Keep your Agents Under Control with agent-belt

Agent-belt is an open-source CLI-based evaluation framework designed for AI coding agents, ensuring that these agents perform correctly before reaching customers. It operates by running the agent's CLI as a subprocess within a real workspace, allowing for accurate evaluation without interfering with the agent's operations. Unlike other evaluation frameworks that focus on models or wrapped functions, agent-belt evaluates the CLI itself, offering a comprehensive and non-deterministic approach to testing across various agents. It supports multiple scenarios and scoring modes, enabling robust assessment through trials, varied user inputs, and multiple judges to ensure reliability and accuracy. Developed by JFrog, agent-belt integrates seamlessly into the development workflow, allowing developers to author scenarios, run evaluations, and diagnose issues directly in their IDE, emphasizing prevention of issues before deployment. This framework is part of JFrog's commitment to providing end-to-end solutions in the AI space, aiming to standardize evaluation practices and improve trust in AI agents.