Compare
How OpenAdapt compares
The worst thing an automation can do is quietly the wrong thing. It saves your note to the wrong patient's chart, shows a green checkmark, and moves on. Most tools confirm that something was saved. Almost none confirm whose record it saved to. That is the risk we think should decide this comparison, so we measured it, on our own tool first.
There are three common ways to automate desktop work: traditional RPA, AI agents that drive a computer with a large model, and browser recorders. OpenAdapt is a fourth. You record the task once, and it compiles that recording into a script that replays for free, repairs itself when the screen changes, and, in a certified workflow, halts rather than guess when it can't confirm it's acting on the right record. Each approach wins somewhere. Here is the honest picture, safety first.
The measurement
How often does it act on the wrong record?
We spent seven rounds trying to make our own tool save to the wrong patient, and fixed every hole we found. The answer that held: confirm identity on the name and date of birth (which survive a misread), and stop and write nothing when only a look-alike ID separates two records.
name + DOB
it confirms the record by name and date of birth, which a single misread character can't quietly change
it stops
when two records differ only by a look-alike ID, it writes nothing and asks for a human, rather than risk the wrong one
Honest exception, published against ourselves: inside a browser, an identity-keyed competitor is just as safe. We pull ahead where there is no browser to lean on, which is most of this work: desktop EMRs, Citrix, and Windows.
“Zero wrong actions” is a target, not a boast; every round started from a version we thought was correct. What we promise is measured, disclosed, and fail-closed, with the open problems written down (display-scale drift, icon-only targets, small agent samples).
Versus traditional RPA platforms
UiPath, Automation Anywhere, and Blue Prism make you hand-author selectors and flowcharts and maintain them: a vendor UI update breaks the selectors, and someone reopens the studio. Licensing is per robot or seat, and it's proprietary. OpenAdapt skips the authoring: record the task once, it compiles to a script, and when the UI drifts it heals and proposes the fix as a reviewable diff, on your own machines, MIT open source.
Versus AI computer-use agents
Cloud agents (OpenAI Operator, Claude computer-use) re-reason through your task with a large model on every run: impressive on novel work, but for repetitive work every run is slow, differently-pathed, billed, and usually sends your screen to the cloud. OpenAdapt uses a model only at compile and heal time. A healthy run is a local compiled replay: same steps, no model calls, no per-run bill, screen stays on your machines.
What repetition costs
Cost, on a test you can run yourself
MockMed ships with openadapt-flow, so you can rerun this: 100 compiled replays vs 20 agent runs, one independent check. Both finish every run. The agent doesn't fail here; it just costs money and time on every run where the compiled script costs neither.
7.6× faster
median run: 4.9s compiled vs 37.5s agent
$0 vs $0.27
model cost per run, at list price
0 vs ~24
model calls per run
Compiled $0 per run; agent $0.27 per run.
Deterministic and CI-reproducible: 100 compiled replays vs 20 agent runs. Same orchestrator, same agent setup, same style of arm-independent OCR check as OpenEMR. Both arms succeed 100% of the time; the difference is cost and latency per run. Parity on success is not a capability claim — the agent does not fail here. Agent cost is computed at the model's list price ($3/$15 per Mtok in/out); an introductory $2/$10 rate applies through 2026-08-31, which lowers the agent figure further.
On the same setup under injected UI drift, a hybrid mode (compiled replay first, agent fallback only on a detected halt) matched agent reliability (20/20) at roughly one-eighth the agent's cost per successful run. Details and caveats (synthetic detected-halt drift, assumed drift mix) in the repo.
The same result on a real EMR
The same head-to-head on a real app: an 18-step add-patient-note workflow on the official OpenEMR public demo, one independent OCR check, a distinct note per run. Both succeeded every time (20/20 compiled, 10/10 agent). The demo resets daily, so treat this as a field cross-check on cost and latency, not a reproducible number or a reliability claim.
1.8× faster
median run: 39.2s compiled vs 70.4s agent
$0 vs $0.55
model cost per run, at list price
0 vs ~24
model calls per run
Compiled $0 per run; agent $0.55 per run.
Run the task 500 times and the ratios compound: about $275 and ten hours of wall clock through the agent, versus $0 and about five and a half hours compiled, with every action auditable against the demonstrated script. The compiled arm's price of entry is a one-minute human demonstration; the agent needs only a prompt.
Caveats: a shared demo that resets daily (a field result, not CI-reproducible); the agent arm is N=10, so its 100% has wide error bars; success is the arm-independent check for both, never self-report; pinned to claude-sonnet-5 on 2026-07-08. Full method and raw data below.
OpenEMR methodology and raw dataVersus browser recording tools
Skyvern and browser-use record or drive workflows inside the browser via DOM selectors or model inference. If your whole workflow lives in a browser tab, they're worth a look. The structural limit: they can't reach the desktop EMR, the Windows loan system, or anything over Citrix. OpenAdapt works from pixels and inputs, so the same approach extends to desktop and VDI/RDP (adapters in progress), all 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 (model-free on the hot path) | 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 another tool is the better fit
We'd rather tell you than have you find out mid-pilot: computer-use agents are better for novel one-off tasks (compiling a demo is overhead with no second run) and for exploratory work across unfamiliar sites. OpenAdapt is for the opposite: work your team repeats, where repeatability and cost beat improvisation.
And if the system you need to automate already has a solid API, use it; a direct integration will always beat driving a screen. OpenAdapt is for everything that doesn't: the legacy EMRs, Citrix estates, and desktop systems where that integration was never built, and a recorded demonstration is the only way in.
See it on your workflow
The fastest way to compare is to bring a real task. Book 15 minutes, or read the code first.