# Integrate Mastra in your Astro project

In this guide, you'll build a tool-calling AI agent using Mastra, then connect it to Astro by importing and calling the agent directly from your routes.

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.

> **Note:** While this guide shows you how to use Astro with React and full server-side rendering (SSR), there are many ways to use Astro with Mastra. You can [opt-in to SSR](https://docs.astro.build/en/guides/on-demand-rendering/#enabling-on-demand-rendering) on a per-file basis and use other frameworks like Svelte, Vue, Solid, or Preact. You can use Astro to build out a chat interface and call endpoints [natively in Astro](https://docs.astro.build/en/recipes/call-endpoints/).

## 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 Astro app (optional)

If you already have an Astro app, skip to the next step. Your Astro app should be setup like this:

- Use SSR (`output: "server"` in `astro.config.mjs`)
- Use the [React integration](https://docs.astro.build/en/guides/integrations-guide/react/)
- Tailwind installed

To support [on-demand rendering](https://docs.astro.build/en/guides/on-demand-rendering/) this guide will use the [Node.js adapter](https://docs.astro.build/en/guides/integrations-guide/node/) but any of the supported server adapters will work.

**npm**:

```bash
npm create astro@latest mastra-astro -- --add node --add react --add tailwind --install --skip-houston --template minimal --git
```

**pnpm**:

```bash
pnpm create astro mastra-astro --add node --add react --add tailwind --install --skip-houston --template minimal --git
```

**Yarn**:

```bash
yarn create astro mastra-astro --add node --add react --add tailwind --install --skip-houston --template minimal --git
```

**Bun**:

```bash
bunx create-astro mastra-astro --add node --add react --add tailwind --install --skip-houston --template minimal --git
```

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

`cd` into your project and edit the `astro.config.mjs` file to set the [`output` setting](https://docs.astro.build/en/reference/configuration-reference/#output) to `"server"`:

```js
// @ts-check
import { defineConfig } from 'astro/config'

import node from '@astrojs/node'
import react from '@astrojs/react'
import tailwindcss from '@tailwindcss/vite'

// https://astro.build/config
export default defineConfig({
  output: 'server',
  adapter: node({
    mode: 'standalone',
  }),

  integrations: [react()],

  vite: {
    plugins: [tailwindcss()],
  },
})
```

Lastly, edit the `tsconfig.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 Astro routes 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

Create `src/pages/api/chat.ts`:

```ts
import type { APIRoute } from 'astro'
import { handleChatStream } from '@mastra/ai-sdk'
import { toAISdkV5Messages } from '@mastra/ai-sdk/ui'
import { createUIMessageStreamResponse } from 'ai'
import { mastra } from '@/mastra'

const THREAD_ID = 'example-user-id'
const RESOURCE_ID = 'weather-chat'

export const POST: APIRoute = async ({ request }) => {
  const params = await request.json()
  const stream = await handleChatStream({
    mastra,
    agentId: 'weather-agent',
    params: {
      ...params,
      memory: {
        ...params.memory,
        thread: THREAD_ID,
        resource: RESOURCE_ID,
      },
    },
  })

  return createUIMessageStreamResponse({ stream })
}

export const GET: APIRoute = async () => {
  const memory = await mastra.getAgentById('weather-agent').getMemory()
  let response = null

  try {
    response = await memory?.recall({
      threadId: THREAD_ID,
      resourceId: RESOURCE_ID,
    })
  } catch {
    console.log('No previous messages found.')
  }

  const uiMessages = toAISdkV5Messages(response?.messages || [])

  return Response.json(uiMessages)
}
```

The `POST` route accepts a prompt and streams the agent's response back in AI SDK format, while the `GET` route fetches message history from memory so the UI can be hydrated when the client reloads.

## Create a chat component

Create `src/components/chat.tsx`:

```tsx
import '@/styles/global.css'
import { useEffect, useState } 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'

function Chat() {
  const [input, setInput] = useState<string>('')

  const { messages, setMessages, sendMessage, status } = useChat({
    transport: new DefaultChatTransport({
      api: '/api/chat',
    }),
  })

  useEffect(() => {
    const fetchMessages = async () => {
      const res = await fetch('/api/chat')
      const data = await res.json()
      setMessages([...data])
    }
    fetchMessages()
  }, [setMessages])

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

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

  return (
    <div className="relative size-full h-screen w-full 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="Type your message..."
              disabled={status !== 'ready'}
            />
          </PromptInputBody>
        </PromptInput>
      </div>
    </div>
  )
}

export default Chat
```

This component connects [`useChat()`](https://ai-sdk.dev/docs/reference/ai-sdk-ui/use-chat) to the `api/chat` 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.

## Render the chat component

The last step is to render the chat component on your index page. Edit `src/pages/index.astro`:

```html
---
import Chat from '@/components/chat';
---

<html lang="en">
  <head>
    <meta charset="utf-8" />
    <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
    <meta name="viewport" content="width=device-width" />
    <meta name="generator" content="{Astro.generator}" />
    <title>Astro</title>
  </head>
  <body>
    <Chat client:load />
  </body>
</html>
```

Import the `Chat` component and add it to the body with the `client:load` [directive](https://docs.astro.build/en/reference/directives-reference/#client-directives) so it runs on the client side.

## Test your agent

1. Run your Astro app with `npm run dev`
2. Open the chat at <http://localhost:4321>
3. 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 Astro! 🎉

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 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)