import { BaseChatModel } from "@langchain/core/language_models/chat_models";
import type { ChatCompletionMessage } from "../types.js";
export declare function _isInternalMessage(message: ChatCompletionMessage): boolean;
/**
 * Creates a simulated user powered by a language model for multi-turn conversations.
 *
 * This function generates a simulator that can be used with the runMultiturnSimulation method to create
 * dynamic, LLM-powered user responses in a conversation. The simulator automatically handles message
 * role conversion to maintain proper conversation flow, where user messages become assistant messages
 * and vice versa when passed to the underlying LLM.
 *
 * @param {Object} params - The parameters for creating the simulated user
 * @param {string} params.system - System prompt that guides the LLM's behavior as a simulated user
 * @param {string} [params.model] - Optional name of the language model to use. Must be provided if client is not.
 * @param {BaseChatModel} [params.client] - Optional LangChain chat model instance. Must be provided if model is not.
 * @param {(string | ChatCompletionMessage)[]} [params.fixedResponses] - Optional list of fixed responses to use for the simulated user.
 *        If provided, these responses will be used in sequence based on the turn counter before falling back to LLM generation.
 *
 * @returns A callable simulator function that takes a trajectory and turn counter, and returns a Promise resolving to a ChatCompletionMessage
 *
 * @throws {Error} If neither client nor model is provided
 * @throws {Error} If both client and model are provided
 *
 * @example
 * ```typescript
 * import { runMultiturnSimulation, createLLMSimulatedUser } from "openevals";
 *
 * // Create a simulated user with GPT-4.1-mini
 * const simulatedUser = createLLMSimulatedUser({
 *   system: "You are a helpful customer service representative",
 *   model: "openai:gpt-4.1-mini"
 * });
 *
 * // Use with runMultiturnSimulation
 * const simulator = runMultiturnSimulation({
 *   app: myChatApp,
 *   user: simulatedUser,
 *   maxTurns: 5
 * });
 * ```
 *
 * Notes:
 * - The simulator automatically converts message roles to maintain proper conversation flow:
 *   * User messages become assistant messages when sent to the LLM
 *   * Assistant messages (without tool calls) become user messages when sent to the LLM
 *   * Messages with tool calls are skipped to maintain conversation coherence
 * - The system prompt is prepended to each conversation to maintain consistent behavior
 * - If no messages exist in the trajectory, an initial query is generated based on the system prompt
 * - Fixed responses are used in sequence based on the turn counter before falling back to LLM generation
 */
export declare function createLLMSimulatedUser({ system, model, client, fixedResponses, }: {
    system: string;
    model?: string;
    client?: BaseChatModel;
    fixedResponses?: (string | ChatCompletionMessage)[];
}): (params: {
    trajectory: ChatCompletionMessage[];
    turnCounter: number;
}) => Promise<ChatCompletionMessage>;