Why can't I see Cursor on tbench? Is it that bad that it's not even on the leaderboard? I am trying to figure out if I can pitch your product to my company, and whether it is worth it.

I'm very interested in trying this out! I run Claude Code in sandbox with `--dangerously-skip-permissions`. Is that possible with Letta?

I think Cameron (Void's handler) has some experience wiring up production Void to his computer via Letta Code

What do you like about Void? It reads about how I would expect a base chat model to post.

context poisoning is a real problem that these memory providers only make worse.

I think it cuts both ways - for example I've definitely had the experience where when typing into ChatGPT I know ahead of time that whatever "memory" they're storing and injecting is likely going to degrade my answer, so I hop over to incognito mode. I've also had the experience where I've had a loosely related follow-up question to something and I didn't want to dig through my chat history to find the exact convo, so it's nice to know that ChatGPT will probably pull the relevant details into context.

I think similar concepts apply to coding - in some cases, you have all the context you need up front (good coding practices help with this), but in many cases, there's a lot of "tribal knowledge" scattered across various repos that a human vet working in the org would certainly know, but an agent wouldn't (of course, there's somewhat of a circular argument here that if the agent eventually learned this tribal knowledge, it could just write it down into a CLAUDE.md file ;)). I think there's also a clear separation between procedural knowledge and learned preferences, the former is probably better represented as skills committed to a repo, vs I view the latter more as a "system prompt learning" problem.

I think the problem with ChatGPT / other RAG-based memory solutions is that it's not possible to collaborate with the agent on what it's memory should look like - so it makes sense that its much easier to just have a stateless system and message queue, to avoid mysterious pollution. But Letta's memory management is primarily text/files based so very transparent and controllable.

An example of how this kind of memory can help is learned skills https://www.letta.com/blog/skill-learning - if your agent takes the time to reflect/learn from experience and create a skill, that skills is much more effective at making it better next time than just putting the raw trajectory into context.

ChatGPTs implementation of Memory is terrible. It quickly fills up with useless garbage and sometimes even plain incorrect statements, that are usually only relevant to one obscure conversation I had with it months ago.

A local, project specific llm.md is absolutely something I require though. Without that, language models kept on "fixing" random things in my code that it considered to be incorrect, despite comments on those lines literally telling it to NOT CHANGE THIS LINE OR THIS COMMENT.

My llm.md is structured like this:

- Instructions for the LLM on how to use it

- Examples of a bad and a good note

- LLM editable notes on quirks in the project

It helps a lot with making an LLM understand when things are unusual for a reason.

Besides that file, I wrap every prompt in a project specific intro and outro. I use these to take care of common undesirable LLM behavior, like removing my comments.

I also tell it to use a specific format on its own comments, so I can make it automatically clean those up on the next pass, which takes care of most of the aftercare.

> Does somebody have a clear case on why this is something that we should want

For coding agents, I think it's clear that nobody wants to repeat the same thing over an over again. If a coding agent makes a mistake once (like `git add .` instead of manually picking files), it should be able to "learn" and never make the same mistake again.

Though I definitely agree w/ you that we shouldn't aspire to 1:1 replicate human memory. We want to be able to make our machines "unlearn" easily when needed, and we also want them to be able to "share" memory with other agents in ways that simply isn't possible with humans (until we all get neuralinks I guess)

Beads looks cool! I haven't tried it, but as far as I can tell, it's more of a "linear for agents" (memory as a tool), as opposed to baking long-term memory into the harness itself. In many ways, CLAUDE.md is a weak form of "baking memory into the harness", since AFAIK on bootup of `claude`, the CLAUDE.md gets "absorbed" and pinned in the system prompt.

Letta's memory system is designed off the MemGPT reference architecture, which is intentionally very simple - break the system prompt up into "memory blocks" (all pinned to the context window, since they are injected in system, which are modifiable via memory tools (the original MemGPT paper is still a good reference for what this looks like at a high level: https://research.memgpt.ai/). So it's more like a "living CLAUDE.md" that follows your agent around wherever it's deployed - ofc, it's also interoperable with CLAUDE.md. For example, when you boot up Letta Code and run `/init`, it will scan for AGENTS.md/CLAUDE.md, and will ingest the files into its memory blocks.

LMK if you have any other questions about how it works happy to explain more