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Functionize sells a platform. agent-qa is the QA agent that learns inside your workflow.

Functionize packages AI testing as an enterprise platform sale. agent-qa keeps test ownership, model choice, and verification evidence in the developer workflow, no platform between you and your proof.

Try agent-qa, the source-available workflow your coding agents can run and remember.

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agent-qa vs Functionize

Capabilityagent-qaFunctionizeDetails
Source accessFunctionize 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 YAMLFunctionize 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 Functionize

You pick the model, not the vendor

Functionize decides which AI runs your tests, when it changes, and what it costs you. agent-qa is bring-your-own-model: swap providers in one line, put a cheap model on smoke tests and a strong one on the flow that matters, or run against the internal endpoint your security team already signed off.

Your tests stop being hostages

Every test you write in Functionize makes leaving Functionize 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.

Platform AI is a black box; repo AI is a diff

When Functionize's cloud AI adapts a test, the adaptation happens inside the platform. When agent-qa adapts, the evidence lands in run artifacts and memory files you can read, and the test itself stays a reviewable YAML document with a git history.

Functionize wraps AI testing in an enterprise platform. agent-qa strips the platform away and leaves what teams actually need: intent in YAML, adaptation with receipts, memory in the repo.

Frequently asked questions

Is agent-qa a good Functionize alternative?

Yes, and the reason is structural rather than a feature count. Functionize is an enterprise AI testing platform where authoring, execution, and AI adaptation happen 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 Functionize?

Functionize is priced on enterprise platform contracts, 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 Functionize to agent-qa?

You are re-describing intent, not porting code, which is why this is far smaller than a normal test migration. Inventory the journeys your Functionize suites cover and restate each as an agent-qa plain-English test; the AI adaptation you relied on comes along, but inspectable this time. 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 Functionize?

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's mobile support uses the same YAML contract and CLI as web, so one skill set covers both surfaces.

How does agent-qa's self-healing compare to Functionize's?

Both adapt when the UI changes. The difference is transparency and ownership: agent-qa re-plans from natural-language intent, records what changed in file-backed memory, and caches the corrected plan, all in artifacts your team can audit rather than a platform's internal state.

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 Functionize

The parts of agent-qa that answer what Functionize 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.