I still haven't found useful "memory". It's either an agents.md with a high level summary, which is fairly useless for specific details (eg "editing this element needs to mark this other element as a draft") or something detailed and explaining the nitty gritty, which seems to give too much detail such that it gets ignored, or detail from one functional area contaminates the intended changes in another functional area.
The only approach I've found that works is no memory, and manually choosing the context that matters for a given agent session/prompt.
I’m certainly on the lookout for something like this and I’m happy to see your account has published software from before the LLM boom as well. I guess I’d like some kind of LLM-use-statement attached to projects: did you use an LLM to generate this, and if so, how much and what stages (design, build, test)? How carefully did you review the output? Do you feel the quality is at least what you could have produced by yourself? That sort of thing.
Not casting aspersions on you personally, I’d really like this from every project, and would do the same myself.
This is a fair question, but not one I feel we can let people self answer.
I doubt many people will honestly admit they did no design, testing and that they believe the code is sub par.
It does give me an idea that maybe we need a third party system which can try and answer some of the questions you are asking… of course it too would be LLM driven and quite subjective.
Well the project is promising something without providing any details how exactly this is achieved which to me is always a huge red flag.
Digging deeper I can see it is effectively pg_vector plus mcp with two functions: "recall" and "remember".
It is effectively a RAG.
You can make the argument that perhaps the data structure matters but all of these "memory" systems effectively do the same and none of them have so far proven that retrieval is improved compared to baseline vector db search.
If I am working on a real project with real people, it won’t have the complete memory of the project. I won’t have the complete memory. My memory will be outdated when other PRs are merged. I only care about my tickets.
I am starting to think this is not meant for that kind of work.
LLM Memeory (in general, any implementation) is good in theory.
In practice, as it grows it gets just as messy as not having it.
In the example you have on front page you say “continue working on my project”, but you’re rarely working on just one project, you might want to have 5 or 10 in memory, each one made sense to have at the time.
So now you still have to say, “continue working on the sass project”, sure there’s some context around details, but you pay for it by filling up your llm context , and doing extra mcp calls
Isn’t “memory” just another markdown file that the LLM reads when starting a new session?
I keep two files in each project - AGENTS (generic) and PROJECT (duh). All the “memory” is manually curated in PROJECT, no messy consolidation, no Russian roulette.
I do understand that this is different because the vector search and selective unstash, but the messy consolidation risk remains.
Also not sure about tools that further detach us from the driver seat. To me, this seems to encourage vibe coding instead of engineering-plus-execution.
Not a criticism on the product itself, just rambling.
Platform memory is locked to one model and one company. Stash brings the same capability to any agent — local, cloud, or custom. MCP server, 28 tools, background consolidation, Apache 2.0.
I still haven't found useful "memory". It's either an agents.md with a high level summary, which is fairly useless for specific details (eg "editing this element needs to mark this other element as a draft") or something detailed and explaining the nitty gritty, which seems to give too much detail such that it gets ignored, or detail from one functional area contaminates the intended changes in another functional area.
The only approach I've found that works is no memory, and manually choosing the context that matters for a given agent session/prompt.
All the memories Claude created for me fell in the category remember-to-not-forget, so I disabled it altogether.
I’m certainly on the lookout for something like this and I’m happy to see your account has published software from before the LLM boom as well. I guess I’d like some kind of LLM-use-statement attached to projects: did you use an LLM to generate this, and if so, how much and what stages (design, build, test)? How carefully did you review the output? Do you feel the quality is at least what you could have produced by yourself? That sort of thing.
Not casting aspersions on you personally, I’d really like this from every project, and would do the same myself.
This is a fair question, but not one I feel we can let people self answer.
I doubt many people will honestly admit they did no design, testing and that they believe the code is sub par.
It does give me an idea that maybe we need a third party system which can try and answer some of the questions you are asking… of course it too would be LLM driven and quite subjective.
Well the project is promising something without providing any details how exactly this is achieved which to me is always a huge red flag.
Digging deeper I can see it is effectively pg_vector plus mcp with two functions: "recall" and "remember".
It is effectively a RAG.
You can make the argument that perhaps the data structure matters but all of these "memory" systems effectively do the same and none of them have so far proven that retrieval is improved compared to baseline vector db search.
Is this only for vibecoders who work alone?
If I am working on a real project with real people, it won’t have the complete memory of the project. I won’t have the complete memory. My memory will be outdated when other PRs are merged. I only care about my tickets.
I am starting to think this is not meant for that kind of work.
LLM Memeory (in general, any implementation) is good in theory.
In practice, as it grows it gets just as messy as not having it.
In the example you have on front page you say “continue working on my project”, but you’re rarely working on just one project, you might want to have 5 or 10 in memory, each one made sense to have at the time.
So now you still have to say, “continue working on the sass project”, sure there’s some context around details, but you pay for it by filling up your llm context , and doing extra mcp calls
True! But this is a very naive implementation, a proper implementation could surpass these challenges.
And once you're being specific about what it needs to remember you are 0 steps away from having just told AI to write and read files with the "memory"
There is already memory palace ?
Isn’t “memory” just another markdown file that the LLM reads when starting a new session?
I keep two files in each project - AGENTS (generic) and PROJECT (duh). All the “memory” is manually curated in PROJECT, no messy consolidation, no Russian roulette.
I do understand that this is different because the vector search and selective unstash, but the messy consolidation risk remains.
Also not sure about tools that further detach us from the driver seat. To me, this seems to encourage vibe coding instead of engineering-plus-execution.
Not a criticism on the product itself, just rambling.
Congratulations on the launch!
There is lots of competition in this space, how is your tool different?
Platform memory is locked to one model and one company. Stash brings the same capability to any agent — local, cloud, or custom. MCP server, 28 tools, background consolidation, Apache 2.0.