ADK Cheatsheet

Before using this skill, activate

/google-agents-cli-workflow

first — it contains the required development phases and scaffolding steps.

Prerequisites

1. Run

   agents-cli info

   — if it shows project config, skip to the cheatsheet below
2. If no project exists: run

   agents-cli scaffold create <name>

3. If user has existing code: run

   agents-cli scaffold enhance .

Python only for now. This cheatsheet currently covers the Python ADK SDK. Support for other languages is coming soon.

Quick Reference — Most Common Patterns

Agent Creation

from google.adk.agents import Agent

root_agent = Agent(
    name="my_agent",
    model="gemini-flash-latest",
    instruction="You are a helpful assistant that ...",
    tools=[my_tool],
)

NEVER change an existing agent's

model=

value unless the user explicitly asks. If a Gemini model returns a 404, it's almost always a

GOOGLE_CLOUD_LOCATION

issue — run the listing command to verify availability before changing anything. For model docs, fetch

https://adk.dev/agents/models/google-gemini/index.md

.

bash

uv run --with google-genai python -c "
from google import genai
client = genai.Client(vertexai=True, location='global')
for m in client.models.list(): print(m.name)
"

Basic Tool

from google.adk.tools import FunctionTool

def get_weather(city: str) -> dict:
    """Get current weather for a city."""
    return {"city": city, "temp": "22°C", "condition": "sunny"}

weather_tool = FunctionTool(func=get_weather)

ADK built-in tool imports: Import the tool instance, not the module.

python

from google.adk.tools.load_web_page import load_web_page  # CORRECT
from google.adk.tools import load_web_page                 # WRONG

Simple Callback

from google.adk.agents.callback_context import CallbackContext

async def initialize_state(callback_context: CallbackContext) -> None:
    state = callback_context.state
    if "history" not in state:
        state["history"] = []

root_agent = Agent(
    name="my_agent",
    model="gemini-flash-latest",
    instruction="...",
    before_agent_callback=initialize_state,
)

Reference Files

references/adk-python.md

references/adk-2.0.md

ADK 2.0 Workflows — Opt-in

The Workflow API is experimental, pre-GA (ADK 2.0). Do NOT recommend it by default.

Before suggesting workflow patterns, explain the following to the user and ask if they want to proceed:

1. What it is: ADK 2.0 introduces a graph-based Workflow API — nodes (functions, LLM agents, tools) connected by edges with conditional routing, fan-out/fan-in parallelism, and human-in-the-loop interrupts.
2. When it helps: Complex multi-step pipelines needing deterministic control flow, parallel processing of list items, structured approval gates, or retry logic — cases where SequentialAgent/ParallelAgent/LoopAgent feel limiting.
3. Risks: Pre-GA — APIs may change before GA. Requires

   google-adk >= 2.0.0

   and Python >= 3.11. Incompatible with Live Streaming. Scaffolded projects need

   pyproject.toml

   changes before upgrade — see the reference file for step-by-step instructions.

Only read

references/adk-2.0.md

after the user explicitly opts in. If they decline or are unsure, use the standard ADK 1.x orchestration patterns from

references/adk-python.md

(SequentialAgent, ParallelAgent, LoopAgent, BaseAgent).

ADK Documentation

curl https://adk.dev/llms.txt

Related Skills

• /google-agents-cli-workflow

  — Development workflow, coding guidelines, and operational rules

• /google-agents-cli-scaffold

  — Project creation and enhancement with

  agents-cli scaffold create

  /

  scaffold enhance

• /google-agents-cli-eval

  — Evaluation methodology, evalset schema, and the eval-fix loop

• /google-agents-cli-deploy

  — Deployment targets, CI/CD pipelines, and production workflows