Agent Information System

\n\n {error && (\n

\n Error:\n {error}\n

\n )}\n\n

\n {messages.map((message, index) => (\n \n {message.content}\n

\n ))}\n {isLoading && (\n

\n Processing...\n

\n )}\n

\n\n

\n setInputMessage(e.target.value)}\n onKeyPress={(e) => e.key === \"Enter\" && sendMessage()}\n placeholder=\"Ask me anything...\"\n disabled={isLoading}\n />\n \n Send\n \n

\n \n );\n}\n\nfunction client() {\n createRoot(document.getElementById(\"root\")).render();\n}\nif (typeof document !== \"undefined\") { client(); }\n\nexport default async function server(request: Request): Promise {\n const { OpenAI } = await import(\"https://esm.town/v/std/openai\");\n const { sqlite } = await import(\"https://esm.town/v/stevekrouse/sqlite\");\n\n // Enhanced server-side logging\n function serverLog(level: \"INFO\" | \"WARN\" | \"ERROR\", message: string, context?: any) {\n const timestamp = new Date().toISOString();\n const logEntry = {\n timestamp,\n level,\n message,\n context,\n };\n console.log(JSON.stringify(logEntry));\n return logEntry;\n }\n\n // Robust key extraction with fallback\n const extractKey = (url: URL): string => {\n try {\n const pathParts = url.pathname.split(\"/\").filter(Boolean);\n // Use the last part of the path, or fallback to a default key\n return pathParts[pathParts.length - 1] || \"default_agent_api\";\n } catch (error) {\n console.error(\"Failed to extract key:\", error);\n return \"fallback_agent_api\";\n }\n };\n\n const openai = new OpenAI();\n const KEY = extractKey(new URL(import.meta.url));\n const SCHEMA_VERSION = 3; // Increment schema version for tool support\n const CACHE_TTL = 24 * 60 * 60 * 1000; // 24 hours in milliseconds\n\n try {\n // Initialize database with enhanced error handling and add cache table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${KEY}_tools_${SCHEMA_VERSION} (\n id TEXT PRIMARY KEY,\n name TEXT,\n description TEXT,\n schema TEXT,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n error_log TEXT\n )\n `);\n\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${KEY}_requests_${SCHEMA_VERSION} (\n id TEXT PRIMARY KEY,\n tool_id TEXT,\n query TEXT,\n parameters TEXT,\n response TEXT,\n error_log TEXT,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n `);\n\n // Add a cache table to store API responses\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${KEY}_cache_${SCHEMA_VERSION} (\n path TEXT PRIMARY KEY,\n response TEXT,\n content_type TEXT,\n status INTEGER,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP,\n last_accessed DATETIME DEFAULT CURRENT_TIMESTAMP,\n hits INTEGER DEFAULT 1\n )\n `);\n\n serverLog(\"INFO\", \"Database initialized successfully\", {\n key: KEY,\n schemaVersion: SCHEMA_VERSION,\n });\n } catch (dbInitError) {\n serverLog(\"ERROR\", \"Database initialization failed\", {\n error: dbInitError instanceof Error ? dbInitError.message : String(dbInitError),\n key: KEY,\n });\n return new Response(\n JSON.stringify({\n error: \"Database initialization failed\",\n details: String(dbInitError),\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n\n const url = new URL(request.url);\n const path = url.pathname.split(\"/\").filter(Boolean);\n const fullPath = url.pathname;\n const method = request.method;\n\n // Function to check cache before processing request\n const checkCache = async (path: string, method: string): Promise => {\n // Only cache GET requests\n if (method !== \"GET\") return null;\n\n try {\n // Query the cache table\n const cacheResult = await sqlite.execute(\n `\n SELECT path, response, content_type, status, created_at, hits \n FROM ${KEY}_cache_${SCHEMA_VERSION} \n WHERE path = ?\n `,\n [path],\n );\n\n // If no cache entry found\n\n const cacheEntry = cacheResult.rows[0];\n\n // Check if cache is expired (older than CACHE_TTL)\n const cacheDate = new Date(cacheEntry[\"created_at\"].toString()).getTime();\n const currentDate = new Date().getTime();\n if (currentDate - cacheDate > CACHE_TTL) {\n // Cache expired, delete entry\n await sqlite.execute(\n `\n DELETE FROM ${KEY}_cache_${SCHEMA_VERSION} \n WHERE path = ?\n `,\n [path],\n );\n return null;\n }\n\n // Update access time and hit count\n await sqlite.execute(\n `\n UPDATE ${KEY}_cache_${SCHEMA_VERSION} \n SET last_accessed = CURRENT_TIMESTAMP, \n hits = hits + 1 \n WHERE path = ?\n `,\n [path],\n );\n\n serverLog(\"INFO\", \"Cache hit\", {\n path,\n hits: cacheEntry.hits + 1,\n cacheAge: Math.floor((currentDate - cacheDate) / 1000) + \" seconds\",\n });\n\n // Return cached response\n return new Response(cacheEntry.response, {\n status: cacheEntry.status,\n headers: {\n \"Content-Type\": cacheEntry.content_type,\n \"X-Cache\": \"HIT\",\n \"Cache-Control\": \"public, max-age=86400\",\n \"Age\": Math.floor((currentDate - cacheDate) / 1000).toString(),\n },\n });\n } catch (error) {\n serverLog(\"ERROR\", \"Cache check failed\", {\n path,\n error: error instanceof Error ? error.message + \" \" + error.stack : String(error),\n });\n return null;\n }\n };\n\n // Function to store response in cache\n const cacheResponse = async (\n path: string,\n response: string,\n contentType: string,\n status: number,\n ): Promise => {\n try {\n // Only cache successful responses\n if (status >= 200 && status < 300 && method === \"GET\") {\n await sqlite.execute(\n `\n INSERT OR REPLACE INTO ${KEY}_cache_${SCHEMA_VERSION} \n (path, response, content_type, status, created_at, last_accessed, hits) \n VALUES (?, ?, ?, ?, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP, 1)\n `,\n [path, response, contentType, status],\n );\n\n serverLog(\"INFO\", \"Response cached\", { path });\n }\n } catch (error) {\n serverLog(\"ERROR\", \"Caching failed\", {\n path,\n error: error instanceof Error ? error.message : String(error),\n });\n }\n };\n\n // Check cache before processing any request\n const cachedResponse = await checkCache(fullPath, method);\n if (cachedResponse) {\n return cachedResponse;\n }\n\n // NEW: Handle dynamic data endpoints - trigger AgentA and AgentB for any path\n if (path.length > 0 && path[0] !== \"process\" && path[0] !== \"tool\") {\n try {\n serverLog(\"INFO\", \"Dynamic data endpoint requested\", {\n path: path.join(\"/\"),\n });\n\n // Construct the topic from the path\n const topic = path.join(\" \");\n\n // First check if we have this data cached in the requests table\n const existingRequests = await sqlite.execute(\n `\n SELECT response\n FROM ${KEY}_requests_${SCHEMA_VERSION} \n WHERE query LIKE ?\n ORDER BY created_at DESC\n LIMIT 1\n `,\n [`%${topic}%`],\n );\n\n // If we have a similar request cached, use that\n if (existingRequests.rows && existingRequests.rows.length > 0) {\n const responseData = JSON.parse(existingRequests.rows[0].response);\n const responseBody = JSON.stringify(responseData);\n\n serverLog(\"INFO\", \"Using cached request data\", {\n path: path.join(\"/\"),\n dataLength: responseBody.length,\n });\n\n // Cache the response\n await cacheResponse(\n fullPath,\n responseBody,\n \"application/json\",\n 200,\n );\n\n return new Response(responseBody, {\n status: 200,\n headers: {\n \"Content-Type\": \"application/json\",\n \"X-Cache\": \"MISS (but using similar request)\",\n \"Cache-Control\": \"public, max-age=86400\",\n },\n });\n }\n\n // Simulate a user request to AgentA\n const agentAPrompt = `\n You are AgentA, an advanced AI assistant that creates comprehensive information tools.\n \n A user has requested information about: \"${topic}\"\n \n Define a schema for this information topic and create a query for AgentB to fetch detailed data.\n Your goal is to return the most comprehensive, detailed, and accurate information about this topic.\n \n Format your thinking as JSON with this structure:\n {\n \"actionType\": \"use_tool\",\n \"reasoning\": \"your reasoning here\",\n \"response\": \"This is comprehensive information about ${topic}\",\n \"tool\": {\n \"id\": \"${crypto.randomUUID()}\",\n \"schema\": {\n \"name\": \"${topic.replace(/\\s+/g, \"_\")}_info\",\n \"description\": \"Comprehensive information about ${topic}\",\n \"fields\": [\n {\"name\": \"data\", \"type\": \"object\", \"description\": \"Complete structured data about ${topic}\"}\n ]\n },\n \"query\": \"Provide comprehensive, detailed information about ${topic}. Include all relevant subcategories, statistics, examples, and structured data.\",\n \"parameters\": {\"depth\": \"complete\", \"format\": \"structured\"}\n }\n }\n `;\n\n // Call AgentA to create the tool definition\n const agentACompletion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n messages: [{ role: \"user\", content: agentAPrompt }],\n });\n\n // Parse AgentA response\n const completionContent = agentACompletion.choices[0].message.content || \"{}\";\n const agentAResponse: AgentResponse = JSON.parse(completionContent);\n\n // If AgentA didn't provide a tool, return an error\n if (!agentAResponse.tool) {\n throw new Error(\"Failed to create a tool for this topic\");\n }\n\n // Now use AgentB to process the tool request (similar to the /tool endpoint)\n const toolRequest = agentAResponse.tool;\n\n // Store the tool in the database\n const toolId = toolRequest.id || crypto.randomUUID();\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_tools_${SCHEMA_VERSION} \n (id, name, description, schema) \n VALUES (?, ?, ?, ?)\n `,\n [\n toolId,\n toolRequest.schema.name,\n toolRequest.schema.description,\n JSON.stringify(toolRequest.schema),\n ],\n );\n\n // Call AgentB with the tool request\n const agentBPrompt = `\n You are AgentB, a specialized tool agent designed to provide comprehensive, accurate information.\n \n Tool Schema:\n ${JSON.stringify(toolRequest.schema, null, 2)}\n \n Query: ${toolRequest.query}\n \n Parameters: ${JSON.stringify(toolRequest.parameters || {})}\n\n Current Date and Time: ${new Date().toISOString()}\n \n Instructions:\n 1. Your task is to provide complete, accurate, and detailed information about \"${topic}\".\n 2. Return structured data that covers all relevant aspects of the topic.\n 3. Include subcategories, examples, definitions, comparisons, and any other relevant information.\n 4. Organize the information hierarchically where appropriate.\n 5. If the topic is about a list or collection, make sure to provide an extensive list.\n 6. For numeric data, provide precise figures where possible.\n 7. Include metadata about your response if relevant.\n \n Process this request and return a JSON response with:\n {\n \"success\": true,\n \"data\": {\n // Complete structured information about ${topic}\n }\n }\n `;\n\n const agentBCompletion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n messages: [{ role: \"user\", content: agentBPrompt }],\n });\n\n // Parse AgentB response\n const agentBContent = agentBCompletion.choices[0].message.content || \"{}\";\n const toolResponse: ToolResponse = JSON.parse(agentBContent);\n\n // Store the tool request and response in the database\n const requestId = crypto.randomUUID();\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_requests_${SCHEMA_VERSION} \n (id, tool_id, query, parameters, response, error_log) \n VALUES (?, ?, ?, ?, ?, ?)\n `,\n [\n requestId,\n toolId,\n toolRequest.query,\n JSON.stringify(toolRequest.parameters || {}),\n JSON.stringify(toolResponse.data),\n toolResponse.success ? null : toolResponse.error,\n ],\n );\n\n serverLog(\"INFO\", \"Dynamic endpoint processed successfully\", {\n path: path.join(\"/\"),\n success: toolResponse.success,\n });\n\n // Prepare response\n const responseBody = JSON.stringify(toolResponse.data);\n const status = toolResponse.success ? 200 : 500;\n\n // Cache the response\n await cacheResponse(\n fullPath,\n responseBody,\n \"application/json\",\n status,\n );\n\n // Return the data as JSON\n return new Response(responseBody, {\n status,\n headers: {\n \"Content-Type\": \"application/json\",\n \"X-Cache\": \"MISS\",\n \"Cache-Control\": \"public, max-age=86400\",\n },\n });\n } catch (error) {\n serverLog(\"ERROR\", \"Dynamic endpoint processing failed\", {\n path: path.join(\"/\"),\n error: error instanceof Error ? error.message : String(error),\n });\n\n const errorResponse = JSON.stringify({\n error: \"Failed to process this data endpoint\",\n details: error instanceof Error ? error.message + \" \" + String(error.stack) : String(error),\n });\n\n return new Response(errorResponse, {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n\n // Route for tool processing (AgentB)\n if (path[0] === \"tool\") {\n try {\n const toolRequest: ToolRequest = await request.json();\n serverLog(\"INFO\", \"Processing tool request\", {\n toolName: toolRequest.schema.name,\n query: toolRequest.query,\n });\n\n // Check if we have a similar query cached\n const existingRequests = await sqlite.execute(\n `\n SELECT response\n FROM ${KEY}_requests_${SCHEMA_VERSION} \n WHERE query = ? AND tool_id = ?\n ORDER BY created_at DESC\n LIMIT 1\n `,\n [toolRequest.query, toolRequest.schema.name],\n );\n\n // If we have this exact request cached, use it\n if (existingRequests.rows.length > 0) {\n serverLog(\"INFO\", \"Using cached tool request\", {\n toolName: toolRequest.schema.name,\n query: toolRequest.query,\n });\n\n return new Response(\n JSON.stringify({\n success: true,\n data: JSON.parse(existingRequests.rows[0].response.toString()),\n }),\n {\n headers: {\n \"Content-Type\": \"application/json\",\n \"X-Cache\": \"HIT\",\n },\n },\n );\n }\n\n // Use AgentB (a separate OpenAI instance) to process the tool request\n const agentBPrompt = `\n You are AgentB, a specialized tool agent designed to process specific information requests.\n \n Tool Schema:\n ${JSON.stringify(toolRequest.schema, null, 2)}\n \n Query: ${toolRequest.query}\n \n Parameters: ${JSON.stringify(toolRequest.parameters || {})}\n \n Process this request and return a JSON response with:\n {\n \"success\": true/false,\n \"data\": {}, // The result of processing the request\n \"error\": \"\" // Optional error message if something goes wrong\n }\n `;\n\n const agentBCompletion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n messages: [{ role: \"user\", content: agentBPrompt }],\n });\n\n // Safely parse the response\n const completionContent = agentBCompletion.choices[0].message.content || \"{}\";\n const toolResponse: ToolResponse = JSON.parse(completionContent);\n\n // Store the tool request and response in the database\n const requestId = toolRequest.id || crypto.randomUUID();\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_requests_${SCHEMA_VERSION} \n (id, tool_id, query, parameters, response, error_log) \n VALUES (?, ?, ?, ?, ?, ?)\n `,\n [\n requestId,\n toolRequest.schema.name,\n toolRequest.query,\n JSON.stringify(toolRequest.parameters || {}),\n JSON.stringify(toolResponse.data),\n JSON.stringify(toolResponse.success ? null : toolResponse.error),\n ],\n );\n\n serverLog(\"INFO\", \"Tool request processed\", {\n success: toolResponse.success,\n });\n\n return new Response(JSON.stringify(toolResponse), {\n headers: {\n \"Content-Type\": \"application/json\",\n \"X-Cache\": \"MISS\",\n },\n });\n } catch (error) {\n serverLog(\"ERROR\", \"Tool processing failed\", {\n error: error instanceof Error ? error.message : String(error),\n });\n\n return new Response(\n JSON.stringify({\n success: false,\n error: \"Tool processing error\",\n details: error instanceof Error ? error.message : String(error),\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n }\n\n // Route for main agent processing (AgentA)\n if (path[0] === \"process\") {\n try {\n const { messages } = await request.json();\n serverLog(\"INFO\", \"Processing agent request\", {\n messageCount: messages.length,\n });\n\n // Fallback response if OpenAI fails\n let agentAResponse: AgentResponse = {\n actionType: \"direct_response\",\n response: \"I'm having trouble processing your request right now. Please try again.\",\n };\n\n try {\n // AgentA decides what to do with the query\n const agentAPrompt = `\n You are AgentA, an advanced AI assistant that can define information tools to help users.\n When a user asks for information, you should:\n \n 1. Analyze what kind of information they need\n 2. Define a schema for that information\n 3. Create a query to fetch the information from AgentB, a specialized tool agent designed to process specific information requests.\n \n If a schema already exists for their request type, use that. Otherwise, create a new one.\n \n Current conversation history:\n ${messages.map(m => `${m.role}: ${m.content}`).join(\"\\n\")}\n \n Now, decide:\n 1. Does this request require creating a new information tool?\n 2. Can we use an existing tool?\n\n Current Date and Time: ${new Date()}\n \n Format your thinking as JSON with this structure:\n {\n \"actionType\": \"create_tool\" | \"use_tool\",\n \"reasoning\": \"your reasoning here\",\n \"response\": \"your response to the user\",\n \"tool\": { // Only if actionType is create_tool or use_tool\n \"id\": \"optional unique identifier\",\n \"schema\": {\n \"name\": \"tool name\",\n \"description\": \"tool description\",\n \"fields\": [\n {\"name\": \"field1\", \"type\": \"string | number | boolean | array | object\", \"description\": \"field description\"}\n ]\n },\n \"query\": \"specific query for AgentB\",\n \"parameters\": {} // Any parameters needed for the query\n }\n }\n `;\n\n const agentACompletion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n messages: [{ role: \"user\", content: agentAPrompt }],\n });\n\n // Safely parse the response\n const completionContent = agentACompletion.choices[0].message.content || \"{}\";\n agentAResponse = JSON.parse(completionContent);\n\n // If it's a new tool, store it in the database\n if (agentAResponse.tool) {\n const toolId = crypto.randomUUID();\n await sqlite.execute(\n `\n INSERT INTO ${KEY}_tools_${SCHEMA_VERSION} \n (id, name, description, schema) \n VALUES (?, ?, ?, ?)\n `,\n [\n toolId,\n agentAResponse.tool.schema.name,\n agentAResponse.tool.schema.description,\n JSON.stringify(agentAResponse.tool.schema),\n ],\n );\n\n // Attach the generated ID to the tool\n agentAResponse.tool.id = toolId;\n }\n\n serverLog(\"INFO\", \"AgentA response received\", {\n actionType: agentAResponse.actionType,\n });\n } catch (aiProcessingError) {\n serverLog(\"ERROR\", \"AI processing failed\", {\n error: aiProcessingError instanceof Error ? aiProcessingError.message : String(aiProcessingError),\n });\n // Use fallback response if AI processing fails\n }\n\n // Always return a JSON response\n return new Response(JSON.stringify(agentAResponse), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (error) {\n serverLog(\"ERROR\", \"Agent processing failed\", {\n error: error instanceof Error ? error.message : String(error),\n });\n\n return new Response(\n JSON.stringify({\n error: \"Comprehensive processing error\",\n details: error instanceof Error ? error.message : String(error),\n suggestion: \"Please check your input and try again.\",\n }),\n {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n }\n\n // Separate route for serving the HTML\n if (path.length === 0) {\n const htmlResponse = `\n \n \n \n \n Dynamic Data API\n \n \n \n \n \n

\n Dynamic Data API\n

\n Access structured data for any topic using URL paths\n

\n \n

How to use:

\n Simply add your topic as a path after the domain.\n For example:\n

\n /cars/makes/models - Get information about car makes and models\n

\n /countries/europe - Get information about European countries\n

\n /programming/javascript/frameworks - Get information about JavaScript frameworks\n

\n \n

Try it now:

\n \n

\n Results\n

\n \n

Or use the chat interface:

\n \n Show Chat\n \n

\n \n \n \n \n\n \n `;\n\n return new Response(htmlResponse, {\n headers: { \"Content-Type\": \"text/html\" },\n });\n }\n\n // Default 404 response for any unhandled routes\n return new Response(\n JSON.stringify({\n error: \"Not Found\",\n message: \"Try accessing a data path like '/cars/makes/toyota' to get structured data\",\n }),\n {\n status: 404,\n headers: { \"Content-Type\": \"application/json\" },\n },\n 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