by OpenAdaptAI · Agent Tool · ★ 1.7k
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🔒 Is OpenAdapt safe to install? View the security audit →
OpenAdapt: AI-First Process Automation with Large Multimodal Models (LMMs) OpenAdapt is the open source software adapter 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. Join us on Discord OpenAdapt.ai Architecture OpenAdapt v1.0+ uses a modular meta-package architecture. The main package provides a unified CLI and depends on focused sub-packages via PyPI: | openad
pip install openadapt # Minimal CLI only
pip install openadapt[capture] # GUI capture/recordingTaken from the project's README on GitHub. Check the security audit before running it.
No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 1,731 |
| Forks | 260 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71.5749975713113/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-14 |
| Created | 2023-04-12 |
| Platforms | gemini, python |
| Est. Tokens | ~16k |
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OpenAdapt is Compile a demonstrated GUI workflow into a deterministic, locally executable program. Zero model calls on healthy runs; governed repair; halts instead of guessing. Launcher for openadapt-flow: pip ins. It is categorized as a Agent Tool with 1.7k GitHub stars.
OpenAdapt is primarily written in Python. It covers topics such as agents, ai-agents, ai-agents-framework.
The README on GitHub (github.com/OpenAdaptAI/OpenAdapt) gives: pip install openadapt # Minimal CLI only , then pip install openadapt[capture] # GUI capture/recording. Usage details are in the same README.
OpenAdapt is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpenAdapt on Agent Skills Hub include agent-squad, gptme, ruby_llm. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: