ChatOllama

Ollama allows you to run open-source large language models, such as gpt-oss, locally.

ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile.

It optimizes setup and configuration details, including GPU usage.

For a complete list of supported models and model variants, see the Ollama model library.

Overview

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatOllama	langchain-ollama	✅	❌	✅

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	✅	✅	❌	❌	✅	✅	❌	❌

Setup

First, follow these instructions to set up and run a local Ollama instance:

• Download and install Ollama onto the available supported platforms (including Windows Subsystem for Linux aka WSL, macOS, and Linux)
  • macOS users can install via Homebrew with brew install ollama and start with brew services start ollama
• Fetch available LLM model via ollama pull <name-of-model>
  • View a list of available models via the model library
  • e.g., ollama pull gpt-oss:20b
• This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.

On Mac, the models will be download to ~/.ollama/models

On Linux (or WSL), the models will be stored at /usr/share/ollama/.ollama/models

• Specify the exact version of the model of interest as such ollama pull gpt-oss:20b (View the various tags for the Vicuna model in this instance)
• To view all pulled models, use ollama list
• To chat directly with a model from the command line, use ollama run <name-of-model>
• View the Ollama documentation for more commands. You can run ollama help in the terminal to see available commands.

To enable automated tracing of your model calls, set your LangSmith API key:

# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation

The LangChain Ollama integration lives in the langchain-ollama package:

%pip install -qU langchain-ollama

warning

Make sure you're using the latest Ollama version!

Update by running:

%pip install -U ollama

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_ollama import ChatOllama

llm = ChatOllama(
    model="llama3.1",
    temperature=0,
    # other params...
)

Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content='The translation of "I love programming" in French is:\n\n"J\'adore le programmation."', additional_kwargs={}, response_metadata={'model': 'llama3.1', 'created_at': '2025-06-25T18:43:00.483666Z', 'done': True, 'done_reason': 'stop', 'total_duration': 619971208, 'load_duration': 27793125, 'prompt_eval_count': 35, 'prompt_eval_duration': 36354583, 'eval_count': 22, 'eval_duration': 555182667, 'model_name': 'llama3.1'}, id='run--348bb5ef-9dd9-4271-bc7e-a9ddb54c28c1-0', usage_metadata={'input_tokens': 35, 'output_tokens': 22, 'total_tokens': 57})

print(ai_msg.content)

The translation of "I love programming" in French is:

"J'adore le programmation."

Chaining

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)

API Reference:ChatPromptTemplate

AIMessage(content='"Programmieren ist meine Leidenschaft."\n\n(I translated "programming" to the German word "Programmieren", and added "ist meine Leidenschaft" which means "is my passion")', additional_kwargs={}, response_metadata={'model': 'llama3.1', 'created_at': '2025-06-25T18:43:29.350032Z', 'done': True, 'done_reason': 'stop', 'total_duration': 1194744459, 'load_duration': 26982500, 'prompt_eval_count': 30, 'prompt_eval_duration': 117043458, 'eval_count': 41, 'eval_duration': 1049892167, 'model_name': 'llama3.1'}, id='run--efc6436e-2346-43d9-8118-3c20b3cdf0d0-0', usage_metadata={'input_tokens': 30, 'output_tokens': 41, 'total_tokens': 71})

Tool calling

We can use tool calling with an LLM that has been fine-tuned for tool use such as gpt-oss:

ollama pull gpt-oss:20b

Details on creating custom tools are available in this guide. Below, we demonstrate how to create a tool using the @tool decorator on a normal python function.

from typing import List

from langchain_core.messages import AIMessage
from langchain_core.tools import tool
from langchain_ollama import ChatOllama


@tool
def validate_user(user_id: int, addresses: List[str]) -> bool:
    """Validate user using historical addresses.

    Args:
        user_id (int): the user ID.
        addresses (List[str]): Previous addresses as a list of strings.
    """
    return True


llm = ChatOllama(
    model="gpt-oss:20b",
    validate_model_on_init=True,
    temperature=0,
).bind_tools([validate_user])

result = llm.invoke(
    "Could you validate user 123? They previously lived at "
    "123 Fake St in Boston MA and 234 Pretend Boulevard in "
    "Houston TX."
)

if isinstance(result, AIMessage) and result.tool_calls:
    print(result.tool_calls)

API Reference:AIMessage | tool

[{'name': 'validate_user', 'args': {'addresses': ['123 Fake St, Boston, MA', '234 Pretend Boulevard, Houston, TX'], 'user_id': '123'}, 'id': 'aef33a32-a34b-4b37-b054-e0d85584772f', 'type': 'tool_call'}]

Multi-modal

Ollama has limited support for multi-modal LLMs, such as gemma3

Be sure to update Ollama so that you have the most recent version to support multi-modal.

%pip install pillow

import base64
from io import BytesIO

from IPython.display import HTML, display
from PIL import Image


def convert_to_base64(pil_image):
    """
    Convert PIL images to Base64 encoded strings

    :param pil_image: PIL image
    :return: Re-sized Base64 string
    """

    buffered = BytesIO()
    pil_image.save(buffered, format="JPEG")  # You can change the format if needed
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return img_str


def plt_img_base64(img_base64):
    """
    Disply base64 encoded string as image

    :param img_base64:  Base64 string
    """
    # Create an HTML img tag with the base64 string as the source
    image_html = f'<img src="data:image/jpeg;base64,{img_base64}" />'
    # Display the image by rendering the HTML
    display(HTML(image_html))


file_path = "../../../static/img/ollama_example_img.jpg"
pil_image = Image.open(file_path)

image_b64 = convert_to_base64(pil_image)
plt_img_base64(image_b64)

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/> 

from langchain_core.messages import HumanMessage
from langchain_ollama import ChatOllama

llm = ChatOllama(model="bakllava", temperature=0)


def prompt_func(data):
    text = data["text"]
    image = data["image"]

    image_part = {
        "type": "image_url",
        "image_url": f"data:image/jpeg;base64,{image}",
    }

    content_parts = []

    text_part = {"type": "text", "text": text}

    content_parts.append(image_part)
    content_parts.append(text_part)

    return [HumanMessage(content=content_parts)]


from langchain_core.output_parsers import StrOutputParser

chain = prompt_func | llm | StrOutputParser()

query_chain = chain.invoke(
    {"text": "What is the Dollar-based gross retention rate?", "image": image_b64}
)

print(query_chain)

API Reference:HumanMessage | StrOutputParser

90%

Reasoning models and custom message roles

Some models, such as IBM's Granite 3.2, support custom message roles to enable thinking processes.

To access Granite 3.2's thinking features, pass a message with a "control" role with content set to "thinking". Because "control" is a non-standard message role, we can use a ChatMessage object to implement it:

from langchain_core.messages import ChatMessage, HumanMessage
from langchain_ollama import ChatOllama

llm = ChatOllama(model="granite3.2:8b")

messages = [
    ChatMessage(role="control", content="thinking"),
    HumanMessage("What is 3^3?"),
]

response = llm.invoke(messages)
print(response.content)

API Reference:ChatMessage | HumanMessage

Here is my thought process:
The user is asking for the value of 3 raised to the power of 3, which is a basic exponentiation operation.

Here is my response:

3^3 (read as "3 to the power of 3") equals 27. 

This calculation is performed by multiplying 3 by itself three times: 3*3*3 = 27.

Note that the model exposes its thought process in addition to its final response.

API reference

For detailed documentation of all ChatOllama features and configurations head to the API reference.

Related

• Chat model conceptual guide
• Chat model how-to guides