Do you have a link to some background? I'm curious why it was banned.

That has really been the "OpenCode and Anthropic" fight, OMO is still a tiny player compared to all OpenCode (and other such clients) usage.

It’s not hard to set up a router/proxy for Claude Code to use something else.

No. Just, no to all of this

If you're an engineering manager, you should communicate with your team, know their strengths and weaknesses, stay sharp on modern technique, and, most importantly, ask them what workflows work best for them, not us on Hacker News.

If you're hiring a consultancy or a pile of freelancers it's a bit different, but the question here would make me believe you don't trust their capability to start and I would be looking for teams that better align with what you expect as their outputs.

In my experience multi-agent orchestration frameworks usually accomplish vague to unnoticable to straight up worse results compared just getting used to the vanilla tools before impulsively installing the daily flavor of "I made Claude Code better". I'm guessing you've probably already noticed by now these come out daily. But a look at the repo shows that they do at least halfway use sub-agents in the way most people are starting to realize they're (currently at least) most helpful imo, which is managing context bloat in the main chats. Not a fan of wishfully creating "expert" agents which amount to little more than prompts asking Claude to a good job at the task. I'm honestly not sure why that couldn't be a slash command at that point.

Enforce?

At least its open source ?

It's hard to accurately measure but one advantage that the multi-agent approach has seems to be speed. I routinely see Sisyphus launching up to 4 sub agents to read/analyse a file and/or to do things in parallel.

The quality of the output depends more on the underlying LLM. GLM 4.7 isn't going to beat Opus but Opus with an orchestra seems to be faster and perhaps marginally better than with a more linear approach.

Ofcourse this burns a lot of tokens but with a cheap subscription like z. ai or with a corporate budget does it really matter?

Reasoning density.

I have a specific use case (financial analysis), that is at the edge of what is possible with this models (accuracy wise).

Gemini 2 was the beginning, you could see this technology could be helpful in this specific analysis but plenty of errors (not unlike a junior analyst). Gemini 2.5 flash was great actually useable, errors made were consistent.

This is where it gets interesting, I could add additional points to my system prompt, yes it would fix those errors but it would degrade the answer elsewhere, often it wouldn't be incorrect but merely much simpler less nuanced and less clever.

This is where multi-agents helped it actually meant the prompt can be broken down so that answers remain "clever". There is a big con to this, it is slow, slow to the point that I chose to stick with a single prompt (the request didn't work well operating in parallel as the other prompt surfaced factors for it to consider).

However Gemini 3 flash is now smart enough that I'd now consider my financial analysis solved. All with one prompt.