Env

An Env defines the control flow between Agents. In the simplest case, it is just a SingleAgentEnv where a single agent solves a task from a taskset. Its core signature is Env.run(task: Task, agents: Agents) -> None — it is passed an initial task and pre-initialized agents and then programs the full multi-agent control flow; every finished agent run automatically joins the resulting Episode, which holds all the traces of all the agents.

class Env(ABC):
    @abstractmethod
    async def run(self, task: Task, agents: Agents) -> None:
        """Run a single multi-agent episode."""
        ...

verifiers comes with different pre-built Envs to use:

The AgenticJudgeEnv defines the sequential interaction between a solver and judge agent. The judge can re-use the same runtime after the solver (SharedAgenticJudgeEnv) or use its own, new runtime IsolatedAgenticJudgeEnv.
The UserSimEnv models users as agents, and the episode is a turn-by-turn conversation between the user and assistant agents.
The BestOfNEnv runs n independent attempts at the same task, then marks which attempt achieved the highest reward (best) and whether any attempt crossed a success threshold (pass_at_n), which is useful for rejection sampling and pass@k evaluation.

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