Google Antigravity is a standalone Electron-based fork of VS Code that feels like a heavier, extension-rich VS Code derivative: familiar file tree, keybindings and editor panes, augmented by a mission-control layer for autonomous workflows. The primary developer value is high-throughput, context-aware automation across repositories—agentic planning, cross-file execution and visual verification—trading lightweight responsiveness for deeper native integration and multi-step automation capability.
Intelligence & Context Management
Antigravity is built around long-context model usage and agentic orchestration rather than a pure retrieval-first design. The product streams large file and chat histories (Gemini 3 Pro handles 10k+ line contexts in practice) into model sessions for reasoning and planning. For multi-step agent work it runs asynchronous, multi-agent operations that can plan, execute and validate changes across files; complex reasoning phases may be executed on cloud endpoints while local LLMs (Ollama/Llama) are available as fallbacks for privacy-sensitive tasks.
Agent actions that require environment interaction are executed with controlled outbound operations: shell/terminal access and browser interactions may send code or traces to cloud reasoning endpoints, but Antigravity enforces Allow/Deny lists for agent privileges and endpoint access. Multimodal inputs (screenshots and short videos) are fed to the multimodal stack to reconstruct UI state for “vibe coding” and visual diffs. A fast-mode path exists for immediate, low-latency edits where the client applies small changes locally with minimal roundtrips to the model.
Key Workflow Tools
- Composer (mission-control UI): a centralized orchestration panel showing active missions, agent roles, plan steps, and progress; designed for batching and supervising parallel agents rather than single-chat prompts.
- Terminal Agents integration: a terminal pane that permits agent-driven shell commands with explicit allow/deny controls, execution logs, and replayable command traces for auditing.
- Browser sub-agent UI: an embedded Chromium panel that records and replays clicks/forms and surfaces visual verification artifacts (screenshots, DOM snapshots) alongside test results and agent actions.
- Multimodal “vibe coding” UI: an overlay workflow to convert screenshots/videos into UI replication edits and to preview generated UI changes visually before applying code edits.
- Fast mode (instant edits): an immediate-edit conduit for small fixes that applies changes locally with prioritized, minimal model interaction to reduce latency for trivial tasks.
- Asynchronous multi-agent monitor: visual conflict resolution and merge guidance when multiple agents operate in parallel on overlapping files, with progress timelines and rollback controls.
- Extension/packaging behavior: native fork-level integration means Antigravity uses Open VSX and blocks proprietary Microsoft extensions; file rendering and editor state are handled by the IDE itself rather than an external plugin bridge.
Model Ecosystem & Security
- Primary hosted and local models: Gemini 3 Pro (High/Low variants, primary), Claude Sonnet 4.5 (Thinking/Standard), GPT-OSS 120B (Medium).
- Local and custom endpoints: supports running models locally via Ollama / Llama, and custom endpoints (OpenRouter, Together AI); Granite 4.0 has been demonstrated via localhost:11434.
- Deployment coverage: macOS, Windows, Linux.
- Privacy and security stance: local LLM execution is supported to reduce cloud exposure, but agentic reasoning tasks and environment interactions can send code and traces to cloud endpoints. Antigravity enforces agent privilege boundaries with explicit Allow/Deny lists for shell access and network endpoints. No SOC2/ISO certification claims are presented in the platform itself.
The Verdict
Technically, Antigravity is a VS Code fork with native IDE-level integration that enables agentic automation and multimodal verification at a depth a plugin cannot match: the AI has direct access to editor state, file management, embedded browser frames and terminal IO without inter-process bridge constraints. That native access is the principal advantage for teams building end-to-end agent workflows, visual E2E testing, or workflows that require very large model contexts (Gemini 3 Pro). The trade-offs are higher RAM and CPU usage, UI lag as history grows, blocked proprietary MS extensions, and the potential for cloud data egress during agent reasoning.
Recommendation: adopt Antigravity when you need high-throughput, context-aware automation across repositories and multimodal verification tied to native editor state. If you require lower resource consumption, strict compliance boundaries with guaranteed zero cloud data retention, or you must retain full compatibility with Microsoft’s extension ecosystem, prefer a standard IDE plus plugin architecture and defer Antigravity to mission-specific pipelines where its agentic capabilities are essential.