Summary
The tutorial ships several orchestration patterns (arena_score.py, ensemble_score.py, judge_panel_score.py, tournament.py, ...) but there is no side-by-side comparison showing what each pattern actually buys you on the same task. New users have to guess which pattern to reach for.
This task is to run a small, fixed benchmark task through at least two tutorial patterns and contribute the measured comparison as an example doc. It requires actually installing and using h5i + h5i-python end to end, which also makes it a great smoke test of our onboarding docs. No engine (Rust) knowledge needed.
What to do
- Follow the README install steps (h5i engine +
pip install h5i-orchestra) and get the Quickstart ensemble.py running once. If anything in the install or Quickstart is wrong or confusing, note it: fixing that is in scope and welcome.
- Pick 2 or 3 small, self-contained benchmark tasks with an objective pass signal, for example:
- "implement quicksort in Python with unit tests" (the Quickstart task),
- "fix this off-by-one bug so the provided pytest file passes",
- "add input validation to this function; tests included".
Each task should live in a tiny scratch Git repo with a pytest suite the verifier can run.
- Run each task through at least two patterns, e.g.
examples/tutorial/arena_score.py (independent arena) vs examples/tutorial/ensemble_score.py (mutual review). If you only have one agent CLI installed, hire the same runtime under two seat names; the comparison between patterns is still valid.
- For each (task, pattern) run, record:
- the verdict (winning submission) and whether verification passed,
- wall-clock per step (
h5i team trace prints recorded duration_ms per step),
- diff size of the winning submission,
- total number of agent turns.
- Contribute
examples/benchmarks/README.md with the setup, the tasks, the results table, and a short "which pattern when" takeaway. Include the h5i and h5i-orchestra versions.
Acceptance criteria
Hints
- The default
launcher="resident" starts agent sessions via tmux, so run this on a machine where tmux and at least one agent CLI (Claude Code or Codex) are installed and logged in.
- Agent runs cost real tokens. Keep the tasks tiny; the point is comparing patterns, not solving hard problems.
h5i team trace --dot renders the recorded DAG if you want a figure for the doc.
Summary
The tutorial ships several orchestration patterns (
arena_score.py,ensemble_score.py,judge_panel_score.py,tournament.py, ...) but there is no side-by-side comparison showing what each pattern actually buys you on the same task. New users have to guess which pattern to reach for.This task is to run a small, fixed benchmark task through at least two tutorial patterns and contribute the measured comparison as an example doc. It requires actually installing and using h5i + h5i-python end to end, which also makes it a great smoke test of our onboarding docs. No engine (Rust) knowledge needed.
What to do
pip install h5i-orchestra) and get the Quickstartensemble.pyrunning once. If anything in the install or Quickstart is wrong or confusing, note it: fixing that is in scope and welcome.Each task should live in a tiny scratch Git repo with a
pytestsuite the verifier can run.examples/tutorial/arena_score.py(independent arena) vsexamples/tutorial/ensemble_score.py(mutual review). If you only have one agent CLI installed, hire the same runtime under two seat names; the comparison between patterns is still valid.h5i team traceprints recordedduration_msper step),examples/benchmarks/README.mdwith the setup, the tasks, the results table, and a short "which pattern when" takeaway. Include the h5i and h5i-orchestra versions.Acceptance criteria
examples/benchmarks/README.mdexists with a results table covering at least 2 tasks x 2 patterns.h5i team traceoutput for at least one run).Hints
launcher="resident"starts agent sessions via tmux, so run this on a machine wheretmuxand at least one agent CLI (Claude Code or Codex) are installed and logged in.h5i team trace --dotrenders the recorded DAG if you want a figure for the doc.