import { BaseGrader } from "./baseGrader.js";
import type { Grade, GraderInput, GraderOptions, Input, JSON } from "./types.js";
/** What a metric function receives. `input` is the typed Input; the gold answer is
 *  `input.expected`, and any extra per-input data lives under `input.metadata`. */
export type GraderContext = {
    output: JSON;
    input: Input;
    /** Run the bundled LLM goal judge and get back its 0..1 score + reasoning.
     *  Defaults: `output` falls back to `run.output`, `expected` falls back to
     *  `input.expected` (so the bundled judge grades against the gold answer when
     *  one is present, matching `LlmJudge`). */
    judge: (args: {
        goal: string;
        output?: JSON;
        expected?: JSON;
    }) => Promise<{
        score: number;
        reasoning: string;
    }>;
};
/** A metric: return a 0..1 number, a pass/fail boolean, or a full Grade. */
export type GraderFn = (ctx: GraderContext) => number | boolean | Grade | Promise<number | boolean | Grade>;
/** Public "grader" union: a metric function or a configured grader instance. */
export type Grader = GraderFn | BaseGrader;
/** Adapts a metric function into a single-shot BaseGrader so the whole grading
 *  pipeline (sampling, gating, weighting, scoring) treats it like any grader. */
export declare class FunctionGrader extends BaseGrader {
    private readonly fn;
    protected readonly defaultName = "fn";
    constructor(fn: GraderFn, options?: GraderOptions);
    protected _run({ input, run, runAgency }: GraderInput): Promise<Grade>;
}
/** Wrap a metric function so it carries policy (mustPass/weight/threshold/inputScope/samples/name). */
export declare function grader(fn: GraderFn, options?: GraderOptions): BaseGrader;
/** Normalize a user-supplied grader (function or instance) into a BaseGrader. */
export declare function toGrader(spec: Grader): BaseGrader;