@e2e-dev/decision

Run agent.act and agent.assert through a decision model instead of an LLM. Any AI SDK decision model that answers choice questions with probability distributions works, such as TypeSafe Jev or OpenAI gpt-6-luna through @ai-sdk/openai 4.0.90 or later. A small language model writes field values and upload paths. Needs ai 7.0.134 or later. An answer with no distribution, as a gateway fallback to a language model returns, counts as a committed choice with probability 1 and confidence 0.

import type { E2EConfig } from 'e2e';
import { decisionExecutor } from '@e2e-dev/decision';
import { typeSafeAi } from '@ai-sdk/typesafe-ai';
import { openrouter } from '@openrouter/ai-sdk-provider';

export default {
  agents: {
    default: {
      executor: decisionExecutor({
        model: typeSafeAi.decisionModel('jev-latest'),
        textModel: openrouter('inception/mercury-2.5'),
      }),
    },
  },
} satisfies E2EConfig;

Tests stay plain natural language, with no params. Each turn asks the operation, then the target for it. Every operation the engine declares is offered, and every target question carries a none option. With vision: true and a model that takes images, such as typeSafeAi.decisionModel('jev-latest') with @ai-sdk/typesafe-ai 3.0.19 or later, verdicts see a masked screenshot and tap_at and type_at reach a drawn control or field. The runner authorizes every action and records every model call against the step budget. Secrets stay declared handles that only typeSecret fills.

See the decision models guide for setup, vision, gates, provider options, and limits.