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Listed in Coding & Development with 168 other toolsPart of 969+ curated AI tools on AISO
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Lark

Continuous self-healing tests across UI, API, CLI, and mobile, with first-class coding-agent support

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paidNo public pricing page — a direct fetch of /pricing returns 404 and the site routes to 'Create account' and 'Get a demo'. No figure is quoted here.View full pricing →

Visit Lark

https://getlark.ai/

About Lark

Lark, built by ex-Stripe engineers, runs UI, API, CLI, and mobile tests on a continuous cadence rather than only on commit — the framing is 'every surface, every minute', with the team watching green while Lark watches red. That cadence is the product difference: most suites only tell you something broke when someone pushes, whereas Lark keeps checking against production and staging so a third-party outage, an expired credential, or a config drift surfaces on its own. The dashboard shown on the site reports over 12,400 runs completed in a day at a 99.6% pass rate with 31 tests auto-repaired, which points at the second core feature — self-healing. Tests adapt when you revamp a dashboard or introduce a new API instead of breaking, and Lark alerts you to the change rather than silently failing. It is built for developers rather than a separate QA org, with first-class support for coding agents like Claude Code, Cursor, and Codex, so tests can be authored and maintained from inside the agent workflow that generated the code. When something does fail, Lark produces reproducible debugging artifacts: downloadable test scripts, logs, screenshots, and videos. Coverage spans UIs, APIs, SDKs, and async workflows.

Key Features

Tests run continuously, not only on push
Coverage across UI, API, CLI, SDK, mobile, and async workflows
Self-healing tests that adapt to product changes and alert on the diff
First-class support for Claude Code, Cursor, and Codex
Reproducible artifacts: scripts, logs, screenshots, and videos

Lark Pros & Cons

Pros

  • +Continuous cadence catches failures that are not caused by your own commits
  • +Designed around agent-written code from the start
  • +Strong debugging artifacts on failure

⚠️ Cons

  • No published pricing
  • Continuous runs against production need careful test-data hygiene

Tags

testingmonitoringself-healing testsclaude codecursordeveloper tools
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