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### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

// @ts-nocheck
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// --- AI BEHAVIORAL GUIDELINES ---
  if (url.pathname === "/generatePrompts" && req.method === "POST") {
    try {
      const openai = new OpenAI();
      const { subject } = await req.json();
      }
      const completion = await openai.chat.completions.create({
        model: "gpt-4o",
        messages: [

import { streamText } from "npm:hono@4.4.12/streaming";
// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// --- TYPE DEFINITIONS ---
// --- API HELPER ---
async function callOpenAI(
  systemPrompt: string,
  userContent: string | object,
  isJson = true,
) {
  const openai = new OpenAI();
  const content = typeof userContent === "string"
    ? userContent
    : JSON.stringify(userContent);
  const completion = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [
  }
  const { context } = await c.req.json();
  const result = await callOpenAI(LIST_GENERATOR_PROMPT(type, context), "");
  return c.json(result);
});
app.post("/api/prompt/dynamic", async (c) => {
  const body = await c.req.json();
  const result = await callOpenAI(PROMPT_REFINER_PROMPT, body);
  return c.json(result);
});
app.post("/api/inputs", async (c) => {
  const { refined_prompt } = await c.req.json();
  const result = await callOpenAI(FORM_GENERATOR_PROMPT, { refined_prompt });
  return c.json(result);
});
app.post("/api/clarify", async (c) => {
  const { refined_prompt } = await c.req.json();
  const result = await callOpenAI(CLARIFICATION_PROMPT, { refined_prompt });
  return c.json(result);
});
    inputs: { ...user_inputs, ...clarifications },
  };
  const raw_output_v1 = await callOpenAI(
    "Execute the provided prompt template using the given inputs. Produce only the raw output.",
    v1UserContent,
    output: raw_output_v1,
  };
  const { criteria } = await callOpenAI(
    CRITERIA_GENERATOR_PROMPT,
    criteriaContext,
    criteria: criteria,
  };
  const evaluation_v1: Evaluation = await callOpenAI(
    EVALUATOR_PROMPT,
    v1EvalContext,
    evaluation: evaluation_v1,
  };
  const raw_output_v2 = await callOpenAI(
    REFINER_PROMPT,
    v2RefineContext,
    criteria: criteria,
  };
  const evaluation_v2: Evaluation = await callOpenAI(
    EVALUATOR_PROMPT,
    v2EvalContext,

import { Hono } from "npm:hono@4.4.12";
// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai";
import type { Context } from "npm:hono@4.4.12";
import { streamText } from "npm:hono/streaming";
const services = {
  /**
   * A centralized function for making calls to the OpenAI API.
   * @param systemPrompt - The system prompt to guide the AI.
   * @param userContent - The user's input.
   * @param options - Additional options like model, streaming, and response format.
   * @returns A promise that resolves to the parsed JSON response or an OpenAI stream.
   */
  async callOpenAI(
    systemPrompt: string,
    userContent: string | object,
    } = options;
    const openai = new OpenAI();
    const messages: any[] = [
      { role: "system", content: systemPrompt },
    try {
      const completion = await openai.chat.completions.create(requestPayload);
      if (stream && c) {
      return completion;
    } catch (e) {
      console.error(`Error calling OpenAI: ${e.message}`);
      throw new Error("AI service failed.");
    }
    ...body,
  };
  return services.callOpenAI(
    config.prompts.DYNAMIC_LIST_GENERATOR,
    userContent,
    JSON.stringify(company_context)
  }\n\nOccupation: ${occupation_title}\n\nTask: ${task}`;
  return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {
    c,
    isJson: true,
app.post("/api/inputs", async (c: Context) => {
  const { refined_prompt } = await c.req.json<InputsBody>();
  return services.callOpenAI(config.prompts.INPUT_EXTRACTOR, refined_prompt, {
    c,
    isJson: true,
app.post("/api/clarify", async (c: Context) => {
  const { refined_prompt } = await c.req.json<ClarifyBody>();
  return services.callOpenAI(
    config.prompts.CLARIFICATION_AGENT,
    refined_prompt,
  return streamText(c, async (stream) => {
    try {
      const openai = new OpenAI();
      // 2. First call to the AI to see if it wants to use a tool
      const initialResponse = await openai.chat.completions.create({
        model: config.models.major,
        messages,
        // 5. Make the final call with tool results included, and stream the response
        const finalStream = await openai.chat.completions.create({
          model: config.models.major,
          messages,
    JSON.stringify(company_context)
  }\n\nTask Briefing:\n${refined_prompt}`;
  return services.callOpenAI(
    config.prompts.EVALUATION_CRITERIA_GENERATOR,
    userContent,
  const systemPrompt =
    `${config.prompts.EVALUATOR_AGENT}\n\nOutput Language: ${language}`;
  return services.callOpenAI(systemPrompt, userContent, { c, stream: true });
});
  const systemPrompt =
    `${config.prompts.EVALUATOR_AGENT}\n\nOutput Language: ${language}`;
  return services.callOpenAI(systemPrompt, userContent, { c, stream: true });
});
  const systemPrompt =
    `${config.prompts.REFINER_AGENT}\n\nOutput Language: ${language}`;
  return services.callOpenAI(systemPrompt, userContent, { c, stream: true });
});

// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// --- TYPE DEFINITIONS ---
  const url = new URL(req.url);
  const action = url.searchParams.get("action");
  const openai = new OpenAI();
  if (req.method === "GET") {
            ? "occupations"
            : "tasks";
          const completion = await openai.chat.completions.create({
            model: "gpt-4o",
            messages: [{ role: "system", content: DYNAMIC_LIST_GENERATOR }, {
            JSON.stringify(company_context)
          }\n\nOccupation: ${occupation_title}\n\nTask: ${task}`;
          const completion = await openai.chat.completions.create({
            model: "gpt-4o",
            messages: [{ role: "system", content: PROMPT_REFINER }, {
        case "getInputs": {
          const completion = await openai.chat.completions.create({
            model: "gpt-4o",
            messages: [{ role: "system", content: INPUT_EXTRACTOR }, {
          ];
          const initialResponse = await openai.chat.completions.create({
            model: "gpt-4o",
            messages: messages,
            if (functionName === "createSpecialistAgent") {
              const refineResponse = await openai.chat.completions.create({
                model: "gpt-4o",
                messages: [{ role: "system", content: PROMPT_REFINER }, {
              );
              const inputsResponse = await openai.chat.completions.create({
                model: "gpt-4o",
                messages: [{ role: "system", content: INPUT_EXTRACTOR }, {
              };
              const finalResponse = await openai.chat.completions.create({
                model: "gpt-4o",
                messages: [...messages, responseMessage, {
              }
              const executionResponse = await openai.chat.completions.create({
                model: "gpt-4o",
                messages: [

  const triggerCodeAnalysis = async (codeToAnalyze) => {
    try {
      const { OpenAI } = await import("https://esm.town/v/std/openai");
      const openai = new OpenAI();
      
      const analysis = await openai.chat.completions.create({
        messages: [
          {

import { Hono } from "npm:hono@4.4.12";
// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai";
import type { Context } from "npm:hono@4.4.12";
import { streamText } from "npm:hono/streaming";
app.post("/api/blueprint/simulate-node", async (c: Context) => {
  const { node } = await c.req.json();
  const openai = new OpenAI();
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [
app.post("/api/blueprint/execute", async (c: Context) => {
  const { blueprint } = await c.req.json();
  const openai = new OpenAI();
  try {
    const stream = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [

The system automatically selects the most appropriate AI model based on the notification characteristics:
- **Fast Model** (`openai/gpt-oss-20b`): Simple notifications with low severity and minimal description - ultra-cheap at $0.10/$0.50 per million tokens
- **Balanced Model** (`anthropic/claude-3.5-sonnet`): Standard triage operations (default)
- **Advanced Model** (`openai/gpt-4o`): Critical issues requiring sophisticated analysis
- **Reasoning Model** (`moonshotai/kimi-k2`): Complex scenarios with multiple factors, excellent for tool use and coding
| Model | Provider | Use Case | Cost (per 1M tokens) | Max Tokens | Context |
|-------|----------|----------|---------------------|------------|---------|
| GPT-OSS 20B | OpenAI | Fast | $0.10/$0.50 | 131K | Open-weight MoE |
| Claude 3.5 Sonnet | Anthropic | Balanced | $3/$15 | 8K | Best overall |
| GPT-4o Mini | OpenAI | Fast | $0.15/$0.60 | 16K | Fallback fast |
| GPT-4o | OpenAI | Advanced | $5/$15 | 4K | Complex analysis |
| Kimi K2 Instruct | MoonshotAI | Reasoning | $1/$3 | 131K | Tool use expert |
| Claude 3 Opus | Anthropic | Reasoning | $15/$75 | 4K | Most capable |
  modelSelection: {
    default: 'anthropic/claude-3.5-sonnet',  // Change default model
    fast: 'openai/gpt-oss-20b',             // Ultra-cheap for simple notifications  
    advanced: 'openai/gpt-4o',              // For critical analysis
    reasoning: 'moonshotai/kimi-k2'         // For complex scenarios with tool use
  }

// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// @ts-ignore
import { APIError } from "npm:openai/error";
// --- AI BEHAVIORAL GUIDELINES ---
const sleep = (ms: number) => new Promise((resolve) => setTimeout(resolve, ms));
async function createCompletionWithRetry(openai, options, maxRetries = 3) {
  let lastError;
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await openai.chat.completions.create(options);
    } catch (error) {
      lastError = error;
      if (error instanceof APIError && error.status && error.status >= 500) {
        console.log(`OpenAI API request failed. Retrying in ${i + 1}s...`);
        await sleep((i + 1) * 1000);
        continue;
  if (req.method === "POST") {
    try {
      const openai = new OpenAI();
      const body = await req.json();
      const mood = body.mood;
      const completionOptions:
        OpenAI.Chat.Completions.ChatCompletionCreateParams = {
          model: "gpt-4o",
          messages: [{ role: "system", content: MOOD_ANALYSIS_PROMPT }, {
      const completion = await createCompletionWithRetry(
        openai,
        completionOptions,
      );

  modelId: string, 
  prompt: string = "Hello! Please respond with a simple greeting.",
  provider?: 'groq' | 'openai' | 'anthropic' | 'auto'
): Promise<{ success: boolean; response?: string; error?: string; duration?: number; provider?: string }> {
  try {