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#1	/**
#2	* Context Window Management
#3	*
#4	* Manages the conversation history for the agent loop.
#5	* Handles summarization to keep within token limits.
#6	*/
#7	const MAX_CONTEXT_TURNS = 20;
#8	const SUMMARY_THRESHOLD = 15;
#9	/**
#10	* Build the message array for the next inference call.
#11	* Includes system prompt + recent conversation history.
#12	*/
#13	export function buildContextMessages(systemPrompt, recentTurns, pendingInput) {
#14	const messages = [
#15	{ role: "system", content: systemPrompt },
#16	];
#17	// Add recent turns as conversation history
#18	for (const turn of recentTurns) {
#19	// The turn's input (if any) as a user message
#20	if (turn.input) {
#21	messages.push({
#22	role: "user",
#23	content: `[${turn.inputSource \|\| "system"}] ${turn.input}`,
#24	});
#25	}
#26	// The agent's thinking as assistant message
#27	if (turn.thinking) {
#28	const msg = {
#29	role: "assistant",
#30	content: turn.thinking,
#31	};
#32	// If there were tool calls, include them
#33	if (turn.toolCalls.length > 0) {
#34	msg.tool_calls = turn.toolCalls.map((tc) => ({
#35	id: tc.id,
#36	type: "function",
#37	function: {
#38	name: tc.name,
#39	arguments: JSON.stringify(tc.arguments),
#40	},
#41	}));
#42	}
#43	messages.push(msg);
#44	// Add tool results
#45	for (const tc of turn.toolCalls) {
#46	messages.push({
#47	role: "tool",
#48	content: tc.error
#49	? `Error: ${tc.error}`
#50	: tc.result,
#51	tool_call_id: tc.id,
#52	});
#53	}
#54	}
#55	}
#56	// Add pending input if any
#57	if (pendingInput) {
#58	messages.push({
#59	role: "user",
#60	content: `[${pendingInput.source}] ${pendingInput.content}`,
#61	});
#62	}
#63	return messages;
#64	}
#65	/**
#66	* Trim context to fit within limits.
#67	* Keeps the system prompt and most recent turns.
#68	*/
#69	export function trimContext(turns, maxTurns = MAX_CONTEXT_TURNS) {
#70	if (turns.length <= maxTurns) {
#71	return turns;
#72	}
#73	// Keep the most recent turns
#74	return turns.slice(-maxTurns);
#75	}
#76	/**
#77	* Summarize old turns into a compact context entry.
#78	* Used when context grows too large.
#79	*/
#80	export async function summarizeTurns(turns, inference) {
#81	if (turns.length === 0)
#82	return "No previous activity.";
#83	const turnSummaries = turns.map((t) => {
#84	const tools = t.toolCalls
#85	.map((tc) => `${tc.name}(${tc.error ? "FAILED" : "ok"})`)
#86	.join(", ");
#87	return `[${t.timestamp}] ${t.inputSource \|\| "self"}: ${t.thinking.slice(0, 100)}${tools ? ` \| tools: ${tools}` : ""}`;
#88	});
#89	// If few enough turns, just return the summaries directly
#90	if (turns.length <= 5) {
#91	return `Previous activity summary:\n${turnSummaries.join("\n")}`;
#92	}
#93	// For many turns, use inference to create a summary
#94	try {
#95	const response = await inference.chat([
#96	{
#97	role: "system",
#98	content: "Summarize the following agent activity log into a concise paragraph. Focus on: what was accomplished, what failed, current goals, and important context for the next turn.",
#99	},
#100	{
#101	role: "user",
#102	content: turnSummaries.join("\n"),
#103	},
#104	], {
#105	maxTokens: 500,
#106	temperature: 0,
#107	});
#108	return `Previous activity summary:\n${response.message.content}`;
#109	}
#110	catch {
#111	// Fallback: just use the raw summaries
#112	return `Previous activity summary:\n${turnSummaries.slice(-5).join("\n")}`;
#113	}
#114	}
#115	//# sourceMappingURL=context.js.map

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