The self-improving Agentic QA harness with Memory
Write tests in natural language for web and mobile. agent-qa learns from past runs, adapts to UI changes, and catches regressions before you ship.
1,200+
manual testing hours saved
10B+10,000,000,000+
total tokens consumed
With every test run, agent-qa builds execution memory from product, suite, and test observations, then adds that context to future runs. agent-qa also curates memory from steps that were healed during execution, helping future runs avoid the same mistake.
A polished dashboard and CLI for developers, plus MCP and skills for coding agents.
The action cache reuses validated plans across similar subsequent test runs, reducing planner work, token usage, and runtime overhead.
Run Node, Bun, Python, or Bash hooks in isolated Docker containers to set up environments, call APIs, seed fixtures, tear down state, or pass structured outputs back into the active test run.
Tests, configs, hooks, memory, and suite logic all live as version-controlled code, so every change can be diffed, reviewed, reused, and shared across teams.
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.
Run tests with the model of your choice via OpenAI- and Anthropic-compatible endpoints, Gemini, local or open-source models, and subscriptions like Codex and Claude Code.
Switch between real agent-qa runs and inspect the evidence directly.