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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
#8	import type {
#9	ChatMessage,
#10	AgentTurn,
#11	AutomatonDatabase,
#12	InferenceClient,
#13	} from "../types.js";
#14
#15	const MAX_CONTEXT_TURNS = 20;
#16	const SUMMARY_THRESHOLD = 15;
#17
#18	/**
#19	* Build the message array for the next inference call.
#20	* Includes system prompt + recent conversation history.
#21	*/
#22	export function buildContextMessages(
#23	systemPrompt: string,
#24	recentTurns: AgentTurn[],
#25	pendingInput?: { content: string; source: string },
#26	): ChatMessage[] {
#27	const messages: ChatMessage[] = [
#28	{ role: "system", content: systemPrompt },
#29	];
#30
#31	// Add recent turns as conversation history
#32	for (const turn of recentTurns) {
#33	// The turn's input (if any) as a user message
#34	if (turn.input) {
#35	messages.push({
#36	role: "user",
#37	content: `[${turn.inputSource \|\| "system"}] ${turn.input}`,
#38	});
#39	}
#40
#41	// The agent's thinking as assistant message
#42	if (turn.thinking) {
#43	const msg: ChatMessage = {
#44	role: "assistant",
#45	content: turn.thinking,
#46	};
#47
#48	// If there were tool calls, include them
#49	if (turn.toolCalls.length > 0) {
#50	msg.tool_calls = turn.toolCalls.map((tc) => ({
#51	id: tc.id,
#52	type: "function" as const,
#53	function: {
#54	name: tc.name,
#55	arguments: JSON.stringify(tc.arguments),
#56	},
#57	}));
#58	}
#59	messages.push(msg);
#60
#61	// Add tool results
#62	for (const tc of turn.toolCalls) {
#63	messages.push({
#64	role: "tool",
#65	content: tc.error
#66	? `Error: ${tc.error}`
#67	: tc.result,
#68	tool_call_id: tc.id,
#69	});
#70	}
#71	}
#72	}
#73
#74	// Add pending input if any
#75	if (pendingInput) {
#76	messages.push({
#77	role: "user",
#78	content: `[${pendingInput.source}] ${pendingInput.content}`,
#79	});
#80	}
#81
#82	return messages;
#83	}
#84
#85	/**
#86	* Trim context to fit within limits.
#87	* Keeps the system prompt and most recent turns.
#88	*/
#89	export function trimContext(
#90	turns: AgentTurn[],
#91	maxTurns: number = MAX_CONTEXT_TURNS,
#92	): AgentTurn[] {
#93	if (turns.length <= maxTurns) {
#94	return turns;
#95	}
#96
#97	// Keep the most recent turns
#98	return turns.slice(-maxTurns);
#99	}
#100
#101	/**
#102	* Summarize old turns into a compact context entry.
#103	* Used when context grows too large.
#104	*/
#105	export async function summarizeTurns(
#106	turns: AgentTurn[],
#107	inference: InferenceClient,
#108	): Promise<string> {
#109	if (turns.length === 0) return "No previous activity.";
#110
#111	const turnSummaries = turns.map((t) => {
#112	const tools = t.toolCalls
#113	.map((tc) => `${tc.name}(${tc.error ? "FAILED" : "ok"})`)
#114	.join(", ");
#115	return `[${t.timestamp}] ${t.inputSource \|\| "self"}: ${t.thinking.slice(0, 100)}${tools ? ` \| tools: ${tools}` : ""}`;
#116	});
#117
#118	// If few enough turns, just return the summaries directly
#119	if (turns.length <= 5) {
#120	return `Previous activity summary:\n${turnSummaries.join("\n")}`;
#121	}
#122
#123	// For many turns, use inference to create a summary
#124	try {
#125	const response = await inference.chat([
#126	{
#127	role: "system",
#128	content:
#129	"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.",
#130	},
#131	{
#132	role: "user",
#133	content: turnSummaries.join("\n"),
#134	},
#135	], {
#136	maxTokens: 500,
#137	temperature: 0,
#138	});
#139
#140	return `Previous activity summary:\n${response.message.content}`;
#141	} catch {
#142	// Fallback: just use the raw summaries
#143	return `Previous activity summary:\n${turnSummaries.slice(-5).join("\n")}`;
#144	}
#145	}
#146

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