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: ${e.message}`,\n });\n }\n }\n continue;\n }\n\n streamWriter(\"log\", {\n message:\n \"Worker Agent finished gathering data. Proceeding to verification.\",\n level: \"verification\",\n });\n\n const verifierResponse = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: VERIFIER_AGENT_PROMPT },\n {\n role: \"user\",\n content:\n `Original Prompt: \"${prompt}\"\\n\\nData Summary: \"${collectedDataSummary}\"`,\n },\n ],\n response_format: { type: \"json_object\" },\n });\n const verification = JSON.parse(\n verifierResponse.choices[0].message.content,\n );\n\n if (verification.status === \"complete\") {\n streamWriter(\"log\", {\n message: \"Verification successful. Generating final output.\",\n level: \"verification\",\n });\n\n const formatterResponse = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: FORMATTER_AGENT_PROMPT },\n {\n role: \"user\",\n content:\n `Original Prompt: \"${prompt}\"\\n\\nVerified Data: \"${collectedDataSummary}\"`,\n },\n ],\n });\n streamWriter(\"final_output\", {\n content: formatterResponse.choices[0].message.content,\n });\n break;\n } else {\n streamWriter(\"log\", {\n message:\n `Verification failed. Feedback: ${verification.feedback}`,\n level: \"error\",\n });\n workerMessages.push({\n role: \"user\",\n content:\n `Feedback from Verifier: ${verification.feedback}. Please revise your plan and gather the missing information.`,\n });\n }\n }\n\n if (loopCount >= maxLoops) {\n streamWriter(\"log\", {\n message:\n \"Agent reached maximum cycles without completion. Please refine your prompt or reset.\",\n level: \"error\",\n });\n }\n } catch (error) {\n console.error(\"Stream Orchestrator Error:\", error);\n streamWriter(\"log\", {\n message: `A critical error occurred: ${error.message}`,\n level: \"error\",\n });\n } finally {\n controller.close();\n }\n },\n });\n return new Response(stream, {\n headers: { \"Content-Type\": \"application/x-ndjson\" },\n });\n } catch (e) {\n return new Response(JSON.stringify({ error: e.message }), {\n status: 500,\n });\n }\n }\n\n return new Response(\"Not Found\", { status: 404 });\n}","deleted","label",[98],"permalink","https://www.val.town/x/real/Marketer/code/main.tsx","extension","actionData","errors"]