import * as Types from "../generated/graphql-types.js";
/** Returns `true` when js-tiktoken is installed and the encoder loaded successfully. */
export declare function isAccurateTokenCounting(): boolean;
/**
 * Token estimation.
 *
 * When js-tiktoken is installed, returns an accurate BPE token count (o200k_base encoding).
 * Otherwise falls back to a conservative heuristic: chars / 3.5.
 */
export declare function estimateTokens(text: string): number;
/**
 * Configuration for context window management during agentic tool loops.
 * Values can be provided by the server (via ConversationStrategy) or set client-side.
 */
export interface ContextStrategyConfig {
    /** Max tokens for any single tool result. Results exceeding this are truncated. Default: 128000 */
    toolResultTokenLimit: number;
    /** Max tool call/response rounds to keep in context. Older rounds are dropped FIFO. Default: 10 */
    toolRoundLimit: number;
    /** Fraction of token budget at which client-side windowing is triggered. Default: 0.75 */
    rebudgetThreshold: number;
}
export declare const DEFAULT_CONTEXT_STRATEGY: ContextStrategyConfig;
/**
 * Tracks token budget during streaming agent tool loops.
 *
 * Initialized from server-provided accurate token counts (via formatConversation details),
 * then uses character-based heuristic estimation for incremental additions during the loop.
 */
export declare class TokenBudgetTracker {
    private readonly tokenLimit;
    private readonly completionTokenLimit;
    private _usedTokens;
    constructor(tokenLimit: number, completionTokenLimit: number, initialUsedTokens: number);
    /**
     * Create a tracker from formatConversation response details.
     * Returns undefined if the details lack token information.
     */
    static fromDetails(details: {
        tokenLimit?: number | null;
        completionTokenLimit?: number | null;
        messages?: Array<{
            tokens?: number | null;
        } | null> | null;
    }): TokenBudgetTracker | undefined;
    /** Total available token budget (tokenLimit - completionTokenLimit, at 95% ceiling) */
    get budget(): number;
    /** Current estimated token usage */
    get usedTokens(): number;
    /** Remaining tokens before budget is exhausted */
    get remaining(): number;
    /** Current usage as a percentage (0-100) */
    get usagePercent(): number;
    /** Model's full context token limit */
    get maxTokens(): number;
    /** Track addition of new message content */
    addMessage(text: string, serverTokenCount?: number): void;
    /** Check if we need to trigger windowing/re-budgeting */
    needsRebudget(threshold: number): boolean;
    /** Reset tracker from a fresh set of messages (after windowing) */
    resetFromMessages(messages: Array<{
        message?: string | null;
        tokens?: number | null;
    }>): void;
    /** Get current usage snapshot for emitting events */
    getUsageSnapshot(): {
        usedTokens: number;
        maxTokens: number;
        percentage: number;
        remainingTokens: number;
    };
}
/**
 * Truncates a tool result to fit within a token budget.
 *
 * Attempts to find a clean break point (JSON boundary or newline).
 * Appends a [truncated] marker so the LLM knows data was cut.
 */
export declare function truncateToolResult(result: unknown, maxTokens: number, toolName: string): string;
/**
 * Windows tool rounds to keep the messages array within budget.
 *
 * Preserves:
 * - "Header" messages (system prompt, conversation history, initial user message)
 * - The most recent `keepRounds` tool rounds
 *
 * Drops older tool rounds and inserts a system message noting what was removed.
 *
 * @returns The windowed messages array
 */
export declare function windowToolRounds(messages: Types.ConversationMessage[], keepRounds: number): Types.ConversationMessage[];