import { MemorySaver } from '@langchain/langgraph';
import type { AgentContext, AgentResult, AgentFile } from '../types/agent.types';
import { LLMService } from '../llm/llm-service';
import { Logger } from '../utils/logger';
export declare const AgentWorkflowState: import("@langchain/langgraph").AnnotationRoot<{
    context: import("@langchain/langgraph").BaseChannel<AgentContext, AgentContext | import("@langchain/langgraph").OverwriteValue<AgentContext>, unknown>;
    iteration: import("@langchain/langgraph").BaseChannel<number, number | import("@langchain/langgraph").OverwriteValue<number>, unknown>;
    currentAnalysis: import("@langchain/langgraph").BaseChannel<string, string | import("@langchain/langgraph").OverwriteValue<string>, unknown>;
    missingInformation: import("@langchain/langgraph").BaseChannel<string[], string[] | import("@langchain/langgraph").OverwriteValue<string[]>, unknown>;
    clarityScore: import("@langchain/langgraph").BaseChannel<number, number | import("@langchain/langgraph").OverwriteValue<number>, unknown>;
    previousGapCount: import("@langchain/langgraph").BaseChannel<number, number | import("@langchain/langgraph").OverwriteValue<number>, unknown>;
    gapReductionRate: import("@langchain/langgraph").BaseChannel<number, number | import("@langchain/langgraph").OverwriteValue<number>, unknown>;
    selfQuestions: import("@langchain/langgraph").BaseChannel<string[], string[] | import("@langchain/langgraph").OverwriteValue<string[]>, unknown>;
    retrievedFiles: import("@langchain/langgraph").BaseChannel<{
        path: string;
        content: string;
    }[], {
        path: string;
        content: string;
    }[] | import("@langchain/langgraph").OverwriteValue<{
        path: string;
        content: string;
    }[]>, unknown>;
    finalAnalysis: import("@langchain/langgraph").BaseChannel<string, string | import("@langchain/langgraph").OverwriteValue<string>, unknown>;
    refinementNotes: import("@langchain/langgraph").BaseChannel<string[], string[] | import("@langchain/langgraph").OverwriteValue<string[]>, unknown>;
    allSeenGaps: import("@langchain/langgraph").BaseChannel<Set<string>, Set<string> | import("@langchain/langgraph").OverwriteValue<Set<string>>, unknown>;
    forceStop: import("@langchain/langgraph").BaseChannel<boolean, boolean | import("@langchain/langgraph").OverwriteValue<boolean>, unknown>;
    totalInputTokens: import("@langchain/langgraph").BaseChannel<number, number | import("@langchain/langgraph").OverwriteValue<number>, unknown>;
    totalOutputTokens: import("@langchain/langgraph").BaseChannel<number, number | import("@langchain/langgraph").OverwriteValue<number>, unknown>;
}>;
export interface AgentWorkflowConfig {
    maxIterations: number;
    clarityThreshold: number;
    minImprovement: number;
    enableSelfQuestioning: boolean;
    skipSelfRefinement?: boolean;
    maxQuestionsPerIteration?: number;
    evaluationTimeout?: number;
}
export declare abstract class BaseAgentWorkflow {
    protected llmService: LLMService;
    protected logger: Logger;
    protected workflow: ReturnType<typeof this.buildWorkflow>;
    protected checkpointer: MemorySaver;
    constructor();
    private buildWorkflow;
    execute(context: AgentContext, options?: import('../types/agent.types').AgentExecutionOptions): Promise<AgentResult>;
    protected executeWorkflow(context: AgentContext, config: AgentWorkflowConfig, runnableConfig?: Record<string, unknown>): Promise<AgentResult>;
    private executeFastPath;
    private analyzeInitialNode;
    private evaluateClarityNode;
    private normalizeGap;
    private deduplicateGaps;
    private deduplicateCurrentGaps;
    private areGapsSimilar;
    private generateQuestionsNode;
    private retrieveFilesNode;
    private vectorSearchFiles;
    private keywordSearchFiles;
    private prioritizeGaps;
    private refineAnalysisNode;
    private finalizeOutputNode;
    private shouldRefine;
    protected shouldForceStopFromOutput(parsedData: Record<string, unknown>): boolean;
    protected enhancePromptWithUserQuery(basePrompt: string, context: AgentContext): Promise<string>;
    protected shouldRunIncrementalMode(context: AgentContext): Promise<boolean>;
    protected determineMergeStrategy(context: AgentContext, _data: Record<string, unknown>): Promise<Map<string, 'replace' | 'append' | 'section-update'>>;
    protected getResponseLengthGuidance(context: AgentContext): string;
    protected getTargetTokenRanges(): Record<'quick' | 'normal' | 'deep' | 'exhaustive', {
        min: number;
        max: number;
    }>;
    protected getDepthSpecificGuidance(mode: 'quick' | 'normal' | 'deep' | 'exhaustive'): string;
    protected getMaxOutputTokens(isQuickMode: boolean, _context: AgentContext): number;
    protected abstract getAgentName(): string;
    protected abstract buildSystemPrompt(context: AgentContext): Promise<string>;
    protected abstract buildHumanPrompt(context: AgentContext): Promise<string>;
    protected abstract parseAnalysis(analysis: string): Promise<Record<string, unknown>>;
    protected abstract formatMarkdown(data: Record<string, unknown>, state: typeof AgentWorkflowState.State): Promise<string>;
    protected abstract generateSummary(data: Record<string, unknown>): string;
    protected abstract generateFiles(data: Record<string, unknown>, state: typeof AgentWorkflowState.State): Promise<AgentFile[]>;
}
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