import { BaseChatModel, BaseChatModelParams } from '@langchain/core/language_models/chat_models';
import { BaseMessage } from '@langchain/core/messages';
import { ChatResult } from '@langchain/core/outputs';
export interface AgentGraphLLMResponse {
    text: string;
    reasoning?: string;
    token?: number;
    duration?: number;
}
/** 流式输出的回调类型 */
export type StreamChunkCallback = (chunk: string) => void;
export declare abstract class AgentGraphModel extends BaseChatModel {
    protected boundTools?: any[];
    private mcpEnabled?;
    private mcpTools?;
    protected chatId: string;
    protected targetDir?: string;
    resetChat(): void;
    setMcpTools(tools: any[]): void;
    setMcpEnabled(enabled?: boolean): void;
    getMcpEnabled(): boolean;
    getMcpTools(): any[];
    private isMcpTool;
    constructor(fields?: BaseChatModelParams & {
        targetDir?: string;
    });
    bindTools(tools: any[]): any;
    abstract callApi(prompt: string, lastMsg: BaseMessage): Promise<AgentGraphLLMResponse>;
    /**
     * 流式调用 API，子类可覆盖以实现真正的 SSE 流式传输。
     * 默认回退到 callApi 非流式调用。
     * @param prompt 序列化后的提示词
     * @param onChunk 每收到一段文本时的回调
     * @returns 完整的响应结果
     */
    callApiStream(prompt: string, lastMsg: BaseMessage, onChunk: StreamChunkCallback): Promise<AgentGraphLLMResponse>;
    _generate(messages: BaseMessage[]): Promise<ChatResult>;
    private parseToolCalls;
    /** 从 text[start] 开始，按括号配对提取完整的 JSON 对象字符串 */
    private extractBalancedJson;
    /**
     * 流式生成：调用 callApiStream 进行流式输出，完成后构建完整的 ChatResult。
     * @param messages LangChain 消息列表
     * @param onChunk 每收到一段文本时的回调
     */
    streamGenerate(messages: BaseMessage[], onChunk: StreamChunkCallback): Promise<ChatResult>;
    serializeMessages(messages: BaseMessage[]): string;
    _llmType(): string;
}