Lifecycle: Beta launcher/meta-package. The active compiler and governed runtime are developed in
openadapt-flow. This repository preserves the high-visibilityopenadaptinstall and unified CLI; it is not a second implementation of the engine. The pre-1.0 monolith is historical and frozen underlegacy/.
Compile repeated GUI work into deterministic, governed workflows.
OpenAdapt compiles a demonstrated workflow into a locally executable program. Healthy runs make no model calls. When an interface drifts, the runtime first tries deterministic re-resolution, can optionally propose a reviewable repair, and halts when configured identity, postcondition, effect, or certification checks fail. The proven self-serve path today is browser automation; native desktop and remote-display backends remain experimental.
pip install openadapt installs the launcher and the compiler, exposed as
openadapt flow .... Native capture, privacy scrubbing, and the separate ML
research toolkits remain optional extras.
Join us on Discord | Documentation | OpenAdapt.ai
Every automation tool assumes an API. The systems that actually run regulated work often don't: legacy EMRs, Citrix desktops, and internal apps a team still drives by hand. General computer-use agents can operate those GUIs, but re-reasoning through every step with a large model is slow, expensive, and non-deterministic, and a wrong click writes to the wrong record.
OpenAdapt takes the opposite path for workflows you run over and over. You
demonstrate the task once. openadapt-flow compiles that demonstration into
a script that replays deterministically and locally, with no model calls on
a healthy run. When the UI changes, the resolution ladder may re-resolve the
target and persist a reviewable repair. When configured checks cannot establish
identity or success, the run halts with a report instead of continuing.
pip install openadapt # CLI + demonstration compiler (openadapt flow …)
pip install openadapt[capture] # + native GUI capture/recording
pip install openadapt[privacy] # + Presidio-backed PII/PHI scrubbing
pip install openadapt[all] # Everything, including research extrasThe flagship compiler ships in the base install, so openadapt flow … works
right after pip install openadapt. Install openadapt-flow[hosted] when you
want OS-keychain token storage; environment-based token configuration remains
available on headless systems. This launcher requires openadapt-flow>=1.7.0,<2
so clean installs cannot resolve an older engine that lacks the governed hosted
artifact commands documented below.
Requirements: Python 3.10+
Run the bundled MockMed workflow first. This is the reproducible path exercised
by openadapt-flow CI and requires no account or target application:
openadapt flow demo-record --out rec
openadapt flow compile rec --out bundle --name mockmed-triage
openadapt flow certify bundle --policy permissive
openadapt flow replay bundle --run-dir run-baseline
openadapt flow replay bundle --drift theme --run-dir run-drift \
--save-healed-to bundle-healedEach replay writes an illustrated REPORT.md in its run directory. The drift
run demonstrates bounded deterministic re-resolution; it is not a claim that
arbitrary UI changes can be repaired.
Now apply the stricter deployment gates:
openadapt flow lint bundle
openadapt flow certify bundle --policy clinical-writeThese two commands currently exit nonzero. That is intentional: the bundled workflow is runnable under the permissive demo policy, but it has unarmed clicks, a vacuous postcondition, and no configured system-of-record effect for its write. The strict policy refuses to call it safe. A policy pass means only that the bundle satisfies that named policy.
To run the workflow through OpenAdapt Cloud, create and review a sanitized derivative locally, approve its exact bytes, then upload that immutable derivative with an ingest token created in the Cloud dashboard:
openadapt flow sanitize rec --kind recording --out rec-sanitized
openadapt flow review-sanitized rec-sanitized --original rec
openadapt flow approve-sanitized rec-sanitized --original rec --reviewer "$USER"
openadapt flow login --token oai_ingest_...
openadapt flow push rec-sanitized --kind recording
openadapt flow compile rec-sanitized --out bundle --name workflow
openadapt flow lint bundle --strict
openadapt flow certify bundle --policy permissive
openadapt flow replay bundle --url https://app.example/login --run-dir run
openadapt flow sanitize bundle --kind bundle --out bundle-sanitized
openadapt flow review-sanitized bundle-sanitized --original bundle
openadapt flow approve-sanitized bundle-sanitized --original bundle \
--reviewer "$USER"
openadapt flow validate-hosted --recording rec-sanitized \
--bundle bundle-sanitized --run-dir run --policy permissive \
--risk-class low --environment staging-v1 \
--target-url https://app.example/login --out validation.json
openadapt flow push bundle-sanitized --kind bundle \
--validation-attestation validation.jsonThe original recording remains local. Sanitization accounts for every file and refuses unsupported content rather than copying it. Review is local and approval is bound to the exact archive hash; live observations can contain PHI again and therefore remain inside the workflow's declared execution boundary.
Then record your own browser application:
openadapt flow record --url https://your.app --out rec
openadapt flow compile rec --out bundle --name my-workflow
openadapt flow replay bundle --url https://your.app
openadapt flow <verb>is the recommended path. The standaloneopenadapt-flow <verb>command keeps working and behaves identically.
Each compiled step carries a template crop, an OCR label, geometry landmarks, and postconditions derived from what the demo changed on screen. At replay time a resolution ladder tries them in order (local match, global match, OCR, landmark geometry, then optionally a grounding model), so healthy runs cost milliseconds and make no model calls.
- Deterministic replay. No large model on the hot path, so a compiled run is repeatable and has no model API charge on a healthy run. It still consumes local or hosted compute, storage, monitoring, and exception-handling effort.
- Bounded re-resolution and repair. Deterministic fallback rungs can recover from supported drift. Optional model- or human-assisted repairs are governed changes to the bundle, not unconstrained reasoning on every run.
- Explicit verification. Compiled steps can carry postconditions. For consequential writes, effect verification must also be configured against a system of record; screen evidence alone cannot prove a transaction committed.
- Refusal where armed. Identity-verified or policy-gated steps can refuse a low-confidence match and halt with a report. Coverage is not automatic for every click, so lint and certification are part of deployment.
The reference backend is a headless browser, which is why the whole loop runs in CI with no OS permissions. Desktop, Citrix, and RDP backends are adapters in progress that we are validating with design partners, not yet production paths. Compiled workflows can also be emitted as Agent Skills or MCP servers so other agents can invoke them.
In one field test against a computer-use agent on a real third-party EMR (OpenEMR's public demo), compiled replay matched the agent's success (20/20 compiled vs 10/10 agent) at roughly half the median latency and with zero model calls in the measured runs; the agent's measured model charge was about $0.55 per run. This comparison does not include authoring, maintenance, compute, storage, review, or exception-handling cost. It is a small-sample result on a shared, daily-resetting public demo, so it is not CI-reproducible; a CI-reproducible control and the adversarial safety measurements are published alongside it.
See openadapt-flow for the compiler, validation methodology, and known limits.
This repository is the stable URL and install route; openadapt-flow is the
canonical implementation. Lifecycle labels describe support intent, not a
blanket production-readiness claim.
| Package | Role | Maturity | Repository |
|---|---|---|---|
openadapt |
Installer + unified CLI (openadapt flow ...) |
Beta | This repo |
openadapt-flow |
Compiler + governed runtime | Beta; browser path proven, other backends experimental | openadapt-flow |
openadapt-capture |
Optional native recorder | Experimental | openadapt-capture |
openadapt-privacy |
Optional PII/PHI scrubbing | Experimental | openadapt-privacy |
openadapt-flow also ships standalone on PyPI (pip install openadapt-flow);
the standalone openadapt-flow <verb> command behaves identically to
openadapt flow <verb>.
openadapt flow record --url <app> --out <dir> Record a workflow once
openadapt flow compile <rec> --out <bundle> Compile a recording into a bundle
openadapt flow replay <bundle> Replay a bundle (local, $0)
openadapt flow lint <bundle> Report a bundle's coverage gaps
openadapt flow certify <bundle> --policy <name> Enforce a safety policy on a bundle
openadapt flow sanitize <path> --kind <kind> --out <dir>
openadapt flow review-sanitized <dir> --original <path>
openadapt flow approve-sanitized <dir> --original <path> --reviewer <identity>
openadapt flow login --token <oai_ingest_token>
openadapt flow validate-hosted --recording <dir> --bundle <dir> --run-dir <dir> ...
openadapt flow push <sanitized-recording-dir> --kind recording
openadapt flow push <sanitized-bundle-dir> --kind bundle \
--validation-attestation <attestation.json> [--workflow-id <uuid>] \
[--resolves-run-id <halted-run-uuid>]
openadapt flow report-break <run-dir> --workflow-id <id>
openadapt capture start --name <name> Start a native recording (requires [capture])
openadapt capture stop Stop recording
openadapt capture list List captures
openadapt capture view <name> Open capture viewer
openadapt version Show installed versions
openadapt doctor Check system requirements
These packages are research, not part of the supported product. They explore whether human demonstrations can improve the accuracy of general computer-use models. They are not required to record, compile, or replay a workflow, and the compiler above makes no model calls on its hot path.
| Package | Research focus | Repository |
|---|---|---|
openadapt-ml |
Training and inference for multimodal GUI-action models | openadapt-ml |
openadapt-evals |
Benchmark evaluation for GUI agents | openadapt-evals |
openadapt-retrieval |
Multimodal demonstration retrieval | openadapt-retrieval |
openadapt-grounding |
UI element localization / grounding models | openadapt-grounding |
Install with pip install openadapt[ml,evals]. The openadapt train and
openadapt eval commands become available once those extras are installed.
Research thesis: demonstration-conditioned agents
The research line asks a different question from the compiler: instead of compiling one demonstration into a deterministic script, can a model use demonstrations at inference time to disambiguate unfamiliar GUIs?
- Demonstrate — record user actions and screenshots, scrub PII/PHI, build a searchable demonstration library.
- Learn — embed and index demonstrations for retrieval, and/or fine-tune Vision-Language Models on them.
- Execute — condition a policy on retrieved demonstrations, ground intent to coordinates, act behind safety gates, and evaluate to feed results back.
Validated result (research, not product): on a controlled macOS benchmark (45 System Settings tasks sharing a common navigation entry point), demonstration-conditioned prompting improved first-action accuracy from 46.7% to 100%, with a length-matched control (+11.1 pp) confirming the benefit is semantic, not token-length. Phase 2 (retrieval-only prompting) is validated; Phase 3 (demo-conditioned fine-tuning) is in progress. See the research thesis for methodology and limits.
Industry note: OpenCUA (NeurIPS 2025 Spotlight, XLANG Lab) reused OpenAdapt's macOS accessibility capture code in their AgentNetTool, but uses demonstrations only for model training, not runtime conditioning.
- New automation work: install
openadaptand useopenadapt flow ...; make engine changes inopenadapt-flow. openadapt-agent: Deprecated. Do not build new integrations on it. Its execution responsibilities have moved to the governed runtime inopenadapt-flow.- Pre-1.0 monolith: Historical. Version 0.46.0 and its source remain available for existing users, but receive no feature development.
The monolithic OpenAdapt codebase (v0.46.0) is preserved in the legacy/
directory.
pip install openadapt==0.46.0See docs/LEGACY_FREEZE.md for the migration guide.
Early demonstrations of the legacy version: Twitter demo · Loom walkthrough. For the current architecture, see the documentation.
The default headless-browser path needs no OS permissions. Native desktop capture does:
macOS: Grant Accessibility, Screen Recording, and Input Monitoring permissions to your terminal. See permissions guide.
Windows: Run as Administrator if needed for input capture.
- Join Discord
- Pick an issue from the relevant repository
- Submit a PR
For sub-package development:
git clone https://github.com/OpenAdaptAI/openadapt-flow # or another package
cd openadapt-flow
pip install -e ".[dev]"- OpenAdaptAI/SoM — Set-of-Mark prompting
- OpenAdaptAI/pynput — input monitoring fork
- OpenAdaptAI/atomacos — macOS accessibility
Product surfaces: OpenAdapt Cloud provides the hosted browser-workflow control plane, with live execution enabled only when its production dependencies pass readiness checks.
openadapt-desktopand native/remote backends remain experimental. Internal tooling:openadapt-wright,openadapt-herald,openadapt-crier,openadapt-consilium,openadapt-telemetry, andopenadapt-viewersupport development and operations; they are not required by the compiler runtime.
- Discord: https://discord.gg/yF527cQbDG
- Documentation: https://docs.openadapt.ai
- Issues: use the relevant repository
MIT License — see LICENSE for details.