import { LearningMemory, LearningMemoryConfig, LearningMemoryRecord, LearningSemanticSearch } from '@happyvertical/smrt-core';
import { DatabaseInterface } from '@happyvertical/sql';
import { Feedback, FeedbackOutcomeOptions } from './feedback.js';
/** The minimal persona shape the memory router needs. */
export interface PersonaMemoryLike {
    id?: string | null;
    memoryScope?: string;
    agentClass?: string;
    tenantId?: string | null;
}
/**
 * The effective memory partition key for a persona: its explicit `memoryScope`,
 * or a stable `persona:<id>` fallback when none is set.
 *
 * @throws if neither a `memoryScope` nor an id is available.
 */
export declare function personaMemoryScope(persona: PersonaMemoryLike): string;
/**
 * Build a {@link LearningMemory} partitioned to a persona's memory scope.
 *
 * The persona's `memoryScope` is used as the memory `owner_id`, so recall and
 * capture are isolated per persona. `owner_class` defaults to the persona's
 * agent class (falling back to `'AgentPersona'`).
 */
export declare function personaLearningMemory(options: {
    db: DatabaseInterface;
    persona: PersonaMemoryLike;
    ownerClass?: string;
    tenantId?: string | null;
    semanticSearch?: LearningSemanticSearch;
    config?: Partial<LearningMemoryConfig>;
}): LearningMemory;
/**
 * Apply a single {@link Feedback} signal to a persona's memory as
 * confidence-only reinforcement.
 *
 * Maps the signal onto a `LearningOutcome` and captures it against the episode
 * the signal names (`feedback.scope` / `feedback.key`). A `correction` signal
 * additionally supersedes the stored strategy with the corrected value.
 *
 * @returns The updated/seeded memory record, or `null` when the signal carries
 *   no reinforcement value (or there was nothing to reinforce).
 */
export declare function reinforceFromFeedback(memory: LearningMemory, feedback: Feedback, options?: FeedbackOutcomeOptions): Promise<LearningMemoryRecord | null>;
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