Mixture-of-Agents: Making LLMs Collaborate Instead of Compete

What if instead of picking the best model for your prompt, you made all models collaborate on the answer? That's the core idea behind Mixture-of-Agents (MoA) — a technique from a 2024 research paper that showed LLMs produce better outputs when they can see and improve upon each other's responses. The paper demonstrated that even weaker models can boost the quality of stronger ones through this iterative refinement. I implemented MoA as a production API endpoint. This post covers the architecture, the six strategies I built, the engineering decisions that weren't obvious, and the parts that surprised me. The Problem With "Just Pick the Best Model" Most developers approach multi-model setups with a simple question: which model is best for this task? But the answer changes depending on the prompt, the domain, the time of day, and honestly a bit of luck. I noticed something while building a Compare mode that runs the same prompt through multiple models simultaneously. When I looked at the