compaction.ts

/**
 * Context compaction for long sessions.
 *
 * Pure functions for compaction logic. The session manager handles I/O,
 * and after compaction the session is reloaded.
 */

import type { AgentMessage, ThinkingLevel } from "@earendil-works/pi-agent-core";
import type { AssistantMessage, Model, Usage } from "@earendil-works/pi-ai";
import { completeSimple } from "@earendil-works/pi-ai";
import type { ProviderPayloadHook } from "../extension-provider-hooks.js";
import {
	convertToLlm,
	createBranchSummaryMessage,
	createCompactionSummaryMessage,
	createCustomMessage,
} from "../messages.js";
import { buildSessionContext, type CompactionEntry, type SessionEntry } from "../session-manager.js";
import {
	computeFileLists,
	createFileOps,
	extractFileOpsFromMessage,
	type FileOperations,
	formatFileOperations,
	SUMMARIZATION_SYSTEM_PROMPT,
	serializeConversation,
} from "./utils.js";
/** Details stored in CompactionEntry.details for file tracking */
export interface CompactionDetails {
	readFiles: string[];
	modifiedFiles: string[];
}

/**
 * Extract file operations from messages and previous compaction entries.
 */
/** Preserve file operations recorded by prior compactions and current tool calls. */
function extractFileOperations(
	messages: AgentMessage[],
	entries: SessionEntry[],
	prevCompactionIndex: number,
): FileOperations {
	const fileOps = createFileOps();
	if (prevCompactionIndex >= 0) {
		const prevCompaction = entries[prevCompactionIndex] as CompactionEntry;
		if (!prevCompaction.fromHook && prevCompaction.details) {
			// fromHook field kept for session file compatibility
			const details = prevCompaction.details as CompactionDetails;
			if (Array.isArray(details.readFiles)) {
				for (const f of details.readFiles) fileOps.read.add(f);
			}
			if (Array.isArray(details.modifiedFiles)) {
				for (const f of details.modifiedFiles) fileOps.edited.add(f);
			}
		}
	}
	for (const msg of messages) {
		extractFileOpsFromMessage(msg, fileOps);
	}

	return fileOps;
}
/**
 * Extract AgentMessage from an entry if it produces one.
 * Returns undefined for entries that don't contribute to LLM context.
 */
function getMessageFromEntry(entry: SessionEntry): AgentMessage | undefined {
	if (entry.type === "message") {
		return entry.message;
	}
	if (entry.type === "custom_message") {
		return createCustomMessage(entry.customType, entry.content, entry.display, entry.details, entry.timestamp);
	}
	if (entry.type === "branch_summary") {
		return createBranchSummaryMessage(entry.summary, entry.fromId, entry.timestamp);
	}
	if (entry.type === "compaction") {
		return createCompactionSummaryMessage(
			entry.summary,
			entry.tokensBefore,
			entry.timestamp,
			entry.customInstructions,
		);
	}
	return undefined;
}

function getMessageFromEntryForCompaction(entry: SessionEntry): AgentMessage | undefined {
	if (entry.type === "compaction") {
		return undefined;
	}
	return getMessageFromEntry(entry);
}

/** Result from compact() - SessionManager adds uuid/parentUuid when saving */
export interface CompactionResult<T = unknown> {
	summary: string;
	firstKeptEntryId: string;
	tokensBefore: number;
	/** Extension-specific data (e.g., ArtifactIndex, version markers for structured compaction) */
	details?: T;
}
export const COMPACT_SKILL_NAME = "compact";

export interface CompactionSettings {
	enabled: boolean;
	reserveTokens: number;
	keepRecentTokens: number;
}

export const DEFAULT_COMPACTION_SETTINGS: CompactionSettings = {
	enabled: true,
	reserveTokens: 16384,
	keepRecentTokens: 20000,
};
/**
 * Calculate total context tokens from usage.
 * Uses the native totalTokens field when available, falls back to computing from components.
 *
 * Includes output: the assistant's response becomes part of the prompt on the next
 * request, so it counts toward the context the next turn will send.
 */
export function calculateContextTokens(usage: Usage): number {
	return usage.totalTokens || usage.input + usage.output + usage.cacheRead + usage.cacheWrite;
}

/**
 * Get usage from an assistant message if available.
 * Skips aborted and error messages as they don't have valid usage data.
 */
function getAssistantUsage(msg: AgentMessage): Usage | undefined {
	if (msg.role === "assistant" && "usage" in msg) {
		const assistantMsg = msg as AssistantMessage;
		if (assistantMsg.stopReason !== "aborted" && assistantMsg.stopReason !== "error" && assistantMsg.usage) {
			return assistantMsg.usage;
		}
	}
	return undefined;
}

/**
 * Find the last non-aborted assistant message usage from session entries.
 */
export function getLastAssistantUsage(entries: SessionEntry[]): Usage | undefined {
	for (let i = entries.length - 1; i >= 0; i--) {
		const entry = entries[i];
		if (entry.type === "message") {
			const usage = getAssistantUsage(entry.message);
			if (usage) return usage;
		}
	}
	return undefined;
}

export interface ContextUsageEstimate {
	tokens: number;
	usageTokens: number;
	trailingTokens: number;
	lastUsageIndex: number | null;
}

function getLastAssistantUsageInfo(messages: AgentMessage[]): { usage: Usage; index: number } | undefined {
	for (let i = messages.length - 1; i >= 0; i--) {
		const usage = getAssistantUsage(messages[i]);
		if (usage) return { usage, index: i };
	}
	return undefined;
}

/**
 * Estimate context tokens from messages, using the last assistant usage when available.
 * If there are messages after the last usage, estimate their tokens with estimateTokens.
 */
export function estimateContextTokens(messages: AgentMessage[]): ContextUsageEstimate {
	const usageInfo = getLastAssistantUsageInfo(messages);

	if (!usageInfo) {
		let estimated = 0;
		for (const message of messages) {
			estimated += estimateTokens(message);
		}
		return {
			tokens: estimated,
			usageTokens: 0,
			trailingTokens: estimated,
			lastUsageIndex: null,
		};
	}

	const usageTokens = calculateContextTokens(usageInfo.usage);
	let trailingTokens = 0;
	for (let i = usageInfo.index + 1; i < messages.length; i++) {
		trailingTokens += estimateTokens(messages[i]);
	}

	return {
		tokens: usageTokens + trailingTokens,
		usageTokens,
		trailingTokens,
		lastUsageIndex: usageInfo.index,
	};
}

/**
 * Check if compaction should trigger based on context usage.
 */
export function shouldCompact(contextTokens: number, contextWindow: number, settings: CompactionSettings): boolean {
	if (!settings.enabled) return false;
	if (contextWindow <= 0) return false;
	return contextTokens > contextWindow - settings.reserveTokens;
}
/**
 * Estimate token count for a message using chars/4 heuristic.
 * This is conservative (overestimates tokens).
 */
export function estimateTokens(message: AgentMessage): number {
	let chars = 0;

	switch (message.role) {
		case "user": {
			const content = (message as { content: string | Array<{ type: string; text?: string }> }).content;
			if (typeof content === "string") {
				chars = content.length;
			} else if (Array.isArray(content)) {
				for (const block of content) {
					if (block.type === "text" && block.text) {
						chars += block.text.length;
					}
				}
			}
			return Math.ceil(chars / 4);
		}
		case "assistant": {
			const assistant = message as AssistantMessage;
			for (const block of assistant.content) {
				if (block.type === "text") {
					chars += block.text.length;
				} else if (block.type === "thinking") {
					chars += block.thinking.length;
				} else if (block.type === "toolCall") {
					chars += block.name.length + JSON.stringify(block.arguments).length;
				}
			}
			return Math.ceil(chars / 4);
		}
		case "custom":
		case "toolResult": {
			if (typeof message.content === "string") {
				chars = message.content.length;
			} else {
				for (const block of message.content) {
					if (block.type === "text" && block.text) {
						chars += block.text.length;
					}
					if (block.type === "image") {
						chars += 4800; // Estimate images as 4000 chars, or 1200 tokens
					}
				}
			}
			return Math.ceil(chars / 4);
		}
		case "bashExecution": {
			chars = message.command.length + message.output.length;
			return Math.ceil(chars / 4);
		}
		case "branchSummary":
		case "compactionSummary": {
			chars = message.summary.length;
			return Math.ceil(chars / 4);
		}
	}

	return 0;
}

/**
 * Find valid cut points: indices of user, assistant, custom, or bashExecution messages.
 * Never cut at tool results (they must follow their tool call).
 * When we cut at an assistant message with tool calls, its tool results follow it
 * and will be kept.
 * BashExecutionMessage is treated like a user message (user-initiated context).
 */
function findValidCutPoints(entries: SessionEntry[], startIndex: number, endIndex: number): number[] {
	const cutPoints: number[] = [];
	for (let i = startIndex; i < endIndex; i++) {
		const entry = entries[i];
		switch (entry.type) {
			case "message": {
				const role = entry.message.role;
				switch (role) {
					case "bashExecution":
					case "custom":
					case "branchSummary":
					case "compactionSummary":
					case "user":
					case "assistant":
						cutPoints.push(i);
						break;
					case "toolResult":
						break;
				}
				break;
			}
			case "thinking_level_change":
			case "model_change":
			case "compaction":
			case "branch_summary":
			case "custom":
			case "custom_message":
			case "label":
			case "session_info":
				break;
		}
		// Branch summaries and custom messages are user-role turn boundaries.
		if (entry.type === "branch_summary" || entry.type === "custom_message") {
			cutPoints.push(i);
		}
	}
	return cutPoints;
}

/**
 * Find the user message (or bashExecution) that starts the turn containing the given entry index.
 * Returns -1 if no turn start found before the index.
 * BashExecutionMessage is treated like a user message for turn boundaries.
 */
export function findTurnStartIndex(entries: SessionEntry[], entryIndex: number, startIndex: number): number {
	for (let i = entryIndex; i >= startIndex; i--) {
		const entry = entries[i];
		if (entry.type === "branch_summary" || entry.type === "custom_message") {
			return i;
		}
		if (entry.type === "message") {
			const role = entry.message.role;
			if (role === "user" || role === "bashExecution") {
				return i;
			}
		}
	}
	return -1;
}

export interface CutPointResult {
	/** Index of first entry to keep */
	firstKeptEntryIndex: number;
	/** Index of user message that starts the turn being split, or -1 if not splitting */
	turnStartIndex: number;
	/** Whether this cut splits a turn (cut point is not a user message) */
	isSplitTurn: boolean;
}

/**
 * Find the cut point in session entries that keeps approximately `keepRecentTokens`.
 *
 * Algorithm: Walk backwards from newest, accumulating estimated message sizes.
 * Stop when we've accumulated >= keepRecentTokens. Cut at that point.
 *
 * Can cut at user OR assistant messages (never tool results). When cutting at an
 * assistant message with tool calls, its tool results come after and will be kept.
 *
 * Returns CutPointResult with:
 * - firstKeptEntryIndex: the entry index to start keeping from
 * - turnStartIndex: if cutting mid-turn, the user message that started that turn
 * - isSplitTurn: whether we're cutting in the middle of a turn
 *
 * Only considers entries between `startIndex` and `endIndex` (exclusive).
 */
export function findCutPoint(
	entries: SessionEntry[],
	startIndex: number,
	endIndex: number,
	keepRecentTokens: number,
): CutPointResult {
	const cutPoints = findValidCutPoints(entries, startIndex, endIndex);

	if (cutPoints.length === 0) {
		return { firstKeptEntryIndex: startIndex, turnStartIndex: -1, isSplitTurn: false };
	}
	let accumulatedTokens = 0;
	let cutIndex = cutPoints[0]; // Default: keep from first message (not header)

	for (let i = endIndex - 1; i >= startIndex; i--) {
		const entry = entries[i];
		if (entry.type !== "message") continue;
		const messageTokens = estimateTokens(entry.message);
		accumulatedTokens += messageTokens;
		if (accumulatedTokens >= keepRecentTokens) {
			for (let c = 0; c < cutPoints.length; c++) {
				if (cutPoints[c] >= i) {
					cutIndex = cutPoints[c];
					break;
				}
			}
			break;
		}
	}
	while (cutIndex > startIndex) {
		const prevEntry = entries[cutIndex - 1];
		if (prevEntry.type === "compaction") {
			break;
		}
		if (prevEntry.type === "message") {
			break;
		}
		cutIndex--;
	}
	const cutEntry = entries[cutIndex];
	const isUserMessage = cutEntry.type === "message" && cutEntry.message.role === "user";
	// A cut in a non-user turn requires a prefix summary.
	const turnStartIndex = isUserMessage ? -1 : findTurnStartIndex(entries, cutIndex, startIndex);

	return {
		firstKeptEntryIndex: cutIndex,
		turnStartIndex,
		isSplitTurn: !isUserMessage && turnStartIndex !== -1,
	};
}
const SUMMARIZATION_PROMPT = `The messages above are a conversation to summarize. Create a structured context checkpoint summary that another LLM will use to continue the work.

Use this EXACT format:

## Goal
[What is the user trying to accomplish? Can be multiple items if the session covers different tasks.]

## Constraints & Preferences
- [Any constraints, preferences, or requirements mentioned by user]
- [Or "(none)" if none were mentioned]

## Progress
### Done
- [x] [Completed tasks/changes]

### In Progress
- [ ] [Current work]

### Blocked
- [Issues preventing progress, if any]

## Key Decisions
- **[Decision]**: [Brief rationale]

## Next Steps
1. [Ordered list of what should happen next]

## Critical Context
- [Any data, examples, or references needed to continue]
- [Or "(none)" if not applicable]

Keep each section concise. Preserve exact file paths, function names, and error messages.`;

const KERNEL_PERSIST_SUMMARY_NOTE =
	"Note: the Python kernel keeps running after this summary — every Python variable, import, and helper you defined stays available. The cells that defined them won't appear above, so record in the summary any names worth remembering so you reuse them instead of redefining them.";

const UPDATE_SUMMARIZATION_PROMPT = `The messages above are NEW conversation messages to incorporate into the existing summary provided in <previous-summary> tags.

Update the existing structured summary with new information. RULES:
- PRESERVE all existing information from the previous summary
- ADD new progress, decisions, and context from the new messages
- UPDATE the Progress section: move items from "In Progress" to "Done" when completed
- UPDATE "Next Steps" based on what was accomplished
- PRESERVE exact file paths, function names, and error messages
- If something is no longer relevant, you may remove it

Use this EXACT format:

## Goal
[Preserve existing goals, add new ones if the task expanded]

## Constraints & Preferences
- [Preserve existing, add new ones discovered]

## Progress
### Done
- [x] [Include previously done items AND newly completed items]

### In Progress
- [ ] [Current work - update based on progress]

### Blocked
- [Current blockers - remove if resolved]

## Key Decisions
- **[Decision]**: [Brief rationale] (preserve all previous, add new)

## Next Steps
1. [Update based on current state]

## Critical Context
- [Preserve important context, add new if needed]

Keep each section concise. Preserve exact file paths, function names, and error messages.`;

/**
 * Build the instruction portion of the summarization prompt: the initial or
 * update template, optional user instructions, and the kernel persistence note.
 */
export function buildSummarizationPrompt(customInstructions?: string, previousSummary?: string): string {
	let basePrompt = previousSummary ? UPDATE_SUMMARIZATION_PROMPT : SUMMARIZATION_PROMPT;
	if (customInstructions) {
		basePrompt += `\n\n<user-instructions>\nThe user provided these instructions for this summary. Follow them with high priority while keeping the section format above: emphasize what they ask to focus on, and preserve verbatim anything they ask to remember.\n${customInstructions}\n</user-instructions>`;
	}
	return `${basePrompt}\n\n${KERNEL_PERSIST_SUMMARY_NOTE}`;
}

/**
 * Generate a summary of the conversation using the LLM.
 * If previousSummary is provided, uses the update prompt to merge.
 *
 * `onPayload` carries the owning session's provider identity so the call is
 * not anonymous at the provider; it is expected to be scoped, because the
 * summarization prompt is not the session's own conversation.
 */
export async function generateSummary(
	currentMessages: AgentMessage[],
	model: Model<any>,
	reserveTokens: number,
	apiKey: string,
	headers?: Record<string, string>,
	signal?: AbortSignal,
	customInstructions?: string,
	previousSummary?: string,
	thinkingLevel?: ThinkingLevel,
	onPayload?: ProviderPayloadHook,
): Promise<string> {
	const maxTokens = Math.floor(0.8 * reserveTokens);

	const basePrompt = buildSummarizationPrompt(customInstructions, previousSummary);
	// Serialize before the LLM call so it summarizes rather than continues this conversation.
	const llmMessages = convertToLlm(currentMessages);
	const conversationText = serializeConversation(llmMessages);
	let promptText = `<conversation>\n${conversationText}\n</conversation>\n\n`;
	if (previousSummary) {
		promptText += `<previous-summary>\n${previousSummary}\n</previous-summary>\n\n`;
	}
	promptText += basePrompt;

	const summarizationMessages = [
		{
			role: "user" as const,
			content: [{ type: "text" as const, text: promptText }],
			timestamp: Date.now(),
		},
	];

	const completionOptions =
		model.reasoning && thinkingLevel && thinkingLevel !== "off"
			? { maxTokens, signal, apiKey, headers, onPayload, reasoning: thinkingLevel }
			: { maxTokens, signal, apiKey, headers, onPayload };

	const response = await completeSimple(
		model,
		{ systemPrompt: SUMMARIZATION_SYSTEM_PROMPT, messages: summarizationMessages },
		completionOptions,
	);

	if (response.stopReason === "error") {
		throw new Error(`Summarization failed: ${response.errorMessage || "Unknown error"}`);
	}

	const textContent = response.content
		.filter((c): c is { type: "text"; text: string } => c.type === "text")
		.map((c) => c.text)
		.join("\n");

	return textContent;
}
export interface CompactionPreparation {
	/** UUID of first entry to keep */
	firstKeptEntryId: string;
	/** Messages that will be summarized and discarded */
	messagesToSummarize: AgentMessage[];
	/** Messages that will be turned into turn prefix summary (if splitting) */
	turnPrefixMessages: AgentMessage[];
	/** Whether this is a split turn (cut point in middle of turn) */
	isSplitTurn: boolean;
	tokensBefore: number;
	/** Summary from previous compaction, for iterative update */
	previousSummary?: string;
	/** File operations extracted from messagesToSummarize */
	fileOps: FileOperations;
	/** Compaction settions from settings.jsonl	*/
	settings: CompactionSettings;
}

export function prepareCompaction(
	pathEntries: SessionEntry[],
	settings: CompactionSettings,
): CompactionPreparation | undefined {
	if (pathEntries.length > 0 && pathEntries[pathEntries.length - 1].type === "compaction") {
		return undefined;
	}

	let prevCompactionIndex = -1;
	for (let i = pathEntries.length - 1; i >= 0; i--) {
		if (pathEntries[i].type === "compaction") {
			prevCompactionIndex = i;
			break;
		}
	}

	let previousSummary: string | undefined;
	let boundaryStart = 0;
	if (prevCompactionIndex >= 0) {
		const prevCompaction = pathEntries[prevCompactionIndex] as CompactionEntry;
		previousSummary = prevCompaction.summary;
		const firstKeptEntryIndex = pathEntries.findIndex((entry) => entry.id === prevCompaction.firstKeptEntryId);
		boundaryStart = firstKeptEntryIndex >= 0 ? firstKeptEntryIndex : prevCompactionIndex + 1;
	}
	const boundaryEnd = pathEntries.length;

	const tokensBefore = estimateContextTokens(buildSessionContext(pathEntries).messages).tokens;

	const cutPoint = findCutPoint(pathEntries, boundaryStart, boundaryEnd, settings.keepRecentTokens);
	const firstKeptEntry = pathEntries[cutPoint.firstKeptEntryIndex];
	if (!firstKeptEntry?.id) {
		return undefined; // Session needs migration
	}
	const firstKeptEntryId = firstKeptEntry.id;

	const historyEnd = cutPoint.isSplitTurn ? cutPoint.turnStartIndex : cutPoint.firstKeptEntryIndex;
	const messagesToSummarize: AgentMessage[] = [];
	for (let i = boundaryStart; i < historyEnd; i++) {
		const msg = getMessageFromEntryForCompaction(pathEntries[i]);
		if (msg) messagesToSummarize.push(msg);
	}
	const turnPrefixMessages: AgentMessage[] = [];
	if (cutPoint.isSplitTurn) {
		for (let i = cutPoint.turnStartIndex; i < cutPoint.firstKeptEntryIndex; i++) {
			const msg = getMessageFromEntryForCompaction(pathEntries[i]);
			if (msg) turnPrefixMessages.push(msg);
		}
	}

	// Avoid a compaction that would summarize no history.
	if (messagesToSummarize.length === 0 && turnPrefixMessages.length === 0 && !previousSummary) {
		return undefined;
	}
	const fileOps = extractFileOperations(messagesToSummarize, pathEntries, prevCompactionIndex);
	// Split turns retain their suffix, but their prefix file operations still belong in the summary.
	if (cutPoint.isSplitTurn) {
		for (const msg of turnPrefixMessages) {
			extractFileOpsFromMessage(msg, fileOps);
		}
	}

	return {
		firstKeptEntryId,
		messagesToSummarize,
		turnPrefixMessages,
		isSplitTurn: cutPoint.isSplitTurn,
		tokensBefore,
		previousSummary,
		fileOps,
		settings,
	};
}
const TURN_PREFIX_SUMMARIZATION_PROMPT = `This is the PREFIX of a turn that was too large to keep. The SUFFIX (recent work) is retained.

Summarize the prefix to provide context for the retained suffix:

## Original Request
[What did the user ask for in this turn?]

## Early Progress
- [Key decisions and work done in the prefix]

## Context for Suffix
- [Information needed to understand the retained recent work]

Be concise. Focus on what's needed to understand the kept suffix.`;

/**
 * Generate summaries for compaction using prepared data.
 * Returns CompactionResult - SessionManager adds uuid/parentUuid when saving.
 *
 * @param preparation - Pre-calculated preparation from prepareCompaction()
 * @param customInstructions - Optional custom focus for the summary
 */
export async function compact(
	preparation: CompactionPreparation,
	model: Model<any>,
	apiKey: string,
	headers?: Record<string, string>,
	customInstructions?: string,
	signal?: AbortSignal,
	thinkingLevel?: ThinkingLevel,
	onPayload?: ProviderPayloadHook,
): Promise<CompactionResult> {
	const {
		firstKeptEntryId,
		messagesToSummarize,
		turnPrefixMessages,
		isSplitTurn,
		tokensBefore,
		previousSummary,
		fileOps,
		settings,
	} = preparation;
	let summary: string;

	if (isSplitTurn && turnPrefixMessages.length > 0) {
		const [historyResult, turnPrefixResult] = await Promise.all([
			messagesToSummarize.length > 0
				? generateSummary(
						messagesToSummarize,
						model,
						settings.reserveTokens,
						apiKey,
						headers,
						signal,
						customInstructions,
						previousSummary,
						thinkingLevel,
						onPayload,
					)
				: Promise.resolve("No prior history."),
			generateTurnPrefixSummary(
				turnPrefixMessages,
				model,
				settings.reserveTokens,
				apiKey,
				headers,
				signal,
				thinkingLevel,
				onPayload,
			),
		]);
		summary = `${historyResult}\n\n---\n\n**Turn Context (split turn):**\n\n${turnPrefixResult}`;
	} else {
		summary = await generateSummary(
			messagesToSummarize,
			model,
			settings.reserveTokens,
			apiKey,
			headers,
			signal,
			customInstructions,
			previousSummary,
			thinkingLevel,
			onPayload,
		);
	}
	const { readFiles, modifiedFiles } = computeFileLists(fileOps);
	summary += formatFileOperations(readFiles, modifiedFiles);

	if (!firstKeptEntryId) {
		throw new Error("First kept entry has no UUID - session may need migration");
	}

	return {
		summary,
		firstKeptEntryId,
		tokensBefore,
		details: { readFiles, modifiedFiles } as CompactionDetails,
	};
}

/**
 * Generate a summary for a turn prefix (when splitting a turn).
 */
async function generateTurnPrefixSummary(
	messages: AgentMessage[],
	model: Model<any>,
	reserveTokens: number,
	apiKey: string,
	headers?: Record<string, string>,
	signal?: AbortSignal,
	thinkingLevel?: ThinkingLevel,
	onPayload?: ProviderPayloadHook,
): Promise<string> {
	const maxTokens = Math.floor(0.5 * reserveTokens); // Smaller budget for turn prefix
	const llmMessages = convertToLlm(messages);
	const conversationText = serializeConversation(llmMessages);
	const promptText = `<conversation>\n${conversationText}\n</conversation>\n\n${TURN_PREFIX_SUMMARIZATION_PROMPT}`;
	const summarizationMessages = [
		{
			role: "user" as const,
			content: [{ type: "text" as const, text: promptText }],
			timestamp: Date.now(),
		},
	];

	const response = await completeSimple(
		model,
		{ systemPrompt: SUMMARIZATION_SYSTEM_PROMPT, messages: summarizationMessages },
		model.reasoning && thinkingLevel && thinkingLevel !== "off"
			? { maxTokens, signal, apiKey, headers, onPayload, reasoning: thinkingLevel }
			: { maxTokens, signal, apiKey, headers, onPayload },
	);

	if (response.stopReason === "error") {
		throw new Error(`Turn prefix summarization failed: ${response.errorMessage || "Unknown error"}`);
	}

	return response.content
		.filter((c): c is { type: "text"; text: string } => c.type === "text")
		.map((c) => c.text)
		.join("\n");
}