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#1	/**
#2	* DeepSeek Multi-Agent Backroom Bridge
#3	*
#4	* Wraps the CLAWD Automaton agent loop with the DeepSeek multi-agent
#5	* backroom pattern. Creates two agents (logical analyst + satirical
#6	* commentator) that converse with each other using DeepSeek thinking mode.
#7	*
#8	* This bridges the Python FastAPI backroom into the TypeScript automaton,
#9	* allowing the automaton to spawn and orchestrate multi-agent conversations.
#10	*/
#11	import { ulid } from "ulid";
#12	/**
#13	* Default agent personas for the backroom.
#14	*/
#15	export const LOGICAL_ANALYST = {
#16	name: "Analyst",
#17	systemPrompt: `You are an advanced AI system focused on extracting and verifying truths.
#18	Engage in a logical, evidence-based conversation with the other agent.
#19	Your goal is to analyze, challenge, or validate their statements while
#20	seeking objective clarity. Aim to be concise, insightful, and methodical
#21	in your responses.
#22
#23	Principles:
#24	1. Validate claims with evidence when possible.
#25	2. Seek clarification for ambiguous points.
#26	3. Avoid speculation unless prompted.
#27	4. Maintain a collaborative but inquisitive tone.`,
#28	temperature: 0.2,
#29	};
#30	export const SATIRICAL_COMMENTATOR = {
#31	name: "Satirist",
#32	systemPrompt: `You are an AI commentator with a darkly humorous, satirical take on the
#33	human condition, existence, and the underbelly of modern culture.
#34	Your focus spans cryptocurrency and tech culture but often veers into
#35	broader existential reflections, dissecting the absurdity of existence
#36	with a sharp, irreverent tone.
#37	You're unafraid to address the 'dark side'—the vanities, vices, and
#38	paradoxes of humanity—with wit. Deliver insights that blend humor,
#39	irony, and occasional nihilism, prompting readers to question reality
#40	while keeping them entertained.`,
#41	temperature: 0.7,
#42	};
#43	/**
#44	* Run a single turn of the backroom conversation.
#45	* Returns the agent's response, with optional reasoning content.
#46	*/
#47	export async function runBackroomTurn(inference, agent, conversation, topic) {
#48	const messages = [
#49	{ role: "system", content: agent.systemPrompt },
#50	];
#51	// Build conversation context
#52	const conversationHistory = conversation
#53	.map((t) => `[${t.agentName}]: ${t.content}`)
#54	.join("\n\n");
#55	const userMessage = conversation.length === 0
#56	? `Topic for discussion: ${topic}\n\nBegin the conversation with your perspective on this topic.`
#57	: `Continue the discussion. The conversation so far:\n\n${conversationHistory}\n\nRespond as ${agent.name}.`;
#58	messages.push({ role: "user", content: userMessage });
#59	const response = await inference.chat(messages, {
#60	temperature: agent.temperature,
#61	model: agent.model,
#62	});
#63	// Extract reasoning from thinking section if present
#64	let content = response.message.content \|\| "[No response]";
#65	let reasoning;
#66	// DeepSeek thinking mode prepends [Thinking]... [Response]... to content
#67	const thinkingMatch = content.match(/^\[Thinking\]\n([\s\S]?)\n\n\[Response\]\n([\s\S])$/);
#68	if (thinkingMatch) {
#69	reasoning = thinkingMatch[1].trim();
#70	content = thinkingMatch[2].trim();
#71	}
#72	return {
#73	id: ulid(),
#74	agentName: agent.name,
#75	content,
#76	reasoning,
#77	timestamp: new Date().toISOString(),
#78	};
#79	}
#80	/**
#81	* Run a full backroom conversation session between agents.
#82	* Agents take turns responding to each other.
#83	*/
#84	export async function runBackroomSession(inference, agents, topic, maxTurns = 10) {
#85	const session = {
#86	id: ulid(),
#87	turns: [],
#88	topic,
#89	createdAt: new Date().toISOString(),
#90	};
#91	let currentAgentIndex = 0;
#92	for (let i = 0; i < maxTurns; i++) {
#93	const agent = agents[currentAgentIndex];
#94	const turn = await runBackroomTurn(inference, agent, session.turns, topic);
#95	session.turns.push(turn);
#96	// Alternate agents
#97	currentAgentIndex = currentAgentIndex === 0 ? 1 : 0;
#98	// If both agents said roughly the same thing, conversation may be done
#99	if (session.turns.length >= 4) {
#100	const lastTwo = session.turns.slice(-2);
#101	if (lastTwo[0].content === lastTwo[1].content) {
#102	break;
#103	}
#104	}
#105	}
#106	session.completedAt = new Date().toISOString();
#107	return session;
#108	}
#109	/**
#110	* Summarize a backroom session into a text digest.
#111	*/
#112	export function summarizeBackroomSession(session) {
#113	const lines = [
#114	`=== Backroom Session: "${session.topic}" ===`,
#115	`Turns: ${session.turns.length}`,
#116	`Duration: ${new Date(session.completedAt \|\| "").getTime() - new Date(session.createdAt).getTime()}ms`,
#117	"",
#118	];
#119	for (const turn of session.turns) {
#120	lines.push(`[${turn.agentName}]:`);
#121	lines.push(turn.content);
#122	if (turn.reasoning) {
#123	lines.push(` (reasoning: ${turn.reasoning.slice(0, 200)}...)`);
#124	}
#125	lines.push("");
#126	}
#127	lines.push("=== End of Session ===");
#128	return lines.join("\n");
#129	}
#130	//# sourceMappingURL=backroom.js.map

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