Train on it

environments/gyrell/ packages the engine as a verifiers environment. The policy plays whole teams, each seat with its own private conversation, against configurable opposition. No LLM judge anywhere: the reward comes from spec.score.

import gyrell

env = gyrell.load_environment(
    role="team",            # play every red seat
    opponent="scripted",    # or "selfplay", or any OpenAI-compatible endpoint
    partner="scripted",
)

cd environments/gyrell && uv pip install -e .
vf-eval gyrell -m openai/gpt-4.1-mini -n 5

To train on a new game, follow Build a game, then mirror the environment package: vendor the engine, expose a load_environment that seats your policy, and let spec.score be the reward. Per-seat observations map directly onto per-seat conversation threads.

Hub installs, training configs, and publishing: Prime Intellect.