/**
 * Layer 2: Conversation Summarization.
 *
 * Replaces older conversation history with an LLM-generated summary
 * while preserving a tail of recent turns. Uses the primary model
 * for summarization quality (the conversation history is structurally
 * complex with interleaved tool calls and multi-turn reasoning).
 *
 * Fires at 70% of context window (configurable). Emits lifecycle
 * events (onBeforeCompaction, onPostCompaction) for consumer
 * coordination (e.g., observational memory flush).
 *
 * References:
 *   - compaction-strategy.md (Layer 2: Conversation Summarization)
 *   - phase-5-compaction.md (5.3)
 */
import type { AgentMessage } from '../context-manager.js';
import type { CompactionConfig, CompactionResult, CompactionTarget } from '../types.js';
export declare const COMPACTION_DEFAULTS: CompactionConfig;
/**
 * Extract the <summary> content from the LLM's compaction output.
 * The prompt asks for <analysis> (scratchpad) then <summary> (the actual summary).
 * We strip the analysis and keep only the summary content.
 * If no <summary> tags are found, return the full output (the model may
 * have skipped the tags but still produced useful content).
 */
export declare function extractSummaryContent(raw: string): string;
/**
 * Partition conversation history into compaction target and preserved tail.
 *
 * @param history - The full conversation history (post-slot region)
 * @param preserveRecentTurns - Number of recent turns to preserve
 * @returns [target, preserved] where target is summarized and preserved is kept verbatim
 */
export declare function partitionHistory(history: AgentMessage[], preserveRecentTurns: number): [AgentMessage[], AgentMessage[]];
/**
 * Build the compaction summary message wrapping it in XML tags.
 *
 * @param summary - The LLM-generated summary text
 * @param turnsCompacted - Number of turns that were summarized
 * @returns A user-role message containing the tagged summary
 */
export declare function buildSummaryMessage(summary: string, turnsCompacted: number): AgentMessage;
/**
 * Format conversation turns for the summarization prompt.
 * Extracts text content and labels each turn with role.
 */
export declare function formatTurnsForSummarization(turns: AgentMessage[]): string;
/**
 * Type for the LLM completion function.
 * Matches the signature of CortexAgent.directComplete().
 */
export type CompleteFn = (context: {
    systemPrompt: string;
    messages: unknown[];
}) => Promise<string>;
/**
 * Type for the consumer's onBeforeCompaction handler.
 */
export type BeforeCompactionHandler = (target: CompactionTarget) => Promise<void>;
/**
 * Type for the consumer's onPostCompaction handler.
 */
export type PostCompactionHandler = (result: CompactionResult) => void;
/**
 * Type for the consumer's onCompactionError handler.
 */
export type CompactionErrorHandler = (error: Error) => void;
/**
 * Run Layer 2 conversation summarization.
 *
 * Steps:
 * 1. Partition history into target and preserved tail
 * 2. Emit onBeforeCompaction (awaited)
 * 3. Generate summary via LLM
 * 4. Build new history: [summary message] + [preserved tail]
 * 5. Emit onPostCompaction
 *
 * @param history - Current conversation history (post-slot region)
 * @param config - Compaction configuration
 * @param complete - LLM completion function
 * @param handlers - Consumer lifecycle handlers
 * @returns The new conversation history and compaction result
 */
export declare function runCompaction(history: AgentMessage[], config: CompactionConfig, complete: CompleteFn, handlers?: {
    onBeforeCompaction?: BeforeCompactionHandler[];
    onPostCompaction?: PostCompactionHandler[];
    onCompactionError?: CompactionErrorHandler[];
}, 
/** Actual full-context token count (includes system prompt, slots, tools). When provided, used as tokensBefore instead of text-only heuristic. */
actualContextTokens?: number): Promise<{
    newHistory: AgentMessage[];
    result: CompactionResult;
}>;
/**
 * Check if compaction should trigger based on token count and threshold.
 */
export declare function shouldCompact(currentTokens: number, contextWindow: number, threshold: number): boolean;
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