Google Antigravity vs Gemini CLI: Agent-First Development vs Terminal-Based AI (2026)

Google Antigravity and Gemini CLI serve fundamentally different purposes: Antigravity functions as a standalone IDE replacement with agent-orchestration capabilities, whereas Gemini CLI operates as a terminal-native assistant that integrates with existing development workflows.

I spent weeks testing both Google Antigravity and Gemini CLI across multiple development scenarios, and the most important finding was not which tool performed better. It was recognizing that these tools operate in fundamentally different architectural layers of the development stack. Google Antigravity functions as an IDE-focused development platform (a VSCode fork), whereas Gemini CLI operates as a terminal-native AI assistant; thus, they address different development workflows and require different levels of adoption commitment.

Google Antigravity was launched on November 18, 2025, and is described by Google as an "agentic development platform." The keyword is platform. Antigravity is a VSCode fork that implements a three-surface architecture: an Editor for synchronous coding, a Manager for autonomous agent orchestration, and browser integration for automated testing.

Gemini CLI, released under the Apache 2.0 license, takes the opposite approach. It operates as a terminal-native assistant that enhances existing workflows without requiring changes to the environment. The tool provides access to Gemini's 1-million-token context window via a command-line interface that integrates with Visual Studio Code (490,967+ installs on the official extension) and GitHub Actions.

For enterprise teams managing complex codebases, the architectural distinction between an IDE-first platform and a terminal-native assistant determines everything from adoption strategy to migration costs to workflow disruption.

Augment Code's Context Engine processes 400,000+ files through semantic dependency analysis without requiring IDE migration. Book a demo →

Google Antigravity vs Gemini CLI at a Glance

While both tools use Google's Gemini model family, their architectures differ fundamentally in implementation and purpose. Google Antigravity uses specialized Gemini 3 model configurations for different agent roles, including coding, browser automation, and terminal operations. Gemini CLI accesses Gemini 2.5 Pro (free tier) or higher-tier models through API authentication, operating as a command-line interface rather than a standalone development environment.

Google Antigravity's Agent-First Development Approach

When I tested Google Antigravity's Manager surface on a complex feature-planning task, the tool generated a detailed, step-by-step execution plan before any code was written. This "plan-first" approach differs fundamentally from traditional AI assistants that jump directly into implementation.

Google Antigravity is designed to support parallel agent execution and to orchestrate multiple autonomous workflows simultaneously. The platform includes specialized agents for browser automation, terminal operations, and file handling. However, as of January 2026, Antigravity remains in public preview, with documented operational issues, including frequent crashes and severe quota-management problems that impede practical deployment.

While the architecture theoretically enables complex multi-component projects, recent reports of multi-day quota lockouts, including for some paid subscribers, raise concerns about Antigravity's readiness to support reliable, continuous AI agent delegation at scale.

Antigravity's Global Skills system allows organizations to create custom instructions for code review standards, style guidelines, and best practices that agents are designed to follow during execution. However, this customization layer has documented limitations: current implementation shows agents sometimes fail to consistently execute user-specified instructions, particularly when task complexity requires balancing multiple competing directives. These multi-file refactoring challenges appear across multiple AI coding tools at enterprise scale.

Gemini CLI's Terminal-Native Integration Model

When I tested Gemini CLI's VSCode integration in a debugging workflow, the tool maintained context awareness of my cursor position, selected text, and open files because the companion extension shares IDE state via JSON-RPC. This tight integration enables contextual assistance without the friction of copying code into a separate interface.

Gemini CLI's Model Context Protocol (MCP) extensibility enables custom tooling integration via settings-file configuration. However, according to Zitnik Lab's documentation, a critical constraint limits this capability: a 500-tool limit per MCP server. This architectural limitation requires organizations with extensive development toolchains to carefully prioritize which tools to integrate, as not all tools in a comprehensive development environment can be simultaneously accessible through a single MCP server instance.

Google Antigravity vs Gemini CLI: Multi-Repository Support

Enterprise engineering teams consistently ask about multi-repository support, and according to GitHub Issue #6209 in the official Gemini CLI repository, the single-folder workspace limitation represents a critical documented constraint: "Currently, only single folder workspaces are supported for the VSCode IDE integration." Google Antigravity lacks any documented multi-repository architecture in its published materials.

Gemini CLI has a confirmed limitation documented in GitHub Issue #6209: "Currently, only single folder workspaces are supported for the VSCode IDE integration." The single-folder workspace constraint precludes common enterprise patterns, such as Chromium-style multi-root workspaces, in which teams must exclude build artifacts while including source code from multiple repositories. This context loss problem affects multiple AI coding tools when working across project boundaries.

As of January 2026, GitHub Issue #6209 remains unresolved. Google Antigravity's documentation contains no references to multi-repository architecture. All published codelabs and tutorials demonstrate single-application projects. Without documented cross-repository dependency mapping or service boundary navigation capabilities, enterprise teams managing microservice architectures face significant capability gaps with both tools.

When I tested Augment Code's Context Engine on a similar multi-repository microservices architecture, cross-repository dependency tracking remained functional because the system indexes up to 400,000+ files across repositories while maintaining semantic understanding of service boundaries. This architectural approach demonstrates how tools designed with cross-repository support from inception can address the gap that both Google tools currently face.

Google Antigravity vs Gemini CLI: Production Reliability

My hands-on testing revealed reliability issues that align with community-reported problems documented in official GitHub issue trackers.

Google Antigravity Quota Management Failures

The most severe operational issue I encountered with Google Antigravity was the management of quotas. Multiple developers on Google's AI forum report multi-day lockouts, even as paying subscribers. In documented cases, developers reported waiting multiple days for quota restoration only to face additional lockouts without explanation. According to research on Google Antigravity's critical operational failures, quota management problems represent "the #1 blocker to adoption," with developers on the official forum describing multi-day wait periods that persisted even for pro account subscribers.

Developer advocate Sascha Heyer provided direct feedback to Google, noting: "The Antigravity rate limits were a major bottleneck for developers. The magic is definitely there, but the infrastructure or plans aren't quite ready for professional users yet." According to Heyer's LinkedIn post, this assessment highlighted critical quota-management issues, including multi-day lockouts for paid subscribers.

The existence of community-built tools such as antigravity-dashboard, which tracks 5-hour quota windows with reset countdowns and burn rate calculations, indicates that developers have found the official quota management insufficient and have built their own infrastructure to work around its limitations.

Gemini CLI Execution Failures

Gemini CLI exhibits systematic failures in tool execution that affect core functionality. According to GitHub Issue #12975, a priority/p1 issue describes a file-editing bug in which "after reading code and retrieving the information from that file, if I've deleted code, it adds back the code," thereby undoing user changes.

A comprehensive GitHub discussion documents failures of shell commands, regular expressions, and web fetch, as well as journal overwrite incidents that caused data loss. One developer created a permanent record specifically to document "Gemini CLI's systemic failures and the data loss it caused."

Users of the Windows platform face particularly severe issues. According to GitHub Issue #13734, version 0.17.1 introduced a regression that made the CLI "completely unusable" on Windows, displaying a misleading TypeError: fetch failed error message that suggests network connectivity issues when the actual cause is a CLI client bug in the Windows build.

Google Antigravity vs Gemini CLI: Enterprise Integration Capabilities

Both tools offer integration pathways with varying levels of maturity. Gemini CLI provides production-ready VSCode integration with 490,967+ installs and official GitHub Actions support, whereas Google Antigravity's integration is constrained by its architecture as a standalone IDE that requires a complete development environment migration.

When I tested Augment Code's workflow augmentation layer on our existing VSCode setup, team developers retained access to familiar tooling because the system operates as an enhancement layer rather than requiring IDE migration. This approach contrasts with Antigravity's requirement for a complete replacement of the environment.

For organizations seeking to enhance existing workflows without IDE migration, tools designed as workflow augmentation layers (rather than IDE replacements) can provide AI assistance while preserving team investment in current development environments and established processes.

IDE Integration Pathways

Gemini CLI's VSCode extension represents the most production-ready option with 522,669 verified installs from Google's publisher account. Setup requires VSCode 1.99.0+, Node.js 20+, and a single /ide install command.

A JetBrains plugin exists in the marketplace claiming to provide Gemini CLI Companion integration with JetBrains IDEs. However, according to GitHub issue #15538, JetBrains support was documented as a requested feature for the Gemini CLI integration, even though the primary JetBrains Gemini plugin (Gemini Code Assist) is officially published and supported by Google. Unlike the VSCode extension with verified Google publisher status, enterprise teams should verify the JetBrains plugin's publisher identity, maintenance commitment, and alignment with the official Gemini CLI roadmap before production deployment.

Google Antigravity requires a complete IDE migration. Users cannot utilize Microsoft/GitHub sync functionality and must manually import extensions via .vsix files. According to the AppSoftware migration guide, the C# Dev Kit extension is explicitly incompatible because Microsoft licensing prohibits its use in third-party IDEs.

CI/CD Pipeline Support

Gemini CLI provides official CI/CD support through the google-github-actions/run-gemini-cli action, enabling pull request code reviews, issue triage, and code analysis through @gemini-cli mentions. GitLab integration is implemented using a community-developed Model Context Protocol (MCP) server pattern, with implementation details documented by a Google Cloud engineer, who uses the official Gemini CLI Docker container and API-key authentication.

Google Antigravity's agent-first architecture prioritizes interactive, asynchronous development and autonomous agent execution over direct integration with CI/CD pipelines. The platform lacks documented CI/CD integration capabilities, as current deployment patterns rely on the Manager Surface for task orchestration rather than on automated pipeline execution workflows.

Enterprise Environment Constraints

Google Antigravity faces significant documented enterprise-environment concerns, whereas Gemini CLI has documented limitations primarily related to cloud connectivity, quotas, and workflow constraints.

Gemini CLI requires mandatory internet connectivity. According to Milvus documentation, "The most significant limitation is its requirement for constant internet connectivity, as the AI processing occurs entirely in Google's cloud infrastructure rather than locally." The mandatory internet connectivity requirement eliminates air-gapped development environments, network-restricted secure development enclaves, and offline development workflows.

Proxy configuration presents documented issues. GitHub Issue #8616 confirms that while command-line proxy flags work, settings.json proxy configuration fails during authentication. Organizations that require an HTTP/HTTPS proxy for all external connections may encounter authentication failures when relying on configuration-file-based proxy settings rather than command-line arguments.

No documented SSO integration (SAML or OAuth-based corporate SSO) is available for Google Antigravity or the Gemini CLI. Available authentication methods instead include interactive Google Account login (unsuitable for CI/CD), API key authentication for non-interactive use, and Application Default Credentials (ADC) for Google Cloud environments.

Google Antigravity vs Gemini CLI: Security and Compliance Assessment

Enterprise teams concerned about code confidentiality and regulatory requirements face a critical distinction: between third-party tools (e.g., Antigravity from antigravity.google and Gemini CLI from geminicli.com) and Google's official enterprise products (Gemini Code Assist Enterprise and Gemini Enterprise).

Google Antigravity and Gemini CLI lack documented compliance certifications. Google Antigravity has not published SOC 2 Type II, ISO 27001, HIPAA, or FedRAMP certifications. Gemini CLI similarly does not provide verified SOC 2 Type II, ISO 27001, HIPAA, or FedRAMP certifications, which Fortune 500 companies typically require for code assistance tools that access proprietary codebases.

According to Google Antigravity's privacy policy, the platform collects code and project information, usage patterns, and communications. Notably absent from the documentation: explicit data residency commitments, formal code confidentiality guarantees, enterprise data processing agreements, and compliance certifications (GDPR, HIPAA, SOC 2, ISO 27001, or FedRAMP).

In contrast, Google's official enterprise products (Gemini Code Assist Enterprise and Gemini Enterprise) provide comprehensive compliance frameworks including HIPAA, GDPR, FedRAMP High, SOC 2 Type II, and ISO 27001 certifications. These official products offer explicit stateless processing guarantees, but their publicly available terms and documentation do not explicitly commit that customer code or prompts are not used to train Google's models.

Google Antigravity vs Gemini CLI: Pricing and Cost Analysis

Enterprise pricing information varies by tool and access model, with some transparent per-seat costs available but significant gaps for team deployments. Gemini CLI Code Assist offers a published price of $19/user/month, whereas Gemini API provides transparent, token-based pricing ($1.25-$10.00 per million tokens). However, Antigravity team and organization plans, along with Google AI Ultra for Business enterprise bundles, require direct sales contact, leaving mid-sized teams (15-50 engineers) unable to forecast deployment costs.

Google Antigravity's enterprise access is bundled within Google AI Ultra for Business, which requires a sales contact for pricing. While Google offers a pricing page at antigravity.google/pricing that references access tiers (Individual, Developer, Team, Organization), these tiers refer to access methods through Google One, Google Workspace, and Google Cloud rather than standalone Antigravity pricing. Specific per-seat costs for Team or Organization plans are not publicly disclosed.

Gemini CLI offers the only publicly confirmed pricing: $19/user/month for Gemini Code Assist subscriptions. For a 15-engineer team, this translates to $285/month ($3,420 annually). For 50 engineers, costs reach $950/month ($11,400 annually).

Pay-as-you-go API pricing provides an alternative: Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens. However, usage-based pricing complicates budget forecasting because token consumption varies with code-generation volume and query complexity.

How to Choose Between Google Antigravity and Gemini CLI

Based on my testing and the documented evidence, here is when each tool might make sense for your team:

Critical limitations to evaluate before adoption:

The mandatory internet connectivity requirement eliminates air-gapped and network-restricted environments. The single-folder workspace limitation (GitHub Issue #6209) prevents work on multi-repository codebases. Documented Windows platform regressions render the tool unusable after auto-updates. Quota management failures cause multi-day lockouts even for paid subscribers. Multiple priority/p1 issues remain open, indicating unresolved critical bugs affecting production use.

Consider deferring both tools if

You manage multi-repository codebases, require air-gapped or network-restricted environments, need compliance certifications (SOC 2 or ISO 27001), or require production-grade reliability and support response times under 72 hours.

Evaluate Production-Ready AI Coding Tools for Enterprise Codebases

The comparison between Google Antigravity and Gemini CLI reveals that both tools remain in early preview and face fundamental barriers to production readiness. Google Antigravity, launched on November 18, 2025, is an IDE-focused agentic development platform with promising autonomous agent orchestration capabilities but severe operational failures, including multi-day quota lockouts for paid subscribers, frequent crashes, and unresolved agent behavior-control issues. Gemini CLI, an open-source, terminal-first AI agent, demonstrates strong capabilities but has documented issues, including Windows platform regressions after updates and critical failures in API response parsing that can render the tool unusable.

Neither tool has documented a multi-repository architecture, published security certifications (SOC 2, ISO 27001, FedRAMP), verified enterprise case studies, or comprehensive documentation of limitations. Both tools have been available for fewer than 70 days, which is insufficient time to validate production-grade stability.

The fundamental question is not which Google tool performs better; it is whether either tool currently meets production requirements for complex enterprise codebases. Based on my testing, the evidence indicates that waiting for maturity improvements before committing to either platform is warranted.

For enterprise teams requiring multi-repository support, documented compliance certifications, and production-grade reliability today, Augment Code provides Context Engine architecture that indexes 400,000+ files across repositories with SOC 2 Type II and ISO 42001 certifications. Teams should evaluate pricing based on their scale and verify feature compatibility with their existing toolchain before committing to any AI coding assistant.

Book a demo →

✓ Context Engine analysis on your actual architecture

✓ Enterprise security evaluation (SOC 2 Type II, ISO 42001)

✓ Scale assessment for 100M+ LOC repositories

✓ Integration review for your IDE and Git platform

✓ Custom deployment options discussion

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