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Build an AI Chatbot with Nuxt, Nuxt UI, and AI SDK

Learn how to build a full-featured AI chatbot with streaming responses, multiple models support, and a beautiful UI using Nuxt, Nuxt UI, and Vercel AI SDK.
Hugo Richard
Hugo Richard@hugorcd
Benjamin Canac
Benjamin Canac@benjamincanac

Building AI-powered applications has never been more accessible. This guide walks through creating a full-featured AI chatbot using Nuxt, Nuxt UI, and the Vercel AI SDK. Each step is explained in detail so you understand how every piece works together.

What we're building

By the end of this tutorial, you'll have a fully functional AI chatbot with:

  • Streaming responses that appear in real-time as the AI generates them
  • A beautiful chat interface built with Nuxt UI's purpose-built chat components
  • Markdown rendering for rich AI responses with code highlighting
  • Multi-model support allowing users to switch between OpenAI, Anthropic, and Google models
  • Server-side AI integration using Nitro API routes and the AI SDK
Check out the complete AI Chat template on GitHub for a production-ready implementation with authentication, database persistence, and more.

Prerequisites

Before we start, make sure you have:

  • Node.js 20+ installed
  • A Vercel AI Gateway API key (provides access to multiple AI providers through a single endpoint)

Project setup

Start by creating a new Nuxt project:

npx nuxi@latest init nuxt-ai-chat
cd nuxt-ai-chat

Installing dependencies

Install Nuxt UI and the AI-specific dependencies:

pnpm add @nuxt/ui @nuxtjs/mdc @nuxthub/core drizzle-orm drizzle-kit @libsql/client ai @ai-sdk/vue zod
If you're using pnpm, create a .npmrc file at the root of your project with shamefully-hoist=true:
.npmrc
shamefully-hoist=true

Configuration

Update your nuxt.config.ts to register the modules:

nuxt.config.ts
export default defineNuxtConfig({
  modules: [
    '@nuxt/ui',
    '@nuxtjs/mdc',
    '@nuxthub/core'
  ],

  hub: {
    db: 'sqlite'
  },

  css: ['~/assets/css/main.css'],

  mdc: {
    headings: {
      anchorLinks: false // Disable anchor links in AI responses
    }
  },
})

Create the main CSS file to import Tailwind CSS and Nuxt UI:

app/assets/css/main.css
@import "tailwindcss";
@import "@nuxt/ui";

Setting up the app

Nuxt UI requires wrapping your app with UApp for modals, toasts, and overlays to work properly:

app/app.vue
<template>
  <UApp>
    <UDashboardGroup unit="rem">
      <NuxtPage />
    </UDashboardGroup>
  </UApp>
</template>

Create a .env file with your AI Gateway API key:

.env
AI_GATEWAY_API_KEY=your-api-key-here
With Vercel AI Gateway, you don't need individual API keys for OpenAI, Anthropic, or Google. The AI Gateway provides a unified API to access hundreds of models through a single endpoint.

Setting up the database

NuxtHub provides a zero-config database powered by Drizzle ORM. Here is the schema for the chat application:

server/db/schema.ts
import { sqliteTable, text, integer, index } from 'drizzle-orm/sqlite-core'
import { relations } from 'drizzle-orm'

export const chats = sqliteTable('chats', {
  id: text().primaryKey().$defaultFn(() => crypto.randomUUID()),
  title: text(),
  createdAt: integer({ mode: 'timestamp' }).notNull().$defaultFn(() => new Date())
})

export const chatsRelations = relations(chats, ({ many }) => ({
  messages: many(messages)
}))

export const messages = sqliteTable('messages', {
  id: text('id').primaryKey().$defaultFn(() => crypto.randomUUID()),
  chatId: text('chat_id').notNull().references(() => chats.id, { onDelete: 'cascade' }),
  role: text('role', { enum: ['user', 'assistant', 'system'] }).notNull(),
  parts: text('parts', { mode: 'json' }),
  createdAt: integer({ mode: 'timestamp' }).notNull().$defaultFn(() => new Date()),
}, table => [
  index('messages_chat_id_idx').on(table.chatId)
])

export const messagesRelations = relations(messages, ({ one }) => ({
  chat: one(chats, {
    fields: [messages.chatId],
    references: [chats.id]
  })
}))

Generate the database migrations from your schema:

npx nuxt db generate
Migrations are automatically applied when you start the development server with npx nuxt dev. NuxtHub uses SQLite locally, so no external database is required during development.

Building the backend

This section covers integrating AI on the server. The following API endpoints handle chat creation, AI streaming, and data persistence using Nitro.

Creating a chat

First, create the endpoint that initializes a new chat and saves the first message to the database. This uses the UIMessage type from the AI SDK:

server/api/chats.post.ts
import { defineEventHandler, readValidatedBody } from 'h3'
import type { UIMessage } from 'ai'
import { db, schema } from 'hub:db'
import { z } from 'zod'

export default defineEventHandler(async (event) => {
  const { message } = await readValidatedBody(event, z.object({
    message: z.custom<UIMessage>()
  }).parse)

  // Create a new chat
  const [chat] = await db.insert(schema.chats).values({}).returning()

  // Save the first user message
  await db.insert(schema.messages).values({
    chatId: chat.id,
    role: 'user',
    parts: message.parts
  })

  return chat
})

Streaming AI responses

Next, create the endpoint that handles the AI conversation. This endpoint uses streamText, createUIMessageStream, and createUIMessageStreamResponse from the AI SDK:

server/api/chats/[id].post.ts
import { createError, defineEventHandler, getValidatedRouterParams, readValidatedBody } from 'h3'
import { eq } from 'drizzle-orm'
import { db, schema } from 'hub:db'
import { z } from 'zod'
import {
  convertToModelMessages,
  createUIMessageStream,
  createUIMessageStreamResponse,
  generateText,
  streamText
} from 'ai'
import type { UIMessage } from 'ai'

export default defineEventHandler(async (event) => {
  const { id } = await getValidatedRouterParams(event, z.object({
    id: z.string()
  }).parse)

  const { model, messages } = await readValidatedBody(event, z.object({
    model: z.string().default('openai/gpt-4o-mini'),
    messages: z.array(z.custom<UIMessage>())
  }).parse)

  // Fetch the chat from the database
  const chat = await db.query.chats.findFirst({
    where: (chat, { eq }) => eq(chat.id, id as string)
  })

  if (!chat) {
    throw createError({ statusCode: 404, statusMessage: 'Chat not found' })
  }

  // Generate a title for the chat if it doesn't have one
  if (!chat.title) {
    const { text: title } = await generateText({
      model: 'openai/gpt-4o-mini',
      system: `Generate a short title (max 30 characters) based on the user's message. No quotes or punctuation.`,
      prompt: JSON.stringify(messages[0])
    })

    await db.update(schema.chats).set({ title }).where(eq(schema.chats.id, id))
  }

  // Save the user message if it's a follow-up
  const lastMessage = messages[messages.length - 1]
  if (lastMessage?.role === 'user' && messages.length > 1) {
    await db.insert(schema.messages).values({
      chatId: id,
      role: 'user',
      parts: lastMessage.parts
    })
  }

  // Create the streaming response
  const stream = createUIMessageStream({
    execute: ({ writer }) => {
      const result = streamText({
        model,
        system: `You are a helpful AI assistant. Be concise and friendly.`,
        messages: convertToModelMessages(messages)
      })

      // Notify the client that a title was generated
      if (!chat.title) {
        writer.write({
          type: 'data-chat-title',
          data: { message: 'Title generated' },
          transient: true
        })
      }

      writer.merge(result.toUIMessageStream())
    },
    onFinish: async ({ messages }) => {
      // Save the assistant's response to the database
      await db.insert(schema.messages).values(messages.map(message => ({
        chatId: chat.id,
        role: message.role as 'user' | 'assistant',
        parts: message.parts
      })))
    }
  })

  return createUIMessageStreamResponse({ stream })
})

Here's what each part does:

AI Gateway

Thanks to Vercel AI Gateway, we can use any AI model supported by the gateway just by specifying the model name.

Automatic Title Generation

When a chat doesn't have a title yet, we use generateText to create one based on the first message. This provides a better UX by showing meaningful titles in the chat history instead of "Untitled".

Streaming with streamText

The streamText function generates a streaming response from the AI model. Key options include:

  • model: The AI model to use
  • system: Instructions that guide the AI's behavior
  • messages: The conversation history

UIMessageStream

The createUIMessageStream and createUIMessageStreamResponse functions create a stream that the AI SDK client can consume. The response streams chunks as they're generated, creating the real-time typing effect.

The writer.write() method allows sending custom data events to the client (like data-chat-title), while onFinish is called when streaming completes, perfect for persisting the assistant's response.

Fetching a chat

Add an endpoint to fetch existing chat data from your database:

server/api/chats/[id].get.ts
import { createError, defineEventHandler, getValidatedRouterParams } from 'h3'
import { asc, eq } from 'drizzle-orm'
import { db, schema } from 'hub:db'
import { z } from 'zod'

export default defineEventHandler(async (event) => {
  const { id } = await getValidatedRouterParams(event, z.object({
    id: z.string()
  }).parse)

  const chat = await db.query.chats.findFirst({
    where: (eq(schema.chats.id, id)),
    with: {
      messages: {
        orderBy: () => asc(schema.messages.createdAt)
      }
    }
  })

  if (!chat) {
    throw createError({ statusCode: 404, statusMessage: 'Chat not found' })
  }

  return chat
})

Wire up the UI

Nuxt UI provides purpose-built components for AI chat interfaces: UChatPrompt for the input area and UChatMessages for displaying the conversation.

Creating the home page

The home page is where users start a new conversation. The UChatPrompt component provides a textarea with auto-resize, keyboard shortcuts, and a submit button:

app/pages/index.vue
<script setup lang="ts">
const input = ref('')
const loading = ref(false)

async function createChat() {
  if (!input.value.trim()) return

  loading.value = true

  // Create a new chat on the server
  const chat = await $fetch('/api/chats', {
    method: 'POST',
    body: {
      message: {
        role: 'user',
        parts: [{ type: 'text', text: input.value }]
      }
    }
  })

  // Navigate to the chat page
  navigateTo(`/chat/${chat.id}`)
}
</script>

<template>
  <UDashboardPanel :ui="{ body: 'p-0 sm:p-0' }">
    <template #body>
      <UContainer class="min-h-dvh flex flex-col justify-center gap-6 py-8">
        <h1 class="text-3xl sm:text-4xl text-highlighted font-bold">
          How can I help you today?
        </h1>

        <UChatPrompt
          v-model="input"
          :status="loading ? 'streaming' : 'ready'"
          variant="subtle"
          placeholder="Ask me anything..."
          @submit="createChat"
        >
          <UChatPromptSubmit color="neutral" />
        </UChatPrompt>
      </UContainer>
    </template>
  </UDashboardPanel>
</template>

The UChatPrompt component automatically handles:

  • Form submission when pressing ↵
  • Auto-resizing as you type
  • A loading state when status is set to streaming
  • Focus management and keyboard shortcuts

Creating the chat page

The chat page is where the actual conversation happens. It integrates the AI SDK's Chat class and DefaultChatTransport for real-time streaming.

app/pages/chat/[id].vue
<script setup lang="ts">
import { getTextFromMessage } from '@nuxt/ui/utils/ai'
import { DefaultChatTransport } from 'ai'
import { Chat } from '@ai-sdk/vue'

const route = useRoute()
const toast = useToast()

// Fetch existing chat data
const { data: chatData } = await useFetch(`/api/chats/${route.params.id}`)

if (!chatData.value) {
  throw createError({ statusCode: 404, statusMessage: 'Chat not found', fatal: true })
}

const input = ref('')

// Initialize the Chat class from AI SDK
const chat = new Chat({
  id: chatData.value.id,
  messages: chatData.value.messages,
  transport: new DefaultChatTransport({
    api: `/api/chats/${chatData.value.id}`
  }),
  onData(dataPart) {
    // Refresh the chat list when a title is generated
    if (dataPart.type === 'data-chat-title') {
      refreshNuxtData('chats')
    }
  },
  onError(error) {
    toast.add({
      title: 'Error',
      description: error.message,
      color: 'error'
    })
  }
})

function handleSubmit(e: Event) {
  e.preventDefault()
  if (input.value.trim()) {
    chat.sendMessage({ text: input.value })
    input.value = ''
  }
}

// Auto-generate response for first message
onMounted(() => {
  if (chatData.value?.messages.length === 1) {
    chat.regenerate()
  }
})
</script>

<template>
  <UDashboardPanel :ui="{ body: 'p-0 sm:p-0' }">
    <template #body>
      <UContainer class="min-h-dvh flex flex-col py-4 sm:py-6">
        <UChatMessages
          :messages="chat.messages"
          :status="chat.status"
          should-auto-scroll
          class="flex-1"
        >
          <template #content="{ message }">
            <MDC
              :value="getTextFromMessage(message)"
              :cache-key="message.id"
              class="*:first:mt-0 *:last:mb-0"
            />
          </template>
        </UChatMessages>

        <UChatPrompt
          v-model="input"
          :error="chat.error"
          variant="subtle"
          class="sticky bottom-0"
          @submit="handleSubmit"
        >
          <UChatPromptSubmit
            :status="chat.status"
            color="neutral"
            @stop="chat.stop()"
            @reload="chat.regenerate()"
          />
        </UChatPrompt>
      </UContainer>
    </template>
  </UDashboardPanel>
</template>

Here's a breakdown of the key parts:

The Chat Class

The Chat class from @ai-sdk/vue manages the entire conversation state. It handles:

  • Message history with chat.messages
  • Connection status with chat.status (ready, submitted, streaming, error)
  • Sending messages with chat.sendMessage()
  • Stopping generation with chat.stop()
  • Regenerating responses with chat.regenerate()

The onData callback receives custom data events from the server (like data-chat-title), allowing you to react to server-side events during streaming.

UChatMessages Component

The UChatMessages component is purpose-built for AI chatbots with:

  • Auto-scroll to bottom on load
  • Continuous scrolling as messages stream in
  • A loading indicator while the assistant processes
  • An "Auto scroll" button when scrolled up

Rendering Markdown with MDC

AI models often respond with markdown formatting (code blocks, lists, bold text, etc.). We use the MDC component from @nuxtjs/mdc to render this content beautifully. The getTextFromMessage utility extracts the text content from AI SDK v5 message parts.

Nuxt UI provides pre-styled prose components, so your markdown content will be automatically styled to match your theme.

UChatPromptSubmit Component

The UChatPromptSubmit component adapts based on the chat status:

  • Shows a send button when ready
  • Shows a stop button while streaming
  • Shows a reload button after an error

Adding chat history

This section adds a dropdown menu to list previous chats and navigate between them.

Listing chats API

First, create an endpoint to fetch all chats:

server/api/chats.get.ts
import { defineEventHandler } from 'h3'
import { db, schema } from 'hub:db'
import { desc } from 'drizzle-orm'

export default defineEventHandler(async () => {
  return await db.query.chats.findMany({
    orderBy: () => desc(schema.chats.createdAt)
  })
})

Building the chats history dropdown

The component uses UDropdownMenu with a UButton as trigger. Use useFetch with a key to fetch and cache the chat list:

app/components/ChatsHistory.vue
<script setup lang="ts">
const route = useRoute()

const { data: chats } = useFetch('/api/chats', {
  key: 'chats',
  default: () => []
})

const items = computed(() => [
  {
    label: 'New chat',
    to: '/',
    icon: 'i-lucide-plus-square',
    active: route.name === 'index'
  },
  ...chats.value.map(chat => ({
    label: chat.title || 'Untitled',
    to: `/chat/${chat.id}`,
    active: route.params.id === chat.id
  }))
])
</script>

<template>
  <UDropdownMenu :items="items" class="m-2">
    <UButton
      icon="i-lucide-messages-square"
      variant="ghost"
      label="Chats History"
      color="neutral"
      class="w-fit"
    />
  </UDropdownMenu>
</template>

Integrating history in the home page

app/pages/index.vue
<script setup lang="ts">
const input = ref('')
const loading = ref(false)

async function createChat() {
  if (!input.value.trim()) return

  loading.value = true

  // Create a new chat on the server
  const chat = await $fetch('/api/chats', {
    method: 'POST',
    body: {
      message: {
        role: 'user',
        parts: [{ type: 'text', text: input.value }]
      }
    }
  })

  // Navigate to the chat page
  navigateTo(`/chat/${chat.id}`)
}
</script>

<template>
  <UDashboardPanel :ui="{ body: 'p-0 sm:p-0' }">
    <template #header>
      <ChatsHistory />
    </template>
    <template #body>
      <UContainer class="min-h-dvh flex flex-col justify-center gap-6 py-8">
        <h1 class="text-3xl sm:text-4xl text-highlighted font-bold">
          How can I help you today?
        </h1>

        <UChatPrompt
          v-model="input"
          :status="loading ? 'streaming' : 'ready'"
          variant="subtle"
          placeholder="Ask me anything..."
          @submit="createChat"
        >
          <UChatPromptSubmit color="neutral" />
        </UChatPrompt>
      </UContainer>
    </template>
  </UDashboardPanel>
</template>

Integrating history in the chat page

app/pages/chat/[id].vue
<script setup lang="ts">
import { getTextFromMessage } from '@nuxt/ui/utils/ai'
import { DefaultChatTransport } from 'ai'
import { Chat } from '@ai-sdk/vue'

const route = useRoute()
const toast = useToast()

// Fetch existing chat data
const { data: chatData } = await useFetch(`/api/chats/${route.params.id}`)

if (!chatData.value) {
  throw createError({ statusCode: 404, statusMessage: 'Chat not found', fatal: true })
}

const input = ref('')

// Initialize the Chat class from AI SDK
const chat = new Chat({
  id: chatData.value.id,
  messages: chatData.value.messages,
  transport: new DefaultChatTransport({
    api: `/api/chats/${chatData.value.id}`
  }),
  onData(dataPart) {
    // Refresh the chat list when a title is generated
    if (dataPart.type === 'data-chat-title') {
      refreshNuxtData('chats')
    }
  },
  onError(error) {
    toast.add({
      title: 'Error',
      description: error.message,
      color: 'error'
    })
  }
})

function handleSubmit(e: Event) {
  e.preventDefault()
  if (input.value.trim()) {
    chat.sendMessage({ text: input.value })
    input.value = ''
  }
}

// Auto-generate response for first message
onMounted(() => {
  if (chatData.value?.messages.length === 1) {
    chat.regenerate()
  }
})
</script>

<template>
  <UDashboardPanel :ui="{ body: 'p-0 sm:p-0' }">
    <template #header>
      <ChatsHistory />
    </template>
    <template #body>
      <UContainer class="min-h-dvh flex flex-col py-4 sm:py-6">
        <UChatMessages
          :messages="chat.messages"
          :status="chat.status"
          should-auto-scroll
          class="flex-1"
        >
          <template #content="{ message }">
            <MDC
              :value="getTextFromMessage(message)"
              :cache-key="message.id"
              class="*:first:mt-0 *:last:mb-0"
            />
          </template>
        </UChatMessages>

        <UChatPrompt
          v-model="input"
          :error="chat.error"
          variant="subtle"
          class="sticky bottom-0"
          @submit="handleSubmit"
        >
          <UChatPromptSubmit
            :status="chat.status"
            color="neutral"
            @stop="chat.stop()"
            @reload="chat.regenerate()"
          />
        </UChatPrompt>
      </UContainer>
    </template>
  </UDashboardPanel>
</template>

The refreshNuxtData('chats') call in the chat page's onData callback (as shown earlier) ensures the chat list updates automatically when a new title is generated.

Adding multi-model support

One of the benefits of using AI Gateway is the ability to switch between models seamlessly. This section adds a model selector to the chat.

Creating a models composable

Define the available models and persist the user's selection using useCookie:

app/composables/useModels.ts
export function useModels() {
  const models = [
    { value: 'openai/gpt-4o-mini', label: 'GPT-4o Mini', icon: 'i-simple-icons-openai' },
    { value: 'anthropic/claude-3-5-haiku-latest', label: 'Claude 3.5 Haiku', icon: 'i-simple-icons-anthropic' },
    { value: 'google/gemini-2.0-flash', label: 'Gemini 2.0 Flash', icon: 'i-simple-icons-google' }
  ]

  const model = useCookie<string>('ai-model', {
    default: () => 'openai/gpt-4o-mini'
  })

  return {
    models,
    model
  }
}

Building the model selector

Create a USelectMenu component that displays the available models:

app/components/ModelSelect.vue
<script setup lang="ts">
const model = defineModel<string>({ required: true })

const { models } = useModels()

const selectedModel = computed(() =>
  models.find(m => m.value === model.value)
)
</script>

<template>
  <USelectMenu
    v-model="model"
    :items="models"
    :icon="selectedModel?.icon"
    variant="ghost"
    value-key="value"
  />
</template>

Integrating with the chat

Update the chat page to include the model selector and pass the selected model to the server:

app/pages/chat/[id].vue
<script setup lang="ts">
import { getTextFromMessage } from '@nuxt/ui/utils/ai'
import { DefaultChatTransport } from 'ai'
import { Chat } from '@ai-sdk/vue'

const route = useRoute()
const toast = useToast()
const { model } = useModels()

const { data: chatData } = await useFetch(`/api/chats/${route.params.id}`)

if (!chatData.value) {
  throw createError({ statusCode: 404, statusMessage: 'Chat not found', fatal: true })
}

const input = ref('')

const chat = new Chat({
  id: chatData.value.id,
  messages: chatData.value.messages,
  transport: new DefaultChatTransport({
    api: `/api/chats/${chatData.value.id}`,
    body: {
      model: model.value // Pass the selected model
    }
  }),
  onData(dataPart) {
    if (dataPart.type === 'data-chat-title') {
      refreshNuxtData('chats')
    }
  },
  onError(error) {
    toast.add({
      title: 'Error',
      description: error.message,
      color: 'error'
    })
  }
})

function handleSubmit(e: Event) {
  e.preventDefault()
  if (input.value.trim()) {
    chat.sendMessage({ text: input.value })
    input.value = ''
  }
}

onMounted(() => {
  if (chatData.value?.messages.length === 1) {
    chat.regenerate()
  }
})
</script>

<template>
  <UDashboardPanel :ui="{ body: 'p-0 sm:p-0' }">
    <template #header>
      <ChatsHistory />
    </template>
    <template #body>
      <UContainer class="min-h-dvh flex flex-col py-4 sm:py-6">
        <UChatMessages
          :messages="chat.messages"
          :status="chat.status"
          should-auto-scroll
          class="flex-1"
        >
          <template #content="{ message }">
            <MDC
              :value="getTextFromMessage(message)"
              :cache-key="message.id"
              class="*:first:mt-0 *:last:mb-0"
            />
          </template>
        </UChatMessages>

        <UChatPrompt
          v-model="input"
          :error="chat.error"
          variant="subtle"
          class="sticky bottom-0"
          @submit="handleSubmit"
        >
          <template #footer>
            <ModelSelect v-model="model" />
          </template>

          <UChatPromptSubmit
            :status="chat.status"
            color="neutral"
            @stop="chat.stop()"
            @reload="chat.regenerate()"
          />
        </UChatPrompt>
      </UContainer>
    </template>
  </UDashboardPanel>
</template>

Going further

You now have a working AI chatbot with database persistence! To take it further, consider adding:

User Authentication

Add authentication with nuxt-auth-utils to let users access their chat history across devices and keep conversations private.

AI Tools

Extend your chatbot with AI SDK tools that can fetch real-time data, generate charts, or interact with external APIs:

import { tool } from 'ai'
import { z } from 'zod'

const weatherTool = tool({
  description: 'Get the current weather for a location',
  parameters: z.object({
    location: z.string().describe('The city name')
  }),
  execute: async ({ location }) => {
    // Fetch weather data from an API
    return { location, temperature: 22, condition: 'Sunny' }
  }
})
The official AI Chat template includes all these features and more. Get started instantly with npx nuxi@latest init -t ui/chat my-chat-app.

Deploying to Vercel

Deploy your chatbot to Vercel with zero configuration:

npx vercel deploy

Then, in the Vercel dashboard:

  • Enable AI Gateway and add credits so requests can be processed.
  • Add a Turso database from the Vercel Marketplace and connect it to your project (it will provision the database and add the required environment variables automatically).

Note: On Vercel, you don’t need to manually add AI_GATEWAY_API_KEY — Vercel handles the gateway configuration for deployments. Keep using .env locally for development.

Learn more about setting up AI Gateway in the Vercel AI Gateway documentation.

Conclusion

You've built a complete AI chatbot with:

  • A complete chat interface using Nuxt UI components
  • Real-time streaming responses with the AI SDK
  • Markdown rendering with MDC for rich content display
  • Multi-model support via AI Gateway
  • Database persistence with SQLite (local) / Turso (production) and Drizzle ORM

The combination of Nuxt's full-stack capabilities, Nuxt UI's purpose-built chat components, a local SQLite dev database with a production Turso database, and the AI SDK's streaming infrastructure makes building AI applications straightforward and enjoyable.

Resources:

We're excited to see what you'll build!