Adding New Experiments

This guide walks you through the process of adding a new experiment to the Cloudflare Experiments repository.

Before You Start

Always open an issue using the Experiment idea template to discuss your experiment with maintainers before implementing it.

What Makes a Good Experiment?

A good experiment:

Demonstrates a specific Cloudflare capability (Workers AI, Browser Rendering, D1, KV, R2, etc.)
Solves a real developer need (not just “Hello World”)
Is edge-first and runs in under 60 seconds
Has a single responsibility (one experiment = one capability)
Is independently deployable without dependencies on other experiments
Provides practical value that developers would actually use

Step-by-Step Process

Open an experiment idea issue

Use the Experiment idea template to propose your experiment. Include:

What Cloudflare capability it demonstrates
What problem it solves
Basic description of how it works
Any special bindings or resources needed (D1, KV, AI, etc.)

Wait for maintainer feedback before proceeding.

Create the experiment directory

Create a new directory under experiments/<name>/:

mkdir -p experiments/my-experiment
cd experiments/my-experiment

Use kebab-case for the experiment name (e.g. is-it-down, link-shortener).

Set up the project structure

Create the standard directory structure:

mkdir -p src/{routes,lib,utils,types,constants}

Your directory should look like:

experiments/my-experiment/
├── src/
│   ├── index.ts
│   ├── routes/
│   ├── lib/
│   ├── utils/
│   ├── types/
│   └── constants/
├── package.json
├── wrangler.toml
├── tsconfig.json
└── README.md

Initialize package.json

Create a package.json with the required dependencies:

{
  "name": "my-experiment",
  "version": "1.0.0",
  "type": "module",
  "scripts": {
    "dev": "wrangler dev",
    "deploy": "wrangler deploy"
  },
  "dependencies": {
    "hono": "^4.0.0"
  },
  "devDependencies": {
    "@cloudflare/workers-types": "^4.0.0",
    "typescript": "^5.0.0",
    "wrangler": "^3.0.0"
  }
}

Then install dependencies:

npm install

Create wrangler.toml

Configure your Worker with wrangler.toml:

name = "my-experiment"
main = "src/index.ts"
compatibility_date = "2024-01-01"

# Add bindings as needed:
# [[d1_databases]]
# binding = "DB"
# database_name = "my-database"
# database_id = "..."

# [[kv_namespaces]]
# binding = "MY_KV"
# id = "..."

Or use wrangler.json if you prefer JSON format.

Set up TypeScript configuration

Create tsconfig.json:

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "ES2022",
    "lib": ["ES2022"],
    "types": ["@cloudflare/workers-types"],
    "strict": true,
    "moduleResolution": "node",
    "skipLibCheck": true,
    "esModuleInterop": true,
    "resolveJsonModule": true
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules"]
}

Implement the Worker

Follow the code standards to implement your Worker:Create src/types/env.d.ts:

/// <reference types="@cloudflare/workers-types" />

export interface Env {
  // Add your bindings here
  // DB: D1Database;
  // MY_KV: KVNamespace;
}

Create src/utils/response.ts:

import type { Context } from "hono";

export function jsonError(c: Context, message: string, code: string, status: 400 | 404 | 502 = 400) {
  return c.json({ error: message, code }, { status });
}

export function jsonSuccess<T>(c: Context, data: T, status: 200 = 200) {
  return c.json(data, { status });
}

Create src/lib/url.ts (if handling URL params):

/**
 * Validates and normalizes a URL for safe fetching.
 * Returns the URL string or null if invalid/not allowed.
 */
export function validateUrl(input: string | undefined): string | null {
  if (!input || typeof input !== "string") return null;
  const trimmed = input.trim();
  if (!trimmed) return null;
  try {
    const url = new URL(trimmed);
    if (url.protocol !== "http:" && url.protocol !== "https:") {
      return null;
    }
    return url.href;
  } catch {
    return null;
  }
}

Create your route handlers in src/routes/:

import { Hono } from "hono";
import type { Env } from "../types/env";
import { jsonError, jsonSuccess } from "../utils/response";

const app = new Hono<{ Bindings: Env }>();

app.get("/my-route", async (c) => {
  // Your logic here
  return jsonSuccess(c, { message: "Success" });
});

export default app;

Create src/index.ts:

import { Hono } from "hono";
import type { Env } from "./types/env";
import myRoutes from "./routes/my-route";

const app = new Hono<{ Bindings: Env }>();

app.route("/", myRoutes);

app.get("/", (c) => {
  return c.json({
    name: "my-experiment",
    description: "What this experiment does",
    usage: "GET /my-route",
  });
});

app.onError((err, c) => {
  return c.json({ error: err.message, code: "INTERNAL_ERROR" }, 500);
});

export default {
  fetch: app.fetch,
};

Test locally

Run your experiment locally:

npm run dev

Test your endpoints:

curl http://localhost:8787/my-route

Verify:

All routes work as expected
Error handling works correctly
TypeScript has no errors

Create README.md

Document your experiment with a comprehensive README:

# My Experiment

Brief description of what this experiment does.

## Features

- Feature 1
- Feature 2

## API

### `GET /my-route`

| Query | Required | Description |
| ----- | -------- | ----------- |
| `param` | Yes | Description |

**Example**

```http
GET /my-route?param=value

Response

{
  "result": "data"
}

Errors

400 - Invalid parameter

Run locally

cd experiments/my-experiment
npm install
npm run dev

Deploy

Cloudflare features used

Workers
List relevant features

</Step>

<Step title="Add to root README.md">
Add a row to the experiments table in the root `README.md`:
```markdown
| [My Experiment](experiments/my-experiment/) | Brief description | [Deploy](https://deploy.workers.cloudflare.com/?url=https://github.com/YOUR_USERNAME/cloudflare-experiments/tree/main/experiments/my-experiment) |

Submit a pull request

Create a PR with:

Clear title: “Add [experiment-name] experiment”
Description linking to the experiment idea issue
Screenshots or examples of the API in action (if applicable)
Confirmation that npx wrangler deploy works

Fill in the PR template with all required information.

Common Patterns and Examples

Using Reference Experiments

You can use existing experiments as references:

Simple stateless experiment

Reference: whereami or is-it-downThese experiments:

Don’t use persistent storage
Have simple route handlers
Focus on edge networking

Good for: Simple APIs, URL validators, fetch-based tools

Experiment with database

Reference: link-shortenerThis experiment:

Uses D1 for primary storage
Uses KV for read cache
Handles database errors gracefully
Implements retry logic for unique constraints

Good for: CRUD operations, data persistence, caching patterns

Experiment with AI

Reference: ai-website-summary or github-repo-explainerThese experiments:

Use Workers AI binding
Handle streaming responses
Process external content

Good for: AI-powered tools, content analysis, summarization

Experiment with Browser Rendering

Reference: screenshot-apiThis experiment:

Uses Browser Rendering API
Handles binary responses (images)
Implements proper timeouts

Good for: Screenshot tools, browser automation, PDF generation

Error Handling Patterns

Handling fetch errors:

export async function fetchWithTiming(url: string): Promise<FetchResult> {
  const start = Date.now();
  const controller = new AbortController();
  const timeoutId = setTimeout(() => controller.abort(), FETCH_TIMEOUT_MS);

  try {
    const res = await fetch(url, {
      method: "GET",
      signal: controller.signal,
      headers: { "User-Agent": "Cloudflare-Experiments/1.0" },
    });
    const responseTimeMs = Date.now() - start;
    clearTimeout(timeoutId);
    return {
      ok: res.ok,
      statusCode: res.status,
      responseTimeMs,
    };
  } catch (e) {
    clearTimeout(timeoutId);
    const responseTimeMs = Date.now() - start;
    const message = e instanceof Error ? e.message : "Unknown error";
    return {
      ok: false,
      statusCode: 0,
      responseTimeMs,
      error: message,
    };
  }
}

Handling JSON parsing errors:

let body: RequestBody;
try {
  body = (await c.req.json()) as RequestBody;
} catch {
  return jsonError(c, "Invalid or missing JSON body", "INVALID_BODY");
}

Handling database constraint errors:

try {
  await db.prepare("INSERT INTO table (id, data) VALUES (?, ?)").bind(id, data).run();
  return jsonSuccess(c, { id });
} catch (e) {
  const err = e as { message?: string };
  if (err.message?.includes("UNIQUE constraint")) {
    return jsonError(c, "Record already exists", "DUPLICATE_ENTRY");
  }
  throw e; // Let global error handler deal with it
}

Deployment Checklist

Before submitting your PR, verify:

Getting Help

What if I'm not sure which Cloudflare feature to use?

Open a Discussion describing what you want to build. The community can help suggest the right Cloudflare features.

Can I use multiple Cloudflare features in one experiment?

Yes, but the experiment should have a single clear purpose. For example, link-shortener uses both D1 and KV, but its purpose is clear: shorten URLs.

Do I need to create database migrations?

Include the SQL schema in your README.md so users can set up the database. For D1, users will run wrangler d1 execute with your schema.

How do I handle secrets?

Use Cloudflare secrets via wrangler secret put SECRET_NAME. Document required secrets in your README.md. Never commit actual secret values.

Next Steps

Review the Code Standards for implementation details
Check the Contributing Guide for PR guidelines
Browse existing experiments for inspiration
Open an experiment idea issue to get started

Thank you for contributing to Cloudflare Experiments!

Code and Structure Standards

⌘I

Get Started

AI & Machine Learning

Web Scraping & Parsing

Browser & Screenshots

Network & Monitoring

Storage & Data

Contributing

Before You Start

What Makes a Good Experiment?

Step-by-Step Process

Run locally

Deploy

Cloudflare features used

Common Patterns and Examples

Using Reference Experiments

Error Handling Patterns

Deployment Checklist

Getting Help

Next Steps

Get Started

AI & Machine Learning

Web Scraping & Parsing

Browser & Screenshots

Network & Monitoring

Storage & Data

Contributing

​Before You Start

​What Makes a Good Experiment?

​Step-by-Step Process

​Run locally

​Deploy

​Cloudflare features used

​Common Patterns and Examples

​Using Reference Experiments

​Error Handling Patterns

​Deployment Checklist

​Getting Help

​Next Steps

Before You Start

What Makes a Good Experiment?

Step-by-Step Process

Run locally

Deploy

Cloudflare features used

Common Patterns and Examples

Using Reference Experiments

Error Handling Patterns

Deployment Checklist

Getting Help

Next Steps