Gemini API Cookbook
by Google
Google's official runnable notebooks for every Gemini API feature, from Live API streaming to File Search RAG.
Overview
The Gemini API Cookbook is Google's official, continuously updated collection of runnable Jupyter notebooks for the Gemini API, maintained in the google-gemini/cookbook repository (~17.7k stars, 700+ commits). It is organised as a learning path rather than a reference dump: you start with Authentication, move to a Get Started notebook covering basic prompting and multimodal input, then branch into feature-specific quickstarts. Those quickstarts cover the Live API and live translation for real-time audio and video streaming, grounding with Google Search, Maps and URL context, code execution, the Batch API for high-volume discounted jobs, webhooks for async operation notifications, inference tiers (Priority and Flex), File Search for RAG-style grounding, Robotics-ER spatial reasoning, and the generative-media stack — Veo and Omni Flash for video, Imagen and Nano-Banana 2 for images, TTS and Lyria for speech and music. A separate Examples section chains features into full applications: browser-as-a-tool automation, animated story video generation, 3D spatial understanding, live plotting and mapping, cost estimation and health monitoring, and Gradio deployment. A Demos section ships end-to-end apps including a Flask API quickstart, a Live API web console and a LangGraph fullstack agent. Every quickstart opens directly in Google Colab, and the notebooks track the current model lineup — Gemini 3.7 Flash, 3.5 Flash-Lite, Omni Flash, Nano-Banana 2, Lyria 3 and Robotics-ER 1.5 — so the code you copy matches the API as it ships today rather than a frozen snapshot. Python is the primary language, with a separate quickstarts-js track for JavaScript and Go, Node.js, Java and C# SDKs alongside the plain REST API.
At a Glance
- Topic
- Frameworks
- Level
- All Levels
- Format
- Interactive
- Cost
- Free
- Duration
- ~10-15 hours to work the core quickstarts; each notebook ~15-30 min, self-paced
- Provider
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Authenticate against the Gemini API and run your first multimodal prompt
- ✓Stream real-time audio and video through the Live API for voice agents
- ✓Ground model output in Google Search, Maps and URL context to reduce hallucination
- ✓Build a RAG pipeline with File Search instead of hosting your own vector store
- ✓Cut cost on high-volume jobs using the Batch API and Priority/Flex inference tiers
- ✓Drive video, image, speech and music generation with Veo, Imagen, Nano-Banana 2 and Lyria
- ✓Chain code execution and function calling into agentic browser-automation workflows
- ✓Deploy a working app from the Flask, Gradio and LangGraph fullstack demos
Highlights
- •Official Google-maintained repo at ~17.7k stars and 700+ commits — notebooks track the current model lineup rather than a frozen snapshot
- •Every quickstart opens straight into Google Colab, so there is no local setup before your first working call
- •Covers the parts of the API most tutorials skip entirely: batch jobs, webhooks, inference tiers and cost estimation
- •Separate quickstarts-js track for JavaScript, plus Go, Node.js, Java and C# SDK coverage against the same REST API
- •Demos section ships complete forkable apps — Flask quickstart, Live API web console, LangGraph fullstack agent
Who It’s For
Best For
- ✓Engineers porting an existing OpenAI or Anthropic integration onto Gemini
- ✓Teams building voice or real-time multimodal agents on the Live API
- ✓Developers who need working generative-media code for video, image, TTS and music
- ✓Anyone evaluating Gemini grounding and File Search before committing to a vector database
Prerequisites
- •Working Python and comfort running Jupyter or Colab notebooks
- •A Google AI Studio API key — the cookbook is free, but heavy API usage is billed
- •Basic familiarity with calling an LLM API and handling JSON responses
FAQ
What is Gemini API Cookbook?
The Gemini API Cookbook is Google's official collection of runnable Colab notebooks for the Gemini API, aimed at engineers who would rather read working code than API reference prose. It walks from authentication and basic multimodal prompting through Live API streaming, grounding, File Search RAG, batch inference and generative media, and finishes with full demo apps you can fork. After working through it you can ship a grounded, multimodal Gemini feature without guessing at request shapes.
Is Gemini API Cookbook free?
Gemini API Cookbook is free to access.
What level is Gemini API Cookbook for?
Gemini API Cookbook is aimed at a all levels audience. Recommended background: Working Python and comfort running Jupyter or Colab notebooks, A Google AI Studio API key — the cookbook is free, but heavy API usage is billed, Basic familiarity with calling an LLM API and handling JSON responses.
How long does Gemini API Cookbook take?
Expect roughly ~10-15 hours to work the core quickstarts; each notebook ~15-30 min, self-paced. Most learners work through it at their own pace.
What will I learn from Gemini API Cookbook?
You'll learn: Authenticate against the Gemini API and run your first multimodal prompt; Stream real-time audio and video through the Live API for voice agents; Ground model output in Google Search, Maps and URL context to reduce hallucination; Build a RAG pipeline with File Search instead of hosting your own vector store; Cut cost on high-volume jobs using the Batch API and Priority/Flex inference tiers; Drive video, image, speech and music generation with Veo, Imagen, Nano-Banana 2 and Lyria; Chain code execution and function calling into agentic browser-automation workflows; Deploy a working app from the Flask, Gradio and LangGraph fullstack demos.
Topics
Sources
This page was written from 2 sources, 1 on domains other than github.com.