StreamMA: Rethinking Latency in Multi-Agent Systems

Multi-agent systems have always struggled with latency. Traditionally, they follow a 'generate-then-transfer' approach. The result? Latency scales linearly with the depth of the pipeline. Enter StreamMA, a new system that promises to change the game by streaming each reasoning step to downstream agents as soon as it's generated. Ship it to testnet first. Always.

Latency Reduction and Beyond

StreamMA doesn't just cut down on waiting time. It enhances overall effectiveness. By focusing on early, reliable steps rather than waiting for the entire chain, the system minimizes error-prone late steps. This isn't just theoretical. Across tests in math, science, and code, StreamMA consistently outperformed traditional methods. Claude Opus 4.6 and GPT-5.4, two leading LLMs, confirmed these results across various topologies like Chain, Tree, and Graph.

Breaking Down the Numbers

Numbers don't lie. StreamMA showed an average improvement of 7.3 percentage points, with a maximum spike of 22.4 percentage points on the HMMT 2026 test. That's impressive. But what's truly groundbreaking is the discovery of a 'step-level scaling law.' By increasing the steps per agent, both effectiveness and efficiency saw consistent gains. This opens a new dimension for scaling, one that can complement agent-count scaling.

Why StreamMA Matters

So, why should you care? Simple: faster and more reliable multi-agent reasoning systems mean better real-time decision-making. Think of applications in autonomous vehicles, real-time data analysis, and more. The SDK handles this in three lines now. But, is speed the only metric that matters? StreamMA suggests otherwise. As developers, we need to rethink how we balance speed and accuracy. Clone the repo. Run the test. Then form an opinion.