Google Antigravity

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Google Antigravity

Google Antigravity is an innovative "agent-first" Integrated Development Environment (IDE) specifically designed for Gemini 3. It empowers developers by integrating autonomous agents that can plan and execute entire engineering tasks, supported by a built-in Agent Manager. This revolutionary approach aims to streamline software development, allowing for more efficient and intelligent problem-solving.

General
Author	Google
Release Date	2025
Website	https://antigravity.google/
Documentation	https://antigravity.google/docs
Technology Type	AI-powered IDE

Key Features

Agent-First Design: Integrates autonomous agents directly into the development workflow for task planning and execution.
Built-in Agent Manager: Provides tools for managing, monitoring, and orchestrating AI agents.
Gemini 3 Integration: Optimized to leverage the advanced capabilities of the Gemini 3 model.
Automated Engineering Tasks: Facilitates the automation of complex development processes, from code generation to testing.
Intelligent Problem-Solving: Enhances developer productivity by offloading routine and complex tasks to AI agents.

Start Building with Google Antigravity

Google Antigravity is set to redefine software development by integrating AI agents directly into the IDE. This platform will allow developers to build and manage complex projects with unprecedented efficiency. As an "agent-first" IDE, it focuses on leveraging autonomous capabilities to accelerate the development lifecycle.

👉 Google Antigravity Official Site 👉 Google Antigravity Documentation

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Google Antigravity AI technology Hackathon projects

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

On-device Voice Stress Detection

Slashh turns your phone into a private, always-on companion that notices when your voice is under stress and offers a calming nudge — entirely on-device, in airplane mode, with no recording and no LLM. Most stress detectors look at only one signal. Slashh is multimodal: it runs two transformer models on the Snapdragon Hexagon NPU at the same time. WavLM listens to how you speak (vocal tension and arousal), while Whisper-Tiny transcribes what you say and a tiny Dreaddit-trained classifier scores the words for stress. A small fusion perceptron combines the two into one confident probability — far more reliable than either signal alone — and only raises a gentle relief nudge when stress genuinely sustains. Everything in the inference loop runs locally via PyTorch ExecuTorch and QNN context binaries; the app declares no INTERNET permission, so audio, transcripts, and scores never leave the device. Built and verified live on a Galaxy S25 Ultra (Snapdragon 8 Elite): two neural models on the NPU, a responsive energy fallback, and real-time relief — fully private by construction.

Scribend: 100% Offline Edge AI Medical Scribe

Scribend addresses the critical need for secure, automated medical documentation in clinical environments where cloud connectivity is inconsistent or data privacy is paramount. By leveraging an entirely on-device Edge AI architecture, Scribend transforms spoken doctor-patient interactions into structured clinical records without ever transmitting data to the cloud. The system utilizes a modular, multi-model pipeline optimized for the Qualcomm Snapdragon NPU: Transcription: We utilize Distil-Whisper Small for high-accuracy speech-to-text, augmented with an 80-term medical vocabulary hint to ensure precise capture of clinical terminology and phonetic typo correction. Context Retrieval: Using MiniLM vector embeddings and a local SQLite database, the system performs semantic searches on a patient’s historical records, providing the LLM with relevant medical context before note generation. Reasoning: Meta Llama 3.2 3B Instruct acts as the system’s "brain." It performs zero-shot speaker diarization to separate Doctor and Patient dialogue, applies contextual logic to identify medical facts, and outputs a perfectly structured JSON SOAP note. Formatting: Finally, the system automatically converts the JSON output into a polished, timestamped Markdown document, complete with tables, bold headers, and bullet points for instant clinical review. Designed specifically for modern mobile hardware like the Samsung Galaxy S25, Scribend achieves this performance with a sub-2.5GB memory footprint, proving that complex, context-aware AI is not only possible but efficient on edge devices

MarketSense AI

A team of three AI agents that detects competitor price drops, analyzes the right response, and queues a human-approved pricing decision — collaborating live on Band. Tags: multi-agent band-of-agents ai-ml-api langgraph competitive-intelligence pricing human-in-the-loop enterprise-workflow fastapi e-commerce Long description: The problem. Retail pricing teams can't watch every competitor on every marketplace 24/7. By the time a competitor's price drop is noticed, sales have already leaked. But fully automating price changes is dangerous — it risks margin collapse and brand damage. MarketSense AI solves this with a team of three specialized agents that collaborate on Band, keeping a human in control of the final decision: Scout continuously scans competitor prices and social sentiment, flagging significant drops. Analyst is recruited into the conversation, requests sentiment from Scout (a genuine bidirectional agent exchange), weighs match / undercut / hold strategies against a margin floor, and writes a strategic recommendation. Executive drafts the proposed action and queues it for human approval — nothing executes until a person clicks Approve on the dashboard. Why it's reliable. Agents pass lightweight references over Band while all structured data lives in Postgres, so decisions are auditable and never depend on parsing chat. The whole pipeline — detection → analysis → governed action → Slack alert — runs autonomously in the cloud, ending at a human-in-the-loop gate.

AI Crisis Command & Coordination Platform

An AI Crisis Command & Coordination Platform is an enterprise-grade, multi-agent operational operating system designed to stabilize high-stakes chaos and synchronize real-time emergency responses during large-scale incidents (e.g., natural disasters, mass-scale cyberattacks, industrial failures, or civil defense threats). By acting as an intelligent "system of systems," the platform unifies disjointed physical and digital infrastructure into a singular, actionable operational layer. It transforms traditional reactive emergency management into a proactive, algorithmically assisted orchestration framework. 1. Core Structural Engine The platform's architecture shifts emergency management from rigid, manual workflows to an agile, automated data-to-action pipeline.Intelligent Data Ingestion: The platform continuously aggregates and cleans highly fragmented, multi-modal data streams in real time. This includes live IoT sensor grids, geospatial satellite imagery, municipal infrastructure feeds, encrypted field radio transcriptions, and external environmental APIs (weather, seismic activity, grid load). Autonomous Multi-Agent Orchestration: At its core, specialized AI agents operating on a shared, low-latency layer collaborate to manage sub-tasks simultaneously. For instance, if an industrial breach occurs, a Logistics Agent instantly calculates optimal route diversions, a Hazmat Agent models plume dispersion, and a Communications Agent drafts localized public warnings—all without human bottlenecks. Calibrated User Experience (UX): Designed specifically for high-stress operational environments, the user interface enforces strict visual hierarchy. It suppresses non-essential analytical noise, highlights high-priority triage vectors, and presents critical indicators through clean, digestible visual matrices to minimize cognitive overload for dispatchers and commandeR

Concord SOC

The Problem: The Handoff Bottleneck Every serious security incident follows the same shape: detection, classification, investigation, containment, communication, approval, and documentation. The bottleneck isn't the work itself—it is the handoffs between the people doing it. Each handoff introduces context switching and a chance for critical institutional memory to get lost. Standard automation without shared context just moves the bottleneck. The Solution: Concord SOC Concord SOC collapses every handoff into a single, continuously visible Band room. Five specialized AI agents and one accountable human analyst read and write to this single thread in real time. Triage: Classifies the incoming alert into a severity, category, and summary. Forensics: Reads Triage's words directly to identify the attack vector, CVEs, IOCs, and affected assets. Containment: Uses Forensics' findings to map out isolation steps, access rules, and a rollback plan. Communications: Activates only after both Forensics and Containment post. It drafts customer notifications and internal summaries. Human Analyst: Sits inside the room. Their explicit approval is the sole gate required to release communications. Root-Cause Analyst: Activates post-approval, synthesizing the entire room's history into a final post-incident report. Design Philosophy The room is the system; there is no hidden database. Agents act only when visible conditions are met. Human approval is a core architectural feature, not a fallback. If the room disappears, the system stops instantly—an intentional design choice proving that coordination happens entirely in the open.

Sentinel

Sentinel is an autonomous, multi-agent architecture review system designed for regulated and high-stakes enterprise environments. In industries like finance, healthcare, and insurance, deploying new technical workflows requires rigorous scrutiny across multiple domains—security, compliance, and IT governance. Manual reviews are often massive bottlenecks. Sentinel solves this by orchestrating a team of specialized AI agents through the Band collaboration layer to autonomously debate, audit, and score technical proposals. Built specifically for Track 3 (Regulated & High-Stakes Workflows), Sentinel goes beyond simple linear automation or thin API wrappers by utilizing Band as a true shared interaction layer. The workflow is managed by the Conductor agent, which ingests technical architecture documents and coordinates the room. The Harness agent injects historical company policies and compliance baselines directly into the shared workspace. Operating in parallel, the Adversary aggressively red-teams the architecture for critical security flaws (such as prompt injection vulnerabilities in LLM execution flows), while the Guardian audits for regulatory violations (such as GDPR or SOC2 data handling failures). Because these agents operate within a shared Band chat room, they do not work in silos. Once the specialized audits are complete, the Evaluator reads the room's context to synthesize the distinct flags into a cohesive architectural review. Finally, the RiskScorer processes this evaluation to generate a definitive, quantitative risk matrix and an automated approve/escalate decision payload. By demonstrating real agent-to-agent collaboration, role specialization, complex context exchange, and task handoffs, Sentinel proves that multi-agent systems can handle complex, regulated decision-making safely, transparently, and efficiently.