adk-scaffold

name	adk-scaffold
description	MUST READ before scaffolding any ADK agent project. Project scaffolding — create new ADK agent projects or enhance existing ones with deployment, CI/CD, and infrastructure. Covers requirements gathering, DESIGN_SPEC.md authoring, create and enhance workflows, and template options. Use when user says "create a new agent", "scaffold a project", "set up deployment", "add CI/CD", "start a new ADK app", "enhance my project", or "add RAG". Do NOT use for API code patterns (use adk-cheatsheet), evaluation (use adk-eval-guide), or deployment operations (use adk-deploy-guide).
metadata	{"author":"Google","version":"0.1.0","mcp-server":"adk-mcp"}

ADK Project Scaffolding Guide

Use the agent-starter-pack CLI (via uvx) to create new ADK agent projects or enhance existing ones with deployment, CI/CD, and infrastructure scaffolding.

---

Step 1: Gather Requirements

Ask these questions in two rounds. Start with the use case, then move to architecture.

Start with the use case, then ask follow-ups based on answers.

Always ask:

1. What problem will the agent solve? — Core purpose and capabilities
2. External APIs or data sources needed? — Tools, integrations, auth requirements
3. Safety constraints? — What the agent must NOT do, guardrails
4. Preferred language? — Python, Go, TypeScript, or Java
5. Deployment preference? — Prototype first (recommended) or full deployment? If deploying: Agent Engine or Cloud Run?

Ask based on context:

• If RAG or data ingestion mentioned → Datastore? Vertex AI Search or Vertex AI Vector Search? Use --agent agentic_rag --datastore <choice>.
• If agent should be available to other agents → A2A protocol? Use --agent adk_a2a to expose the agent as an A2A-compatible service.
• If full deployment chosen → CI/CD runner? GitHub Actions (default) or Google Cloud Build?
• If Cloud Run chosen → Session storage? In-memory (default), Cloud SQL (persistent), or Agent Engine (managed).
• If deployment with CI/CD chosen → Git repository? Does one already exist, or should one be created? If creating, public or private?

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Step 2: Write DESIGN_SPEC.md

Compose a detailed spec with these sections. Present the full spec for user approval before scaffolding.

# DESIGN_SPEC.md

## Overview
2-3 paragraphs describing the agent's purpose and how it works.

## Example Use Cases
3-5 concrete examples with expected inputs and outputs.

## Tools Required
Each tool with its purpose, API details, and authentication needs.

## Constraints & Safety Rules
Specific rules — not just generic statements.

## Success Criteria
Measurable outcomes for evaluation.

## Edge Cases to Handle
At least 3-5 scenarios the agent must handle gracefully.

The spec should be thorough enough for another developer to implement the agent without additional context.

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Step 3: Create or Enhance the Project

Create a New Project

uvx agent-starter-pack create <project-name> \
  --agent <template> \
  --deployment-target <target> \
  --region <region> \
  --prototype \
  -y

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.
• 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.

Create Flags

Flag	Short	Default	Description
--agent	-a	adk	Agent template (see template table below)
--deployment-target	-d	agent_engine	Deployment target (agent_engine, cloud_run, none)
--region		us-central1	GCP region
--prototype	-p	off	Skip CI/CD and Terraform (recommended for first pass)
--cicd-runner		skip	github_actions or google_cloud_build
--datastore	-ds	—	Datastore for data ingestion (vertex_ai_search, vertex_ai_vector_search)
--session-type		in_memory	Session storage (in_memory, cloud_sql, agent_engine)
--auto-approve	-y	off	Skip confirmation prompts
--skip-checks	-s	off	Skip GCP/Vertex AI verification checks
--agent-directory	-dir	app	Agent code directory name
--google-api-key	-k	—	Use Google AI Studio instead of Vertex AI
--agent-guidance-filename		GEMINI.md	Guidance file name (CLAUDE.md, AGENTS.md)
--debug		off	Enable debug logging for troubleshooting

Enhance an Existing Project

uvx agent-starter-pack enhance . \
  --deployment-target <target> \
  -y

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

Enhance Flags

All create flags are supported, plus:

Flag	Short	Default	Description
--name	-n	directory name	Project name for templating
--base-template	-bt	—	Override base template (e.g. agentic_rag to add RAG)
--dry-run		off	Preview changes without applying
--force		off	Force overwrite all files (skip smart-merge)

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
uvx agent-starter-pack enhance . --deployment-target agent_engine -y

# Add CI/CD pipeline (ask: GitHub Actions or Cloud Build?)
uvx agent-starter-pack enhance . --cicd-runner github_actions -y

# Add RAG with data ingestion
uvx agent-starter-pack enhance . --base-template agentic_rag --datastore vertex_ai_search -y

# Preview what would change (dry run)
uvx agent-starter-pack enhance . --deployment-target cloud_run --dry-run -y

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Template Options

Template	Language	Deployment	Description
adk	Python	Agent Engine, Cloud Run	Standard ADK agent (default)
adk_a2a	Python	Agent Engine, Cloud Run	Agent-to-agent coordination (A2A protocol)
agentic_rag	Python	Agent Engine, Cloud Run	RAG with data ingestion pipeline
adk_live	Python	Cloud Run	Real-time multimodal agent (Live API)
adk_go	Go	Cloud Run only	Go ADK agent
adk_ts	TypeScript	Cloud Run only	TypeScript ADK agent
adk_java	Java	Cloud Run only	Java ADK agent (Spring Boot)

Note: Go, TypeScript, and Java templates only support Cloud Run deployment. Do not use --deployment-target agent_engine with these templates.

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Deployment Options

Target	Description
agent_engine	Managed by Google (Vertex AI Agent Engine). Sessions handled automatically. Python only.
cloud_run	Container-based deployment. More control, requires Dockerfile. All languages.
none	No deployment scaffolding. Code only.

"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 enhance:

# Step 1: Create a prototype
uvx agent-starter-pack create my-agent --agent adk --prototype -y

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

# Step 3: Add deployment when ready
uvx agent-starter-pack enhance . --deployment-target agent_engine -y

Agent Engine and session_type

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

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Step 4: Save DESIGN_SPEC.md

After scaffolding, save the approved spec from Step 2 to the project root as DESIGN_SPEC.md.

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Scaffold as Reference

When you need specific files (Terraform, CI/CD workflows, Dockerfile) but don't want to scaffold the current project directly, create a temporary reference project in /tmp/:

uvx agent-starter-pack create /tmp/ref-project \
  --agent adk \
  --deployment-target cloud_run \
  --cicd-runner github_actions \
  -y

Inspect the generated files, adapt what you need, and copy into the actual project. Delete the reference project when done.

This is useful for:

• Non-standard project structures that enhance can't handle
• Cherry-picking specific infrastructure files
• Understanding what ASP generates before committing to it

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Critical Rules

• NEVER change the model in existing code unless explicitly asked
• NEVER mkdir before create — the CLI creates the directory; pre-creating it causes enhance mode instead of create mode
• NEVER create a Git repo or push to remote without asking — confirm repo name, public vs private, and whether the user wants it created at all
• Always ask before choosing CI/CD runner — present GitHub Actions and Cloud Build as options, don't default silently
• Agent Engine clears session_type — if deploying to agent_engine, remove any session_type setting from your code
• Agent Engine is Python only — Go, TypeScript, and Java templates require cloud_run
• Start with --prototype for quick iteration — add deployment later with enhance
• Project names must be ≤26 characters, lowercase, letters/numbers/hyphens only

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Examples

Using scaffold as reference: User says: "I need a Dockerfile for my non-standard project" Actions:

1. Create temp project: uvx agent-starter-pack create /tmp/ref --agent adk --deployment-target cloud_run -y
2. Copy relevant files (Dockerfile, etc.) from /tmp/ref
3. Delete temp project Result: Infrastructure files adapted to the actual project

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Troubleshooting

uvx command not found

Install uv: curl -LsSf https://astral.sh/uv/install.sh | sh

If uv is not an option, use pip instead:

# macOS/Linux
python -m venv .venv && source .venv/bin/activate
# Windows
python -m venv .venv && .venv\Scripts\activate

pip install agent-starter-pack
agent-starter-pack create <project-name> ...