Gemini CLI Is Dead. Here's the Better Thing That Replaced It

Bold opinion: The community Skills library is actually the most underrated thing about this ecosystem. You can drop in a production-grade code-review workflow in 90 seconds. Most people skip it and hand-write prompts forever.

Hooks: Intercept the Agent Loop

Hooks let you fire shell commands at lifecycle moments. Config is JSON:

// .agents/config.json

{

  "hooks": {

    "before_tool_call": [

      { "command": "echo '$TOOL_NAME' >> .agents/tool-log.txt" }

    ],

    "on_loop_stop": [

      { "command": "node scripts/notify.js" }

    ]

  }

}

Available hook points: before_tool_call, after_model_call, on_loop_stop, on_error.

The pattern I use on every project: on_loop_stop writes a summary to a log file, before_tool_call audits tool use so I can see what the agent actually did. Thirty lines of config, total.

Subagents: The Part Gemini CLI Couldn't Do

This is the actual upgrade.

Gemini CLI was synchronous. You waited. The agent finished. You moved on. Antigravity CLI ships async subagents — dispatch a long-running task to a background agent and keep prompting in the foreground, same session.

From inside the TUI:

> Refactor the auth module to use the new token schema.

  [Dispatching to subagent...]



> While that runs — write a changelog entry for the last 10 commits.

Both run in parallel. The subagent reports back when it's done.

From the CLI:

# Dispatch a background agent

agy dispatch "Run the full test suite and write a summary to test-report.md"



# Check active subagents

agy agents list



# Pull output from a specific agent

agy agents output <agent-id>

I Tried It: Parallelizing a Real Refactor

Here's what I actually tested. I had a Next.js project with a type refactor that touched ~40 files.

I dispatched the refactor to a background subagent, then used the foreground agent to write the PR description and update the changelog — simultaneously.

The refactor completed in about 4 minutes. The PR description and changelog were ready before the refactor finished. Total wall-clock time: 4 minutes instead of 8–10 in sequence.

That's not a benchmark. That's just what parallel async agents feel like in practice.

Managed Agents: Serverless Version

Don't want the CLI? There's an API:

import google.generativeai as genai



genai.configure(api_key="YOUR_API_KEY")



agent = genai.create_agent(

    model="gemini-3.5-flash",

    instructions="You are a code reviewer. Review the diff for correctness and security issues.",

    tools=["code_execution"],

)



result = agent.run("Review this diff:\n" + open("changes.diff").read())

print(result.output)

Same agent harness. Same Gemini 3.5 Flash intelligence. Zero local setup. The agent runs in an isolated Linux environment on Google's infrastructure.

Migration Cheat Sheet

Skills files don't need to change. The biggest behavioral win: agy is built in Go. Startup is noticeably faster than the old CLI.

Bold opinion: The deprecation of Gemini CLI felt abrupt. But Google made the right call — maintaining two separate CLIs with diverging capability models would have been worse for the ecosystem than a clean cut.

What This Is Really About

Gemini CLI was a terminal wrapper around a language model.

Antigravity CLI is a terminal interface for an agent harness. The Skills/Hooks/Plugins model is also cross-compatible with Claude Code, Cursor, and Codex CLI — a Skill you write here works elsewhere.

The transition was abrupt. The thing that replaced it is better.

Antigravity CLI at .

Tags: googleio ai productivity devtools