> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runflow.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Multi-Modal (Images & Files)

> Send images and files to vision-capable models

Runflow accepts multimodal content — text + image, text + file, or text + multiple attachments — through three entry points. Pick the one that matches how the media reaches your agent.

## 1. Direct multimodal call

Build the `messages` array yourself and pass it to `agent.process`. Best when you already have URLs or base64 strings on hand.

```typescript theme={null}
import { Agent, openai } from '@runflow-ai/sdk';

const agent = new Agent({
  name: 'vision-agent',
  instructions: 'You can analyze images.',
  model: openai('gpt-4o'),
});

await agent.process({
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'What is in this image?' },
        { type: 'image_url', image_url: { url: 'https://example.com/image.jpg' } },
      ],
    },
  ],
});
```

The same `content` array works across providers — the SDK translates the parts to the native format each model expects (OpenAI, Anthropic, Bedrock, Gemini, Groq, xAI, Azure OpenAI).

## 2. multipart/form-data uploads

When a client sends a file or image directly to the agent endpoint over HTTP, post `multipart/form-data`. Runflow stores the upload and exposes it to your agent as `input.attachments[]`.

```bash theme={null}
curl -X POST "https://executor.runflow.ai/agent/<agentId>?token=<agentToken>" \
  -F "message=quanto custa esse produto?" \
  -F "photo=@./produto.jpg"
```

Enable `media.processAttachments` to let the SDK build the multimodal message for you:

```typescript theme={null}
const agent = new Agent({
  name: 'product-helper',
  model: openai('gpt-4o'),
  instructions: 'Help the user evaluate products.',
  media: { processAttachments: true },
});
```

Routing rule:

* `content_type` starting with `image/` → `image_url`
* anything else                       → `file_url`

For custom routing (OCR a PDF, parse a CSV locally, etc.), leave the flag off and transform `input.attachments[]` yourself — see [Media Processing](/core-concepts/media-processing) for the manual recipe.

## 3. Webhook handlers with `input.file`

Twilio/WhatsApp and Meta/Messenger handlers deliver a single media file per message as `input.file`. Auto-processed when `media.transcribeAudio` or `media.processImages` is enabled.

```typescript theme={null}
const agent = new Agent({
  name: 'whatsapp-bot',
  model: openai('gpt-4o'),
  instructions: 'Reply in the customer\'s language.',
  media: { transcribeAudio: true, processImages: true },
});
```

See [Media Processing](/core-concepts/media-processing) for the full WhatsApp example.

## Provider support at a glance

| Provider            | Images                        | Files (PDF, CSV, …)                  |
| ------------------- | ----------------------------- | ------------------------------------ |
| OpenAI              | URL or base64                 | `file_id` (uploaded via Runtime API) |
| Azure OpenAI        | URL or base64                 | text label only                      |
| Anthropic / Bedrock | URL or base64                 | text label only                      |
| Gemini              | base64 inline / `data:` URI   | text label only                      |
| Groq / xAI          | URL or base64 (vision models) | text label only                      |

For non-image files on providers that don't support arbitrary documents, the SDK emits a `[File: <name>]` text placeholder so the model sees something coherent. If you need the model to actually read a PDF/CSV, parse it locally first and send the extracted text as a `text` part.

## Next Steps

<CardGroup cols={2}>
  <Card title="Media Processing" icon="image" href="/core-concepts/media-processing">
    Full media handling guide with WhatsApp example
  </Card>

  <Card title="Streaming" icon="stream" href="/advanced/streaming">
    Stream multimodal responses
  </Card>
</CardGroup>