/**
 * Random testing model built on the faux provider.
 *
 * Generates deterministic, structured LLM-like responses that exercise the
 * full observational memory pipeline (observer → reflector → pruner → compaction)
 * without real LLM API calls.
 *
 * The model inspects the tool definitions in the context to decide what kind
 * of response to generate:
 *  - `record_observations` → generates observation batches with source entry IDs
 *  - `record_reflections` → generates reflection proposals with supporting IDs
 *  - `drop_observations`   → generates drop-lists with observation IDs
 *  - No tool / unknown     → generates plain text
 */
import { type FauxProviderRegistration } from "../../sdk/ai/providers/faux.js";
import { SeededRandom } from "./seeded-random.js";
export interface RandomModelConfig {
    /** Seed for reproducible sequences. Default: 42. */
    seed?: number;
    /** Target character count per assistant text response. Default: 500. */
    textTargetChars?: number;
    /** Number of observation batches per observer run. Default: 2-4 (random). */
    observationBatches?: {
        min: number;
        max: number;
    };
    /** Observations per batch. Default: 2-5 (random). */
    observationsPerBatch?: {
        min: number;
        max: number;
    };
    /** Number of reflection batches per reflector pass. Default: 1-3. */
    reflectionBatches?: {
        min: number;
        max: number;
    };
    /** Reflections per batch. Default: 1-3. */
    reflectionsPerBatch?: {
        min: number;
        max: number;
    };
    /** Number of drop calls per pruner pass. Default: 1-3. */
    prunerDropCalls?: {
        min: number;
        max: number;
    };
    /** IDs to drop per call. Default: 1-5. */
    prunerIdsPerCall?: {
        min: number;
        max: number;
    };
    /** Tokens per second for streaming simulation. Default: 0 (instant). */
    tokensPerSecond?: number;
}
export interface RandomModelRegistration {
    provider: FauxProviderRegistration;
    config: Required<RandomModelConfig>;
    /** The seeded RNG for direct use by tests. */
    rng: SeededRandom;
}
/**
 * Register a deterministic random model as a faux provider.
 *
 * The model automatically detects which tools are in the context and generates
 * appropriate structured responses for the observer, reflector, and pruner.
 *
 * @example
 * ```typescript
 * const testModel = createRandomModel({ seed: 123 });
 * // Use testModel.provider.getModel() in agentLoop calls
 * // After testing: testModel.provider.unregister()
 * ```
 */
export declare function createRandomModel(config?: RandomModelConfig): RandomModelRegistration;
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