agent-eval

You can benchmark coding agents by running them against reproducible YAML task definitions and collecting metrics like pass rate, cost, time, and consistency. This approach isolates runs using git worktrees to ensure deterministic, comparable results across different tools.

Evaluating coding agents requires comparing them on reproducible tasks within your codebase. By defining tasks in YAML and recording quantitative metrics such as pass rate and execution time, you can identify the best-performing tool through data-driven analysis.

Yes, benchmarking is applicable to evaluating Claude Code, Aider, Codex, and other coding agents. The tool compares these agents across codebases using per-task metrics to identify the highest-performing option.

Git worktrees provide isolation for coding agent runs by creating separate working directories from the same repository. This ensures reproducible, deterministic experiments by preventing agents from interfering with each other or modifying shared state.

Yes, you define success criteria using YAML task definitions that declare what each agent should do and how success is judged. This allows customizable evaluation tailored to specific codebase tasks and expected outcomes.

Reproducible coding agent comparisons record pass rate, cost, time, and consistency metrics. These quantitative results are collected across tasks to generate a comprehensive comparison report for analysis.