Search

1. When a new message is posted in a configured Slack channel (ie. #bugs, or #support), Slack sends an event to this Val
2. The val makes an OpenAI call to determine if the message is a bug 
3. If it is, then it searches GitHub for semantically related open issues with a separate OpenAI call
4. It posts a comment in the Slack thread with links to related GitHub issues, with a "Relevance Score"

// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
import { Hono } from "npm:hono@4.4.12";
app.post("/api/analyze", async (c) => {
  try {
    const openai = new OpenAI({ apiKey: Deno.env.get("OPENAI_API_KEY") });
    const body = await c.req.json();
      .replace("{{supplier_database}}", JSON.stringify(body.supplier_database, null, 2));
    const completion = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [{ role: "user", content: prompt }],

import { Hono } from "npm:hono@4.4.12";
// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// @ts-ignore
import { sqlite } from "https://esm.town/v/std/sqlite?v=4";
  `;
  const openai = new OpenAI();
  const completion = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [

[ SQLite ](https://docs.val.town/std/sqlite) Store and retrieve structured data
[ Blob Storage ](https://docs.val.town/std/blob) Store and retrieve any data
[ OpenAI ](https://docs.val.town/std/openai) Use the OpenAI API
[ Email ](https://docs.val.town/std/email) Send emails
##### API and SDK

Note: When changing a SQLite table's schema, change the table's name (e.g., add \_2 or \_3) to create a fresh table.

### 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" }],
  model: "gpt-4o-mini",

- [ ] Get OpenTownie or Gemini or Claude or OpenAI to synthesize the core of these patterns into a prompt we can use to make more ReactRouter apps, such as...
- [ ] Convert this or into the basic react router guest book (and preserve this forum app in another project?)
- [ ] To what extent can these patterns be packaged up into a Val Town Router project? Would be neat to get the version pinning thing all centralized, can this as-a-library be that centralized place?

import { AgentContext, AgentInput, AgentOutput } from "https://esm.town/v/AIWB/agentRegistry/interfaces.ts";
import { fetch } from "https://esm.town/v/std/fetch";
import { OpenAI } from "https://esm.town/v/std/openai";
// If Deno.env is used, ensure it's for Val Town secrets if possible or clearly noted.
// For direct Deno.env.get('OPENAI_API_KEY'), it relies on the Val Town environment variable being set.
// The std/openai library usually handles API key abstraction using user's Val Town secrets.
// Your provided summarizerAgent
    }
    const openai = new OpenAI(); // Using OpenAI client as per your example
    log("INFO", "SummarizerAgent", "Generating summary with OpenAI...");
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini", // Explicitly setting a model
      messages: [
    if (summary === "Could not generate summary.") {
      log("WARN", "SummarizerAgent", "OpenAI did not return a valid summary content.");
    }
// Your provided combinerAgent (not directly used in the 12-step orchestrator, but kept for reference)
export async function combinerAgent(
  // ... (combinerAgent code as you provided, also updating its OpenAI call) ...
  input: AgentInput<{
    summary?: string;
    }
    const openai = new OpenAI(); // Using OpenAI client as per your example
    log("INFO", "CombinerAgent", "Combining text with OpenAI...");
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini", // Explicitly setting a model
      messages: [
    const combined = completion.choices[0]?.message?.content?.trim() ?? "Could not combine information.";
    if (combined === "Could not combine information.")
      log("WARN", "CombinerAgent", "OpenAI did not return valid combined content.");
    log("SUCCESS", "CombinerAgent", "Combined successfully");
    return { mandateId, correlationId: taskId, payload: { combined } };
  const { userQuery } = input.payload;
  log("INFO", "ConfigurationAgent", `Processing query: ${userQuery}`);
  const openai = new OpenAI();
  const systemPrompt =
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [{ role: "system", content: systemPrompt }, { role: "user", content: userQuery }],
  const { log } = context;
  const { articles, topic, keywords } = input.payload;
  const openai = new OpenAI();
  let relevantArticles: FetchedArticle[] = [];
  log("INFO", "RelevanceAssessmentAgent", `Assessing relevance for ${articles.length} articles on topic: ${topic}`);
    let assessment = { isRelevant: false, rationale: "LLM assessment failed or unclear." };
    try {
      const completion = await openai.chat.completions.create({
        model: "gpt-4o-mini",
        messages: [{ role: "system", content: "You are a relevance assessor. Output JSON." }, {
): Promise<AgentOutput<{ articlesWithSentiment: FetchedArticle[] }>> {
  const { log } = context;
  const openai = new OpenAI();
  let articlesWithSentiment: FetchedArticle[] = [];
  if (!input.payload.articles)
    let sentimentScore: FetchedArticle["sentimentScore"] = 0.0;
    try {
      const completion = await openai.chat.completions.create({
        model: "gpt-4o-mini",
        messages: [{ role: "system", content: "You are a sentiment analysis expert. Output JSON." }, {
): Promise<AgentOutput<{ articlesWithThemes: FetchedArticle[] }>> {
  const { log } = context;
  const openai = new OpenAI();
  let articlesWithThemes: FetchedArticle[] = [];
  log(
    let themeResult: { themes: string[]; entities: FetchedArticle["entities"] } = { themes: [], entities: [] };
    try {
      const completion = await openai.chat.completions.create({
        model: "gpt-4o-mini",
        messages: [{ role: "system", content: "You are a thematic/NER expert. Output JSON." }, {
  const { log } = context;
  const { articlesWithThemes, topic, historicalContextSummary } = input.payload;
  const openai = new OpenAI();
  log(
    "INFO",
  let trendReport: any = { dominantSentiment: "N/A", keyThemes: [], emergingPatterns: "Not analyzed." };
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [{ role: "system", content: "Trend analysis expert. Output JSON." }, {
  let anomalyReport: any = { anomaliesFound: false, anomalyDetails: [] };
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [{ role: "system", content: "Anomaly detection expert. Output JSON." }, {
  const { log } = context;
  const { trendReport, anomalyReport, config, articlesCount } = input.payload;
  const openai = new OpenAI();
  log("INFO", "InsightGenerationAgent", `Generating insights for topic: ${config.topic}`);
  const contextSummary =
  let insights: string[] = ["No specific insights generated."];
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [{ role: "system", content: "Strategic analyst providing concise insights." }, {
  log("INFO", "ReportCompilationAgent", `Compiling report for topic: ${config.topic}`); // Directly use 'config'
  const openai = new OpenAI(); // Ensure API key is configured via environment variables or client options
  // The agent code used 'relevantArticles'. If 'articlesWithThemes' is the correct data source
  let finalReport = "Report generation failed.";
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [
  const { log } = context;
  const { anomalyReport, insights, config } = input.payload;
  const openai = new OpenAI();
  let criticalInfo = "";
  if (anomalyReport?.anomaliesFound && anomalyReport.anomalyDetails.length > 0) {
  let alertMessage: string | undefined = undefined;
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [{ role: "system", content: "Alert generation specialist." }, { role: "user", content: prompt }],

import { AgentContext, AgentInput, AgentOutput } from "https://esm.town/v/AIWB/agentRegistry/interfaces.ts";
import { fetch } from "https://esm.town/v/std/fetch";
import { OpenAI } from "https://esm.town/v/std/openai";
// Summarizer Agent (unchanged, but shown for completeness)
    }
    const openai = new OpenAI();
    log("INFO", "SummarizerAgent", "Generating summary with OpenAI...");
    const completion = await openai.chat.completions.create({
      messages: [
        {
    if (summary === "Could not generate summary.") {
      log("WARN", "SummarizerAgent", "OpenAI did not return a valid summary content.");
    }
    }
    const openai = new OpenAI();
    log("INFO", "CombinerAgent", "Combining text with OpenAI...");
    const completion = await openai.chat.completions.create({
      messages: [
        {
    if (combined === "Could not combine information.") {
      log("WARN", "CombinerAgent", "OpenAI did not return valid combined content.");
    }

import { blob } from "https://esm.town/v/std/blob?v=11";
// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// --- CONSTANTS & CONFIGURATION ---

- [ ] Get OpenTownie or Gemini or Claude or OpenAI to synthesize the core of these patterns into a prompt we can use to make more ReactRouter apps, such as...
- [ ] Convert this or into the basic react router guest book (and preserve this forum app in another project?)
- [ ] To what extent can these patterns be packaged up into a Val Town Router project? Would be neat to get the version pinning thing all centralized, can this as-a-library be that centralized place?