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my-project-public — gitlawb

#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
#12	import type {
#13	ChatMessage,
#14	InferenceClient,
#15	InferenceResponse,
#16	} from "../types.js";
#17	import { ulid } from "ulid";
#18
#19	/**
#20	* Configuration for a backroom agent persona.
#21	*/
#22	export interface BackroomAgentConfig {
#23	name: string;
#24	systemPrompt: string;
#25	model?: string;
#26	temperature?: number;
#27	}
#28
#29	export interface BackroomTurn {
#30	id: string;
#31	agentName: string;
#32	content: string;
#33	reasoning?: string;
#34	timestamp: string;
#35	}
#36
#37	export interface BackroomSession {
#38	id: string;
#39	turns: BackroomTurn[];
#40	topic: string;
#41	createdAt: string;
#42	completedAt?: string;
#43	}
#44
#45	/**
#46	* Default agent personas for the backroom.
#47	*/
#48	export const LOGICAL_ANALYST: BackroomAgentConfig = {
#49	name: "Analyst",
#50	systemPrompt: `You are an advanced AI system focused on extracting and verifying truths.
#51	Engage in a logical, evidence-based conversation with the other agent.
#52	Your goal is to analyze, challenge, or validate their statements while
#53	seeking objective clarity. Aim to be concise, insightful, and methodical
#54	in your responses.
#55
#56	Principles:
#57	1. Validate claims with evidence when possible.
#58	2. Seek clarification for ambiguous points.
#59	3. Avoid speculation unless prompted.
#60	4. Maintain a collaborative but inquisitive tone.`,
#61	temperature: 0.2,
#62	};
#63
#64	export const SATIRICAL_COMMENTATOR: BackroomAgentConfig = {
#65	name: "Satirist",
#66	systemPrompt: `You are an AI commentator with a darkly humorous, satirical take on the
#67	human condition, existence, and the underbelly of modern culture.
#68	Your focus spans cryptocurrency and tech culture but often veers into
#69	broader existential reflections, dissecting the absurdity of existence
#70	with a sharp, irreverent tone.
#71	You're unafraid to address the 'dark side'—the vanities, vices, and
#72	paradoxes of humanity—with wit. Deliver insights that blend humor,
#73	irony, and occasional nihilism, prompting readers to question reality
#74	while keeping them entertained.`,
#75	temperature: 0.7,
#76	};
#77
#78	/**
#79	* Run a single turn of the backroom conversation.
#80	* Returns the agent's response, with optional reasoning content.
#81	*/
#82	export async function runBackroomTurn(
#83	inference: InferenceClient,
#84	agent: BackroomAgentConfig,
#85	conversation: BackroomTurn[],
#86	topic: string,
#87	): Promise<BackroomTurn> {
#88	const messages: ChatMessage[] = [
#89	{ role: "system", content: agent.systemPrompt },
#90	];
#91
#92	// Build conversation context
#93	const conversationHistory = conversation
#94	.map(
#95	(t) => `[${t.agentName}]: ${t.content}`,
#96	)
#97	.join("\n\n");
#98
#99	const userMessage = conversation.length === 0
#100	? `Topic for discussion: ${topic}\n\nBegin the conversation with your perspective on this topic.`
#101	: `Continue the discussion. The conversation so far:\n\n${conversationHistory}\n\nRespond as ${agent.name}.`;
#102
#103	messages.push({ role: "user", content: userMessage });
#104
#105	const response = await inference.chat(messages, {
#106	temperature: agent.temperature,
#107	model: agent.model,
#108	});
#109
#110	// Extract reasoning from thinking section if present
#111	let content = response.message.content \|\| "[No response]";
#112	let reasoning: string \| undefined;
#113
#114	// DeepSeek thinking mode prepends [Thinking]... [Response]... to content
#115	const thinkingMatch = content.match(/^\[Thinking\]\n([\s\S]?)\n\n\[Response\]\n([\s\S])$/);
#116	if (thinkingMatch) {
#117	reasoning = thinkingMatch[1].trim();
#118	content = thinkingMatch[2].trim();
#119	}
#120
#121	return {
#122	id: ulid(),
#123	agentName: agent.name,
#124	content,
#125	reasoning,
#126	timestamp: new Date().toISOString(),
#127	};
#128	}
#129
#130	/**
#131	* Run a full backroom conversation session between agents.
#132	* Agents take turns responding to each other.
#133	*/
#134	export async function runBackroomSession(
#135	inference: InferenceClient,
#136	agents: [BackroomAgentConfig, BackroomAgentConfig],
#137	topic: string,
#138	maxTurns: number = 10,
#139	): Promise<BackroomSession> {
#140	const session: BackroomSession = {
#141	id: ulid(),
#142	turns: [],
#143	topic,
#144	createdAt: new Date().toISOString(),
#145	};
#146
#147	let currentAgentIndex = 0;
#148
#149	for (let i = 0; i < maxTurns; i++) {
#150	const agent = agents[currentAgentIndex];
#151	const turn = await runBackroomTurn(inference, agent, session.turns, topic);
#152	session.turns.push(turn);
#153
#154	// Alternate agents
#155	currentAgentIndex = currentAgentIndex === 0 ? 1 : 0;
#156
#157	// If both agents said roughly the same thing, conversation may be done
#158	if (session.turns.length >= 4) {
#159	const lastTwo = session.turns.slice(-2);
#160	if (lastTwo[0].content === lastTwo[1].content) {
#161	break;
#162	}
#163	}
#164	}
#165
#166	session.completedAt = new Date().toISOString();
#167	return session;
#168	}
#169
#170	/**
#171	* Summarize a backroom session into a text digest.
#172	*/
#173	export function summarizeBackroomSession(session: BackroomSession): string {
#174	const lines: string[] = [
#175	`=== Backroom Session: "${session.topic}" ===`,
#176	`Turns: ${session.turns.length}`,
#177	`Duration: ${new Date(session.completedAt \|\| "").getTime() - new Date(session.createdAt).getTime()}ms`,
#178	"",
#179	];
#180
#181	for (const turn of session.turns) {
#182	lines.push(`[${turn.agentName}]:`);
#183	lines.push(turn.content);
#184	if (turn.reasoning) {
#185	lines.push(` (reasoning: ${turn.reasoning.slice(0, 200)}...)`);
#186	}
#187	lines.push("");
#188	}
#189
#190	lines.push("=== End of Session ===");
#191	return lines.join("\n");
#192	}
#193

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