litert-samples

LiteRT‑samples is a collection of sample applications, model recipes, agent skills and utilities that illustrate how to work with Google's Gemini models. The resources are hosted on the Google for Developers site, which also provides documentation for the Gemini API and related tools. You can browse the samples and see code snippets in several programming languages, including Python, JavaScript, Go, Java and REST.

The sample apps are aimed at developers who want concrete examples of how to call Gemini models for text generation, multimodal reasoning or agent‑based workflows. Each example shows the required import statements, client initialization and a simple call to `generate_content` (or the equivalent method in the chosen language). For instance, the Python snippet creates a `genai.Client`, selects the model `gemini-3.5-flash`, sends a short prompt and prints the response text. Similar patterns appear in the JavaScript, Go and Java examples, giving you a clear template to adapt for your own projects.

If you are building a full‑stack application, the site also mentions tools such as Google AI Studio, which can turn natural‑language prompts into production‑ready code using the Gemini API. For Android developers, Gemini is integrated into Android Studio to assist at various stages of the development lifecycle. The documentation points to deployment options like Google Cloud Run for scalable hosting, and to the Gemini Enterprise Agent Platform for larger‑scale agent deployments. All of these resources are organized under the “Build with Gemini” section, where you can save collections, explore agent‑first platforms like Google Antigravity, and access the Gemini API reference for different languages.

The page provides concrete usage details: you specify the model name (e.g., `gemini-3.5-flash`), pass the prompt as a string, and receive a response object whose `text` field contains the generated output. The examples include error handling for client creation and API calls, and they demonstrate how to set up the request in a minimal, reproducible way. While the site highlights the ability to process large codebases, complex media and real‑time workflows, the sample collection itself focuses on straightforward text generation scenarios that can be expanded as you become familiar with the API.

Key features

• Sample applications covering text generation and agent workflows
• Code snippets in Python, JavaScript, Go, Java and REST
• Model selection using Gemini model identifiers such as `gemini-3.5-flash`