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QuAIA

Free plan available

Open-source AI agents automate end-to-end software testing workflows.

About QuAIA

Launched Nov 08, 2026

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Email :

[email protected]

Industry :

Horizontal

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Description

Open-source AI agents automate end-to-end software testing workflows.

QuAIA is an open-source framework of AI agents designed to automate the entire software testing lifecycle. It connects directly to existing ticket and test management systems, eliminating the need for manual test script creation. The system operates within an organization's own infrastructure, ensuring data privacy and security. It automates processes from requirement analysis through to final test report generation. This solution is designed for development and QA teams seeking to reduce manual testing overhead, accelerate release cycles, and improve test coverage without vendor lock-in. It solves the problems of tedious manual test creation, slow feedback loops, and the high maintenance cost of traditional test automation frameworks.

QuAIA Key Features

  • From requirement to reports, end to end: AI agents review stories, design and run tests, file bugs and write the test execution reports.
  • Connects to existing ticket and test management systems (e.g. Jira, Xray, Zephyr).
  • Zero test scripts, test execution similar to how the real user does it.
  • Show it once, never explain it again: it learns from your experts.
  • One test case for every screen, in every language.
  • Your infrastructure, your data, your choice of AI model.

QuAIA Use Cases

  • Testing that keeps pace with AI-assisted development and ever more frequent releases.
  • One continuous test process, from reviewing requirements before development starts to the final test report.
  • Automated regression testing without scripts to maintain, even when the UI changes often or ships in several languages.
  • QA in regulated or data-sensitive environments where data must stay within your own infrastructure.
  • Increased.
  • Extra QA capacity without new hires: add agent instances for large regression runs; experts focus on decisions.

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