hermes-agent/evals/toolperf_abeval

Core-Toolset A/B Eval Harness

The hard A/B evaluation used for the August 2026 core-toolset performance batch (tracker: #77056). It measures whether a set of tool-layer changes actually reduces model waste — LLM turns, tool calls, tool errors, retries, result bytes, wall clock — on a battery of error-inducing tasks, each derived from a waste class measured in real production traffic.

Design

• Two arms, one variable. baseline and fixes runs differ ONLY by PYTHONPATH (a checkout of origin/main vs your integration branch). Same Hermes home, same model, same tasks, same reps.
• Tasks are traps. Each of the 9 tasks is constructed so a specific failure class fires: python vs python3/venv confusion, an already-applied patch, an ambiguous multi-match edit, wrong-casing search, hidden-dir search, giant truncated output, cd-heavy multi-dir work, a blocklist-tripping inline script, and a paginated big-file read. A change that claims to fix a waste class must move the needle on its trap.
• Scoring is from traces, not self-report. Metrics come from NeMo Relay ATOF traces emitted by the run itself (llm/tool scope events), plus wall clock and a per-task programmatic success check (marker strings + on-disk verification).
• Resume-safe. Completed run_ids in meta.jsonl are skipped, so a killed battery continues where it left off. Startup crashes (nonzero exit with empty output) are NOT recorded — they retry on resume instead of polluting cells (this bit the first pass of the Aug 2026 run).

Setup

1. Create a dedicated Hermes home with credentials for the models under test:

   export ABEVAL_HOME=/tmp/abeval-home
   mkdir -p "$ABEVAL_HOME"
   # minimal config.yaml + provider key, e.g. OpenRouter:
   cat > "$ABEVAL_HOME/config.yaml" <<'YAML'
   model:
     provider: openrouter
   YAML
   printf 'OPENROUTER_API_KEY=%s\n' "$KEY" > "$ABEVAL_HOME/.env"
   

   The runner writes a per-run Relay plugins.toml and points the native SDK integration at it; no Hermes observability plugin needs to be enabled.

2. Prepare the two trees:

   git worktree add /tmp/abeval-baseline origin/main
   # fixes tree = your integration branch checkout
   

Run

cd evals/toolperf_abeval
export ABEVAL_ROOT=/tmp/abeval-workspace   # results + sandboxes land here
export ABEVAL_HOME=/tmp/abeval-home
./run_all.sh /tmp/abeval-baseline /path/to/fixes-tree 3 \
  "anthropic/claude-sonnet-4.5" "qwen/qwen3-coder-30b-a3b-instruct"

108 runs (2 models x 2 arms x 9 tasks x 3 reps) took ~2.5h on the original battery. Re-print tables any time:

python3 ab_eval.py report --models "anthropic/claude-sonnet-4.5,qwen/qwen3-coder-30b-a3b-instruct"

Reading the results

• Weak models are the signal. Strong models recover from most induced errors in one turn, so expect parity there; the fixes' win shows up as fewer turns/tool calls/errors on the weak model. The Aug 2026 batch measured −21% turns, −29% tool calls, errors→0, −23% wall on qwen3-coder-30b, with sonnet-4.5 at parity.
• Success-rate deltas at n=3 are noise. Audit any sub-100% cell run-by-run (read meta.jsonl tail) before calling it a regression.
• The eval can catch product gaps on BOTH arms — e.g. the original run found the hidden-file search probe only fired on total-zero-match searches (fixed on main since).

Extending

Add a task by appending to TASKS (the prompt), make_sandbox (the trap), and SUCCESS (the programmatic check). Keep checks strict and mechanical — marker strings and on-disk state, never judge-by-vibes.