AI agent bench
Compare agents only with the same task, starting commit, and outcome check. The harness preserves result branches and removes temporary worktrees.
Create <repo>/.agent-bench.toml:
prompt = "prompts/task.md"
start_branch = "main" # or start_commit
agents = ["claude", "codex"]
outer_check = "./scripts/full_check.sh"
inner_check = "pytest tests/integration/test_x.py -q"outer_check proves the real outcome before and after, and measures wall time. inner_check gives agents fast feedback.
Require a clean repo, available CLIs, and a passing outer_check. Confirm agents and run ID, then run trials sequentially to avoid load-biased timing:
python <skill>/scripts/run_trial.py --repo "$REPO" --config "$REPO/.agent-bench.toml" --agent "$AGENT" --run "$RUN_ID"Results go to eval-results/<task>/<agent>/run-<id>-<timestamp>/. Record unexpected behavior in ai-agent-bench-anomalies.md per [anomalies](references/anomalies.md).
Aggregate with scripts/parse_transcript.py --aggregate <run-dirs> --output comparison.json --render-report comparison.md. Report gates, branches, time delta, tokens, and cost. Never rank a failed trial.
For plugin behavior rather than a real code task, use the bounded Pydantic runner documented by eval-regression and scripts/run_evals.py.
Never commit on the user's branch. A repeated run creates a new timestamped result and preserves prior evidence.