import type { BaseLanguageModelInterface } from "@langchain/core/language_models/base";
import type { ToolInterface } from "@langchain/core/tools";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import type { AgentStep } from "@langchain/core/agents";
import { type BaseMessage } from "@langchain/core/messages";
import { Optional } from "../../types/type-utils.js";
import { Agent, AgentArgs, OutputParserArgs } from "../agent.js";
import { AgentActionOutputParser, AgentInput } from "../types.js";
/**
 * Interface defining the structure of arguments used to create a prompt
 * for the ChatConversationalAgent class.
 */
export interface ChatConversationalCreatePromptArgs {
    /** String to put after the list of tools. */
    systemMessage?: string;
    /** String to put before the list of tools. */
    humanMessage?: string;
    /** List of input variables the final prompt will expect. */
    inputVariables?: string[];
    /** Output parser to use for formatting. */
    outputParser?: AgentActionOutputParser;
}
/**
 * Type that extends the AgentInput interface for the
 * ChatConversationalAgent class, making the outputParser property
 * optional.
 */
export type ChatConversationalAgentInput = Optional<AgentInput, "outputParser">;
/**
 * Agent for the MRKL chain.
 * @augments Agent
 *
 * @deprecated Use the {@link https://api.js.langchain.com/functions/langchain.agents.createStructuredChatAgent.html | createStructuredChatAgent method instead}.
 */
export declare class ChatConversationalAgent extends Agent {
    static lc_name(): string;
    lc_namespace: string[];
    ToolType: ToolInterface;
    constructor(input: ChatConversationalAgentInput);
    _agentType(): "chat-conversational-react-description";
    observationPrefix(): string;
    llmPrefix(): string;
    _stop(): string[];
    static validateTools(tools: ToolInterface[]): void;
    /**
     * Constructs the agent scratchpad based on the agent steps. It returns an
     * array of base messages representing the thoughts of the agent.
     * @param steps The agent steps to construct the scratchpad from.
     * @returns An array of base messages representing the thoughts of the agent.
     */
    constructScratchPad(steps: AgentStep[]): Promise<BaseMessage[]>;
    /**
     * Returns the default output parser for the ChatConversationalAgent
     * class. It takes optional fields as arguments to customize the output
     * parser.
     * @param fields Optional fields to customize the output parser.
     * @returns The default output parser for the ChatConversationalAgent class.
     */
    static getDefaultOutputParser(fields?: OutputParserArgs & {
        toolNames: string[];
    }): AgentActionOutputParser;
    /**
     * Create prompt in the style of the ChatConversationAgent.
     *
     * @param tools - List of tools the agent will have access to, used to format the prompt.
     * @param args - Arguments to create the prompt with.
     * @param args.systemMessage - String to put before the list of tools.
     * @param args.humanMessage - String to put after the list of tools.
     * @param args.outputParser - Output parser to use for formatting.
     */
    static createPrompt(tools: ToolInterface[], args?: ChatConversationalCreatePromptArgs): ChatPromptTemplate<any, any>;
    /**
     * Creates an instance of the ChatConversationalAgent class from a
     * BaseLanguageModel and a set of tools. It takes optional arguments to
     * customize the agent.
     * @param llm The BaseLanguageModel to create the agent from.
     * @param tools The set of tools to create the agent from.
     * @param args Optional arguments to customize the agent.
     * @returns An instance of the ChatConversationalAgent class.
     */
    static fromLLMAndTools(llm: BaseLanguageModelInterface, tools: ToolInterface[], args?: ChatConversationalCreatePromptArgs & AgentArgs): ChatConversationalAgent;
}