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- Hacker News
- I will say, having built something similar for tracking 'memory' and items at home, it can quickly consume your tokens when dealing with both reading and updating, keeping stale info relevant etc.. when the amount of data starts to grow. Smaller tasks can balloon in their token cost as documents a read, updated, collated, refreshed etc..
however, I have found keeping a good solid reference to my home infrastructure, services, ci/cd setup, hosts, storage , networking etc.. really works wonders as a set of 'memories' to share across projects that I expect to be tested / deployed / acceptance tested etc.. using the home infra bits and pieces.
by alienbaby - Something I started doing recently was writing out principles instead of memories.
Essentially patterns the agents need to always think in. I also implemented a versioning system to the principles that need to be quoted in any comments which are there in the code. That way, when my principles evolve, so does the code.
I did package it up in a way that I can share it with friends [0]. System still evolving, but the last two-ish months that I've used it has served me really, really well, And it's been even better with the latest models.
I've had surprisingly good adherence from agents on this technique.
by bushido - This ignores the lifecycle involved with memory. It doesn't appear to have a concept of time or reconciliation (both issues we're having at work). A memory might be relevant for a week or a month, but it might no longer matter after that ie "we're migrating systems so keep <thing> in mind"--that doesn't matter after the migration is complete. I guess the offered solution implicitly supports reconciliation since the agent can keep history of its memories and rewrite them, but it doesn't appear to be very first-class and it doesn't seem like there's anything intentional unless you prompt it "go check all the docs and make sure everything is consistent".
It also doesn't seem to have a way to separate preference from factual memory which is useful when you have various humans interacting with the same agent. One human might prefer a certain output style over the other and that's something a memory framework can also address.
Like the MCP articles a few months ago, it also seems to assume all agents are cli coding harnesses running on your local machine. We have a handful of other things like chat bots, event-driven agents running on servers, chat driven agents running on servers in sandboxes--there's not a single filesystem and even if there were one, having multiple agents try to edit it at once would corrupt it.
by nijave - I think they even more so need deterministic feedback:
I tried an approach based on the following idea recently and it's amazing - Lint rules where the error messages contain an explanation on how to deal with the issue.
I'm using it to foster IOSP (integration operation segregation principle) for example.
by Garlef - Agreed, and this seems better.
My thought though has always been that I don't want there to be agent-only designated documentation.
I use mattpocock/skills and that generates ADRs (Architectural Decision Records). That only uses skills, including a setup skill that will write a few pointers in AGENTS.md. I always have a CONTRIBUTING.md to document development flow and a CODING_STANDARDS.md. Between those and the README.md and architecture documentation and commit messages the agents seem to be able to find and use docs and keep them up to date. We are also writing a lot of specs and putting those in Github issues.
by gregwebs - I think whichever one is used, there needs to be a way to enforce what's written.
If I say "use jq instead of writing a python script to parse json" it should never write adhoc python scripts to parse json. Yet that constantly happens to me anyway.
by spike021 - This is something I've been fooling with a lot lately. Reading his solution, it look to me like his objections apply to his own solution. The sharpest critique he makes of RAG is that agents can't search for what they don't know. A markdown "brain" has the same problem. How does the "agent" know which documents are relevant before it starts? The index files retrieval done by the agent instead of by embeddings doesn't escape the problem. The same goes for staleness (which for me seems like a constant chase). He criticizes memory systems for treating the past as truth, but documents go stale too (a lot). The fix of having the agent update what's outdated, is the same job he's ridiculing the dreamers and background daemons for doing. He says "putting it to the test"; where's the test? He says "only five of the many problems"; if there's so many, show me, don't just say it. He's absolutely right about auditability, but for me at least Claude uses a regular markdown (MD) file I can read just fine. So every memory plugin on the market does not work the same way.
This is a first draft; his github is better than his article. Looking through it, Consult actually works. The agent doesn't pick documents blind. Every scope has a catalog file that describes each document: what it covers, when to open it. These catalogs seem to load in to the start of each session, so the agent gets a little map without reading every file. Code navigation seems the same. Each index document has a short description and a "read_if", and subindexes are opened when their condition matches the job. This looks pretty well laid out, which I would never have guessed from the article.
- You don't need documentation or the 3rd party memory systems. The code IS the documentation.
All this stuff is LLM rube goldberg machines. It just pollutes context.
I barely use AGENTS.md/CLAUDE.md these days. And where they remain, it's super basic high level stuff.
I'm honestly still kicking myself in the ass on many projects where I did something similar to this. I kept tons of markdown docs and decision docs. Now those things are just causing problems because they got stale. Even after having sessions of reconciling documentation, the LLM just gets confused.
by kaydub