/**
 * toolCallContinuation — Utilities for detecting and classifying LLM finish
 * reasons across providers. Used to detect max_tokens truncation and normalize
 * stop reason extraction from provider-specific metadata formats.
 *
 * @module
 */
import type { AIMessageChunk } from '@langchain/core/messages';
/**
 * Extract the finish/stop reason from an AIMessageChunk's response_metadata.
 * Centralizes provider-specific metadata access patterns.
 *
 * Provider formats:
 * - OpenAI/Azure: `response_metadata.finish_reason`
 * - Anthropic direct: `response_metadata.stop_reason`
 * - Bedrock invoke: `response_metadata.stopReason`
 * - Bedrock streaming: `response_metadata.messageStop.stopReason`
 * - VertexAI/Google: `response_metadata.finishReason`
 *
 * @param chunk - The final accumulated AIMessageChunk
 * @returns The finish reason string, or undefined if not found
 */
export declare function extractFinishReason(chunk: AIMessageChunk | undefined): string | undefined;
/**
 * Check if a finish reason indicates the response was truncated by token limits.
 *
 * @param reason - The finish reason from extractFinishReason()
 * @returns true if the response was cut short by max_tokens
 */
export declare function isMaxTokensFinish(reason: string | undefined): boolean;