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Project Info

Omni QA

Devpost

This project did not submit a demo video on Devpost.

Inspiration

With artificial intelligence, software is now being generated and deployed faster than ever. Entire applications are built in hours, not weeks. But here’s the catch: the code works… until it doesn’t. AI-generated code is particularly error-prone and buggy. For the startup that just went viral on social media, or the major corporation rolling out a critical feature—one crash, one broken flow, one bad user experience can mean lost customers, lost trust, and millions in lost revenue. Traditional quality assurance isn’t keeping up with the pace of AI-enabled software development. For users and developers alike, this represents a critical problem in our ecosystem. That's why we built Omni, natural language-based testing that proactively identifies and resolves issues before they reach your users.

What it does

Omni modernizes QA with a fleet of AI agents that test like real users. Using natural language-based goals, our agents don’t just follow scripts—they explore your app with context, memory, and intent. They react to UI changes, latency, and feedback in real time, surfacing bugs that traditional tests miss. No more brittle test suites. Omni’s agents don’t depend on specific DOM selectors or content. They intelligently navigate websites even as the underlying codebase changes around them. We capture the full story when something breaks. Screenshots, DOM diffs, network logs, user intent—everything developers need to understand and fix bugs fast. Then we go a step further: Omni automatically generates context-aware pull requests in your repository to proactively catch and resolve bugs. QA built for the AI era. While AI accelerates code creation, Omni ensures that what ships actually works. No more tradeoff between speed and quality. Omni finds what others miss—so your users never end up as your QA testers.

How we built it

Frontend: Next.js Tailwind CSS WebRTC Framer Motion Backend: AWS EC2 FastAPI FFMPEG Browser Use Playwright Fetch AI Our frontend was built using Next.js and Tailwind. On the backend, we have a FastAPI server operating as an orchestration layer between our frontend (client) and our fleet of QA agents. We then deploy browser use agents on an EC2 instance. Using natural language tests on the frontend, the QA agents navigate websites to identify bugs. It streams realtime video and events back to the client. If the agent detects an issue, it uses FetchAI and the Github API to create and solve PRs using context from the test cases.

Challenges we ran into

Managing many concurrent agent sessions was difficult API rate limits, browser rate limits kept dragging us down

Accomplishments we're proud of

Built a fully-featured testing suite Built an end-to-end testing pipeline that can identify AND solve bugs

What we learned

Working with browser-use APIs Streaming concurrent video footage streams

What's next

for Omni Higher concurrent agent capacity Stronger corroboration amongst agent journeys Consistent deployment for exhaustive coverage

Analysis

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Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • Next.jsIn code
  • PythonIn code
  • ReactIn code
  • Tailwind CSSIn code
  • TypeScriptIn code
  • FastAPIClaimed

6 of 7 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

547 KB

Source files

69

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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