Agno agents support text, image, audio and video inputs and can generate text, image, audio and video outputs. For a complete overview, please checkout the compatibility matrix.
To get started, feel free to checkout the multimodal examples.

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Multimodal inputs to an agent

Let’s create an agent that can understand images and make tool calls as needed

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Agent with Image Understanding

image_agent.py
from agno.agent import Agent
from agno.media import Image
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[DuckDuckGoTools()],
    markdown=True,
)

agent.print_response(
    "Tell me about this image and give me the latest news about it.",
    images=[
        Image(
            url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg"
        )
    ],
    stream=True,
)
Run the agent: 
python image_agent.py
Similar to images, you can also use audio and video as an input.

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Agent with Audio Understanding

audio_agent.py
import base64

import requests
from agno.agent import Agent, RunOutput  # noqa
from agno.media import Audio
from agno.models.openai import OpenAIChat

# Fetch the audio file and convert it to a base64 encoded string
url = "https://openaiassets.blob.core.windows.net/$web/API/docs/audio/alloy.wav"
response = requests.get(url)
response.raise_for_status()
wav_data = response.content

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini-audio-preview", modalities=["text"]),
    markdown=True,
)
agent.print_response(
    "What is in this audio?", audio=[Audio(content=wav_data, format="wav")]
)

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Agent with Video Understanding

video_agent.py
from pathlib import Path

from agno.agent import Agent
from agno.media import Video
from agno.models.google import Gemini

agent = Agent(
    model=Gemini(id="gemini-2.0-flash-001"),
    markdown=True,
)

# Please download "GreatRedSpot.mp4" using
# wget https://storage.googleapis.com/generativeai-downloads/images/GreatRedSpot.mp4
video_path = Path(__file__).parent.joinpath("GreatRedSpot.mp4")

agent.print_response("Tell me about this video", videos=[Video(filepath=video_path)])

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Multimodal outputs from an agent

Similar to providing multimodal inputs, you can also get multimodal outputs from an agent. You can either use tools to generate image/audio/video or use the agent’s model to generate them (if the model supports this capability).

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Image Generation using a tool

The following example demonstrates how to generate an image using an OpenAI tool with an agent.
image_agent.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.openai import OpenAITools
from agno.utils.media import save_base64_data

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[OpenAITools(image_model="gpt-image-1")],
    markdown=True,
)

response = agent.run(
    "Generate a photorealistic image of a cozy coffee shop interior",
)

if response.images and response.images[0].content:
    save_base64_data(str(response.images[0].content), "tmp/coffee_shop.png")
The output of the tool generating a media also goes to the model’s input as a message so it has access to the media (image, audio, video) and can use it in the response. For example, if you say “Generate an image of a dog and tell me its color.” the model will have access to the image and can use it to describe the dog’s color in the response in the same run.

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Image Model Response

The following example demonstrates how some models can directly generate images as part of their response.
image_agent.py
from io import BytesIO

from agno.agent import Agent, RunOutput  # noqa
from agno.models.google import Gemini
from PIL import Image

# No system message should be provided
agent = Agent(
    model=Gemini(
        id="gemini-2.0-flash-exp-image-generation",
        response_modalities=["Text", "Image"], # This means to generate both images and text
    )
)

# Print the response in the terminal
run_response = agent.run("Make me an image of a cat in a tree.")

if run_response and isinstance(run_response, RunOutput) and run_response.images:
    for image_response in run_response.images:
        image_bytes = image_response.content
        if image_bytes:
            image = Image.open(BytesIO(image_bytes))
            image.show()
            # Save the image to a file
            # image.save("generated_image.png")
else:
    print("No images found in run response")
You can find all generated images in the RunOutput.images list.

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Audio Generation using a tool

The following example demonstrates how to generate an audio using the ElevenLabs tool with an agent. See Eleven Labs for more details.
audio_agent.py
import base64

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.eleven_labs import ElevenLabsTools
from agno.utils.media import save_base64_data

audio_agent = Agent(
    model=Gemini(id="gemini-2.5-pro"),
    tools=[
        ElevenLabsTools(
            voice_id="21m00Tcm4TlvDq8ikWAM",
            model_id="eleven_multilingual_v2",
            target_directory="audio_generations",
        )
    ],
    description="You are an AI agent that can generate audio using the ElevenLabs API.",
    instructions=[
        "When the user asks you to generate audio, use the `generate_audio` tool to generate the audio.",
        "You'll generate the appropriate prompt to send to the tool to generate audio.",
        "You don't need to find the appropriate voice first, I already specified the voice to user."
        "Return the audio file name in your response. Don't convert it to markdown.",
        "The audio should be long and detailed.",
    ],
    markdown=True,
)

response = audio_agent.run(
    "Generate a very long audio of history of french revolution and tell me which subject it belongs to.",
    debug_mode=True,
)

if response.audio:
    print("Agent response:", response.content)
    base64_audio = base64.b64encode(response.audio[0].content).decode("utf-8")
    save_base64_data(base64_audio, "tmp/french_revolution.mp3")
    print("Successfully saved generated speech to tmp/french_revolution.mp3")


audio_agent.print_response("Generate a kick sound effect")

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Audio Model Response

The following example demonstrates how some models can directly generate audio as part of their response.
audio_agent.py
from agno.agent import Agent, RunOutput
from agno.models.openai import OpenAIChat
from agno.utils.audio import write_audio_to_file

agent = Agent(
    model=OpenAIChat(
        id="gpt-5-mini-audio-preview",
        modalities=["text", "audio"],
        audio={"voice": "alloy", "format": "wav"},
    ),
    markdown=True,
)
response: RunOutput = agent.run("Tell me a 5 second scary story")

# Save the response audio to a file
if response.response_audio is not None:
    write_audio_to_file(
        audio=agent.run_response.response_audio.content, filename="tmp/scary_story.wav"
    )

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Video Generation using a tool

The following example demonstrates how to generate a video using FalTools with an agent. See FAL for more details.
video_agent.py
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.fal import FalTools

fal_agent = Agent(
    name="Fal Video Generator Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[
        FalTools(
            model="fal-ai/hunyuan-video",
            enable_generate_media=True,
        )
    ],
    description="You are an AI agent that can generate videos using the Fal API.",
    instructions=[
        "When the user asks you to create a video, use the `generate_media` tool to create the video.",
        "Return the URL as raw to the user.",
        "Don't convert video URL to markdown or anything else.",
    ],
    markdown=True,
)

fal_agent.print_response("Generate video of balloon in the ocean")

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Multimodal inputs and outputs together

You can create agents that can take multimodal inputs and return multimodal outputs. The following example demonstrates how to provide a combination of audio and text inputs to an agent and obtain both text and audio outputs.

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Audio input and Audio output

audio_agent.py
import requests
from agno.agent import Agent
from agno.media import Audio
from agno.models.openai import OpenAIChat
from agno.utils.audio import write_audio_to_file
from rich.pretty import pprint

# Fetch the audio file and convert it to a base64 encoded string
url = "https://openaiassets.blob.core.windows.net/$web/API/docs/audio/alloy.wav"
response = requests.get(url)
response.raise_for_status()
wav_data = response.content

agent = Agent(
    model=OpenAIChat(
        id="gpt-5-mini-audio-preview",
        modalities=["text", "audio"],
        audio={"voice": "sage", "format": "wav"},
    ),
    markdown=True,
)

run_response = agent.run(
    "What's in these recording?",
    audio=[Audio(content=wav_data, format="wav")],
)

if run_response.response_audio is not None:
    pprint(run_response.content)
    write_audio_to_file(
        audio=run_response.response_audio.content, filename="tmp/result.wav"
    )

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Developer Resources