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Google Antigravity

Google Antigravity AI Agent
Rating:
(3 / 5 from 1 ratings)

Overview

Agent-first AI coding environment and IDE from Google where autonomous agents plan, write, test, and debug software across editor, terminal, and browser.

Google Antigravity is an AI-powered, agent-first development platform and IDE from Google, released alongside the Gemini 3 model family. Built as a fork of Visual Studio Code, it lets developers delegate complex software tasks to autonomous agents that can plan, code, test, and debug across an integrated editor, terminal, and browser. Antigravity’s Agent Manager view orchestrates multiple agents in parallel workspaces, while the Artifacts system surfaces verifiable plans, patches, logs, and screenshots so developers can inspect how work was done. The platform supports multiple models, including Gemini 3 Pro and other leading AI models, and is available as a free preview with generous usage limits on Windows, macOS, and Linux.

Product overview

Capabilities, pricing and fit

Google Antigravity is a developer-focused agent suite rather than a general-purpose assistant. Its current surfaces include a multi-agent command center, terminal CLI, Python SDK, and agentic IDE for planning, implementing, testing, and reviewing software work.

Information as of August 15, 2026

What it can do

  • Parallel local-agent orchestration

    Antigravity 2.0 groups conversations into projects, operates across multiple workspaces, and manages multiple local agents and scheduled messages.[1]

  • Terminal coding agents

    The CLI runs autonomous coding agents, shell commands, and background subagents from a terminal-first interface.[1]

  • Agent harness SDK

    The Python SDK supports prototyping custom agents and automating software-engineering tasks and evaluations on the Antigravity harness.[1]

Where it fits best

  • Developers running multi-step coding, implementation, and testing tasks across one or more local repositories.[1], [2]

Pricing and availability

Antigravity has a $0 individual plan with basic weekly rate limits. Google AI Pro and Ultra provide larger limits and flexible AI credits, while organization access is offered through Google Cloud with consumption-based pricing; exact paid-plan prices were not shown on the checked page.[3]

Platforms: macOS, Windows, Linux, Terminal CLI, Python SDK, Agentic IDE[1], [2], [3]

Access: Local project folders, Isolated Git worktrees, Google Cloud organization access[1], [2], [3]

Important considerations
  • Agents operate inside explicitly configured Projects and either work directly in active folders or in an isolated Git worktree, so users must choose the execution boundary appropriate to the task.[2]
  • The no-cost individual plan uses basic weekly rate limits; Google AI Pro and Ultra increase limits, but the checked Antigravity page does not state their subscription prices.[3]
  • Desktop support has operating-system requirements: macOS 12 or later on Apple Silicon, Windows 10 64-bit or later, or a compatible Linux distribution; Intel macOS is not supported.[2]
Sources (3)
  1. Google Antigravity - ProductOfficial site · checked 2026-08-15
  2. Getting Started with Antigravity 2.0 | Google Antigravity DocsDocumentation · checked 2026-08-15
  3. Google Antigravity - PricingPricing page · checked 2026-08-15

Autonomy level

78/100

Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.

Reasoning: Google Antigravity exhibits high autonomy through its agent-first architecture that enables autonomous agents to simultaneously plan, code, execute, and verify complex end-to-end software tasks across editor, terminal, and browser interfaces without constant user intervention. Agents operate independently by writing code, launching applications, te...

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Some of the use cases of Google Antigravity:

  • Delegating end-to-end coding tasks like scaffolding, refactors, and bug fixes to autonomous agents.
  • Running multiple coding agents in parallel to explore different implementations or tackle large backlogs.
  • Using an integrated browser and terminal so agents can run tests, inspect apps, and iterate without manual setup.
  • Reviewing agent-generated artifacts such as plans, diffs, and logs to understand and verify code changes.
  • Experimenting with agentic development workflows on real-world projects while keeping a familiar VS Code-style IDE.

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Popularity level: 67%

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