/**
 * llmExtractor — LLM-backed beat extractor.
 *
 * Uses an LLMProvider (typically a cheap model like Claude Haiku or
 * GPT-4o-mini) to produce semantically rich beats. One extraction LLM
 * call per turn. Opt-in — default is `heuristicExtractor()` which is
 * free.
 *
 * The extractor asks the LLM for a JSON response in this shape:
 *
 * ```json
 * {
 *   "beats": [
 *     {
 *       "summary": "User revealed their name is Alice",
 *       "importance": 0.9,
 *       "refs": ["msg-1-0"],
 *       "category": "identity"
 *     }
 *   ]
 * }
 * ```
 *
 * The extractor parses, clamps importance via `asImportance()`, and
 * returns the beats. Malformed responses fall back to an empty array
 * — a bad extraction should not break the agent turn.
 *
 * Usage:
 * ```ts
 * import { anthropic } from 'agentfootprint';
 * import { llmExtractor, narrativePipeline, InMemoryStore } from 'agentfootprint/memory';
 *
 * const pipeline = narrativePipeline({
 *   store: new InMemoryStore(),
 *   extractor: llmExtractor({ provider: yourLLMProvider }),
 * });
 * ```
 */
import type { LLMProvider } from '../../adapters/types.js';
import type { BeatExtractor } from './extractor.js';
export interface LLMExtractorConfig {
    /** The provider used for extraction. Typically a cheap/fast model. */
    readonly provider: LLMProvider;
    /**
     * Override the system prompt. Defaults to a one-paragraph instruction
     * that elicits the JSON shape described in the module docs.
     */
    readonly systemPrompt?: string;
    /**
     * Dev-mode logger invoked when the LLM response fails to parse.
     * Defaults to `console.warn` — production consumers can route the
     * signal to their telemetry pipeline.
     */
    readonly onParseError?: (error: unknown, rawContent: string) => void;
}
export declare function llmExtractor(config: LLMExtractorConfig): BeatExtractor;