Input and History

Read this file when the user wants multimodal input, message history, or context trimming.

Send Images, Audio, Video, or Documents to the Model

Pass multimodal content as a list mixing text with ImageUrl, AudioUrl, VideoUrl, DocumentUrl, or BinaryContent.

python

from pydantic_ai import Agent, ImageUrl

agent = Agent(model='openai:gpt-5.2')
result = agent.run_sync(
    [
        'What company is this logo from?',
        ImageUrl(url='https://example.com/logo.png'),
    ]
)
print(result.output)

Use BinaryContent(...) when the asset is already in memory instead of at a URL.

Not every model supports every input type. Keep provider expectations in mind when the user chooses a specific model.

Work with Message History

Use message_history= to continue a conversation across runs.

python

from pydantic_ai import Agent

agent = Agent('openai:gpt-5.2', instructions='Be a helpful assistant.')

result1 = agent.run_sync('Tell me a joke.')
result2 = agent.run_sync('Explain?', message_history=result1.new_messages())
print(result2.output)

Important distinctions:

new_messages() returns only the current run
all_messages() returns the full history accumulated so far
when message_history is non-empty, Pydantic AI assumes the history already carries the system prompt

Manage Context Size

Use history_processors=[...] to trim or rewrite message history before each model request.

python

from pydantic_ai import Agent, ModelMessage


async def keep_recent(messages: list[ModelMessage]) -> list[ModelMessage]:
    return messages[-10:] if len(messages) > 10 else messages


agent = Agent('openai:gpt-5.2', history_processors=[keep_recent])

Good uses:

trimming long conversations
removing PII before provider calls
summarizing old messages
applying app-specific history policies