Google ADK Python Skill

When to Use This Skill

• Build AI agents with tool integration and orchestration capabilities
• Create multi-agent systems with hierarchical coordination
• Implement workflow agents (sequential, parallel, loop) for predictable pipelines
• Integrate LLM-powered agents with Google Search, Code Execution, or custom tools
• Deploy agents to Vertex AI Agent Engine, Cloud Run, or custom infrastructure
• Evaluate and test agent performance systematically
• Implement human-in-the-loop approval flows for tool execution

Core Concepts

Agent Types

• Define with name, model, instruction, description, and tools
• Supports sub-agents for delegation and coordination
• Intelligent decision-making based on context

• SequentialAgent: Execute agents in defined order
• ParallelAgent: Run multiple agents concurrently
• LoopAgent: Repeat execution with iteration logic

Key Components

• Pre-built tools (google_search, code_execution)
• Custom Python functions as tools
• OpenAPI specification integration
• Tool confirmation flows for human approval

• Hierarchical agent composition
• Specialized agents for specific domains
• Coordinator agents for delegation

Installation

# Stable release (recommended)
pip install google-adk

# Development version (latest features)
pip install git+https://github.com/google/adk-python.git@main

Implementation Patterns

Single Agent with Tools

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]
)

Multi-Agent System

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]
)

Custom Tool Creation

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]
)

Sequential Workflow

from google.adk.agents import SequentialAgent

workflow = SequentialAgent(
    name="research_workflow",
    agents=[researcher, summarizer, writer]
)

Parallel Workflow

from google.adk.agents import ParallelAgent

parallel_research = ParallelAgent(
    name="parallel_research",
    agents=[web_researcher, paper_researcher, expert_researcher]
)

Human-in-the-Loop

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
)

Deployment Options

Cloud Run Deployment

# Containerize agent
docker build -t my-agent .

# Deploy to Cloud Run
gcloud run deploy my-agent --image my-agent

Vertex AI Agent Engine

# Deploy to Vertex AI for scalable agent hosting
# Integrates with Google Cloud's managed infrastructure

Custom Infrastructure

# Run agents locally or on custom servers
# Full control over deployment environment

Model Support

• gemini-2.5-flash
• gemini-2.5-pro
• gemini-1.5-flash
• gemini-1.5-pro

Best Practices

1. Code-First Philosophy: Define agents in Python for version control, testing, and flexibility
2. Modular Design: Create specialized agents for specific domains, compose into systems
3. Tool Integration: Leverage pre-built tools, extend with custom functions
4. Evaluation: Test agents systematically against test cases
5. Safety: Implement confirmation flows for sensitive operations
6. Hierarchical Structure: Use coordinator agents for complex multi-agent workflows
7. Workflow Selection: Choose workflow agents for predictable pipelines, LLM agents for dynamic routing

Common Use Cases

• Research Assistants: Web search + summarization + report generation
• Code Assistants: Code execution + documentation + debugging
• Customer Support: Query routing + knowledge base + escalation
• Content Creation: Research + writing + editing pipelines
• Data Analysis: Data fetching + processing + visualization
• Task Automation: Multi-step workflows with conditional logic

Development UI

• Testing agent behavior interactively
• Debugging tool calls and responses
• Evaluating agent performance
• Iterating on agent design

Resources

• GitHub: https://github.com/google/adk-python
• Documentation: https://google.github.io/adk-docs/
• llms.txt: https://raw.githubusercontent.com/google/adk-python/refs/heads/main/llms.txt

Implementation Workflow

1. Define Requirements: Identify agent capabilities and tools needed
2. Choose Architecture: Single agent, multi-agent, or workflow-based
3. Select Tools: Pre-built, custom functions, or OpenAPI integrations
4. Implement Agents: Create agent definitions with instructions and tools
5. Test Locally: Use development UI for iteration
6. Add Evaluation: Create test cases for systematic validation
7. Deploy: Choose Cloud Run, Vertex AI, or custom infrastructure
8. Monitor: Track agent performance and iterate