How popular is generative-ai on GitHub?

GoogleCloudPlatform/generative-ai has 17,780 stars and 4,481 forks on GitHub, and gained 21 stars in the last 7 days.

What license does generative-ai use?

GoogleCloudPlatform/generative-ai is released under the Apache-2.0 license.

GoogleCloudPlatform/generative-aiPublic

Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform

AI summary: A comprehensive collection of sample code, notebooks, and tutorials for building generative AI applications on Google Cloud.

Star on GitHub

Stars: 17.8K; +4 today
Forks: 4.5K
Watchers: 283
Open issues: 78
Open PRs: 14
Contributors: ~310
Commits: 2.2K
Branches: 64

Jupyter NotebookApache-2.0Created May 5, 2023Last push 1d ago+21 stars this week+120 this month

Quick answers

What is generative-ai?: A comprehensive collection of sample code, notebooks, and tutorials for building generative AI applications on Google Cloud.
What does generative-ai do?: This repository serves as Google Cloud's official hub for generative AI examples and best practices. It provides developers with concrete, runnable code demonstrating how to integrate Google's foundation models (like Gemini) and AI infrastructure (like Vertex AI) into real-world applications. The repo covers a massive surface area, from simple prompt engineering and text generation to complex RAG (Retrieval-Augmented Generation) pipelines, multimodal analysis, and agentic workflows. By providing reference implementations in Python, Node.js, and Java, it helps teams bridge the gap between reading API documentation and deploying functional AI features in enterprise environments. It essentially acts as a living cookbook for the Google Cloud AI ecosystem.
Who is generative-ai for?: This repository is intended for software engineers, data scientists, and cloud architects building applications on Google Cloud Platform. It requires familiarity with cloud concepts, basic machine learning principles, and programming in Python or Node.js.
How do I get started with generative-ai?: git clone https://github.com/GoogleCloudPlatform/generative-ai.git && cd generative-ai/gemini/getting-started
How popular is generative-ai on GitHub?: GoogleCloudPlatform/generative-ai has 17,780 stars and 4,481 forks on GitHub, and gained 21 stars in the last 7 days.
What license does generative-ai use?: GoogleCloudPlatform/generative-ai is released under the Apache-2.0 license.

Star history

since May 7, 2023

17.8K stars as of Oct 3, 2026. Before Jul 28, 2026, reconstructed from public GitHub event archives (checked against the repository's real star total); since then measured daily.

Contribution activity

commits per day, last 52 weeks

436 commits in the last yearLessMore

Signals and awards

derived from tracked data

Widely adopted
17,780 stars
Actively maintained
Pushed within 48 hours
Community-driven
~310 contributors
Well documented
High community health score
Permissive license
Apache-2.0
Continuous integration
Automated checks passing

What generative-ai does

This repository serves as Google Cloud's official hub for generative AI examples and best practices. It provides developers with concrete, runnable code demonstrating how to integrate Google's foundation models (like Gemini) and AI infrastructure (like Vertex AI) into real-world applications. The repo covers a massive surface area, from simple prompt engineering and text generation to complex RAG (Retrieval-Augmented Generation) pipelines, multimodal analysis, and agentic workflows. By providing reference implementations in Python, Node.js, and Java, it helps teams bridge the gap between reading API documentation and deploying functional AI features in enterprise environments. It essentially acts as a living cookbook for the Google Cloud AI ecosystem.

This repository is intended for software engineers, data scientists, and cloud architects building applications on Google Cloud Platform. It requires familiarity with cloud concepts, basic machine learning principles, and programming in Python or Node.js.

Extensive Jupyter notebooks: Step-by-step tutorials for interacting with Vertex AI and Gemini APIs.
RAG implementations: Reference architectures for grounding LLMs in enterprise data using Vector Search.
Multimodal examples: Code showing how to process and analyze text, images, video, and audio simultaneously.
Agent and tool use: Demonstrations of connecting models to external APIs and databases for action-taking.
Multi-language support: Examples provided in Python, Node.js, Java, and Go.
Enterprise integration: Guides on using LangChain, LlamaIndex, and other frameworks within Google Cloud.

Where teams use it

Learning Vertex AI

Developers new to Google Cloud use the introductory notebooks to understand how to authenticate and call the Gemini API.

Building RAG systems

Enterprise teams copy reference architectures to build internal knowledge bots grounded in their own documents.

Multimodal processing pipelines

Engineers build systems that automatically extract metadata and summaries from uploaded videos and images.

Evaluating LLM outputs

Data scientists use provided scripts to benchmark different prompts and models against custom evaluation datasets.

Getting started: git clone https://github.com/GoogleCloudPlatform/generative-ai.git && cd generative-ai/gemini/getting-started

README

main branch

Generative AI on Google Cloud

The latest Gemini models are available! Try out Gemini 3.8 Flash.

Gemini Enterprise Agent Platform, the latest evolution of Vertex AI, has been released!

Check out the Google-Cloud-AI/agent-platform repository for a curated list of assets for agent building on Google Cloud.

This repository contains notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage generative AI workflows using Generative AI with Agent Platform.

Intro Video

	Description
`gemini/`	Discover Gemini through starter notebooks, use cases, function calling, sample apps, and more.
`search/`	Use this folder if you're interested in using Agent Search, a Google-managed solution to help you rapidly build search engines for websites and across enterprise data. (Formerly known as Enterprise Search on Generative AI App Builder).
`rag-grounding/`	Use this folder for information on Retrieval Augmented Generation (RAG) and Grounding. This is an index of notebooks and samples across other directories focused on this topic.
`vision/`	Use this folder if you're interested in building your own solutions from scratch using features from Imagen and Veo.
`audio/`	Use this folder if you're interested in building your own solutions from scratch using features from Chirp, a version of Google's Universal Speech Model (USM).
`setup-env/`	Instructions on how to set up Google Cloud, the Gen AI Python SDK, and notebook environments on Google Colab and Workbench.
`RESOURCES.md`	Learning resources (e.g. blogs, YouTube playlists) about Generative AI on Google Cloud.

Related Repositories

✨ Agent Development Kit (ADK) Samples: This repository provides ready-to-use agents built on top of the Agent Development Kit, designed to accelerate your development process. These agents cover a range of common use cases and complexities, from simple conversational bots to complex multi-agent workflows.
🚀 Agent Starter Pack
- A collection of production-ready Generative AI Agent templates built for Google Cloud.
- It accelerates development by providing a holistic, production-ready solution, addressing common challenges (Deployment & Operations, Evaluation, Customization, Observability) in building and deploying Gen AI agents.
Gemini Cookbook
genai-factory - A collection of end-to-end infrastructure blueprints to deploy generative AI infrastructures in GCP, using IaC and following security best-practices.
Google Cloud Applied AI Engineering
Vertex AI GenMedia Creative Studio - Experience Google's generative media foundational models + custom workflows.
MCP Servers for GenMedia - Empower your agents with generative media tools.
Generative AI for Marketing using Google Cloud
Generative AI for Developer Productivity
Vertex AI Core
Conversational AI
- Contact Center AI Samples
- Reimagining Customer Experience 360
Document AI
- Document AI Samples
Gemini in Google Cloud
- Cymbal Superstore
Cloud Databases
- Gen AI Databases Retrieval App
Other

Contributing

Contributions welcome! See the Contributing Guide.

Getting help

Please use the issues page to provide suggestions, feedback or submit a bug report.

Disclaimer

This repository itself is not an officially supported Google product. The code in this repository is for demonstrative purposes only.

View on GitHub

Recent activity

commits and pull requests

fix(ge-demo-generator): harden A2UI iframe healer for v0.8 wrappers, DataBindings, and HTML edge cases (v12.26-public)Merged
#3126 by ryotat7 · updated 3d ago
fix(ge-demo-generator): harden A2UI iframe healer for v0.8 wrappers, DataBindings, and HTML edge cases (v12.26-public) (#3126)
ryotat7 committed 567b3d7 · 3d ago
fix(ge-demo-generator): replace IFrameSrcdoc inline dashboards with native A2UI and runtime healer (v12.26-public)Merged
#3125 by ryotat7 · updated 4d ago
fix(ge-demo-generator): replace IFrameSrcdoc inline dashboards with native A2UI and runtime healer (v12.26-public) (#3125)
ryotat7 committed 93fb2d5 · 4d ago
Create Interactions API Intro NotebookMerged
#3124 by holtskinner · updated 4d ago
Create Interactions API Intro Notebook (#3124)
holtskinner committed c1f7b15 · 4d ago
docs(geminidataanalytics): add UpdateConversation sample to HTTP intro notebookDraft
#3123 by copybara-service[bot] · updated 4d ago
feat(agentic-video): add Go CLI and GenAI SDK reference implementationMerged
#3100 by ghchinoy · updated 4d ago
feat(agentic-video): add Go CLI and GenAI SDK reference implementation (#3100)
ghchinoy committed 5939a05 · 4d ago
chore(deps): bump the npm_and_yarn group across 10 directories with 15 updatesOpen
#3122 by dependabot[bot] · updated 4d ago
fix(geminidataanalytics): handle null Vega-Lite values and preserve published_context fields on update in SDK intro notebookDraft
#3121 by copybara-service[bot] · updated 5d ago
feat(ge-demo-generator): align external file IDs/scans with demo prompts (v12.25-public)Merged
#3120 by ryotat7 · updated 6d ago

Recent open issues

view all

Dead links across notebooks and READMEs (68 verified, fixes ready)
#3119 · 6d ago · by pratikgx · 1 comments
[Feat]: refactor(embeddings): migrate legacy gsutil commands to gcloud storage
#3071 · 1mo ago · by Red1-Rahman

Discussions

all 45

Code frequency

additions and deletions

+452.2K lines added, -297.8K removed over the last year.

Commits per week

last 52 weeks

436 commits in the last 52 weeks.

When work happens

weekday and hour

Commit volume by weekday and hour (UTC). Larger dots mean more commits.

Date	List	Rank	Stars gained
Mar 9, 2026	daily	#14	+194
Mar 8, 2026	daily	#18	+252

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