying a db
You could think of it as a more efficient indexing format for data the agent has access to. It’s badly needed as a better representation or pointer map would improve recall time and comprehensiveness dramatically without having to invest further in model training.
they just want to reinvent information retrieval from first principles; also the fact that everyone forgets to check what currently exists and reinvents hexagonal wheels for the sake of agentic development
What the author suggests is remarkably close to the proposition of OpenViking. I've been testing a few memory solutions and OpenViking is one of my favorites so far.
I find that semantic search is substantially better than keyword search even for small corpuses. Being able to find "related" material that doesn't match the keyword is a big advance over traditional full text search.
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.
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.
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.
that sounds horribly token inefficient, just create a tool call if you are all in on the agentic approach and hide memory retrieval behind an optimized api
They already have. The tool is called bash and optimized api is grep, the storage is the file system. We’ve been here many months.
What people are exploring are other options as far as I can tell.
What you are offering is “don’t do that, this already works”. Which I guess is fine, but apparently not everyone is fully satisfied with the current generation of tooling.
> How can I judge what is a good memory to store? How can I avoid filling my memory with crap?
> This is a common fear with memory systems but doesn't really apply to memoryfields. Irrelevant material is simply never surfaced by the semantic search.
This is so wrong. The Achilles' heel of this approach is the RAG. What makes it worse is having lots of memories that are outdated, wrong, hallucinated, or irrelevant.
Nothing beats curated data. Memory should be regularly reviewed, compacted, and cleaned up if it's no longer valid.
- memory systems are a specific type of knowledge base where you generate all the documents. You might as well generate them to be less than your embedding token limit to obviate the need for chunking.
- embedding models are getting better and are no longer just semantic averaging.
- small models are getting dirt cheap, making parallel reads cost manageable
What they describe is sort of the simplest architecture that takes advantage of these observations. I believe them when they say it works well.
I do suspect though that things like keyword lookup will completely fail if every memory is just a vector. Hence why something like Typesense hybrid search can still be useful.
I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database structure and it has been implemented, that information should not be contained in any text document -- it is noise, will drift, and be impossible to debug why the agent keeps producing undesired behavior.
I have a ~/Projects folder. For example, I use Playwright with Chrome DevTools Protocol in order to do performance testing and leak detection. There is a script that handles this. My prompt is "Search ~/Projects for perf testing with CDP and Playwright and implement here". Point being, if I need anything I point to a resource or ask to search a resource and it will find it quick and, most importantly, tends to improve it every iteration.
If I was in an institution, I would have a repository and would rather just point the resource and say use that than have memory of it locally.
Just to start with: memoryfields are possible to use in a server/client system. That was a key aim and I do already use them over Amazon S3 (though not always).
I started, like you did, with a personal library of prompts. But the issue is that as your library of little pieces of prompts increases a) you get tired of constantly editing them yourself b) you have no easy way to export and share them with others c) it's frustrating that the agent doesn't "automatically" find your little bit of prompt on X even when clearly it is relevant - hence sem search.
I think a lot of people are still using the "personal library of bits of prompt" model. It is ok. But I wanted to propose an minimal, interchangeable standard for sharing them. So the idea of being an institution and having a shared memoryfield: that's something I want as well!
The spec, feedback greatly welcome:
https://github.com/calpaterson/memoryfield-spec/blob/main/SP...
I turn it off for local agents because I bounce around a few and I really don't like the mostly implicit nature of it. I want to write my instructions in version control if I have anything to say consistently to an agent.
The markdown explanation I was also unimpressed with, but RAG over hyperlinks is convincing to me.
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)
And I agree: it's not a very special thing. That's why I propose: Markdown + a simple embedding.
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.
Of course, ideally your data would be structured, but the agents will mostly be grepping anyway, and maybe look at sibbling files.
Incredible to watch things come full circle. Next thing you know, someone is going to figure out a binary encoding.
What people are exploring are other options as far as I can tell.
What you are offering is “don’t do that, this already works”. Which I guess is fine, but apparently not everyone is fully satisfied with the current generation of tooling.
Also fwiw grep is pretty poorly suited to semantic search and will only return the most basic of matches.
If you are really trying to build a useful memory search tool there are much better options than plain text search.
> This is a common fear with memory systems but doesn't really apply to memoryfields. Irrelevant material is simply never surfaced by the semantic search.
This is so wrong. The Achilles' heel of this approach is the RAG. What makes it worse is having lots of memories that are outdated, wrong, hallucinated, or irrelevant.
Nothing beats curated data. Memory should be regularly reviewed, compacted, and cleaned up if it's no longer valid.