Open-Source Agentic QA Harness with Memory
The open-source Agentic QA Harness with Memory allows users to write tests in natural language for web and mobile applications. It features self-healing test execution that adapts to UI changes and builds execution memory to improve future test runs. The tool is designed for both developers and machines, providing a polished dashboard and CLI for efficient testing workflows.
- ▪Agentic QA Harness enables writing tests in natural language, making it accessible for users.
- ▪The self-healing capability allows tests to recover from UI drift and flaky interactions.
- ▪Execution memory is built with each run, enhancing the context for future tests.
2 outlets in our directory ran this story, first to last over 11 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Open source during trial — r/OpenAI
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Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/vostride/agent-qa |
| Publication time | Tue, 19 May 2026 09:53:01 +0000 |
| Retrieval time | 2026-05-19T10:04:57.575Z |
| Last seen | 2026-05-19T10:04:57.575Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | yf9mvq08Dgvm · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Docs · Demo · Issues agent-qa Open-source Agentic QA Harness with Memory Write tests in natural language. agent-qa runs them across web and mobile with execution memory, catching regressions before release. Docs | Quickstart Features Write tests in natural language: Define actions and assertions in human language while agents work from visible roles, labels, and screen state. Self-healing test execution: When any sub-action, such as click, fill, or select, fails, agent-qa re-observes the UI and tries a different path in the same run. Tests recover from UI drift and flaky interactions instead of failing on the first broken action. Evolves with every run: With every test run, agent-qa builds execution memory from product, suite, and test observations, then adds that context to future runs.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.
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