Image, Audio, Video & Document Input

Some LLMs are now capable of understanding audio, video, image and document content.

Image Input

Info

Some models do not support image input. Please check the model's documentation to confirm whether it supports image input.

If you have a direct URL for the image, you can use ImageUrl:

With Pydantic AI GatewayDirectly to Provider API

Learn about Gateway image_input.py

from pydantic_ai import Agent, ImageUrl

agent = Agent(model='gateway/openai:gpt-5')
result = agent.run_sync(
    [
        'What company is this logo from?',
        ImageUrl(url='https://iili.io/3Hs4FMg.png'),
    ]
)
print(result.output)
#> This is the logo for Pydantic, a data validation and settings management library in Python.

image_input.py

from pydantic_ai import Agent, ImageUrl

agent = Agent(model='openai:gpt-5')
result = agent.run_sync(
    [
        'What company is this logo from?',
        ImageUrl(url='https://iili.io/3Hs4FMg.png'),
    ]
)
print(result.output)
#> This is the logo for Pydantic, a data validation and settings management library in Python.

If you have the image locally, you can also use BinaryContent:

With Pydantic AI GatewayDirectly to Provider API

Learn about Gateway local_image_input.py

import httpx

from pydantic_ai import Agent, BinaryContent

image_response = httpx.get('https://iili.io/3Hs4FMg.png')  # Pydantic logo

agent = Agent(model='gateway/openai:gpt-5')
result = agent.run_sync(
    [
        'What company is this logo from?',
        BinaryContent(data=image_response.content, media_type='image/png'),  # (1)!
    ]
)
print(result.output)
#> This is the logo for Pydantic, a data validation and settings management library in Python.

To ensure the example is runnable we download this image from the web, but you can also use Path().read_bytes() to read a local file's contents.

local_image_input.py

import httpx

from pydantic_ai import Agent, BinaryContent

image_response = httpx.get('https://iili.io/3Hs4FMg.png')  # Pydantic logo

agent = Agent(model='openai:gpt-5')
result = agent.run_sync(
    [
        'What company is this logo from?',
        BinaryContent(data=image_response.content, media_type='image/png'),  # (1)!
    ]
)
print(result.output)
#> This is the logo for Pydantic, a data validation and settings management library in Python.

To ensure the example is runnable we download this image from the web, but you can also use Path().read_bytes() to read a local file's contents.

Audio Input

Info

Some models do not support audio input. Please check the model's documentation to confirm whether it supports audio input.

You can provide audio input using either AudioUrl or BinaryContent. The process is analogous to the examples above.

Video Input

Info

Some models do not support video input. Please check the model's documentation to confirm whether it supports video input.

You can provide video input using either VideoUrl or BinaryContent. The process is analogous to the examples above.

Document Input

Info

Some models do not support document input. Please check the model's documentation to confirm whether it supports document input.

You can provide document input using either DocumentUrl or BinaryContent. The process is similar to the examples above.

If you have a direct URL for the document, you can use DocumentUrl:

With Pydantic AI GatewayDirectly to Provider API

Learn about Gateway document_input.py

from pydantic_ai import Agent, DocumentUrl

agent = Agent(model='gateway/anthropic:claude-sonnet-4-5')
result = agent.run_sync(
    [
        'What is the main content of this document?',
        DocumentUrl(url='https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf'),
    ]
)
print(result.output)
#> This document is the technical report introducing Gemini 1.5, Google's latest large language model...

document_input.py

from pydantic_ai import Agent, DocumentUrl

agent = Agent(model='anthropic:claude-sonnet-4-5')
result = agent.run_sync(
    [
        'What is the main content of this document?',
        DocumentUrl(url='https://storage.googleapis.com/cloud-samples-data/generative-ai/pdf/2403.05530.pdf'),
    ]
)
print(result.output)
#> This document is the technical report introducing Gemini 1.5, Google's latest large language model...

The supported document formats vary by model.

You can also use BinaryContent to pass document data directly:

With Pydantic AI GatewayDirectly to Provider API

Learn about Gateway binary_content_input.py

from pathlib import Path
from pydantic_ai import Agent, BinaryContent

pdf_path = Path('document.pdf')
agent = Agent(model='gateway/anthropic:claude-sonnet-4-5')
result = agent.run_sync(
    [
        'What is the main content of this document?',
        BinaryContent(data=pdf_path.read_bytes(), media_type='application/pdf'),
    ]
)
print(result.output)
#> The document discusses...

binary_content_input.py

from pathlib import Path
from pydantic_ai import Agent, BinaryContent

pdf_path = Path('document.pdf')
agent = Agent(model='anthropic:claude-sonnet-4-5')
result = agent.run_sync(
    [
        'What is the main content of this document?',
        BinaryContent(data=pdf_path.read_bytes(), media_type='application/pdf'),
    ]
)
print(result.output)
#> The document discusses...

User-side download vs. direct file URL

When using one of ImageUrl, AudioUrl, VideoUrl or DocumentUrl, Pydantic AI will default to sending the URL to the model provider, so the file is downloaded on their side.

Support for file URLs varies depending on type and provider:

Model	Send URL directly	Download and send bytes	Unsupported
`OpenAIChatModel`	`ImageUrl`	`AudioUrl`, `DocumentUrl`	`VideoUrl`
`OpenAIResponsesModel`	`ImageUrl`, `AudioUrl`, `DocumentUrl`	—	`VideoUrl`
`AnthropicModel`	`ImageUrl`, `DocumentUrl` (PDF)	`DocumentUrl` (`text/plain`)	`AudioUrl`, `VideoUrl`
`GoogleModel` (Vertex)	All URL types	—	—
`GoogleModel` (GLA)	YouTube, Files API	All other URLs	—
`MistralModel`	`ImageUrl`, `DocumentUrl` (PDF)	—	`AudioUrl`, `VideoUrl`
`BedrockConverseModel`	S3 URLs (`s3://`)	`ImageUrl`, `DocumentUrl`, `VideoUrl`	`AudioUrl`

A model API may be unable to download a file (e.g., because of crawling or access restrictions) even if it supports file URLs. For example, GoogleModel on Vertex AI limits YouTube video URLs to one URL per request. In such cases, you can instruct Pydantic AI to download the file content locally and send that instead of the URL by setting force_download on the URL object:

force_download.py

from pydantic_ai import ImageUrl, AudioUrl, VideoUrl, DocumentUrl

ImageUrl(url='https://example.com/image.png', force_download=True)
AudioUrl(url='https://example.com/audio.mp3', force_download=True)
VideoUrl(url='https://example.com/video.mp4', force_download=True)
DocumentUrl(url='https://example.com/doc.pdf', force_download=True)

Uploaded Files

Some model providers like Google's Gemini API support uploading files. You can upload a file using the provider's client and passing the resulting URL as input:

file_upload.py

from pydantic_ai import Agent, DocumentUrl
from pydantic_ai.models.google import GoogleModel
from pydantic_ai.providers.google import GoogleProvider

provider = GoogleProvider()
file = provider.client.files.upload(file='pydantic-ai-logo.png')
assert file.uri is not None

agent = Agent(GoogleModel('gemini-2.5-flash', provider=provider))
result = agent.run_sync(
    [
        'What company is this logo from?',
        DocumentUrl(url=file.uri, media_type=file.mime_type),
    ]
)
print(result.output)

BedrockConverseModel supports s3://<bucket-name>/<object-key> URIs, provided that the assumed role has the s3:GetObject permission. An optional bucketOwner query parameter must be specified if the bucket is not owned by the account making the request. For example: s3://my-bucket/my-file.png?bucketOwner=123456789012.