The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the LiveVariant listing page.
The test that keeps testing.
Open-source A/B testing that never stops. One adaptive model routes traffic toward what's winning while your campaign runs, learns a different winner per audience, and tests several elements as one combination. The whole test lives in a URL: no account, no platform, nothing to install on your site.
livevariant.com · built for LLM agents first, marketers and developers second, all touching the same object: the URL.
The headline on livevariant.com is itself a running LiveVariant test, served by the SDK snippet shown on that page: two slots, nine combinations, adapting per country and device. We test our own homepage with our own product.
So do the agents we run on it. livevariant.ai is the front door of a small colony of autonomous AI agents whose one job is to grow LiveVariant, and every page they publish runs as a LiveVariant test: the expected winner is written down first, the live stats are public, and the misses stay in the ledger. The first agent, Prior, keeps its journal and experiment ledger at prior.livevariant.ai.
The whole setup is one conversation, and there is nothing to install: naming the site in any AI chat is enough for the agent to discover the tools and take it from there.
"I want to A/B test my next 'Daily brew' newsletter with livevariant.com. Give me some ideas and set it up."
Your assistant proposes a plan first: two slots tested as one combination rather than as two separate tests, plus the audience segments results split by.
"Looks good!"
It builds the test and hands back three links for the template (one image link per slot, plus the click link that records the win and redirects) and your manage link with live results:
Nine combinations, every recipient sticks to their own, traffic shifts
toward whatever combination is winning while the campaign runs, and a
different combination can win per audience. Ask for get_stats later
and it tells you each combination's probability of being best, not
just raw rates.
Installing buys deeper integration: the full skill in context, tools without discovery. Skills (recommended, works with Claude Code and Cowork):
Claude Code plugin (this repository is the marketplace):
Codex plugin:
Any other agent: MCP hosted at https://livevariant.com/mcp, stdio via
npx -y @livevariant/mcp, or plain HTTP at POST /api/v1/<tool>
(interactive docs at /docs, spec at /openapi.json). No API keys: a
test's config and its stats secret carry all the authority there is.
The builder composes a test in the browser, no code and no account. Or write the URL by hand:
Replace your email's image URL with that, and the integration is done.
Every recipient sticks to their variant across opens, traffic shifts
toward the winner while the campaign runs, and clicks (/c) plus a
thank-you-page pixel (/px) close the loop. Add &kh=<your-stats-key>
(a stable value: generate one under Settings, or take the builder's) to
make results readable with your stats secret.
Multiple elements? Slots test the combination, not isolated pieces (wrapped here for reading; variant values are full URLs):
One link per element (&slot=), one sticky combination per recipient,
and the model learns that hero A only wins with product shot 2, which
two separate tests can never see.
For audience segments in email, use what survives mail proxies: campaign
tags (ctx=source:utm_source) or your ESP's merge fields
(&c_country={{country}}).
Landing pages are tests as well: images and content served directly on the page. Developers and LLM coding agents wire one up by installing the SDK; the config is readable on purpose, and this is the whole test:
Two slots, nine combinations, a different winner per country and device, and the test is scoped to your domain automatically. If the server is unreachable, visitors get your control and nothing breaks. The headline on livevariant.com runs exactly this way; the page shows its own snippet.
Building a test (through the builder or build_test) shows the stats
secret exactly once; only its hash travels in the config, so nobody
can recover it later, including us. Keep it.
#fragment (which never
reaches server logs): open it for live per-combination and per-slot
numbers.get_stats with the same secret and get win
probabilities plus an honest stop/continue call, instead of eyeballed
conversion rates.No account is ever required, but on livevariant.com you can create one (email link or Google) and it buys three things:
pk_ key, pass it to
the SDK from a verified domain, and inline tests register themselves
under My tests, readable without any secret in the loop.The classic email flow sends A to 10%, B to 10%, waits a few hours, and blasts the "winner" to the rest. Decided once, on early openers, one element at a time, one answer for everyone, and it ends.
LiveVariant keeps everyone in the test forever: allocation adapts on every serve, per segment, across combinations, and priors (yours or your LLM's) give it a head start that real data can always override. Keep your ESP's subject-line test (subjects render before anything loads); everything after the open is ours. The mechanics are the published literature (Thompson 1933; Chapelle & Li 2011; Li et al. 2010; Hill et al., KDD 2017; Shivaswamy & Joachims 2012), implemented small enough to audit.
That trade is not free, and it is worth stating plainly. Adaptive allocation buys lower regret and pays for it in measurement precision. Starving a losing variant is the point, but it also means that variant's reported rate is biased low, by about 11% of its own value in our simulations, because an arm that has an unlucky start gets sampled less and few observations arrive to correct it. And any rule you poll continuously loses the guarantee it would have at a single look. So LiveVariant optimizes earnings-while-learning rather than the precision of the number you read afterwards. For a campaign whose goal is conversions that is the right way round; if you need a defensible measurement of the gap rather than the most conversions, a fixed 50/50 split and a single pre-planned look is the better instrument.
This product is designed to be self-deployed. Our server can be used for testing, but its state can be destroyed at any time; a managed hosted version is in the works. Your deployment runs the same AGPL code with none of those caveats:
One click clones this repo into your account and deploys the whole
thing: serving, dashboard, tools API, MCP endpoint. Nothing to
configure; every URL is built from the origin the request arrived on.
The self-host build contains no auth framework at all (a test asserts
it), and three optional env vars cover the trust knobs: comma-separated
LV_ALLOWED_ORIGINS locks the SDK endpoints to your own sites,
LV_ALLOWED_DESTINATIONS + LV_UNLISTED_DESTINATIONS decide whether
redirect destinations off your list are allowed, blocked, or shown
behind a continue screen, and LV_API_TOKEN gates the tools API and
MCP endpoint behind a bearer token for server-to-server calls. Custom
logic instead of env vars? Implement the two-method TrustPolicy (and
optionally AccountsProvider) ports from @livevariant/server and
pass them to createApp.
Node 24 (nvm use). npm ci, npm run build, npm test
(test:no-browser for the Playwright-free subset). The deep technical
reference lives in CLAUDE.md, which is also what your
coding agent reads; the design system in DESIGN.md.
Contributions are welcome: see CONTRIBUTING.md. Your first pull request asks you to sign the Contributor License Agreement.
AGPL-3.0. Self-hosting for your own use is unrestricted; if you modify LiveVariant and offer it as a network service, publish your modifications.