While it can be super powerful, I wish there was a quicker "in memory" agent solution where each agent keeps in its own RAM the list of files modifications ("patch") it recommends to apply to solve current issue. Then we could apply that patch depending on what we're doing, if we have others patches to apply before etc.
Also even if agents can work in parallel, sometimes we only have 1 of them in front of us and if we already know what's the next thing we're gonna ask, we'll still wait for the previous task to be completed before sending the new prompt. I'm not sure how to improve this async problem, I guess I could launch multiple agents in parallel but I wouldn't get sharing of the chat history between the different agents, and when I work I usually work on related issues that depend on each others, thus I do need some kind of global or shared context between agents analyzing codebases and creating patches.
Anyone has ideas over how to improve those AI coding agents workflows ? Maybe latest versions of GitButler https://gitbutler.com/ but I'm not sure, and it does use git worktree behind the hood
I would love something similar that lets me plug in actual Claude Code/Codex with their original agent loop, prompting etc, and just handles the multiplexing, worktrees, isolation, etc automatically (it looks like this tool doesn’t support that). Because I think a lot of the power of eg CC comes from the engineering they’ve done to the tool rather than the underlying model.
How are people doing this at the moment?
Very similar features, Catnip is Claude Code specific and does everything in a Docker container so you can more safely run in YOLO mode and the Git worktrees don't make a mess on your host filesystem or checkout. Also is mobile responsive which is cute.
I have not yet tried either one, but here is the other project for those who want to compare and contrast them: