Google Antigravity is Google's agentic development platform. Orchestrate multiple autonomous coding agents in parallel across an IDE, a terminal-first CLI, and an SDK, with Gemini, Claude, and open-weight models available on a free individual tier.
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Google describes Antigravity as its agentic development platform, allowing anyone to build in the agent-first era. That single sentence is worth reading carefully, because it sets Antigravity apart from the category most people assume it belongs to. This is not an autocomplete plugin bolted onto an existing editor, and it is not a chat window that happens to know about your repository. It is a platform whose organising unit is the agent: a process that receives a goal, plans an approach, writes and edits files, runs terminal commands, opens a browser to check its own work, and reports back with something a human can inspect.
The product launched on 18 November 2025 as a public preview, offered to individuals at no charge. In its first incarnation it was recognisably an IDE, built around two surfaces. The Editor view gave developers the familiar experience of a modern code editor with inline completions and a capable agent in a side panel. The Manager view did something less familiar: it inverted the usual arrangement so that instead of an agent living inside an editor, the editor became one of several surfaces available to the agent. Google framed this as an agent-first Manager surface, which flips the paradigm of agents being embedded within surfaces to one where the surfaces are embedded into the agent. It is a genuinely different mental model, and it explains most of what the product has become since.
The platform has moved substantially since that launch. Antigravity 2.0 is now positioned as a dedicated platform to work with agents, able to orchestrate multiple autonomous agents working in parallel across independent projects. Alongside the desktop application there is now a terminal-first command line interface and a software development kit, so the same agent infrastructure can be reached from whichever surface fits the task. If you have read an older description of Antigravity as a single editor application, or as a creative idea-generation playground, that description is out of date and was never accurate for this product.
It is worth stating plainly what Antigravity is not. It is not a general-purpose creative tool for brainstorming concepts, and it is not an experimental sandbox for generating speculative ideas. Every surface, every feature, and every pricing tier is oriented toward producing working software. If your interest is in idea generation rather than code, this is the wrong tool, and the rest of this page will not change that.
The central claim of any agentic coding tool is that it can be trusted with a task rather than a keystroke. The difficulty is verification: if an agent has spent twenty minutes editing files and running commands, how do you know it did the right thing without reading every diff line by line? Antigravity's answer is a concept it calls Artifacts.
Artifacts are tangible deliverables in formats that are easier to validate than raw tool calls, such as task lists, implementation plans, walkthroughs, screenshots, and browser recordings. The design intent is to move the human review burden up a level of abstraction. Instead of auditing a sequence of individual file writes and shell invocations, you review a plan before work begins and a walkthrough after it ends, with screenshots or a browser recording as evidence that the resulting software behaves as described. This is a meaningful ergonomic difference from tools that surface only a stream of tool calls, though it is not a substitute for code review on anything that matters.
The agent's reach is deliberately broad. In the IDE surface it is described as being able to autonomously operate across your editor, terminal, and browser. The browser leg is the most consequential of the three. An agent that can open a page, interact with it, and observe the result can close its own feedback loop on front-end work, checking whether a layout change actually rendered correctly rather than merely asserting that it wrote the CSS. That capability is also the one that most deserves caution, because an agent with browser and terminal access is an agent with a large blast radius.
Parallelism is the other half of the model. Rather than one agent working through a queue, Antigravity 2.0 supports subagents that are defined and instantiated dynamically to tackle parallel parts of complex problems, leading to faster and better results. The Manager surface exists to make that tractable, giving a place to spawn, observe, and steer several agents at once across multiple workspaces. Whether this is genuinely faster depends heavily on how decomposable your task is; work with tight sequential dependencies will not parallelise well no matter how the tool is designed.
Parallel agent orchestration. Multiple autonomous agents can run at once across independent projects, each with its own context and workspace. The Manager surface is where they are dispatched and monitored.
Dynamic subagents. For a single complex problem, the platform can define and instantiate subagents on the fly to work on separate parts of it concurrently, rather than requiring you to decompose the task manually in advance.
Scheduled Tasks. Routine work can be automated on a timer. Scheduled Tasks let you define a cron schedule so agents start and run autonomously in the background. This turns the platform from something you drive interactively into something that can perform recurring checks, dependency reviews, or maintenance passes without a human present.
Artifacts and verification. Task lists, implementation plans, walkthroughs, screenshots, and browser recordings are produced as reviewable outputs, with artifact review available directly in the editor.
Editor capabilities. The Editor view provides tab autocompletion, natural language code commands, and a context-aware configurable agent in the side panel, so conventional editing workflows remain available alongside delegated work.
Terminal-first CLI. The Antigravity CLI is the terminal-first surface to interact with Antigravity agents, aimed at developers who prefer to stay in the shell. It supports running multiple agents in parallel and is designed for a minimal resource footprint, with configuration through shortcuts and slash commands.
SDK. A software development kit exposes the agent infrastructure programmatically, for teams building their own integrations rather than working through a provided interface.
Customisation through Skills, MCP, and hooks. You can define global or workspace-specific Skills, MCPs and JSON Hooks to encourage custom agent behavior. Model Context Protocol support matters here, because it means the agent can be connected to external tools and data sources through an open standard rather than a proprietary plugin format.
Projects. Conversations can be grouped into Projects that span multiple folders and carry custom settings and scoped permissions. Scoped permissions are the practical control surface for limiting what an agent is allowed to touch.
Live voice transcription. Speech can be converted into prompts using Gemini audio models, for developers who prefer dictating intent over typing it.
Model choice is one of Antigravity's more distinctive properties, because Google has not restricted the platform to its own models. The Individual tier costs $0/month and lists access to Gemini 3.5 Flash, Gemini 3.1 Pro, Gemini 3 Flash, Claude Sonnet and Opus 4.6, and gpt-oss-120b.
Three things follow from that list. First, third-party models are genuinely available: Anthropic's Claude Sonnet and Opus sit alongside Google's own Gemini models, which is unusual for a first-party vendor tool and gives you a real choice when one model family handles a particular codebase better than another. Second, an open-weight model is included, which matters for developers who want a lighter or more predictable option for routine work. Third, this lineup has changed since launch, when the announcement referenced Gemini 3, Claude Sonnet 4.5, and GPT-OSS. Model naming in this space moves quickly, so treat the pricing page as the authoritative current list rather than any secondary description, including this one.
Model availability is tied to your plan, and higher tiers are described as offering more generous rate limits and a flexible AI credit pool rather than exclusive access to different models.
The pricing structure has four levels, and the honest summary is that the free tier is real but the limits around it are not fully disclosed.
Individual — $0/month. This tier includes agent model access to the lineup described above, unlimited Tab completions, and unlimited Command requests. Against that, it carries what Google calls basic weekly rate limits. The specific numbers behind that phrase are not published on the pricing page, and this page will not invent them.
Google AI Pro. Everything in the Individual tier, plus more generous rate limits and a flexible AI credit pool. The pricing page does not itself state a monthly figure for this tier; independent reporting has referred to it as a $20 per month plan.
Google AI Ultra. Everything in Pro, with further increases to rate limits and credit allowance. Again the pricing page does not state a figure, while independent coverage has cited roughly $250 per month.
Organization, via Google Cloud. Access is granted under Google Cloud Terms of Service, with Google Cloud project integration and consumption-based API pricing with the Gemini Enterprise Agent Platform. This is a usage-billed arrangement rather than a per-seat subscription, and it is the tier with the materially different data handling described later on this page.
The critical caveat for anyone budgeting around this tool concerns quota behaviour rather than headline price. Independent coverage in March 2026 reported that users protested changes to quota behaviour on the paid AI Pro and AI Ultra plans. The specific complaint was that a quota previously understood to refresh every few hours came to behave as a weekly allowance, with some developers reporting dramatic reductions in the volume of work they could complete before being locked out. Google has publicly acknowledged adjusting rate limits in response to demand. If you intend to rely on this tool for daily professional work, treat the published tier list as a starting point and validate the actual throughput you get before committing.
Antigravity is a desktop application with broad but specific platform coverage. The download page offers builds for three operating systems across six architectures: macOS on Apple Silicon and Intel, Windows on x64 and ARM64, and Linux on x64 and ARM64.
The system requirements are stated precisely. On macOS the minimum supported release is version 12 (Monterey), and Google supports releases that still receive Apple security updates, which it describes as typically the current and two previous versions. On Windows the floor is Windows 10 64-bit. On Linux the requirement is expressed in terms of system libraries rather than distributions: glibc 2.28 or newer and glibcxx 3.4.25 or newer, with Ubuntu 20, Debian 10, Fedora 36, and RHEL 8 given as examples that satisfy it. Older long-term-support Linux installations that predate those library versions will not run the application without an upgrade.
The CLI is installed separately through command-line installation scripts and is available for macOS, Linux, and Windows. Because the IDE and CLI are versioned independently, it is possible to run one without the other.
Two points are genuinely undetermined from official documentation and are stated here as such. The getting-started documentation does not explicitly set out account sign-in requirements, and it does not describe regional availability restrictions. Given that the free tier is tied to Google AI plan levels, some form of Google account association is the practical expectation, but this page will not assert specifics that the documentation does not state.
This is the section that deserves the most attention, because the answer differs sharply depending on which door you come in through, and the difference is not cosmetic.
For individual users on the standard service, the terms are explicit. The terms state that Google uses Interactions to evaluate, develop, and improve Google and Alphabet research, products, services and machine learning technologies, and that Google employees and contractors may access, view, review and use Interactions. Interactions are defined broadly, covering user data, interaction data about your usage of the service, related metadata, and any feedback you provide, collected while the service is running.
Two mitigations are provided alongside that. There is a preference control: if you do not want your Interactions used in that way, the terms direct you to change the relevant preference in settings. And there is a deletion route, since you have the option to delete your Interactions, with a support email address given for deletion requests. The terms also note that Interactions will be used according to the agreement unless and until you request deletion.
For organisations, the position is materially stronger and stated without ambiguity. Logging in with Google Cloud credentials inherits Google Cloud's standard data privacy protections and ensures corporate data is never used for training. Enterprise deployments also carry built-in security guardrails and workspace sandboxing intended to make delegating complex workflows safer.
The practical conclusion is straightforward. If you are working on proprietary code and your compliance posture does not tolerate broad data usage for model improvement or human review, the individual free tier is not the appropriate entry point regardless of how attractive its price is. Use the Google Cloud path, and confirm the current terms yourself before onboarding a team, since terms change and this page is a snapshot rather than legal advice.
Developers working on decomposable projects. The parallel agent model pays off when a task genuinely splits into independent pieces. Greenfield feature work, broad refactors across many files, and test-writing passes fit this shape well.
Front-end and full-stack developers. The browser leg of the agent's reach is most valuable where visual verification matters. An agent that can render a page and inspect the result closes a loop that would otherwise need a human or a hand-written browser test.
Terminal-centric developers. The CLI exists precisely so that adopting the agent model does not require adopting a new editor. This is the lowest-friction entry point for anyone with an established shell workflow.
Teams standardising agent behaviour. Skills, MCP connections, and JSON hooks defined at workspace level let a team encode its conventions once, rather than each developer prompting from scratch.
Enterprises with strict data requirements. The Google Cloud path with its no-training guarantee, sandboxing, and scoped permissions is the configuration that makes this tool viable in regulated environments.
Who should look elsewhere. Developers who need a lightweight inline assistant and nothing more will find the platform heavier than necessary. Anyone whose work depends on guaranteed high-volume daily throughput should weigh the documented quota complaints seriously. And anyone looking for a creative ideation tool has the wrong product entirely.
Start with scoping rather than ambition. Projects support scoped permissions, and configuring them before delegating anything is the single highest-value setup step. An agent with terminal and browser access can do a great deal of damage when pointed at the wrong directory, and several publicly reported bad experiences involve agents modifying or deleting files the user did not expect them to touch.
Work inside version control with a clean tree. This is not specific to Antigravity, but it is the difference between an unhelpful agent run being an annoyance and being an incident. Commit before delegating, and review the diff after.
Use the plan artifact as a checkpoint. Because the platform produces implementation plans as reviewable artifacts, the natural workflow is to read the plan before letting execution proceed on anything substantial. Correcting a misunderstanding at the plan stage costs a sentence; correcting it after twenty file edits costs a revert.
Match the model to the task. With Gemini, Claude, and an open-weight option all available, routine mechanical work does not need the most expensive reasoning model. Given that quota consumption is the most common complaint about this platform, model selection is also the main lever you have over how quickly you exhaust your allowance.
Parallelise only what is genuinely parallel. Dispatching several agents at a shared set of files invites conflicting edits. Separate concerns by directory or by project before separating them by agent.
Treat Scheduled Tasks conservatively at first. Background agents running on a cron schedule with no human present is a powerful capability and an easy way to accumulate surprises. Start with read-only or reporting tasks before granting write authority on a timer.
Verify artifacts rather than trusting them. A screenshot proves a page rendered; it does not prove the logic beneath it is correct. Artifacts reduce review effort but do not eliminate the need for review.
Antigravity sits in a crowded and fast-moving category, and the honest framing is that it competes on the agent-orchestration axis rather than on raw code completion quality.
Anthropic's Claude Code is the closest comparison for terminal-centric agentic development, and is frequently named by reviewers as the benchmark Antigravity is measured against. OpenAI's Codex occupies similar ground and appears repeatedly in user comparisons. Cursor remains the reference point for an AI-native editor experience, with a stronger emphasis on the editing loop than on parallel autonomous agents. GitHub Copilot is the incumbent for inline assistance integrated into existing editors, and is the natural comparison for developers who want augmentation rather than delegation. Google's own Gemini CLI is worth noting specifically, since some existing Google users have questioned the move from that simpler tool toward the heavier Antigravity platform.
The differentiators that consistently favour Antigravity are its parallel orchestration model, its inclusion of third-party models including Claude on a free tier, and the browser-based self-verification loop. The differentiators that favour competitors are maturity, predictable quota behaviour, and stability, all three of which have been recurring criticisms of Antigravity through 2026.
Quota behaviour is the dominant complaint. Independent coverage in March 2026 reported that users protested changes to quota behaviour on the paid AI Pro and AI Ultra plans, with developers describing quotas that refreshed far less often than they expected and, in some accounts, allowances that fell dramatically compared with earlier months. This is the single most important thing to validate for yourself before depending on the tool.
The free tier's limits are unpublished. Basic weekly rate limits is the only description given. There is no public number to plan against.
The 2.0 transition was disruptive. On Trustpilot the product carries a low score across a small number of reviews, with criticism concentrated on the 2.0 transition and on how quickly quotas are consumed. A recurring theme is that the update changed the working model for existing users with little warning. One review notes that thankfully the IDE version is still accessible, forcing users to update to an agent only was very annoying for anyone who enjoyed the older versions, which indicates the older surface remained available but that the transition was handled poorly.
Review data is thin and self-selected. The Trustpilot profile is unclaimed, carries only 31 reviews, and skews heavily negative, which is a normal pattern for unprompted developer-tool reviews. It is a real signal about the shape of user frustration, not a reliable measure of overall quality.
Reliability reports vary. Users have reported capacity errors on specific third-party models and agent termination errors during long-running tasks. Availability of any given model at any given moment is not guaranteed.
Autonomy carries risk. An agent with editor, terminal, and browser access can take destructive actions. Scoped permissions and version control are mitigations, not guarantees.
Older Linux systems are excluded. The glibc 2.28 floor rules out a number of long-lived enterprise installations.
Some facts are simply not published. Regional availability and explicit sign-in requirements are not stated in the getting-started documentation, and exact quota figures are not stated on the pricing page. Where this page has been unable to verify something, it says so rather than estimating.
Yes, there is a genuinely free tier. The Individual plan is listed at $0 per month and includes agent model access, unlimited Tab completions, and unlimited Command requests. The constraint is that it operates under what Google calls basic weekly rate limits, and the specific size of that allowance is not published. Paid Google AI Pro and Ultra tiers offer more generous limits and a flexible credit pool, and an organisation tier bills by consumption through Google Cloud.
On the standard individual service, the terms state that Google uses Interactions to evaluate, develop, and improve Google and Alphabet research, products, services and machine learning technologies, and that employees and contractors may access, view, review and use them. You can change how this data is used through a preference in settings, and you can request deletion of your Interactions by email. For organisations, the position is different and explicit: logging in with Google Cloud credentials ensures corporate data is never used for training. If your code is proprietary, use the Google Cloud path.
The pricing page lists Gemini 3.5 Flash, Gemini 3.1 Pro, Gemini 3 Flash, Claude Sonnet and Opus 4.6, and gpt-oss-120b for the Individual tier. Notably this includes Anthropic's models and an open-weight model alongside Google's own, so you are not restricted to Gemini. This lineup has changed since the November 2025 launch, so check the pricing page for the current list.
macOS, Windows, and Linux, with builds for six architectures in total. macOS requires version 12 (Monterey) or later on Apple Silicon or Intel; Windows requires Windows 10 64-bit and is available for x64 and ARM64; Linux requires glibc 2.28 or newer and glibcxx 3.4.25 or newer, with Ubuntu 20, Debian 10, Fedora 36, and RHEL 8 cited as qualifying examples.
It launched as a public preview on 18 November 2025. Since then the product has moved through a substantial 2.0 release that added the CLI, the SDK, and the parallel orchestration platform, and current official pages present it as generally available rather than carrying a preview label. Because the labelling has shifted over time, verify the current status on the official site if it matters to your adoption decision.
A conventional assistant completes code inside your editor while you drive. Antigravity inverts that relationship. Its Manager surface flips the paradigm of agents being embedded within surfaces to one where the surfaces are embedded into the agent, so you delegate goals to agents that plan, edit files, run commands, and check their own work in a browser. It then returns Artifacts such as task lists, implementation plans, walkthroughs, screenshots, and browser recordings so you can validate the outcome without reading every tool call.
Yes, parallel operation is the core design. Multiple autonomous agents can run across independent projects, and subagents are defined and instantiated dynamically to tackle parallel parts of complex problems. Official current documentation does not state a fixed maximum number of concurrent agents, and practical throughput will be governed by your plan's rate limits rather than by an agent count.
The Antigravity CLI is the terminal-first surface to interact with Antigravity agents, aimed at developers who prefer to stay in the shell. It supports running multiple agents in parallel, is designed for a minimal resource footprint, and is configured through terminal shortcuts and slash commands. It is installed and versioned separately from the desktop application, so you can adopt the agent model without changing editors.
Yes. You can define global or workspace-specific Skills, MCPs and JSON Hooks to encourage custom agent behavior. Model Context Protocol support means external tools and data sources can be attached through an open standard, and Projects allow conversations to span multiple folders with custom settings and scoped permissions.
Quota predictability is the main one. Independent coverage in March 2026 reported that users protested changes to quota behaviour on the paid AI Pro and AI Ultra plans, and the free tier's limits are unpublished. Stability during long-running tasks and the disruption caused by the 2.0 transition are the other recurring complaints. If you need guaranteed daily throughput or a mature, unchanging workflow, evaluate carefully before switching.
