Letta Code

Thanks for sharing!! (Charles here from Letta) The original MemGPT (the starting point for Letta) was actually an agent CLI as well, so it's fun to see everything come full circle.

If you're a Claude Code user (I assume much of HN is) some context on Letta Code: it's a fully open source coding harness (#1 model-agnostic OSS on Terminal-Bench, #4 overall).

It's specifically designed to be "memory-first" - the idea is that you use the same coding agents perpetually, and have them build learned context (memory) about you / your codebase / your org over time. There are some built-in memory tools like `/init` and `/remember` to help guide this along (if your agent does something stupid, you can 'whack it' with /remember). There's also a `/clear` command, which resets the message buffer, but keeps the learned context / memory inside the context window.

We built this for ourselves - Letta Code co-authors the majority of PRs on the letta-code GitHub repo. I personally have been the same agent for ~2+ weeks (since the latest stable build) and it's fun to see its memory become more and more valuable over time.

LMK if you have any q's! The entire thing is OSS and designed to be super hackable, and can run completely locally when combined with the Letta docker image.

Void is the greatest ad for Letta. I'm interested to see if it's as good at coding as it is at posting. https://bsky.app/profile/void.comind.network

I do have some experience but haven't deployed Void on actual tasks, mostly because I want to keep Void focused on day-to-day social operations. I have considered giving Void subagents to handle coding tasks, which may be a good use case for Void-2: https://bsky.app/profile/void-2.comind.network

In my experience, "memory" is really not that helpful in most cases. For all of my projects, I keep the documentation files and feature specs up to date, so that LLMs are always aware of where to find what and which coding style guides the project is based on.

Maintaining the memory is a considerable burden, and make sure that simple "fix this linting" doesn't end up in the memory, as we always fix that type of issue in that particular way. That's also the major problem I have with ChatGPT's memory: it starts to respond from the perspective of "this is correct for this person".

I am curious who sees the benefits of the memory in coding? Is it like "learns how to code better" or it learns "how the project is structured". Either way, to me, this sounds like an easy project setup thing.

I find the long-term memory concepts with regards to AI curiously dubious.

On first glance, of course it's something we want. It's how we do it, after all! Learning on the job is what enables us to do our jobs and so many other things.

On the other hand humans are frustratingly stuck in their ways and not all that happy to change and that is something that societies or orgs fight a lot. Do I want to convince my coding agent to learn new behavior, conflicting with existing memory?

It's not at all obvious to me in how far memory is a bug or a feature. Does somebody have a clear case on why this is something that we should want and why it's not a problem?