/**
 * Calls the internal LLM via OpenRouter for intelligent analysis.
 * @param systemPrompt - The system instruction for the LLM.
 * @param userPrompt - The user's request or the data to analyze.
 * @param maxTokens - Maximum number of tokens to generate.
 * @returns The LLM's response text or null if an error occurs.
 */
export declare function callInternalLlm(systemPrompt: string, userPrompt: string, maxTokens?: number): Promise<string | null>;
/**
 * Analyzes text to generate improved metrics using the internal LLM.
 * @param textToAnalyze - The text content of the thought or related data.
 * @param metricType - The type of metric to generate (e.g., 'confidence', 'relevance', 'quality').
 * @param context - Optional context for the analysis.
 * @returns A numerical score (0.0-1.0) or null if analysis fails.
 */
export declare function analyzeForMetric(textToAnalyze: string, metricType: 'confidence' | 'relevance' | 'quality' | 'bias' | 'verification_need', context?: {
    previousThoughtContent?: string;
    connectionType?: string;
}): Promise<number | null>;
/**
 * Uses the internal LLM to suggest improvements for a thought.
 * @param thoughtContent - The content of the thought to improve.
 * @returns An array of suggested improvement strings, or null if analysis fails.
 */
export declare function suggestLlmImprovements(thoughtContent: string): Promise<string[] | null>;
/**
 * Uses the internal LLM to verify a statement or calculation.
 * @param statement - The statement or calculation to verify.
 * @returns An object containing verification status, confidence, and notes, or null if analysis fails.
 */
export declare function verifyWithLlm(statement: string): Promise<{
    status: 'verified' | 'contradicted' | 'unverified';
    confidence: number;
    notes: string;
    key_factors?: string[];
} | null>;