Google Agents CLI Scaffold

ADK Project Scaffolding Guide

Requires: agents-cli (uv tool install google-agents-cli) — install uv first if needed.

Use the agents-cli CLI to create new ADK agent projects or enhance existing ones with deployment, CI/CD, and infrastructure scaffolding.

Prerequisite: Clarify Requirements (MANDATORY for new projects)

Before scaffolding a new project, load /google-agents-cli-workflow and complete Phase 0 — clarify the user's requirements before running any scaffold create command. Ask what the agent should do, what tools/APIs it needs, and whether they want a prototype or full deployment.

Step 1: Choose Architecture

Mapping user choices to CLI flags:

Product name mapping

Older names → CLI values (vertexai SDK package name unchanged):

• Agent Engine / Vertex AI Agent Engine → --deployment-target agent_runtime
• Vertex AI Search / Agent Search → --datastore agent_platform_search
• Vertex AI Vector Search / Vector Search → --datastore agent_platform_vector_search
• Agent Engine sessions / Agent Platform Sessions → --session-type agent_platform_sessions

Step 2: Create or Enhance the Project

Create a New Project

agents-cli scaffold create <project-name> \
  --agent <template> \
  --deployment-target <target> \
  --region <region> \
  --prototype

Constraints:

• Project name must be 26 characters or less, lowercase letters, numbers, and hyphens only.
• Do NOT mkdir the project directory before running create — the CLI creates it automatically. If you mkdir first, create will fail or behave unexpectedly.
• Auto-detect the guidance filename based on the IDE you are running in and pass --agent-guidance-filename accordingly (AGENTS.md for Antigravity CLI/OpenAI Codex/other, CLAUDE.md for Claude Code, GEMINI.md for Gemini CLI).
• When enhancing an existing project, check where the agent code lives. If it's not in app/, pass --agent-directory <dir> (e.g. --agent-directory agent). Getting this wrong causes enhance to miss or misplace files.

Reference Files

Enhance an Existing Project

agents-cli scaffold enhance . --deployment-target <target>
agents-cli scaffold enhance . --cicd-runner <runner>

Run this from inside the project directory (or pass the path instead of .).

Upgrade a Project

Upgrade an existing project to a newer agents-cli version, intelligently applying updates while preserving your customizations:

agents-cli scaffold upgrade                # Upgrade current directory
agents-cli scaffold upgrade <project-path> # Upgrade specific project
agents-cli scaffold upgrade --dry-run      # Preview changes without applying
agents-cli scaffold upgrade --auto-approve  # Auto-apply non-conflicting changes

Execution Modes

The CLI defaults to strict programmatic mode — all required params must be supplied as CLI flags or a UsageError is raised. No approval flags needed. Pass all required params explicitly.

Common Workflows

Always ask the user before running these commands. Present the options (CI/CD runner, deployment target, etc.) and confirm before executing.

# Add deployment to an existing prototype (strict programmatic)
agents-cli scaffold enhance . --deployment-target agent_runtime

# Add CI/CD pipeline (ask: GitHub Actions or Cloud Build?)
agents-cli scaffold enhance . --cicd-runner github_actions

Template Options

Deployment Options

"Prototype First" Pattern (Recommended)

Start with --prototype to skip CI/CD and Terraform. Focus on getting the agent working first, then add deployment later with scaffold enhance:

# Step 1: Create a prototype
agents-cli scaffold create my-agent --agent adk --prototype

# Step 2: Iterate on the agent code...

# Step 3: Add deployment when ready
agents-cli scaffold enhance . --deployment-target agent_runtime

Agent Runtime and session_type

When using agent_runtime as the deployment target, Agent Runtime manages sessions internally. If your code sets a session_type, clear it — Agent Runtime overrides it.

Step 3: Load Dev Workflow

After scaffolding, immediately load /google-agents-cli-workflow — it contains the development workflow, coding guidelines, and operational rules you must follow when implementing the agent.

Key files to customize: app/agent.py (instruction, tools, model), app/tools.py (custom tool functions), .env (project ID, location, API keys). Files to preserve: agents-cli-manifest.yaml (CLI reads this), deployment configs under deployment/, Makefile, app/__init__.py (the App(name=...) must match the directory name — default app), and the generated runtime/A2A infra (app/fast_api_app.py, app/app_utils/a2a.py, app/app_utils/services.py, Dockerfile) — these wire up serving, sessions, and the built-in A2A surface; don't hand-edit them.

RAG projects (agentic_rag) — provision datastore first: Before running agents-cli playground or testing your RAG agent, you must provision the datastore and ingest data:

agents-cli infra datastore   # Provision datastore infrastructure
agents-cli data-ingestion    # Ingest data into the datastore

Use infra datastore — not infra single-project. Both provision the datastore, but infra datastore is faster because it skips unrelated Terraform. Without this step, the agent won't have data to search over.

Vector Search region: vector_search_location defaults to us-central1, separate from region (us-east1). It sets both the Vector Search collection region and the BQ ingestion dataset region, kept colocated to avoid cross-region data movement. Override per-invocation with agents-cli data-ingestion --vector-search-location <region>.

Verifying your agent works: Use agents-cli run "test prompt" for quick smoke tests, then agents-cli eval generate and agents-cli eval grade for systematic validation. Do NOT write pytest tests that assert on LLM response content — that belongs in eval.