Complete Guide to Google Antigravity (2026)

Google Antigravity now has two related surfaces: an agentic IDE for direct editing and Antigravity 2.0, a standalone desktop command centre for running and monitoring agents. Google documents agents that can work across the editor, terminal, and browser, with artifacts for reviewing the result.

I have used Antigravity, and I don't like it enough to make it my main coding tool. My problem is the supervision it needs when a small request turns into a larger change. The example below explains why I still prefer Cursor.

What Is Google Antigravity?

Google Antigravity is a family of AI development tools that lets agents inspect projects, edit code, run terminal commands, and use a browser. The Antigravity App coordinates agent tasks; the VS Code-derived Antigravity IDE combines agent work with direct code editing. The family also includes a CLI, editor extensions, and an SDK.

Start by deciding whether you need an editor or a place to supervise longer-running tasks. The useful distinction is how you plan, inspect, and review the work. Parallel agents are also available in competing tools, so a dashboard alone does not establish an advantage.

Getting Started

Choose the Antigravity App or IDE from Google's official download page, sign in, and open a small project you already understand. Check its permissions and review settings before asking it to make changes.

1. Ask the agent to locate one feature and explain the files involved without editing.
2. Request a small change, such as a loading state for one button, with an explicit file boundary.
3. Ask for the proposed approach and the check that will prove it works.
4. Review the diff and any browser artifacts, then run the project's own checks.

The IDE inherits many VS Code conventions, but the standalone App is a different surface. Use the shortcut list and settings for the product you installed instead of assuming every command works in both.

Manager View

Antigravity's agent-management interface organizes active tasks so you can inspect what each agent is doing and review its output. It is useful when several distinct jobs need supervision. Compare that experience with the multi-agent workflows in other tools using the same tasks.

Parallel work helps only when the tasks do not overlap. A database migration and a UI polish pass may be separate; two agents rewriting the same component are not. Keep each task in a separate scope, review the artifacts and diff, then run the project's own checks. More autonomy means more output to inspect, not less responsibility.

The Models You Can Run

Model access depends on the Antigravity surface and your plan. Google's current model table lists Gemini 4 Argon for eligible paid accounts in the App, CLI, and extensions, but not Antigravity IDE. Gemini 3.8 Flash has broader access, while Claude 5.5 models require eligible paid plans.

Google lists November 2, 2026 as the removal date for its older Claude 4.6 and GPT-OSS options. Check the live picker before committing a workflow to one of those models. Model availability and quota are separate questions.

The model menu is useful only when it changes a result you can review. Run the same representative task through the models available to your account, then record elapsed time, output quality, and usage. Do not pick a plan from a model name alone.

Skills and Rules

The first time I let Antigravity's agent loose on a real task, I asked it to add a loading state to a button and it restructured my entire component folder instead. It moved files, renamed things, and introduced a folder layout I never asked for. The code worked, the architecture was arguably better, and it was still nowhere near what I'd actually requested. That's the core problem with agents that have judgment but no guardrails: their preferences are reasonable, they're just not necessarily yours.

Rules give an agent project instructions; skills package task-specific procedures; MCP connects it to additional tools. These mechanisms solve different problems. None replaces permissions or review.

Google documents plain AGENTS.md or GEMINI.md files for directory-scoped instructions. Modular rules belong in .agents/rules/*.md and require valid trigger frontmatter; the legacy .agent/rules directory is also supported. Save this plain example as GEMINI.md:

# Project Rules

## Framework Preferences
- Use React for UI components
- Use Tailwind CSS for styling (no CSS-in-JS)
- Use Zod for validation

## Constraints
- Do not add new dependencies without asking first
- Do not restructure existing folder organization
- Do not create abstraction layers unless explicitly requested

The headings aren't fixed; agents parse the content as context, and the more specific you are, the more consistently they follow it. A few patterns earn their keep in daily use. Framework consistency stops the worst mid-project surprises, the agent reaching for styled-components in one file and Tailwind in the next. Guardrails against over-engineering matter more than people expect, which I think of as the black coffee principle: if I ask for coffee, don't assume I want milk, sugar, and sprinkles, so "start simple" and "don't add abstractions without asking" pull real weight. File-organization rules head off the restructuring mess I described above.

Both extremes cause real problems in practice. A rule like "write good code" constrains nothing; micromanaging rules that try to script every decision leave the agent no room to operate. "Use early returns instead of nested conditionals" gives an agent something concrete to act on.

Skills contain a SKILL.md file and optional supporting resources. Antigravity can select a relevant skill from its description, and the App also supports explicit invocation with /<skill-name>. Automatic selection does not mean a skill is guaranteed to load for every matching task.

When adapting instructions from another tool, check the file location and activation format. Cursor project rules use .cursor/rules/*.mdc, Claude Code uses CLAUDE.md, and Antigravity supports AGENTS.md, GEMINI.md, and modular rules. Keep shared conventions consistent without assuming the configuration formats are interchangeable.

One caution is worth flagging on the Armoury side. The more MCP connections you wire up, the more your agents can reach, and that surface is genuinely powerful. Shortly after launch there was a reported persistent code-execution vulnerability involving malicious workspace rules that abused this access. I treat MCP permissions the way I treat any other credential: connect what a task needs and nothing I can't account for.

Google AI Ecosystem

Antigravity doesn't exist on its own in Google's lineup. Google has shipped a confusing pile of AI developer tools in a short span, and knowing where Antigravity sits saves you from picking the wrong one. Antigravity is the desktop, agent-first surface, and the others occupy different lanes.

Gemini Code Assist is the plugin model, dropping completions and an Agent Mode into VS Code, JetBrains, and Android Studio rather than asking you to switch editors. It's Google's enterprise-facing answer to Copilot; its IDE extensions remain available to Gemini Code Assist Standard and Enterprise licensees.

Antigravity CLI brings Gemini to the terminal with natural-language commands and the same agent harness as Antigravity 2.0. It replaced Gemini CLI as the free consumer option when Google stopped serving Gemini CLI to free, Google AI Pro, and Ultra users on June 18, 2026. Gemini CLI remains available through enterprise licenses and paid API keys. Jules is the asynchronous one: it connects to GitHub, clones your repo into a secure Google Cloud VM, works in the background while you close the browser, and hands back a pull request when it's done, with tiered limits that scale across Google's AI plans. Firebase Studio is the cloud-IDE lane for Firebase-centric prototyping, and it has carried preview caveats, so I'd keep a backup plan before standardizing on it.

The overlap is real, and Google hasn't always explained when to choose which. In practice, if you want Gemini in your current editor, use Gemini Code Assist; if you want background tasks delivered as PRs, use Jules; if you want to supervise several agents from a dashboard and you're curious about the agent-first workflow, that's Antigravity. All of these run Gemini underneath, which is the throughline, and it's also why Google can afford to keep several of them alive at once.

How Much Does Antigravity Cost?

Antigravity has a quota-limited free tier and paid access through Google AI plans. Google's May 2026 announcement priced Pro at $20 per month and the two Ultra tiers at $100 and $200; confirm current local pricing before subscribing. Google's May 19 plan update set the current individual tiers below, while the live plans page explains how their quotas refresh. Check both before committing a budget because model access and capacity language have already moved since launch.

Plan	Price	Usage limit
Free	$0	Rate-limited; quota refreshes weekly
AI Pro	$20/month	Higher weekly limit; quota refreshes every five hours until the weekly cap
AI Ultra 5x	$100/month	Five times the Pro token allowance
AI Ultra 20x	$200/month	Twenty times the Pro token allowance

AI Pro and Ultra users can enable purchased AI credits after the baseline quota runs out. Google consumes those credits at Gemini Enterprise Agent Platform pricing, and the setting can wait for the next refresh or allow overages automatically. Third-party models use a separate fixed quota where available; Antigravity's plans page explicitly says there is no bring-your-own-key route for extra limits. Team and enterprise access runs through Google's Gemini Enterprise Agent Platform.

Both Antigravity and Cursor combine subscriptions with usage limits or additional charges. Cursor's current Plus and Premium plans cost $8 and $30 per month, with different model and agent access; the older $20 Pro price is a legacy plan. Compare the features and quota needed for your actual tasks.

My take: the AI Pro tier at $20/month is the right starting point if you're evaluating seriously. Try the free quota first, then use Pro if its limits interrupt a useful evaluation. The Ultra tiers only make sense after you have validated the workflow on your own tasks.

Limits and Fit

I like the idea and I keep it installed, but my experience has not persuaded me to replace Cursor with it. The product churns at a pace that's hard to track: features, limits, and pricing can change between when I write this and when you read it, so anything I peg too precisely will drift. The extension ecosystem is smaller than Cursor's; VS Code compatibility is partial, and every extension you depend on needs a real test. Google's enterprise route now runs through Gemini Enterprise Agent Platform, while the individual plans still use capacity-based limits and changing model availability. For a company evaluation, I would keep an established editor available and treat Antigravity as a measured pilot until its exact integrations and controls pass your requirements.

There's also the Google factor to weigh. Antigravity is one of several AI developer products Google maintains at once, and Google has a long history of sunsetting tools, even popular ones, when interest moves elsewhere. The talent and the technology behind Antigravity are real, but whether Google commits to it for the long haul is genuinely unknown, including, I'd guess, to Google.

So if you're a solo developer happy in Cursor, you don't need to switch, and Antigravity probably won't feel like a compelling primary tool. If you're coordinating a larger project with several distinct work streams, or you want heavy Gemini access tied to limits instead of API spend, spend a weekend with it and see whether the agent-team model solves a problem you didn't realize you had. Just keep your rules files portable and don't build anything irreversible on top of it yet.

Compare Google Antigravity

• Cursor vs Google Antigravity: file-by-file editing against the agent-first, dashboard-driven model, and the most common comparison for developers already in Cursor

The full set of AI coding tools comparisons covers the rest of the field, and the best AI coding tools hub lists every tool we cover.

Verdict

Antigravity is worth a contained pilot when managing parallel agent tasks is the problem you need to solve. Start with one Project, a reversible task, and a review step that you already trust. Keep your existing editor when direct file work and one active task are the better fit. Compare it with Cursor vs Google Antigravity before turning it into a team standard.

Updated September 23, 2026: explained Google's move of consumer accounts from Gemini CLI to Antigravity CLI, and that enterprise licenses and paid API keys still get Gemini CLI.