How to Build a Free Calendly Clone Using AI Coding Agents

How do you actually clone an app like Calendly with AI coding agents?

You do it by treating the AI agent like a product team, not a code generator. Instead of asking for a scheduling app in one shot, you break the work into phases: research the competitors, plan the architecture, build the features, then test relentlessly with automated agents acting as fake users. One builder followed this exact process with a coding agent (Codex) and produced a working scheduling app, event types, calendar sync, and Stripe payments included, using a handful of large prompts over about five days of actual agent runtime. The result was a self-hosted tool free to run, since it skips the subscription fees of commercial scheduling platforms.

TL;DR

A builder cloned Calendly’s core booking flow, including event types, live calendar sync, and paid bookings via Stripe, using an AI coding agent instead of a traditional dev team.
The project relied on a four-phase mega-prompt, research, planning, building, and testing, rather than dozens of small back-and-forth requests.
The agent ran an autonomous testing loop, simulating around 50 virtual users clicking through signup, admin, and booking flows to catch bugs before a human ever touched the app.
Total hands-on development time was about five days and five hours of agent runtime, spread out over roughly two weeks alongside other work.
Human review still mattered: the builder caught a UX flaw agents missed (a progress bar that looked clickable but wasn’t) and had to manually rebrand and redesign the app.
The finished app runs locally for free, and going live only requires hosting it (the builder mentioned Vercel) plus connecting Google Calendar, a backend database, and Stripe.
The builder was explicit that cloning a working prototype is not the same as building a scalable SaaS product, since real-world usage brings far more bugs, edge cases, and infrastructure costs.

REMY IS NOT

✕a coding agent
✕no-code
✕vibe coding
✕a faster Cursor

IT IS

✓a general contractor for software

The one that tells the coding agents what to build.

What features did the AI-built Calendly clone actually include?

The cloned app, named Snag Time after a rebrand, replicated most of the functionality people associate with Calendly or Cal.com. It included a dashboard showing upcoming bookings, monthly booking counts, and hours scheduled. Users could create multiple “event types” (for example, a free strategy call versus a paid consulting session), each with its own name, description, booking link, color, location, availability rules, buffer times, and custom intake questions.

The booking flow itself synced live to a real calendar. Blocking off time in the actual calendar app immediately removed those slots from the public booking page, and confirmed bookings sent calendar invites to both parties. The clone also integrated Stripe, so event types could be marked as paid, with the checkout flow (tested in Stripe’s sandbox mode) working the same way a production payment would, just with test card numbers instead of real ones. Workspace settings covered branding, team member permissions (admin versus member), password management, and profile customization, mirroring the account-level features found in commercial scheduling tools.

What was the actual prompting workflow?

The builder didn’t rely on constant micromanagement. The process started with one large “goal prompt” instructing the agent to clone Calendly’s core functionality, run locally, and cost nothing to operate. That prompt explicitly asked the agent to work in four stages:

Research, studying Calendly and Cal.com, including what users like and dislike about each.
Planning, translating that research into a feature list and a technical plan covering the database, integrations, and how sub-agents would divide the work.
Building, where the agent began writing and assembling the actual application.
Testing, where the agent looped between building, finding bugs, fixing them, and testing again.

After that first mega-prompt did most of the heavy lifting, only a handful of follow-up prompts were needed: one for rebranding and visual redesign (the app was initially auto-named “Tempo Cove,” which the builder changed), one focused purely on performance (cutting a laggy, unresponsive booking page down to a fraction of its original load time), and additional rounds of UX polish and manual testing.

How did the autonomous testing loop work?

Rather than manually clicking through the app looking for bugs, the builder had the agent simulate roughly 50 virtual agents walking through different user journeys, signing up, creating event types, going through the booking flow, and using the admin dashboard. This ran as a loop: build, test with simulated users, catch bugs, fix them, and test again, repeating for days without direct human involvement in the testing itself.

This caught a lot of functional bugs on its own. But it didn’t catch everything. The builder found one clear UX problem the agents missed: a progress bar in the booking flow looked clickable (the natural instinct is to click it to go back a step) but was actually non-functional, since the interface only responded to separate arrow buttons. Agents can verify that a feature works as coded; they don’t always intuit how a human will instinctively try to use it. That gap is where manual review still added value.

How long did it take and what did it cost?

The builder reported about five days and five hours of actual agent runtime, though the project stretched over roughly two weeks in calendar time since prompts were sent in between working on other projects. The transcript doesn’t specify exact dollar costs for compute or API usage, but the core claim is that the finished app runs locally at no ongoing cost, and that self-hosting it (for example on Vercel) plus connecting a real Google account, backend database, and live Stripe keys is what’s needed to make it usable with real clients.

Is a cloned app like this actually ready to replace Calendly?

Not entirely, and the builder was upfront about that. The clone reproduces the core booking and payment loop well, but it’s missing some of the automation features (reminder sequences, workflow triggers, integrations) that mature platforms like Calendly and Cal.com have built out over years. The builder also drew a clear line between building something for personal or small-team use and building a SaaS product meant to scale to real customer volume. Scaling introduces problems that a five-day prototype won’t surface: inference and database costs at volume, edge-case bugs from real user behavior, and ongoing feature requests that someone has to own and maintain. Cloning the surface-level functionality of an established product is now realistic with AI coding agents. Turning that clone into a durable, revenue-generating business is a separate and much harder problem.

Frequently Asked Questions

What AI coding agent was used to build the Calendly clone?

The builder used Codex, an AI coding agent, to research, plan, build, and test the application, noting that a similar tool like Claude Code could take over from that point to keep extending the app.

Can this AI-built scheduling app handle payments?

Yes. It integrates with Stripe so event types can be set as paid sessions, with the checkout flow working the same way in Stripe’s sandbox test mode as it would with live payment credentials.

Does the calendar sync in real time?

Yes. The booking page reflected changes made in the connected calendar almost immediately. Blocking time off in the real calendar removed those slots from the public booking link once refreshed.

How much manual work was still required after the AI built the app?

The builder still had to rebrand the app (it was auto-named “Tempo Cove”), fix a UX issue involving a non-clickable progress bar, and run additional prompts specifically to fix performance lag and refine the design.

Is this a real replacement for commercial tools like Calendly or Cal.com?

For personal or internal use, it covers the core booking and payment functionality. It lacks some automation features found in mature platforms, and the builder cautioned that scaling a cloned prototype into a full SaaS product involves far more engineering than the initial build.