VibeSDK by CloudFlare

The agent works through a model-and-tool loop rather than a rigid sequence of build phases, which means it can read a file, decide what to change, run the code, and react to whatever comes back instead of blindly marching through fixed steps. It reads and edits files, installs what's needed, and keeps going until the app runs. That means you can change your mind mid-build and it adapts instead of starting over.

When your request is vague, the agent asks structured follow-up questions before it starts. You get fewer wasted generations because the agent confirms what you actually want. It's a small thing that saves a lot of rework.

Every file the agent touches shows up in an integrated editor as it works. You can read the code, jump into a file, and suggest a change without leaving the browser. Watching the edits stream in makes the whole process feel less like a black box.

Generated code bundles and loads as an edge Worker, so each preview runs without a long-lived development server. Your app is live seconds after the agent finishes a change. No waiting on a build pipeline. The downside is that previews depend on Cloudflare's runtime, which differs from a local Node setup.

The agent inspects browser console output, spots runtime errors, and redeploys a fix. You'll still hit bugs it can't solve, but routine breakage gets handled without you digging through logs. Not perfect, but a real time saver.

Each generated app gets its own SQLite-backed storage through a Durable Object Facet, with tools to inspect and reset the database. Your projects don't share data, which matters when you're running several at once.

The agent saves restore points in Cloudflare Artifacts, and you can roll back to an earlier state without rewriting the history or losing the record of how the project reached it. If a change makes things worse, you recover in a click instead of reconstructing files by hand.

You route your chosen model providers through AI Gateway, which adds centralized observability and caching across them. Costs stay visible, popular responses get cached, and swapping models doesn't mean rewriting the agent.