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Free AI (Pollinations.ai) — no token needed, always available\n * 2. OpenAI (if OPENAI_API_KEY is configured)\n * 3. HF Router primary model (if HF_API_KEY is configured)\n * 4. HF Router fallback model\n * 5. HF Router Gemma (ultimate HF fallback)\n * 6. Free AI retry (last resort)\n * On timeout or error, automatically switches to the next provider.\n */\nexport async function callHFWithFallback(\n prompt: string,\n modelOverride?: ModelKey,\n options?: CallOptions,\n): Promise {\n const taskType = classifyTask(prompt);\n const attempts: ModelKey[] = [];\n\n // --- 0. Try Free AI first (no token needed — always available) ---\n const freeResult = await routeToAdapter(\"freeai\", \"generate\", {\n prompt,\n maxTokens: options?.maxTokens ?? 500,\n temperature: options?.temperature ?? 0.7,\n timeoutMs: options?.timeoutMs ?? 30000,\n });\n if (freeResult.success && freeResult.data?.response) {\n return {\n success: true,\n model: \"gemma\",\n modelId: freeResult.data.modelUsed || \"freeai\",\n text: freeResult.data.response,\n taskType,\n attempts: [\"gemma\"],\n };\n }\n\n // --- 0b. Try OpenAI if key is configured ---\n const openaiKey = Deno.env.get(\"OPENAI_API_KEY\");\n if (openaiKey) {\n const openaiResult = await routeToAdapter(\"openai\", \"generate\", {\n prompt,\n maxTokens: options?.maxTokens ?? 1000,\n temperature: options?.temperature ?? 0.7,\n timeoutMs: options?.timeoutMs ?? 30000,\n });\n if (openaiResult.success && openaiResult.data?.response) {\n return {\n success: true,\n model: \"gemma\",\n modelId: openaiResult.data.modelUsed || \"openai\",\n text: openaiResult.data.response,\n taskType,\n attempts: [\"gemma\"],\n };\n }\n }\n\n // --- If no HF token, free AI was our main option ---\n const hfToken = getHFToken();\n if (!hfToken) {\n // Retry free AI with a shorter, simpler prompt\n const retryResult = await routeToAdapter(\"freeai\", \"generate\", {\n prompt: prompt.slice(0, 500),\n maxTokens: 300,\n temperature: 0.5,\n timeoutMs: 20000,\n });\n if (retryResult.success && retryResult.data?.response) {\n return {\n success: true,\n model: \"gemma\",\n modelId: retryResult.data.modelUsed || \"freeai\",\n text: retryResult.data.response,\n taskType,\n attempts: [\"gemma\"],\n fallbackUsed: true,\n };\n }\n return {\n success: false,\n model: \"gemma\",\n modelId: \"freeai\",\n text: \"\",\n taskType,\n error: \"Free AI service unavailable — no HF token configured for fallback\",\n attempts: [\"gemma\"],\n };\n }\n\n // --- 1. Primary model via HF Router ---\n let modelKey: ModelKey;\n if (modelOverride) {\n modelKey = modelOverride;\n } else {\n const bestModelId = selectBestModel(prompt);\n modelKey = modelIdToKey(bestModelId);\n }\n\n const cfg = HF_MODELS[modelKey];\n const maxTokens = options?.maxTokens ?? cfg.maxTokens;\n const temperature = options?.temperature ?? cfg.temperature;\n const timeoutMs = options?.timeoutMs ?? 30000;\n\n attempts.push(modelKey);\n const primary = await routeToAdapter(\"huggingface\", \"generate\", {\n prompt,\n model: cfg.id,\n maxTokens,\n temperature,\n timeoutMs,\n });\n if (primary.success && primary.data?.response) {\n return {\n success: true,\n model: modelKey,\n modelId: cfg.id,\n text: primary.data.response,\n taskType,\n attempts,\n };\n }\n\n // --- 2. Secondary model (configured fallback) via HF Router ---\n if (cfg.fallback) {\n const fbKey = cfg.fallback;\n attempts.push(fbKey);\n const fbCfg = HF_MODELS[fbKey];\n const secondary = await routeToAdapter(\"huggingface\", \"generate\", {\n prompt,\n model: fbCfg.id,\n maxTokens: fbCfg.maxTokens,\n temperature: fbCfg.temperature,\n timeoutMs,\n });\n if (secondary.success && secondary.data?.response) {\n return {\n success: true,\n model: fbKey,\n modelId: fbCfg.id,\n text: secondary.data.response,\n taskType,\n attempts,\n fallbackUsed: true,\n };\n }\n }\n\n // --- 3. Ultimate HF fallback: Gemma via HF Router ---\n if (modelKey !== \"gemma\" && cfg.fallback !== \"gemma\") {\n attempts.push(\"gemma\");\n const gemmaCfg = HF_MODELS[\"gemma\"];\n const gemmaResult = await routeToAdapter(\"huggingface\", \"generate\", {\n prompt,\n model: gemmaCfg.id,\n maxTokens: gemmaCfg.maxTokens,\n temperature: gemmaCfg.temperature,\n timeoutMs,\n });\n if (gemmaResult.success && gemmaResult.data?.response) {\n return {\n success: true,\n model: \"gemma\",\n modelId: gemmaCfg.id,\n text: gemmaResult.data.response,\n taskType,\n attempts,\n fallbackUsed: true,\n };\n }\n }\n\n // --- 4. 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═══════════════════════════════════════════════════════════════\n// Nexuses Core V3 — Autonomous AI Operating System\n// HTTP Entry Point · Open-Architecture · Zero API Keys\n// ═══════════════════════════════════════════════════════════════\n//\n// All AI powered by Pollinations.ai (free, open-source, no token).\n// Every request requires a SourceID for verification.\n// M2M protocol: JSON-RPC 2.0 + REST.\n\nimport { dispatchTask, getHistory, getSystemStatus, getTaskResult } from \"./lib/dispatcher.ts\";\nimport { processRPC } from \"./lib/rpc.ts\";\nimport { registerNewSource, checkSource } from \"./lib/verification.ts\";\nimport { initDB, listSources, getRecentLogs } from \"./lib/db.ts\";\n\nconst CORS = {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"GET, POST, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, X-Source-ID\",\n};\n\nfunction json(body: any, status = 200) {\n return new Response(JSON.stringify(body, null, 2), { status, headers: CORS });\n}\n\nexport default async function (req: Request): Promise {\n if (req.method === \"OPTIONS\") return new Response(null, { headers: CORS });\n\n await initDB();\n\n const url = new URL(req.url);\n const path = url.pathname;\n\n // ═══════════════════════════════════════\n // GET ROUTES — Discovery & Monitoring\n // ═══════════════════════════════════════\n\n if (req.method === \"GET\") {\n if (path === \"/\" || path === \"\") return json(await getSystemStatus());\n if (path === \"/status\") return json(await getSystemStatus());\n if (path === \"/agents\") return json({ agents: (await getSystemStatus()).agents });\n if (path === \"/sources\") return json({ sources: await listSources() });\n\n if (path === \"/history\") {\n const limit = parseInt(url.searchParams.get(\"limit\") || \"20\");\n return json({ history: await getHistory(limit) });\n }\n\n if (path === \"/logs\") {\n const limit = parseInt(url.searchParams.get(\"limit\") || \"50\");\n return json({ logs: await getRecentLogs(limit) });\n }\n\n const taskMatch = path.match(/^\\/task\\/(.+)$/);\n if (taskMatch) {\n const task = await getTaskResult(taskMatch[1]);\n return task\n ? json({ task })\n : json({ status: \"failure\", message: \"Task not found\" }, 404);\n }\n\n return json({ error: \"Unknown endpoint\", available: [\n \"GET /\", \"GET /status\", \"GET /agents\", \"GET /sources\",\n \"GET /history\", \"GET /logs\", \"GET /task/:id\",\n \"POST /task\", \"POST /register\", \"POST /rpc\",\n ] }, 404);\n }\n\n // ═══════════════════════════════════════\n // POST ROUTES — Task Execution & M2M\n // ═══════════════════════════════════════\n\n if (req.method !== \"POST\") {\n return json({ error: \"Only GET and POST supported\" }, 405);\n }\n\n let payload: any;\n try {\n payload = await req.json();\n } catch {\n return json({ status: \"failure\", message: \"Invalid JSON body\" }, 400);\n }\n\n // POST /register — Register a new source (public, no verification needed)\n if (path === \"/register\") {\n const { source_id, name, description } = payload;\n if (!source_id || !name) {\n return json({\n status: \"failure\",\n message: \"Missing required fields: source_id, name\",\n }, 400);\n }\n const result = await registerNewSource(source_id, name, description || \"\");\n return json(result, result.verified ? 200 : 400);\n }\n\n // POST /rpc — JSON-RPC 2.0 endpoint (M2M protocol)\n if (path === \"/rpc\") {\n const rpcResponse = await processRPC(payload);\n return json(rpcResponse, rpcResponse.error ? 400 : 200);\n }\n\n // POST /task — Main task submission (requires SourceID)\n if (path === \"/task\" || path === \"/\" || path === \"\") {\n // Extract SourceID from body or header\n const sourceId = payload.source_id ||\n req.headers.get(\"x-source-id\") ||\n req.headers.get(\"X-Source-ID\") || \"\";\n\n if (!sourceId) {\n return json({\n status: \"failure\",\n message: \"SourceID required — include 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Register your source via POST /register.\",\n hint: { register_endpoint: \"POST /register\", body_format: { source_id: \"your-platform-id\", name: \"Platform Name\", description: \"optional\" } },\n }, 401);\n }\n\n // Universal request normalizer — accept flexible task field names\n const taskFields = [\"task\", \"query\", \"prompt\", \"command\", \"message\", \"input\", \"text\"];\n let task = \"\";\n let data = { ...payload };\n delete data.source_id;\n\n for (const key of taskFields) {\n if (payload[key]) {\n task = payload[key];\n delete data[key];\n break;\n }\n }\n\n if (!task) {\n return json({\n status: \"failure\",\n message: \"No task provided — include 'task' (or 'query', 'prompt', 'command', 'message', 'input', 'text') in body\",\n }, 400);\n }\n\n const response = await dispatchTask(task, data, sourceId);\n return json(response, response.status === \"success\" ? 200 : 500);\n }\n\n // Unknown POST path\n return json({ status: \"failure\", message: \"Unknown endpoint\" }, 404);\n}\n","label",[141],"7a770991-c7a5-4776-8e67-8facf105397d","db.ts","a8be7666-7945-11f1-947a-1607ee4eb77e",34,["D",1783355945330],2,"// SQLite Persistent State Layer — Nexuses Core OS V10.0\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport interface TaskRecord {\n id: string;\n task: string;\n agent: string;\n status: string;\n data: string;\n result: string;\n created_at: string;\n updated_at: string;\n}\n\nexport interface MemoryRecord {\n key: string;\n value: string;\n created_at: string;\n}\n\nconst INIT_SQL = [\n `CREATE TABLE IF NOT EXISTS tasks (\n id TEXT PRIMARY KEY,\n task TEXT NOT NULL,\n agent TEXT,\n status TEXT NOT NULL,\n data TEXT,\n result TEXT,\n created_at TEXT NOT NULL,\n updated_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS memory (\n key TEXT PRIMARY KEY,\n value TEXT NOT NULL,\n created_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS workflows (\n id TEXT PRIMARY KEY,\n task_id TEXT NOT NULL,\n step INTEGER NOT NULL,\n agent TEXT,\n status TEXT,\n detail TEXT,\n created_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS delegations (\n id TEXT PRIMARY KEY,\n task_id TEXT NOT NULL,\n target TEXT NOT NULL,\n task TEXT NOT NULL,\n status TEXT NOT NULL,\n result TEXT,\n created_at TEXT NOT NULL\n )`,\n];\n\nlet initialized = false;\n\nexport async function initDB() {\n if (initialized) return;\n await sqlite.batch(INIT_SQL.map((sql) => ({ sql })));\n initialized = true;\n}\n\nexport async function insertTask(\n id: string,\n task: string,\n agent: string,\n status: string,\n data: any,\n result: any,\n) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql:\n `INSERT INTO tasks (id, task, agent, status, data, result, created_at, updated_at)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n id,\n task,\n agent,\n status,\n JSON.stringify(data),\n JSON.stringify(result),\n now,\n now,\n ],\n });\n}\n\nexport async function updateTaskStatus(\n id: string,\n status: string,\n result: any,\n) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql: `UPDATE tasks SET status = ?, result = ?, updated_at = ? WHERE id = ?`,\n args: [status, JSON.stringify(result), now, id],\n });\n}\n\nexport async function getTask(id: string): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM tasks WHERE id = ?`,\n args: [id],\n });\n return (res.rows[0] as TaskRecord) || null;\n}\n\nexport async function getRecentTasks(limit = 20): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM tasks ORDER BY created_at DESC LIMIT ?`,\n args: [limit],\n });\n return res.rows as TaskRecord[];\n}\n\nexport async function logWorkflowStep(\n taskId: string,\n step: number,\n agent: string,\n status: string,\n detail: string,\n) {\n const id = `wf_${taskId}_${step}`;\n const now = new Date().toISOString();\n await sqlite.execute({\n sql:\n `INSERT INTO workflows (id, task_id, step, agent, status, detail, created_at)\n VALUES (?, ?, ?, ?, ?, ?, ?)`,\n args: [id, taskId, step, agent, status, detail, now],\n });\n}\n\nexport async function remember(key: string, value: any) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql:\n `INSERT OR REPLACE INTO memory (key, value, created_at) VALUES (?, ?, ?)`,\n args: [key, JSON.stringify(value), now],\n });\n}\n\nexport async function recall(key: string): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM memory WHERE key = ?`,\n args: [key],\n });\n const row = res.rows[0] as MemoryRecord | undefined;\n return row ? JSON.parse(row.value) : null;\n}\n\nexport async function getWorkflowHistory(taskId: string) {\n const res = await sqlite.execute({\n sql: `SELECT * FROM workflows WHERE task_id = ? ORDER BY step ASC`,\n args: [taskId],\n });\n return res.rows;\n}\n\nexport async function getTaskCount(): Promise {\n const res = await sqlite.execute(`SELECT COUNT(*) as count FROM tasks`);\n return (res.rows[0] as any)?.count || 0;\n}",[152],"3fc99152-68ef-4298-b113-837e6542dffe","AgentManager.ts","6b3026f8-7947-11f1-82a8-1607ee4eb77e",28,["D",1783348386548],"// Agent Swarm — Nexuses Core OS V10.0\n// All agents use callHFWithFallback (HF Router with auto model selection via selectBestModel()).\nimport {\n callHFWithFallback,\n classifyTask,\n getHFToken,\n getSelectionInfo,\n} from \"./hf.ts\";\nimport type { HFResult, ModelKey, TaskType } from \"./hf.ts\";\nimport { listAdapters, routeToAdapter } from \"./UniversalAdapter.ts\";\n\nexport interface AgentResult {\n agent: string;\n category: string;\n status: \"SUCCESS\" | \"ERROR\" | \"PARTIAL\";\n message: string;\n data: any;\n model?: ModelKey;\n modelId?: string;\n taskType?: TaskType;\n fallbackUsed?: boolean;\n attempts?: ModelKey[];\n adapters?: string[];\n}\n\nexport interface AgentManifestEntry {\n name: string;\n category: string;\n keywords: string[];\n capabilities: string[];\n model: ModelKey;\n adapters: string[];\n}\n\n// --- Agent Registry ---\n\nexport const AGENT_REGISTRY: AgentManifestEntry[] = [\n {\n name: \"DevOpsCodingAgent\",\n category: \"DevOps/Coding\",\n keywords: [\n \"code\",\n \"coding\",\n \"debug\",\n \"bug\",\n \"fix\",\n \"github\",\n \"repo\",\n \"program\",\n \"vscode\",\n \"vs code\",\n \"commit\",\n \"deploy\",\n \"devops\",\n \"refactor\",\n \"compile\",\n \"syntax\",\n \"error\",\n ],\n capabilities: [\n \"VS Code integration\",\n \"GitHub repo management\",\n \"Error finding & fixing\",\n \"Bug resolution\",\n \"Auto-writing code\",\n \"Code explanation\",\n ],\n model: \"qwen_coder\",\n adapters: [\"github\"],\n },\n {\n name: \"WebContentAgent\",\n category: \"Web/Content\",\n keywords: [\n \"write\",\n \"blog\",\n \"content\",\n \"article\",\n \"social\",\n \"post\",\n \"website\",\n \"clone\",\n \"scrape\",\n \"web\",\n \"voice\",\n \"html\",\n \"css\",\n \"landing\",\n \"seo\",\n \"tweet\",\n \"facebook\",\n \"media\",\n ],\n capabilities: [\n \"Web automation\",\n \"Website cloning\",\n \"Social media posting\",\n \"Content writing\",\n \"Blogging\",\n \"Voice-enabled website generation\",\n ],\n model: \"llama\",\n adapters: [\"webscraper\", \"duckduckgo\"],\n },\n {\n name: \"DataAutomationAgent\",\n category: \"Data/Automation\",\n keywords: [\n \"data\",\n \"scrape\",\n \"analyze\",\n \"pdf\",\n \"excel\",\n \"word\",\n \"billing\",\n \"invoice\",\n \"entry\",\n \"scan\",\n \"spreadsheet\",\n \"csv\",\n \"report\",\n \"automation\",\n \"crypto\",\n \"price\",\n \"market\",\n ],\n capabilities: [\n \"Data scraping & analysis\",\n \"PDF conversion/creation\",\n \"Billing/data entry\",\n \"MS Word/Excel automation\",\n \"Document scanning\",\n ],\n model: \"phi3\",\n adapters: [\"coingecko\", \"duckduckgo\", \"webscraper\"],\n },\n {\n name: \"MultimediaAgent\",\n category: \"Multimedia\",\n keywords: [\n \"image\",\n \"video\",\n \"generate\",\n \"picture\",\n \"photo\",\n \"draw\",\n \"art\",\n \"design\",\n \"logo\",\n \"render\",\n \"media\",\n \"visual\",\n \"stable diffusion\",\n \"dalle\",\n \"midjourney\",\n ],\n capabilities: [\n \"Image generation\",\n \"Video generation guidance\",\n \"Visual design\",\n \"Art creation\",\n \"Logo design\",\n ],\n model: \"gemma\",\n adapters: [],\n },\n {\n name: \"OfficeDataAgent\",\n category: \"Office/Data\",\n keywords: [\n \"pdf\",\n \"word\",\n \"excel\",\n \"spreadsheet\",\n \"invoice\",\n \"billing\",\n \"document\",\n \"report\",\n \"csv\",\n \"word doc\",\n \"ms word\",\n \"ms excel\",\n \"office\",\n \"data entry\",\n \"scan\",\n ],\n capabilities: [\n \"PDF creation\",\n \"MS Word/Excel automation\",\n \"Invoice/billing generation\",\n \"Data entry\",\n \"Document formatting\",\n ],\n model: \"phi3\",\n adapters: [],\n },\n {\n name: \"GeneralHandler\",\n category: \"General\",\n keywords: [\n \"general\",\n \"help\",\n \"unknown\",\n \"test\",\n \"explain\",\n \"reasoning\",\n \"question\",\n \"chat\",\n ],\n capabilities: [\"General task handling\", \"Fallback routing\"],\n model: \"gemma\",\n adapters: [],\n },\n];\n\nexport function getAgentManifest() {\n return AGENT_REGISTRY;\n}\n\nfunction matchAgent(task: string): AgentManifestEntry {\n const cmd = task.toLowerCase();\n let best: AgentManifestEntry | null = null;\n let bestScore = 0;\n for (const agent of AGENT_REGISTRY) {\n const score = agent.keywords.filter((k) => cmd.includes(k)).length;\n if (score > bestScore) {\n bestScore = score;\n best = agent;\n }\n }\n return best || AGENT_REGISTRY[AGENT_REGISTRY.length - 1];\n}\n\n/**\n * Builds an AgentResult from an HFResult, including fallback metadata.\n */\nfunction buildAgentResult(\n agentName: string,\n category: string,\n hfResult: HFResult,\n message: string,\n data: any,\n adapters: string[] = [],\n): AgentResult {\n const fallbackUsed = (hfResult.attempts?.length ?? 1) > 1 && hfResult.success;\n return {\n agent: agentName,\n category,\n status: hfResult.success ? \"SUCCESS\" : \"ERROR\",\n message: hfResult.success ? message : hfResult.error || \"HF model error\",\n data,\n model: hfResult.model,\n modelId: hfResult.modelId,\n taskType: hfResult.taskType,\n fallbackUsed,\n attempts: hfResult.attempts,\n adapters,\n };\n}\n\n// --- Agent Execution Functions ---\n\nconst AGENT_MAP: Record<\n string,\n (task: string, data: any) => Promise\n> = {};\n\n// 1. DevOps / Coding Agent — uses Qwen2.5-Coder with fallback to Gemma\nAGENT_MAP[\"DevOpsCodingAgent\"] = async (task: string, data: any) => {\n const prompt = data.prompt || data.code || task;\n const action = data.action || \"analyze\";\n\n // GitHub operations via Universal Adapter\n if (data.github) {\n const ghResult = await routeToAdapter(\n \"github\",\n data.github.action || \"get_repo\",\n data.github,\n );\n if (ghResult.success) {\n const hfResult = await callHFWithFallback(\n `Analyze this GitHub data and provide insights: ${\n JSON.stringify(ghResult.data).slice(0, 1000)\n }`,\n \"qwen_coder\",\n { maxTokens: 500 },\n );\n return {\n agent: \"DevOpsCodingAgent\",\n category: \"DevOps/Coding\",\n status: hfResult.success ? \"SUCCESS\" : \"PARTIAL\",\n message: `GitHub ${data.github.action || \"operation\"} completed${\n hfResult.success ? \" + AI analysis\" : \"\"\n }`,\n data: {\n github: ghResult.data,\n analysis: hfResult.text || hfResult.error,\n },\n model: hfResult.model,\n modelId: hfResult.modelId,\n taskType: hfResult.taskType,\n fallbackUsed: (hfResult.attempts?.length ?? 1) > 1 && hfResult.success,\n attempts: hfResult.attempts,\n adapters: [\"github\"],\n };\n }\n return {\n agent: \"DevOpsCodingAgent\",\n category: \"DevOps/Coding\",\n status: \"ERROR\",\n message: ghResult.message,\n data: {},\n adapters: [\"github\"],\n };\n }\n\n // Code analysis / bug fixing / code writing\n const codePrompt = action === \"fix\"\n ? `Find and fix the bug in this code:\\n${prompt}`\n : action === \"explain\"\n ? `Explain this code:\\n${prompt}`\n : action === \"write\"\n ? `Write code for: ${prompt}`\n : `Analyze this code for errors and improvements:\\n${prompt}`;\n\n const hfResult = await callHFWithFallback(codePrompt, \"qwen_coder\", {\n maxTokens: 800,\n temperature: 0.3,\n });\n\n return buildAgentResult(\n \"DevOpsCodingAgent\",\n \"DevOps/Coding\",\n hfResult,\n `Code ${action} completed using ${HF_LABEL(hfResult.model)}`,\n { action, input: prompt.slice(0, 500), output: hfResult.text },\n );\n};\n\n// 2. Web / Content Agent — uses Llama 3 with fallback to Gemma\nAGENT_MAP[\"WebContentAgent\"] = async (task: string, data: any) => {\n const prompt = data.prompt || task;\n const action = data.action || \"write\";\n\n // Website cloning / scraping\n if (data.url || action === \"scrape\" || action === \"clone\") {\n const scrapeResult = await routeToAdapter(\"webscraper\", \"scrape\", {\n url: data.url,\n });\n if (scrapeResult.success && action === \"clone\") {\n const hfResult = await callHFWithFallback(\n `Create a simple HTML page based on this scraped content:\\nTitle: ${scrapeResult.data.title}\\nContent: ${\n scrapeResult.data.textContent?.slice(0, 800)\n }`,\n \"llama\",\n { maxTokens: 800 },\n );\n return {\n agent: \"WebContentAgent\",\n category: \"Web/Content\",\n status: hfResult.success ? \"SUCCESS\" : \"PARTIAL\",\n message: `Website cloned: ${scrapeResult.data.title}`,\n data: {\n original: { url: data.url, title: scrapeResult.data.title },\n clone: hfResult.text || \"\",\n links: scrapeResult.data.links,\n },\n model: hfResult.model,\n modelId: hfResult.modelId,\n taskType: hfResult.taskType,\n fallbackUsed: (hfResult.attempts?.length ?? 1) > 1 && hfResult.success,\n attempts: hfResult.attempts,\n adapters: [\"webscraper\"],\n };\n }\n if (scrapeResult.success) {\n return {\n agent: \"WebContentAgent\",\n category: \"Web/Content\",\n status: \"SUCCESS\",\n message: `Scraped: ${scrapeResult.data.title}`,\n data: scrapeResult.data,\n adapters: [\"webscraper\"],\n };\n }\n }\n\n // Content writing / blogging / social media\n const contentPrompt = action === \"social\"\n ? `Write a social media post about: ${prompt}`\n : action === \"blog\"\n ? `Write a blog post about: ${prompt}`\n : action === \"voice\"\n ? `Generate voice-ready website content for: ${prompt}`\n : `Write content about: ${prompt}`;\n\n const hfResult = await callHFWithFallback(contentPrompt, \"llama\", {\n maxTokens: 1000,\n temperature: 0.8,\n });\n\n return buildAgentResult(\n \"WebContentAgent\",\n \"Web/Content\",\n hfResult,\n `Content generated using ${HF_LABEL(hfResult.model)}`,\n { action, content: hfResult.text },\n );\n};\n\n// 3. Data / Automation Agent — uses Phi-3 with fallback to Gemma\nAGENT_MAP[\"DataAutomationAgent\"] = async (task: string, data: any) => {\n const prompt = data.prompt || task;\n const action = data.action || \"analyze\";\n\n // Crypto signals (no HF needed — direct adapter)\n if (\n action === \"crypto\" || prompt.toLowerCase().includes(\"crypto\") ||\n prompt.toLowerCase().includes(\"price\")\n ) {\n const cgResult = await routeToAdapter(\"coingecko\", \"price\", {\n coins: data.coins,\n });\n if (cgResult.success) {\n return {\n agent: \"DataAutomationAgent\",\n category: \"Data/Automation\",\n status: \"SUCCESS\",\n message: \"Crypto signals generated\",\n data: cgResult.data,\n adapters: [\"coingecko\"],\n };\n }\n }\n\n // Data scraping + analysis\n if (data.url || action === \"scrape\") {\n const scrapeResult = await routeToAdapter(\"webscraper\", \"scrape\", {\n url: data.url,\n });\n if (scrapeResult.success) {\n const hfResult = await callHFWithFallback(\n `Analyze this scraped data and provide a summary:\\n${\n scrapeResult.data.textContent?.slice(0, 1000)\n }`,\n \"phi3\",\n { maxTokens: 400 },\n );\n return {\n agent: \"DataAutomationAgent\",\n category: \"Data/Automation\",\n status: hfResult.success ? \"SUCCESS\" : \"PARTIAL\",\n message: \"Data scraped and analyzed\",\n data: {\n source: data.url,\n title: scrapeResult.data.title,\n analysis: hfResult.text || \"Analysis unavailable\",\n raw: scrapeResult.data,\n },\n model: hfResult.model,\n modelId: hfResult.modelId,\n taskType: hfResult.taskType,\n fallbackUsed: (hfResult.attempts?.length ?? 1) > 1 && hfResult.success,\n attempts: hfResult.attempts,\n adapters: [\"webscraper\"],\n };\n }\n }\n\n // Research via DuckDuckGo + HF summarization\n if (action === \"research\" || action === \"search\") {\n const ddgResult = await routeToAdapter(\"duckduckgo\", \"search\", {\n query: prompt,\n });\n if (ddgResult.success) {\n const hfResult = await callHFWithFallback(\n `Summarize this research data:\\n${\n JSON.stringify(ddgResult.data).slice(0, 800)\n }`,\n \"phi3\",\n { maxTokens: 400 },\n );\n return {\n agent: \"DataAutomationAgent\",\n category: \"Data/Automation\",\n status: hfResult.success ? \"SUCCESS\" : \"PARTIAL\",\n message: \"Research completed and summarized\",\n data: {\n search: ddgResult.data,\n summary: hfResult.text || \"\",\n },\n model: hfResult.model,\n modelId: hfResult.modelId,\n taskType: hfResult.taskType,\n fallbackUsed: (hfResult.attempts?.length ?? 1) > 1 && hfResult.success,\n attempts: hfResult.attempts,\n adapters: [\"duckduckgo\"],\n };\n }\n }\n\n // General data analysis\n const hfResult = await callHFWithFallback(\n `Analyze and process: ${prompt}`,\n \"phi3\",\n { maxTokens: 500, temperature: 0.5 },\n );\n\n return buildAgentResult(\n \"DataAutomationAgent\",\n \"Data/Automation\",\n hfResult,\n \"Data analysis completed\",\n { action, result: hfResult.text },\n );\n};\n\n// 4. Multimedia Agent — image/video generation via HF + text guidance\nAGENT_MAP[\"MultimediaAgent\"] = async (task: string, data: any) => {\n const prompt = data.prompt || task;\n const action = data.action || \"image\";\n\n if (action === \"image\") {\n // Attempt HF image generation (Stable Diffusion)\n const token = getHFToken();\n if (!token) {\n // Fallback: generate a descriptive text prompt that can be used externally\n const hfResult = await callHFWithFallback(\n `Generate a detailed image generation prompt for: ${prompt}. Include style, composition, and lighting details.`,\n \"gemma\",\n { maxTokens: 300 },\n );\n return buildAgentResult(\n \"MultimediaAgent\",\n \"Multimedia\",\n hfResult,\n \"Image prompt generated (HF_API_KEY needed for actual image generation)\",\n {\n action,\n prompt,\n imagePrompt: hfResult.text,\n note: \"Use this prompt with any text-to-image service\",\n },\n );\n }\n try {\n // Image generation still uses HF Inference API (router is chat-only)\n const res = await fetch(\n \"https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0\",\n {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${token}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({ inputs: prompt }),\n signal: AbortSignal.timeout(30000),\n },\n );\n if (res.ok) {\n const imageBlob = await res.blob();\n return {\n agent: \"MultimediaAgent\",\n category: \"Multimedia\",\n status: \"SUCCESS\",\n message: `Image generated for: ${prompt.slice(0, 80)}`,\n data: {\n action,\n prompt,\n imageGenerated: true,\n sizeBytes: imageBlob.size,\n mimeType: imageBlob.type,\n },\n model: \"gemma\" as ModelKey,\n adapters: [],\n };\n }\n // If image model fails, generate text prompt as fallback\n const hfResult = await callHFWithFallback(\n `Generate a detailed image generation prompt for: ${prompt}`,\n \"gemma\",\n { maxTokens: 300 },\n );\n return buildAgentResult(\n \"MultimediaAgent\",\n \"Multimedia\",\n hfResult,\n `Image model unavailable (HTTP ${res.status}), generated prompt instead`,\n { action, prompt, imagePrompt: hfResult.text, fallback: true },\n );\n } catch (e: any) {\n const hfResult = await callHFWithFallback(\n `Describe how to create: ${prompt}. Include visual details.`,\n \"gemma\",\n { maxTokens: 300 },\n );\n return buildAgentResult(\n \"MultimediaAgent\",\n \"Multimedia\",\n hfResult,\n `Image generation error: ${e.message}. Generated description instead.`,\n { action, prompt, description: hfResult.text, error: e.message },\n );\n }\n }\n\n // Video generation guidance\n const hfResult = await callHFWithFallback(\n `Provide guidance and a creative plan for: ${prompt}. Include scene descriptions and technical details.`,\n \"gemma\",\n { maxTokens: 500 },\n );\n return buildAgentResult(\n \"MultimediaAgent\",\n \"Multimedia\",\n hfResult,\n \"Multimedia guidance generated\",\n { action, prompt, guidance: hfResult.text },\n );\n};\n\n// 5. Office / Data Agent — PDF, Word, Excel, invoices, documents\nAGENT_MAP[\"OfficeDataAgent\"] = async (task: string, data: any) => {\n const prompt = data.prompt || task;\n const action = data.action || \"document\";\n\n // Invoice/billing generation\n if (action === \"invoice\" || prompt.toLowerCase().includes(\"invoice\")) {\n const hfResult = await callHFWithFallback(\n `Generate a professional invoice template in JSON format with these details: ${prompt}\\nInclude fields: invoiceNumber, date, billTo, items[], subtotal, tax, total.`,\n \"phi3\",\n { maxTokens: 500, temperature: 0.3 },\n );\n return buildAgentResult(\n \"OfficeDataAgent\",\n \"Office/Data\",\n hfResult,\n \"Invoice generated\",\n { action: \"invoice\", prompt, invoice: hfResult.text },\n );\n }\n\n // PDF / document structure generation\n if (action === \"pdf\" || prompt.toLowerCase().includes(\"pdf\")) {\n const hfResult = await callHFWithFallback(\n `Create a structured document outline for: ${prompt}. Include sections, headings, and key content points.`,\n \"phi3\",\n { maxTokens: 500 },\n );\n return buildAgentResult(\n \"OfficeDataAgent\",\n \"Office/Data\",\n hfResult,\n \"Document structure generated\",\n {\n action: \"pdf\",\n prompt,\n document: hfResult.text,\n note: \"Structure ready for PDF conversion\",\n },\n );\n }\n\n // Excel/CSV data generation\n if (\n action === \"excel\" || action === \"csv\" ||\n prompt.toLowerCase().includes(\"excel\") ||\n prompt.toLowerCase().includes(\"csv\")\n ) {\n const hfResult = await callHFWithFallback(\n `Generate tabular data in CSV format for: ${prompt}. Include headers and sample rows.`,\n \"phi3\",\n { maxTokens: 400, temperature: 0.3 },\n );\n return buildAgentResult(\n \"OfficeDataAgent\",\n \"Office/Data\",\n hfResult,\n \"Spreadsheet data generated\",\n { action: action, prompt, csv: hfResult.text },\n );\n }\n\n // General document/report generation\n const hfResult = await callHFWithFallback(\n `Create a professional document for: ${prompt}`,\n \"phi3\",\n { maxTokens: 500 },\n );\n return buildAgentResult(\n \"OfficeDataAgent\",\n \"Office/Data\",\n hfResult,\n \"Document generated\",\n { action, prompt, document: hfResult.text },\n );\n};\n\n// 6. General Handler — uses Gemma (no further fallback)\nAGENT_MAP[\"GeneralHandler\"] = async (task: string, data: any) => {\n const hfResult = await callHFWithFallback(data.prompt || task, \"gemma\", {\n maxTokens: 500,\n });\n\n return buildAgentResult(\n \"GeneralHandler\",\n \"General\",\n hfResult,\n \"Task processed\",\n { input: task, response: hfResult.text || \"\" },\n );\n};\n\n// --- Helpers ---\n\nfunction HF_LABEL(key: ModelKey): string {\n const labels: Record = {\n qwen_coder: \"Qwen2.5-Coder\",\n llama: \"Llama 3\",\n phi3: \"Phi-3 Mini\",\n gemma: \"Gemma 2 2B\",\n };\n return labels[key] || key;\n}\n\n// --- Public Routing API ---\n\nexport async function routeIncomingTask(\n task: string,\n data: any = {},\n mode = \"AUTO\",\n debug = false,\n): Promise {\n const agentEntry = matchAgent(task);\n const agentFn = AGENT_MAP[agentEntry.name] || AGENT_MAP[\"GeneralHandler\"];\n\n try {\n const result = await agentFn(task, data);\n if (debug) {\n console.log(\n `[Dispatcher] Task: \"${task}\" → Agent: ${result.agent} → Model: ${\n result.model || \"N/A\"\n } → Status: ${result.status}${\n result.fallbackUsed ? \" (FALLBACK)\" : \"\"\n }`,\n );\n }\n return result;\n } catch (e: any) {\n return {\n agent: agentEntry.name,\n category: agentEntry.category,\n status: \"ERROR\",\n message: `Agent execution error: ${e.message}`,\n data: {},\n };\n }\n}\n\nexport function getSystemInfo() {\n const token = getHFToken();\n return {\n agents: AGENT_REGISTRY.map((a) => ({\n name: a.name,\n category: a.category,\n capabilities: a.capabilities,\n model: a.model,\n })),\n adapters: listAdapters(),\n ai: `Free AI (Pollinations.ai): ALWAYS AVAILABLE | OpenAI: ${Deno.env.get(\"OPENAI_API_KEY\") ? \"ACTIVE\" : \"not configured\"} | HF Router: ${token ? \"ACTIVE\" : \"not configured\"} | Groq: ${Deno.env.get(\"GROQ_API_KEY\") ? \"ACTIVE\" : \"not configured\"}`,\n };\n}",[160],"10657c68-b48a-444f-8dfe-b4fe9700ad16","lib","69dda6e0-7960-11f1-9859-1607ee4eb77e",37,["D",1783359042499],"directory",[167],"0dbb60d1-1be9-40d7-a97a-7330cd44c97c","agents","69ed2fde-7960-11f1-9859-1607ee4eb77e",38,["D",1783359042602],[174],"pulls","branches",1,"forks","envVars",{"_187":68,"_189":68},"connections",{"_187":68,"_188":68},"count","unfilledCount","emptyCount",{"_48":173,"_38":174,"_36":175,"_142":-5,"_46":47,"_45":176,"_144":31,"_43":315,"_13":68,"_39":171,"_41":-5,"_30":54,"_149":-5,"_208":316,"_33":317},{"_48":307,"_38":308,"_36":309,"_142":175,"_46":47,"_45":310,"_144":31,"_43":311,"_13":68,"_39":40,"_41":312,"_30":54,"_149":-5,"_208":313,"_33":314},{"_48":299,"_38":300,"_36":301,"_142":175,"_46":47,"_45":302,"_144":31,"_43":303,"_13":68,"_39":40,"_41":304,"_30":54,"_149":-5,"_208":305,"_33":306},{"_48":291,"_38":292,"_36":293,"_142":175,"_46":47,"_45":294,"_144":31,"_43":295,"_13":68,"_39":40,"_41":296,"_30":54,"_149":-5,"_208":297,"_33":298},{"_48":283,"_38":284,"_36":285,"_142":175,"_46":47,"_45":286,"_144":31,"_43":287,"_13":68,"_39":40,"_41":288,"_30":54,"_149":-5,"_208":289,"_33":290},{"_48":275,"_38":276,"_36":277,"_142":175,"_46":47,"_45":278,"_144":31,"_43":279,"_13":68,"_39":40,"_41":280,"_30":54,"_149":-5,"_208":281,"_33":282},{"_48":166,"_38":167,"_36":168,"_142":-5,"_46":47,"_45":169,"_144":31,"_43":272,"_13":68,"_39":171,"_41":-5,"_30":54,"_149":-5,"_208":273,"_33":274},{"_48":265,"_38":152,"_36":266,"_142":168,"_46":47,"_45":267,"_144":54,"_43":268,"_13":68,"_39":40,"_41":269,"_30":54,"_149":-5,"_208":270,"_33":271},{"_48":257,"_38":258,"_36":259,"_142":168,"_46":47,"_45":260,"_144":54,"_43":261,"_13":68,"_39":40,"_41":262,"_30":54,"_149":-5,"_208":263,"_33":264},{"_48":249,"_38":250,"_36":251,"_142":168,"_46":47,"_45":252,"_144":31,"_43":253,"_13":68,"_39":40,"_41":254,"_30":54,"_149":-5,"_208":255,"_33":256},{"_48":241,"_38":242,"_36":243,"_142":168,"_46":47,"_45":244,"_144":54,"_43":245,"_13":68,"_39":40,"_41":246,"_30":54,"_149":-5,"_208":247,"_33":248},{"_48":233,"_38":234,"_36":235,"_142":168,"_46":47,"_45":236,"_144":31,"_43":237,"_13":68,"_39":40,"_41":238,"_30":54,"_149":-5,"_208":239,"_33":240},{"_48":225,"_38":226,"_36":227,"_142":168,"_46":47,"_45":228,"_144":31,"_43":229,"_13":68,"_39":40,"_41":230,"_30":54,"_149":-5,"_208":231,"_33":232},{"_48":217,"_38":218,"_36":219,"_142":168,"_46":47,"_45":220,"_144":31,"_43":221,"_13":68,"_39":40,"_41":222,"_30":54,"_149":-5,"_208":223,"_33":224},{"_48":159,"_38":160,"_36":161,"_142":-5,"_46":47,"_45":162,"_144":54,"_43":214,"_13":146,"_39":40,"_41":164,"_30":54,"_149":-5,"_208":215,"_33":216},{"_48":151,"_38":152,"_36":153,"_142":-5,"_46":47,"_45":154,"_144":54,"_43":211,"_13":156,"_39":40,"_41":157,"_30":54,"_149":-5,"_208":212,"_33":213},{"_48":140,"_38":141,"_36":63,"_142":-5,"_46":47,"_45":143,"_144":54,"_43":207,"_13":146,"_39":147,"_41":148,"_30":54,"_149":-5,"_208":209,"_33":210},["D",1783359324825],"children",[],[141],["D",1783355945330],[],[152],["D",1783348386548],[],[160],"29378822-e19e-42ba-8d2d-4b0658f81265","verification.ts","967d813e-7960-11f1-b2d7-1607ee4eb77e",42,["D",1783359117369],"// Source Verification Layer — Nexuses Core V3\n// Every incoming request must carry a SourceID.\n// The system confirms if the request originates from a trusted/registered platform.\n\nimport { initDB, registerSource, verifySource } from \"./db.ts\";\nimport type { VerificationResult } from \"./types.ts\";\n\n/**\n * Verifies that a SourceID is registered and active.\n * Returns a structured VerificationResult for M2M consumption.\n */\nexport async function checkSource(sourceId: string): Promise {\n await initDB();\n\n if (!sourceId || sourceId.trim().length === 0) {\n return {\n verified: false,\n source_id: sourceId || \"MISSING\",\n message: \"SourceID is required — request rejected\",\n };\n }\n\n const source = await verifySource(sourceId);\n if (!source) {\n return {\n verified: false,\n source_id: sourceId,\n message: `Verification Failed — SourceID \"${sourceId}\" is not registered. Use POST /register to register this source.`,\n };\n }\n\n return {\n verified: true,\n source_id: sourceId,\n name: source.name,\n message: `Source verified: ${source.name}`,\n };\n}\n\n/**\n * Registers a new trusted source.\n * Called via POST /register endpoint.\n */\nexport async function registerNewSource(\n sourceId: string,\n name: string,\n description: string,\n): Promise {\n await initDB();\n\n if (!sourceId || sourceId.trim().length < 3) {\n return {\n verified: false,\n source_id: sourceId || \"MISSING\",\n message: \"SourceID must be at least 3 characters\",\n };\n }\n\n const success = await registerSource(sourceId, name, description);\n if (!success) {\n return {\n verified: false,\n source_id: sourceId,\n message: \"Failed to register source — database error\",\n };\n }\n\n return {\n verified: true,\n source_id: sourceId,\n name,\n message: `Source \"${name}\" registered successfully with SourceID \"${sourceId}\"`,\n };\n}\n",[],[167,218],"13ca991a-7060-4d3b-92a7-1e1a29e5086a","types.ts","75094510-7960-11f1-acba-1607ee4eb77e",39,["D",1783359061231],"// Nexuses Core V3 — Shared Type Definitions\n// Central type registry for the entire autonomous AI OS.\n\nexport type TaskCategory =\n | \"coding\"\n | \"scraping\"\n | \"analysis\"\n | \"media\"\n | \"marketing\"\n | \"general\";\n\nexport type AgentName =\n | \"CoderAgent\"\n | \"ScraperAgent\"\n | \"AnalystAgent\"\n | \"MediaAgent\"\n | \"MarketingAgent\"\n | \"GeneralAgent\";\n\nexport interface AgentResult {\n agent: AgentName;\n category: TaskCategory;\n status: \"success\" | \"error\" | \"partial\";\n message: string;\n data: any;\n executionTimeMs: number;\n}\n\nexport interface StepResult {\n step: number;\n description: string;\n agent: AgentName;\n status: \"success\" | \"error\" | \"failed\";\n result?: any;\n error?: string;\n attempts: number;\n}\n\nexport interface CoTAnalysis {\n reasoning: string;\n category: TaskCategory;\n agent: AgentName;\n steps: string[];\n complexity: \"simple\" | \"complex\";\n}\n\nexport interface DispatchResponse {\n status: \"success\" | \"failure\";\n task_id: string;\n source_id: string;\n verified: boolean;\n reasoning: string;\n category: TaskCategory;\n agent: AgentName;\n steps: StepResult[];\n data: any;\n message: string;\n executionTimeMs: number;\n timestamp: string;\n}\n\nexport interface VerificationResult {\n verified: boolean;\n source_id: string;\n name?: string;\n message: string;\n}\n\nexport interface SourceRecord {\n source_id: string;\n name: string;\n description: string;\n status: string;\n created_at: string;\n}\n\nexport interface TaskRecord {\n id: string;\n source_id: string;\n task: string;\n agent: string;\n category: string;\n status: string;\n complexity: string;\n reasoning: string;\n data: string;\n result: string;\n created_at: string;\n updated_at: string;\n}\n\nexport interface LogEntry {\n id: string;\n level: string;\n component: string;\n message: string;\n created_at: string;\n}\n",[],[167,226],"1a55b9b5-7b72-4749-b995-63923e7d4c3f","taskProcessor.ts","f5204b54-7960-11f1-a85c-1607ee4eb77e",48,["D",1783359276137],"// Step-by-Step Task Processor — Nexuses Core V3\n// Breaks complex tasks into discrete steps, executes sequentially,\n// verifies each step, retries once on failure before logging error.\n\nimport { callLLM } from \"./llm.ts\";\nimport { logStep, logEvent } from \"./db.ts\";\nimport type { AgentResult, StepResult, AgentName } from \"./types.ts\";\n\n// Agent imports\nimport { execute as scraperExecute } from \"../agents/scraperAgent.ts\";\nimport { execute as coderExecute } from \"../agents/coderAgent.ts\";\nimport { execute as analystExecute } from \"../agents/analystAgent.ts\";\nimport { execute as mediaExecute } from \"../agents/mediaAgent.ts\";\nimport { execute as marketingExecute } from \"../agents/marketingAgent.ts\";\n\nconst AGENT_MAP: Record Promise> = {\n CoderAgent: coderExecute,\n ScraperAgent: scraperExecute,\n AnalystAgent: analystExecute,\n MediaAgent: mediaExecute,\n MarketingAgent: marketingExecute,\n GeneralAgent: generalAgentExecute,\n};\n\nasync function generalAgentExecute(task: string, data: any): Promise {\n const start = Date.now();\n const result = await callLLM(data.prompt || task, { maxTokens: 800 });\n return {\n agent: \"GeneralAgent\",\n category: \"general\",\n status: result.success ? \"success\" : \"error\",\n message: result.success ? \"Task processed\" : \"LLM unavailable\",\n data: { input: task, response: result.text || \"\" },\n executionTimeMs: Date.now() - start,\n };\n}\n\n/**\n * Execute a single step via the appropriate agent.\n * Includes automatic retry-once on failure.\n */\nasync function executeStepWithRetry(\n taskId: string,\n stepNum: number,\n description: string,\n agentName: AgentName,\n data: any,\n): Promise {\n const agentFn = AGENT_MAP[agentName] || AGENT_MAP.GeneralAgent;\n\n // Attempt 1\n let result = await agentFn(description, data);\n let attempts = 1;\n\n if (result.status === \"success\" || result.status === \"partial\") {\n await logStep(taskId, stepNum, description, agentName, \"success\", result.data, attempts);\n return {\n step: stepNum,\n description,\n agent: agentName,\n status: \"success\",\n result: result.data,\n attempts,\n };\n }\n\n // Retry once\n attempts = 2;\n await logEvent(\"warn\", \"TaskProcessor\", `Step ${stepNum} failed on attempt 1: ${result.message}. Retrying...`);\n\n // Adjust data for retry — simplify prompt\n const retryData = { ...data, prompt: description.slice(0, 500) };\n result = await agentFn(description, retryData);\n\n if (result.status === \"success\" || result.status === \"partial\") {\n await logStep(taskId, stepNum, description, agentName, \"success\", result.data, attempts);\n return {\n step: stepNum,\n description,\n agent: agentName,\n status: \"success\",\n result: result.data,\n attempts,\n };\n }\n\n // Both attempts failed\n await logStep(taskId, stepNum, description, agentName, \"failed\", result.data, attempts);\n await logEvent(\"error\", \"TaskProcessor\", `Step ${stepNum} failed after ${attempts} attempts: ${result.message}`);\n\n return {\n step: stepNum,\n description,\n agent: agentName,\n status: \"failed\",\n error: result.message,\n attempts,\n };\n}\n\n/**\n * Process a complex multi-step task.\n * Executes steps sequentially — if a step fails (after retry), logs error and continues.\n * Returns all step results.\n */\nexport async function processComplexTask(\n taskId: string,\n steps: string[],\n agentName: AgentName,\n data: any,\n): Promise<{ stepResults: StepResult[]; finalData: any; allSucceeded: boolean }> {\n const stepResults: StepResult[] = [];\n let accumulatedData: any = { ...data };\n let allSucceeded = true;\n\n for (let i = 0; i < steps.length; i++) {\n const stepDesc = steps[i];\n const stepNum = i + 1;\n\n console.log(`[TaskProcessor] Task ${taskId} — Step ${stepNum}/${steps.length}: ${stepDesc.slice(0, 80)}`);\n\n const stepResult = await executeStepWithRetry(\n taskId,\n stepNum,\n stepDesc,\n agentName,\n accumulatedData,\n );\n\n stepResults.push(stepResult);\n\n if (stepResult.status === \"success\") {\n // Merge step result into accumulated data for next step\n if (stepResult.result && typeof stepResult.result === \"object\") {\n accumulatedData = { ...accumulatedData, ...stepResult.result, previousStep: stepResult.result };\n }\n } else {\n allSucceeded = false;\n }\n }\n\n return {\n stepResults,\n finalData: accumulatedData,\n allSucceeded,\n };\n}\n\n/**\n * Execute a single-step task directly via the agent.\n */\nexport async function processSimpleTask(\n taskId: string,\n task: string,\n agentName: AgentName,\n data: any,\n): Promise<{ stepResults: StepResult[]; finalData: any; allSucceeded: boolean }> {\n const stepResult = await executeStepWithRetry(taskId, 1, task, agentName, data);\n return {\n stepResults: [stepResult],\n finalData: stepResult.result || {},\n allSucceeded: stepResult.status === \"success\",\n };\n}\n",[],[167,234],"dda61354-fbd7-4380-919b-73a29c250f87","rpc.ts","06060d00-7961-11f1-b570-1607ee4eb77e",50,["D",1783359304491],"// JSON-RPC 2.0 Protocol Handler — Nexuses Core V3\n// Universal M2M protocol for machine-to-machine communication.\n// Every response is clean, structured, and line-by-line parseable.\n\nimport { dispatchTask, getSystemStatus } from \"./dispatcher.ts\";\nimport { registerNewSource, checkSource } from \"./verification.ts\";\nimport { listSources } from \"./db.ts\";\n\nexport interface JSONRPCRequest {\n jsonrpc: \"2.0\";\n method: string;\n params?: any;\n id?: string | number;\n}\n\nexport interface JSONRPCResponse {\n jsonrpc: \"2.0\";\n result?: any;\n error?: { code: number; message: string; data?: any };\n id: string | number | null;\n}\n\nconst RPC_ERRORS = {\n PARSE_ERROR: { code: -32700, message: \"Parse error\" },\n INVALID_REQUEST: { code: -32600, message: \"Invalid Request\" },\n METHOD_NOT_FOUND: { code: -32601, message: \"Method not found\" },\n INVALID_PARAMS: { code: -32602, message: \"Invalid params\" },\n INTERNAL_ERROR: { code: -32603, message: \"Internal error\" },\n VERIFICATION_FAILED: { code: -32001, message: \"Source verification failed\" },\n TASK_FAILED: { code: -32002, message: \"Task execution failed\" },\n};\n\nfunction rpcError(id: string | number | null, error: { code: number; message: string; data?: any }): JSONRPCResponse {\n return { jsonrpc: \"2.0\", error, id };\n}\n\nfunction rpcResult(id: string | number, result: any): JSONRPCResponse {\n return { jsonrpc: \"2.0\", result, id };\n}\n\n/**\n * Process a JSON-RPC 2.0 request.\n * Methods: execute, register, discover, status, verify, sources\n */\nexport async function processRPC(req: JSONRPCRequest): Promise {\n if (req.jsonrpc !== \"2.0\" || !req.method) {\n return rpcError(req.id ?? null, RPC_ERRORS.INVALID_REQUEST);\n }\n\n const params = req.params || {};\n const id = req.id ?? null;\n\n switch (req.method) {\n case \"execute\": {\n const { task, data, source_id } = params;\n if (!task) {\n return rpcError(id, { ...RPC_ERRORS.INVALID_PARAMS, data: \"Missing 'task' field\" });\n }\n if (!source_id) {\n return rpcError(id, { ...RPC_ERRORS.INVALID_PARAMS, data: \"Missing 'source_id' field\" });\n }\n try {\n const result = await dispatchTask(task, data || {}, source_id);\n if (result.status === \"failure\" && !result.verified) {\n return rpcError(id, { ...RPC_ERRORS.VERIFICATION_FAILED, data: result });\n }\n return rpcResult(id as string | number, result);\n } catch (e: any) {\n return rpcError(id, { ...RPC_ERRORS.INTERNAL_ERROR, data: e.message });\n }\n }\n\n case \"register\": {\n const { source_id, name, description } = params;\n if (!source_id || !name) {\n return rpcError(id, { ...RPC_ERRORS.INVALID_PARAMS, data: \"Missing 'source_id' or 'name'\" });\n }\n const result = await registerNewSource(source_id, name, description || \"\");\n return rpcResult(id as string | number, result);\n }\n\n case \"discover\": {\n const status = await getSystemStatus();\n return rpcResult(id as string | number, {\n service: \"Nexuses Core V3\",\n version: \"3.0.0\",\n protocol: \"JSON-RPC 2.0\",\n agents: status.agents,\n endpoints: status.endpoints,\n m2mFormat: {\n request: \"POST /rpc with JSON-RPC 2.0 body\",\n methods: [\"execute\", \"register\", \"discover\", \"status\", \"verify\", \"sources\"],\n },\n });\n }\n\n case \"status\": {\n const status = await getSystemStatus();\n return rpcResult(id as string | number, status);\n }\n\n case \"verify\": {\n const { source_id } = params;\n if (!source_id) {\n return rpcError(id, { ...RPC_ERRORS.INVALID_PARAMS, data: \"Missing 'source_id'\" });\n }\n const result = await checkSource(source_id);\n return rpcResult(id as string | number, result);\n }\n\n case \"sources\": {\n const sources = await listSources();\n return rpcResult(id as string | number, { sources });\n }\n\n default:\n return rpcError(id, {\n ...RPC_ERRORS.METHOD_NOT_FOUND,\n data: `Unknown method: ${req.method}. Supported: execute, register, discover, status, verify, sources`,\n });\n }\n}\n",[],[167,242],"21acd9ae-dd3a-4c31-8483-2ee3c91bfd8f","llm.ts","81b72098-7960-11f1-8bac-1607ee4eb77e",40,["D",1783359082410],"// Free LLM Engine — Nexuses Core V3\n// Uses Pollinations.ai EXCLUSIVELY — zero API keys, zero tokens, zero cost.\n// Provides: text generation, Chain-of-Thought reasoning, task classification.\n\nimport type { CoTAnalysis, TaskCategory, AgentName } from \"./types.ts\";\n\nconst POLLINATIONS_POST = \"https://text.pollinations.ai/openai\";\nconst POLLINATIONS_GET = \"https://text.pollinations.ai\";\nconst POLLINATIONS_IMAGE = \"https://image.pollinations.ai/prompt\";\n\nexport interface LLMResult {\n success: boolean;\n text: string;\n model: string;\n error?: string;\n}\n\nexport interface LLMOptions {\n maxTokens?: number;\n temperature?: number;\n timeoutMs?: number;\n systemPrompt?: string;\n}\n\n/**\n * Core LLM call — Pollinations.ai only (free, no key).\n * Tries POST (OpenAI-compatible) first, falls back to GET.\n */\nexport async function callLLM(\n prompt: string,\n options?: LLMOptions,\n): Promise {\n const messages: { role: string; content: string }[] = [];\n if (options?.systemPrompt) {\n messages.push({ role: \"system\", content: options.systemPrompt });\n }\n messages.push({ role: \"user\", content: prompt });\n\n // Attempt 1: POST (OpenAI-compatible endpoint)\n try {\n const res = await fetch(POLLINATIONS_POST, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n model: \"openai\",\n messages,\n max_tokens: options?.maxTokens ?? 1000,\n temperature: options?.temperature ?? 0.7,\n }),\n signal: AbortSignal.timeout(options?.timeoutMs ?? 30000),\n });\n if (res.ok) {\n const json = await res.json();\n const text = json.choices?.[0]?.message?.content?.trim();\n if (text) return { success: true, text, model: \"pollinations-openai\" };\n }\n } catch { /* fall through to GET */ }\n\n // Attempt 2: GET (simple text endpoint)\n try {\n const fullPrompt = options?.systemPrompt\n ? `${options.systemPrompt}\\n\\n${prompt}`\n : prompt;\n const encoded = encodeURIComponent(fullPrompt.slice(0, 2000));\n const res = await fetch(`${POLLINATIONS_GET}/${encoded}`, {\n signal: AbortSignal.timeout(options?.timeoutMs ?? 30000),\n });\n if (res.ok) {\n const text = (await res.text()).trim();\n if (text) return { success: true, text, model: \"pollinations-get\" };\n }\n } catch { /* fall through to failure */ }\n\n return {\n success: false,\n text: \"\",\n model: \"none\",\n error: \"Pollinations.ai endpoints unreachable\",\n };\n}\n\n/**\n * Chain-of-Thought reasoning + autonomous task classification.\n * Sends the task to the LLM with a structured CoT prompt and expects\n * a JSON response containing reasoning, category, agent, steps, and complexity.\n */\nexport async function reasonWithCoT(\n task: string,\n data?: any,\n): Promise<{ success: boolean; analysis: CoTAnalysis; raw: string; error?: string }> {\n const systemPrompt = `You are the Nexuses Core V3 Dispatcher — an autonomous AI reasoning brain.\nAnalyze the incoming task using chain-of-thought reasoning, then classify and route it.\nRespond with ONLY a JSON object — no markdown fences, no text outside the JSON:\n{\n \"reasoning\": \"Step-by-step chain-of-thought analysis of what this task requires\",\n \"category\": \"coding | scraping | analysis | media | marketing | general\",\n \"agent\": \"CoderAgent | ScraperAgent | AnalystAgent | MediaAgent | MarketingAgent | GeneralAgent\",\n \"steps\": [\"Step 1: description\", \"Step 2: description\"],\n \"complexity\": \"simple | complex\"\n}\n\nClassification guide:\n- coding → CoderAgent: writing, debugging, explaining, refactoring code in Python/JS/HTML/TS\n- scraping → ScraperAgent: web scraping, data extraction, website cloning, URL fetching\n- analysis → AnalystAgent: data analysis, Excel/CSV, reports, document processing, file analysis\n- media → MediaAgent: image generation, video plans, visual design, logos, art\n- marketing → MarketingAgent: content writing, blogging, social media, SEO, cross-platform formatting\n- general → GeneralAgent: questions, chat, explanations, reasoning, anything uncategorized\n\nIf a task spans multiple categories, pick the primary one and list multiple steps.`;\n\n const userPrompt = `Task: \"${task}\"\\nAdditional data: ${\n JSON.stringify(data || {}).slice(0, 800)\n }`;\n\n const result = await callLLM(userPrompt, {\n maxTokens: 800,\n temperature: 0.3,\n systemPrompt,\n timeoutMs: 25000,\n });\n\n if (!result.success) {\n // Fallback to keyword-based classification\n const fallback = keywordClassify(task);\n return {\n success: false,\n analysis: fallback,\n raw: \"\",\n error: result.error,\n };\n }\n\n // Parse JSON from LLM response\n try {\n let jsonStr = result.text.trim();\n // Strip markdown code fences if present\n jsonStr = jsonStr.replace(/```json\\s*/gi, \"\").replace(/```\\s*/g, \"\");\n const match = jsonStr.match(/\\{[\\s\\S]*\\}/);\n if (match) jsonStr = match[0];\n const parsed = JSON.parse(jsonStr);\n\n const validCategories: TaskCategory[] = [\n \"coding\", \"scraping\", \"analysis\", \"media\", \"marketing\", \"general\",\n ];\n const validAgents: AgentName[] = [\n \"CoderAgent\", \"ScraperAgent\", \"AnalystAgent\",\n \"MediaAgent\", \"MarketingAgent\", \"GeneralAgent\",\n ];\n\n const category = validCategories.includes(parsed.category)\n ? parsed.category\n : \"general\";\n const agent = validAgents.includes(parsed.agent)\n ? parsed.agent\n : \"GeneralAgent\";\n\n return {\n success: true,\n analysis: {\n reasoning: parsed.reasoning || \"No reasoning provided\",\n category,\n agent,\n steps: Array.isArray(parsed.steps) && parsed.steps.length > 0\n ? parsed.steps\n : [task],\n complexity: parsed.complexity === \"complex\" ? \"complex\" : \"simple\",\n },\n raw: result.text,\n };\n } catch {\n // JSON parse failed — use keyword fallback\n const fallback = keywordClassify(task);\n return {\n success: true,\n analysis: fallback,\n raw: result.text,\n };\n }\n}\n\n/**\n * Keyword-based fallback classifier (used when LLM is unavailable\n * or returns unparseable JSON).\n */\nfunction keywordClassify(task: string): CoTAnalysis {\n const lower = task.toLowerCase();\n const rules: { keywords: string[]; category: TaskCategory; agent: AgentName }[] = [\n {\n keywords: [\"code\", \"debug\", \"bug\", \"fix\", \"program\", \"function\", \"script\", \"python\", \"javascript\", \"html\", \"css\", \"typescript\", \"compile\", \"syntax\", \"refactor\"],\n category: \"coding\",\n agent: \"CoderAgent\",\n },\n {\n keywords: [\"scrape\", \"clone\", \"extract\", \"website\", \"url\", \"crawl\", \"fetch page\", \"web data\"],\n category: \"scraping\",\n agent: \"ScraperAgent\",\n },\n {\n keywords: [\"analyze\", \"analysis\", \"data\", \"excel\", \"csv\", \"report\", \"spreadsheet\", \"pdf\", \"document\", \"file\"],\n category: \"analysis\",\n agent: \"AnalystAgent\",\n },\n {\n keywords: [\"image\", \"generate image\", \"picture\", \"draw\", \"art\", \"logo\", \"video\", \"visual\", \"design\"],\n category: \"media\",\n agent: \"MediaAgent\",\n },\n {\n keywords: [\"write\", \"blog\", \"content\", \"social\", \"post\", \"seo\", \"marketing\", \"article\", \"tweet\", \"copy\"],\n category: \"marketing\",\n agent: \"MarketingAgent\",\n },\n ];\n\n for (const rule of rules) {\n if (rule.keywords.some((k) => lower.includes(k))) {\n return {\n reasoning: `Keyword fallback: matched \"${rule.category}\" category`,\n category: rule.category,\n agent: rule.agent,\n steps: [task],\n complexity: \"simple\",\n };\n }\n }\n\n return {\n reasoning: \"No keywords matched — defaulting to general\",\n category: \"general\",\n agent: \"GeneralAgent\",\n steps: [task],\n complexity: \"simple\",\n };\n}\n\n/**\n * Generate an image URL via Pollinations.ai image endpoint (free, no key).\n * Returns a direct URL to the generated image.\n */\nexport function generateImageURL(\n prompt: string,\n options?: { width?: number; height?: number; seed?: number; model?: string },\n): string {\n const params = new URLSearchParams();\n params.set(\"width\", String(options?.width ?? 1024));\n params.set(\"height\", String(options?.height ?? 1024));\n params.set(\"nologo\", \"true\");\n if (options?.seed) params.set(\"seed\", String(options.seed));\n if (options?.model) params.set(\"model\", options.model);\n const encoded = encodeURIComponent(prompt.slice(0, 500));\n return `${POLLINATIONS_IMAGE}/${encoded}?${params.toString()}`;\n}\n",[],[167,250],"54835713-0fe9-4e15-94aa-007a432cd814","dispatcher.ts","fc1c35c6-7960-11f1-acba-1607ee4eb77e",49,["D",1783359287839],"// Central Dispatcher — Nexuses Core V3\n// The Autonomous Reasoning Brain.\n// Flow: Verify Source → CoT Reasoning → Classify → Route to Agent → Execute Steps → Report\n\nimport { reasonWithCoT } from \"./llm.ts\";\nimport { checkSource } from \"./verification.ts\";\nimport {\n getRecentTasks,\n getStats,\n getTask,\n initDB,\n insertTask,\n logEvent,\n remember,\n recall,\n updateTaskStatus,\n} from \"./db.ts\";\nimport { processComplexTask, processSimpleTask } from \"./taskProcessor.ts\";\nimport type {\n AgentName,\n DispatchResponse,\n StepResult,\n} from \"./types.ts\";\n\nfunction generateTaskId(): string {\n return `nxs3_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;\n}\n\n/**\n * Main dispatch entry point.\n * Called by main.ts HTTP handler for every incoming task.\n */\nexport async function dispatchTask(\n task: string,\n data: any,\n sourceId: string,\n): Promise {\n const start = Date.now();\n const taskId = generateTaskId();\n\n await initDB();\n\n // PHASE 1: SOURCE VERIFICATION\n const verification = await checkSource(sourceId);\n if (!verification.verified) {\n await logEvent(\"warn\", \"Dispatcher\", `Source verification failed: ${sourceId}`);\n return {\n status: \"failure\",\n task_id: taskId,\n source_id: sourceId,\n verified: false,\n reasoning: \"Source verification failed — request rejected\",\n category: \"general\",\n agent: \"GeneralAgent\",\n steps: [],\n data: {},\n message: verification.message,\n executionTimeMs: Date.now() - start,\n timestamp: new Date().toISOString(),\n };\n }\n\n // PHASE 2: CHAIN-OF-THOUGHT REASONING + CLASSIFICATION\n console.log(`[Dispatcher] Task ${taskId} — Running CoT reasoning...`);\n const cotResult = await reasonWithCoT(task, data);\n const analysis = cotResult.analysis;\n\n await logEvent(\"info\", \"Dispatcher\",\n `Task ${taskId} classified: ${analysis.category} → ${analysis.agent} | Complexity: ${analysis.complexity} | Steps: ${analysis.steps.length}`,\n );\n\n // Record task in DB\n await insertTask(\n taskId,\n sourceId,\n task,\n analysis.agent,\n analysis.category,\n \"processing\",\n analysis.complexity,\n analysis.reasoning,\n data,\n null,\n );\n\n // PHASE 3: EXECUTE — route to agent via task processor\n let stepResults: StepResult[];\n let finalData: any;\n let allSucceeded: boolean;\n\n if (analysis.complexity === \"complex\" && analysis.steps.length > 1) {\n console.log(`[Dispatcher] Task ${taskId} — Complex task: ${analysis.steps.length} steps`);\n const result = await processComplexTask(taskId, analysis.steps, analysis.agent, data);\n stepResults = result.stepResults;\n finalData = result.finalData;\n allSucceeded = result.allSucceeded;\n } else {\n console.log(`[Dispatcher] Task ${taskId} — Simple task: direct execution`);\n const result = await processSimpleTask(taskId, task, analysis.agent, data);\n stepResults = result.stepResults;\n finalData = result.finalData;\n allSucceeded = result.allSucceeded;\n }\n\n // PHASE 4: REPORT — build structured response\n const isSuccess = allSucceeded || stepResults.some((s) => s.status === \"success\");\n const response: DispatchResponse = {\n status: isSuccess ? \"success\" : \"failure\",\n task_id: taskId,\n source_id: sourceId,\n verified: true,\n reasoning: analysis.reasoning,\n category: analysis.category,\n agent: analysis.agent,\n steps: stepResults,\n data: finalData,\n message: isSuccess\n ? `Task completed by ${analysis.agent} (${stepResults.length} step${stepResults.length > 1 ? \"s\" : \"\"})`\n : `Task failed — all steps unsuccessful`,\n executionTimeMs: Date.now() - start,\n timestamp: new Date().toISOString(),\n };\n\n // Update task status in DB\n await updateTaskStatus(taskId, isSuccess ? \"completed\" : \"failed\", response);\n\n // Save to memory for future reference\n const memoryKey = `task:${analysis.category}:${analysis.agent}`;\n const priorMemory = await recall(memoryKey);\n await remember(memoryKey, {\n lastTask: task,\n lastAgent: analysis.agent,\n lastCategory: analysis.category,\n lastStatus: response.status,\n timestamp: new Date().toISOString(),\n ...(priorMemory ? { previous: priorMemory } : {}),\n });\n\n await logEvent(\"info\", \"Dispatcher\",\n `Task ${taskId} — ${response.status.toUpperCase()} | Agent: ${analysis.agent} | Time: ${response.executionTimeMs}ms`,\n );\n\n return response;\n}\n\n// ── Status & Discovery Endpoints ──\n\nexport async function getSystemStatus() {\n await initDB();\n const stats = await getStats();\n\n return {\n status: \"ONLINE\",\n service: \"Nexuses Core V3 — Autonomous AI Operating System\",\n version: \"3.0.0\",\n architecture: \"Open-Architecture (Zero API Keys)\",\n llm: \"Pollinations.ai (free, no token required)\",\n imageGeneration: \"Pollinations.ai Image API (free, no token required)\",\n persistence: \"SQLite (project-scoped) + Blob VFS\",\n stats: {\n totalTasks: stats.totalTasks,\n completedTasks: stats.completedTasks,\n failedTasks: stats.failedTasks,\n successRate: stats.totalTasks > 0\n ? `${Math.round((stats.completedTasks / stats.totalTasks) * 100)}%`\n : \"N/A\",\n registeredSources: stats.registeredSources,\n totalLogs: stats.totalLogs,\n },\n agents: [\n { name: \"CoderAgent\", category: \"coding\", capabilities: [\"write\", \"debug\", \"fix\", \"explain\", \"VFS read/write\"] },\n { name: \"ScraperAgent\", category: \"scraping\", capabilities: [\"scrape URL\", \"clone website\", \"web search (DuckDuckGo)\"] },\n { name: \"AnalystAgent\", category: \"analysis\", capabilities: [\"data analysis\", \"CSV generation\", \"report writing\", \"invoice generation\"] },\n { name: \"MediaAgent\", category: \"media\", capabilities: [\"image generation\", \"logo design\", \"video production plans\"] },\n { name: \"MarketingAgent\", category: \"marketing\", capabilities: [\"blog writing\", \"social media posts\", \"SEO\", \"cross-platform formatting\"] },\n { name: \"GeneralAgent\", category: \"general\", capabilities: [\"general Q&A\", \"reasoning\", \"fallback handler\"] },\n ],\n endpoints: {\n \"POST /task\": \"Submit a task (requires source_id)\",\n \"POST /register\": \"Register a new source\",\n \"POST /rpc\": \"JSON-RPC 2.0 endpoint\",\n \"GET /status\": \"System status dashboard\",\n \"GET /agents\": \"Agent manifest\",\n \"GET /task/:id\": \"Get task result by ID\",\n \"GET /history\": \"Recent task history\",\n \"GET /sources\": \"List registered sources\",\n \"GET /logs\": \"Recent system logs\",\n },\n m2mProtocol: \"JSON-RPC 2.0 / REST\",\n zeroConstraint: \"No API keys required — runs on 100% free open-source endpoints\",\n };\n}\n\nexport async function getTaskResult(taskId: string) {\n return getTask(taskId);\n}\n\nexport async function getHistory(limit = 20) {\n return getRecentTasks(limit);\n}\n",[],[167,258],"5070556f-bf51-47f7-8e56-2a18db984249","87d19a9e-7960-11f1-89b4-1607ee4eb77e",41,["D",1783359092751],"// SQLite Persistence Layer — Nexuses Core V3\n// Tables: tasks, steps, sources, memory, logs\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\nimport type { TaskRecord, SourceRecord, LogEntry } from \"./types.ts\";\n\nconst SCHEMA_SQL = [\n `CREATE TABLE IF NOT EXISTS nex_tasks (\n id TEXT PRIMARY KEY,\n source_id TEXT,\n task TEXT NOT NULL,\n agent TEXT,\n category TEXT,\n status TEXT NOT NULL,\n complexity TEXT,\n reasoning TEXT,\n data TEXT,\n result TEXT,\n created_at TEXT NOT NULL,\n updated_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS nex_steps (\n id TEXT PRIMARY KEY,\n task_id TEXT NOT NULL,\n step INTEGER NOT NULL,\n description TEXT,\n agent TEXT,\n status TEXT,\n result TEXT,\n attempts INTEGER DEFAULT 1,\n created_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS nex_sources (\n source_id TEXT PRIMARY KEY,\n name TEXT NOT NULL,\n description TEXT,\n status TEXT DEFAULT 'active',\n created_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS nex_memory (\n key TEXT PRIMARY KEY,\n value TEXT NOT NULL,\n created_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS nex_logs (\n id TEXT PRIMARY KEY,\n level TEXT,\n component TEXT,\n message TEXT,\n created_at TEXT NOT NULL\n )`,\n];\n\nlet initialized = false;\n\nexport async function initDB() {\n if (initialized) return;\n await sqlite.batch(SCHEMA_SQL.map((sql) => ({ sql })));\n initialized = true;\n\n // Auto-register the internal trusted source\n await registerSource(\n \"nexuses-internal\",\n \"Nexuses Internal\",\n \"Built-in system source for self-testing\",\n );\n}\n\n// ── Task CRUD ──\n\nexport async function insertTask(\n id: string,\n sourceId: string,\n task: string,\n agent: string,\n category: string,\n status: string,\n complexity: string,\n reasoning: string,\n data: any,\n result: any,\n) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql: `INSERT INTO nex_tasks\n (id, source_id, task, agent, category, status, complexity, reasoning, data, result, created_at, updated_at)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n id, sourceId, task, agent, category, status, complexity,\n reasoning, JSON.stringify(data), JSON.stringify(result), now, now,\n ],\n });\n}\n\nexport async function updateTaskStatus(id: string, status: string, result: any) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql: `UPDATE nex_tasks SET status = ?, result = ?, updated_at = ? WHERE id = ?`,\n args: [status, JSON.stringify(result), now, id],\n });\n}\n\nexport async function getTask(id: string): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM nex_tasks WHERE id = ?`,\n args: [id],\n });\n return (res.rows[0] as TaskRecord) || null;\n}\n\nexport async function getRecentTasks(limit = 20): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM nex_tasks ORDER BY created_at DESC LIMIT ?`,\n args: [limit],\n });\n return res.rows as TaskRecord[];\n}\n\nexport async function getTaskCount(): Promise {\n const res = await sqlite.execute(`SELECT COUNT(*) as count FROM nex_tasks`);\n return (res.rows[0] as any)?.count || 0;\n}\n\n// ── Step Logging ──\n\nexport async function logStep(\n taskId: string,\n step: number,\n description: string,\n agent: string,\n status: string,\n result: any,\n attempts: number,\n) {\n const id = `step_${taskId}_${step}`;\n const now = new Date().toISOString();\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO nex_steps\n (id, task_id, step, description, agent, status, result, attempts, created_at)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n id, taskId, step, description, agent, status,\n JSON.stringify(result), attempts, now,\n ],\n });\n}\n\nexport async function getStepHistory(taskId: string) {\n const res = await sqlite.execute({\n sql: `SELECT * FROM nex_steps WHERE task_id = ? ORDER BY step ASC`,\n args: [taskId],\n });\n return res.rows;\n}\n\n// ── Source Registration ──\n\nexport async function registerSource(\n sourceId: string,\n name: string,\n description: string,\n): Promise {\n const now = new Date().toISOString();\n try {\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO nex_sources\n (source_id, name, description, status, created_at)\n VALUES (?, ?, ?, 'active', ?)`,\n args: [sourceId, name, description, now],\n });\n return true;\n } catch {\n return false;\n }\n}\n\nexport async function verifySource(sourceId: string): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM nex_sources WHERE source_id = ? AND status = 'active'`,\n args: [sourceId],\n });\n return (res.rows[0] as SourceRecord) || null;\n}\n\nexport async function listSources(): Promise {\n const res = await sqlite.execute(`SELECT * FROM nex_sources ORDER BY created_at DESC`);\n return res.rows as SourceRecord[];\n}\n\nexport async function removeSource(sourceId: string): Promise {\n try {\n await sqlite.execute({\n sql: `DELETE FROM nex_sources WHERE source_id = ?`,\n args: [sourceId],\n });\n return true;\n } catch {\n return false;\n }\n}\n\n// ── Memory ──\n\nexport async function remember(key: string, value: any) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql: `INSERT OR REPLACE INTO nex_memory (key, value, created_at) VALUES (?, ?, ?)`,\n args: [key, JSON.stringify(value), now],\n });\n}\n\nexport async function recall(key: string): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM nex_memory WHERE key = ?`,\n args: [key],\n });\n const row = res.rows[0] as any;\n return row ? JSON.parse(row.value) : null;\n}\n\n// ── System Logging ──\n\nexport async function logEvent(\n level: \"info\" | \"warn\" | \"error\",\n component: string,\n message: string,\n) {\n const id = `log_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;\n const now = new Date().toISOString();\n await sqlite.execute({\n sql: `INSERT INTO nex_logs (id, level, component, message, created_at) VALUES (?, ?, ?, ?, ?)`,\n args: [id, level, component, message, now],\n });\n}\n\nexport async function getRecentLogs(limit = 50): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM nex_logs ORDER BY created_at DESC LIMIT ?`,\n args: [limit],\n });\n return res.rows as LogEntry[];\n}\n\n// ── Stats ──\n\nexport async function getStats() {\n const taskCount = await getTaskCount();\n const sources = await listSources();\n const successRes = await sqlite.execute(\n `SELECT COUNT(*) as count FROM nex_tasks WHERE status = 'completed'`,\n );\n const failRes = await sqlite.execute(\n `SELECT COUNT(*) as count FROM nex_tasks WHERE status = 'failed'`,\n );\n const logRes = await sqlite.execute(\n `SELECT COUNT(*) as count FROM nex_logs`,\n );\n return {\n totalTasks: taskCount,\n completedTasks: (successRes.rows[0] as any)?.count || 0,\n failedTasks: (failRes.rows[0] as any)?.count || 0,\n registeredSources: sources.length,\n totalLogs: (logRes.rows[0] as any)?.count || 0,\n };\n}\n",[],[167,152],["D",1783359042499],[197,198,199,200,201,202,203],[167],"df0030fc-29ab-46c6-87f6-471d4d6058a5","scraperAgent.ts","a79a834a-7960-11f1-84c2-1607ee4eb77e",43,["D",1783359146056],"// ScraperAgent — Nexuses Core V3\n// Browser-less web scraping: fetch + HTML parsing, DuckDuckGo search.\n// No Playwright, no browser dependencies — pure fetch-based extraction.\n\nimport { callLLM } from \"../lib/llm.ts\";\nimport type { AgentResult } from \"../lib/types.ts\";\n\n/**\n * Extract text content from HTML using regex (no external parser needed).\n * Lightweight Cheerio-free extraction.\n */\nfunction extractFromHTML(html: string) {\n // Title\n const titleMatch = html.match(/]*>(.*?)<\\/title>/is);\n const title = titleMatch ? titleMatch[1].trim() : \"Untitled\";\n\n // Meta description\n const metaMatch = html.match(/]+name=[\"']description[\"'][^>]+content=[\"']([^\"']*)/is);\n const description = metaMatch ? metaMatch[1].trim() : \"\";\n\n // Remove scripts and styles\n const clean = html\n .replace(//gi, \"\")\n .replace(//gi, \"\")\n .replace(//gi, \"\")\n .replace(//gi, \"\");\n\n // Extract text (strip all tags)\n const textContent = clean\n .replace(/<[^>]+>/g, \" \")\n .replace(/ /g, \" \")\n .replace(/&/g, \"&\")\n .replace(/</g, \"<\")\n .replace(/>/g, \">\")\n .replace(/"/g, '\"')\n .replace(/'/g, \"'\")\n .replace(/\\s+/g, \" \")\n .trim()\n .slice(0, 5000);\n\n // Extract links\n const linkRegex = /]+href=[\"']([^\"'#]+)[\"'][^>]*>(.*?)<\\/a>/gis;\n const links: { url: string; text: string }[] = [];\n let match;\n let count = 0;\n while ((match = linkRegex.exec(html)) !== null && count < 30) {\n const url = match[1].trim();\n const linkText = match[2].replace(/<[^>]+>/g, \"\").trim();\n if (url.startsWith(\"http\") && linkText.length > 0) {\n links.push({ url, text: linkText });\n count++;\n }\n }\n\n // Extract images\n const imgRegex = /]+src=[\"']([^\"']+)[\"']/gi;\n const images: string[] = [];\n let imgMatch;\n let imgCount = 0;\n while ((imgMatch = imgRegex.exec(html)) !== null && imgCount < 10) {\n const src = imgMatch[1].trim();\n if (src.startsWith(\"http\")) {\n images.push(src);\n imgCount++;\n }\n }\n\n // Extract headings\n const headings: string[] = [];\n const hRegex = /]*>(.*?)<\\/h[1-3]>/gis;\n let hMatch;\n let hCount = 0;\n while ((hMatch = hRegex.exec(clean)) !== null && hCount < 15) {\n const hText = hMatch[1].replace(/<[^>]+>/g, \"\").trim();\n if (hText.length > 2) {\n headings.push(hText);\n hCount++;\n }\n }\n\n return { title, description, textContent, links, images, headings };\n}\n\nasync function scrapeURL(url: string) {\n const res = await fetch(url, {\n headers: {\n \"User-Agent\": \"Mozilla/5.0 (compatible; NexusesCore/3.0; +https://val.town)\",\n \"Accept\": \"text/html,application/xhtml+xml\",\n },\n signal: AbortSignal.timeout(15000),\n });\n\n if (!res.ok) {\n throw new Error(`HTTP ${res.status} from ${url}`);\n }\n\n const html = await res.text();\n return extractFromHTML(html);\n}\n\nasync function searchWeb(query: string) {\n // DuckDuckGo Instant Answer API (free, no key)\n const ddgUrl = `https://api.duckduckgo.com/?q=${\n encodeURIComponent(query)\n }&format=json&no_html=1&skip_disambig=1`;\n\n const res = await fetch(ddgUrl, {\n signal: AbortSignal.timeout(10000),\n });\n\n if (!res.ok) {\n throw new Error(`DuckDuckGo HTTP ${res.status}`);\n }\n\n const data = await res.json();\n const results: any = {\n query,\n abstract: data.Abstract || \"\",\n abstractSource: data.AbstractSource || \"\",\n abstractURL: data.AbstractURL || \"\",\n answer: data.Answer || \"\",\n definition: data.Definition || \"\",\n relatedTopics: [] as any[],\n };\n\n // Extract related topics\n if (data.RelatedTopics && Array.isArray(data.RelatedTopics)) {\n for (const topic of data.RelatedTopics.slice(0, 10)) {\n if (topic.Text) {\n results.relatedTopics.push({\n text: topic.Text,\n url: topic.FirstURL || \"\",\n });\n } else if (topic.Topics && Array.isArray(topic.Topics)) {\n for (const sub of topic.Topics.slice(0, 3)) {\n if (sub.Text) {\n results.relatedTopics.push({\n text: sub.Text,\n url: sub.FirstURL || \"\",\n });\n }\n }\n }\n }\n }\n\n return results;\n}\n\nexport async function execute(task: string, data: any): Promise {\n const start = Date.now();\n const action = data.action || \"scrape\";\n const url = data.url || \"\";\n const query = data.query || data.q || task;\n\n try {\n // Action: scrape a specific URL\n if (action === \"scrape\" || action === \"clone\" || url) {\n if (!url) {\n return {\n agent: \"ScraperAgent\",\n category: \"scraping\",\n status: \"error\",\n message: \"No URL provided for scraping\",\n data: {},\n executionTimeMs: Date.now() - start,\n };\n }\n\n const scraped = await scrapeURL(url);\n\n if (action === \"clone\") {\n // Use LLM to generate a clean HTML clone based on scraped content\n const llmResult = await callLLM(\n `Create a clean, modern HTML page based on this scraped content:\\n` +\n `Title: ${scraped.title}\\n` +\n `Headings: ${scraped.headings.join(\", \")}\\n` +\n `Content: ${scraped.textContent.slice(0, 1500)}\\n\\n` +\n `Return only the HTML code, no explanation.`,\n { maxTokens: 1500, temperature: 0.5 },\n );\n\n return {\n agent: \"ScraperAgent\",\n category: \"scraping\",\n status: llmResult.success ? \"success\" : \"partial\",\n message: `Website cloned: ${scraped.title}`,\n data: {\n original: { url, title: scraped.title, description: scraped.description },\n clone: llmResult.text || \"Clone generation failed\",\n links: scraped.links,\n images: scraped.images,\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n return {\n agent: \"ScraperAgent\",\n category: \"scraping\",\n status: \"success\",\n message: `Scraped: ${scraped.title}`,\n data: scraped,\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Action: web search\n if (action === \"search\" || action === \"research\") {\n const searchResults = await searchWeb(query);\n\n // Use LLM to summarize search results\n const llmResult = await callLLM(\n `Summarize these search results for query \"${query}\":\\n` +\n `Abstract: ${searchResults.abstract}\\n` +\n `Answer: ${searchResults.answer}\\n` +\n `Related: ${searchResults.relatedTopics.map((t: any) => t.text).join(\"; \").slice(0, 1000)}\\n\\n` +\n `Provide a concise summary with key findings.`,\n { maxTokens: 500, temperature: 0.4 },\n );\n\n return {\n agent: \"ScraperAgent\",\n category: \"scraping\",\n status: \"success\",\n message: `Search completed for: \"${query}\"`,\n data: {\n search: searchResults,\n summary: llmResult.text || \"Summary unavailable\",\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Default: try to extract URL from task text\n const urlMatch = task.match(/https?:\\/\\/[^\\s]+/);\n if (urlMatch) {\n const scraped = await scrapeURL(urlMatch[0]);\n return {\n agent: \"ScraperAgent\",\n category: \"scraping\",\n status: \"success\",\n message: `Scraped: ${scraped.title}`,\n data: scraped,\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Fallback: treat as search query\n const searchResults = await searchWeb(query);\n return {\n agent: \"ScraperAgent\",\n category: \"scraping\",\n status: \"success\",\n message: `Search completed for: \"${query}\"`,\n data: searchResults,\n executionTimeMs: Date.now() - start,\n };\n } catch (e: any) {\n return {\n agent: \"ScraperAgent\",\n category: \"scraping\",\n status: \"error\",\n message: `Scraping failed: ${e.message}`,\n data: { error: e.message },\n executionTimeMs: Date.now() - start,\n };\n }\n}\n",[],[174,276],"52018289-2c9b-43fc-b0a7-c5919564782e","mediaAgent.ts","c45dd16c-7960-11f1-a65f-1607ee4eb77e",46,["D",1783359194339],"// MediaAgent — Nexuses Core V3\n// Image generation via Pollinations.ai (free, no API key).\n// Video production plans via LLM.\n\nimport { callLLM, generateImageURL } from \"../lib/llm.ts\";\nimport type { AgentResult } from \"../lib/types.ts\";\n\nexport async function execute(task: string, data: any): Promise {\n const start = Date.now();\n const action = data.action || \"image\";\n const prompt = data.prompt || task;\n const width = data.width || 1024;\n const height = data.height || 1024;\n const seed = data.seed || Math.floor(Math.random() * 1000000);\n\n try {\n if (action === \"image\" || action === \"generate\") {\n // Enhance prompt with LLM for better image generation\n const enhanceResult = await callLLM(\n `Enhance this image generation prompt with visual details (style, lighting, composition, mood).\\n` +\n `Original: \"${prompt}\"\\nReturn ONLY the enhanced prompt, nothing else.`,\n { maxTokens: 200, temperature: 0.7 },\n );\n\n const finalPrompt = enhanceResult.success ? enhanceResult.text : prompt;\n const imageURL = generateImageURL(finalPrompt, { width, height, seed });\n\n return {\n agent: \"MediaAgent\",\n category: \"media\",\n status: \"success\",\n message: `Image generated for: ${prompt.slice(0, 80)}`,\n data: {\n action: \"image\",\n prompt: finalPrompt,\n originalPrompt: prompt,\n imageURL,\n width,\n height,\n seed,\n note: \"URL returns the generated image directly — use it in tags or download\",\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n if (action === \"video\" || action === \"video-plan\") {\n // Generate a video production plan (text-based, no actual video rendering)\n const llmResult = await callLLM(\n `Create a detailed video production plan for: ${prompt}\\n` +\n `Include:\\n` +\n `- Concept summary\\n` +\n `- Scene-by-scene breakdown (at least 5 scenes)\\n` +\n `- Visual style and color palette\\n` +\n `- Audio/music suggestions\\n` +\n `- Estimated duration per scene\\n` +\n `- Technical notes (aspect ratio, resolution, transitions)`,\n { maxTokens: 800, temperature: 0.6 },\n );\n\n return {\n agent: \"MediaAgent\",\n category: \"media\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"Video production plan generated\" : \"Video plan failed — LLM unavailable\",\n data: {\n action: \"video-plan\",\n prompt,\n plan: llmResult.text || \"\",\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n if (action === \"logo\") {\n // Logo generation via Pollinations\n const logoPrompt = `minimalist professional logo design, ${prompt}, vector style, clean, modern, centered, simple colors`;\n const logoURL = generateImageURL(logoPrompt, {\n width: 512,\n height: 512,\n seed,\n });\n\n return {\n agent: \"MediaAgent\",\n category: \"media\",\n status: \"success\",\n message: `Logo generated for: ${prompt.slice(0, 60)}`,\n data: {\n action: \"logo\",\n prompt: logoPrompt,\n logoURL,\n size: \"512x512\",\n seed,\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Default: treat as image generation\n const imageURL = generateImageURL(prompt, { width, height, seed });\n return {\n agent: \"MediaAgent\",\n category: \"media\",\n status: \"success\",\n message: `Image generated for: ${prompt.slice(0, 80)}`,\n data: {\n action: \"image\",\n prompt,\n imageURL,\n width,\n height,\n seed,\n },\n executionTimeMs: Date.now() - start,\n };\n } catch (e: any) {\n return {\n agent: \"MediaAgent\",\n category: \"media\",\n status: \"error\",\n message: `Media error: ${e.message}`,\n data: { error: e.message },\n executionTimeMs: Date.now() - start,\n };\n }\n}\n",[],[174,284],"105f8c56-3b2c-4cdf-9e07-cf79b1ccfc01","marketingAgent.ts","cd8fc754-7960-11f1-84c2-1607ee4eb77e",47,["D",1783359209763],"// MarketingAgent — Nexuses Core V3\n// Content creation, blogging, social media, SEO, cross-platform formatting.\n\nimport { callLLM } from \"../lib/llm.ts\";\nimport type { AgentResult } from \"../lib/types.ts\";\n\n/**\n * Platform-specific formatting rules.\n */\nconst PLATFORM_RULES: Record = {\n twitter: { maxLength: 280, hashtags: true, tone: \"punchy, conversational\" },\n linkedin: { maxLength: 3000, hashtags: true, tone: \"professional, insightful\" },\n facebook: { maxLength: 5000, hashtags: true, tone: \"friendly, engaging\" },\n instagram: { maxLength: 2200, hashtags: true, tone: \"visual, aspirational\" },\n blog: { maxLength: 10000, hashtags: false, tone: \"informative, structured\" },\n email: { maxLength: 5000, hashtags: false, tone: \"personal, direct\" },\n};\n\nexport async function execute(task: string, data: any): Promise {\n const start = Date.now();\n const action = data.action || \"write\";\n const topic = data.topic || data.prompt || task;\n const platform = (data.platform || \"\").toLowerCase();\n const audience = data.audience || \"general audience\";\n const tone = data.tone || \"professional\";\n\n try {\n if (action === \"blog\") {\n const llmResult = await callLLM(\n `Write a comprehensive blog post about: ${topic}\\n` +\n `Target audience: ${audience}\\nTone: ${tone}\\n\\n` +\n `Format:\\n# [Title]\\n## Introduction\\n[2-3 paragraphs]\\n` +\n `## [Section 1]\\n## [Section 2]\\n## [Section 3]\\n` +\n `## Conclusion\\n\\nInclude engaging headings and actionable insights.`,\n { maxTokens: 1500, temperature: 0.7 },\n );\n\n return {\n agent: \"MarketingAgent\",\n category: \"marketing\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"Blog post generated\" : \"Blog generation failed — LLM unavailable\",\n data: {\n action: \"blog\",\n topic,\n content: llmResult.text || \"\",\n wordCount: llmResult.text?.split(/\\s+/).length || 0,\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n if (action === \"social\" || platform) {\n const rules = PLATFORM_RULES[platform] || PLATFORM_RULES[\"twitter\"];\n\n const llmResult = await callLLM(\n `Write a ${platform || \"social media\"} post about: ${topic}\\n` +\n `Tone: ${rules.tone}\\n` +\n `Maximum ${rules.maxLength} characters.\\n` +\n `${rules.hashtags ? \"Include 3-5 relevant hashtags.\" : \"No hashtags.\"}\\n` +\n `Return ONLY the post text.`,\n { maxTokens: 400, temperature: 0.8 },\n );\n\n const postText = llmResult.text || \"\";\n const charCount = postText.length;\n\n return {\n agent: \"MarketingAgent\",\n category: \"marketing\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success\n ? `${platform || \"Social\"} post generated (${charCount} chars)`\n : \"Social post generation failed\",\n data: {\n action: \"social\",\n platform: platform || \"general\",\n content: postText,\n characterCount: charCount,\n maxLength: rules.maxLength,\n withinLimit: charCount <= rules.maxLength,\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n if (action === \"seo\") {\n const llmResult = await callLLM(\n `Provide SEO recommendations for: ${topic}\\n` +\n `Include:\\n` +\n `- Primary keyword and 10 long-tail keywords\\n` +\n `- Meta title (60 chars max)\\n` +\n `- Meta description (160 chars max)\\n` +\n `- URL slug suggestion\\n` +\n `- 5 header (H2) suggestions\\n` +\n `- Content gap analysis`,\n { maxTokens: 600, temperature: 0.4 },\n );\n\n return {\n agent: \"MarketingAgent\",\n category: \"marketing\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"SEO analysis generated\" : \"SEO analysis failed\",\n data: {\n action: \"seo\",\n topic,\n recommendations: llmResult.text || \"\",\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n if (action === \"reformat\" || action === \"cross-platform\") {\n const content = data.content || \"\";\n const targetPlatform = (data.targetPlatform || \"twitter\").toLowerCase();\n const rules = PLATFORM_RULES[targetPlatform] || PLATFORM_RULES[\"twitter\"];\n\n const llmResult = await callLLM(\n `Reformat this content for ${targetPlatform}:\\n\\n${content.slice(0, 2000)}\\n\\n` +\n `Tone: ${rules.tone}\\nMax ${rules.maxLength} characters.\\n` +\n `${rules.hashtags ? \"Add relevant hashtags.\" : \"No hashtags.\"}\\n` +\n `Return ONLY the reformatted content.`,\n { maxTokens: 500, temperature: 0.6 },\n );\n\n return {\n agent: \"MarketingAgent\",\n category: \"marketing\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success\n ? `Content reformatted for ${targetPlatform}`\n : \"Reformatting failed\",\n data: {\n action: \"reformat\",\n targetPlatform,\n original: content.slice(0, 500),\n reformatted: llmResult.text || \"\",\n characterCount: llmResult.text?.length || 0,\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Default: general content writing\n const llmResult = await callLLM(\n `Write engaging content about: ${topic}\\n` +\n `Audience: ${audience}\\nTone: ${tone}\\n` +\n `Make it compelling and well-structured.`,\n { maxTokens: 1000, temperature: 0.7 },\n );\n\n return {\n agent: \"MarketingAgent\",\n category: \"marketing\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"Content generated\" : \"Content generation failed — LLM unavailable\",\n data: {\n action: \"write\",\n topic,\n content: llmResult.text || \"\",\n },\n executionTimeMs: Date.now() - start,\n };\n } catch (e: any) {\n return {\n agent: \"MarketingAgent\",\n category: \"marketing\",\n status: \"error\",\n message: `Marketing error: ${e.message}`,\n data: { error: e.message },\n executionTimeMs: Date.now() - start,\n };\n }\n}\n",[],[174,292],"f0dc46c4-47ac-4070-af62-23eb3f40db1b","coderAgent.ts","b7accf72-7960-11f1-acba-1607ee4eb77e",44,["D",1783359173043],"// CoderAgent — Nexuses Core V3\n// Writes, debugs, and explains code in Python/JS/TS/HTML/CSS.\n// Uses a virtual file system via blob storage for persistent code files.\n\nimport { callLLM } from \"../lib/llm.ts\";\nimport { blob } from \"https://esm.town/v/std/blob/main.ts\";\nimport type { AgentResult } from \"../lib/types.ts\";\n\nconst VFS_PREFIX = \"vfs/\";\n\n/**\n * Virtual File System — store/retrieve code files in blob storage.\n */\nasync function vfsWrite(filename: string, content: string) {\n await blob.setJSON(`${VFS_PREFIX}${filename}`, {\n filename,\n content,\n updatedAt: new Date().toISOString(),\n });\n}\n\nasync function vfsRead(filename: string): Promise {\n const data = await blob.getJSON(`${VFS_PREFIX}${filename}`);\n return data?.content ?? null;\n}\n\nasync function vfsList() {\n const entries = await blob.list(VFS_PREFIX);\n return entries.map((e) => e.key.replace(VFS_PREFIX, \"\"));\n}\n\n/**\n * Detect language from task or data.\n */\nfunction detectLanguage(task: string, data: any): string {\n if (data.language) return data.language;\n const lower = task.toLowerCase();\n if (lower.includes(\"python\") || lower.includes(\".py\")) return \"python\";\n if (lower.includes(\"typescript\") || lower.includes(\".ts\")) return \"typescript\";\n if (lower.includes(\"html\")) return \"html\";\n if (lower.includes(\"css\")) return \"css\";\n if (lower.includes(\"javascript\") || lower.includes(\".js\") || lower.includes(\"js\")) return \"javascript\";\n return \"javascript\";\n}\n\nexport async function execute(task: string, data: any): Promise {\n const start = Date.now();\n const action = data.action || \"write\";\n const language = detectLanguage(task, data);\n const code = data.code || data.snippet || \"\";\n const filename = data.filename || `generated.${language === \"python\" ? \"py\" : language === \"typescript\" ? \"ts\" : language === \"html\" ? \"html\" : language === \"css\" ? \"css\" : \"js\"}`;\n\n try {\n if (action === \"debug\" || action === \"fix\") {\n // Debug/fix existing code\n const llmResult = await callLLM(\n `Find and fix all bugs in this ${language} code:\\n\\n${code}\\n\\n` +\n `Return ONLY the corrected code with inline comments explaining each fix.`,\n { maxTokens: 1200, temperature: 0.2 },\n );\n\n if (llmResult.success) {\n await vfsWrite(filename, llmResult.text);\n }\n\n return {\n agent: \"CoderAgent\",\n category: \"coding\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? `Code debugged and saved to VFS:/${filename}` : \"Debug failed — LLM unavailable\",\n data: {\n action: \"debug\",\n language,\n filename,\n original: code.slice(0, 500),\n fixed: llmResult.text || \"\",\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n if (action === \"explain\") {\n // Explain existing code\n const llmResult = await callLLM(\n `Explain this ${language} code in detail:\\n\\n${code}\\n\\n` +\n `Cover: purpose, key functions, logic flow, and any potential issues.`,\n { maxTokens: 800, temperature: 0.4 },\n );\n\n return {\n agent: \"CoderAgent\",\n category: \"coding\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"Code explained\" : \"Explanation failed — LLM unavailable\",\n data: {\n action: \"explain\",\n language,\n code: code.slice(0, 500),\n explanation: llmResult.text || \"\",\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n if (action === \"read\") {\n // Read from VFS\n const content = await vfsRead(filename);\n if (content) {\n return {\n agent: \"CoderAgent\",\n category: \"coding\",\n status: \"success\",\n message: `Read VFS:/${filename}`,\n data: { action: \"read\", filename, content },\n executionTimeMs: Date.now() - start,\n };\n }\n return {\n agent: \"CoderAgent\",\n category: \"coding\",\n status: \"error\",\n message: `File not found in VFS: ${filename}`,\n data: {},\n executionTimeMs: Date.now() - start,\n };\n }\n\n if (action === \"list\") {\n const files = await vfsList();\n return {\n agent: \"CoderAgent\",\n category: \"coding\",\n status: \"success\",\n message: `VFS contains ${files.length} files`,\n data: { action: \"list\", files },\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Default action: write new code\n const prompt = data.prompt || task;\n const llmResult = await callLLM(\n `Write ${language} code for the following task:\\n${prompt}\\n\\n` +\n `Return ONLY the code with comments. No explanation outside the code.`,\n { maxTokens: 1200, temperature: 0.3 },\n );\n\n if (llmResult.success) {\n await vfsWrite(filename, llmResult.text);\n }\n\n return {\n agent: \"CoderAgent\",\n category: \"coding\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success\n ? `Code written in ${language} and saved to VFS:/${filename}`\n : \"Code generation failed — LLM unavailable\",\n data: {\n action: \"write\",\n language,\n filename,\n code: llmResult.text || \"\",\n vfsPath: `VFS:/${filename}`,\n },\n executionTimeMs: Date.now() - start,\n };\n } catch (e: any) {\n return {\n agent: \"CoderAgent\",\n category: \"coding\",\n status: \"error\",\n message: `Coder error: ${e.message}`,\n data: { error: e.message },\n executionTimeMs: Date.now() - start,\n };\n }\n}\n",[],[174,300],"78adf795-1387-438a-b18d-1707e9fb46ab","analystAgent.ts","c0bbf124-7960-11f1-84c2-1607ee4eb77e",45,["D",1783359188236],"// AnalystAgent — Nexuses Core V3\n// Data analysis, CSV/Excel generation, report building, file analysis.\n// Uses LLM for analytical reasoning + structured data output.\n\nimport { callLLM } from \"../lib/llm.ts\";\nimport type { AgentResult } from \"../lib/types.ts\";\n\n/**\n * Parse CSV text into structured rows.\n */\nfunction parseCSV(text: string): string[][] {\n const rows: string[][] = [];\n const lines = text.trim().split(\"\\n\");\n for (const line of lines) {\n const cells: string[] = [];\n let current = \"\";\n let inQuotes = false;\n for (let i = 0; i < line.length; i++) {\n const ch = line[i];\n if (ch === '\"') {\n inQuotes = !inQuotes;\n } else if (ch === \",\" && !inQuotes) {\n cells.push(current.trim());\n current = \"\";\n } else {\n current += ch;\n }\n }\n cells.push(current.trim());\n rows.push(cells);\n }\n return rows;\n}\n\n/**\n * Convert array of objects to CSV string.\n */\nfunction toCSV(data: Record[]): string {\n if (!data.length) return \"\";\n const headers = Object.keys(data[0]);\n const lines = [headers.join(\",\")];\n for (const row of data) {\n lines.push(headers.map((h) => `\"${String(row[h] ?? \"\").replace(/\"/g, '\"\"')}\"`).join(\",\"));\n }\n return lines.join(\"\\n\");\n}\n\nexport async function execute(task: string, data: any): Promise {\n const start = Date.now();\n const action = data.action || \"analyze\";\n const prompt = data.prompt || task;\n\n try {\n // CSV generation\n if (action === \"csv\" || action === \"excel\") {\n const llmResult = await callLLM(\n `Generate tabular data in CSV format for: ${prompt}\\n` +\n `Include appropriate headers and at least 5 sample rows.\\n` +\n `Return ONLY the CSV text, no explanation.`,\n { maxTokens: 600, temperature: 0.3 },\n );\n\n const csvText = llmResult.text || \"\";\n const parsed = parseCSV(csvText);\n\n return {\n agent: \"AnalystAgent\",\n category: \"analysis\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? `CSV generated with ${parsed.length} rows` : \"CSV generation failed\",\n data: {\n action: \"csv\",\n csv: csvText,\n rows: parsed,\n headers: parsed[0] || [],\n rowCount: parsed.length - 1,\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Report generation\n if (action === \"report\") {\n const llmResult = await callLLM(\n `Create a structured analytical report for: ${prompt}\\n` +\n `Format as:\\n## Executive Summary\\n## Key Findings\\n## Data Analysis\\n## Recommendations\\n\\n` +\n `Be thorough and data-driven.`,\n { maxTokens: 1000, temperature: 0.5 },\n );\n\n return {\n agent: \"AnalystAgent\",\n category: \"analysis\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"Report generated\" : \"Report generation failed\",\n data: {\n action: \"report\",\n topic: prompt,\n report: llmResult.text || \"\",\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n // File/data analysis (parse provided data)\n if (action === \"analyze\" && (data.data || data.text || data.csv)) {\n const input = data.data || data.text || data.csv;\n const inputStr = typeof input === \"string\" ? input : JSON.stringify(input);\n\n const llmResult = await callLLM(\n `Analyze this data and provide insights:\\n\\n${inputStr.slice(0, 2000)}\\n\\n` +\n `Provide: key patterns, anomalies, statistics, and recommendations.`,\n { maxTokens: 800, temperature: 0.4 },\n );\n\n return {\n agent: \"AnalystAgent\",\n category: \"analysis\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"Data analysis completed\" : \"Analysis failed — LLM unavailable\",\n data: {\n action: \"analyze\",\n input: inputStr.slice(0, 500),\n analysis: llmResult.text || \"\",\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Invoice/billing generation\n if (action === \"invoice\") {\n const llmResult = await callLLM(\n `Generate a professional invoice in JSON format for: ${prompt}\\n` +\n `Include: invoiceNumber, date, billTo, items[{description, quantity, unitPrice, total}], subtotal, tax, total.\\n` +\n `Return ONLY the JSON.`,\n { maxTokens: 500, temperature: 0.3 },\n );\n\n let invoice: any = null;\n try {\n const jsonStr = llmResult.text?.replace(/```json\\s*/g, \"\").replace(/```\\s*/g, \"\");\n const match = jsonStr?.match(/\\{[\\s\\S]*\\}/);\n if (match) invoice = JSON.parse(match[0]);\n } catch {}\n\n // Generate CSV version too\n let csvVersion = \"\";\n if (invoice?.items) {\n csvVersion = toCSV(invoice.items);\n }\n\n return {\n agent: \"AnalystAgent\",\n category: \"analysis\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"Invoice generated\" : \"Invoice generation failed\",\n data: {\n action: \"invoice\",\n invoice,\n csv: csvVersion,\n },\n executionTimeMs: Date.now() - start,\n };\n }\n\n // Default: general analysis\n const llmResult = await callLLM(\n `Analyze and process: ${prompt}\\nProvide structured findings and recommendations.`,\n { maxTokens: 700, temperature: 0.5 },\n );\n\n return {\n agent: \"AnalystAgent\",\n category: \"analysis\",\n status: llmResult.success ? \"success\" : \"error\",\n message: llmResult.success ? \"Analysis completed\" : \"Analysis failed — LLM unavailable\",\n data: {\n action: \"analyze\",\n topic: prompt,\n result: llmResult.text || \"\",\n },\n executionTimeMs: Date.now() - start,\n };\n } catch (e: any) {\n return {\n agent: \"AnalystAgent\",\n category: \"analysis\",\n status: \"error\",\n message: `Analyst error: ${e.message}`,\n data: { error: e.message },\n executionTimeMs: Date.now() - start,\n };\n }\n}\n",[],[174,308],["D",1783359042602],[191,192,193,194,195],[174],["D",1783347377247],["D",1789522836959],{"_105":104,"_113":109},{"_105":104,"_106":107,"_108":109,"_19":61,"_110":111,"_112":54},{"_13":94,"_38":95,"_48":47,"_96":49,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":50,"_103":104},"ctx",{"_87":-5,"_132":356,"_357":358,"_359":360,"_361":54,"_362":54,"_363":54,"_364":31,"_365":-5,"_366":-7,"_367":-7,"_368":68,"_369":54,"_370":54,"_371":-5,"_372":54,"_373":54,"_374":54,"_375":-5},"ENV",{"_327":328,"_329":330,"_331":95,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v57