ollama-python/README.md

# Ollama Python Library

The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with [Ollama](https://github.com/ollama/ollama).

## Prerequisites

- [Ollama](https://ollama.com/download) should be installed and running
- Pull a model to use with the library: `ollama pull <model>` e.g. `ollama pull gemma3`
  - See [Ollama.com](https://ollama.com/search) for more information on the models available.

## Install

```sh
pip install ollama
```

## Usage

```python
from ollama import chat
from ollama import ChatResponse

response: ChatResponse = chat(model='gemma3', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response['message']['content'])
# or access fields directly from the response object
print(response.message.content)
```

See [_types.py](ollama/_types.py) for more information on the response types.

## Streaming responses

Response streaming can be enabled by setting `stream=True`.

```python
from ollama import chat

stream = chat(
    model='gemma3',
    messages=[{'role': 'user', 'content': 'Why is the sky blue?'}],
    stream=True,
)

for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)
```

## Cloud Models

Run larger models by offloading to Ollama’s cloud while keeping your local workflow.

- Supported models: `deepseek-v3.1:671b-cloud`, `gpt-oss:20b-cloud`, `gpt-oss:120b-cloud`, `kimi-k2:1t-cloud`, `qwen3-coder:480b-cloud`, `kimi-k2-thinking` See [Ollama Models - Cloud](https://ollama.com/search?c=cloud) for more information

### Run via local Ollama

1) Sign in (one-time):

```
ollama signin
```

2) Pull a cloud model:

```
ollama pull gpt-oss:120b-cloud
```

3) Make a request:

```python
from ollama import Client

client = Client()

messages = [
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
]

for part in client.chat('gpt-oss:120b-cloud', messages=messages, stream=True):
  print(part.message.content, end='', flush=True)
```

### Cloud API (ollama.com)

Access cloud models directly by pointing the client at `https://ollama.com`.

1) Create an API key from [ollama.com](https://ollama.com/settings/keys) , then set:

```
export OLLAMA_API_KEY=your_api_key
```

2) (Optional) List models available via the API:

```
curl https://ollama.com/api/tags
```

3) Generate a response via the cloud API:

```python
import os
from ollama import Client

client = Client(
    host='https://ollama.com',
    headers={'Authorization': 'Bearer ' + os.environ.get('OLLAMA_API_KEY')}
)

messages = [
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
]

for part in client.chat('gpt-oss:120b', messages=messages, stream=True):
  print(part.message.content, end='', flush=True)
```

## Custom client
A custom client can be created by instantiating `Client` or `AsyncClient` from `ollama`.

All extra keyword arguments are passed into the [`httpx.Client`](https://www.python-httpx.org/api/#client).

```python
from ollama import Client
client = Client(
  host='http://localhost:11434',
  headers={'x-some-header': 'some-value'}
)
response = client.chat(model='gemma3', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
```

## Async client

The `AsyncClient` class is used to make asynchronous requests. It can be configured with the same fields as the `Client` class.

```python
import asyncio
from ollama import AsyncClient

async def chat():
  message = {'role': 'user', 'content': 'Why is the sky blue?'}
  response = await AsyncClient().chat(model='gemma3', messages=[message])

asyncio.run(chat())
```

Setting `stream=True` modifies functions to return a Python asynchronous generator:

```python
import asyncio
from ollama import AsyncClient

async def chat():
  message = {'role': 'user', 'content': 'Why is the sky blue?'}
  async for part in await AsyncClient().chat(model='gemma3', messages=[message], stream=True):
    print(part['message']['content'], end='', flush=True)

asyncio.run(chat())
```

## API

The Ollama Python library's API is designed around the [Ollama REST API](https://github.com/ollama/ollama/blob/main/docs/api.md)

### Chat

```python
ollama.chat(model='gemma3', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}])
```

### Generate

```python
ollama.generate(model='gemma3', prompt='Why is the sky blue?')
```

### List

```python
ollama.list()
```

### Show

```python
ollama.show('gemma3')
```

### Create

```python
ollama.create(model='example', from_='gemma3', system="You are Mario from Super Mario Bros.")
```

### Copy

```python
ollama.copy('gemma3', 'user/gemma3')
```

### Delete

```python
ollama.delete('gemma3')
```

### Pull

```python
ollama.pull('gemma3')
```

### Push

```python
ollama.push('user/gemma3')
```

### Embed

```python
ollama.embed(model='gemma3', input='The sky is blue because of rayleigh scattering')
```

### Embed (batch)

```python
ollama.embed(model='gemma3', input=['The sky is blue because of rayleigh scattering', 'Grass is green because of chlorophyll'])
```

### Ps

```python
ollama.ps()
```


## Errors

Errors are raised if requests return an error status or if an error is detected while streaming.

```python
model = 'does-not-yet-exist'

try:
  ollama.chat(model)
except ollama.ResponseError as e:
  print('Error:', e.error)
  if e.status_code == 404:
    ollama.pull(model)
```