惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

S
SegmentFault 最新的问题
B
Blog
P
Proofpoint News Feed
美团技术团队
The GitHub Blog
The GitHub Blog
Y
Y Combinator Blog
A
About on SuperTechFans
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Vercel News
Vercel News
有赞技术团队
有赞技术团队
小众软件
小众软件
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Google DeepMind News
Google DeepMind News
Martin Fowler
Martin Fowler
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
aimingoo的专栏
aimingoo的专栏
H
Help Net Security
罗磊的独立博客
L
LangChain Blog
GbyAI
GbyAI
腾讯CDC
T
The Blog of Author Tim Ferriss
Microsoft Security Blog
Microsoft Security Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Building an agentic PR reviewer with Antigravity SDK
Remigiusz Samborski · 2026-06-19 · via DEV Community

As announced in this blog post on June 18, 2026, Gemini CLI and Gemini Code Assist IDE extensions will stop serving requests for Google AI Pro and Ultra, as well as those using it free of charge using Gemini Code Assist for individuals. Google is unifying its AI terminal tools by transitioning the community-focused Gemini CLI into Antigravity CLI, a new agent-first platform built for complex, multi-agent workflows.

With this transition timeline in place, development teams relying on Gemini CLI for repository management and automated tasks must establish a migration path. In this post, I will show you how to transition seamlessly by building an automated "first-pass" pull request reviewer using the Google Antigravity SDK and the run-agy-sdk composite GitHub Action.

The orchestration tax

The approach I am proposing also solves another pressing issue for modern engineering teams: cognitive overload. As Addy Osmani recently pointed out, there is an orchestration tax to using AI for coding. The time developers save generating code is often pushed onto reviewers as large, complex PRs, causing context switching and cognitive fatigue.

By offloading the tedious "first pass" search to an Antigravity agent, human reviewers can mitigate this tax and focus on high-level architecture and safeguarding quality.

Why we need automated agentic code reviews

AI-generated code can be deceptively good. It is often clean, well-documented, and syntactically correct. This makes it harder for human reviewers to spot subtle logical bugs or security vulnerabilities that might not be immediately obvious.

In a large codebase, manually verifying every change is simply not feasible. This is why we need autonomous agents that can step into the codebase and analyze it from a fresh perspective.

But if a developer used an LLM to generate the code, how can we trust another AI to find the bugs? The answer lies in the agent architecture and context separation.

Developers might write code using any tool — whether it's CLI, an IDE extension, or various models like Gemini 3.5 Flash or Gemini 3.1 Pro. The reviewer, however, is a managed Antigravity Agent running via a separate SDK integration. This agent has a specialized, low-freedom persona and strict system instructions that force it to act as an adversarial code auditor rather than a developer. Furthermore, it operates in an isolated environment. Because it has a different system prompt, safety guardrails, and context boundaries, the agent reviews the changes with a completely fresh perspective, catching logical bugs and vulnerabilities that the original generator might miss.

To demonstrate it in practice I created an agentic review pipeline, which:

  1. Leverages a managed Antigravity Agent configured via the SDK to review the code. The agent uses advanced reasoning to explore files and verify logic under strict guidelines.
  2. Runs reviews inside isolated workspaces or sandboxes with custom policies to prevent shell or arbitrary code execution risks.
  3. Enables the agent to use the GitHub MCP server to interact directly with the environment to write pull request comments and reviews.
  4. Avoids using the synchronize trigger in pull request workflows to prevent redundant review runs and endless loops. Instead, runs reviews on opened and reopened events, and triggers subsequent passes manually by posting a @agy /review comment on the PR.

Agentic review pipeline

You can find the code at run-agy-sdk.

What is run-agy-sdk?

The run-agy-sdk is a composite GitHub Action that runs the Google Antigravity SDK (google-antigravity) directly on the GitHub Actions host runner.

Why run on the host instead of a container?

By running directly on the host, the Antigravity SDK has access to the host's Docker daemon. This allows the SDK to spawn Docker-based MCP servers (like the GitHub MCP server) to read files, run tests, and post reviews.

Sub-containers should ideally run with restricted network access and read-only filesystems where possible to prevent an LLM from being tricked into executing arbitrary destructive commands. The limited set of permissions is handled in the GitHub Action configuration (see here). Whereas the Antigravity agent has a limited number of tools it can use from GitHub MCP (see here).

Moreover the workflow is explicitly protected from running automatically on forks, preventing unauthorized code execution. The automated review job will only run if the pull request originates from the same repository (see here). On-demand reviews triggered by commenting @agy /review are restricted so that they can only be initiated by maintainers (see here).

Demonstration walkthrough

The demo below shows the action triggered by a new PR:

Implementation: How to install the action in your repo

Let's walk through the setup process step-by-step.

Step 1: Add your API key to GitHub secrets

The action requires a Google Gemini or Antigravity API key to authenticate language model interactions.

  1. Generate your API key.
  2. Navigate to your target GitHub repository and go to Settings > Secrets and variables > Actions.
  3. Create a new Repository Secret named ANTIGRAVITY_API_KEY and paste your API key as the value.

Step 2: Configure the GitHub Actions workflow

Add a new file in your repository at .github/workflows/antigravity-review.yml and add the following configuration:

name: '🔎 Antigravity PR Review'

on:
  pull_request:
    types: [opened, reopened]
  workflow_dispatch:

concurrency:
  group: '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}'
  cancel-in-progress: true

jobs:
  antigravity-review:
    runs-on: 'ubuntu-latest'
    timeout-minutes: 20

    permissions:
      contents: 'read'
      pull-requests: 'write'
      issues: 'write'

    steps:
      - name: 'Checkout Repository'
        uses: 'actions/checkout@v6'
        with:
          persist-credentials: false

      - name: 'Run Antigravity PR Review'
        uses: 'rsamborski/run-agy-sdk@main'
        id: 'agy_pr_review'
        with:
          api-key: '${{ secrets.ANTIGRAVITY_API_KEY }}'
          github-token: '${{ secrets.GITHUB_TOKEN }}'
          mode: 'review'
          prompt: '/antigravity-review'
          trust-workspace: 'true'
          sandbox-profile: 'true'

Pro Tip: Pin the action version to a specific commit SHA (e.g., rsamborski/run-agy-sdk@<commit-sha>) rather than using @main. This prevents unexpected breaks from upstream updates.

While you can reference run-agy-sdk directly in your workflows, its real power lies in using it as a blueprint. I encourage you to fork the repository and use it as a template to build your own custom, agentic GitHub Actions. By modifying the safety policies, custom tools, or prompts in run_agent.py, you can tailor the agent's review behavior to your team's specific codebase, style guidelines, and compliance rules.

For a full workflow template supporting both automated PR reviews and comment-triggered reviews, refer to the workflows folder in the repository.

Conclusions

Automating code reviews is a necessity as AI-generated code volumes increase. By using run-agy-sdk, you can run the Antigravity SDK to review PRs automatically and shift more of the burden of code quality assurance away from human reviewers.

  • Access the full source code in the GitHub Repository.
  • Read the documentation to customize the prompts and mode.
  • Feel free to fork the repository and build your own automation.

Acknowledgments

This project was inspired by the run-gemini-cli action, while shifting to the recently released Antigravity SDK. It is a personal sample implementation of how to run the Antigravity SDK in a GitHub Action, and is not an officially supported Google product.

Let’s connect!

I’d love to hear how you’re using Antigravity for your agentic workflows. Are you building automated code review loops or keeping a tighter leash on your agents?