openadapt-flow

CI PyPI Python License: MIT

Record a GUI workflow once. Replay it deterministically, locally, for free. A model only touches the script to repair it.

One demonstration, two UIs, same compiled workflow — the right side self-heals under a theme it has never seen

Real screenshots from the two runs in docs/showcase/. Left: the UI the demo was recorded on. Right: a theme it had never seen — each step re-resolves through OCR or geometry, and each fix is written back to the script as a reviewable diff. Zero model calls on either side.

Safety, stated honestly. It halts instead of guessing, and we measure how often it could still resolve the wrong target under UI drift — then publish it. Read what it doesn't do yet and how we test it, including five adversarial rounds against our own wrong-target check.

Try it

pip install openadapt-flow && playwright install chromium

openadapt-flow demo-record --out rec                     # record a demonstration
openadapt-flow compile rec --out bundle --name my-task   # compile it
openadapt-flow replay bundle                             # replay: local, $0
openadapt-flow replay bundle --drift theme               # drift the UI, watch it heal

The last two commands serve the bundled MockMed demo app and write an illustrated REPORT.md per run. Pass --url to replay against your own app; recorded parameter values are the defaults and --param overrides them.

How it works

Computer-use agents re-reason through your task with a large model on every run. That's the right shape for a task nobody has automated before, and the wrong one for the 500th referral this month. openadapt-flow compiles the demonstration instead.

Each compiled step carries a template crop, an OCR label, geometry landmarks, and postconditions derived from what the demo actually changed on screen. At replay time a resolution ladder tries them in order: local template match, global template match, OCR, landmark geometry, then (optionally) a grounding model. Healthy scripts never leave the first rung. Milliseconds, no model calls, no per-run cost.

When the UI drifts, a lower rung still finds the target and the fix lands in the bundle as a diff you can review. When the screen stops matching expectations entirely, the run halts with a report instead of guessing, and steps tagged irreversible won't act on a low-confidence match at all.

The runtime is vision-only (PNG in, clicks and keys out) behind a small Backend protocol. The reference backend is a headless browser, which is why the whole loop runs in CI with no OS permissions. Desktop and RDP backends are adapters to come, not rewrites.

Proof

Every CI run records a demonstration, compiles it, and checks:

Scenario Outcome
Baseline replay ×3 all steps template rung, 0 heals, 0 model calls
Theme drift succeeds; 8/8 anchors healed; healed bundle replays clean
Moved buttons succeeds via global template search
Renamed buttons succeeds via landmark geometry
Surprise modal fails loudly, naming the violated postcondition
Non-recorded parameter substituted and verified by OCR of the final screen

Artifacts: baseline run report · theme-drift run report.

Compiled workflows can also be emitted as Agent Skills or MCP servers (emit-skill / emit-mcp), so other agents can invoke them.

Benchmark

OpenEMR: compiled replay vs computer-use agent, latency and cost

The lead result is on a real third-party app: the official OpenEMR public demo (fake patients only, resets daily). We ran an 18-step add-patient-note workflow both ways — log in, find a patient, scroll a dense dashboard, add a note — with a distinct note value each run and the same OCR success check on both arms: 20 compiled replays against 10 runs of a claude-sonnet-5 computer-use agent. Compiled went 20/20 at 39.2s (p50) with zero model calls; the agent went 10/10 at 70.4s (p50), about $0.55 per run at list price ($5.52 total for the 10 runs, with prompt caching and hard cost caps enforced in the harness). It's a shared public demo that other users mutate and that resets daily — not CI-reproducible, and the sample is small. Correctness alone (no agent arm, 5/5 fresh browsers, zero model calls, closed-loop scrolling) is in docs/showcase-openemr/FINDINGS.md. Full numbers, methodology, and caveats: benchmark/openemr/BENCHMARK.md.

For a controlled, CI-reproducible comparison — the methodology anchor — we ran the bundled MockMed task both ways on 2026-07-08 with the same OCR success check: 100 compiled replays against 20 runs of the same agent. Both arms went 100 for 100 and 20 for 20, so on an app this simple the story isn't success rate. It's that a compiled replay finishes in 4.9s (p50; 5.1s p95) with zero model calls, while the agent takes 37.5s (p50; 43.4s p95) at about $0.27 per run at list price, every run, forever. Full numbers, methodology, and caveats: benchmark/BENCHMARK.md.

Status

v0: 580 tests, drift matrix in CI. Solid for the reference browser backend. DESIGN.md has the module contracts; docs/L1_INTEGRATION.md covers feeding layered clinical-data platforms.

Development

git clone https://github.com/OpenAdaptAI/openadapt-flow && cd openadapt-flow
pip install -e '.[dev]' && playwright install chromium
pytest -q

The demo GIF is generated from real run artifacts by scripts/make_demo_gif.py. MIT license.

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Credits

Author: OpenAdapt.AI

License

MIT

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Python >=3.10

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