AgentHub

AgentHub provides a comprehensive platform for identifying and resolving errors in AI agents before they impact end-users. By simulating real-world scenarios within carefully designed reinforcement learning environments, AgentHub enables users to trace, evaluate, and enhance agent behavior at scale. The platform facilitates the generation of large, annotated datasets and allows for experimentation with various prompts, models, and configurations, all within a unified environment. AgentHub offers clear visualizations to highlight successful strategies and pinpoint areas for improvement.

Developed by a team with experience from Apple's Foundation Model Eval team and MIT, AgentHub streamlines the integration process, allowing users to quickly establish their agent evaluation environment without extensive setup. This facilitates a rapid feedback loop, empowering developers to build superior AI agents with increased speed and confidence.