From 867a8245b7c2a39a0c13d4227251f4ac1f547826 Mon Sep 17 00:00:00 2001 From: Richard Abrich Date: Sun, 12 Jul 2026 22:00:17 -0400 Subject: [PATCH] docs: refresh README (accurate positioning + demonstration compiler prominence) Sharpen the top-of-README positioning to reflect that OpenAdapt now compiles a demonstration into a deterministic replay (openadapt-flow), in addition to conditioning/training/evaluating agents, and state the modular optional-extras design as a strength. Strengthen the Demonstration Compiler section: note the standalone PyPI package and v0.3.x, sharpen the wrong-record identity halt, state the backend status honestly (browser shipped; desktop/RDP adapters in progress), mention Skill/MCP emit, and add the OpenEMR field result with its small-sample, not-CI-reproducible caveats. No new capability claims beyond what openadapt-flow ships and documents. Co-Authored-By: Claude Opus 4.8 Claude-Session: https://claude.ai/code/session_01CKrVJJy5jWVCkXAqgUqtqZ --- README.md | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 90c0486ee..3b32c2173 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ **OpenAdapt** is the **open** source software **adapt**er between Large Multimodal Models (LMMs) and traditional desktop and web GUIs. -Record GUI demonstrations, train ML models, and evaluate agents - all from a unified CLI. +Record a GUI workflow once, then compile it into a deterministic replay, condition a model on it, or train and evaluate agents against it — all from a unified CLI. OpenAdapt is a modular meta-package: the base install is just the CLI, and each capability (capture, compiler, ML, evals, privacy) is an optional extra you add as you need it. [Join us on Discord](https://discord.gg/yF527cQbDG) | [Documentation](https://docs.openadapt.ai) | [OpenAdapt.ai](https://openadapt.ai) @@ -93,7 +93,8 @@ openadapt capture view my-task For workflows you run over and over, re-reasoning through every step with a large model is slow, expensive, and non-deterministic. `openadapt-flow` compiles a single demonstration into a script that replays **deterministically -and locally**, with no model calls on the hot path. +and locally**, with no model calls on the hot path. It ships standalone on PyPI +(`pip install openadapt-flow`, currently v0.3.x) or as an extra: ```bash pip install openadapt[flow] @@ -106,8 +107,22 @@ landmark geometry, then optionally a grounding model), so healthy runs cost milliseconds. When the UI drifts, a lower rung re-resolves the target and the fix lands back in the bundle as a reviewable diff — self-healing without a human in the loop. When the screen stops matching expectations, the run halts -with a report instead of guessing, and identity-verified steps refuse to act on -a low-confidence match. +with a report instead of guessing, and identity-verified steps (for example a +wrong-record check) refuse to act on a low-confidence match rather than click +the wrong target. + +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 in +progress, 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 near-zero +marginal cost — the agent cost about $0.55 per run, the compiled replay makes +zero model calls. This 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. Model-free on the hot path, deterministic, self-healing under drift, and honest about what it can't resolve. See