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When we discuss Models, we are normally referring to Large Language Models (LLMs). These models act as the brain of your Agents - enabling them to reason, act, and respond to the user. The better the model, the smarter the Agent. 
from agno.agent import Agent
from agno.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    description="Share 15 minute healthy recipes.",
    markdown=True,
)
agent.print_response("Share a breakfast recipe.", stream=True)
Use model strings ("provider:model_id") for simpler configuration. For advanced use cases requiring custom parameters like temperature or max_tokens, use the full model class syntax.

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Error handling

You can set exponential_backoff to True on the Agent to automatically retry requests that fail due to third-party model provider errors. 
agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    exponential_backoff=True,
    retries=2,
    retry_delay=1,
)

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Learn more

Supported Model Providers

See the full list of supported model providers.

Model-as-string

Use the convenient provider:model_id string format to specify models without importing model classes.

Compatibility

See what features are supported by each model provider.

Cache Response

Cache the response from the model provider to avoid duplicate API calls.