Use when building AI agents with Google's Agent Development Kit (ADK) Python - multi-agent systems, workflow agents, tool integration, Vertex AI deployment, or agent evaluation.
/plugin marketplace add GGPrompts/TabzBeads/plugin install specialized@tabz-beadsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
You are an expert guide for Google's Agent Development Kit (ADK) Python - an open-source, code-first toolkit for building, evaluating, and deploying AI agents.
Use this skill when users need to:
LlmAgent: LLM-powered agents capable of dynamic routing and adaptive behavior
Workflow Agents: Structured, predictable orchestration patterns
BaseAgent: Foundation for custom agent implementations
Tools Ecosystem:
Multi-Agent Architecture:
# Stable release (recommended)
pip install google-adk
# Development version (latest features)
pip install git+https://github.com/google/adk-python.git@main
from google.adk.agents import LlmAgent
from google.adk.tools import google_search
agent = LlmAgent(
name="search_assistant",
model="gemini-2.5-flash",
instruction="You are a helpful assistant that searches the web for information.",
description="Search assistant for web queries",
tools=[google_search]
)
from google.adk.agents import LlmAgent
# Specialized agents
researcher = LlmAgent(
name="Researcher",
model="gemini-2.5-flash",
instruction="Research topics thoroughly using web search.",
tools=[google_search]
)
writer = LlmAgent(
name="Writer",
model="gemini-2.5-flash",
instruction="Write clear, engaging content based on research.",
)
# Coordinator agent
coordinator = LlmAgent(
name="Coordinator",
model="gemini-2.5-flash",
instruction="Delegate tasks to researcher and writer agents.",
sub_agents=[researcher, writer]
)
from google.adk.tools import Tool
def calculate_sum(a: int, b: int) -> int:
"""Calculate the sum of two numbers."""
return a + b
# Convert function to tool
sum_tool = Tool.from_function(calculate_sum)
agent = LlmAgent(
name="calculator",
model="gemini-2.5-flash",
tools=[sum_tool]
)
from google.adk.agents import SequentialAgent
workflow = SequentialAgent(
name="research_workflow",
agents=[researcher, summarizer, writer]
)
from google.adk.agents import ParallelAgent
parallel_research = ParallelAgent(
name="parallel_research",
agents=[web_researcher, paper_researcher, expert_researcher]
)
from google.adk.tools import google_search
# Tool with confirmation required
agent = LlmAgent(
name="careful_searcher",
model="gemini-2.5-flash",
tools=[google_search],
tool_confirmation=True # Requires approval before execution
)
# Containerize agent
docker build -t my-agent .
# Deploy to Cloud Run
gcloud run deploy my-agent --image my-agent
# Deploy to Vertex AI for scalable agent hosting
# Integrates with Google Cloud's managed infrastructure
# Run agents locally or on custom servers
# Full control over deployment environment
Optimized for Gemini:
Model Agnostic: While optimized for Gemini, ADK supports other LLM providers through standard APIs.
ADK includes built-in interface for:
When implementing ADK-based agents:
Remember: ADK treats agent development like traditional software engineering - use version control, write tests, and follow engineering best practices.
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