AgentHub

AgentHub helps you catch and fix errors in your AI agents - before your users ever see them. Simulate real-world scenarios in curated RL environments for your agents - trace, evaluate, and improve agent behavior at scale. Generate large annotated datasets and experiment with different prompts, models, and configurations - all in one place - with clear visualizations of what’s working (and what’s not). Built by a former tech lead from Apple’s Foundation Model Eval team and a product engineer from MIT with experience working on agents at startups, AgentHub makes it easy to integrate your use case and start improving your agents quickly. Our streamlined onboarding process lets you get your agent eval environment up and running fast—no heavy lifting on your end. Close the feedback loop and build better agents, faster - with confidence.