Ollama Python Library

The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with Ollama.

Prerequisites

• Ollama should be installed and running
• Pull a model to use with the library: ollama pull <model> e.g. ollama pull gemma3
  • See Ollama.com for more information on the models available.

Install

pip install ollama

Usage

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

See _types.py for more information on the response types.

Streaming responses

Response streaming can be enabled by setting stream=True.

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

fromollamaimportClientclient=Client()
messages= [
{
'role': 'user',
'content': 'Why is the sky blue?',
},
]
forpartinclient.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 , 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:

importosfromollamaimportClientclient=Client(
host='https://ollama.com',
headers={'Authorization': 'Bearer '+os.environ.get('OLLAMA_API_KEY')}
)
messages= [
{
'role': 'user',
'content': 'Why is the sky blue?',
},
]
forpartinclient.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.

fromollamaimportClientclient=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.

importasynciofromollamaimportAsyncClientasyncdefchat():
message= {'role': 'user', 'content': 'Why is the sky blue?'}
response=awaitAsyncClient().chat(model='gemma3', messages=[message])
asyncio.run(chat())

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

importasynciofromollamaimportAsyncClientasyncdefchat():
message= {'role': 'user', 'content': 'Why is the sky blue?'}
asyncforpartinawaitAsyncClient().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

Chat

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

Generate

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

List

ollama.list()

Show

ollama.show('gemma3')

Create

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

Copy

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

Delete

ollama.delete('gemma3')

Pull

ollama.pull('gemma3')

Push

ollama.push('user/gemma3')

Embed

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

Embed (batch)

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

Ps

ollama.ps()

Errors

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

model='does-not-yet-exist'try:
ollama.chat(model)
exceptollama.ResponseErrorase:
print('Error:', e.error)
ife.status_code==404:
ollama.pull(model)