What is Google Antigravity and how does it work?

Google Antigravity is an AI-first coding platform where agents built on Gemini 3 Pro can plan, write, run, and verify code inside a full development environment. They have controlled access to the editor, terminal, and browser, and report their work via artifacts such as task lists and screenshots so you can review changes before they land.

Is Google Antigravity free to use?

Yes, as of 18 November 2025 Antigravity is available as a free public preview for Windows, macOS, and Linux. Usage of Gemini 3 Pro inside the IDE is gated by rate limits that refresh roughly every five hours, so only heavy users are likely to hit them.

Do I still need to learn to code if I use Antigravity?

Absolutely. Antigravity can automate boilerplate and suggest implementations, but you still need core skills in programming, testing, and security to judge whether an agent’s output is correct and safe. Tools like this shift your role toward architect and reviewer rather than replacing foundational coding knowledge.

Google unveils Antigravity, an AI-first coding platform

Updated on November 28, 2025 6 minutes read

Photorealistic developer workspace with laptop and dual monitors showing abstract AI agent network for Google Antigravity coding platform

On 18 November 2025, Google launched Antigravity, an AI-first coding platform built on its new Gemini 3 Pro model. Google describes Antigravity as an agentic development environment where AI agents can plan, execute, and verify coding tasks across the editor, terminal, and browser. The public preview is free on Windows, macOS, and Linux, with usage limits for Gemini 3 Pro traffic. For learners, developers, and engineering teams, Antigravity is an early look at how everyday programming may shift from writing every line yourself to supervising fleets of autonomous agents.

What happened

Antigravity arrived alongside the wider Gemini 3 launch, which Google framed as the start of a new era for AI-powered products across Search, the Gemini app, and developer tools. In the launch material, Google positioned Antigravity as an agentic development platform that shows what Gemini 3 can do when it has controlled access to a full development environment instead of just a chat window. Rather than acting only as autocomplete, Antigravity lets you spin up agents that can read and modify code, run shell commands, and drive a browser to test or debug applications. Those agents report their work through structured artifacts such as task lists, execution plans, screenshots, and browser recordings, which are intended to make it easier to verify what the system has done.

The interface is split into two main views. The Editor view resembles a modern IDE with an AI side panel. The Manager view acts as a mission control dashboard for orchestrating multiple agents and workspaces in parallel.

Agents in Antigravity keep context over longer sessions, learn from past work, and accept feedback on specific artifacts without losing track of their current tasks. That capability matters once they are managing larger repositories, dependency upgrades, or multi-step refactors.

Gemini 3 Pro provides the core intelligence behind these agents and improves both coding quality and tool use compared to earlier Gemini releases. On the Terminal-Bench 2.0 benchmark, which measures how well a model can operate a computer via the command line, Gemini 3 Pro scores 54.2 percent, significantly ahead of previous versions. Antigravity is available as a free public preview for Windows, macOS, and Linux, with rate limits that refresh roughly every five hours. According to Google, only heavy power users are likely to hit those limits in normal development work.

Under the hood, Antigravity is built around Gemini 3 Pro, but it can also call other models, such as Claude Sonnet 4.5 and OpenAI-compatible models. This makes it a multi-mode agent platform rather than a single vendor IDE.

Why it matters

For individual developers, Antigravity represents a shift from writing code line by line to supervising systems that write and run code on your behalf. The agents can plan and carry out multi-step tasks such as scaffolding a new service, wiring up tests, and producing a first draft user interface without you touching every intermediate change. That can shorten feedback loops, but it also demands stronger skills in reading diffs, interpreting logs, and deciding when to roll back or override the agent.

Learners and bootcamp graduates gain a new kind of safety net. Antigravity can unblock you on boilerplate and repetitive fixes, so you can spend more time on concepts like data modeling, security boundaries, and API design instead of wrestling with syntax. For teams, the agent-first approach may make it easier to automate chores such as migrating frameworks, updating dependencies, or regenerating tests, while reserving human attention for architecture, review, and risk decisions.

Gemini 3 includes what Google calls vibe coding, where you describe the feel of a product or interface in natural language and let the model sketch an implementation. With Antigravity, that idea extends to whole workflows. You can describe a simple dashboard or mini app, let agents build the initial version, then refine it manually and through further prompts. The upside is faster iteration and cleaner prototypes. The downside is that poorly supervised agents can propagate subtle bugs or security issues at scale, so governance and education remain critical.

Key numbers

18 November 2025: Launch date for Gemini 3 Pro and the public preview of Google Antigravity.

54.2 percent: Gemini 3 Pro score on Terminal-Bench 2.0, a benchmark for terminal-based tool use.

1487 Elo: Gemini 3 Pro WebDev Arena score for web development agents, indicating strong performance against other coding models.

3 platforms: Antigravity public preview availability on Windows, macOS, and Linux.

About every 5 hours: Interval at which Antigravity refreshes its rate limits for Gemini 3 Pro usage.

1 million tokens: Approximate context window size available for Gemini 3 in multimodal tasks, relevant when agents work over large code bases or documentation sets.

Context

Antigravity does not appear in a vacuum, since developers already have access to powerful AI coding tools such as GitHub Copilot, Cursor, Claude-based assistants, and IDE extensions from JetBrains and others. Most of those tools center a single assistant inside the editor, while Google’s approach leans harder into distributed agents with their own management layer and artifact system.

Earlier Gemini releases, especially Gemini 2 and 2.5, improved tool calling and long context reasoning. Gemini 3 extends that work with stronger benchmarks in planning, coding, and multimodal understanding, then surfaces those capabilities directly in Antigravity. From a market perspective, Antigravity pushes the competition beyond autocomplete quality and toward questions such as which tool offers the best control over agents, observability, and integration into existing stacks.

For education providers and training programs, this continues a clear trend. Curricula that stop at basic syntax and simple CRUD applications will feel dated next to tools where one prompt can scaffold an entire stack. Learners now need skills in code review, security thinking, and prompt-level architecture. That includes deciding what they want an agent to do, how they will validate the output, and when they should override it.

What’s next

Officially, Antigravity is a public preview and an experiment that showcases what is possible with Gemini 3, so you should expect rough edges and frequent updates rather than a fully locked-down enterprise IDE. Google’s wider roadmap points to deeper agentic features, including a Gemini 3 Deep Think mode and expanded use of computer use models that can control browsers and desktops more reliably.

If you are an individual learner, a practical next step is to install Antigravity on your machine, connect it to a small personal project, and observe how the agents plan work, propose changes, and surface artifacts. Treat the agent as a junior collaborator. Give it high-level goals, review every pull request or diff it proposes, and maintain a manual testing checklist so you do not outsource all judgment to the model.

Teams can start with time-boxed pilots in non-critical repositories, define which tasks are appropriate for automation, and add checks such as security scanning and human approvals before merging AI-generated changes. Across all of this, a clear skill signal appears. People who can explain how an agent arrived at a change and how they validated it will be more valuable than those who simply accept suggestions.

How to go deeper

Explore AI engineering foundations in the Data Science and AI Bootcamp to build the Python, machine learning, and reasoning skills that sit underneath tools like Antigravity.

Strengthen your full-stack fundamentals with the Web Development Bootcamp, then use Antigravity to automate parts of your project work.