> Discover all available pages from the documentation index: https://mastra.ai/llms.txt

# Integrate Mastra in your React + Vite project

In this guide, you'll build a tool-calling AI agent using Mastra, then connect it to React by calling the agent directly from Mastra's standalone server.

You'll use [AI SDK UI](https://ai-sdk.dev/docs/ai-sdk-ui/overview) and [AI Elements](https://ai-sdk.dev/elements) to create a beautiful, interactive chat experience.

## Before you begin

- You'll need an API key from a supported [model provider](https://mastra.ai/models). If you don't have a preference, use [OpenAI](https://mastra.ai/models/providers/openai).
- Install Node.js `v22.13.0` or later

## Create a new React + Vite app (optional)

If you already have a React + Vite app using Tailwind, skip to the next step.

### Project scaffold

Run the following command to [create a new React + Vite app](https://vite.dev/guide/#scaffolding-your-first-vite-project):

**npm**:

```bash
npm create vite@latest mastra-react -- --template react-ts
```

**pnpm**:

```bash
pnpm create vite mastra-react --template react-ts
```

**Yarn**:

```bash
yarn create vite mastra-react --template react-ts
```

**Bun**:

```bash
bunx create-vite mastra-react --template react-ts
```

This creates a project called `mastra-react`, but you can replace it with any name you want.

Navigate to your project directory:

```bash
cd mastra-react
```

### Tailwind

Next, install Tailwind:

**npm**:

```bash
npm install tailwindcss @tailwindcss/vite
```

**pnpm**:

```bash
pnpm add tailwindcss @tailwindcss/vite
```

**Yarn**:

```bash
yarn add tailwindcss @tailwindcss/vite
```

**Bun**:

```bash
bun add tailwindcss @tailwindcss/vite
```

Configure the Vite plugins:

```typescript
import { defineConfig } from 'vite'
import react from '@vitejs/plugin-react'
import path from 'path'
import tailwindcss from '@tailwindcss/vite'

export default defineConfig({
  plugins: [react(), tailwindcss()],
  resolve: {
    alias: {
      '@': path.resolve(__dirname, './src'),
    },
  },
})
```

Replace everything in `src/index.css` with the following:

```css
@import 'tailwindcss';
```

Add these `compilerOptions` to `tsconfig.json`:

```jsonc
{
  // ...
  "compilerOptions": {
    "baseUrl": ".",
    "paths": {
      "@/*": ["./src/*"],
    },
  },
}
```

Edit `tsconfig.app.json` to resolve paths:

```jsonc
{
  "compilerOptions": {
    // ...
    "baseUrl": ".",
    "paths": {
      "@/*": ["./src/*"],
    },
    // ...
  },
}
```

## Initialize Mastra

Run [`mastra init`](https://mastra.ai/reference/cli/mastra). When prompted, choose a provider (e.g. OpenAI) and enter your key:

**npm**:

```bash
npx mastra@latest init
```

**pnpm**:

```bash
pnpm dlx mastra@latest init
```

**Yarn**:

```bash
yarn dlx mastra@latest init
```

**Bun**:

```bash
bun x mastra@latest init
```

This creates a `src/mastra` folder with an example weather agent and the following files:

- `index.ts` - Mastra config, including memory
- `tools/weather-tool.ts` - a tool to fetch weather for a given location
- `agents/weather-agent.ts`- a weather agent with a prompt that uses the tool

You'll call `weather-agent.ts` from your chat UI in the next steps.

## Install AI SDK UI & AI elements

Install AI SDK UI along with the Mastra adapter:

**npm**:

```bash
npm install @mastra/ai-sdk@latest @ai-sdk/react ai
```

**pnpm**:

```bash
pnpm add @mastra/ai-sdk@latest @ai-sdk/react ai
```

**Yarn**:

```bash
yarn add @mastra/ai-sdk@latest @ai-sdk/react ai
```

**Bun**:

```bash
bun add @mastra/ai-sdk@latest @ai-sdk/react ai
```

Next, initialize AI Elements. When prompted, choose the default options:

**npm**:

```bash
npx ai-elements@latest
```

**pnpm**:

```bash
pnpm dlx ai-elements@latest
```

**Yarn**:

```bash
yarn dlx ai-elements@latest
```

**Bun**:

```bash
bun x ai-elements@latest
```

This downloads the entire AI Elements UI component library into a `@/components/ai-elements` folder.

## Create a chat route

Open ⁠`src/mastra/index.ts` and add a [⁠chatRoute()](https://mastra.ai/reference/ai-sdk/chat-route) to your config. This creates an API route your React frontend can call for AI SDK-compatible chat responses, which you’ll use with ⁠useChat() next.

```ts
import { Mastra } from '@mastra/core/mastra'
// Existing imports...
import { chatRoute } from '@mastra/ai-sdk'

export const mastra = new Mastra({
  // Existing config...
  server: {
    apiRoutes: [
      chatRoute({
        path: '/chat/:agentId',
      }),
    ],
  },
})
```

## Add the chat UI

Replace the `src/App.tsx` file to create a chat interface:

```tsx
import * as React from 'react'
import { DefaultChatTransport, type ToolUIPart } from 'ai'
import { useChat } from '@ai-sdk/react'

import {
  PromptInput,
  PromptInputBody,
  PromptInputTextarea,
} from '@/components/ai-elements/prompt-input'

import {
  Conversation,
  ConversationContent,
  ConversationScrollButton,
} from '@/components/ai-elements/conversation'

import { Message, MessageContent, MessageResponse } from '@/components/ai-elements/message'

import { Tool, ToolHeader, ToolContent, ToolInput, ToolOutput } from '@/components/ai-elements/tool'

export default function App() {
  const [input, setInput] = React.useState<string>('')

  const { messages, sendMessage, status } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/chat/weather-agent',
    }),
  })

  const handleSubmit = async () => {
    if (!input.trim()) return

    sendMessage({ text: input })
    setInput('')
  }

  return (
    <div className="relative mx-auto size-full h-screen max-w-4xl p-6">
      <div className="flex h-full flex-col">
        <Conversation className="h-full">
          <ConversationContent>
            {messages.map(message => (
              <div key={message.id}>
                {message.parts?.map((part, i) => {
                  if (part.type === 'text') {
                    return (
                      <Message key={`${message.id}-${i}`} from={message.role}>
                        <MessageContent>
                          <MessageResponse>{part.text}</MessageResponse>
                        </MessageContent>
                      </Message>
                    )
                  }

                  if (part.type?.startsWith('tool-')) {
                    return (
                      <Tool key={`${message.id}-${i}`}>
                        <ToolHeader
                          type={(part as ToolUIPart).type}
                          state={(part as ToolUIPart).state || 'output-available'}
                          className="cursor-pointer"
                        />
                        <ToolContent>
                          <ToolInput input={(part as ToolUIPart).input || {}} />
                          <ToolOutput
                            output={(part as ToolUIPart).output}
                            errorText={(part as ToolUIPart).errorText}
                          />
                        </ToolContent>
                      </Tool>
                    )
                  }

                  return null
                })}
              </div>
            ))}
            <ConversationScrollButton />
          </ConversationContent>
        </Conversation>
        <PromptInput onSubmit={handleSubmit} className="mt-20">
          <PromptInputBody>
            <PromptInputTextarea
              onChange={e => setInput(e.target.value)}
              className="md:leading-10"
              value={input}
              placeholder="Ask about the weather..."
              disabled={status !== 'ready'}
            />
          </PromptInputBody>
        </PromptInput>
      </div>
    </div>
  )
}
```

This component connects [`useChat()`](https://ai-sdk.dev/docs/reference/ai-sdk-ui/use-chat) to the `chat/weather-agent` endpoint, sending prompts there and streaming the response back in chunks.

It renders the response text using the [`<MessageResponse>`](https://ai-sdk.dev/elements/components/message#messageresponse-) component, and shows any tool invocations with the [`<Tool>`](https://ai-sdk.dev/elements/components/tool) component.

## Test your agent

To test your agent with the chat interface, run both the Mastra server and the Vite development server.

1. Start the Mastra development server:

   **npm**:

   ```bash
   npx mastra dev
   ```

   **pnpm**:

   ```bash
   pnpm dlx mastra dev
   ```

   **Yarn**:

   ```bash
   yarn dlx mastra dev
   ```

   **Bun**:

   ```bash
   bun x mastra dev
   ```

2. In a separate terminal window, start the Vite development server:

   **npm**:

   ```bash
   npm run dev
   ```

   **pnpm**:

   ```bash
   pnpm run dev
   ```

   **Yarn**:

   ```bash
   yarn dev
   ```

   **Bun**:

   ```bash
   bun run dev
   ```

3. Open your application at <http://localhost:5173>

4. Try asking about the weather. If your API key is set up correctly, you'll get a response

## Next steps

Congratulations on building your Mastra agent with React! 🎉

From here, you can extend the project with your own tools and logic:

- Learn more about [agents](https://mastra.ai/docs/agents/overview)
- Give your agent its own [tools](https://mastra.ai/docs/agents/using-tools)
- Add human-like [memory](https://mastra.ai/docs/memory/overview) to your agent

When you're ready, read more about how Mastra integrates with AI SDK UI and React, and how to deploy your agent anywhere:

- Integrate Mastra with [AI SDK UI](https://mastra.ai/guides/build-your-ui/ai-sdk-ui)
- Deploy your agent [anywhere](https://mastra.ai/docs/deployment/overview)