Basic Agent

This is a simple agent that doesn't use any external APIs.

\n \n

✅ Agent is working correctly!

Current time: ${timestamp}

\n \n \n \n

Response

\n \n \n\n`, {\n headers: { \"Content-Type\": \"text/html\" }\n });\n } else if (req.method === \"POST\") {\n // Process JSON for POST requests\n return new Response(JSON.stringify({\n success: true,\n timestamp,\n response: \"I received your message and I'm responding without using any external APIs!\",\n request_method: req.method,\n agent_type: \"basic\"\n }), {\n headers: { \"Content-Type\": \"application/json\" }\n });\n } else {\n // Handle other methods\n return new Response(JSON.stringify({\n error: \"Method not supported\",\n supported_methods: [\"GET\", \"POST\"],\n timestamp\n }), {\n status: 405,\n headers: { \"Content-Type\": \"application/json\" }\n });\n }\n}",[],[165],{"_103":173,"_95":174,"_145":175,"_146":-5,"_147":134,"_148":176,"_150":151,"_105":177,"_100":178,"_154":168,"_156":179,"_158":27,"_159":-5,"_160":180,"_98":181},"9ddbac34-31be-11f0-9ddd-569c3dd06744","deepResearchAgent.ts","d8597e74-31bc-11f0-bc3c-569c3dd06744",15,["D",1747335521033],4,"import { OpenAI } from \"https://esm.town/v/std/openai\";\n\n// Initialize OpenAI client\nconst openai = new OpenAI();\n\n// For debugging\nconsole.log(\"Deep Research Agent initialized\");\n\n/**\n * Deep Research Agent\n * \n * This agent performs comprehensive research on a given topic by:\n * 1. Breaking down the topic into subtopics\n * 2. Researching each subtopic in detail\n * 3. Compiling findings into a comprehensive report\n */\nexport default async function(req: Request): Promise {\n console.log(\"Received request:\", req.method, req.url);\n \n try {\n // Handle OPTIONS request for CORS\n if (req.method === \"OPTIONS\") {\n return new Response(null, {\n headers: {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"POST, OPTIONS, GET\",\n \"Access-Control-Allow-Headers\": \"Content-Type\",\n }\n });\n }\n\n // For GET requests, return a simple HTML form\n if (req.method === \"GET\") {\n return new Response(`\n\n\n Deep Research Agent\n \n\n\n

Deep Research Agent

This is a direct API endpoint. For a better experience, use the research interface.

\n \n \n \n

Result

\n \n \n\n`, {\n headers: { \"Content-Type\": \"text/html\" }\n });\n }\n\n // Only accept POST requests for API functionality\n if (req.method !== \"POST\") {\n return new Response(JSON.stringify({ error: \"Method not allowed\" }), {\n status: 405,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\"\n }\n });\n }\n\n // Parse request body\n let requestData;\n try {\n requestData = await req.json();\n } catch (error) {\n return new Response(JSON.stringify({ error: \"Invalid JSON in request body\" }), {\n status: 400,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\"\n }\n });\n }\n\n const { topic, depth = \"medium\" } = requestData;\n\n if (!topic) {\n return new Response(JSON.stringify({ error: \"Topic is required\" }), {\n status: 400,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\"\n }\n });\n }\n\n // Start the research process\n const researchResult = await conductResearch(topic, depth);\n\n return new Response(JSON.stringify({ \n success: true, \n topic,\n research: researchResult \n }), {\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\"\n }\n });\n } catch (error) {\n console.error(\"Error in Deep Research Agent:\", error);\n return new Response(JSON.stringify({ \n error: \"Failed to complete research\", \n details: error.message \n }), {\n status: 500,\n headers: {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\"\n }\n });\n }\n}\n\n/**\n * Conducts comprehensive research on a topic\n */\nasync function conductResearch(topic: string, depth: \"light\" | \"medium\" | \"deep\" = \"medium\"): Promise {\n // Step 1: Break down the topic into subtopics\n const subtopics = await generateSubtopics(topic, depth);\n \n // Step 2: Research each subtopic\n const subtopicResearch = await Promise.all(\n subtopics.map(subtopic => researchSubtopic(topic, subtopic, depth))\n );\n \n // Step 3: Generate an executive summary\n const executiveSummary = await generateExecutiveSummary(topic, subtopicResearch);\n \n // Step 4: Compile the final report\n return {\n topic,\n executiveSummary,\n subtopics: subtopicResearch,\n generatedAt: new Date().toISOString(),\n depth\n };\n}\n\n/**\n * Generates a list of subtopics for the main research topic\n */\nasync function generateSubtopics(topic: string, depth: string): Promise {\n const depthMap = {\n \"light\": 3,\n \"medium\": 5,\n \"deep\": 8\n };\n \n const numSubtopics = depthMap[depth] || 5;\n \n try {\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content: `You are a research planning assistant. Your task is to break down a research topic into ${numSubtopics} specific subtopics that would provide comprehensive coverage when researched individually. Return ONLY a JSON array of strings with the subtopics.`\n },\n {\n role: \"user\",\n content: `Break down the following research topic into ${numSubtopics} specific subtopics: \"${topic}\"`\n }\n ]\n });\n\n const content = response.choices[0]?.message?.content || \"\";\n \n // Try to extract a JSON array from the content\n try {\n // First, try to parse the entire content as JSON\n const parsedContent = JSON.parse(content);\n if (Array.isArray(parsedContent)) {\n return parsedContent;\n } else if (parsedContent.subtopics && Array.isArray(parsedContent.subtopics)) {\n return parsedContent.subtopics;\n }\n } catch (e) {\n // If that fails, try to extract an array using regex\n const match = content.match(/\\\\[([^\\\\]]+)\\\\]/);\n if (match) {\n try {\n return JSON.parse('[' + match[1] + ']');\n } catch (e) {\n // If regex extraction fails, split by commas and clean up\n return match[1].split(',').map(s => s.trim().replace(/^\"|\"$/g, ''));\n }\n }\n }\n \n // If all parsing attempts fail, extract subtopics using a simple approach\n const lines = content.split('\\n');\n const extractedSubtopics = lines\n .map(line => line.replace(/^\\\\d+\\\\.\\\\s*|^-\\\\s*|^•\\\\s*|^\\\\*\\\\s*|\"/g, '').trim())\n .filter(line => line.length > 0 && !line.includes('{') && !line.includes('}'));\n \n if (extractedSubtopics.length > 0) {\n return extractedSubtopics.slice(0, numSubtopics);\n }\n \n // Fallback to default subtopics if all else fails\n return generateDefaultSubtopics(topic, numSubtopics);\n } catch (error) {\n console.error(\"Error generating subtopics:\", error);\n return generateDefaultSubtopics(topic, numSubtopics);\n }\n}\n\n/**\n * Generates default subtopics if the AI-generated ones fail\n */\nfunction generateDefaultSubtopics(topic: string, count: number): string[] {\n const defaultSubtopics = [\n `Overview of ${topic}`,\n `History and background of ${topic}`,\n `Current state of ${topic}`,\n `Key challenges in ${topic}`,\n `Future trends in ${topic}`,\n `Applications of ${topic}`,\n `Controversies surrounding ${topic}`,\n `Economic impact of ${topic}`,\n `Social implications of ${topic}`,\n `Technological aspects of ${topic}`\n ];\n \n return defaultSubtopics.slice(0, count);\n}\n\n/**\n * Researches a specific subtopic in detail\n */\nasync function researchSubtopic(mainTopic: string, subtopic: string, depth: string): Promise {\n const depthMap = {\n \"light\": 300,\n \"medium\": 500,\n \"deep\": 800\n };\n \n const wordCount = depthMap[depth] || 500;\n \n try {\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content: `You are a research assistant specializing in detailed analysis. Provide a comprehensive analysis of the subtopic in relation to the main topic. Your response should be thorough, well-structured, and approximately ${wordCount} words. Include key facts, current understanding, and important considerations.`\n },\n {\n role: \"user\",\n content: `Research the following subtopic: \"${subtopic}\" as part of the broader topic \"${mainTopic}\".`\n }\n ]\n });\n\n const content = response.choices[0]?.message?.content || \"No research content generated\";\n \n return {\n title: subtopic,\n content: content,\n wordCount: content.split(/\\\\s+/).length\n };\n } catch (error) {\n console.error(`Error researching subtopic \"${subtopic}\":`, error);\n return {\n title: subtopic,\n content: `Error researching this subtopic: ${error.message}. Please try again later.`,\n wordCount: 0\n };\n }\n}\n\n/**\n * Generates an executive summary based on all the research\n */\nasync function generateExecutiveSummary(topic: string, subtopicResearch: SubtopicResearch[]): Promise {\n try {\n // Create a condensed version of the research to fit within token limits\n const condensedResearch = subtopicResearch.map(item => \n `${item.title}: ${item.content.substring(0, 200)}...`\n ).join(\"\\n\\n\");\n \n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content: \"You are an executive summary writer. Based on the research provided, create a concise yet comprehensive executive summary that highlights the key findings, insights, and conclusions. The summary should be approximately 400-500 words.\"\n },\n {\n role: \"user\",\n content: `Create an executive summary for research on \"${topic}\" based on the following subtopic research:\\n\\n${condensedResearch}`\n }\n ]\n });\n\n return response.choices[0]?.message?.content || \"No executive summary generated\";\n } catch (error) {\n console.error(\"Error generating executive summary:\", error);\n return `Error generating executive summary: ${error.message}. Please try again later.`;\n }\n}\n\n// Type definitions\ninterface SubtopicResearch {\n title: string;\n content: string;\n wordCount: number;\n}\n\ninterface ResearchReport {\n topic: string;\n executiveSummary: string;\n subtopics: SubtopicResearch[];\n generatedAt: string;\n depth: string;\n}",[],[174],{"_103":183,"_95":184,"_145":185,"_146":-5,"_147":134,"_148":186,"_150":151,"_105":187,"_100":188,"_154":168,"_156":189,"_158":27,"_159":-5,"_160":190,"_98":191},"e6dc2b58-31bd-11f0-a08b-569c3dd06744","researchInterface.ts","f39233fc-31bc-11f0-9526-569c3dd06744",7,["D",1747335214011],2,"/**\n * Research Interface\n * \n * A simple web interface to interact with the Deep Research Agent\n */\nexport default function(req: Request): Response {\n // Get the current URL to determine the agent URL\n const url = new URL(req.url);\n const baseUrl = `${url.protocol}//${url.host}`;\n const agentUrl = `${baseUrl}/deepResearchAgent`;\n\n const html = `\n\n\n \n \n Deep Research Agent\n \n \n \n\n\n

Deep Research Agent

Conduct comprehensive research on any topic

\n \n

Researching topic...

This may take a few minutes depending on the research depth.

\n \n

Research Results

\n \n

Executive Summary

\n \n

\n \n \n

\n\n \n\n`;\n\n return new Response(html, {\n headers: {\n \"Content-Type\": \"text/html\"\n }\n });\n}",[],[184],{"_103":193,"_95":99,"_145":194,"_146":-5,"_147":134,"_148":195,"_150":151,"_105":196,"_100":153,"_154":168,"_156":197,"_158":27,"_159":-5,"_160":198,"_98":199},"900259b4-31be-11f0-8ad3-569c3dd06744","8eb9aa3a-31be-11f0-9ca4-569c3dd06744",13,["D",1747335497801],"/**\n * Simple test to verify that HTTP endpoints are working\n */\nexport default function(req: Request): Response {\n const html = `\n\n\n \n \n Simple Test\n \n\n\n

✅ Simple Test Successful!

\n \n

This endpoint is working correctly.

\n \n

Request URL

${req.url}

\n \n

Request Method

${req.method}

\n \n

Current Time

${new Date().toISOString()}

\n \n

Available Endpoints

\n\n`;\n\n return new Response(html, {\n headers: { 'Content-Type': 'text/html' }\n });\n}",[],[99],{"_103":201,"_95":202,"_145":203,"_146":-5,"_147":134,"_148":204,"_150":151,"_105":205,"_100":188,"_154":168,"_156":206,"_158":27,"_159":-5,"_160":207,"_98":208},"88441942-31be-11f0-ace6-569c3dd06744","testAgent.ts","f054e0ee-31bd-11f0-9861-569c3dd06744",11,["D",1747335484807],"/**\n * Test file for the Deep Research Agent\n * This will make a simple request to the agent and log the results\n */\nexport default async function(req: Request): Promise {\n try {\n // Get the URL of the current request to determine the agent URL\n const url = new URL(req.url);\n const baseUrl = `${url.protocol}//${url.host}`;\n const agentUrl = `${baseUrl}/deepResearchAgent`;\n \n console.log(`Testing Deep Research Agent at: ${agentUrl}`);\n \n // Make a simple test request\n const response = await fetch(agentUrl, {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json'\n },\n body: JSON.stringify({\n topic: \"Climate change adaptation strategies\",\n depth: \"light\" // Using light for faster testing\n })\n });\n \n console.log(`Response status: ${response.status}`);\n \n if (!response.ok) {\n const errorText = await response.text();\n console.error(`Error response: ${errorText}`);\n return new Response(JSON.stringify({\n success: false,\n error: `Test failed with status ${response.status}`,\n errorDetails: errorText\n }), {\n status: 500,\n headers: { 'Content-Type': 'application/json' }\n });\n }\n \n const data = await response.json();\n \n // Log a summary of the results\n console.log(\"Test successful! Research summary:\");\n console.log(`Topic: ${data.topic}`);\n console.log(`Subtopics: ${data.research.subtopics.length}`);\n console.log(`Executive summary length: ${data.research.executiveSummary.length} characters`);\n console.log(`First subtopic: ${data.research.subtopics[0].title}`);\n \n // Create a nice HTML response\n const html = `\n\n\n \n \n Deep Research Agent Test\n \n\n\n

✅ Deep Research Agent Test Successful!

\n \n

The agent is working correctly and returned a complete research report.

\n \n

Research Topic

${data.topic}

\n \n

Number of Subtopics

${data.research.subtopics.length}

\n \n

First Subtopic

${data.research.subtopics[0].title}

\n \n

Executive Summary Preview

${data.research.executiveSummary.substring(0, 150)}...

\n \n

Next Steps

You can now use the Deep Research Agent through:

The web interface: ${baseUrl}/researchInterface
Direct API calls to: ${baseUrl}/deepResearchAgent

\n\n`;\n\n return new Response(html, {\n headers: { 'Content-Type': 'text/html' }\n });\n } catch (error) {\n console.error(\"Test failed:\", error);\n \n return new Response(JSON.stringify({\n success: false,\n error: error.message\n }), {\n status: 500,\n headers: { 'Content-Type': 'application/json' }\n });\n }\n}",[],[202],"files",[142,163,172,182,192,200],"counts",{"_213":31,"_214":153,"_215":31,"_216":31},"pulls","branches","forks","envVars","contents",[],{"_103":193,"_95":99,"_145":194,"_146":-5,"_147":134,"_148":195,"_150":151,"_105":220,"_100":153,"_154":168,"_156":197,"_158":27,"_159":-5,"_98":221},["D",1747335497801],[99],"upstream","emptyEnvVars","isNew","routes/_app.x.$handle.$name.code.$",{"_93":94,"_95":96,"_97":41,"_100":-7,"_98":99,"_101":227,"_140":231,"_209":250,"_155":251,"_217":253,"_222":-5,"_254":27,"_255":-5},{"_103":104,"_95":96,"_105":228,"_107":-5,"_108":27,"_109":110,"_111":-5,"_112":-5,"_12":113,"_114":115,"_116":27,"_117":-5,"_118":31,"_119":120,"_121":120,"_122":123,"_124":123,"_125":123,"_126":120,"_127":27,"_21":229,"_131":230},["D",1747334673999],{"_93":94,"_129":130},{"_100":133,"_95":41,"_103":134,"_135":104,"_136":-5,"_137":-5,"_138":-5,"_139":-5},[232,235,238,241,244,247],{"_103":143,"_95":144,"_145":115,"_146":-5,"_147":134,"_148":149,"_150":151,"_105":152,"_100":153,"_154":155,"_156":157,"_158":27,"_159":-5,"_160":233,"_98":234},[],[144],{"_103":164,"_95":165,"_145":166,"_146":-5,"_147":134,"_148":133,"_150":151,"_105":167,"_100":153,"_154":168,"_156":169,"_158":27,"_159":-5,"_160":236,"_98":237},[],[165],{"_103":173,"_95":174,"_145":175,"_146":-5,"_147":134,"_148":176,"_150":151,"_105":177,"_100":178,"_154":168,"_156":179,"_158":27,"_159":-5,"_160":239,"_98":240},[],[174],{"_103":183,"_95":184,"_145":185,"_146":-5,"_147":134,"_148":186,"_150":151,"_105":187,"_100":188,"_154":168,"_156":189,"_158":27,"_159":-5,"_160":242,"_98":243},[],[184],{"_103":193,"_95":99,"_145":194,"_146":-5,"_147":134,"_148":195,"_150":151,"_105":196,"_100":153,"_154":168,"_156":197,"_158":27,"_159":-5,"_160":245,"_98":246},[],[99],{"_103":201,"_95":202,"_145":203,"_146":-5,"_147":134,"_148":204,"_150":151,"_105":205,"_100":188,"_154":168,"_156":206,"_158":27,"_159":-5,"_160":248,"_98":249},[],[202],[232,235,238,241,244,247],{"_103":193,"_95":99,"_145":194,"_146":-5,"_147":134,"_148":195,"_150":151,"_105":220,"_100":153,"_154":168,"_156":197,"_158":27,"_159":-5,"_98":252},[99],[],"showCompareCallout","pull","routes/_app","actionData","errors"]