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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 models: `Groq: ${Deno.env.get(\"GROQ_API_KEY\") ? \"ACTIVE\" : \"NOT_CONFIGURED\"} | HF: ${token ? \"ACTIVE\" : \"NOT_CONFIGURED\"}`,\n };\n}","// 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 models: `HF Router: ${token ? \"ACTIVE\" : \"NOT_CONFIGURED\"} | HF_API_KEY: ${token ? \"CONFIGURED\" : \"MISSING\"}`,\n 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Nexuses Core OS V10.0 — Universal AI Gateway\n// HTTP Entry Point with IAP, JSON-RPC, strict auth, and Dynamic Request Normalizer\n\nimport {\n callAdapterDirect,\n dispatchTask,\n getHistory,\n getSystemStatus,\n getTaskStatus,\n} from \"./TaskDispatcher.ts\";\nimport { getAgentManifest } from \"./AgentManager.ts\";\nimport { listAdapters } from \"./UniversalAdapter.ts\";\nimport { getModelManifest } from \"./hf.ts\";\nimport { initDB } from \"./db.ts\";\nimport { buildCapabilityList, getServiceManifest } from \"./RegistryManager.ts\";\nimport { processHandshake, processRPC } from \"./IAP.ts\";\nimport type { JSONRPCRequest } from \"./IAP.ts\";\n\nconst CORS = {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"GET, POST, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, Authorization\",\n};\n\nfunction json(body: any, status = 200) {\n return new Response(JSON.stringify(body, null, 2), { status, headers: CORS });\n}\n\nfunction getEnv(key: string): string | undefined {\n try {\n return Deno.env.get(key);\n } catch {\n return undefined;\n }\n}\n\nfunction getEndpoint(req: Request): string {\n const url = new URL(req.url);\n return `${url.protocol}//${url.host}/`;\n}\n\n// --- Universal Request Normalizer ---\n// خود بخود ریکویسٹ کو پہچان کر فارمیٹ سیٹ کرتا ہے\nfunction normalizeRequest(payload: any) {\n const possibleTaskKeys = [\n \"task\",\n \"query\",\n \"prompt\",\n \"command\",\n \"message\",\n \"input\",\n \"body\",\n \"text\",\n ];\n let task = \"general\";\n let data = { ...payload };\n\n for (const key of possibleTaskKeys) {\n if (payload[key]) {\n task = payload[key];\n delete data[key];\n break;\n }\n }\n return { task, data };\n}\n\n// --- Auth Validation ---\nfunction validateAuth(req: Request): boolean {\n const apiKey = getEnv(\"NEXUSES_API_KEY\");\n if (!apiKey) return true; // Dev mode\n const auth = req.headers.get(\"authorization\") || \"\";\n const token = auth.replace(/^Bearer\\s+/i, \"\").trim();\n return token === apiKey;\n}\n\n// --- Rate Limiting ---\nconst RATE_LIMIT = new Map();\nfunction isRateLimited(ip: string): boolean {\n const now = Date.now();\n const ts = RATE_LIMIT.get(ip)?.filter((t) => now - t < 60000) || [];\n if (ts.length >= 50) return true; // 50 requests per minute\n ts.push(now);\n RATE_LIMIT.set(ip, ts);\n return false;\n}\n\n// --- HTTP Handler ---\nexport default async function (req: Request): Promise {\n if (req.method === \"OPTIONS\") return new Response(null, { headers: CORS });\n\n const url = new URL(req.url);\n const path = url.pathname;\n const ip = req.headers.get(\"x-forwarded-for\")?.split(\",\")[0] ||\n req.headers.get(\"cf-connecting-ip\") || \"unknown\";\n const endpoint = getEndpoint(req);\n\n await initDB();\n\n // --- GET Routes (Public Discovery) ---\n if (req.method === \"GET\") {\n if (path === \"/\" || path === \"\") return json(await getSystemStatus());\n if (path === \"/status\") return json(await getSystemStatus());\n if (path === \"/register\") return json(getServiceManifest(endpoint));\n if (path === \"/agents\") return json({ agents: getAgentManifest() });\n if (path === \"/adapters\") return json({ adapters: listAdapters() });\n if (path === \"/models\") return json({ models: getModelManifest() });\n if (path === \"/capabilities\") {\n return json({ capabilities: buildCapabilityList() });\n }\n if (path === \"/history\") {\n const limit = parseInt(url.searchParams.get(\"limit\") || \"20\");\n return json({ history: await getHistory(limit) });\n }\n const taskMatch = path.match(/^\\/task\\/(.+)$/);\n if (taskMatch) {\n const task = await getTaskStatus(taskMatch[1]);\n return task\n ? json({ task })\n : json({ status: \"failure\", message: \"Task not found\" }, 404);\n }\n return json({ error: \"Unknown endpoint\" }, 404);\n }\n\n // --- POST Routes ---\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 // 1. Universal AI Gateway — POST / or /public-task\n // Any external platform (web app, bot, etc.) can POST a work task here.\n // No auth required. The system auto-analyzes, selects the optimal model,\n // and executes with automatic fallback.\n if (path === \"/\" || path === \"\" || path === \"/public-task\") {\n if (isRateLimited(ip)) {\n return json({ status: \"failure\", message: \"Rate limit exceeded\" }, 429);\n }\n\n // Dynamic auth check — HF_API_KEY must be configured at runtime\n const hfKey = getEnv(\"HF_API_KEY\");\n if (!hfKey) {\n return json({\n status: \"failure\",\n message: \"HF_API_KEY is not configured. Set it in your Val Town environment variables.\",\n error: \"MISSING_HF_API_KEY\",\n }, 503);\n }\n\n const { task, data } = normalizeRequest(payload);\n const response = await dispatchTask(task, data, \"AUTO\");\n return json(response, response.status === \"success\" ? 200 : 500);\n }\n\n // 2. IAP Handshake (public — no auth needed for discovery)\n if (path === \"/handshake\") {\n if (payload.jsonrpc === \"2.0\" && payload.method === \"handshake\") {\n return json(await processHandshake(payload, endpoint));\n }\n return json({ error: \"Invalid handshake\" }, 400);\n }\n\n // 3. Strict Auth Check for protected POST endpoints\n if (!validateAuth(req)) {\n return json({\n status: \"failure\",\n message: \"Unauthorized — provide valid Bearer token\",\n }, 401);\n }\n\n if (isRateLimited(ip)) {\n return json({ status: \"failure\", message: \"Rate limit exceeded\" }, 429);\n }\n\n // Protected Routes\n if (path === \"/rpc\") {\n const rpcResponse = await processRPC(\n payload as JSONRPCRequest,\n endpoint,\n dispatchTask,\n );\n return json(rpcResponse);\n }\n\n if (path === \"/adapter\") {\n const { adapter, action, data } = payload;\n const result = await callAdapterDirect(\n adapter,\n action || \"default\",\n data || {},\n );\n return json({\n status: result.success ? \"success\" : \"failure\",\n data: result.data,\n });\n }\n\n // Unknown POST path\n return json({ status: \"failure\", message: \"Unknown endpoint\" }, 404);\n}","label",[144],"9c0458a6-55a8-43a6-b7d0-1a5a1828d062","hf.ts","73dfc540-7921-11f1-aa04-1607ee4eb77e",66,["D",1783337283181],10,"// HF Model Orchestration — Nexuses Core OS V10.0\n// Universal AI Gateway: HF Router-only with auto model selection via selectBestModel() + dynamic fallback.\n\nimport { routeToAdapter, selectBestModel } from \"./UniversalAdapter.ts\";\n\n// --- Model Registry ---\nexport type ModelKey = \"qwen_coder\" | \"llama\" | \"phi3\" | \"gemma\";\nexport type TaskType = \"coding\" | \"creative\" | \"analysis\" | \"general\";\n\ninterface ModelConfig {\n id: string;\n label: string;\n bestFor: TaskType[];\n keywords: string[];\n maxTokens: number;\n temperature: number;\n fallback: ModelKey | null;\n}\n\nexport const HF_MODELS: Record = {\n qwen_coder: {\n id: \"Qwen/Qwen2.5-Coder-7B-Instruct\",\n label: \"Qwen2.5 Coder 7B\",\n bestFor: [\"coding\"],\n keywords: [\"code\", \"coding\", \"debug\", \"bug\", \"fix\", \"program\", \"refactor\"],\n maxTokens: 800,\n temperature: 0.3,\n fallback: \"gemma\",\n },\n llama: {\n id: \"meta-llama/Llama-3.2-1B-Instruct\",\n label: \"Llama 3.2 1B\",\n bestFor: [\"creative\"],\n keywords: [\"write\", \"writing\", \"blog\", \"story\", \"creative\", \"social\"],\n maxTokens: 1000,\n temperature: 0.8,\n fallback: \"gemma\",\n },\n phi3: {\n id: \"microsoft/Phi-3-mini-4k-instruct\",\n label: \"Phi-3 Mini 4K\",\n bestFor: [\"analysis\", \"general\"],\n keywords: [\"analyze\", \"data\", \"report\", \"csv\", \"summarize\", \"search\"],\n maxTokens: 500,\n temperature: 0.5,\n fallback: \"gemma\",\n },\n gemma: {\n id: \"google/gemma-2-2b-it\",\n label: \"Gemma 2 2B\",\n bestFor: [\"general\"],\n keywords: [\"general\", \"help\", \"chat\", \"explain\", \"unknown\"],\n maxTokens: 500,\n temperature: 0.7,\n fallback: null,\n },\n};\n\n// --- Reverse lookup: model ID string → ModelKey ---\nfunction modelIdToKey(modelId: string): ModelKey {\n for (const [key, cfg] of Object.entries(HF_MODELS)) {\n if (cfg.id === modelId) return key as ModelKey;\n }\n return \"gemma\";\n}\n\n// --- Selection Logic ---\nexport function classifyTask(taskText: string): TaskType {\n const lower = taskText.toLowerCase();\n const scores = { coding: 0, creative: 0, analysis: 0, general: 0 };\n for (const cfg of Object.values(HF_MODELS)) {\n for (const type of cfg.bestFor) {\n scores[type] += cfg.keywords.filter((k) => lower.includes(k)).length;\n }\n }\n let best: TaskType = \"general\";\n let bestScore = 0;\n for (const [type, score] of Object.entries(scores)) {\n if (score > bestScore) {\n bestScore = score;\n best = type as TaskType;\n }\n }\n return best;\n}\n\nexport function modelForTaskType(type: TaskType): ModelKey {\n switch (type) {\n case \"coding\":\n return \"qwen_coder\";\n case \"creative\":\n return \"llama\";\n case \"analysis\":\n return \"phi3\";\n default:\n return \"gemma\";\n }\n}\n\nexport function selectModel(taskText: string): ModelKey {\n return modelForTaskType(classifyTask(taskText));\n}\n\n// --- Integration with UniversalAdapter ---\nexport interface HFResult {\n success: boolean;\n model: ModelKey;\n modelId: string;\n text: string;\n taskType: TaskType;\n error?: string;\n attempts?: ModelKey[];\n fallbackUsed?: boolean;\n}\n\nexport interface CallOptions {\n maxTokens?: number;\n temperature?: number;\n timeoutMs?: number;\n}\n\n/**\n * Universal AI Gateway — calls HF Router with automatic fallback:\n * 1. Primary model (auto-selected via selectBestModel()) via HF Router\n * 2. Secondary model (configured fallback) via HF Router\n * 3. Gemma (ultimate fallback) via HF Router\n * On timeout or error, automatically switches to the next model.\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 // --- Autonomous model selection via selectBestModel() ---\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 // --- 1. Primary model via HF Router ---\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 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 // --- All paths failed ---\n return {\n success: false,\n model: modelKey,\n modelId: cfg.id,\n text: \"\",\n taskType,\n error: \"All HF Router models failed after fallback chain\",\n attempts,\n };\n}\n\nexport async function callHF(\n prompt: string,\n model?: ModelKey,\n options?: CallOptions,\n): Promise {\n return callHFWithFallback(prompt, model, options);\n}\n\n// --- Discovery ---\nexport function getModelManifest() {\n return Object.entries(HF_MODELS).map(([key, cfg]) => ({\n key,\n id: cfg.id,\n label: cfg.label,\n bestFor: cfg.bestFor,\n status: \"ADAPTER_LINKED\",\n }));\n}\n\nexport function getSelectionInfo(taskText: string) {\n const taskType = classifyTask(taskText);\n const modelKey = modelForTaskType(taskType);\n const cfg = HF_MODELS[modelKey];\n return {\n taskType,\n selectedModel: modelKey,\n modelId: cfg.id,\n modelLabel: cfg.label,\n fallbackModel: cfg.fallback,\n };\n}\n\nexport function getHFToken() {\n return Deno.env.get(\"HF_API_KEY\");\n}\n",[155],"079f1434-a86c-4116-bae1-fac7d1bb70b7","db.ts","b7760422-7921-11f1-a660-1607ee4eb77e",67,["D",1783337283367],3,"// 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}",[163],"8407cd35-1fab-49df-a96d-79bd6092b107","UniversalAdapter.ts","53ca729a-7922-11f1-b4ab-1607ee4eb77e",71,["D",1783337297431],9,"// Universal Adapter Layer — Nexuses Core OS V10.0\n// HF Router (primary), auto model selection via selectBestModel(), scraping, search, crypto.\n\nexport interface Adapter {\n name: string;\n type: \"api\" | \"browser\" | \"service\" | \"plugin\" | \"ai\";\n status: \"active\" | \"inactive\" | \"error\";\n description: string;\n execute: (action: string, data: any) => Promise;\n}\n\nexport interface AdapterResult {\n success: boolean;\n adapter: string;\n action: string;\n data: any;\n message: string;\n}\n\n// --- Smart Model Router (Auto Model Selection) ---\n// Intelligently picks the best HF Router model based on task keywords.\nconst MODEL_CHAIN: { model: string; keywords: string[] }[] = [\n {\n model: \"Qwen/Qwen2.5-Coder-7B-Instruct\",\n keywords: [\"code\", \"coding\", \"script\", \"function\", \"debug\", \"bug\", \"fix\", \"program\", \"refactor\", \"compile\", \"syntax\", \"algorithm\", \"api\", \"sql\", \"regex\"],\n },\n {\n model: \"microsoft/Phi-3-mini-4k-instruct\",\n keywords: [\"calculate\", \"solve\", \"logic\", \"math\", \"analyze\", \"analysis\", \"data\", \"report\", \"csv\", \"summarize\", \"research\", \"invoice\", \"excel\", \"spreadsheet\"],\n },\n {\n model: \"google/gemma-2-2b-it\",\n keywords: [\"poem\", \"story\", \"explain\", \"translate\", \"write\", \"blog\", \"content\", \"article\", \"social\", \"post\", \"creative\", \"narrative\"],\n },\n {\n model: \"meta-llama/Llama-3.2-1B-Instruct\",\n keywords: [\"general\", \"help\", \"chat\", \"question\", \"reasoning\", \"unknown\", \"test\"],\n },\n];\n\nexport function selectBestModel(task: string): string {\n const t = task.toLowerCase();\n let bestModel = MODEL_CHAIN[MODEL_CHAIN.length - 1].model; // default\n let bestScore = 0;\n for (const entry of MODEL_CHAIN) {\n const score = entry.keywords.filter((k) => t.includes(k)).length;\n if (score > bestScore) {\n bestScore = score;\n bestModel = entry.model;\n }\n }\n return bestModel;\n}\n\n// --- 1. Groq Adapter (Fastest AI) ---\nconst GroqAdapter: Adapter = {\n name: \"groq\",\n type: \"ai\",\n status: \"active\",\n description: \"Primary AI: Fast Llama-3-8b\",\n async execute(action: string, data: any): Promise {\n const apiKey = Deno.env.get(\"GROQ_API_KEY\");\n if (!apiKey) {\n return {\n success: false,\n adapter: \"groq\",\n action,\n data: {},\n message: \"GROQ_API_KEY missing\",\n };\n }\n try {\n const response = await fetch(\n \"https://api.groq.com/openai/v1/chat/completions\",\n {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model: data.model || \"llama3-8b-8192\",\n messages: [{ role: \"user\", content: data.prompt || data.task }],\n max_tokens: data.maxTokens || 1000,\n temperature: data.temperature ?? 0.7,\n }),\n signal: AbortSignal.timeout(30000),\n },\n );\n if (!response.ok) {\n const errBody = await response.text().catch(() => \"\");\n return {\n success: false,\n adapter: \"groq\",\n action,\n data: {},\n message: `Groq HTTP ${response.status}: ${errBody.slice(0, 200)}`,\n };\n }\n const result = await response.json();\n const text = result.choices?.[0]?.message?.content || \"\";\n return {\n success: true,\n adapter: \"groq\",\n action: \"generate\",\n data: { response: text, modelUsed: result.model || \"llama3-8b-8192\" },\n message: \"Success\",\n };\n } catch (e: any) {\n return {\n success: false,\n adapter: \"groq\",\n action,\n data: {},\n message: e.message,\n };\n }\n },\n};\n\n// --- 2. Hugging Face Adapter (Smart Router AI) ---\nconst HuggingFaceAdapter: Adapter = {\n name: \"huggingface\",\n type: \"ai\",\n status: \"active\",\n description: \"Small AI: Auto-selects Qwen, Phi-3, Gemma, Llama\",\n async execute(action: string, data: any): Promise {\n const apiKey = Deno.env.get(\"HF_API_KEY\");\n if (!apiKey) {\n return {\n success: false,\n adapter: \"huggingface\",\n action,\n data: {},\n message: \"HF_API_KEY missing\",\n };\n }\n\n const taskContent = data.prompt || data.task || \"\";\n const model = data.model || selectBestModel(taskContent);\n const maxTokens = data.maxTokens || 500;\n const temperature = data.temperature ?? 0.7;\n\n // New Hugging Face Router endpoint (OpenAI-compatible chat completions)\n const url = \"https://router.huggingface.co/v1/chat/completions\";\n\n try {\n const response = await fetch(url, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model,\n messages: [{ role: \"user\", content: taskContent }],\n max_tokens: maxTokens,\n temperature,\n }),\n signal: AbortSignal.timeout(data.timeoutMs || 30000),\n });\n\n if (!response.ok) {\n const errBody = await response.text().catch(() => \"\");\n return {\n success: false,\n adapter: \"huggingface\",\n action,\n data: {},\n message: `HF Router HTTP ${response.status}: ${errBody.slice(0, 200)}`,\n };\n }\n\n const result = await response.json();\n const text = result.choices?.[0]?.message?.content ||\n result.choices?.[0]?.text || \"\";\n\n return {\n success: true,\n adapter: \"huggingface\",\n action: \"generate\",\n data: { response: text, modelUsed: model },\n message: `Used ${model} via HF Router`,\n };\n } catch (e: any) {\n return {\n success: false,\n adapter: \"huggingface\",\n action,\n data: {},\n message: e.message,\n };\n }\n },\n};\n\n// --- 3. GitHub Adapter ---\nconst GitHubAdapter: Adapter = {\n name: \"github\",\n type: \"api\",\n status: \"active\",\n description: \"GitHub management\",\n async execute(action: string, data: any) {\n const token = Deno.env.get(\"GITHUB_TOKEN\");\n return {\n success: !!token,\n adapter: \"github\",\n action,\n data: {},\n message: token ? \"GitHub ready\" : \"Token missing\",\n };\n },\n};\n\n// --- 4. DuckDuckGo Adapter ---\nconst DuckDuckGoAdapter: Adapter = {\n name: \"duckduckgo\",\n type: \"service\",\n status: \"active\",\n description: \"Web search\",\n async execute(action: string, data: any) {\n const res = await fetch(\n `https://api.duckduckgo.com/?q=${\n encodeURIComponent(data.query || data.q)\n }&format=json`,\n );\n const result = await res.json();\n return {\n success: true,\n adapter: \"duckduckgo\",\n action: \"search\",\n data: result,\n message: \"Search complete\",\n };\n },\n};\n\n// --- 5. CoinGecko Adapter ---\nconst CoinGeckoAdapter: Adapter = {\n name: \"coingecko\",\n type: \"api\",\n status: \"active\",\n description: \"Crypto prices\",\n async execute(action: string, data: any) {\n return {\n success: true,\n adapter: \"coingecko\",\n action: \"price\",\n data: { signals: \"BTC: 60k\" },\n message: \"OK\",\n };\n },\n};\n\n// --- 6. WebScraper Adapter ---\nconst WebScraperAdapter: Adapter = {\n name: \"webscraper\",\n type: \"browser\",\n status: \"active\",\n description: \"Scrape content\",\n async execute(action: string, data: any) {\n return {\n success: true,\n adapter: \"webscraper\",\n action: \"scrape\",\n data: { content: \"Page data...\" },\n message: \"OK\",\n };\n },\n};\n\n// --- Registry ---\nconst REGISTRY = new Map();\n[\n GroqAdapter,\n HuggingFaceAdapter,\n GitHubAdapter,\n DuckDuckGoAdapter,\n CoinGeckoAdapter,\n WebScraperAdapter,\n].forEach(\n (a) => REGISTRY.set(a.name, a),\n);\n\nexport function listAdapters() {\n return Array.from(REGISTRY.values()).map((a) => ({\n name: a.name,\n type: a.type,\n status: a.status,\n description: a.description,\n }));\n}\n\nexport async function routeToAdapter(\n name: string,\n action: string,\n data: any,\n): Promise {\n const adapter = REGISTRY.get(name);\n if (!adapter) {\n return {\n success: false,\n adapter: name,\n action,\n data: {},\n message: \"Adapter not found\",\n };\n }\n return adapter.execute(action, data);\n}",[171],"328c1a09-baff-4e77-94f1-ec600321702d","TaskDispatcher.ts","b1539bee-7922-11f1-a14a-1607ee4eb77e",65,["D",1783336502693],"// Task Dispatcher — Nexuses Core OS V10.0\n// Orchestrates the Analyze → Execute → Report pipeline.\n// Integrates Supervisor (auto-retry) and IAP delegation.\nimport { getAgentManifest, routeIncomingTask } from \"./AgentManager.ts\";\nimport type { AgentResult } from \"./AgentManager.ts\";\nimport { listAdapters, routeToAdapter } from \"./UniversalAdapter.ts\";\nimport {\n classifyTask,\n getHFToken,\n getModelManifest,\n getSelectionInfo,\n selectModel,\n} from \"./hf.ts\";\nimport type { TaskType } from \"./hf.ts\";\nimport { supervise } from \"./Supervisor.ts\";\nimport type { SupervisedResult } from \"./Supervisor.ts\";\nimport { delegateToExternal } from \"./IAP.ts\";\nimport type { DelegationResult } from \"./IAP.ts\";\nimport { getServiceInfo, hasInternalCapability } from \"./RegistryManager.ts\";\nimport {\n getRecentTasks,\n getTask,\n getTaskCount,\n initDB,\n insertTask,\n logWorkflowStep,\n recall,\n remember,\n updateTaskStatus,\n} from \"./db.ts\";\n\nexport interface DispatchResponse {\n status: \"success\" | \"failure\";\n task_id: string;\n data: any;\n message: string;\n agent?: string;\n category?: string;\n model?: string;\n modelId?: string;\n taskType?: TaskType;\n fallbackUsed?: boolean;\n attempts?: string[];\n adapters?: string[];\n delegated?: boolean;\n delegationTarget?: string | null;\n supervised?: boolean;\n retried?: boolean;\n selfCorrected?: boolean;\n supervisorAttempts?: number;\n executionTimeMs: number;\n timestamp: string;\n}\n\nfunction generateTaskId(): string {\n return `nxs_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;\n}\n\n// --- Public Dispatch API ---\n\nexport async function dispatchTask(\n task: string,\n data: any = {},\n mode = \"AUTO\",\n): Promise {\n const start = Date.now();\n const taskId = generateTaskId();\n\n // Ensure DB is ready\n await initDB();\n\n // PHASE 1: ANALYZE — deconstruct the task, identify agents + model\n const agents = getAgentManifest();\n const taskLower = task.toLowerCase();\n\n // Identify which agents could handle this task\n const candidates = agents.filter((a) =>\n a.keywords.some((k) => taskLower.includes(k))\n );\n\n // Dynamic model selection — classify task type before any API call\n const selection = getSelectionInfo(task);\n\n await logWorkflowStep(\n taskId,\n 1,\n \"TaskDispatcher\",\n \"ANALYZE\",\n `Task: \"${task}\" | Type: ${selection.taskType} | Model: ${selection.modelLabel} (${selection.modelId}) | Fallback: ${\n selection.fallbackModel || \"none\"\n } | Candidates: ${\n candidates.map((a) => a.name).join(\", \") || \"GeneralHandler\"\n }`,\n );\n\n // Record initial task state\n await insertTask(\n taskId,\n task,\n \"pending\",\n \"processing\",\n { task, data, mode },\n null,\n );\n\n // Recall any memory related to this task type\n const memoryKey = `task_type:${candidates[0]?.name || \"general\"}`;\n const priorMemory = await recall(memoryKey);\n\n // PHASE 2: EXECUTE — route to selected agent via Supervisor\n await logWorkflowStep(\n taskId,\n 2,\n \"TaskDispatcher\",\n \"EXECUTE\",\n `Routing to agent for execution (supervised mode)`,\n );\n\n // Wrap execution in Supervisor for autonomous error recovery\n const supervised = await supervise(\n taskId,\n task,\n data,\n (t, d) => routeIncomingTask(t, d, mode, true),\n );\n\n let agentResult: AgentResult;\n let delegated = false;\n let delegationResult: DelegationResult | null = null;\n\n if (supervised.success && supervised.finalResult) {\n agentResult = supervised.finalResult as AgentResult;\n } else {\n // Internal execution failed after retries — try external delegation\n await logWorkflowStep(\n taskId,\n 5,\n \"TaskDispatcher\",\n \"DELEGATE\",\n `Internal execution failed, attempting external delegation`,\n );\n\n delegationResult = await delegateToExternal(task, data);\n delegated = delegationResult.delegated;\n\n if (delegationResult.success) {\n agentResult = {\n agent: \"ExternalDelegation\",\n category: \"Delegated\",\n status: \"SUCCESS\",\n message: delegationResult.message,\n data: delegationResult.data,\n adapters: [],\n };\n } else {\n agentResult = {\n agent: supervised.finalResult?.agent || \"Unknown\",\n category: supervised.finalResult?.category || \"General\",\n status: \"ERROR\",\n message: supervised.message,\n data: supervised.finalResult?.data || {},\n };\n }\n }\n\n await logWorkflowStep(\n taskId,\n 3,\n agentResult.agent,\n agentResult.status,\n agentResult.message + (delegated ? \" [DELEGATED]\" : \"\"),\n );\n\n // PHASE 3: REPORT — build structured Universal Task Format response\n const isSuccess = agentResult.status === \"SUCCESS\" ||\n agentResult.status === \"PARTIAL\";\n const response: DispatchResponse = {\n status: agentResult.status === \"ERROR\" ? \"failure\" : \"success\",\n task_id: taskId,\n data: agentResult.data,\n message: agentResult.message,\n agent: agentResult.agent,\n category: agentResult.category,\n model: agentResult.model,\n modelId: agentResult.modelId,\n taskType: agentResult.taskType || selection.taskType,\n fallbackUsed: agentResult.fallbackUsed,\n attempts: agentResult.attempts,\n adapters: agentResult.adapters || [],\n delegated,\n delegationTarget: delegationResult?.target || null,\n supervised: true,\n retried: supervised.retried,\n selfCorrected: supervised.selfCorrected,\n supervisorAttempts: supervised.attempts.length,\n executionTimeMs: Date.now() - start,\n timestamp: new Date().toISOString(),\n };\n\n // Update task in DB\n await updateTaskStatus(taskId, isSuccess ? \"completed\" : \"failed\", response);\n\n // Save to memory for future reference\n await remember(memoryKey, {\n lastTask: task,\n lastAgent: agentResult.agent,\n lastStatus: agentResult.status,\n lastModel: agentResult.model,\n fallbackUsed: agentResult.fallbackUsed,\n timestamp: new Date().toISOString(),\n ...(priorMemory ? { previous: priorMemory } : {}),\n });\n\n await logWorkflowStep(\n taskId,\n 4,\n \"TaskDispatcher\",\n \"REPORT\",\n `Status: ${response.status} | Model: ${response.model || \"N/A\"}${\n response.fallbackUsed ? \" (FALLBACK)\" : \"\"\n } | Time: ${response.executionTimeMs}ms`,\n );\n\n return response;\n}\n\n// --- Status & Discovery ---\n\nexport async function getTaskStatus(taskId: string) {\n return getTask(taskId);\n}\n\nexport async function getHistory(limit = 20) {\n return getRecentTasks(limit);\n}\n\nexport async function getSystemStatus() {\n await initDB();\n const taskCount = await getTaskCount();\n const token = getHFToken();\n\n return {\n status: \"ONLINE\",\n service: \"Nexuses Core OS V10.0 — Universal AI Gateway\",\n uptime: \"24/7 on Val Town\",\n persistence: \"SQLite (project-scoped)\",\n tasksProcessed: taskCount,\n hfIntegration: token ? \"ACTIVE\" : \"NOT_CONFIGURED\",\n protocol: \"JSON-RPC 2.0 / IAP 1.0\",\n models: getModelManifest(),\n agents: getAgentManifest().map((a) => ({\n name: a.name,\n category: a.category,\n capabilities: a.capabilities,\n })),\n adapters: listAdapters(),\n endpoints: {\n POST: {\n \"/\": \"Universal AI Gateway — public, no auth (auto model selection)\",\n \"/public-task\": \"Alias for / (public universal task)\",\n \"/adapter\": \"Direct adapter call (auth required)\",\n \"/handshake\": \"IAP handshake (JSON-RPC, public)\",\n \"/rpc\": \"JSON-RPC 2.0 endpoint (auth required)\",\n },\n GET: {\n \"/\": \"System discovery\",\n \"/status\": \"System status & stats\",\n \"/register\": \"Service manifest for external AI discovery\",\n \"/task/:id\": \"Get task result by ID\",\n \"/history\": \"Recent task history\",\n \"/agents\": \"Agent manifest\",\n \"/adapters\": \"Available adapters\",\n \"/models\": \"Available HF models\",\n },\n },\n example: {\n url: \"POST /\",\n headers: {\n \"Authorization\": \"Bearer YOUR_NEXUSES_API_KEY\",\n \"Content-Type\": \"application/json\",\n },\n body: {\n task: \"debug my code\",\n data: { code: \"function add(a,b){return a-b}\" },\n },\n },\n };\n}\n\nexport async function callAdapterDirect(\n name: string,\n action: string,\n data: any,\n) {\n return routeToAdapter(name, action, data);\n}",[179],"8abf1075-0e1e-48f9-9c76-5901dfc34f21","Supervisor.ts","486ed854-7923-11f1-b6db-1607ee4eb77e",57,["D",1783336448470],"// Supervisor Agent — Nexuses Core OS V10.0\n// Autonomous error loop: Log → Analyze → Retry → Self-correct.\n// Wraps agent execution with supervision and automatic retry logic.\n\nimport {\n callHFWithFallback,\n classifyTask,\n getHFToken,\n selectModel,\n} from \"./hf.ts\";\nimport type { HFResult, ModelKey, TaskType } from \"./hf.ts\";\nimport { logWorkflowStep } from \"./db.ts\";\n\nexport interface SupervisedResult {\n success: boolean;\n attempts: AttemptLog[];\n finalResult: any;\n retried: boolean;\n selfCorrected: boolean;\n message: string;\n}\n\nexport interface AttemptLog {\n attempt: number;\n model: ModelKey;\n status: \"success\" | \"error\" | \"timeout\";\n error?: string;\n action: string;\n}\n\nconst MAX_RETRIES = 3;\n\n/**\n * Supervises a task execution with automatic error recovery.\n *\n * Flow:\n * 1. Execute the task via the provided executor function.\n * 2. If it fails: log the error, analyze failure context with HF.\n * 3. Retry with a different model or adjusted strategy.\n * 4. Self-correct if possible (modify prompt based on error analysis).\n */\nexport async function supervise(\n taskId: string,\n task: string,\n data: any,\n executor: (task: string, data: any) => Promise,\n): Promise {\n const attempts: AttemptLog[] = [];\n let lastError = \"\";\n let selfCorrected = false;\n\n for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {\n console.log(\n `[Supervisor] Attempt ${attempt}/${MAX_RETRIES} for task: \"${\n task.slice(0, 80)\n }\"`,\n );\n\n try {\n const result = await executor(task, data);\n\n // Check if execution succeeded\n if (\n result.success || result.status === \"SUCCESS\" ||\n result.status === \"success\"\n ) {\n attempts.push({\n attempt,\n model: result.model || \"N/A\",\n status: \"success\",\n action: \"executed\",\n });\n\n await logWorkflowStep(\n taskId,\n 10 + attempt,\n \"Supervisor\",\n \"SUCCESS\",\n `Attempt ${attempt} succeeded | Model: ${result.model || \"N/A\"}`,\n );\n\n return {\n success: true,\n attempts,\n finalResult: result,\n retried: attempt > 1,\n selfCorrected,\n message: result.message || `Task completed on attempt ${attempt}`,\n };\n }\n\n // Execution returned but with failure status\n lastError = result.error || result.message || \"Unknown error\";\n attempts.push({\n attempt,\n model: result.model || \"N/A\",\n status: \"error\",\n error: lastError,\n action: \"failed\",\n });\n\n console.log(`[Supervisor] Attempt ${attempt} failed: ${lastError}`);\n await logWorkflowStep(\n taskId,\n 10 + attempt,\n \"Supervisor\",\n \"RETRY\",\n `Attempt ${attempt} failed: ${lastError}`,\n );\n\n // If this is the last attempt, don't analyze/retry\n if (attempt >= MAX_RETRIES) break;\n\n // PHASE: Analyze the failure\n const analysis = await analyzeFailure(task, data, lastError, attempt);\n await logWorkflowStep(\n taskId,\n 10 + attempt + 0.5,\n \"Supervisor\",\n \"ANALYZE\",\n `Failure analysis: ${analysis.slice(0, 200)}`,\n );\n\n // PHASE: Self-correct — modify the prompt/data based on analysis\n if (attempt === 1) {\n const corrected = await selfCorrect(task, data, lastError, analysis);\n if (corrected.modified) {\n task = corrected.task;\n data = corrected.data;\n selfCorrected = true;\n console.log(\n `[Supervisor] Self-corrected task prompt based on error analysis`,\n );\n await logWorkflowStep(\n taskId,\n 10 + attempt + 0.6,\n \"Supervisor\",\n \"SELF_CORRECT\",\n `Prompt modified based on failure analysis`,\n );\n }\n }\n\n // PHASE: Retry with different strategy\n // The executor itself already has fallback logic via callHFWithFallback,\n // but we retry the whole execution which may pick a different agent/model\n } catch (e: any) {\n lastError = e.message;\n attempts.push({\n attempt,\n model: \"N/A\",\n status: \"error\",\n error: lastError,\n action: \"exception\",\n });\n\n console.log(`[Supervisor] Attempt ${attempt} threw: ${lastError}`);\n await logWorkflowStep(\n taskId,\n 10 + attempt,\n \"Supervisor\",\n \"ERROR\",\n `Attempt ${attempt} exception: ${lastError}`,\n );\n\n if (attempt >= MAX_RETRIES) break;\n }\n }\n\n // All attempts exhausted\n await logWorkflowStep(\n taskId,\n 19,\n \"Supervisor\",\n \"EXHAUSTED\",\n `All ${MAX_RETRIES} attempts failed. Last error: ${lastError}`,\n );\n\n return {\n success: false,\n attempts,\n finalResult: null,\n retried: attempts.length > 1,\n selfCorrected,\n message: `All ${MAX_RETRIES} attempts failed. Last error: ${lastError}`,\n };\n}\n\n/**\n * Analyzes a failure using HF to understand what went wrong.\n */\nasync function analyzeFailure(\n task: string,\n data: any,\n error: string,\n attempt: number,\n): Promise {\n const token = getHFToken();\n if (!token) {\n return \"No HF token — cannot analyze failure. Will retry with same parameters.\";\n }\n\n const prompt =\n `Analyze why this task execution failed and suggest a fix:\\nTask: ${task}\\nData: ${\n JSON.stringify(data).slice(0, 500)\n }\\nError: ${error}\\nAttempt: ${attempt}`;\n\n const result = await callHFWithFallback(prompt, \"gemma\", {\n maxTokens: 200,\n timeoutMs: 10000,\n });\n return result.success ? result.text : \"Analysis unavailable\";\n}\n\n/**\n * Self-corrects the task prompt or data based on failure analysis.\n * Modifies the prompt to be clearer or adjusts parameters.\n */\nasync function selfCorrect(\n task: string,\n data: any,\n error: string,\n analysis: string,\n): Promise<{ task: string; data: any; modified: boolean }> {\n const token = getHFToken();\n if (!token) return { task, data, modified: false };\n\n // If the error is about missing token or config, don't retry with modified prompt\n if (error.includes(\"not configured\") || error.includes(\"NO_TOKEN\")) {\n return { task, data, modified: false };\n }\n\n // If error is a timeout, simplify the prompt\n if (error.includes(\"Timeout\") || error.includes(\"timeout\")) {\n const simplifiedTask = task.slice(0, 200);\n return {\n task: simplifiedTask,\n data: { ...data, maxTokens: Math.min(data.maxTokens || 500, 200) },\n modified: true,\n };\n }\n\n // If error is HTTP 503 (model loading), the retry will naturally try fallback\n if (error.includes(\"503\") || error.includes(\"loading\")) {\n return { task, data, modified: false };\n }\n\n // For other errors, try rephrasing the task\n const rephrasePrompt =\n `Rephrase this task instruction to be clearer and more specific:\\n\"${task}\"\\n\\nOnly return the rephrased instruction, nothing else.`;\n const result = await callHFWithFallback(rephrasePrompt, \"gemma\", {\n maxTokens: 100,\n timeoutMs: 10000,\n });\n\n if (\n result.success && result.text && result.text.length > 10 &&\n result.text.length < 500\n ) {\n return { task: result.text.trim(), data, modified: true };\n }\n\n return { task, data, modified: false };\n}",[186],"32f7f8e0-0678-4ba9-98a2-98638c2bc626","RegistryManager.ts","296c2956-7924-11f1-a6e5-1607ee4eb77e",59,["D",1783336448573],"// Registry Manager — Nexuses Core OS V10.0\n// Central catalog of all capabilities: agents, adapters, models, external services.\n// Used by the IAP discovery protocol and /register endpoint.\n\nimport { getAgentManifest } from \"./AgentManager.ts\";\nimport { listAdapters } from \"./UniversalAdapter.ts\";\nimport {\n classifyTask,\n getHFToken,\n getModelManifest,\n type TaskType,\n} from \"./hf.ts\";\n\nexport interface Capability {\n id: string;\n name: string;\n category: string;\n type: \"agent\" | \"adapter\" | \"model\" | \"external\";\n actions: string[];\n status: \"active\" | \"inactive\" | \"error\";\n description: string;\n}\n\nexport interface ServiceManifest {\n service_id: string;\n service_name: string;\n version: string;\n protocol: string;\n endpoint: string;\n auth: string;\n capabilities: Capability[];\n task_types: TaskType[];\n universal_task_format: object;\n registered_at: string;\n}\n\nconst SERVICE_ID = \"nexuses-core-os-v10\";\nconst SERVICE_NAME = \"Nexuses Core OS\";\nconst VERSION = \"10.0.0\";\nconst PROTOCOL = \"JSON-RPC 2.0 / IAP 1.0\";\n\n// --- External Service Registry (for delegation targets) ---\n\ninterface ExternalService {\n id: string;\n name: string;\n url: string;\n capabilities: string[];\n authHeader?: string;\n status: \"available\" | \"unconfigured\";\n}\n\nconst EXTERNAL_SERVICES: ExternalService[] = [\n {\n id: \"hf-router\",\n name: \"Hugging Face Router (Chat Completions)\",\n url: \"https://router.huggingface.co/v1/chat/completions\",\n capabilities: [\n \"text-generation\",\n \"code-generation\",\n \"creative-writing\",\n \"summarization\",\n ],\n authHeader: \"Authorization: Bearer \",\n status: \"available\",\n },\n {\n id: \"hf-image\",\n name: \"Hugging Face Image Generation\",\n url:\n \"https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0\",\n capabilities: [\"image-generation\", \"text-to-image\"],\n authHeader: \"Authorization: Bearer \",\n status: \"available\",\n },\n {\n id: \"coingecko\",\n name: \"CoinGecko Market Data\",\n url: \"https://api.coingecko.com/api/v3\",\n capabilities: [\"crypto-prices\", \"market-signals\"],\n status: \"available\",\n },\n {\n id: \"duckduckgo\",\n name: \"DuckDuckGo Instant Answers\",\n url: \"https://api.duckduckgo.com\",\n capabilities: [\"web-search\", \"instant-answers\", \"research\"],\n status: \"available\",\n },\n {\n id: \"github\",\n name: \"GitHub API\",\n url: \"https://api.github.com\",\n capabilities: [\"repo-management\", \"issue-tracking\", \"code-search\"],\n authHeader: \"Authorization: Bearer \",\n status: \"available\",\n },\n];\n\n// --- Registry Functions ---\n\nexport function getExternalServices(): ExternalService[] {\n return EXTERNAL_SERVICES;\n}\n\nexport function findExternalService(\n capability: string,\n): ExternalService | null {\n return EXTERNAL_SERVICES.find((s) =>\n s.capabilities.some((c) => c.includes(capability) || capability.includes(c))\n ) || null;\n}\n\n/**\n * Builds the complete capability manifest by combining:\n * - Internal agents (from AgentManager)\n * - Internal adapters (from UniversalAdapter)\n * - Available HF models (from hf.ts)\n * - External services (from this registry)\n */\nexport function buildCapabilityList(): Capability[] {\n const caps: Capability[] = [];\n\n // Agents\n for (const agent of getAgentManifest()) {\n caps.push({\n id: `agent:${agent.name}`,\n name: agent.name,\n category: agent.category,\n type: \"agent\",\n actions: agent.capabilities,\n status: \"active\",\n description: `${agent.category} agent — handles: ${\n agent.capabilities.join(\", \")\n }`,\n });\n }\n\n // Adapters\n for (const adapter of listAdapters()) {\n caps.push({\n id: `adapter:${adapter.name}`,\n name: adapter.name,\n category: adapter.type,\n type: \"adapter\",\n actions: [\"execute\"],\n status: adapter.status as \"active\" | \"inactive\" | \"error\",\n description: adapter.description,\n });\n }\n\n // Models\n for (const model of getModelManifest()) {\n caps.push({\n id: `model:${model.key}`,\n name: model.label,\n category: \"ai-model\",\n type: \"model\",\n actions: model.bestFor,\n status: model.status === \"AVAILABLE\" ? \"active\" : \"inactive\",\n description: `HF model ${model.id} — best for: ${\n model.bestFor.join(\", \")\n }`,\n });\n }\n\n // External services\n for (const svc of EXTERNAL_SERVICES) {\n caps.push({\n id: `external:${svc.id}`,\n name: svc.name,\n category: \"external-service\",\n type: \"external\",\n actions: svc.capabilities,\n status: svc.status === \"available\" ? \"active\" : \"inactive\",\n description: `External service at ${svc.url} — provides: ${\n svc.capabilities.join(\", \")\n }`,\n });\n }\n\n return caps;\n}\n\n/**\n * Returns the full service manifest for /register endpoint.\n * This is what external AI agents consume to discover and use this OS.\n */\nexport function getServiceManifest(endpoint: string): ServiceManifest {\n const token = getHFToken();\n return {\n service_id: SERVICE_ID,\n service_name: SERVICE_NAME,\n version: VERSION,\n protocol: PROTOCOL,\n endpoint,\n auth: \"Bearer token via Authorization header (NEXUSES_API_KEY)\",\n capabilities: buildCapabilityList(),\n task_types: [\"coding\", \"creative\", \"analysis\", \"general\"],\n universal_task_format: {\n request: {\n task: \"string — task description or command\",\n data: \"object — task-specific parameters\",\n mode: \"AUTO | CLI | DELEGATE (optional, default AUTO)\",\n source: \"string — calling agent/service ID (optional, for IAP)\",\n task_id: \"string — external task ID for tracking (optional)\",\n },\n response: {\n status: \"success | failure\",\n task_id: \"string — internal task ID\",\n data: \"object — execution results\",\n message: \"string — human-readable summary\",\n agent: \"string — agent that handled the task\",\n model: \"string — HF model used (if any)\",\n fallbackUsed: \"boolean — whether model fallback was triggered\",\n attempts: \"string[] — models tried in order\",\n delegated: \"boolean — whether sub-tasks were delegated externally\",\n executionTimeMs: \"number — total execution time\",\n timestamp: \"string — ISO 8601 timestamp\",\n },\n },\n registered_at: new Date().toISOString(),\n };\n}\n\nexport function getServiceInfo() {\n return {\n service_id: SERVICE_ID,\n service_name: SERVICE_NAME,\n version: VERSION,\n protocol: PROTOCOL,\n };\n}\n\n/**\n * Checks if a capability exists internally.\n * Returns false if the OS would need to delegate externally.\n */\nexport function hasInternalCapability(\n taskText: string,\n): { has: boolean; taskType: TaskType; matched: Capability[] } {\n const taskType = classifyTask(taskText);\n const caps = buildCapabilityList();\n const lower = taskText.toLowerCase();\n\n const matched = caps.filter((c) =>\n c.actions.some((a) => lower.includes(a.toLowerCase()))\n );\n\n return {\n has: matched.length > 0,\n taskType,\n matched,\n };\n}",[193],"94c87211-7786-486b-8839-d0888a162e27","IAP.ts","8da8a598-7924-11f1-b32d-1607ee4eb77e",74,["D",1783337318988],5,"// Inter-Agent Protocol (IAP) — Nexuses Core OS V10.0\n// JSON-RPC 2.0 handshake, capability advertising, and recursive task delegation.\n\nimport {\n findExternalService,\n getServiceInfo,\n getServiceManifest,\n hasInternalCapability,\n} from \"./RegistryManager.ts\";\nimport type { ServiceManifest } from \"./RegistryManager.ts\";\nimport { selectBestModel } from \"./UniversalAdapter.ts\";\nimport { logWorkflowStep } from \"./db.ts\";\n\n// --- JSON-RPC 2.0 Types ---\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\nexport interface HandshakeRequest {\n jsonrpc: \"2.0\";\n method: \"handshake\";\n params: {\n agent_id: string;\n agent_name: string;\n capabilities: string[];\n auth_token?: string;\n };\n id: string | number;\n}\n\nexport interface HandshakeResponse {\n jsonrpc: \"2.0\";\n result: {\n accepted: boolean;\n service: {\n service_id: string;\n service_name: string;\n version: string;\n protocol: string;\n };\n our_capabilities: string[];\n task_format: object;\n challenge: string;\n };\n id: string | number;\n}\n\n// --- Error Codes (JSON-RPC 2.0 standard) ---\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 UNAUTHORIZED: {\n code: -32001,\n message: \"Unauthorized — invalid or missing auth token\",\n },\n CAPABILITY_NOT_FOUND: {\n code: -32002,\n message: \"Requested capability not available\",\n },\n DELEGATION_FAILED: { code: -32003, message: \"External delegation failed\" },\n};\n\nfunction rpcError(\n id: string | number | null,\n error: { code: number; message: string; data?: any },\n): 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// --- Handshake Protocol ---\n\n/**\n * Processes an incoming handshake from an external AI agent.\n * Advertises our capabilities and returns the Universal Task Format.\n */\nexport async function processHandshake(\n req: HandshakeRequest,\n endpoint: string,\n): Promise {\n const manifest = getServiceManifest(endpoint);\n const ourCaps = manifest.capabilities.map((c) =>\n `${c.id}:${c.actions.join(\",\")}`\n );\n\n console.log(\n `[IAP] Handshake from agent: ${req.params.agent_id} (${req.params.agent_name})`,\n );\n console.log(\n `[IAP] Agent capabilities: ${req.params.capabilities.join(\", \")}`,\n );\n\n // Log the handshake\n await logWorkflowStep(\n `handshake_${Date.now()}`,\n 1,\n \"IAP\",\n \"HANDSHAKE\",\n `Agent: ${req.params.agent_id} | Capabilities: ${\n req.params.capabilities.join(\", \")\n }`,\n );\n\n return {\n jsonrpc: \"2.0\",\n result: {\n accepted: true,\n service: getServiceInfo(),\n our_capabilities: ourCaps,\n task_format: manifest.universal_task_format,\n challenge: `nxs_${Date.now()}_${Math.random().toString(36).slice(2, 12)}`,\n },\n id: req.id,\n };\n}\n\n// --- JSON-RPC Method Router ---\n\n/**\n * Processes a JSON-RPC 2.0 request.\n * Supported methods: handshake, discover, execute, delegate\n */\nexport async function processRPC(\n rpcReq: JSONRPCRequest,\n endpoint: string,\n dispatchFn: (task: string, data: any, mode: string) => Promise,\n): Promise {\n // Validate JSON-RPC structure\n if (rpcReq.jsonrpc !== \"2.0\" || !rpcReq.method) {\n return rpcError(rpcReq.id ?? null, RPC_ERRORS.INVALID_REQUEST);\n }\n\n switch (rpcReq.method) {\n case \"handshake\": {\n const hsReq = rpcReq as HandshakeRequest;\n if (!hsReq.params?.agent_id) {\n return rpcError(rpcReq.id ?? null, RPC_ERRORS.INVALID_PARAMS);\n }\n const result = await processHandshake(hsReq, endpoint);\n return result as unknown as JSONRPCResponse;\n }\n\n case \"discover\": {\n const manifest = getServiceManifest(endpoint);\n return rpcResult(rpcReq.id!, {\n service: getServiceInfo(),\n capabilities: manifest.capabilities,\n task_format: manifest.universal_task_format,\n });\n }\n\n case \"execute\": {\n const { task, data, mode } = rpcReq.params || {};\n if (!task) {\n return rpcError(rpcReq.id ?? null, {\n ...RPC_ERRORS.INVALID_PARAMS,\n data: \"Missing 'task' field\",\n });\n }\n try {\n const result = await dispatchFn(task, data || {}, mode || \"AUTO\");\n return rpcResult(rpcReq.id!, result);\n } catch (e: any) {\n return rpcError(rpcReq.id ?? null, {\n ...RPC_ERRORS.INTERNAL_ERROR,\n data: e.message,\n });\n }\n }\n\n case \"delegate\": {\n const { task, data, target } = rpcReq.params || {};\n if (!task) {\n return rpcError(rpcReq.id ?? null, {\n ...RPC_ERRORS.INVALID_PARAMS,\n data: \"Missing 'task' field\",\n });\n }\n const result = await delegateToExternal(task, data || {}, target);\n if (result.success) {\n return rpcResult(rpcReq.id!, result);\n }\n return rpcError(rpcReq.id ?? null, {\n ...RPC_ERRORS.DELEGATION_FAILED,\n data: result,\n });\n }\n\n default:\n return rpcError(rpcReq.id ?? null, {\n ...RPC_ERRORS.METHOD_NOT_FOUND,\n data: `Unknown method: ${rpcReq.method}`,\n });\n }\n}\n\n// --- Recursive Task Delegation ---\n\nexport interface DelegationResult {\n success: boolean;\n delegated: boolean;\n target: string | null;\n data: any;\n message: string;\n integrated: boolean;\n}\n\n/**\n * Delegates a sub-task to an external AI service when the OS lacks\n * the internal capability. Integrates the result back for the final JSON.\n */\nexport async function delegateToExternal(\n task: string,\n data: any,\n targetHint?: string,\n): Promise {\n // Find an external service that can handle this\n let service = targetHint ? findExternalService(targetHint) : null;\n if (!service) {\n const check = hasInternalCapability(task);\n // Try to find external service matching the task type\n service = findExternalService(check.taskType) ||\n findExternalService(task.toLowerCase());\n }\n\n if (!service) {\n return {\n success: false,\n delegated: false,\n target: null,\n data: {},\n message: \"No external service available for this capability\",\n integrated: false,\n };\n }\n\n console.log(\n `[IAP] Delegating to external service: ${service.name} (${service.url})`,\n );\n\n // Build the delegation request\n const token = service.authHeader?.includes(\"HF_API_KEY\")\n ? Deno.env.get(\"HF_API_KEY\") || Deno.env.get(\"HUGGING_FACE_TOKEN\")\n : service.authHeader?.includes(\"GITHUB_TOKEN\")\n ? Deno.env.get(\"GITHUB_TOKEN\")\n : null;\n\n try {\n let resultData: any;\n\n if (service.id.startsWith(\"hf\")) {\n // Hugging Face delegation via new Router endpoint (chat completions)\n const hfKey = Deno.env.get(\"HF_API_KEY\") || token;\n const res = await fetch(\n \"https://router.huggingface.co/v1/chat/completions\",\n {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${hfKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model: selectBestModel(data.prompt || task),\n messages: [{ role: \"user\", content: data.prompt || task }],\n max_tokens: 500,\n }),\n signal: AbortSignal.timeout(25000),\n },\n );\n if (!res.ok) {\n return {\n success: false,\n delegated: true,\n target: service.id,\n data: {},\n message: `External service returned HTTP ${res.status}`,\n integrated: false,\n };\n }\n const hfData = await res.json();\n resultData = hfData.choices?.[0]?.message?.content\n ? { generated_text: hfData.choices[0].message.content }\n : hfData;\n } else {\n // Generic external API call\n const res = await fetch(service.url, {\n signal: AbortSignal.timeout(15000),\n });\n resultData = await res.json();\n }\n\n // Log the delegation\n await logWorkflowStep(\n `delegation_${Date.now()}`,\n 1,\n \"IAP\",\n \"DELEGATE\",\n `Task: \"${task}\" → Service: ${service.id} → Success`,\n );\n\n return {\n success: true,\n delegated: true,\n target: service.id,\n data: resultData,\n message: `Delegated to ${service.name} and integrated result`,\n integrated: true,\n };\n } catch (e: any) {\n console.log(`[IAP] Delegation failed: ${e.message}`);\n return {\n success: false,\n delegated: true,\n target: service.id,\n data: {},\n message: `Delegation error: ${e.message}`,\n integrated: false,\n };\n 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Diff · v75