Automate the UI-only work your APIs can't reach.

OpenAdapt compiles demonstrations into governed workflows across browser, native desktop, RDP, and Citrix. The default healthy path executes deterministically and makes no generative-model API calls. Consequential actions are identity-gated, results are checked against the workflow's evidence contract, and uncertainty halts for review instead of being reported as success.

OpenAdapt is for repeated work trapped behind browser, desktop, and virtual-desktop interfaces: too visual or variable for brittle selectors, but too consequential to hand to a free-form computer-use agent on every run.

Install OpenAdapt · Watch the live demo · Read the docs · Visit openadapt.ai

The installer is OpenAdapt. The canonical engine is openadapt-flow.

python -m pip install --upgrade 'openadapt[browser]'
openadapt quickstart

This runs the bundled MockMed lifecycle. It records, compiles, certifies, replays, independently verifies the synthetic effect, and writes a human-readable REPORT.md. Use the five-minute guide to add lint, drift, repair, and deployment.

Published head-to-head comparisons, each graded by an external success check that is independent of every arm:

• Live third-party EMR (OpenEMR public demo, fake patients, 18-step add-patient-note workflow): compiled replay went 19/20 at 39.2s p50 with zero model calls; the agent went 10/10 at 70.4s p50 at about $0.55 of model charge per run. Compiled run 20 didn't pass. The saved-row oracle, tightened on 2026-07-28, refuses to count a note still sitting in the unsaved entry form, and the replayer had already halted at step 17 rather than press on. Small sample on a shared, daily-resetting demo — not CI-reproducible. Methodology and caveats.
• Historical MockMed control (bundled task): retained rows marked 100/100 compiled runs and 20/20 agent runs successful under the 2026-07-08 OCR check. The final frames were not retained, so the current verifier cannot rescore those outcomes. Use these rows only for latency and estimated model API charge comparison. The compiled arm recorded 4.9s p50 and $0 per run in model API charges; the agent arm recorded 37.5s p50 and about $0.27 per run. Methodology and caveats.
• Independent effect verification (fault-injection study, 90 runs per arm, end to end through the real replayer into an on-disk SQLite system of record, graded by a direct read-only database connection that bypasses the service): a screen-only "success banner" oracle silently accepted 75.0% of the wrong effects that actually occurred (54 of 90 runs). Adding one out-of-band verifier that reads the system of record cut that to 12.5% (9 of 90). A complete read path over every mutable surface reaches 0 of 90, but the realistic deployment number is the middle rung — one out-of-band oracle — not the 0%. All nine residual misses are a single named class: a collateral write to a surface the oracle does not read. Every run terminates in an explicit transaction outcome (VERIFIED, HALTED_BEFORE_EFFECT, RECONCILIATION_REQUIRED, and others), so uncertain delivery is surfaced for reconciliation, never reported as success. Methodology and caveats.

The recorded zero model API calls mean no generative-model API charge for that benchmark run. The figure excludes authoring, review, maintenance, and infrastructure, and it is not a production-reliability or clinical-safety claim. Read the limits before extrapolating either result.

openadapt-evals is a Research repository. Runnable references live in openadapt-flow/docs/showcase, with methods and evidence under benchmark.

These targets form one product across browser, Windows, macOS, Linux, RDP, and Citrix/VDI. Qualification is per workflow, not a blanket Production claim. Current signed admissions are in the live record.

Model training, retrieval, grounding, and general computer-use work remain Research: openadapt-ml, openadapt-retrieval, and openadapt-grounding. They are not required for healthy deterministic replay.

OmniMCP, SoM, and PydanticPrompt are Labs, not product dependencies. Historical, Superseded, Deprecated, Archived, and Internal repositories are classified in the public lifecycle registry rather than presented as the product.

Product-engine changes belong in openadapt-flow. Packaging and launcher changes belong in OpenAdapt. Use each repository's issues for scoped work, or visit openadapt.ai for deployment inquiries.

Unless a repository says otherwise, OpenAdapt code is MIT licensed.