Google ADK Python Skill
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.
When to Use This Skill
Use this skill when users need to:
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Build AI agents with tool integration and orchestration capabilities
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Create multi-agent systems with hierarchical coordination
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Implement workflow agents (sequential, parallel, loop) for predictable pipelines
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Integrate LLM-powered agents with Google Search, Code Execution, or custom tools
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Deploy agents to Vertex AI Agent Engine, Cloud Run, or custom infrastructure
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Evaluate and test agent performance systematically
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Implement human-in-the-loop approval flows for tool execution
Core Concepts
Agent Types
LlmAgent: LLM-powered agents capable of dynamic routing and adaptive behavior
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Define with name, model, instruction, description, and tools
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Supports sub-agents for delegation and coordination
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Intelligent decision-making based on context
Workflow Agents: Structured, predictable orchestration patterns
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SequentialAgent: Execute agents in defined order
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ParallelAgent: Run multiple agents concurrently
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LoopAgent: Repeat execution with iteration logic
BaseAgent: Foundation for custom agent implementations
Key Components
Tools Ecosystem:
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Pre-built tools (google_search, code_execution)
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Custom Python functions as tools
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OpenAPI specification integration
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Tool confirmation flows for human approval
Multi-Agent Architecture:
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Hierarchical agent composition
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Specialized agents for specific domains
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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
Optimized for Gemini:
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gemini-2.5-flash
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gemini-2.5-pro
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gemini-1.5-flash
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gemini-1.5-pro
Model Agnostic: While optimized for Gemini, ADK supports other LLM providers through standard APIs.
Best Practices
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Code-First Philosophy: Define agents in Python for version control, testing, and flexibility
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Modular Design: Create specialized agents for specific domains, compose into systems
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Tool Integration: Leverage pre-built tools, extend with custom functions
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Evaluation: Test agents systematically against test cases
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Safety: Implement confirmation flows for sensitive operations
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Hierarchical Structure: Use coordinator agents for complex multi-agent workflows
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Workflow Selection: Choose workflow agents for predictable pipelines, LLM agents for dynamic routing
Common Use Cases
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Research Assistants: Web search + summarization + report generation
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Code Assistants: Code execution + documentation + debugging
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Customer Support: Query routing + knowledge base + escalation
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Content Creation: Research + writing + editing pipelines
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Data Analysis: Data fetching + processing + visualization
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Task Automation: Multi-step workflows with conditional logic
Development UI
ADK includes built-in interface for:
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Testing agent behavior interactively
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Debugging tool calls and responses
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Evaluating agent performance
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Iterating on agent design
Resources
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Documentation: https://google.github.io/adk-docs/
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llms.txt: https://raw.githubusercontent.com/google/adk-python/refs/heads/main/llms.txt
Implementation Workflow
When implementing ADK-based agents:
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Define Requirements: Identify agent capabilities and tools needed
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Choose Architecture: Single agent, multi-agent, or workflow-based
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Select Tools: Pre-built, custom functions, or OpenAPI integrations
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Implement Agents: Create agent definitions with instructions and tools
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Test Locally: Use development UI for iteration
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Add Evaluation: Create test cases for systematic validation
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Deploy: Choose Cloud Run, Vertex AI, or custom infrastructure
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Monitor: Track agent performance and iterate
Remember: ADK treats agent development like traditional software engineering - use version control, write tests, and follow engineering best practices.
Task Planning Notes
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Always plan and break many small todo tasks
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Always add a final review todo task to review the works done at the end to find any fix or enhancement needed