Published: May 20, 2025, Last updated: September 21, 2025

Explainer	Web	Extensions	Chrome Status	Intent
GitHub	Origin trial	Chrome 138	View	Intent to Experiment
GitHub	Origin trial for sampling parameters	Chrome 148	View	Intent to Experiment

With the Prompt API, you can send natural language requests to Gemini Nano in the browser.

There are many ways you can use the Prompt API. For example, you could build:

• AI-powered search: Answer questions based on the content of a web page.
• Personalized news feeds: Build a feed that dynamically classifies articles with categories and allow for users to filter for that content.
• Custom content filters. Analyze news articles and automatically blur or hide content based on user-defined topics.
• Calendar event creation. Develop a Chrome Extension that automatically extracts event details from web pages, so users can create calendar entries in just a few steps.
• Seamless contact extraction. Build an extension that extracts contact information from websites, making it easier for users to contact a business or add details to their list of contacts.

These are just a few possibilities, and we're excited to see what you create.

Review the hardware requirements

The following requirements exist for developers and the users who operate features using these APIs in Chrome. Other browsers may have different operating requirements.

The Language Detector and Translator APIs work in Chrome on desktop. These APIs do not work on mobile devices.

The Prompt API, Summarizer API, Writer API, Rewriter API, and Proofreader API work in Chrome when the following conditions are met:

• Operating system: Windows 10 or 11; macOS 13+ (Ventura and onwards); Linux; or ChromeOS (from Platform 16389.0.0 and onwards) on Chromebook Plus devices. Chrome for Android, iOS, and ChromeOS on non-Chromebook Plus devices are not yet supported by the APIs which use Gemini Nano.
• Storage: At least 22 GB of free space on the volume that contains your Chrome profile.
• GPU or CPU: Built-in models can run with GPU or CPU.
  • GPU: Strictly more than 4 GB of VRAM.
  • CPU: 16 GB of RAM or more and 4 CPU cores or more.
  • Note: The Prompt API with audio input requires a GPU.
• Network: Unlimited data or an unmetered connection.

Gemini Nano's exact size may vary as the browser updates the model. To determine the current size, visit chrome://on-device-internals.

The Prompt API uses the Gemini Nano model in Chrome. While the API is built into Chrome, the model is downloaded separately the first time an origin uses the API. Before you use this API, acknowledge Google's Generative AI Prohibited Uses Policy.

To determine if the model is ready to use, call LanguageModel.availability().

const availability = await LanguageModel.availability({ // The same options in prompt() or promptStreaming() });

To trigger the download and instantiate the language model, check for user activation. Then, call the create() function.

const session = await LanguageModel.create({ monitor(m) { m.addEventListener('downloadprogress', (e) => { console.log(Downloaded ${e.loaded * 100}%); }); }, });

If the response to availability() was downloading, listen for download progress and inform the user, as the download may take time.

Use on localhost

All of the built-in AI APIs are available on localhost in Chrome. Set the following flags to Enabled:

• chrome://flags/#optimization-guide-on-device-model
• chrome://flags/#prompt-api-for-gemini-nano-multimodal-input

Then click Relaunch or restart Chrome. If you encounter errors, troubleshoot localhost.

Model parameters

The params() function informs you of the language model's parameters. The object has the following fields:

• defaultTopK: The default top-K value.
• maxTopK: The maximum top-K value.
• defaultTemperature: The default temperature.
• maxTemperature: The maximum temperature.

// Only available when using the Prompt API for Chrome Extensions. await LanguageModel.params(); // {defaultTopK: 3, maxTopK: 128, defaultTemperature: 1, maxTemperature: 2}

Create a session

Once the Prompt API can run, you create a session with the create() function.

const session = await LanguageModel.create();

Create a session with the Prompt API for Chrome Extensions

When you use the Prompt API for Chrome Extensions, each session can be customized with topK and temperature using an optional options object. The default values for these parameters are returned from LanguageModel.params().

// Only available when using the Prompt API for Chrome Extensions. const params = await LanguageModel.params(); // Initializing a new session must either specify both topK and // temperature or neither of them. // Only available when using the Prompt API for Chrome Extensions. const slightlyHighTemperatureSession = await LanguageModel.create({ temperature: Math.max(params.defaultTemperature * 1.2, 2.0), topK: params.defaultTopK, });

The create() function's optional options object also takes a signal field, which lets you pass an AbortSignal to destroy the session.

const controller = new AbortController(); stopButton.onclick = () => controller.abort();

const session = await LanguageModel.create({
  signal: controller.signal,
});

Add context with initial prompts

With initial prompts, you can provide the language model with context about previous interactions, for example, to allow the user to resume a stored session after a browser restart.

const session = await LanguageModel.create({ initialPrompts: [ { role: 'system', content: 'You are a helpful and friendly assistant.' }, { role: 'user', content: 'What is the capital of Italy?' }, { role: 'assistant', content: 'The capital of Italy is Rome.' }, { role: 'user', content: 'What language is spoken there?' }, { role: 'assistant', content: 'The official language of Italy is Italian. [...]', }, ], });

Constrain responses with a prefix

You can add an "assistant" role, in addition to previous roles, to elaborate on the model's previous responses. For example:

const followup = await session.prompt([ { role: "user", content: "I'm nervous about my presentation tomorrow" }, { role: "assistant", content: "Presentations are tough!" } ]);

In some cases, instead of requesting a new response, you may want to prefill part of the "assistant"-role response message. This can be helpful to guide the language model to use a specific response format. To do this, add prefix: true to the trailing "assistant"-role message. For example:

const characterSheet = await session.prompt([ { role: 'user', content: 'Create a TOML character sheet for a gnome barbarian', }, { role: 'assistant', content: '```toml\n', prefix: true, }, ]);

Add expected input and output

The Prompt API has multimodal capabilities and supports multiple languages. Set the expectedInputs and expectedOutputs modalities and languages when creating your session.

• type: Modality expected.
  • For expectedInputs, this can be text, image, or audio.
  • For expectedOutputs, the Prompt API allows text only.
• languages: Array to set the language or languages expected. The Prompt API accepts "en", "ja", and "es". Support for additional languages is in development.
  • For expectedInputs, set the system prompt language and one or more expected user prompt languages.
  • Set one or more expectedOutputs languages.

const session = await LanguageModel.create({ expectedInputs: [ { type: "text", languages: ["en" /* system prompt /, "ja" / user prompt */] } ], expectedOutputs: [ { type: "text", languages: ["ja"] } ] });

You may receive a "NotSupportedError" DOMException if the model encounters an unsupported input or output.

Multimodal capabilities

With these capabilities, you could:

• Allow users to transcribe audio messages sent in a chat application.
• Describe an image uploaded to your website for use in a caption or alt text.

Take a look at the Mediarecorder Audio Prompt demo for using the Prompt API with audio input and the Canvas Image Prompt demo for using the Prompt API with image input.

The Prompt API supports the following input types:

• Audio:
  • AudioBuffer
  • ArrayBufferView
  • ArrayBuffer
  • Blob
• Visual:
  • HTMLImageElement
  • SVGImageElement
  • HTMLVideoElement (uses the video frame at the current video position)
  • HTMLCanvasElement
  • ImageBitmap
  • OffscreenCanvas
  • VideoFrame
  • Blob
  • ImageData

This snippet shows a multimodal session that first processes two visuals (one image Blob and one HTMLCanvasElement) and has the AI compare them, and that second lets the user respond with an audio recording (as an AudioBuffer).

const session = await LanguageModel.create({ expectedInputs: [ { type: "text", languages: ["en"] }, { type: "audio" }, { type: "image" }, ], expectedOutputs: [{ type: "text", languages: ["en"] }], });

const referenceImage = await (await fetch("reference-image.jpeg")).blob();
const userDrawnImage = document.querySelector("canvas");

const response1 = await session.prompt([
  {
    role: "user",
    content: [
      {
        type: "text",
        value:
          "Give a helpful artistic critique of how well the second image matches the first:",
      },
      { type: "image", value: referenceImage },
      { type: "image", value: userDrawnImage },
    ],
  },
]);
console.log(response1);

const audioBuffer = await captureMicrophoneInput({ seconds: 10 });

const response2 = await session.prompt([
  {
    role: "user",
    content: [
      { type: "text", value: "My response to your critique:" },
      { type: "audio", value: audioBuffer },
    ],
  },
]);
console.log(response2);

Append messages

Inference may take some time, especially when prompting with multimodal inputs. It can be useful to send predetermined prompts in advance to populate the session, so the model can get a head start on processing.

While initialPrompts are useful at session creation, the append() method can be used in addition to the prompt() or promptStreaming() methods, to give additional additional contextual prompts after the session is created.

For example:

const session = await LanguageModel.create({ initialPrompts: [ { role: 'system', content: 'You are a skilled analyst who correlates patterns across multiple images.', }, ], expectedInputs: [{ type: 'image' }], });

fileUpload.onchange = async () => {
  await session.append([
    {
      role: 'user',
      content: [
        {
          type: 'text',
          value: `Here's one image. Notes: ${fileNotesInput.value}`,
        },
        { type: 'image', value: fileUpload.files[0] },
      ],
    },
  ]);
};

analyzeButton.onclick = async (e) => {
  analysisResult.textContent = await session.prompt(userQuestionInput.value);
};

The promise returned by append() fulfills once the prompt has been validated, processed, and appended to the session. The promise is rejected if the prompt cannot be appended.

Pass a JSON Schema

Add the responseConstraint field to prompt() or promptStreaming() method to pass a JSON Schema as the value. You can then use structured output with the Prompt API.

In the following example, the JSON Schema makes sure the model responds with true or false to classify if a given message is about pottery.

const session = await LanguageModel.create();

const schema = {
  "type": "boolean"
};

const post = "Mugs and ramen bowls, both a bit smaller than intended, but that
happens with reclaim. Glaze crawled the first time around, but pretty happy
with it after refiring.";

const result = await session.prompt(
  `Is this post about pottery?\n\n${post}`,
  {
    responseConstraint: schema,
  }
);
console.log(JSON.parse(result));
// true

Your implementation can include a JSON Schema or regular expression as part of the message sent to the model. This uses some of the context window. You can measure how much of the context window it will use by passing the responseConstraint option to session.measureContextUsage().

You can avoid this behavior with the omitResponseConstraintInput option. If you do so, we recommend that you include some guidance in the prompt:

const result = await session.prompt( Summarize this feedback into a rating between 0-5. Only output a JSON object { rating }, with a single property whose value is a number: The food was delicious, service was excellent, will recommend. , { responseConstraint: schema, omitResponseConstraintInput: true });

Prompt the model

You can prompt the model with either the prompt() or the promptStreaming() functions.

Request-based output

If you expect a short result, you can use the prompt() function that returns the response once it's available.

// Start by checking if it's possible to create a session based on the // availability of the model, and the characteristics of the device. const available = await LanguageModel.availability({ expectedInputs: [{type: 'text', languages: ['en']}], expectedOutputs: [{type: 'text', languages: ['en']}], });

if (available !== 'unavailable') {
  const session = await LanguageModel.create();

  // Prompt the model and wait for the whole result to come back.
  const result = await session.prompt('Write me a poem!');
  console.log(result);
}

Streamed output

If you expect a longer response, you should use the promptStreaming() function which lets you show partial results as they come in from the model. The promptStreaming() function returns a ReadableStream.

const available = await LanguageModel.availability({ expectedInputs: [{type: 'text', languages: ['en']}], expectedOutputs: [{type: 'text', languages: ['en']}], }); if (available !== 'unavailable') { const session = await LanguageModel.create();

  // Prompt the model and stream the result:
  const stream = session.promptStreaming('Write me an extra-long poem!');
  for await (const chunk of stream) {
    console.log(chunk);
  }
}

Stop prompting

Both prompt() and promptStreaming() accept an optional second parameter with a signal field, which lets you stop running prompts.

const controller = new AbortController(); stopButton.onclick = () => controller.abort();

const result = await session.prompt('Write me a poem!', {
  signal: controller.signal,
});

Session management

Each session keeps track of the context of the conversation. Previous interactions are taken into account for future interactions until the session's context window is full.

Each session has a maximum number of tokens it can process. Check your progress towards this limit with the following:

console.log(${session.contextUsage}/${session.contextWindow});

It's possible to send a prompt that causes the context window to overflow. In such cases, the initial portions of the conversation with the language model will be removed, one prompt and response pair at a time, until enough tokens are available to process the new prompt. The exception is the system prompt, which is never removed.

Such overflows can be detected by listening for the contextoverflow event on the session:

session.addEventListener("contextoverflow", () => { console.log("We've gone past the context window, and some inputs will be dropped!"); });

If it's not possible to remove enough tokens from the conversation history to process the new prompt, then the prompt() or promptStreaming() call will fail with a QuotaExceededError exception and nothing will be removed. The QuotaExceededError has the following properties:

• requested: how many tokens the input consists of
• contextWindow: how many tokens were available

Learn more about session management.

Clone a session

To preserve resources, you can copy an existing session with the clone() function. This creates a fork of the conversation, where the context and initial prompt are preserved.

The clone() function takes an optional options object with a signal field, which lets you pass an AbortSignal to destroy the cloned session.

const controller = new AbortController(); stopButton.onclick = () => controller.abort();

const clonedSession = await session.clone({
  signal: controller.signal,
});

Terminate a session

Call destroy() to free resources if you no longer need a session. When a session is destroyed, it can no longer be used, and any ongoing execution is aborted. You may want to keep the session around if you intend to prompt the model often since creating a session can take some time.

await session.prompt( "You are a friendly, helpful assistant specialized in clothing choices." );

session.destroy();

// The promise is rejected with an error explaining that
// the session is destroyed.
await session.prompt(
  "What should I wear today? It is sunny, and I am choosing between a t-shirt
  and a polo."
);

Demos

We've built multiple demos to explore the many use cases for the Prompt API. The following demos are web applications:

• Prompt API playground
• Mediarecorder Audio Prompt
• Canvas Image Prompt

To test the Prompt API in Chrome Extensions, install the demo extension. The extension source code is available on GitHub.

Performance strategy

The Prompt API for the web is still being developed. While we build this API, refer to our best practices on session management for optimal performance.

Permission Policy, iframes, and Web Workers

By default, the Prompt API is only available to top-level windows and to their same-origin iframes. Access to the API can be delegated to cross-origin iframes using the Permission Policy allow="" attribute:

<iframe src="https://cross-origin.example.com/" allow="language-model"></iframe>

The Prompt API isn't available in Web Workers for now, due to the complexity of establishing a responsible document for each worker in order to check the permissions policy status.

Your input can directly impact how we build and implement future versions of this API and all built-in AI APIs.

• For feedback on Chrome's implementation, file a bug report or a feature request.
• Share your feedback on the API shape by commenting on an existing Issue or by opening a new one in the Prompt API GitHub repository.
• Join the early preview program.