WildflowerJS Reactive JS, No BS*

A no-build reactive JavaScript framework, rooted in the web platform.
No build step. No dependencies. No lock-in.

Latest release: v1.3.0 · see what's new
<script src="wildflower.min.js"></script> ...and start building.

Back to Basics

The code you write is 100% web standard code. HTML stays HTML. JavaScript stays JavaScript. CSS stays CSS. No JSX, no templating language, no custom syntax to learn. If you know the web platform, you already know how to use WildflowerJS.

WildflowerJS extends the web platform. It doesn't replace it.

Your Development Simplified

Because you develop with 100% web standards, every tool in your existing chain already understands the code: IDE, browser DevTools, linter, formatter, screen reader, SEO crawler. Nothing to install, no custom file types, no sourcemaps. Save the file, refresh, and your change is live.

Just be a web developer.

Batteries Included: One Mental Model

Router, SSR, stores, computed properties, two-way binding, event modifiers, data pools, and TypeScript types, all built in, all speaking the same language. Learn data-bind once and you know binding everywhere: lists, pools, stores, forms. There's no five-library stack to keep in sync.

One script tag. Everything you need.

<div data-component="counter">
  <span data-bind="count"></span>
  <button data-action="increment">
    +1
  </button>
</div>

<script>
wildflower.component('counter', {
  state: { count: 0 },
  increment() { this.count++ }
})
</script>

How It Works

data-bind connects state to the DOM.

data-action connects events to methods.

this.count++ triggers a precise DOM update.

Mutate state. The DOM updates.

Two Reactivity Modes

data-list for automatic reactivity: mutate state, DOM updates. data-pool for explicit control: plain objects, zero proxy overhead, you say what changed.

Same template syntax. Different performance profile. From interactive forms to per-frame particle systems. You choose the right tradeoff for the job.

Try it. Right-click, inspect this demo. Every dot is a real DOM element.

See full demo →

* Build Step

Zero Toolchain

Modern frameworks ask you to install a compiler, a bundler, a package manager, hundreds of fragile transitive dependencies, and a framework-specific file format, before you write a single line of your application.

WildflowerJS was built starting from a single principle: no build step, no tooling. Ever.

WildflowerJS asks you to add a script tag.

There's no CLI scaffolding step, no config files, no .vue/.jsx/.svelte source format. You don't debug through sourcemaps or wait on a build pipeline. Your project has zero dependencies.

Performance isn't a tradeoff. Build steps optimize bundle delivery, not the runtime work that follows it. WildflowerJS writes directly to the DOM, with no virtual DOM or reconciliation pass between state change and update, so it doesn't need a build step to be fast.

The framework is full-featured without the toolchain: router, SSR, stores, computed properties, transitions, pools. You don't need a toolchain to use any of it.

my-app/
  index.html
  app.js
  style.css
  wildflower.min.js

That's the entire project. No package.json.
No node_modules. No config files. Ship it.

Zero Install. Zero Attack Surface.

Every dependency you install is trust extended to a maintainer you've never met, running scripts on your dev machine and in your CI. A typical React + Vite + UI‑lib setup pulls in 300+ transitive packages before you write a feature.

Each one is a potential intrusion vector. NPM worms, OAuth chains compromising deploy platforms, postinstall hijacking: the supply chain is now where production code gets compromised, not the deploy. And signing isn't a backstop: Mini Shai‑Hulud (May 2026) compromised 170+ packages whose malicious versions carried valid SLSA Build Level 3 provenance, because the attestation came from build infrastructure the worm had already taken over.

WildflowerJS users don't have this attack surface, by construction. There is no npm install, no postinstall script, no transitive package graph. The framework is one file you copy or pin by hash.

As of v1.1, the same holds for building the framework itself. WildflowerJS bundles with a vendored rollup and terser pipeline pulled as three SHA‑512‑pinned tarballs: no npm install, no transitive packages, no postinstall scripts in the build path. The entire toolchain is three files verified by hash.

Zero dependencies is the absence of a problem the rest of the industry has not properly addressed.

A typical React/Vue project:

  npm install
  ├── hundreds of packages
  ├── from hundreds of maintainers
  ├── postinstall scripts run on install
  └── tens to hundreds of MB of transitive code

WildflowerJS:

  <script src="wildflower.min.js"></script>
  └── 1 file.
      No transitive dependencies.

Zero Compromise

WildflowerJS doesn't compromise performance for ease-of-use. Even with no build step, on the js-framework-benchmark, WildflowerJS performs at the level of frontier frameworks, level with the fastest signal-based frameworks across list creation, updates, selection, swaps, and removal. And for per-frame workloads, data pools lead every framework we tested in our Lorenz attractor simulation demo.

The charts here are the overall geomean standings and the operation breakdown from our latest full-field run, plus the sustained frame rate from our per-frame animation sweep. Click any chart to see it full size.

Delivery is fast too, because there's less to deliver. One file, no framework runtime split across chunks, no hydration pass. Lighthouse scores hold their own against compiled frameworks without a single build artifact.

Simplicity in the interface, performance in the implementation. WildflowerJS doesn't trade one for the other.

Benchmark setup for these charts: js-framework-benchmark operations 1 through 9, 15 samples per cell, all frameworks in a single run; total-duration medians, lower is better. The frame-rate chart runs each framework's fastest variant on the Lorenz attractor for 8 seconds per particle count, fullscreen on a 120 Hz panel; higher is better. Apple M5 Pro, 24 GB RAM, macOS 26.5.2, Google Chrome 150 (stable, headed).

Bar chart of geomean slowdown versus the fastest framework per operation, vanilla JS baseline 1.00: WF-pool 1.054, Vue Vapor 1.056, Solid 1.095, WF 1.120, Svelte 1.165, Vue 1.263. Lower is better.
Geomean slowdown vs fastest per operation. Lower is better.
Grouped bar chart of all nine js-framework-benchmark operations for Solid, Svelte, Vue, Vue Vapor, WF, and WF-pool, with per-operation rankings. WF-pool is fastest on most operations.
All nine operations, side by side. Stars mark the fastest.
Line chart of sustained FPS versus particle count on the Lorenz attractor for Solid, Svelte, Vue, Vue Vapor, WF, and WF-pool. WF-pool holds the highest frame rate at every count, staying above 60 FPS past 4500 particles.
Per-frame animation. Sustained FPS as particle count grows; higher is better.

Zero Lock-in

WildflowerJS works with the DOM, not instead of it. There's no virtual DOM intercepting your code and no compiler rewriting your markup. The render cycle is yours.

That means Leaflet, DataTables, Chart.js, D3, Three.js, any library that touches the DOM, just works. No wrapper packages or framework-specific escape hatches required. Drop in a script tag and use it.

Because your code is standard HTML and JavaScript, you're never locked in. Your skills transfer and your code is more portable. If you outgrow the framework, your knowledge doesn't expire.

This also means your "ecosystem" is all of the world of vanilla JS. Without compromises or hacks.

<!-- Use any library directly -->
<div data-component="map-view">
  <div id="map" style="height: 400px"></div>
</div>
wildflower.component('map-view', {
  state: { lat: 51.505, lng: -0.09 },
  init() {
    // Leaflet works as-is. No wrappers.
    this._map = L.map('map')
      .setView([this.lat, this.lng], 13);
    L.tileLayer('https://{s}.tile.osm.org'
      + '/{z}/{x}/{y}.png').addTo(this._map);
  }
})

Precise Reactivity

When you write this.count++, WildflowerJS updates the single DOM node bound to count. Nothing else is touched. There's no tree diffing or reconciliation pass to figure that out.

You get fine-grained updates and a simple mental model. Change a property, the bound element updates. That's the entire reactivity model.

Other frameworks ask you to learn signals, accessors, memos, effects, and subscription lifecycles to achieve what WildflowerJS does with a property assignment.

wildflower.component('dashboard', {
  state: {
    users: 1420,
    status: 'healthy'
  },
  computed: {
    summary() {
      return this.users + ' users, ' + this.status;
    }
  },
  refresh() {
    this.users = 1421;
    // Only the elements bound to 'users'
    // and 'summary' update. Everything
    // else on the page is untouched.
  }
})

One Reactivity Model. Everywhere.

Components, Stores, and Plugins all share the same reactive foundation. State, computed properties, and methods work identically no matter where they live. Learn it once, it works the same way in a UI component, a global store, or a framework plugin.

Other frameworks make you learn a different system for each layer. React components use hooks, but stores need Redux or Zustand, which are completely different APIs. Vue components use reactive data, but Pinia stores have their own patterns. Every layer is a new mental model.

In WildflowerJS, there's one model. A store is a component without a template. A plugin is an entity that extends the framework itself, adding directives, lifecycle hooks, and services. The same this.count++ triggers the same reactivity everywhere.

This unlocks patterns other frameworks can't express. A store can run headless physics simulations with tick(), feeding data into a component that renders it through a pool, all using the same reactive primitives, no glue code required.

// Component: reactive UI
wildflower.component('cart', {
  state: { items: [] },
  computed: {
    total() { return this.items.length; }
  }
})

// Store: global shared state
wildflower.store('user', {
  state: { name: '', role: 'guest' },
  computed: {
    isAdmin() { return this.role === 'admin'; }
  }
})

// Plugin: extends the framework
wildflower.plugin({
  name: 'notifications',
  state: { items: [], unreadCount: 0 },
  computed: {
    hasUnread() { return this.unreadCount > 0; }
  },
  add(msg) { this.items.push(msg); this.unreadCount++; }
})
// Access globally: wildflower.$notifications.add(...)

// Same state. Same computed. Same methods.

Live Server Data: Built In, Stays True

With WildflowerJS SSR, the page arrives already true. The server (your server, whatever back-end you prefer) renders your data into real HTML, so the first paint is real content, indexable and readable before a line of JavaScript runs. And because the markup is genuine HTML, hydration reads the page's state straight back out of the document. Server-rendered components end up exactly equivalent to client-rendered ones.

v1.3 brings data-query, which does for the rest of the page's life what SSR does for first load. Most frameworks hand you fetch() and leave the rest to you. There's an entire ecosystem of client data libraries that exists to fill that gap. WildflowerJS makes it a declaration instead. Name a source, point an element at it, say how fresh it should stay. Loading and error states, refresh on demand, request racing, and the whole refresh ladder (poll, conditional GET, focus, reconnect, server push) come with it. Oh, and there's no query language. Refinement is an ordinary computed property, and filtering happens client-side without a network round trip.

Together, Wildflower's SSR and data-query tell one story. The server renders the page with real data. Because hydration reads the page itself, there's no flash of empty content, no loading spinner over data the user can already see, and no hydration scripts locking up the main thread. The server's render is the actual UI. When paired with data-query, your SSR becomes the first result of a standing query. The query adopts that markup and keeps it updated from there.

And as you can see in the example, your markup is 100% HTML. From bean to cup, what you see is what you get.

<div data-component="product-board">
  <p data-show="$products.isLoading">
    Loading…
  </p>
  <p data-show="$products.error">
    Failed.
    <button data-action="retry">Retry</button>
  </p>

  <span data-bind="$products.count"></span>
  products

  <tbody data-query="products">
    <template>
      <tr>
        <td data-bind="name"></td>
        <td data-bind="stock"></td>
      </tr>
    </template>
  </tbody>
</div>
// The entire data layer:
wildflower.query('products', {
  from: '/api/products',
  key: 'id',
  refresh: ['focus', 'etag:60']
});

// Server-rendered page? Add data-ssr="true"
// and the markup the server sent becomes the
// query's first result. Live from there.

Data Pools

Every framework wraps collection items in reactive proxies, whether the item needs it or not. WildflowerJS gives you a choice: data-list for push reactivity (automatic), data-pool for pull reactivity (explicit control, zero proxy overhead).

Pools render plain objects with the same template syntax as lists. Mutate the object, call markDirty(), and only that item updates. Full CRUD, selection, bulk operations, all faster than the push-reactive path.

And because pools use pull-based rendering, they scale to simulations, games, particle systems, and data visualizations at native frame rate. Use cases that would choke a virtual DOM. No other framework has anything like this.

<div data-component="user-table">
  <tbody data-pool="users" data-key="id">
    <template>
      <tr>
        <td data-bind="name"></td>
        <td data-bind="status"
            data-bind-class="status === 'active'
              ? 'badge success'
              : 'badge inactive'"></td>
      </tr>
    </template>
  </tbody>
</div>
wildflower.component('user-table', {
  pools: { users: {} },

  init() {
    // Populate: plain objects, no proxies
    data.forEach(u => this.pools.users.add(u));
  },

  // Optional: add tick() and the same pool
  // renders every frame. Same template, same
  // data, different rendering frequency.
  // That's the only difference between a
  // display table and a particle system.
})

Built for AI-Assisted Development

Because WildflowerJS is standard HTML and JavaScript, AI code assistants already know how to write it. There's no custom syntax to hallucinate or compiler quirks to work around. The code an AI generates runs exactly as written, with no build step between generation and execution.

WildflowerJS ships an AI-optimized reference page with patterns, anti-patterns, and examples designed for code generation context windows. Our llms.txt file follows the llms.txt convention for machine-readable documentation.

And for structured app generation, our Universal App Manifest lets you describe an entire application as a JSON schema (components, state, computed properties, methods, templates) and have an AI generate the working code from the manifest, mediated through framework-specific idiom files.

You: "Build me a todo app with
WildflowerJS"

AI reads llms.txt or ai-assistant.html
     ↓
Generates standard HTML + JS
     ↓
<div data-component="todo-app">
  <input data-model="newItem">
  <button data-action="addItem">
    Add
  </button>
  <ul data-list="items">
    <template>
      <li data-bind="text"></li>
    </template>
  </ul>
</div>
     ↓
Open in your browser. It works, and you can read and understand the code.

Do You Need data-list?

data-list is the powerful option for rendering a collection, not the only one. It buys two things: per-item reactivity (change one row's field and only that cell repaints) and keyed diffing (add, remove, or reorder without re-rendering the rest). Many lists need neither. When yours does not, a lighter pattern is simpler to reason about, faster, and works in every build, including Nano, which ships no list renderer at all.

The short version: reach for the lightest tool that fits. Static data is a one-time stamp. A filtered or sorted view is a re-render on change. Interactive rows are each their own component. Save data-list for the case it was built for: a frequently changing list whose individual cells update live.

Every list uses a <template>

The markup is nearly identical no matter which approach you pick. A data-list block and a hand-rendered list hold the same HTML <template>; the difference is who stamps it. With data-list, the framework stamps and diffs it for you. Without it, you clone the template yourself with one or two lines of plain DOM.

Pattern 1: Static data (stamp once)

For data that is fixed when the page renders, such as a navigation menu, a footer, or a snapshot table, clone a template per item and set its text. The items are not components and carry no reactivity, so the framework never touches them. This is the cheapest list there is.

<ul id="menu"></ul>

<template id="menu-item">
  <li><a></a></li>
</template>
const tpl = document.getElementById('menu-item');
const menu = document.getElementById('menu');
for (const link of links) {
  const li = tpl.content.firstElementChild.cloneNode(true);
  const a = li.querySelector('a');
  a.textContent = link.label;
  a.href = link.href;
  menu.appendChild(li);
}

Pattern 2: A filtered or sorted view (re-render on change)

When a search, filter, or sort changes which items are visible, you are replacing the whole visible set anyway, so keyed diffing gains you nothing. Keep the data in state, derive the visible slice with a computed, and re-stamp the list body when it changes. The parts that are cheap to keep reactive, like the query and the result count, stay declarative with data-bind.

<input data-action="input:filter" placeholder="Search">
<p><span data-bind="count"></span> results</p>
<div id="results"></div>

<template id="row">
  <div class="row"><span class="name"></span></div>
</template>
wildflower.component('finder', {
  state: { query: '' },
  computed: {
    results() {
      const q = this.query.toLowerCase();
      return DATA.filter(d => d.name.toLowerCase().includes(q));
    },
    count() { return this.results.length; }
  },
  init() { this.render(); },
  filter(event) { this.query = event.target.value; this.render(); },
  render() {
    const list = this.element.querySelector('#results');
    list.textContent = '';                        // clear
    const tpl = document.getElementById('row');
    for (const item of this.results) {            // re-stamp the visible set
      const el = tpl.content.firstElementChild.cloneNode(true);
      el.querySelector('.name').textContent = item.name;
      list.appendChild(el);
    }
  }
});

The example above re-renders from the filter action. You can also trigger it declaratively with a watch, so the list refreshes whenever the query changes, however it was set:

watch: {
  query() { this.render(); }   // re-render on any change to `query`
}

Live example: the Searchable List demo filters an array of HTTP status codes this way, in the Nano build.

Variant: filter a fixed set in place

When the full set is fixed and you are only narrowing it, such as a reference table or a docs index, you do not need to re-stamp at all. Render every item once as static HTML, tag each with what it belongs to, and toggle its display as the filter changes. The content stays in the page, so it is indexable and readable with JavaScript disabled, and filtering is one loop over the elements you already have.

<input data-action="input:filter" placeholder="Search">

<div class="entry" data-categories="core">WF-101 …</div>
<div class="entry" data-categories="state bindings">WF-EFFECT …</div>
wildflower.component('reference', {
  state: { query: '' },
  watch: { query() { this.apply(); } },
  filter(event) { this.query = event.target.value; },
  apply() {
    const q = this.query.trim().toLowerCase();
    for (const el of this.element.querySelectorAll('.entry')) {
      el.style.display = el.textContent.toLowerCase().includes(q) ? '' : 'none';
    }
  }
});

Live example: the Error Codes reference in these docs filters about fifty static entries exactly this way, with a watch and one show/hide loop.

Pattern 3: Interactive rows (each row a component)

When rows have their own behavior, such as a toggle, an input, or a live value, make each row its own component. You get per-item reactivity from each row's own state, while list membership stays under your control. Appending a data-component element initializes it, and removing it tears it down: the same mutation observer that powers dynamic component detection handles both, so there is no scan() or destroy call to remember.

<ul id="todos"></ul>

<template id="todo-row">
  <li data-component="todo-item">
    <span data-bind="text"></span>
    <button data-action="remove">Done</button>
  </li>
</template>
// add a row
addTodo(text) {
  const el = document.getElementById('todo-row')
    .content.firstElementChild.cloneNode(true);
  el.dataset.text = text;
  document.getElementById('todos').appendChild(el);   // initialized automatically
}

// inside the todo-item component
wildflower.component('todo-item', {
  computed: { text() { return this.element.dataset.text; } },
  remove() { this.element.remove(); }                 // destroyed automatically
});

Live example: the World Clock demo renders its add/remove city wall this way, each clock a self-ticking component.

When data-list earns its place

Reach for data-list when the list changes often and individual cells update in place, so re-rendering the whole body each time would be wasteful and would lose input focus or scroll position. A live-editing table, a reactive to-do list with inline editing, or any large collection that adds, removes, and reorders while cells update is exactly what its keyed diffing was built for. That capability is what separates the Mini build and above from Nano.

Your list is…UseCost
Fixed at render timeStamp a template once (Pattern 1)Almost nothing; works in Nano
A filtered / sorted / searched viewRe-render on change (Pattern 2)One render method; works in Nano
Made of interactive rowsRow as a component (Pattern 3)A per-row component; works in Nano
Frequently mutated with live per-cell updatesdata-listThe list renderer (Mini build and above)
Rule of thumb: if you are not editing individual cells while the list churns, you probably do not need data-list, and one of the three patterns above will be lighter and easier to follow.