Search

_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" },

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" },

_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" },

- **Handwriting canvas** — chisel-nib calligraphy engine with quill physics
- **OCR via Tesseract.js** — your handwriting → text, right in the browser
- **AI memory** — powered by OpenAI, with persistent conversation per session
- **The aesthetic** — parchment textures, ink fade effects, no UI chrome

import { OpenAI } from "https://esm.town/v/std/openai";
import { sqlite } from "https://esm.town/v/std/sqlite@14-main/main.ts";
import { SYSTEM_PROMPT } from "./prompt.ts";
import { html } from "./page.ts";
const openai = new OpenAI();
// --- Session storage via SQLite ---
      // Send last 16 messages for context (fits in token window easily)
      const chatResult = await openai.chat.completions.create({
        model: "gpt-4o-mini",
        max_tokens: 200,

import { OpenAI } from "https://esm.sh/openai@4.20.1";
export default async function handler(req: Request): Promise<Response> {
    console.log(`📸 Processing ${base64Images.length} image(s)`);
    const apiKey = Deno.env.get("OPENAI_API_KEY");
    if (!apiKey) {
      console.error("❌ OPENAI_API_KEY not set");
      return new Response(
        JSON.stringify({
    }
    const openai = new OpenAI({ apiKey });
    console.log("🤖 Calling OpenAI for fish identification...");
    // Language-specific instruction
    }
    const response = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [
    if (!content) {
      throw new Error("No response from OpenAI");
    }
    if (error instanceof Error) {
      if (
        error.message === "No response from OpenAI" ||
        error.message === "Invalid response format from AI"
      ) {

 * Add self-editing capability to any Val. Import this and mount
 * the handler on /editme and /mcp. Then POST instructions
 * and an OpenAI agent will modify your Val's files via a self-hosted MCP.
 * 
 * Usage:
 *   const config = {
 *     val: "yourhandle/yourval",
 *     openaiApiKey: Deno.env.get("OPENAI_API_KEY")!,
 *     valtownApiKey: Deno.env.get("VALTOWN_API_KEY")!,
 *     bearerToken: Deno.env.get("EDITME_SECRET"),  // optional auth
 */
import OpenAI from "npm:openai";
// ============= Types =============
  /** Val identifier: "handle/valName" format */
  val: string;
  /** OpenAI API key */
  openaiApiKey: string;
  /** Val Town API key (needs write access to the val) */
  valtownApiKey: string;
  /** Optional bearer token to protect the /editme endpoint */
  bearerToken?: string;
  /** OpenAI model (default: gpt-5.2) — can also be overridden per-request */
  model?: string;
  /** Reasoning effort: "low" | "medium" | "high" (default: "medium") */
  modelOverride?: string,
): Promise<{ summary: string; steps: string[]; model: string }> {
  const openai = new OpenAI({ apiKey: config.openaiApiKey });
  const model = modelOverride || config.model || "gpt-5.2";
  const reasoningEffort = config.reasoningEffort || "medium";
  }
  console.log(`🔌 OpenAI Responses API (model=${model}, mcp=${mcpUrl})`);
  let response = await openai.responses.create(createOpts);
  for (let turn = 0; turn < MAX_TURNS; turn++) {
        contOpts.reasoning = { effort: reasoningEffort };
      }
      response = await openai.responses.create(contOpts);
    } else {
      console.log(`⚠️ Unexpected status: ${response.status}`);
/**
 * Create an HTTP handler that accepts edit instructions and applies them
 * to the specified Val using an OpenAI agent backed by a self-hosted MCP.
 * 
 * IMPORTANT: You must also mount createMcpHandler on /mcp (or specify mcpPath).

| `TODOS_DB_ID`    | Database ID where todos are synced (32-char ID without hyphens)                       |
### Recommended: OpenAI API Key
| Variable         | Description                                           |
| ---------------- | ----------------------------------------------------- |
| `OPENAI_API_KEY` | OpenAI API key for AI features (strongly recommended) |
**What AI does:**
- Disambiguates when multiple contacts match a name
**Without `OPENAI_API_KEY`:** Falls back to Val Town's shared OpenAI client (10 req/min limit). If AI is unavailable, the system still works but uses raw text, heading/@mention-only owner resolution, and default due dates.
### Required Database Properties
Visit the root URL of your val (`main.http.tsx`) to see a dashboard showing:
- **Connection status**: Notion API and OpenAI connectivity
- **Property mappings**: Which properties are configured and whether they exist in your database
| ----------------------- | --------------------------------------------- |
| `NOTION_WEBHOOK_SECRET` | API key for protecting endpoints              |
| `OPENAI_API_KEY`        | For AI features (see Quick Start for details) |
---

- **Notion API** - Subject to Notion's terms of service and API limitations
- **OpenAI API** (optional) - Subject to OpenAI's terms of service
- **Val Town** - Subject to Val Town's terms of service

1. Click [**Remix**](/?intent=remix)
2. Add environment variables:
   - `OPENAI_API_KEY` — for AI lead qualification
   - `GITHUB_TOKEN` — for accessing GitHub API
     ([create one here](https://github.com/settings/tokens))