Ollama JavaScript Library

The Ollama JavaScript library provides the easiest way to integrate your JavaScript project with Ollama.

Getting Started

npm i ollama

Usage

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'llama3.1',
  messages: [{ role: 'user', content: 'Why is the sky blue?' }],
})
console.log(response.message.content)

Browser Usage

To use the library without node, import the browser module.

import ollama from 'ollama/browser'

Streaming responses

Response streaming can be enabled by setting stream: true, modifying function calls to return an AsyncGenerator where each part is an object in the stream.

import ollama from 'ollama'

const message = { role: 'user', content: 'Why is the sky blue?' }
const response = await ollama.chat({
  model: 'llama3.1',
  messages: [message],
  stream: true,
})
for await (const part of response) {
  process.stdout.write(part.message.content)
}

Cloud Models

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

You can see models currently available on Ollama's cloud here.

Run via local Ollama

1. Sign in (one-time):

ollama signin

2. Pull a cloud model:

ollama pull gpt-oss:120b-cloud

3. Use as usual (offloads automatically):

import { Ollama } from 'ollama'

const ollama = new Ollama()
const response = await ollama.chat({
  model: 'gpt-oss:120b-cloud',
  messages: [{ role: 'user', content: 'Explain quantum computing' }],
  stream: true,
})
for await (const part of response) {
  process.stdout.write(part.message.content)
}

Cloud API (ollama.com)

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

1. Create an API key, then set the OLLAMA_API_KEY environment variable:

export OLLAMA_API_KEY=your_api_key

2. Generate a response via the cloud API:

import { Ollama } from 'ollama'

const ollama = new Ollama({
  host: 'https://ollama.com',
  headers: { Authorization: 'Bearer ' + process.env.OLLAMA_API_KEY },
})

const response = await ollama.chat({
  model: 'gpt-oss:120b',
  messages: [{ role: 'user', content: 'Explain quantum computing' }],
  stream: true,
})

for await (const part of response) {
  process.stdout.write(part.message.content)
}

API

The Ollama JavaScript library's API is designed around the Ollama REST API

chat

ollama.chat(request)

• request <Object>: The request object containing chat parameters.

  • model <string> The name of the model to use for the chat.
  • messages <Message[]>: Array of message objects representing the chat history.
    • role <string>: The role of the message sender ('user', 'system', or 'assistant').
    • content <string>: The content of the message.
    • images <Uint8Array[] | string[]>: (Optional) Images to be included in the message, either as Uint8Array or base64 encoded strings.
    • tool_name <string>: (Optional) Add the name of the tool that was executed to inform the model of the result
  • format <string>: (Optional) Set the expected format of the response (json).
  • stream <boolean>: (Optional) When true an AsyncGenerator is returned.
  • think <boolean | "high" | "medium" | "low">: (Optional) Enable model thinking. Use true/false or specify a level. Requires model support.
  • logprobs <boolean>: (Optional) Return log probabilities for tokens. Requires model support.
  • top_logprobs <number>: (Optional) Number of top log probabilities to return per token when logprobs is enabled.
  • keep_alive <string | number>: (Optional) How long to keep the model loaded. A number (seconds) or a string with a duration unit suffix ("300ms