import { AIMessage, BaseMessage } from "@langchain/core/messages";
import { GraphAgentOptions } from "../types/type";
import { AgentGraphModel } from "../model/AgentGraphModel";
interface TokenUsage {
    total: number;
}
declare const AgentState: import("@langchain/langgraph").AnnotationRoot<{
    messages: import("@langchain/langgraph").BinaryOperatorAggregate<BaseMessage[], BaseMessage[]>;
    tokenUsage: import("@langchain/langgraph").BinaryOperatorAggregate<TokenUsage, TokenUsage>;
    totalDuration: import("@langchain/langgraph").BinaryOperatorAggregate<number, number>;
}>;
export default class McpGraphAgent<T extends AgentGraphModel = any> {
    private model;
    private toolNode;
    private targetDir;
    private options;
    private checkpointer;
    private langchainTools;
    private stopLoadingFunc;
    private verbose;
    private alwaysSystem;
    private recursionLimit;
    private apiConfig;
    private maxTokens;
    private mcpClients;
    private streamEnabled;
    private streamOutputCallback;
    constructor(options?: GraphAgentOptions<T>);
    /**
     * 设置外部流式输出回调（如 VS Code Webview）。
     * 设置后，callModel 的流式输出将通过回调发送而非写入 stdout。
     */
    setStreamOutput(callback: (chunk: string, type: "think" | "text") => void): void;
    /**
     * 清除外部流式输出回调，恢复默认的 stdout 输出。
     */
    clearStreamOutput(): void;
    private printLoadedTools;
    private loadMcpConfigs;
    private initMcpTools;
    private prepareTools;
    ensureInitialized(): Promise<T>;
    private closeMcpClients;
    private showLoading;
    private startLoading;
    private stopLoading;
    private getModel;
    private askForConfig;
    chat(query?: string): Promise<void>;
    /**
     * 流式执行单次查询（编程式 API）。
     * 无论 options.stream 是否开启，此方法始终以流式方式输出。
     */
    stream(query?: string): Promise<void>;
    start(): Promise<void>;
    private renderOutput;
    callModel(state: typeof AgentState.State): Promise<{
        messages: AIMessage[];
        tokenUsage: {
            total: number;
        };
        totalDuration: number;
    }>;
    private getRecentToolCalls;
    private printFinalSummary;
    createGraph(): Promise<import("@langchain/langgraph").CompiledStateGraph<import("@langchain/langgraph").StateType<{
        messages: import("@langchain/langgraph").BinaryOperatorAggregate<BaseMessage[], BaseMessage[]>;
        tokenUsage: import("@langchain/langgraph").BinaryOperatorAggregate<TokenUsage, TokenUsage>;
        totalDuration: import("@langchain/langgraph").BinaryOperatorAggregate<number, number>;
    }>, import("@langchain/langgraph").UpdateType<{
        messages: import("@langchain/langgraph").BinaryOperatorAggregate<BaseMessage[], BaseMessage[]>;
        tokenUsage: import("@langchain/langgraph").BinaryOperatorAggregate<TokenUsage, TokenUsage>;
        totalDuration: import("@langchain/langgraph").BinaryOperatorAggregate<number, number>;
    }>, "tools" | "__start__" | "agent" | "interrupt", {
        messages: import("@langchain/langgraph").BinaryOperatorAggregate<BaseMessage[], BaseMessage[]>;
        tokenUsage: import("@langchain/langgraph").BinaryOperatorAggregate<TokenUsage, TokenUsage>;
        totalDuration: import("@langchain/langgraph").BinaryOperatorAggregate<number, number>;
    }, {
        messages: import("@langchain/langgraph").BinaryOperatorAggregate<BaseMessage[], BaseMessage[]>;
        tokenUsage: import("@langchain/langgraph").BinaryOperatorAggregate<TokenUsage, TokenUsage>;
        totalDuration: import("@langchain/langgraph").BinaryOperatorAggregate<number, number>;
    }, import("@langchain/langgraph").StateDefinition>>;
}
export {};