> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Meta

> Use Meta Llama models with Agno agents.

Meta offers multi-modal language models with strong 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-16E-Instruct-FP8`: Excellent performance for most general tasks, including multi-modal scenarios.
* `Llama-3.3-70B-Instruct`: Strong instruction-following model for complex reasoning tasks.

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

## Installation

```bash theme={null}
uv pip install -U llama-api-client agno
```

## Authentication

Set your `LLAMA_API_KEY` environment variable:

```bash Mac theme={null}
export LLAMA_API_KEY=YOUR_API_KEY
```

```bash Windows theme={null}
setx LLAMA_API_KEY YOUR_API_KEY
```

## Example

Use `Llama` with your `Agent`:

```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.")

```

> [!NOTE]
>
> View more examples [here](https://agno-v2.mintlify.site/models/providers/native/meta/usage/basic.md).

## Parameters

| Parameter | Type | Default | Description |
| - | - | - | - |
| `id` | `str` | `"Llama-4-Maverick-17B-128E-Instruct-FP8"` | The id of the Meta model to use |
| `name` | `str` | `"Llama"` | The name of the model |
| `provider` | `str` | `"Llama"` | The provider of the model |
| `api_key` | `Optional[str]` | `None` | The API key for the Llama API (defaults to `LLAMA_API_KEY` env var) |
| `base_url` | `Optional[Union[str, httpx.URL]]` | `None` | The base URL for the Llama API (defaults to the Llama SDK default) |

### OpenAI-like Parameters

Agno also provides `LlamaOpenAI`, a client that calls the Llama API through its OpenAI-compatible endpoint (`https://api.llama.com/compat/v1/`). It uses the `openai` package. Install it with `uv pip install -U openai`. Use it in place of `Llama` when you want OpenAI-style request handling:

```python theme={null}
from agno.models.meta import LlamaOpenAI

agent = Agent(model=LlamaOpenAI(id="Llama-4-Maverick-17B-128E-Instruct-FP8"))
```

`LlamaOpenAI` supports all parameters from [OpenAI Like](https://agno-v2.mintlify.site/reference/models/openai-like.md).

## Resources

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


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.