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Mabl

Agentic low-code test automation with self-healing tests for web, mobile, and API.

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About

Mabl is an AI-native test automation platform (using ML in testing since 2017) that covers web UI, native mobile, API, accessibility, and performance testing from one dashboard. Tests are built with a low-code recorder or generated with GenAI, then the platform auto-heals them when the UI changes and triages failures with AI-assisted diagnostics. It integrates with GitHub Actions, Jenkins, and Jira, and ships an MCP server for IDE access. Pricing is quote-based with a 14-day trial and no free tier; third-party estimates put entry around $500/month. It is recording-first, so tests capture what you did rather than what the spec says, which matters when verifying AI-generated code.

What it can do

  • Low-code visual test recorder
  • GenAI test generation for web, mobile, and API
  • Self-healing tests that adapt to UI changes
  • Unified web, mobile, API, accessibility, and performance testing
  • Parallel cloud test execution
  • AI-assisted failure diagnostics with screenshots and traces
  • MCP server for IDE and agent access
  • GitHub Actions and CI integrations

Setup

  1. Start a 14-day trial at mabl.com.
  2. Install the mabl Trainer browser extension.
  3. Record user flows or generate tests with GenAI; add assertions visually.
  4. Run in the mabl cloud or trigger from CI via the GitHub Action.

Prerequisites

A mabl account; Chrome for the Trainer extension; an app URL to test.

Use cases

codeautomation

Works with

Any agent (.md)

Trust score — 6/10

/10 trustMetrics-checked

Scored 6/10 (metrics tier, metrics-v1). Evidence: Mature vendor (AI in testing since 2017) with enterprise customers (JetBlue, Mercedes-Benz reported).; Actively developed: GenAI test generation, MCP server, agentic testing positioning through 2026.; Closed-source SaaS; tests live in mabl with limited Git-based version control.; Honest caveats: quote-based pricing (third-party estimates from ~$500/mo) with no free tier; recording-first model captures behavior, not spec.. License: Proprietary SaaS; no public source license..

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
8.0/10
Maintenance · recency, releases, issue load
8.0/10
Openness · license + docs
1.0/10
Manual review · sanity read + red-flag check
6.0/10
  • · Mature vendor (AI in testing since 2017) with enterprise customers (JetBlue, Mercedes-Benz reported).
  • · Actively developed: GenAI test generation, MCP server, agentic testing positioning through 2026.
  • · Closed-source SaaS; tests live in mabl with limited Git-based version control.
  • · Honest caveats: quote-based pricing (third-party estimates from ~$500/mo) with no free tier; recording-first model captures behavior, not spec.

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.