Agents CLI

// Overview

Agents CLI (`agents-cli`) is Google's command-line tool for the lifecycle of an agent built on the Agent Development Kit: scaffold a project, run it, evaluate it, deploy it to Google Cloud, register it with Gemini Enterprise, and observe it in production. It is explicitly not a coding agent and not a replacement for ADK — ADK is the framework you write the agent in, and `agents-cli` is the tooling around it.

Its second half is the part that distinguishes it. `agents-cli setup` installs a set of agent skills into whatever coding assistant you already use — Antigravity CLI, Claude Code, Codex, or another — so that assistant knows the ADK Python API, the scaffolding commands, the evaluation methodology, the deployment targets and the observability wiring without you having to teach it. Seven skills cover workflow, ADK code, scaffolding, eval, deploy, publish and observability. The CLI also works standalone: every command runs directly from a terminal, and the skills are a convenience layer, not a requirement.

Evaluation is unusually well covered for a scaffolding tool. Beyond `eval run`, the CLI can synthesise multi-turn eval scenarios for a local agent, grade generated traces against named metrics, compare two result files, cluster failure modes out of grade results, and auto-tune agent prompts from the eval data. Local development — `create`, `run`, `eval` — works against an AI Studio API key without a Google Cloud project; Cloud is needed for deployment and the platform features. The project is Apache-2.0 licensed and published to PyPI as `google-agents-cli`. It is the successor to Agent Starter Pack, which moved to maintenance mode and points new projects here.

// What it does

• Project scaffolding with `create`, plus `scaffold enhance` to add deployment and CI/CD to an agent project that already exists
• Evaluation suite: run and grade traces, synthesise multi-turn eval scenarios, compare two result files, cluster failure modes, and auto-tune prompts from eval data
• Deployment to Agent Runtime, Cloud Run or GKE, with `infra single-project` and `infra cicd` provisioning the surrounding infrastructure and pipelines
• `publish gemini-enterprise` registers a finished agent with Gemini Enterprise
• Seven agent skills installed into Claude Code, Codex, Antigravity CLI or any other coding assistant, covering the ADK API, scaffolding, eval, deploy, publish and observability
• Observability wiring for Cloud Trace and logging, plus third-party integrations

// Getting started

You need Python 3.11+, uv and Node.js. One command installs both the CLI and the skills; local development needs only an AI Studio API key.

uvx google-agents-cli setup

npx skills add google/agents-cli

agents-cli login
agents-cli create my-agent
cd my-agent
agents-cli install
agents-cli run "summarise the attached report in three bullets"

agents-cli eval dataset synthesize
agents-cli eval run
agents-cli eval analyze
agents-cli eval compare before.json after.json
agents-cli eval optimize

agents-cli infra single-project
agents-cli deploy
agents-cli publish gemini-enterprise

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Take an ADK agent from an empty directory to a deployed, registered Gemini Enterprise agent without assembling the CI/CD and infrastructure yourself
• Give Claude Code or Codex working knowledge of ADK and Google Cloud deployment so it can build agents for you instead of guessing at the API
• Put a real evaluation loop around an agent — synthesised scenarios, graded traces, clustered failure modes, prompt auto-tuning — rather than eyeballing outputs
• Add production deployment, observability and CI/CD to an agent project that was prototyped without them

// How Agents CLI compares

Agents CLI alongside other open-source agent frameworks & builders tools AI/TLDR tracks, ranked by GitHub stars.