@dudko.dev/agent-web-react

React bindings for @dudko.dev/agent-web — the headless, universal in-browser LLM agent. Drop the agent into any React app with a single hook and (optionally) a set of pre-styled components:

• 🪝 useAgent — build the agent from a config, stream its typed events into a ready-to-render state (plan, steps, tool calls, streamed answer, token usage, model-load progress, chat transcript), and get run / stop / reset / reload.
• 🧩 <AgentProvider> + components — a drop-in <AgentChat> panel plus <PlanView>, <StepList>, <Composer>, <ModelLoadBar>, <UsageBadge>, and <ApiKeyForm>. Bring your own CSS or import the optional stylesheet.
• 🔑 useCredentials — store BYOK API keys encrypted at rest (WebCrypto + IndexedDB).
• 🖥️ useWebLLMModel — load a local WebGPU model with download progress.
• 🎛️ Headless-first — the event→UI logic is a pure, exported reducer (agentStateReducer); the components are optional sugar on top.

▶︎ Live demo — an agent that edits a sticky-notes board via tools. Cloud BYOK or local WebGPU, all in your browser. Source in demo/.

Install

npm install @dudko.dev/agent-web-react @dudko.dev/agent-web react react-dom

Then add only the model providers you use (optional peers of the core, dynamically imported):

# cloud, pick what you need
npm install @ai-sdk/openai        # or @ai-sdk/anthropic, @ai-sdk/google, @ai-sdk/xai, @ai-sdk/deepseek, @ai-sdk/openai-compatible
# local WebGPU models
npm install @browser-ai/web-llm @mlc-ai/web-llm

Import the optional stylesheet once (skip it if you style the components yourself):

import '@dudko.dev/agent-web-react/styles.css'

Quick start — a drop-in chat panel

<AgentProvider> builds one agent and shares it; <AgentChat> renders it.

import { useMemo } from 'react'
import {
  AgentProvider,
  AgentChat,
  useCredentials,
  defineTool,
} from '@dudko.dev/agent-web-react'
import { z } from 'zod'
import '@dudko.dev/agent-web-react/styles.css'

export function Assistant() {
  const credentials = useCredentials() // encrypted vault (WebCrypto + IndexedDB)

  // Your app's actions, as tools. Keep the object referentially stable.
  const tools = useMemo(
    () => ({
      add_text: defineTool({
        description: 'Add a text block to the page.',
        inputSchema: z.object({ text: z.string() }),
        execute: async ({ text }) => addTextBlock(text), // your code
      }),
    }),
    [],
  )

  return (
    <AgentProvider
      config={{
        model: { providerType: 'google', model: 'gemini-3.5-flash', credentialRef: 'google' },
        credentials: credentials.store,
        tools,
        describeState: () => serializeMyState(), // optional grounding
      }}
    >
      <AgentChat title="Assistant" style={{ height: 520 }} />
    </AgentProvider>
  )
}

Using a bundler (Vite, Next, CRA)? A provider spec like the one above asks the core to import('@ai-sdk/google') at runtime — but browser bundlers can't resolve that bare, @vite-ignored import, so it fails with “Provider package … is not installed” (and WebLLM fails with “webLLM is not a function”). Build the model yourself and pass it directly instead — see Models in a bundler. It's a few extra lines and works in every bundler.

Store the user's key once (encrypted at rest), e.g. from a settings form:

const credentials = useCredentials()
await credentials.setKey('google', userProvidedKey)
// or drop in <ApiKeyForm credentials={credentials} credentialRef="google" />

The useAgent hook (headless)

useAgent is the whole library in one hook — use it directly if you want your own UI:

import { useAgent } from '@dudko.dev/agent-web-react'

function Custom() {
  const agent = useAgent({
    // In a bundler, pass a model you built (see “Models in a bundler”); a
    // provider spec like this one only resolves where dynamic imports do.
    model: { providerType: 'openai', model: 'gpt-5.4-mini', credentialRef: 'openai' },
    credentials,
    tools,
  })

  return (
    <>
      <button disabled={!agent.isReady} onClick={() => agent.run('Add a totals row')}>
        Run
      </button>
      {agent.isRunning && <button onClick={agent.stop}>Stop</button>}

      {/* Everything below is live, derived from the event stream: */}
      {agent.plan && <p>{agent.plan.thought}</p>}
      {agent.steps.map((s) => (
        <div key={s.id}>
          {s.index}/{s.total} · {s.step.description} · {s.status}
          {s.toolCalls.map((c, i) => (
            <code key={i}>{c.name}</code>
          ))}
        </div>
      ))}
      <p>{agent.finalText}</p>
      <small>{agent.usage.totalTokens} tokens</small>
    </>
  )
}

useAgent(config, options) returns the full AgentUiState plus:

Field	Description
run(goal)	Start a run; resolves with the RunResult
stop()	Abort the in-flight run
reset()	Clear the whole conversation
reload()	Rebuild the agent (e.g. after storing a new key)
status	idle | initializing | ready | running | error
isReady / isRunning	convenience booleans
messages	chat transcript ({ role, content, pending }[])
plan / steps	the live plan and per-step tool calls
finalText	the streamed final answer
usage	running token total
modelLoad	WebLLM download progress, when loading

Options: deps (rebuild the agent when these change — e.g. on a model switch), onEvent (tap the raw event stream), autoStart, maxEvents.

Keep tools and describeState referentially stable (useMemo / useCallback); pass a changed deps array to rebuild the agent on a model/provider switch.

Local WebGPU model (no key, offline)

import { useAgent, useWebLLMModel, ModelLoadBar } from '@dudko.dev/agent-web-react'
import { webLLM } from '@browser-ai/web-llm' // your app's optional peer

// Build WebLLM in your own code so the bundler includes it (see note below).
const createLocalModel = (id: string, opts?: object) => Promise.resolve(webLLM(id, opts as never))

function LocalAgent() {
  const local = useWebLLMModel('Qwen2.5-1.5B-Instruct-q4f16_1-MLC', { create: createLocalModel })
  const agent = useAgent(
    { model: local.model!, tools },
    { deps: [local.model] }, // build once the model is loaded
  )

  if (!local.ready) {
    return local.loading ? (
      <ModelLoadBar load={{ progress: local.progress, text: local.text }} />
    ) : (
      <button disabled={!local.supported} onClick={local.load}>
        Load local model
      </button>
    )
  }
  return <AgentChat controller={agent} />
}

load() eagerly downloads the weights (so the progress bar fills during load, not silently on the first message) and ready flips only once the model can actually answer. The create option is what makes it work under a bundler — see below.

Models in a bundler (Vite, Next, CRA)

The core builds cloud providers with import('@ai-sdk/<provider>') and WebLLM with import('@browser-ai/web-llm'). Those are bare, @vite-ignored dynamic imports — great for Node/SSR/import-map setups, but a browser bundler either leaves them unresolvable at runtime (cloud → “Provider package … is not installed”) or stubs them to an empty module (WebLLM → “webLLM is not a function”).

The fix is the same for both: build the model in your own code (a static import your bundler can see) and hand the agent a direct LanguageModel.

Cloud — construct the provider from the vault-stored key:

import { useEffect, useState } from 'react'
import { createGoogleGenerativeAI } from '@ai-sdk/google'
import type { LanguageModel } from 'ai'
import { useAgent, useCredentials } from '@dudko.dev/agent-web-react'

function CloudAgent() {
  const credentials = useCredentials()
  const [model, setModel] = useState<LanguageModel>()

  useEffect(() => {
    credentials.store.getApiKey('google').then((key) => {
      setModel(key ? createGoogleGenerativeAI({ apiKey: key })('gemini-3.5-flash') : undefined)
    })
  }, [credentials.store, credentials.version])

  const agent = useAgent({ model: model!, credentials: credentials.store, tools }, { deps: [model] })
  // …render <AgentChat controller={agent} /> once model is set
}

Anthropic needs headers: { 'anthropic-dangerous-direct-browser-access': 'true' } passed to createAnthropic({ … }) for direct browser calls.

Local — pass a statically-imported webLLM factory via useWebLLMModel's create option (see the WebLLM example above).

The demo/ app does exactly this — see demo/src/providers.ts.

Components

All components are optional and styled by styles.css (class-prefixed awr-, themeable via --awr-* custom properties, light + dark). Each accepts a className; data components take plain props so you can use them standalone.

Component	Purpose
<AgentChat>	Full panel: transcript + live activity + composer. Reads a controller prop or the <AgentProvider> context.
<MessageList>	The chat transcript.
<Composer>	Textarea + send/stop button (Enter to send).
<PlanView>	A plan's reasoning + step list.
<StepList>	Live execution steps with tool calls.
<ModelLoadBar>	WebLLM download/init progress.
<UsageBadge>	Compact token readout.
<ApiKeyForm>	BYOK key entry that writes to the encrypted vault.

Build your own UI

The components are a thin layer over the exported, pure reducer. Use it directly if you'd rather render everything yourself:

import { agentStateReducer, createInitialAgentState } from '@dudko.dev/agent-web-react'

let state = createInitialAgentState()
state = agentStateReducer(state, { type: 'event', event }) // fold each AgentEvent

How it fits together

@dudko.dev/agent-web (peer)     →  the headless agent + providers + vault
@dudko.dev/agent-web-react      →  useAgent / <AgentProvider> / components
your app                        →  tools, credentials, and where the panel goes

The React package re-exports the core primitives you usually need (createAgent, defineTool, VaultCredentialStore, createWebLLMModel, isWebGPUAvailable, and the key types), so a React app can import everything from one place.

Direct browser calls & CORS: not every provider allows direct BYOK calls from a browser origin. Google (Gemini), openai-compatible and the gateway are the reliable direct paths; Anthropic works (a required header is injected); OpenAI/xAI/DeepSeek usually need a proxy. See the core's providers doc. Ship only the user's own key to the browser — shared/app keys belong behind a proxy or the gateway.

Demo & deployment

The demo/ app (Vite + React) is deployed to GitHub Pages by .github/workflows/deploy-demo.yml on every push to main. To run it locally:

npm install && npm run build      # build the library into dist/
cd demo && npm install && npm run dev

The demo aliases @dudko.dev/agent-web-react to the built ../dist, so rebuild the library (npm run build) after changing its source.

License

MIT © Siarhei Dudko