antigravity-claudekit/skills/ck-google-adk-python/SKILL.md

name, description

name	description
ck-google-adk-python	Builds AI agents using Google Agent Development Kit (ADK) for Python. Activate when user says 'build a Google ADK agent', 'create agent with ADK', 'Google Agent Development Kit', 'multi-agent with ADK', or 'ADK tool calling'. Accepts agent goals, tool definitions, and orchestration requirements.

Overview

Scaffolds and implements AI agents using Google's Agent Development Kit (ADK) for Python, covering single agents, multi-agent pipelines, tool definitions, and deployment to Vertex AI Agent Engine.

When to Use

• Building autonomous agents that use tools to complete tasks
• Creating multi-agent systems with specialized sub-agents
• Implementing agents that need Google Cloud service integrations
• Deploying production agents on Vertex AI Agent Engine
• Building agents with structured tool calling and state management

Don't Use When

• Project uses a different agent framework (LangChain, CrewAI, AutoGen)
• Simple single-turn LLM calls without tool use (use direct Gemini API)
• Frontend-only application with no agent orchestration needs
• Budget constraints prohibit Vertex AI usage (ADK has local dev mode though)

Steps / Instructions

1. Install ADK

pip install google-adk
# For Vertex AI deployment:
pip install google-adk[vertexai]

2. Define a Simple Agent

# agent.py
from google.adk.agents import Agent
from google.adk.tools import FunctionTool
import os

def get_weather(city: str) -> dict:
    """Get current weather for a city."""
    # Replace with real weather API call
    return {"city": city, "temperature": 22, "condition": "sunny"}

weather_tool = FunctionTool(func=get_weather)

root_agent = Agent(
    name="weather_agent",
    model="gemini-1.5-pro",
    description="Answers weather questions for any city.",
    instruction="""You help users get weather information.
    Use the get_weather tool to fetch current conditions.
    Always confirm the city before reporting.""",
    tools=[weather_tool],
)

3. Run Locally

# main.py
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai.types import Content, Part
from agent import root_agent

async def main():
    session_service = InMemorySessionService()
    runner = Runner(
        agent=root_agent,
        app_name="weather_app",
        session_service=session_service,
    )

    session = await session_service.create_session(
        app_name="weather_app",
        user_id="user_001",
    )

    message = Content(parts=[Part(text="What's the weather in Tokyo?")])

    async for event in runner.run_async(
        user_id="user_001",
        session_id=session.id,
        new_message=message,
    ):
        if event.is_final_response():
            print(event.content.parts[0].text)

import asyncio
asyncio.run(main())

4. Multi-Agent Pattern

from google.adk.agents import Agent
from google.adk.agents.sequential import SequentialAgent

research_agent = Agent(
    name="researcher",
    model="gemini-1.5-pro",
    instruction="Research the given topic and return key facts.",
    tools=[search_tool],
)

writer_agent = Agent(
    name="writer",
    model="gemini-1.5-pro",
    instruction="Write a concise summary based on the research provided.",
)

pipeline = SequentialAgent(
    name="research_writer",
    sub_agents=[research_agent, writer_agent],
)

5. Tool Definition Best Practices

from typing import Annotated
from pydantic import Field

def search_database(
    query: Annotated[str, Field(description="Search query string")],
    limit: Annotated[int, Field(description="Max results to return", ge=1, le=50)] = 10,
) -> list[dict]:
    """Search the product database and return matching items."""
    # implementation
    pass

• Use type annotations — ADK generates JSON schema from them
• Write clear docstrings — used as tool description for the model
• Return dicts/lists that serialize cleanly to JSON
• Handle errors gracefully and return error info in the dict

6. Session State Management

from google.adk.sessions import DatabaseSessionService

# Persist sessions across restarts
session_service = DatabaseSessionService(
    db_url=os.environ["DATABASE_URL"]
)

7. Deploy to Vertex AI Agent Engine

from google.adk.deployment import deploy_agent

deploy_agent(
    agent=root_agent,
    project=os.environ["GOOGLE_CLOUD_PROJECT"],
    location="us-central1",
    staging_bucket=os.environ["GCS_STAGING_BUCKET"],
)

Notes

• Set GOOGLE_API_KEY or GOOGLE_APPLICATION_CREDENTIALS for authentication
• Use adk web CLI for interactive local testing with a built-in UI
• ADK auto-traces tool calls — view in Google Cloud Trace
• Always validate tool inputs; the model may pass unexpected types