@mastra/core/dist/agent/goal/scorer.d.ts

import type { AgentMemoryOption, ToolsInput } from '../../agent/types.js';
import type { ScorerJudgeConfig } from '../../evals/index.js';
import type { MastraModelConfig } from '../../llm/index.js';
import type { Mastra } from '../../mastra/index.js';
import type { MastraMemory } from '../../memory/index.js';
import type { RequestContext } from '../../request-context/index.js';
/**
 * Build the default goal scorer: an LLM judge using `judgeModel` and the
 * effective `prompt` (the ported MastraCode judge prompt unless overridden).
 * The objective and the agent's latest output are passed by the goal step on the
 * scorer run input (`originalTask`/`currentText`).
 *
 * When `tools` is provided, the judge agent can call them (read-only verification
 * tools) before deciding, matching the original MastraCode judge's tool surface.
 */
export declare function createGoalScorer({ judgeModel, prompt, tools, memory, defaultMemoryOptions, onStream, maxSteps, mastra, requestContext, }: {
    judgeModel: MastraModelConfig;
    prompt?: string;
    tools?: ToolsInput;
    memory?: MastraMemory;
    defaultMemoryOptions?: AgentMemoryOption;
    onStream?: ScorerJudgeConfig['onStream'];
    maxSteps?: number;
    mastra?: Mastra;
    requestContext?: RequestContext<any>;
}): import("../../evals").MastraScorer<"goal-scorer", any, any, Record<"analyzeStepResult", {
    decision: "continue" | "done" | "waiting";
    reason: string;
}> & Record<"generateScoreStepResult", number> & Record<"generateReasonStepResult", string>>;
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