Builder Shares LLM Council Skill For Multi-Model Agent Research

Dynamic workflows present many interesting ways to fan out tasks to different LLMs/agents, so I think LLM Councils can shine a lot here.

@omarsar0 @dair_ai Kind of silly how they are branding an LLM council tool like it’s some groundbreaking new model or routing concept…

@omarsar0 LLM Council setups tend to outperform single-agent on multi-step research, curious if you’ve benchmarked against a routed chain-of-experts pattern.

I like it. Building the council as a Claude Code skill rather than a standalone service keeps it composable with whatever agent layer you're already running. The anonymized cross-rank before the chairman synthesizes is the part I'd want to test. Curious whether that step actually reduces variance or just adds latency.

@omarsar0 This sounds exactly like what I've been looking for!

A proper LLM Council with your own agent for deep research is 🔥 Love that it's built for Claude Code but flexible with Fireworks/OpenRouter.

Would love to test it on a complex multi-step research task. Mind sharing the link or more details? 👀

@jezell @dair_ai i appreciate that they did the benchmarking, though. maybe not everyone can afford to run those extensive experiments, but it proves that the ideas are slept on these days

@ECLresearch no benchmarking yet. haven't had the time since i released it like 3 months ago, but i might revisit it given the state of things these days and the importance and relevance of llm routing and dynamic workflows.

@omarsar0 this is a super cool use case for deep research.

@omarsar0 @dair_ai Gotta run it on actual benchmarks though. I only saw on X/100 tasks we gave it.

@aixahmad link in the initial post

@captainntan it really is. people are sleeping on this

@DoDataThings Lots to explore on the cross-ranking side of things. Lots of biases in these models. Honestly, I just think this stuff is about to explode in terms of interest, applications, and demand.

@omarsar0 imo polling multiple models is the easy part, what you do when they disagree is where council setups actually get interesting

@omarsar0 I think this is where things are going: orchestration, not just solo models. Parallel responses, ranking, then synthesis. Very strong recipe now that inference is faster and cheaper.

Fan-out to heterogeneous models is powerful when tasks have genuinely different sub-problems. One model's weakness is another's strength. The real challenge is the aggregation layer: naive voting dilutes specialization. A meta-reasoner aware of each model's domain strengths beats a simple consensus.

@omarsar0 Routing research > building. Revisit when cost-diversity ratio shifts.