Exploring Google Antigravity with Gemini 3: Features & Examples

Part 1. What Is Google Antigravity?

Google Antigravity is a groundbreaking, agent‑first integrated development environment (IDE) built around Google’s latest AI model, Gemini 3 Pro.

Rather than being a simple autocomplete assistant, Antigravity treats AI as a full-fledged, autonomous partner. Its agents can write, test, and verify code on behalf of the developer, with direct control of the code editor, the terminal, and even a built-in browser.

Some Google Antigravity's defining characteristics

• Agent-first paradigm: Multiple AI agents can run in parallel, each handling different tasks or subtasks.
• Artifacts: Instead of exposing only low-level tool calls or logs, Antigravity’s agents generate higher‑level “artifacts”—task lists, implementation plans, browser recordings, screenshots, and more—to make their decisions and actions transparent.
• Cross‑surface control: The agents have permission to manipulate not just your code but also the terminal and the browser, enabling them to run, test, and validate UI flows.
• Two views:

– Editor view: like a traditional IDE (similar to VS Code), with side‑panel agents.

– Manager view: a “mission control” for orchestrating multiple agents across different workspaces.

• Learning over time: Agents can learn from past interactions, building a knowledge base to get smarter in future tasks.
• Model flexibility: While Gemini 3 Pro is the backbone, Antigravity also supports Claude Sonnet 4.5 (from Anthropic) and open-source models (e.g., GPT-OSS).
• Availability and pricing: As of its public preview launch on November 18, 2025, Antigravity is free, with generous rate limits that reset periodically.

Part 2. How to Use Google Antigravity Step by Step

Using Antigravity as a developer is relatively intuitive, but because it introduces new paradigms (agent-first, asynchronous orchestration), there are some unique patterns to get used to.

1. Installation and Setup

• Frist download Antigravity. It is available for Windows, macOS, and Linux.
• Once installed, you’ll see the familiar code editor interface, but with an AI agent panel integrated.

2. Selecting Your Model

• You can choose between supported AI models (Gemini 3 Pro, Claude Sonnet 4.5, GPT‑OSS) depending on your needs.
• The model powers the agents, so careful choice may affect performance, cost (when limiting usage), or behavior.

3. Opening the Agent Manager (Mission Control)

• Switch to the Manager view to spawn new agents that operate independently in the background.
• You can assign different tasks (e.g., research, refactoring, testing) to different agents and monitor their progress.

4. Task-Based Workflow

• Rather than micromanaging each tool call, you describe what you want done: e.g., “build a login form,” “add API tests,” or “refactor this module.”
• The agents will break down your request into smaller tasks, create a plan (an artifact), execute it, and then produce verification artifacts.
• You can comment on artifacts (just like commenting in Google Docs) to refine or redirect the agent’s actions.

5. Validation and Feedback

• The built-in browser is controlled by the agent. After building code, the agent can launch the app, interact with it, take screenshots, and even produce a browser recording.
• You can leave feedback on these visual artifacts, guiding future work and helping the agent “learn” what you prefer.

6. Rate Limits & Usage

• During public preview, usage is rate-limited. According to users, quotas refresh approximately every 5 hours.
• Some users have reported hitting these limits even with high-tier subscriptions.
• If you hit the limit, you’ll need to wait until it resets to continue using the higher‑powered model.

Part 3. Google Antigravity for Developers: Key Benefits & Use Cases

From a developer’s perspective, Antigravity offers several compelling advantages — and also some potential pitfalls in its current preview state.

Advantages of Google Antigravity

• Agentic development: Agents don’t just help you — they do the work. That means you can offload long-running or repetitive tasks (e.g., writing boilerplate, scaffolding, testing) while you focus on higher-level design or other work.
• Transparency & trust: Artifacts (plans, screenshots, recordings) make the agent’s work visible and verifiable. This helps build trust: you’re not just trusting an AI’s black-box decisions, but you can see what it's doing.
• Parallelism: Multiple agents can run in parallel from the Manager view, meaning you can delegate several tasks at once — e.g., one agent researches, another codes, another tests.
• End-to-end automation: Because agents have control over terminal and browser, they can not only write code, but also run it, test UI flows, and validate behavior.
• Learning & memory: Over time, the system builds a knowledge base from past work, potentially making future agents more efficient and context-aware.
• Large context window: Built with Gemini 3, which supports very long context windows, so Antigravity can reason across large codebases effectively.
• Free preview: For now, the public preview is free, making it accessible to hobbyists, individual devs, and early adopters.

Challenges of Google Antigravity

• Rate limiting: As mentioned, users have reported being limited by quotas that don’t reflect their subscription tier.
• Stability: Because this is a new product in public preview, there are reports of bugs (e.g., agent commands failing, file corruption, timeouts).
• Trust maturity: While artifacts help, trusting autonomous agents to make complex architectural decisions may require time and careful oversight.
• Learning curve: Developers need to adapt to a new workflow (agent-first, asynchronous orchestration), which differs from traditional manual coding or even code-completion-based AI assistants.

Part 4. Gemini 3 + Antigravity: Examples and Workflowss

Here are some concrete (and hypothetical) examples of how Gemini 3 and Antigravity can work together in real developer workflows:

Building a Flight Tracker App

You ask an agent: “Build a simple flight-track web app: input flight number, fetch status from API, display on a dashboard.” The agent generates a plan (artifact) with subtasks: set up project, design UI, implement API calls, add testing.

It writes the frontend code, sets up API calls, then launches the integrated browser to test the UI. It takes screenshots to show the results. It finds a bug (maybe a button is misplaced), reports via a browser recording, and asks you for feedback.

You comment, it refines, and finally hands over a working prototype.

Refactoring a Legacy Codebase

In Manager mode, you spawn an agent to analyze a monorepo (say, 100k+ lines of code). Because Gemini 3 supports large context, the agent can understand cross-module dependencies.

The agent produces a roadmap artifact: suggests refactoring patterns, dependency decoupling, tests to add, modules to merge.

It then implements refactorings: updating imports, rewriting functions, splitting files. Simultaneously, another agent runs terminal commands (e.g., running lint, test suites) and reports back with test results.

A third agent opens the browser (if it’s a web app), launches the local dev server, and navigates through UI flows to make sure nothing broke. It records a walkthrough and gives you a summary.

Feature Development with Design Intent

Suppose you want to add a new “material card” component for your frontend, with an image header, title, description, and two buttons. You describe it in natural language.

Gemini 3’s reasoning is strong enough to interpret design intent. The agent scaffolds appropriate UI code (React, Flutter, Jetpack Compose, whatever you use), styles it, and writes tests.

It then launches the browser, renders the UI, interacts with it (perhaps clicking the buttons), and captures screenshots or a short video to show you how it behaves. You give feedback on layout or color, and it refines the component.

Automated API Testing & Spec Inference

You have a backend with REST endpoints but no formal OpenAPI spec. You tell an agent: “Infer the OpenAPI schema of my API and generate tests.”

The agent introspects code, perhaps runs the server, makes requests, figures out shape and parameters, and builds a spec. It then writes integration tests (or mocks) based on that spec, runs them in the terminal, aggregates test results, and reports with artifacts.

Tip:

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Part 5. Hot Community Questions About Gemini Antigravity

1. Is Antigravity reliably stable?

Some report that every request in Antigravity throws an error: “every SINGLE request … throws this error … in Cursor or Antigravity.” That makes it “literally unusable” for some.

2. Can I trust the AI agents with my code?

There are reports of Antigravity’s agents corrupting files, deleting functions, and failing to restore from Git checkpoints. That raises concerns about using it for serious production code without full backups.

3. Why does Gemini 3 Pro in Antigravity ask too many clarifying questions?

One user says Gemini 3 is slow and overly cautious, asking for a lot of clarifications instead of confidently making assumptions: “it keeps stopping to clarify things … in agentic coding workflows, you need a model that can move fast.” This disrupts fluid development flows.

4. Are the rate limits for Gemini API clearly documented and predictable?

The official rate-limit documentation shows limits per minute, token, and day.

But community members report “flaky” or unexplained 500 errors, even with low usage. This mismatch raises trust issues in real-world or production use.

5. Is Antigravity’s free preview sustainable?

While Google says it’s free for individuals in public preview. Some believe that quota restrictions today may foreshadow a shift to paid or more restrictive tiers in the future. The long-term cost / pricing plan remains unclear.

Conclusion

Google Antigravity, powered by Gemini 3 Pro, is reshaping software development. By letting AI agents handle code, terminal tasks, and browser actions, developers can delegate repetitive work and focus on design and creativity.

While the system is still in public preview with some limits and stability issues, it shows the potential to become a powerful tool for individuals and teams. Trying Antigravity + Gemini 3 now gives developers a glimpse of the agent-first future of programming.