agent-eval

Comparing coding agent performance involves running head-to-head evaluations on custom tasks defined in YAML files. By executing these tasks across different agents, you capture pass rates, costs, and execution times to measure consistency and inform AI tool adoption decisions.

Agent comparison captures key performance metrics including pass rates, execution costs, completion times, and consistency across runs. These metrics provide detailed insights to help you evaluate and select the best AI coding tool for your specific use cases.

Yes, running agent performance evaluations requires a Python environment with pypdf, pdfplumber, and pdf2image libraries installed. These dependencies support the evaluation scripts and reference components needed to process task definitions and generate performance metrics.

Yes, you can compare multiple coding agents side-by-side by passing multiple agent flags in the run command. This head-to-head comparison on identical custom tasks allows you to directly contrast their pass rates, costs, and times for informed decision-making.

Custom tasks for coding agent comparison are defined using YAML task definition files. These files specify the real-world scenarios and parameters agents must execute, allowing the evaluation to systematically measure performance and consistency across different tools.