AI test authoring and external agents
MagicPod Autopilot creates and edits tests from Japanese text instructions. The MagicPod MCP server also lets Claude, Codex, Cursor, and Devin operate MagicPod through natural language.
MagicPod is a cloud service for automating browser and mobile app tests. Teams build test scenarios by selecting AI-detected screen elements in a no-code editor or issuing instructions to an AI agent. Its readable test scripts let people without programming knowledge participate in test creation and maintenance.
The platform combines test authoring, execution, result reports, and maintenance tools. Teams can use MagicPod's cloud environments, local devices, or supported external device clouds, with integrations for existing development workflows.
MagicPod Autopilot creates and edits tests from Japanese text instructions. The MagicPod MCP server also lets Claude, Codex, Cursor, and Devin operate MagicPod through natural language.
AI assertions evaluate screen content against expectations written in natural language. During batch runs, automatic repair attempts to fix locator definitions and commands after a test fails.
Run tests across browsers and iOS or Android devices, including WebView and Flutter apps. Commands cover file uploads, browser storage, gestures, device rotation, and other platform-specific actions.
Data-driven tests repeat scenarios with different input values, including CSV data. Variables, conditional branches, loops, and explicit waits control execution without writing code.
Execute tests in parallel using cloud environments. Integrations include GitHub Actions, Jenkins, CircleCI, and Bitrise, plus BrowserStack, Sauce Labs, and Remote TestKit for external test infrastructure.
AI failure analysis investigates unsuccessful runs. Result reports show execution outcomes, while project health scores identify areas for improvement and suggest changes.
QA teams can move repeated release checks into scheduled or CI-triggered runs, using reports to investigate failures before shipping changes.
Validate the same customer journey across browser configurations, mobile OS versions, and device models rather than repeating each check manually.
Teams maintaining both interfaces can manage and batch-run their browser and mobile test cases within one project using mixed-platform testing. Each testing type still requires its own contract.
Optional onboarding support covers test reviews, failure diagnosis, training, and operating rules so the team can learn to maintain its own automation. Scope and fees are discussed separately.
A two-week free trial lets you create and execute tests without registering a credit card. Continued use is through a paid plan.
The English Standard plan costs $320/month with an annual contract or $400/month with a monthly contract, excluding tax, for one testing plan. Japanese billing lists ¥39,800 and ¥54,800 respectively. Enterprise pricing is negotiated.
It starts with three projects, 100 test cases, 100 visual difference check steps, and 10 branches, with two concurrent cloud environments each for creation and batch runs. Users and executions are unlimited. Test-case capacity is shared across projects, includes trashed cases, and counts cases over 300 steps as multiple units. Paid add-ons are available.
No. Browser and mobile app testing require separate contracts, including when their cases are managed in one mixed-platform project. Both contracts must use the same contract period and payment method. External device-cloud services also need separate service contracts.
It removes limits on test cases, visual difference check steps, and branches, and adds SAML SSO, IP restrictions, domain control, secure tunneling, contract customization, and premium support. Test results are retained for two years rather than Standard's one year. Project counts and cloud concurrency are arranged with MagicPod.
Test assets can include application binaries, UI images, logs, and prompts. MagicPod uses cloud infrastructure and may send information to providers such as Anthropic and OpenAI for AI features or support. Its terms state that user information is not used to train its own or external generative AI models.
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