The Prompt API

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 localhostAll 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 parametersThe 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 sessionOnce 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 ExtensionsWhen 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 promptsWith 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 prefixYou 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 outputThe 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 capabilitiesWith 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 messagesInference 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.',