Agent-QA vs swarm-test

Agent-QA and swarm-test are both coding assistants tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Agent-QA

The tool lets you write test steps in plain language — 'Click on the Create issue icon', 'Verify that the created issue is shown' — and an agent translates those into browser actions at runtime, reading visible labels and screen state instead of fragile CSS selectors. After each run, it builds execution memory: observations about navigation contracts, UI quirks, and previously healed steps, which get injected into future runs so the agent stops rediscovering the same UI patterns. Self-healing means that when a component shifts, the agent iterates through recovery attempts rather than failing immediately. The ceiling appears when test logic branches on conditional application state — the YAML authoring model is built for linear flows, and complex branching sends teams back to scripting.

swarm-test

The tool works by analyzing the graph structure of your multi-agent system — how agents connect, depend on, and hand off to each other — rather than running live inference. It supports CrewAI, LangGraph, AutoGen, and custom agent graphs out of the box, per the project docs. Output includes interactive reports and Mermaid visualizations suited for CI/CD pipelines and GitHub Actions. The ceiling appears when your reliability concern is runtime behavior rather than topology: swarm-test cannot catch prompt drift, model degradation, or failures that only surface under real load. Teams with those requirements run this alongside live integration testing, not instead of it.

Attribute	Agent-QA	swarm-test
Pricing	Paid	Free
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	Web and mobile (Chromium, mobile drivers)	Python, CLI, GitHub Actions
Pros	Natural language test authoring against visible UI labels rather than DOM selectors, so a component rename or layout shift does not immediately break the test suite the way a hard-coded selector would. Execution memory that accumulates across runs with trust scores and confirmation counts, which means the agent stops wasting run time rediscovering navigation patterns it has already mapped — later assertions stay focused on actual page behavior. Self-healing iteration within a single run — when an action fails, the agent retries with updated screen state observation rather than failing the step immediately, so transient UI delays cause fewer false negatives. Support for custom and open-source LLM models at the infrastructure level, so teams with data-residency requirements or API cost constraints can run inference locally without forking the tool. Open-source codebase with self-hosted deployment option, which means teams are not locked into a vendor's uptime or data pipeline when running tests against internal staging environments.	Zero-cost static analysis — no LLM API calls are made during inspection, so teams can run reliability checks in CI without per-execution API spend or rate-limit exposure. Graph-level cascade detection, so fragile dependency chains that look fine in per-agent unit tests get surfaced before they cause silent end-to-end failures in production. Native GitHub Actions support via a committed action.yml, which means structural checks can be added to a PR workflow without custom scripting. Framework-agnostic graph support for CrewAI, LangGraph, AutoGen, and custom topologies, so teams are not locked into a single agent framework to get pre-deployment analysis. Interactive Mermaid visualizations in generated reports, so a product manager or engineering lead can read the failure risk map without parsing raw JSON or log files.
Cons	The YAML step format is built for linear flows — action, verify, action, verify. Test scenarios that branch based on runtime application state (for example, different assertion paths depending on what a previous step returned from the server) have no native expression in the authoring model. Teams with conditional logic either maintain a parallel scripting layer or restructure tests into multiple flat suites, which defeats the maintenance advantage. Execution memory is only as reliable as the trust scores the agent has accumulated. On a new application or after a major redesign, early runs produce low-confidence observations and the agent behaves closer to a first-run tool — the adaptive advantage appears after repeated runs against a stable-ish UI, not on day one. Teams whose test requirements outgrow linear natural-language flows — particularly those already running Playwright or Cypress suites with custom fixtures, parameterized data, and programmatic assertions — will find agent-qa's authoring model too constrained and switch back to code-first frameworks where branching logic is a function call, not a workaround.	Static analysis only covers the topology you define — it cannot detect failures caused by model behavior, prompt instability, or latency degradation at runtime. Teams whose primary reliability concern is what the model does under load will find the tool answers a different question than the one they are asking, and need a runtime observability tool instead. The project has 3 stars and 1 open issue at time of curation, with no indication of a commercial backer or active contributor community. Teams that hit an edge case in graph parsing have the MIT source to work from, but no support channel and precious little community precedent to draw on. Custom agent graph support requires defining your topology in a format the tool can parse — teams with bespoke orchestration logic that does not map cleanly to a supported framework spend time on schema translation before they get their first report, at which point some switch to writing their own topology-aware test harness rather than adapting an external tool.

Bottom line

Agent-QA is paid while swarm-test is free; only Agent-QA exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Agent-QA and swarm-test?

Agent-QA is Paid and open source, while swarm-test is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Agent-QA better than swarm-test?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.