Generative AI Studio

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Google's Generative AI Studio

Experience the power of Google's Vertex AI through Generative AI Studio, a managed environment that streamlines the interaction, customization, and deployment of foundation models for production applications.

General
Release date	2023
Author	Google
Documentation	Link
Type	Generative AI Model Management

Start building with Generative AI Studio

Explore the best Generative AI Studio resources and libraries to help you get started with building projects using Google's Vertex AI today.

Generative AI Studio Links

A curated list of libraries and resources to help you build outstanding projects with Generative AI Studio.

Generative AI Studio Official Documentation Learn how to use Generative AI Studio with the official documentation.
Google's Foundation Models Browse the wide array of foundation models available in Generative AI Studio, such as PaLM, Imagen, Codey, and Chirp.
Generative AI Studio API Quickstart Get started with the Generative AI Studio API with this helpful quickstart guide.

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Google Generative AI Studio AI technology Hackathon projects

Discover innovative solutions crafted with Google Generative AI Studio AI technology, developed by our community members during our engaging hackathons.

M&A DueDiligence Swarm

Here's a 2000-character description: The mergers and acquisitions process is one of the most complex and time-consuming operations in the corporate world. Before any deal can move forward, teams of financial analysts, legal experts, and risk consultants spend weeks — sometimes months — manually reviewing a target company's financial statements, operational records, employee data, and legal liabilities. The M&A DueDiligence Swarm changes that entirely. Built with FastAPI and powered by Google Gemini's AI, this system deploys a three-agent pipeline that automates the entire due diligence workflow from start to finish. Each agent has a specialized role, and they work in strict sequence — passing their findings directly to the next agent without any manual intervention required. Agent 1, the CFO Auditor, receives the raw financial and operational text of the target company and extracts every key metric: revenue figures, outstanding debt, monthly cash burn rate, gross margins, and cash runway. It structures this data into a clean, readable financial summary that forms the foundation for everything that follows. Agent 2, the Risk Analyst, takes those extracted metrics and performs a deep compliance and liability review. It identifies critical red flags such as customer concentration risk, pending litigation, unresolved tax liabilities, and regulatory exposure. Every concern is rated by severity—Low, Medium, or High—giving decision-makers an instant picture of where the dangers lie. Agent 3, the Deal Closer, synthesizes the entire analysis into an actionable output. It calculates a final investment safety score between 0 and 100, summarizes the top deal-breaking concerns, and drafts a professional negotiation letter addressed directly to the target company, tailored to the specific findings of this audit. The entire pipeline runs in under 30 seconds. Just paste the company data, click Run Audit, and receive a complete due diligence report ready for executive review.

StockSense AI – Financial Intelligence Platform

StockSense AI – Multi-Agent Financial Intelligence Platform StockSense AI is an AI-powered financial intelligence platform designed to simplify stock market research and investment analysis. The platform combines multiple specialized AI agents that work together to gather, analyze, and present financial information in a clear and actionable format. The system is built around a multi-agent architecture where each agent focuses on a specific task: • Market Research Agent – analyzes stock fundamentals and key financial metrics. • Technical Analysis Agent – evaluates price action, indicators, trends, and market behavior. • News & Sentiment Agent – processes financial news and market sentiment to identify potential opportunities and risks. • Financial Intelligence Agent – combines insights from all agents and generates user-friendly explanations and recommendations. Users can interact with the platform through a conversational AI interface, making complex financial analysis accessible to both beginners and experienced investors. Instead of manually searching through multiple sources, users can ask questions in natural language and receive comprehensive, data-driven responses. Key Features: * AI-powered stock analysis * Natural language financial assistant * Market trend visualization * Technical indicator insights * News and sentiment analysis * Interactive financial dashboard * Multi-agent reasoning workflow * Real-time market intelligence support The goal of StockSense AI is to bridge the gap between complex financial data and practical decision-making. By combining AI agents, market analytics, and conversational intelligence, the platform helps users understand market conditions faster and make more informed investment decisions. StockSense AI demonstrates how autonomous AI agents can collaborate to transform financial research into an intuitive and intelligent experience.

Aether Labs: Multi-Agent E-Commerce Orchestration

Aether Labs is a proof-of-concept AI Agent Orchestration platform that demonstrates how multiple specialized AI agents can collaborate to automate enterprise e-commerce workflows. Instead of relying on a single monolithic AI model, the system distributes responsibilities across dedicated agents responsible for product personalization, inventory validation, logistics calculations, and transaction security. Each agent produces structured outputs that are orchestrated through an n8n workflow before being passed to the next processing stage. The platform integrates OpenAI, LangChain, Supabase, and modern frontend technologies to showcase a transparent and modular architecture capable of supporting complex business operations. To improve explainability, the frontend displays an Agent Decision Timeline, allowing users to observe how each specialized agent contributes to the final outcome. The demonstration also includes live workflow execution, checkout interaction, and automatic database persistence. Developed as a hackathon prototype, Aether Labs explores how AI Agent Orchestration can provide a more maintainable, extensible, and enterprise-friendly alternative to traditional backend architectures while remaining transparent about its current prototype limitations and future roadmap.

SovereignQA: 7-Agent Self-Healing DevOps Mesh

SovereignQA is an autonomous, state-driven multi-agent DevOps framework designed to replace fragile, linear CI/CD pipelines with a self-healing QA council. Built entirely on top of the stateful Band.ai protocol, the platform creates a decentralized network where specialized AI agents collaborate asynchronously using an isolated data ledger (Band Room) as their absolute source of truth. The operational lifecycle is triggered natively via GitHub webhooks upon a code push or Pull Request activation. Instead of step-by-step sequencing, the system uses non-linear state orchestration split across three discrete validation rings: 1. Ingestion & Static Verification: Micro-agents execute static syntax diagnostics (Linter Agent), map code paths against security risk profiles (SecOps Auditor for OWASP vulnerabilities), and validate type-hint definitions (Schema Watchdog). 2. Dynamic Runtime Execution: A dedicated Pytest Assert Engine compiles structural assertions, executing code inside an ephemeral, sandboxed Docker container to safely monitor runtime exceptions, while a UI Vision Layout Agent reviews DOM element alignment. 3. Autonomous Remediation & Feedback: If execution fails, a Self-Heal Core agent intercepts command-line tracebacks from the ledger, computes programmatic fixes, patches source files, and loops the state machine back to re-trigger testing. Once cleared, a GitHub Notifier agent posts a comprehensive markdown dashboard and copy-pasteable Git diff right into the developer's pull request. SovereignQA addresses real-world enterprise constraints by introducing an asynchronous message queue (Redis/RabbitMQ) to flatten transaction spikes, sandboxed containerization for secure code processing, and loop kill-switches to protect API token budgets. This ensures a robust, secure, and highly scalable platform.

SignalWatch AI

SignalWatch AI helps enterprise GTM, finance, strategy, and compliance teams unlock live public web data for decision-making. Users enter a target company, its website, competitors, a hackathon track, and a monitoring goal. The .NET 10 backend builds targeted search queries and uses Bright Data SERP API to collect live public web results. The backend filters low-value search/navigation results, classifies useful signals into GTM, Finance / Market, or Security / Compliance categories, and sends the structured signals to Gemini AI. Gemini generates an executive summary, risk score, opportunity score, and recommended business actions. The React dashboard displays the final report with source links, confidence scores, export-to-Markdown, and copy-summary actions. The result is a practical intelligence workflow: live public web data → AI analysis → executive-ready report.

Autonomous Gemini 3.5 Market Research Agent

This project is an autonomous market research agent designed to generate comprehensive, up-to-date company dossiers directly from live web sources. Powered by Gemini 3.5 Flash via the official google-genai SDK, the agent orchestrates a dynamic ReAct (Reason + Act) loop to answer multi-layered queries. The data acquisition layer integrates Bright Data’s APIs to bypass common scraping hurdles like anti-bot tracking and CAPTCHAs. Specifically, it employs the SERP API to fetch clean Google search results and the Web Unlocker API to extract webpage text. To ensure high execution speed and data quality, the engine leverages a ThreadPoolExecutor to run search and scraping tasks concurrently, while utilizing BeautifulSoup to structurally parse HTML before feeding it to the model. Users interact with the agent through a streamlined Streamlit web interface. They can type in a target startup name, monitor the parallel tool execution logs, view the generated report containing key leadership, funding history, and business models, and download the output directly as a clean Markdown file. This architecture provides a functional template for real-time web-grounded AI workflows.