Compare
How OpenAdapt compares
There are three common ways to automate desktop work today: traditional RPA platforms, AI agents that operate a computer with a large model, and browser recording tools. OpenAdapt takes a fourth approach. It compiles a recorded demonstration into a deterministic script that replays for free and heals itself when the UI drifts. Each approach wins somewhere, so here's the honest version.
Versus traditional RPA platforms
Platforms like UiPath, Automation Anywhere, and Blue Prism ask you to build the automation by hand. You author selectors, arrange flowcharts in a studio, and maintain both. Large enterprises run a lot of automation this way and it works, until the UI changes. A vendor ships an update, the selectors stop matching, and someone has to open the studio and repair the flow. Licensing is typically per robot or per seat, and the platforms are proprietary.
OpenAdapt skips the authoring step entirely. You record yourself doing the task once, and the compiler turns that demonstration into a script. When the UI drifts, OpenAdapt heals the script and proposes the fix as a reviewable diff, not a broken bot and a support ticket. It runs on your own machines and it's MIT-licensed open source.
Versus AI computer-use agents
Cloud agents in the OpenAI Operator and Claude computer-use family re-reason through your task with a large model on every single run. On tasks nobody has seen before, they're genuinely impressive. For repetitive work, though, re-reasoning is the wrong shape. Every run is slow. Every run can take a different path than the last. Every run is billed. And most of these services work by sending screenshots of your screen to the cloud.
OpenAdapt uses a model at compile time and at heal time, never on a healthy run. A healthy run is a deterministic local replay: same steps, same order, no model calls, no per-run bill. Your screen stays on your machines.
We measured it
7.7× faster
median run: 4.9s compiled vs 37.5s agent
$0 vs $0.27
model cost per run, at list price
5.1s vs 43.4s
95th-percentile latency
Same task, same success check, 100 compiled replays against 20 runs of a Claude computer-use agent: both arms passed 100% on our simple demo app, so the difference isn't success rate, it's what repetition costs.
Methodology and raw dataVersus browser recording tools
Tools like Skyvern and browser-use record or drive workflows inside the browser, using DOM selectors or model inference. If your whole workflow lives in a browser tab, they're worth a look. The limit is structural: browser-only tools can't reach the desktop EMR, the Windows loan origination system, or anything delivered over Citrix.
OpenAdapt works from pixels and inputs rather than the DOM, so the same approach extends past the browser to desktop applications and VDI/RDP sessions (adapters for these are in progress). Recording, compiling, and replaying all happen on your infrastructure.
Side by side
| OpenAdapt | Traditional RPA | Computer-use agents | Browser recorders | |
|---|---|---|---|---|
| How automations are built | Recorded demonstration, compiled into a script | Hand-authored selectors and flowcharts | A model re-reasons through the task on every run | Browser recording or a prompt |
| Cost per run | None on healthy runs (deterministic local replay) | Licensed per robot or per seat | Metered model calls on every run | Varies; cloud inference is metered |
| When the UI changes | Heals the script; fix arrives as a reviewable diff | Selectors break; someone repairs the flow by hand | The model may adapt, or may take a different path | DOM selectors break, or the model re-infers |
| Where it runs | Your machines | Your infrastructure or vendor cloud | Vendor cloud, with screenshots of your screen | The browser; often with a cloud backend |
| App coverage | Desktop, web, and VDI/RDP (vision-based; adapters in progress) | Desktop and web via connectors | Anything on screen | Browser only |
| License | MIT open source | Proprietary | Proprietary services | Varies; some open source |
Where agents beat us
We'd rather tell you this than have you find out mid-pilot. Computer-use agents are the better tool for novel one-off tasks, since compiling a demonstration is overhead when there's no second run. They win on tasks you can't demonstrate yourself, and on exploratory work like researching something across a dozen unfamiliar sites. OpenAdapt is built for the opposite case: work your team does the same way, over and over, where determinism and cost matter more than improvisation.
See it on your workflow
The fastest way to compare is to bring a real task. Book 15 minutes, or read the code first.