import type { AgentTrace, Expectations, AssertionResult } from './types';
export type ToneLabel = 'friendly' | 'formal' | 'neutral' | 'assertive' | 'empathetic' | 'humorous';
/**
 * Detect if text matches a target tone via keyword heuristics.
 */
export declare function detectTone(text: string, target: ToneLabel): {
    matches: boolean;
    score: number;
};
export interface ConversationExpectations extends Expectations {
    /** Expected tone of the response */
    tone?: ToneLabel;
    /** Agent should maintain context from previous turns */
    context_maintained?: boolean;
    /** Output length constraints */
    output_length?: {
        min?: number;
        max?: number;
    };
    /** Specific context keys that should be retained from prior turns */
    context_retained?: string[];
    /** Tool args must contain these key-value pairs (context carry-forward) */
    args_contain?: Record<string, any>;
}
/**
 * A single turn in a multi-turn conversation test.
 */
export interface ConversationTurn {
    user: string;
    expect: ConversationExpectations;
}
/**
 * A multi-turn conversation test definition.
 */
export interface ConversationTest {
    name: string;
    turns: ConversationTurn[];
    tags?: string[];
}
/**
 * Result of evaluating a single turn.
 */
export interface TurnResult {
    turn: number;
    user: string;
    assertions: AssertionResult[];
    passed: boolean;
}
/**
 * Result of evaluating an entire conversation.
 */
export interface ConversationResult {
    name: string;
    turns: TurnResult[];
    passed: boolean;
    failed_at_turn?: number;
}
/**
 * Split a full trace into per-turn sub-traces based on user messages.
 *
 * The strategy: each time we see an output step that follows an llm_call
 * sequence, we consider it a new "turn boundary". We use a simpler heuristic —
 * split trace.steps into N segments where N = number of turns requested.
 * Steps are divided roughly by looking for sequential groups of
 * tool_call/tool_result/llm_call/output patterns.
 */
export declare function splitTraceByTurns(trace: AgentTrace, turnCount: number): AgentTrace[];
/**
 * Evaluate a multi-turn conversation against a trace.
 */
export declare function evaluateConversation(trace: AgentTrace, conversation: ConversationTest): ConversationResult;
/**
 * Format conversation result for display.
 */
export declare function formatConversationResult(result: ConversationResult): string;
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