import type { BaseLanguageModelInterface } from "@langchain/core/language_models/base";
import { ToolInterface } from "@langchain/core/tools";
import { PromptTemplate } from "@langchain/core/prompts";
import { Optional } from "../../types/type-utils.js";
import { Agent, AgentArgs, OutputParserArgs } from "../agent.js";
import { AgentInput, SerializedZeroShotAgent } from "../types.js";
import { ZeroShotAgentOutputParser } from "./outputParser.js";
/**
 * Interface for creating a prompt for the ZeroShotAgent.
 */
export interface ZeroShotCreatePromptArgs {
    /** String to put after the list of tools. */
    suffix?: string;
    /** String to put before the list of tools. */
    prefix?: string;
    /** List of input variables the final prompt will expect. */
    inputVariables?: string[];
}
/**
 * Type for the input to the ZeroShotAgent, with the 'outputParser'
 * property made optional.
 */
export type ZeroShotAgentInput = Optional<AgentInput, "outputParser">;
/**
 * Agent for the MRKL chain.
 * @augments Agent
 * @example
 * ```typescript
 *
 * const agent = new ZeroShotAgent({
 *   llmChain: new LLMChain({
 *     llm: new ChatOpenAI({ temperature: 0 }),
 *     prompt: ZeroShotAgent.createPrompt([new SerpAPI(), new Calculator()], {
 *       prefix: `Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:`,
 *       suffix: `Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"
 * Question: {input}
 * {agent_scratchpad}`,
 *       inputVariables: ["input", "agent_scratchpad"],
 *     }),
 *   }),
 *   allowedTools: ["search", "calculator"],
 * });
 *
 * const result = await agent.invoke({
 *   input: `Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?`,
 * });
 * ```
 *
 * @deprecated Use the {@link https://api.js.langchain.com/functions/langchain_agents.createReactAgent.html | createReactAgent method instead}.
 */
export declare class ZeroShotAgent extends Agent {
    static lc_name(): string;
    lc_namespace: string[];
    ToolType: ToolInterface;
    constructor(input: ZeroShotAgentInput);
    _agentType(): "zero-shot-react-description";
    observationPrefix(): string;
    llmPrefix(): string;
    /**
     * Returns the default output parser for the ZeroShotAgent.
     * @param fields Optional arguments for the output parser.
     * @returns An instance of ZeroShotAgentOutputParser.
     */
    static getDefaultOutputParser(fields?: OutputParserArgs): ZeroShotAgentOutputParser;
    /**
     * Validates the tools for the ZeroShotAgent. Throws an error if any tool
     * does not have a description.
     * @param tools List of tools to validate.
     */
    static validateTools(tools: ToolInterface[]): void;
    /**
     * Create prompt in the style of the zero shot agent.
     *
     * @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.suffix - String to put after the list of tools.
     * @param args.prefix - String to put before the list of tools.
     * @param args.inputVariables - List of input variables the final prompt will expect.
     */
    static createPrompt(tools: ToolInterface[], args?: ZeroShotCreatePromptArgs): PromptTemplate<any, any>;
    /**
     * Creates a ZeroShotAgent from a Large Language Model and a set of tools.
     * @param llm The Large Language Model to use.
     * @param tools The tools for the agent to use.
     * @param args Optional arguments for creating the agent.
     * @returns A new instance of ZeroShotAgent.
     */
    static fromLLMAndTools(llm: BaseLanguageModelInterface, tools: ToolInterface[], args?: ZeroShotCreatePromptArgs & AgentArgs): ZeroShotAgent;
    static deserialize(data: SerializedZeroShotAgent & {
        llm?: BaseLanguageModelInterface;
        tools?: ToolInterface[];
    }): Promise<ZeroShotAgent>;
}