agentfootprint/dist/esm/memory/facts/llmFactExtractor.d.ts

The explainable agent framework — backtrack a wrong answer to the exact context that caused it (evidence, not guesses). Built on footprintjs.

/**
 * llmFactExtractor — LLM-backed fact extractor.
 *
 * Uses an LLMProvider (typically a cheap model like Claude Haiku or
 * GPT-4o-mini) to pull stable, timeless claims out of a conversation
 * turn. Complements `patternFactExtractor` — regex catches the obvious
 * self-disclosures, the LLM catches the open-ended ones.
 *
 * The extractor asks the LLM for a JSON response in this shape:
 *
 * ```json
 * {
 *   "facts": [
 *     {
 *       "key": "user.name",
 *       "value": "Alice",
 *       "confidence": 0.95,
 *       "category": "identity",
 *       "refs": ["msg-1-0"]
 *     }
 *   ]
 * }
 * ```
 *
 * The extractor parses, clamps confidence via `asConfidence()`, dedups
 * by `key` (last occurrence wins, matching `patternFactExtractor`),
 * and returns the facts. Malformed responses fall back to `[]` — a bad
 * extraction should not break the agent turn.
 *
 * Usage:
 * ```ts
 * import { anthropic } from 'agentfootprint';
 * import { llmFactExtractor, factPipeline, InMemoryStore } from 'agentfootprint/memory';
 *
 * const pipeline = factPipeline({
 *   store: new InMemoryStore(),
 *   extractor: llmFactExtractor({ provider: yourLLMProvider }),
 * });
 * ```
 */
import type { LLMProvider } from '../../provider-contract/types.js';
import type { FactExtractor } from './extractor.js';
export interface LLMFactExtractorConfig {
    /** 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;
    /**
     * Include up to this many existing facts in the user prompt so the
     * model can update / refine rather than duplicate. Set to `0` to
     * skip — cheaper but loses update awareness. Default `16`.
     */
    readonly includeExistingLimit?: number;
}
export declare function llmFactExtractor(config: LLMFactExtractorConfig): FactExtractor;