Comparisonsagent-device alternative

An agent-device alternative for tests that retain context.

Callstack's agent-device gives agents device control and repeatable replay workflows. agent-qa organizes verification around natural-language test contracts and product memory shared across runs.

Try agent-qa, a source-available QA runtime that turns expected behavior into repository-owned tests. Use CLI, MCP, and Skills to run a journey, inspect its evidence, and carry relevant product observations into the next change.

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agent-qa vs agent-device

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agent-qa vs agent-device: capabilities and source evidence
Capability	agent-qa	agent-device	Details
CLI and MCP for coding agents			agent-device exposes its device runtime through CLI, MCP, and a typed Node.js API. agent-qa's CLI and MCP center on test execution and result inspection. Both let coding agents verify changes through structured tools.Sources: 2, 6
Natural-language test contracts			agent-device lets an agent explore an app and save command flows. agent-qa stores natural-language steps and expected outcomes as the test definition. Choose whether repeatability should follow a saved action sequence or a declared journey.Sources: 1, 3, 5
Repository files and CI suites			agent-device has a suite runner for .ad scripts with retries, artifacts, and JUnit reporting. agent-qa runs YAML tests and suites with CI output. Both can preserve release checks as files in your repository.Sources: 3, 6
Curated behavioral memory			Saved agent-device scripts retain actions for replay. agent-qa additionally stores behavioral observations in product, suite, and test Markdown files, retrieves relevant context, and curates it after runs. The cited replay docs do not establish that same memory lifecycle.Sources: 3, 7
Native mobile execution			agent-device supports Android and iOS through its device backends. agent-qa uses configured Appium targets. Compare provisioning, app-state handling, and the devices your team needs; native-mobile support is shared rather than unique to either product.Sources: 2, 8
Browser verification			agent-device documents basic web support through agent-browser within its session and replay system. agent-qa treats web as a configured QA target. The partial rating reflects agent-device's stated basic scope, not an absence of browser support.Sources: 2, 5
Inspectable failure evidence			agent-device captures screenshots, recordings, logs, and network diagnostics. agent-qa associates evidence with test-step outcomes and exposes it to a run inspector and coding-agent tools. Assess which view makes a failed acceptance condition easiest to explain.Sources: 1, 9, 6
Published source			agent-device is MIT licensed. agent-qa is source available under FSL-1.1-ALv2, with a future Apache 2.0 license. Evaluate those different permissions alongside the operational fit, especially when embedding a runtime in another product.Sources: 2, 10

Evaluate agent-qa against agent-device

Choose one app journey with a defined starting state and a precise acceptance condition.
Save an agent-device replay and author an agent-qa YAML test that checks the same behavior on the same build.
Break the acceptance condition and inspect each result, its artifacts, and the CI status before considering a passing run sufficient.
Change the UI without changing behavior, then compare maintenance effort, memory updates, device setup, and repeated-run cost.

Set up your coding agent Use the evaluation guide Inspect recorded QA evidence

Choose the contract your QA suite should preserve

Keep the expected behavior explicit

Write a journey and the outcome that makes it correct. agent-qa's YAML format gives reviewers a stable statement of intent to assess alongside the application change.

Retain observations across journeys

Use memory scoped to a product, suite, or test so later runs can retrieve useful context. The files remain reviewable, and current application evidence still governs the result.

Organize diagnosis around test results

agent-qa links the declared test, step evidence, and follow-up run in one workspace. Use that structure when the team needs a repeatable acceptance-testing process around its coding agents.

agent-qa fits teams that want recurring verification expressed as intent, with reviewable product memory. agent-device fits teams that want a programmable device toolkit and repeatable command flows; both can contribute to a release process.

Frequently asked questions

Which agent-device project is compared here?

This page covers Callstack's agent-device at agent-device.dev and github.com/callstack/agent-device. It is a device automation and verification toolkit, distinct from Vostride's agent-qa runtime.

Does agent-device already run E2E tests in CI?

Yes. Its replay and suite tooling supports repeatable checks, artifacts, retries, and JUnit results. agent-qa offers a different contract based on natural-language YAML and behavioral memory. CI support alone is not a reason to switch.

Can agent-device work with YAML?

Its replay documentation includes Maestro YAML support and export with explicit compatibility limits. Those flows are not agent-qa tests. Treat any move as a translation of actions and expected results, with a fresh check of device setup and app state.

Is agent-qa built on agent-device?

agent-qa's documented native mobile setup uses Appium. The separate tester-army/e2e framework uses agent-device as its mobile engine. These are distinct architectures; do not assume that scripts or device configurations transfer between them.

When is agent-device the better fit?

Choose it for direct device interaction, replay-oriented automation, or a programmable toolkit with MIT licensing. Evaluate agent-qa when a suite of natural-language expectations and retained product context is the main deliverable. Confirm platform-specific capabilities for either tool.

Sources

This page is based on public product and documentation sources. Verify current features and pricing with each vendor before making a purchase decision.

Sources reviewed 2026-10-02 by Vostride.

Support depth varies by device and platform. Partial indicates a different abstraction or the documented scope, not that custom orchestration is impossible. No comparative performance benchmark was run.

Where agent-qa pulls ahead of agent-device

The parts of agent-qa that answer what agent-device leaves you carrying.

Natural-language tests

Write actions and assertions in plain English; agent-qa resolves them against the live interface.

Learn about natural language tests

Plain-English YAML

Describe the behavior once; it stays as reviewable YAML in your repository.

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User-facing targets

Name visible controls and labels instead of brittle selectors.

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One format everywhere

Use the same test structure on web, Android, and iOS.

Learn more

name: Test issue creation on Linear

steps:

-Navigate to {{env:LINEAR_URL}} and make sure the Issues table is visible.

-Click "New issue", enter "Fix mobile checkout" in the title field, enter "Checkout button overlaps the order summary on mobile." in the description field, add the "Checkout" label, then click "Create issue".

-Verify the Issues table contains a row with title "Fix mobile checkout" and label "Checkout".

Execution memory

Turn successful runs into reviewable, evidence-backed memory that makes every future run faster.

Learn about memory

Reviewable bundles

Every learned fact ships with its evidence as files in your repository.

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Proven before it is used

New knowledge must pass replay and live evidence before guiding a run.

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Faster warm runs

Reuse proven flows and supersede stale facts instead of rediscovering them.

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runTest issue creation on Linear29s

Open /issues4.1s

Click "New issue"3.4s

Fill title, description, label9.7s

Verify row VOS-4826.2s

A run leaves evidence

Elements that resolved, a flow that worked, and timings that are real, all attached to the steps that produced them.

Built for Humans & Agents

Humans and agents author the same reviewable YAML, backed by your repository, skills, and MCP.

Learn about MCP and skills

Anyone can author

Product, engineering, and QA write the same plain-language test.

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Skills and MCP for agents

Skills teach the workflow; MCP validates, runs, and returns artifacts.

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One shared artifact

Every author produces the same reviewable YAML in the repository.

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Version controlled, built for teams

Tests, knowledge, and rules stay as reviewable files shared by teammates, agents, and CI.

Learn about configuration

Files, not a database

Tests, config, memory, and rules stay as files your team can inspect and own.

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Learning arrives as a diff

New memory and issues arrive as pull-request diffs, with their evidence.

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One commit everywhere

Humans, coding agents, and CI share the same knowledge from one commit.

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Tests
Configs
Memory
Self improvement
Knowledge

Version control

Engineeragent-qa
QA engineeragent-qa
Coding agentagent-qa
CIagent-qa

* This comparison is based on publicly available information. Product capabilities and pricing can change; verify details with each vendor before making a purchase decision.