> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Meta

> Learn how to use Meta models in Agno.

Meta offers a suite of powerful multi-modal language models known for their strong performance across a wide range of tasks, including superior text understanding and visual intelligence.

We recommend experimenting to find the best-suited model for your use-case. Here are some general recommendations:

* `Llama-4-Scout-17B`: Excellent performance for most general tasks, including multi-modal scenarios.
* `Llama-3.3-70B`: Powerful instruction-following model for complex reasoning tasks.

Explore all the models [here](https://llama.developer.meta.com/docs/models).

## Authentication

Set your `LLAMA_API_KEY` environment variable:

<CodeGroup>
  ```bash Mac theme={null}
  export LLAMA_API_KEY=YOUR_API_KEY
  ```

  ```bash Windows theme={null}
  setx LLAMA_API_KEY YOUR_API_KEY
  ```
</CodeGroup>

## Example

Use `Llama` with your `Agent`:

<CodeGroup>
  ```python agent.py theme={null}
  from agno.agent import Agent
  from agno.models.meta import Llama

  agent = Agent(
      model=Llama(
          id="Llama-4-Maverick-17B-128E-Instruct-FP8",
      ),
      markdown=True
  )

  agent.print_response("Share a 2 sentence horror story.")
  ```
</CodeGroup>

<Note> View more examples [here](../examples/models/meta). </Note>

## Parameters

<Snippet file="model-meta-params.mdx" />

### OpenAI-like Parameters

`LlamaOpenAI` supports all parameters from [OpenAI Like](/reference/models/openai_like).

## Resources

* [Meta AI Models](https://llama.developer.meta.com/docs/models)
* [Llama API Documentation](https://llama.developer.meta.com/docs/overview)