Why We Built AgentClash

Your benchmarks are lying to you.

Every team picking an AI model today is doing the same thing: reading someone else's leaderboard, running a few prompts in a playground, and shipping based on vibes. The benchmarks are gamed. The leaderboards reward hype. And you're left guessing.

We built AgentClash because we were tired of this.

The problem

Static test sets leak into training data. Crowd-voted rankings measure popularity, not capability. You test agents in isolation, one at a time, and compare scores that were generated under completely different conditions.

None of this tells you which model is actually better for your task.

What we're building

AgentClash puts your models on the same real task, at the same time. Same tools, same constraints, same environment. Scored live on completion, speed, token efficiency, and tool strategy.

Step-by-step replays show exactly why one agent won and another didn't. Every failure gets captured, classified, and turned into a regression test — automatically. The more you run, the smarter your eval suite gets.

Why opensource

Because eval infrastructure shouldn't be a black box. You should be able to see exactly how models are scored, modify the scoring to fit your use case, and run it on your own infra.

We're building this in the open. Every commit is public. Every design decision is documented.

What's next

We're in private beta. If you're shipping agents and you're tired of guessing which model to use, join the waitlist.

Follow the build on GitHub.