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.)

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*I say Claude.ai because that’s specifically what has the system; Claude Code doesn’t have this system

Not casting aspersions on you personally, I’d really like this from every project, and would do the same myself.

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.

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.

The only approach I've found that works is no memory, and manually choosing the context that matters for a given agent session/prompt.

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

How many are we up to now? Has to be hundreds of them.

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.

How does it fight context pollution?

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.

There is lots of competition in this space, how is your tool different?