Multimodal inputs

Feed images, audio, video, and PDFs into any labeling or extraction agent.

Every labeler on the other pages takes text. To label other modalities, change the input argument and the model. The schema and the output_schema pattern stay the same.

from typing import Literal

from agno.agent import Agent
from agno.media import Image
from agno.models.google import Gemini
from pydantic import BaseModel, Field


class Classification(BaseModel):
    label: Literal["dog", "cat", "bird", "fish", "other"] = Field(
        ..., description="What kind of animal is in the image"
    )


agent = Agent(
    model=Gemini(id="gemini-3.5-flash"),
    instructions="You classify images by animal type.",
    output_schema=Classification,
)

url = "https://upload.wikimedia.org/wikipedia/commons/4/4d/Cat_November_2010-1a.jpg"
result = agent.run("Classify this image.", images=[Image(url=url)]).content
# Classification(label='cat')

Input argument per modality

Modality	Import	Argument	Model in the cookbook
Image	from agno.media import Image	images=[Image(url=...)]	google:gemini-3.5-flash
Audio	from agno.media import Audio	audio=[Audio(content=...)]	google:gemini-3.5-flash
Video	from agno.media import Video	videos=[Video(content=..., format="mp4")]	google:gemini-3.5-flash
PDF	from agno.media import File	files=[File(url=...)]	google:gemini-3.5-flash

All four classes accept url, filepath, or raw bytes via content. The cookbook fetches audio and video bytes first.

import requests
from agno.media import Audio

audio_bytes = requests.get("https://example.com/clip.mp3").content
agent.run("Transcribe this.", audio=[Audio(content=audio_bytes)])

Bounding boxes

For region detection, return normalized coordinates so the result is resolution-independent.

from pydantic import BaseModel, Field


class BoundingBox(BaseModel):
    label: str = Field(..., description="What the box contains")
    x: float = Field(..., ge=0.0, le=1.0, description="Top-left x in [0, 1]")
    y: float = Field(..., ge=0.0, le=1.0, description="Top-left y in [0, 1]")
    width: float = Field(..., ge=0.0, le=1.0, description="Width in [0, 1]")
    height: float = Field(..., ge=0.0, le=1.0, description="Height in [0, 1]")

The per-field description on x, y, width, and height is load-bearing. Without it, and without the [0, 1] convention spelled out in the instructions, models return degenerate boxes (all-zero or whole-image). Spell out the coordinate system in both places.

Transcription and diarization

Audio extraction covers transcription, speaker diarization, and timestamped segments. Each is a schema change, not a different API.

Output	Schema shape
Flat transcript	{ text: str }
Speaker turns	{ turns: List[{ speaker, text }] }
Timestamped segments	{ segments: List[{ start_seconds, end_seconds, text }] }

Model choice

gemini-3.5-flash handles text, image, audio, video, and PDF natively, so the cookbook uses it across every modality. Agno is model-agnostic, so you can swap in any model that supports the modality.

Next steps

Task	Guide
Define the output schema	Structured extraction
Assign labels to media	Classification
Review media labels	Quality pipeline

Developer Resources

• Image cookbooks
• Audio cookbooks
• Multimodal agents