5 ms·

Open source memory layer so any AI agent can do what Claude.ai and ChatGPT do

185 points by alash3al 5mo ago

alash3al 5mo ago
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.
t_messinis 5mo ago
[dead]
great_psy 5mo ago
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
- dennisy 5mo ago
  True! But this is a very naive implementation, a proper implementation could surpass these challenges.
  - awestroke 5mo ago
    Well let's talk again when the problems have been solved, then. Until then, manually curated skills and documentation will beat this
- vasco 5mo ago
  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"
dennisy 5mo ago
Congratulations on the launch! There is lots of competition in this space, how is your tool different?
_pdp_ 5mo ago
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.
- hirako2000 5mo ago
  It's a cool website..it says memory. It shows LLM suck and this product magically just works. In a way, if it does accomplish that, it is a vectordb needing glorification.
clutter55561 5mo ago
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.
adithyassekhar 5mo ago
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.
bobkb 5mo ago
There is already memory palace ?
dwb 5mo ago
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.
- dennisy 5mo ago
  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.
  - dwb 5mo ago
    > I doubt many people will honestly admit they did no design, testing and that they believe the code is sub par. Well no, but if people want to see a statement like this, and given that most people will want to be at least halfway honest and not admit to slop, maybe it will help nudge things in the right direction.
  - embedding-shape 5mo ago
    > I doubt many people will honestly admit they did no design, testing and that they believe the code is sub par I'd doubt any engineer that doesn't call most of their own code subpar after a week or two after looking back. "Hacking" also famously involves little design or (automated) testing too, so sharing something like that doesn't mean much, unless you're trying to launch a business, but I see no evidence of that for this project.
- chickensong 5mo ago
  What's the point? You can make good or bad software, with or without LLMs. Do you ask a carpenter if they use a hammer or nail gun? Did they only use the nail gun for the roof and the deck? If you care that much and don't have a foundation of trust, you need to either verify the construction is good, or build it yourself. Anything else is just wishful thinking.
Incipient 5mo ago
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.
- clutter55561 5mo ago
  All the memories Claude created for me fell in the category remember-to-not-forget, so I disabled it altogether.
- jvwww 5mo ago
  Yeah I feel the same way. Wonder when/if we'll get continual learning from these models. I feel like they are smart enough already but their lack of real memory makes them a pain to deal with.
  - hirako2000 5mo ago
    Google Gemini does this sort of thing. External to the model k presume. And it's very annoying. A friend told me he would like Claude to remember his personality, which is exactly what Gemini is trying to do. A machine pretending to be human is disturbing enough. A machine pretending to understand you will spiral very far into spitting out exactly what we want to read.
- zby 5mo ago
  I have a wishlist for these systems: https://zby.github.io/commonplace/notes/designing-agent-memory-systems/ https://zby.github.io/commonplace/notes/designing-agent-memo...
- hedgehog 5mo ago
  [dead]
- 5mo ago
kgeist 5mo ago
>Stash makes your AI remember you. Every session. Forever. How does it fight context pollution?
tedggh 5mo ago
A few things seem to work well for me (Codex): 1) An up-to-date detailed functional specification. 2) A codebase structured and organized in multiple projects. 3) Well documented code including good naming conventions; each class, variable or function name should clearly state what its purpose is, no matter how long and silly the name is. These naming conventions are part of a coding guidelines section in Agent.md. My functional specification acts as the Project.md for the agent. Then before each agentic code review I create a tree of my project directory and I merged it with the codebase into one single file, and add the timestamp to the file name. This last bit seems to matter to avoid the LLM to refer to older versions and it’s also useful to do quick diffs without sending the agent to git. So far this simple workflow has been working very well in a fairly large and complex codebase. Not very efficient tokens wise, but it just works. By the way I don’t need to merge the entire codebase every time, I may decide to leave projects out because I consider them done and tested or irrelevant to the area I want to be working on. However I do include them in the printed directory tree so the agent at least knows about them and could request seeing a particular file if it needs to.
- swingboy 5mo ago
  Interesting approach. How do you do the merging? Is it manual? Just changed files? A hybrid?
braiamp 5mo ago
What the heck is happening on this site with the pointer disappearing? For some reason the body tag has "cursor: none" which is never good.
- eleventen 5mo ago
  The traditional cursor is so 2025. It's predictable. Familiar. old. AI is the future, so we need cursors of the future that simulate the frustrating lag and imprecision of LLMS. Dots chase other little dots around and do inscrutable little animations. Actual answer: You need javascript to see their dumb custom cursor.
- finaard 5mo ago
  I didn't read much of the page - just was scrolling a bit to see what the fuck that thing is doing, and that was more than enough to know that I'll never touch whatever those people are doing.
ting0 5mo ago
It's not clear to me how or why this works, and how it compares to just using md files in my project. For something like this, we really need benchmarks.
jFriedensreich 5mo ago
All these agent memory systems seem so simultaneously over and under engineered and like a certain dead end. I cannot imagine any reality in which this does not rot and get out of sync with what the latest model need. For the one time you build a payment provider how many session will be tilted towards thinking about payments because of the "don't use stripe" memory?
- stavros 5mo ago
  I've treated this as an information problem and wrote a small utility that explicitly does not store most things (https://github.com/skorokithakis/gnosis https://github.com/skorokithakis/gnosis). Basically, the premise is that the things the LLM knows will always be there, so store nothing the LLM said, the code will always be there so the code-relevant things should be comments, but there are things that will be neither, and that are never captured. When we create anything, what we ended up not doing is often more important than what we did end up doing. My utility runs at the end of the session and captures all the alternatives we rejected, and the associated rationales, and stores that as system knowledge. Basically, I want to capture all these things that my coworkers know, but that I can't just grep the code for. So far it's worked well, but it's still early.
- devmor 5mo ago
  I have a bespoke memory system that I wrote myself and it avoids this problem entirely by making every memory a contextual search space. The “don’t use stripe” memory would only be recalled into context if the model was prompted to do something with payment processing.
- cush 5mo ago
  What's worse is how obvious the author hasn't even used it themselves. Completely unproven memory layer. No due diligence - just a fancy marketing site with outrageous claims
aprilnya 5mo ago
I clicked this thinking “oh, cool, someone finally made a portable version of the Claude.ai* memory system!” Spoiler, no, it’s not it at all, it’s just a “store”/“remember” memory system… as opposed to the Claude.ai memory system, where it doesn’t make the model actively have to write memories on its own, but rather has a model in the background go through your chat history and generate a summary from it. I’ve found the latter approach to work much, much better than simple “store”/“remember” systems. So, it just feels misleading to say this can do what Claude.ai’s can do… (I’ve been looking for a memory system that works the same for a while, so that I can switch away from Claude.ai to something else like LibreChat, but I just haven’t found any. Might be the only thing keeping me on Claude at this point.) - *I say Claude.ai because that’s specifically what has the system; Claude Code doesn’t have this system
- kimjune01 5mo ago
  I agree. silent agent doing agentic things async is what would be helpful, not requiring a modification to the main prompt
  - aprilnya 5mo ago
    Yeah. The other advantage is a summary-based memory also just… “pieces together” things that a “store”/“remember” memory wouldn’t, because they’re things that the actual main agent would not think to store. i.e. small disconnected things across conversations that alone, would not end up in memory because they’re insignificant. But when there’s an agent looking at multiple conversations at once it can actually reason about this stuff and piece it together.
- kuboble 5mo ago
  That's an interesting concept. So it's like if you're an agent chatting with a user, you have an army of assistants who overhear the conversation and record important facts, or search relevant facts on some database and decide on the fly when to interrupt you with "this memory X looks relevant". Sounds easy enough if tokens were free, but an interesting problem to do it efficiently.
bearjaws 5mo ago
Wow another day, another memory system for AI agents! How many are we up to now? Has to be hundreds of them.
- dominotw 5mo ago
  its the todomvc of ai user. i made one for myself too. I try not to tell anyone about it .
kimjune01 5mo ago
this is a patch on top of the broken flat-compaction caching algorithm used by coding agents. Why not fix the cache algorithm directly? Union-find is a better impl june.kim/union-find-compaction
dominotw 5mo ago
is this backed by a hollywood celebrity ?
Asharma538 5mo ago
[dead]
binary0010 5mo ago
[flagged]
bozdogancih 5mo ago
[dead]
zby 5mo ago
Reviewed: https://zby.github.io/commonplace/agent-memory-systems/reviews/stash/ https://zby.github.io/commonplace/agent-memory-systems/revie... Together with the other hundred llm memory systems: https://zby.github.io/commonplace/agent-memory-systems/ https://zby.github.io/commonplace/agent-memory-systems/ I have also written a wishlist for these systems: https://zby.github.io/commonplace/notes/designing-agent-memory-systems/ https://zby.github.io/commonplace/notes/designing-agent-memo...
OsrsNeedsf2P 5mo ago
I'd like to use this for our locally running game agent[0] but the PostgreSQL and other dependencies is a show stopper. Why so complex? [0] Ziva.sh is a desktop app that brings agentic features to game engines. We can't just bundle a running DB and we won't be sending this sensitive info to a cloud
cush 5mo ago
Now that building software is effectively free, it's astounding that we're still trying to pitch things like this using a vibe-coded fancy marketing site. Who has time to use these and wait weeks or months to find out if they actually work? There's no proof in the site this is better than RAG or even a folder of memory files and grep, yet makes all these fantastic claims (while scrolling at 14fps). This wasn't even coded 24 hours ago... It's honestly so lazy.
kjaw221-33 5mo ago
[dead]
kjaw221-33 5mo ago
[dead]
3strikes 5mo ago
[dead]
Dinuda 5mo ago
here is a production grade solution I made. I was doing AI courses for kids and this was benifitial to do collab sessions between chatgpt and claude. It also sync's their memory automatically. Check it out: https://tallei.com https://tallei.com

185 points by alash3al 5mo ago

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.

t_messinis 5mo ago

[dead]

great_psy 5mo ago

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

dennisy 5mo ago
True! But this is a very naive implementation, a proper implementation could surpass these challenges.
- awestroke 5mo ago
  Well let's talk again when the problems have been solved, then. Until then, manually curated skills and documentation will beat this
vasco 5mo ago
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"

dennisy 5mo ago

Congratulations on the launch! There is lots of competition in this space, how is your tool different?

_pdp_ 5mo ago

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.

hirako2000 5mo ago
It's a cool website..it says memory. It shows LLM suck and this product magically just works. In a way, if it does accomplish that, it is a vectordb needing glorification.

clutter55561 5mo ago

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.

adithyassekhar 5mo ago

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.

bobkb 5mo ago

There is already memory palace ?

dwb 5mo ago

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.

dennisy 5mo ago
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.
- dwb 5mo ago
  > I doubt many people will honestly admit they did no design, testing and that they believe the code is sub par. Well no, but if people want to see a statement like this, and given that most people will want to be at least halfway honest and not admit to slop, maybe it will help nudge things in the right direction.
- embedding-shape 5mo ago
  > I doubt many people will honestly admit they did no design, testing and that they believe the code is sub par I'd doubt any engineer that doesn't call most of their own code subpar after a week or two after looking back. "Hacking" also famously involves little design or (automated) testing too, so sharing something like that doesn't mean much, unless you're trying to launch a business, but I see no evidence of that for this project.
chickensong 5mo ago
What's the point? You can make good or bad software, with or without LLMs. Do you ask a carpenter if they use a hammer or nail gun? Did they only use the nail gun for the roof and the deck? If you care that much and don't have a foundation of trust, you need to either verify the construction is good, or build it yourself. Anything else is just wishful thinking.

Incipient 5mo ago

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.

clutter55561 5mo ago
All the memories Claude created for me fell in the category remember-to-not-forget, so I disabled it altogether.
jvwww 5mo ago
Yeah I feel the same way. Wonder when/if we'll get continual learning from these models. I feel like they are smart enough already but their lack of real memory makes them a pain to deal with.
- hirako2000 5mo ago
  Google Gemini does this sort of thing. External to the model k presume. And it's very annoying. A friend told me he would like Claude to remember his personality, which is exactly what Gemini is trying to do. A machine pretending to be human is disturbing enough. A machine pretending to understand you will spiral very far into spitting out exactly what we want to read.
zby 5mo ago
I have a wishlist for these systems: https://zby.github.io/commonplace/notes/designing-agent-memory-systems/ https://zby.github.io/commonplace/notes/designing-agent-memo...
hedgehog 5mo ago
[dead]
5mo ago

kgeist 5mo ago

>Stash makes your AI remember you. Every session. Forever. How does it fight context pollution?

tedggh 5mo ago

A few things seem to work well for me (Codex): 1) An up-to-date detailed functional specification. 2) A codebase structured and organized in multiple projects. 3) Well documented code including good naming conventions; each class, variable or function name should clearly state what its purpose is, no matter how long and silly the name is. These naming conventions are part of a coding guidelines section in Agent.md. My functional specification acts as the Project.md for the agent. Then before each agentic code review I create a tree of my project directory and I merged it with the codebase into one single file, and add the timestamp to the file name. This last bit seems to matter to avoid the LLM to refer to older versions and it’s also useful to do quick diffs without sending the agent to git. So far this simple workflow has been working very well in a fairly large and complex codebase. Not very efficient tokens wise, but it just works. By the way I don’t need to merge the entire codebase every time, I may decide to leave projects out because I consider them done and tested or irrelevant to the area I want to be working on. However I do include them in the printed directory tree so the agent at least knows about them and could request seeing a particular file if it needs to.

swingboy 5mo ago
Interesting approach. How do you do the merging? Is it manual? Just changed files? A hybrid?

braiamp 5mo ago

What the heck is happening on this site with the pointer disappearing? For some reason the body tag has "cursor: none" which is never good.

eleventen 5mo ago
The traditional cursor is so 2025. It's predictable. Familiar. old. AI is the future, so we need cursors of the future that simulate the frustrating lag and imprecision of LLMS. Dots chase other little dots around and do inscrutable little animations. Actual answer: You need javascript to see their dumb custom cursor.
finaard 5mo ago
I didn't read much of the page - just was scrolling a bit to see what the fuck that thing is doing, and that was more than enough to know that I'll never touch whatever those people are doing.

ting0 5mo ago

It's not clear to me how or why this works, and how it compares to just using md files in my project. For something like this, we really need benchmarks.

jFriedensreich 5mo ago

All these agent memory systems seem so simultaneously over and under engineered and like a certain dead end. I cannot imagine any reality in which this does not rot and get out of sync with what the latest model need. For the one time you build a payment provider how many session will be tilted towards thinking about payments because of the "don't use stripe" memory?

stavros 5mo ago
I've treated this as an information problem and wrote a small utility that explicitly does not store most things (https://github.com/skorokithakis/gnosis https://github.com/skorokithakis/gnosis). Basically, the premise is that the things the LLM knows will always be there, so store nothing the LLM said, the code will always be there so the code-relevant things should be comments, but there are things that will be neither, and that are never captured. When we create anything, what we ended up not doing is often more important than what we did end up doing. My utility runs at the end of the session and captures all the alternatives we rejected, and the associated rationales, and stores that as system knowledge. Basically, I want to capture all these things that my coworkers know, but that I can't just grep the code for. So far it's worked well, but it's still early.
devmor 5mo ago
I have a bespoke memory system that I wrote myself and it avoids this problem entirely by making every memory a contextual search space. The “don’t use stripe” memory would only be recalled into context if the model was prompted to do something with payment processing.
cush 5mo ago
What's worse is how obvious the author hasn't even used it themselves. Completely unproven memory layer. No due diligence - just a fancy marketing site with outrageous claims

aprilnya 5mo ago

I clicked this thinking “oh, cool, someone finally made a portable version of the Claude.ai* memory system!” Spoiler, no, it’s not it at all, it’s just a “store”/“remember” memory system… as opposed to the Claude.ai memory system, where it doesn’t make the model actively have to write memories on its own, but rather has a model in the background go through your chat history and generate a summary from it. I’ve found the latter approach to work much, much better than simple “store”/“remember” systems. So, it just feels misleading to say this can do what Claude.ai’s can do… (I’ve been looking for a memory system that works the same for a while, so that I can switch away from Claude.ai to something else like LibreChat, but I just haven’t found any. Might be the only thing keeping me on Claude at this point.) - *I say Claude.ai because that’s specifically what has the system; Claude Code doesn’t have this system

kimjune01 5mo ago
I agree. silent agent doing agentic things async is what would be helpful, not requiring a modification to the main prompt
- aprilnya 5mo ago
  Yeah. The other advantage is a summary-based memory also just… “pieces together” things that a “store”/“remember” memory wouldn’t, because they’re things that the actual main agent would not think to store. i.e. small disconnected things across conversations that alone, would not end up in memory because they’re insignificant. But when there’s an agent looking at multiple conversations at once it can actually reason about this stuff and piece it together.
kuboble 5mo ago
That's an interesting concept. So it's like if you're an agent chatting with a user, you have an army of assistants who overhear the conversation and record important facts, or search relevant facts on some database and decide on the fly when to interrupt you with "this memory X looks relevant". Sounds easy enough if tokens were free, but an interesting problem to do it efficiently.

bearjaws 5mo ago

Wow another day, another memory system for AI agents! How many are we up to now? Has to be hundreds of them.

dominotw 5mo ago
its the todomvc of ai user. i made one for myself too. I try not to tell anyone about it .

kimjune01 5mo ago

this is a patch on top of the broken flat-compaction caching algorithm used by coding agents. Why not fix the cache algorithm directly? Union-find is a better impl june.kim/union-find-compaction

dominotw 5mo ago

is this backed by a hollywood celebrity ?

Asharma538 5mo ago

[dead]

binary0010 5mo ago

[flagged]

bozdogancih 5mo ago

[dead]

zby 5mo ago

Reviewed: https://zby.github.io/commonplace/agent-memory-systems/reviews/stash/ https://zby.github.io/commonplace/agent-memory-systems/revie... Together with the other hundred llm memory systems: https://zby.github.io/commonplace/agent-memory-systems/ https://zby.github.io/commonplace/agent-memory-systems/ I have also written a wishlist for these systems: https://zby.github.io/commonplace/notes/designing-agent-memory-systems/ https://zby.github.io/commonplace/notes/designing-agent-memo...

OsrsNeedsf2P 5mo ago

I'd like to use this for our locally running game agent[0] but the PostgreSQL and other dependencies is a show stopper. Why so complex? [0] Ziva.sh is a desktop app that brings agentic features to game engines. We can't just bundle a running DB and we won't be sending this sensitive info to a cloud

cush 5mo ago

Now that building software is effectively free, it's astounding that we're still trying to pitch things like this using a vibe-coded fancy marketing site. Who has time to use these and wait weeks or months to find out if they actually work? There's no proof in the site this is better than RAG or even a folder of memory files and grep, yet makes all these fantastic claims (while scrolling at 14fps). This wasn't even coded 24 hours ago... It's honestly so lazy.

kjaw221-33 5mo ago

[dead]

kjaw221-33 5mo ago

[dead]

3strikes 5mo ago

[dead]

Dinuda 5mo ago

here is a production grade solution I made. I was doing AI courses for kids and this was benifitial to do collab sessions between chatgpt and claude. It also sync's their memory automatically. Check it out: https://tallei.com https://tallei.com