/**
 * ToT — Tree of Thoughts: breadth-first iterative expansion + pruning.
 *
 * Paper: "Tree of Thoughts: Deliberate Problem Solving with Large
 *        Language Models" — Yao et al., 2023
 *        (https://arxiv.org/abs/2305.10601).
 *
 * Pattern: Factory → produces a `Runner` built from `Loop(depth times,
 *          Parallel(K thought branches) + prune-to-top-M)`.
 * Role:    patterns/ layer. Pure composition over existing primitives.
 *          Build-time-fixed depth + branching factor; runtime frontier
 *          is pruned to `beamWidth` per level via a consumer-supplied
 *          scorer.
 *
 * Tradeoff vs. full DFS: this shipped variant is BFS with constant
 * width. True DFS with backtracking or adaptive branching factor would
 * need runtime-variable Parallel (DynamicParallel) or recursion.
 */
import type { LLMProvider } from '../provider-contract/types.js';
import type { Runner } from '../core/runner.js';
export interface ToTOptions {
    readonly provider: LLMProvider;
    readonly model: string;
    /** System prompt for the thought-generation LLMCall. */
    readonly thoughtPrompt: string;
    /** Depth of the tree (number of expansion iterations). */
    readonly depth: number;
    /** Branching factor — K thoughts generated per frontier node per iteration. */
    readonly branchingFactor: number;
    /**
     * Scorer: given a thought, return a numeric score. Higher is better.
     * The top `beamWidth` thoughts survive each level; the rest are pruned.
     * Synchronous so pruning is deterministic.
     */
    readonly score: (thought: string) => number;
    /** Beam width — how many thoughts survive after each level. Default 1 (greedy). */
    readonly beamWidth?: number;
    readonly temperature?: number;
    readonly maxTokens?: number;
    readonly name?: string;
    readonly id?: string;
}
/**
 * Build a ToT Runner. At run time:
 *   1. Seed — treat the input message as the initial frontier of 1 thought.
 *   2. For each of `depth` iterations:
 *      a. Parallel fan-out: generate `branchingFactor` new thoughts.
 *      b. Score all new thoughts, keep top `beamWidth`, pass to next iteration.
 *   3. Return the single best-scoring thought from the final frontier.
 */
export declare function tot(opts: ToTOptions): Runner<{
    message: string;
}, string>;