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TestSprite generates tests in its cloud. agent-qa learns in your repo.

TestSprite focuses on autonomous generation and cloud verification. agent-qa turns agentic QA into explicit YAML, hooks, memory, and evidence that stay with the code your agents change.

Try agent-qa, the source-available way to turn generated QA into remembered evidence.

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agent-qa vs TestSprite

Capabilityagent-qaTestSpriteDetails
Source accessTestSprite is not positioned as a repo-owned framework with published source, so behaviour you disagree with is a support ticket. With agent-qa it is a pull request.
Repo-owned YAMLTestSprite keeps the test intent inside its own product. agent-qa keeps intent, config, hooks, memory and suites beside the code they cover, where your engineering process already works.
Coding-agent nativeA coding agent cannot click through a hosted editor. agent-qa ships MCP tools, packaged Skills and a CLI, so the agent that changed the code writes the test, runs it and reads the failure without leaving the loop.
Bring your own LLMWhoever picks the model sets your quality ceiling and your bill. agent-qa lets you point at any provider, any compatible endpoint, or a model on your own hardware, and change it in one line.
Local and CI executionOne command on a laptop, in CI, and from an agent. No run depends on somebody else's control plane being up, and nothing queues behind another tenant.
Web and mobile QAWeb, Android and iOS from the same natural-language flow and the same evidence model. The surface is a target named in a file, not a different product tier.
Memory, cache, hooksExecution memory, a validated action cache and sandboxed hooks compound. A suite that has been running a month is faster, cheaper and better informed about your app than the day it was written.
No platform lock-inEvery durable asset stays in your repository. Cancel agent-qa tomorrow and the tests, the memory and the evidence are still there and still readable.

Why teams switch from TestSprite

Built for coding agents, not dashboards

Your team already ships code with coding agents, and a coding agent cannot click around TestSprite's interface. agent-qa ships MCP tools, packaged Skills and a CLI, so Claude Code, Cursor and their peers author the test, run it and triage the failure inside the same loop that wrote the change.

Your tests stop being hostages

Every test you write in TestSprite makes leaving TestSprite more expensive. That is not an accident, it is the business model. Every test you write with agent-qa is a YAML file in your repository: reviewed in a pull request, portable to any runner, and still yours the day you cancel.

Generated tests you can't review are liabilities

Autonomous generation without code review produces coverage you can't reason about. agent-qa keeps generation agentic but lands every test as reviewable YAML in a pull request, so a human (or another agent) can see exactly what's being promised.

TestSprite automates testing into its cloud. agent-qa automates it into your codebase, where review, memory, and your coding agents already live.

Frequently asked questions

Is agent-qa a good TestSprite alternative?

Yes, and the reason is structural rather than a feature count. TestSprite is an autonomous testing platform that generates and verifies tests in the vendor's cloud, which means the asset you are building lives on their side of the line. agent-qa is a source-available QA agent with no paid tier, governed by FSL-1.1-ALv2: tests are plain-English YAML in your repository, runs execute on your laptop, in your CI, or from your coding agent, and every run writes back into memory committed beside the tests. The suite gets better at your app whether or not you renew anything.

How much does agent-qa cost compared to TestSprite?

TestSprite is priced on cloud platform subscriptions, so the bill tracks how much you test. agent-qa has no paid tier, no seats and no platform fee; FSL-1.1-ALv2 governs permitted use. You pay for the model tokens and infrastructure you already control, on the provider you choose, and the validated action cache takes roughly 60% of the tokens off a matched rerun. Adding coverage does not add a line item.

How do I migrate from TestSprite to agent-qa?

You are re-describing intent, not porting code, which is why this is far smaller than a normal test migration. Export the intent of your TestSprite coverage, what each generated test verifies, and re-state it as agent-qa YAML flows your team reviews once and owns forever. Run npx agent-qa init, write each critical flow as a plain-English YAML test, and let the runtime work out the selectors and the recovery. Most teams move a smoke suite in an afternoon, and there is nothing to un-pick later because the output is files in your own repository.

Does agent-qa cover web and mobile like TestSprite?

Yes, and from the same file. agent-qa runs end-to-end tests on web, Android and iOS with one natural-language format, one memory store and one evidence model, so a flow written once survives being pointed at another surface. agent-qa runs mobile E2E natively alongside web; generated or hand-written, the tests share one repo-owned format.

Can coding agents generate agent-qa tests the way TestSprite generates tests?

Yes. That's the native workflow. agent-qa ships MCP tools and packaged Skills so Claude Code, Cursor, and similar agents can author tests from product context, validate them, run them, and file the results, with everything landing in your repo instead of a vendor cloud.

Sources

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

Where agent-qa pulls ahead of TestSprite

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

Natural-language tests

Describe actions and assertions in natural language. agent-qa resolves them against the live interface using visible roles, labels, and screen state.

Learn about natural language tests

Natural-language YAML

Write the behavior and expected outcome in plain English. The test stays as reviewable YAML in your repository.

Targets users recognize

Refer to “New issue,” “Checkout,” or the “Issues table.” agent-qa finds the matching control in the live interface.

One format, every surface

Use the same natural-language structure across web, Android, and iOS without maintaining selector-heavy variants.

Execution memory

A run that passes leaves evidence: elements that resolved, a flow that worked, and timings that are real. agent-qa distils that evidence into durable facts, procedures, cautions and measurements. Each becomes a reviewable bundle committed next to your tests. Nothing steers a run until the evidence says it should, and once it does, the next run replays what already works instead of deriving it again.

Learn about memory

Bundles you can review

Each thing learned is a directory in your repository: the record, the evidence behind it, and the artifacts that justify it. Read it in a pull request.

Proven before it is used

New knowledge is born a candidate. Offline replay and attributed evidence from live runs decide whether it ever reaches a planner.

Warm runs get faster

A flow that already worked is replayed rather than re-derived, and stale facts are superseded instead of quietly served.

Version controlled, built for teams

Tests, suites, hooks, and the workspace config are files you write. What it learns about your app, the bugs it files, the rules you assert, and the skills an authoring agent uses are files agent-qa writes, in a visible directory beside your tests rather than in a database you cannot read. All of it is committed, so a new memory bundle arrives as a diff in a pull request with the evidence that justified it. Only derived indexes and binary run artifacts are gitignored, because they rebuild from what is committed. A teammate, a coding agent, and CI check out one commit and get the same brief.

Learn about configuration

Files, not a database

What you author and what the agent learns sit next to each other as committed files. Nothing it knows is locked in a store you cannot open.

Learning arrives as a diff

A new memory bundle or a filed issue shows up in a pull request, with the evidence behind it, and is approved the way any other change is.

Everyone reads the same commit

A teammate, a coding agent, and CI work from one checkout, so no run is quietly using state that nobody else has.

Bring your own model

The model is a setting, not a rewrite. Point a workspace at an OpenAI or Anthropic compatible endpoint, at Gemini, at an open-weight model running on your own hardware, or at a subscription your team already pays for like Codex or Claude Code, and override it on the one test that needs something stronger than the rest. Nothing about how a test is written changes when the model does, because the test says what should happen and the model is only what works out how to get there. There is no vendor to be locked to and no key of ours to buy.

Learn about LLM providers

Swap it in one line

The provider, the endpoint, and the model are config. Change them and every test you have already written runs against the new one, unedited.

The seat you already pay for

Account-backed authentication works where the provider allows it, so a Codex or Claude Code subscription can drive runs without a second bill.

Or nothing leaves the building

Point it at an open-weight model on your own hardware when the code under test is the kind that cannot go to somebody else's API.

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