agent-eval

To benchmark coding agents, you run reproducible task suites using structured YAML definitions and deterministic git worktrees for isolation. This process collects objective pass rates, costs, runtimes, and consistency metrics to compare tools like Claude Code, Aider, and Codex for informed selection.

Evaluating LLM code generation tools requires running comparative task suites with per-task judges that validate file modifications and success criteria. This yields objective data on tool suitability, highlighting strengths and trade-offs across agents for real-world decision making.

Yes, agent-eval requires structured YAML task definitions to specify what to do, how to touch files, and how success is judged. These declarative definitions are necessary to execute deterministic evaluations and generate comparative reports across multiple coding agents.

To ensure reproducibility when comparing coding agents, the benchmark uses per-run git worktrees to provide deterministic isolation. This guarantees that each agent operates in a clean, controlled environment, preventing cross-contamination of results across different evaluation runs.

Yes, you can validate updates across agent versions by applying reproducible task suites to compare model-driven code generation tools. This approach collects consistency metrics and pass rates, allowing teams to objectively verify performance changes between different agent versions.