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

# Ollama

> Run local models with Ollama in Agno agents.

Run large language models with Ollama, either locally or through Ollama Cloud.

[Ollama](https://ollama.com) is a fantastic tool for running models both locally and in the cloud.

**Local Usage**: Run models on your own hardware using the Ollama client.

**Cloud Usage**: Access cloud-hosted models via [Ollama Cloud](https://ollama.com) with an API key.

Ollama supports multiple open-source models. See the library [here](https://ollama.com/library).

Experiment with different models to find the best fit for your use case. Here are some general recommendations:

* `gpt-oss:120b-cloud` is an excellent general-purpose cloud model for most tasks.
* `llama3.3` models are good for most basic use-cases.
* `qwen` models perform specifically well with tool use.
* `deepseek-r1` models have strong reasoning capabilities.
* `phi4` models are powerful, while being really small in size.

## Authentication (Ollama Cloud Only)

To use Ollama Cloud, set your `OLLAMA_API_KEY` environment variable. You can get an API key from [Ollama Cloud](https://ollama.com).

<CodeGroup>
  ```bash Mac theme={null}
  export OLLAMA_API_KEY=***
  ```

  ```bash Windows theme={null}
  setx OLLAMA_API_KEY ***
  ```
</CodeGroup>

When using Ollama Cloud, the host is automatically set to `https://ollama.com`. For local usage, no API key is required.

## Set up a model

### Local Usage

Install [ollama](https://ollama.com) and run a model:

```bash run model theme={null}
ollama run llama3.1
```

This starts an interactive session with the model.

To download the model for use in an Agno agent:

```bash pull model theme={null}
ollama pull llama3.1
```

### Cloud Usage

For Ollama Cloud, no local Ollama server installation is required. Install the Ollama library, set up your API key as described in the Authentication section above, and access cloud-hosted models directly.

## Examples

### Local Usage

Once the model is available locally, use the `Ollama` model class to access it:

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

  agent = Agent(
      model=Ollama(id="llama3.1"),
      markdown=True
  )

  # Print the response in the terminal
  agent.print_response("Share a 2 sentence horror story.")
  ```
</CodeGroup>

### Cloud Usage

<Note>When using Ollama Cloud with an API key, the host is automatically set to `https://ollama.com`. You can omit the `host` parameter.</Note>

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

  agent = Agent(
      model=Ollama(id="gpt-oss:120b-cloud"),
      markdown=True
  )

  # Print the response in the terminal
  agent.print_response("Share a 2 sentence horror story.")
  ```
</CodeGroup>

<Note> View more examples [here](/models/providers/local/ollama/usage/basic). </Note>

## Params

| Parameter    | Type                          | Default                    | Description                                                  |
| ------------ | ----------------------------- | -------------------------- | ------------------------------------------------------------ |
| `id`         | `str`                         | `"llama3.2"`               | The name of the Ollama model to use                          |
| `name`       | `str`                         | `"Ollama"`                 | The name of the model                                        |
| `provider`   | `str`                         | `"Ollama"`                 | The provider of the model                                    |
| `host`       | `str`                         | `"http://localhost:11434"` | The host URL for the Ollama server                           |
| `timeout`    | `Optional[int]`               | `None`                     | Request timeout in seconds                                   |
| `format`     | `Optional[str]`               | `None`                     | The format to return the response in (e.g., "json")          |
| `options`    | `Optional[Dict[str, Any]]`    | `None`                     | Additional model options (temperature, top\_p, etc.)         |
| `keep_alive` | `Optional[Union[float, str]]` | `None`                     | How long to keep the model loaded (e.g., "5m", 3600 seconds) |
| `template`   | `Optional[str]`               | `None`                     | The prompt template to use                                   |
| `system`     | `Optional[str]`               | `None`                     | System message to use                                        |
| `raw`        | `Optional[bool]`              | `None`                     | Whether to return raw response without formatting            |
| `stream`     | `bool`                        | `True`                     | Whether to stream the response                               |

`Ollama` is a subclass of the [Model](/reference/models/model) class and has access to the same params.

## Responses API

Ollama v0.13.3+ supports the OpenAI Responses API via the `/v1/responses` endpoint. Use `OllamaResponses` for this interface:

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

  agent = Agent(
      model=OllamaResponses(id="gpt-oss:20b"),
      markdown=True,
  )

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

The Responses API is stateless. Each request is independent with no `previous_response_id` chaining.

See [OllamaResponses reference](/reference/models/ollama-responses) for full parameters.