We’re still preparing for our internally tested Alpha release — the system isn’t production-ready yet. Our initial internal release was made on 18 Oct 2025, and we plan to roll out four revisions before alpha release become available for public testing.
Open-source automation with purpose.
ParseOS is an open-source initiative to advance automation at the OS level.
We do not pay for research; instead, our focus is on creating value by helping students and professionals start strong in data science and machine learning.
Our goal is to foster a positive shift in tech through collaboration and shared learning.
Below are the broad technical and community goals of the OS Automation project.
Our focus is to build a smooth, flexible, and cost-effective automation system that can be confidently used in production environments while staying open-source and community-driven.
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Build Smooth & Production-Ready Automation
Our first and foremost goal is to make operating system automation as smooth and reliable as possible.
We focus on using open-source solutions and tools that work efficiently in real-world, production-level systems — not just prototypes or demos. -
Create a Flexible & Extensible Architecture
We design the system architecture to be highly modular and flexible, allowing easy integration or replacement of components.
This makes it simple to adopt new popular tools, MCP-based solutions, or open-source alternatives in the future without breaking the existing system. -
Keep It Cost-Effective and Scalable
A key goal is to make the system cost-efficient without compromising performance.
We aim to reduce unnecessary API calls, heavy dependencies, and overhead so that users can run automation tasks at low operational cost while still being scalable for production use. -
Ensure Cross-Platform Compatibility
OS Automation is built with the goal of supporting multiple operating systems and environments.
Whether users are on Linux, macOS, or Windows, our target is to deliver consistent behavior and easy setup across all supported platforms. -
Strengthen Testing & Reliability
We aim to include robust testing, logging, and validation mechanisms to ensure each automation command behaves predictably.
Error handling and retry logic are part of our core design to make the system production-ready and self-healing. -
Improve Developer Experience & Documentation
We want every developer to get started easily.
Comprehensive documentation, clear examples, and simple “getting started” commands are part of our ongoing goal to make automation beginner-friendly and transparent. -
Build an Open Community
Our long-term vision includes growing an open developer community that collaborates, contributes new automation modules, and improves the ecosystem together.
Open collaboration ensures that OS Automation evolves through shared knowledge, transparency, and innovation.
| Run | Total Cases | Passed | X-Failed | Total Fail | % Success |
|---|---|---|---|---|---|
| Run 1 | 5 | 3 | 1 | 1 | 60.00% |
| Run 2 | 4 | 3 | 0 | 1 | 75.00% |
| Run 3 | 5 | 4 | 0 | 1 | 80.00% |
| Run 4 | 6 | 5 | 0 | 1 | 83.33% |
To get started, just run the following command in your terminal:
python -m os_automation.cli.cli run "open browser and search for python tutorials"
💡 What it does: This command automatically opens your browser and searches for Python tutorials — helping you start learning Python right away!
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Clone this repository to your system using:
git clone <repo_url> -
Make sure Python is installed on your computer.
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Open your terminal or command prompt.
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Navigate into the cloned project folder.
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Run the following command:
python -m os_automation.cli.cli run "open browser and search for python tutorials" -
Press Enter, and the automation will take care of the rest! 🎉
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Three AI Agents — (Main Planner, OmniParserEventExecutor, Validator)
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Task Abstraction Layer (TAL) — the shared contract schema between all agents
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Adapters — thin wrappers that let you plug in any repo or model without rewriting the system
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Registry — keeps track of which adapters are available
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It stays open and modular, allowing seamless integration with popular automation repositories, MCP-native tools, and visual automation frameworks such as Sikuli and pyautogui.
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Is future-proof (ready to integrate new tools easily).
We welcome contributions of all sizes — from documentation fixes to major feature improvements.
Please follow the simple flow below to keep reviews fast and collaboration smooth.
- Fork and Clone
git clone https://github.com/EmptyOps/parse-os.git cd parse-os git remote add upstream https://github.com/<main-org>/parse-os.git - Create a New Branch
git checkout -b issue/your-issue-name
• Branches help keep your changes separate from the main code.Create a new branch whenever you fix a bug, add a new feature, or improve existing code — this keeps your work clean and easy to review.
- Make and Test Changes
- Follow the project’s coding and commit style.
- Run existing tests or add new ones if needed.
- Push and Create Pull Request (PR)
git push origin feature/your-feature-name
• Use a clear title (e.g. feat: add plugin manager).
- After Submitting PR
- Maintainers will check your PR in a few days.
- If they ask for changes, make updates in the same branch.
- After approval, it will be squash-merged into the main branch to keep history clean.
- Keep PRs small, modular, and focused — easier to review and merge.
- Discuss new ideas or architecture-level changes in Discussions or Issues.
- Be kind, respectful, and collaborative.
- For help or clarifications, comment directly on your PR or open a short discussion thread.