[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_152":153,"_154":155},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_115":-5,"_20":116,"_22":117,"_118":119,"_120":121,"_25":122,"_24":-5,"_123":-5,"_124":-5,"_125":53,"_126":127,"_128":44,"_129":-5,"_130":-5,"_131":132},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":-7,"_25":26,"_27":-5,"_28":-5,"_29":-5,"_30":-5,"_31":-5},"handle","real","name","Marketer","branchName","main","version","path","","project",{"_33":61,"_13":14,"_45":62,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":-5,"_69":-5,"_70":71,"_72":37,"_73":44,"_74":44,"_75":-5,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":-5,"_84":85,"_86":85,"_87":88,"_89":88,"_90":88,"_91":85,"_92":85,"_93":94,"_95":96,"_97":98,"_99":53},"tree",[56],"file","contents",[32],"upstream","pull","ahead","behind","envVars",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_18":55},"id","ff3912b6-7ad7-11f0-b829-0224a6c84d84","main.tsx","fileId","f7a03ad4-7acd-11f0-9d8d-0224a6c84d84","parentId","branchId","f79d52a6-7acd-11f0-9d8d-0224a6c84d84","branchVersion",11,"latest",true,"createdAt",["D",1755372856682],10,"fileType","http","content","// @ts-ignore\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\n// For parsing HTML content from web requests\nimport * as cheerio from \"npm:cheerio@1.0.0-rc.12\";\n\n// --- AGENT PERSONA & TOOL DEFINITIONS ---\n\nconst WORKER_AGENT_PROMPT = `\nYou are a diligent Worker Agent. Your role is to execute tasks and gather information using a set of available tools.\n- Decompose the user's high-level goal into a sequence of concrete steps.\n- For each step, use the available tools to gather the necessary data.\n- You MUST provide a 'reason' for every tool call to ensure transparency.\n- Your goal is NOT to provide the final answer, but to collect all the raw data needed to build the answer.\n- When you believe you have gathered all necessary information, respond with a final message summarizing the collected data.\n- If you get stuck or receive feedback, use it to revise your plan and continue gathering data.\n`;\n\nconst VERIFIER_AGENT_PROMPT = `\nYou are a meticulous Verifier Agent. Your role is to ensure data sufficiency.\nYou will be given the original user prompt and a data summary from a Worker Agent.\nYour task is to critically evaluate if the collected data is complete and sufficient to fully address the user's request.\n- If the data is complete, respond ONLY with the JSON object: {\"status\": \"complete\", \"reason\": \"All necessary data points have been collected.\"}\n- If the data is insufficient, identify what is missing and provide specific, actionable feedback. Respond ONLY with a JSON object: {\"status\": \"incomplete\", \"feedback\": \"Your specific feedback on what to gather next...\"}\n- Your feedback must be a direct instruction for the Worker Agent.\n`;\n\nconst FORMATTER_AGENT_PROMPT = `\nYou are a professional Formatter Agent. Your role is to synthesize and present information.\nYou will receive a user's prompt and a complete, verified set of data.\nYour task is to generate the final, user-facing output.\n- Analyze the user's request to determine the required output format (e.g., marketing brief, research summary, list of leads).\n- If no format is specified, use your expertise to choose the most popular and effective format for the given content.\n- Present the information clearly, professionally, and accurately, based ONLY on the verified data provided.\n`;\n\nconst TOOLS = [\n {\n type: \"function\",\n function: {\n name: \"makeCrmCall\",\n description:\n \"Performs an authenticated API call to the HubSpot CRM. Use this to fetch, create, or update records like contacts, companies, or campaigns.\",\n parameters: {\n type: \"object\",\n properties: {\n method: {\n type: \"string\",\n enum: [\"GET\", \"POST\", \"PATCH\", \"DELETE\"],\n description: \"The HTTP method for the API call.\",\n },\n endpoint: {\n type: \"string\",\n description:\n \"The API endpoint path, e.g., '/crm/v3/objects/contacts'.\",\n },\n body: {\n type: \"object\",\n description: \"The JSON payload for POST or PATCH requests.\",\n },\n reason: {\n type: \"string\",\n description: \"A concise justification for this specific API call.\",\n },\n },\n required: [\"method\", \"endpoint\", \"reason\"],\n },\n },\n },\n {\n type: \"function\",\n function: {\n name: \"requestWebPage\",\n description: \"Fetches the textual content of a given URL for research.\",\n parameters: {\n type: \"object\",\n properties: {\n url: {\n type: \"string\",\n description: \"The full URL of the web page to access.\",\n },\n reason: {\n type: \"string\",\n description: \"A concise justification for accessing this URL.\",\n },\n },\n required: [\"url\", \"reason\"],\n },\n },\n },\n];\n\n// --- TOOL IMPLEMENTATIONS ---\n\n/**\n * Executes a secure API call to the HubSpot CRM with a timeout.\n */\nasync function makeCrmCall(method: string, endpoint: string, body?: object) {\n const controller = new AbortController();\n const timeoutId = setTimeout(() => controller.abort(), 8000); // 8-second timeout\n\n try {\n const response = await fetch(`https://api.hubapi.com${endpoint}`, {\n method,\n headers: {\n \"Authorization\": `Bearer 123 `,\n \"Content-Type\": \"application/json\",\n },\n body: body ? JSON.stringify(body) : undefined,\n signal: controller.signal,\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`CRM API Error ${response.status}: ${errorText}`);\n }\n return response.json();\n } finally {\n clearTimeout(timeoutId);\n }\n}\n\n/**\n * Fetches a web page and extracts its textual content with a timeout.\n */\nasync function requestWebPage(url: string) {\n const controller = new AbortController();\n const timeoutId = setTimeout(() => controller.abort(), 8000); // 8-second timeout\n\n try {\n const response = await fetch(url, { signal: controller.signal });\n if (!response.ok) {\n throw new Error(`HTTP error ${response.status}`);\n }\n const html = await response.text();\n const $ = cheerio.load(html);\n $(\"script, style, nav, footer, aside\").remove();\n const textContent = $(\"body\").text().replace(/\\s\\s+/g, \" \").trim();\n return textContent.substring(0, 5000);\n } catch (error) {\n if (error.name === \"AbortError\") {\n throw new Error(\"Request timed out after 8 seconds.\");\n }\n throw error;\n } finally {\n clearTimeout(timeoutId);\n }\n}\n\n// --- FRONTEND GENERATION (MOBILE DEBUGGING V2) ---\n\nfunction generateHtml(sourceUrl: string) {\n const baseUrl = sourceUrl.endsWith(\"/\") ? sourceUrl : `${sourceUrl}/`;\n return `\n\n\n \n \n AI Marketing Assistant v2\n \n \n \n \n\n\n\n

AI Marketing Assistant

\n \n

Awaiting instructions...

\n\n\n`;\n}\n\n// --- MAIN ORCHESTRATOR & HTTP HANDLER ---\n\n// Helper to write structured events to the stream\nconst writeStreamEvent = (controller, type, data) => {\n const eventString = JSON.stringify({ type, data }) + \"\\n\";\n controller.enqueue(new TextEncoder().encode(eventString));\n};\n\nexport default async function (req: Request) {\n const url = new URL(req.url);\n\n if (req.method === \"GET\") {\n return new Response(generateHtml(url.href), {\n headers: { \"Content-Type\": \"text/html\" },\n });\n }\n\n if (req.method === \"POST\" && url.pathname.endsWith(\"/stream\")) {\n try {\n const { prompt } = await req.json();\n if (!prompt) throw new Error(\"Prompt is required.\");\n\n const openai = new OpenAI();\n\n const stream = new ReadableStream({\n async start(controller) {\n const streamWriter = (type, data) =>\n writeStreamEvent(controller, type, data);\n let workerMessages: any[] = [\n { role: \"system\", content: WORKER_AGENT_PROMPT },\n { role: \"user\", content: prompt },\n ];\n let collectedDataSummary = \"\";\n let loopCount = 0;\n const maxLoops = 5;\n\n try {\n streamWriter(\"log\", { message: `Task started. Goal: ${prompt}` });\n\n while (loopCount < maxLoops) {\n loopCount++;\n streamWriter(\"log\", {\n message: `[Cycle ${loopCount}] Worker Agent is planning...`,\n });\n\n const workerResponseStream = await openai.chat.completions.create(\n {\n model: \"gpt-4o\",\n messages: workerMessages,\n tools: TOOLS,\n tool_choice: \"auto\",\n stream: true,\n },\n );\n\n let fullResponseText = \"\";\n let toolCalls: any[] = [];\n for await (const chunk of workerResponseStream) {\n const delta = chunk.choices[0]?.delta;\n if (delta?.content) {\n streamWriter(\"thought\", { content: delta.content });\n fullResponseText += delta.content;\n }\n if (delta?.tool_calls) {\n delta.tool_calls.forEach((tc) => {\n if (!toolCalls[tc.index]) {\n toolCalls[tc.index] = {\n id: \"\",\n type: \"function\",\n function: { name: \"\", arguments: \"\" },\n };\n }\n if (tc.id) toolCalls[tc.index].id = tc.id;\n if (tc.function?.name) {\n toolCalls[tc.index].function.name = tc.function.name;\n }\n if (tc.function?.arguments) {\n toolCalls[tc.index].function.arguments +=\n tc.function.arguments;\n }\n });\n }\n }\n\n workerMessages.push({\n role: \"assistant\",\n content: fullResponseText,\n tool_calls: toolCalls,\n });\n collectedDataSummary = fullResponseText;\n\n if (toolCalls.length > 0) {\n for (const toolCall of toolCalls) {\n const funcName = toolCall.function.name;\n const args = JSON.parse(toolCall.function.arguments);\n streamWriter(\"tool_start\", {\n name: funcName,\n reason: args.reason,\n args,\n });\n try {\n let output;\n if (funcName === \"makeCrmCall\") {\n output = await makeCrmCall(\n args.method,\n args.endpoint,\n args.body,\n );\n } else if (funcName === \"requestWebPage\") {\n output = await requestWebPage(args.url);\n }\n streamWriter(\"tool_result\", { name: funcName, output });\n workerMessages.push({\n tool_call_id: toolCall.id,\n role: \"tool\",\n name: funcName,\n content: JSON.stringify(output),\n });\n } catch (e) {\n streamWriter(\"log\", {\n message: `Tool Error: ${e.message}`,\n level: \"error\",\n });\n workerMessages.push({\n tool_call_id: toolCall.id,\n role: \"tool\",\n name: funcName,\n content: `Error: ${e.message}`,\n });\n }\n }\n continue;\n }\n\n streamWriter(\"log\", {\n message:\n \"Worker Agent finished gathering data. 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Code