Low maintenance automation
The world's most advanced testing harness
The engineering underneath: independent, reliable, resilient.
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Per-task LLM routing
The best model for each testing job. You don't need to worry about upgrades, prompt rewrites, version pinning, or evals.
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Reliable tools for your agent
An MCP for the full testing loop. Broad tool coverage, progressive disclosure, idempotent writes, token-efficient, streaming with keepalive, OAuth.
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Self-healing off a model, not a selector
Every element is modeled as dozens of weighted attributes, so a page change re-identifies it by best overall match, falling back to locating it visually when attributes aren't enough.
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Structural objectivity
An LLM told to make a test pass will edit the DOM or weaken the assertion to get there. mabl runs your deployed app in a real browser, in a separate context, and checks it against assertions you defined separately.
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Your intent, translated
You describe what to test and set your guardrails; mabl's tuned metaprompts turn that into the structured instructions the agent runs on, scoped per agent with mabl's own guardrails winning on conflict. You get an effective agent without prompt-engineering it yourself.
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Deterministic first, LLM when needed
Cheap deterministic locators run first; the model engages only when they fail. More efficiency, lower cost, faster.
Rely on resilient tests
Multi-signal find model
Tests identify elements by inspecting their attributes and find history, falling back to a visual description when DOM signals are insufficient.
Agentic auto-healing
Tests stay passing through unexpected selector changes and UI shifts while preserving original intent.
Intelligent wait
Tests wait for a stable, ready state based on your app’s historical loading behavior, avoiding hard-coded wait steps and false failures.
Fix real issues, fast
Conversational results analysis
Quickly understand test failures with analysis and recommendations, optimized for corrective action.
Agentic test editing
Update tests using plain language and curated failure analyses as input context, from your agent or the UI.
Agent instructions
Define situation-specific guidance for how mabl should update tests, so agentic modifications adhere to your best practices.
Monitor and act on trends
Quality score reporting
Spot flaky and broken tests at a glance with composite quality scores that combine pass rate, stability, and reliability.
Automatic failure categorization
Results are automatically labeled by failure category, sourced from a pre-defined set and customizable by your team.
Suite health monitoring
Track pass rates, stability, and performance across your apps, environments, and teams.