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name: adk description: Comprehensive guide for building AI agents using Google's Agent Development Kit (ADK). Use when building agentic systems, orchestrating multi-agent workflows, creating AI assistants with custom tools, implementing LLM-powered applications, or working with Google Gemini models. Covers agent types (LlmAgent, SequentialAgent, ParallelAgent, LoopAgent), tool integration (FunctionTool, built-in tools, MCP), session management, deployment strategies, and multi-agent coordination patterns.

Agent Development Kit (ADK)

Overview

This skill provides comprehensive guidance for building sophisticated AI agents using Google's Agent Development Kit (ADK), an open-source Python framework optimized for Gemini models but supporting any LLM. ADK enables code-first agent development with modular multi-agent systems, rich tool ecosystems, and flexible deployment options.

How to Use This Skill

Quick Start: Follow the Quick Start section below for basic agent setup

Learning & Building:

• Patterns & Examples: See SKILL.md sections (Tool Integration, Multi-Agent Coordination, Common Patterns)
• Code Snippets: references/examples.md for quick copy-paste examples
• Working Samples: samples/adk-samples/ for complete runnable examples
• API Reference: references/core_concepts.md for condensed API docs or docs/adk-docs/docs/ for complete documentation

A2A Protocol:

• Quick Guide: See "Agent as Tool" and "Multi-Agent Coordination" sections in SKILL.md
• Protocol Spec: samples/A2A/docs/specification.md
• Samples: samples/a2a-samples/samples/python/ for client/server examples
• Detailed Docs: docs/adk-docs/docs/a2a/ for ADK-specific A2A integration

Production Deployment:

• Quick Guide: references/deployment_best_practices.md
• Complete Guides: docs/adk-docs/docs/deploy/ for Cloud Run, Vertex AI, GKE

When to Use ADK

Build ADK agents when you need:

• Multi-step agentic workflows with planning, tool use, and dynamic decision-making
• Multi-agent systems with specialized agents coordinating on complex tasks
• Custom tools that extend LLM capabilities with external APIs, databases, or computations
• Production-grade deployment on Google Cloud (Cloud Run, Vertex AI Agent Engine)
• Flexible orchestration combining deterministic workflows (Sequential, Parallel, Loop) with LLM-driven routing
• Session persistence and conversation memory across multiple turns

Quick Start

Installation

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # macOS/Linux

# Install ADK
pip install google-adk

Environment Setup

Create .env file with your API credentials:

# Option 1: Google AI Studio (free tier)
GOOGLE_API_KEY=your-api-key

# Option 2: Vertex AI (production)
GOOGLE_GENAI_USE_VERTEXAI=TRUE
GOOGLE_CLOUD_PROJECT=your-project-id
GOOGLE_CLOUD_LOCATION=us-central1

Minimal Agent

# agent.py
from google.adk.agents import Agent

root_agent = Agent(  # Must be named 'root_agent'
    name='assistant',
    model='gemini-2.5-flash',
    description='A helpful AI assistant',
    instruction='Help users with their questions in a clear, friendly manner'
)

Running Your Agent

# Interactive CLI
adk run agent_module.root_agent "Hello, how can you help me?"

# Web UI (browser-based testing)
adk web  # Access at http://localhost:8000

# API Server (REST endpoint)
adk api_server  # POST /run at http://localhost:8080

Core Agent Types

LlmAgent (Agent)

Powered by LLMs for dynamic reasoning, tool selection, and response generation.

Key Configuration:

from google.adk.agents import LlmAgent

agent = LlmAgent(
    name='weather_bot',                    # Unique identifier
    model='gemini-2.5-flash',              # Model name
    description='Provides weather info',   # For delegation routing
    instruction='Use tools to get weather', # System prompt
    tools=[get_weather_tool],              # Available tools
    output_key='weather_data',             # Save response to state
    sub_agents=[specialist1, specialist2]  # Child agents
)

When to Use:

• Need flexible, language-driven behavior
• Dynamic tool selection based on context
• Coordination and routing to sub-agents
• Natural language understanding and generation

SequentialAgent

Executes sub-agents in strict order. Deterministic flow without LLM.

Example:

from google.adk.agents import SequentialAgent, LlmAgent

pipeline = SequentialAgent(
    name='content_pipeline',
    sub_agents=[
        LlmAgent(name='writer', output_key='draft'),
        LlmAgent(name='reviewer', output_key='feedback'),
        LlmAgent(name='reviser')  # Final output
    ]
)

When to Use:

• Fixed, predictable process steps
• Each step depends on previous results
• Data pipelines (ETL, processing, reporting)

ParallelAgent

Executes sub-agents concurrently. Returns when all complete.

Example:

from google.adk.agents import ParallelAgent

parallel = ParallelAgent(
    name='multi_source_fetch',
    sub_agents=[
        LlmAgent(name='fetch_api1', output_key='data1'),
        LlmAgent(name='fetch_api2', output_key='data2'),
        LlmAgent(name='fetch_api3', output_key='data3')
    ]
)

When to Use:

• Independent tasks that can run simultaneously
• Multiple data sources to query
• Performance-critical parallel operations

LoopAgent

Repeats sub-agents until termination condition met.

Example:

from google.adk.agents import LoopAgent

def is_approved(context) -> bool:
    return context.state.get('status') == 'approved'

loop = LoopAgent(
    name='refinement_loop',
    sub_agents=[generator, critic, reviser],
    termination_condition=is_approved,
    max_iterations=5
)

When to Use:

• Iterative refinement processes
• Retry logic with dynamic conditions
• Quality-driven loops (generate until threshold met)

Tool Integration

Function Tools

Convert Python functions to agent tools. ADK generates schema automatically from function signature.

Critical Requirements:

1. Docstring: LLM uses this to understand when/how to use the tool (MOST IMPORTANT!)
2. Type hints: Required for parameter schema generation
3. Return dict: Preferred for structured responses

Example:

def get_weather(city: str, units: str = "celsius") -> dict:
    """
    Get current weather for a specified city.
    
    Use this tool when users ask about weather, temperature, or conditions.
    
    Args:
        city: City name (e.g., "San Francisco", "Tokyo")
        units: Temperature units - "celsius" or "fahrenheit"
    
    Returns:
        dict with temperature, condition, humidity, wind_speed
    """
    # API call or mock implementation
    return {
        "temperature": 22,
        "condition": "sunny",
        "humidity": 65,
        "city": city
    }

agent = Agent(
    name='weather_agent',
    tools=[get_weather]  # ADK auto-wraps as FunctionTool
)

Best Practices:

• Descriptive docstrings: Explain what, when, and why
• Clear parameter names: Use descriptive, obvious names
• Type hints: Always include for all parameters
• Structured returns: Return dicts with meaningful keys
• Error handling: Return error info in dict format

Built-in Tools

GoogleSearchTool:

from google.adk.tools import GoogleSearchTool

agent = Agent(
    tools=[GoogleSearchTool()]
)

CodeExecutionTool:

from google.adk.tools import CodeExecutionTool

agent = Agent(
    tools=[CodeExecutionTool()]  # Execute Python in sandbox
)

GoogleRagEngineTool:

from google.adk.tools import GoogleRagEngineTool

agent = Agent(
    tools=[GoogleRagEngineTool(
        data_store_id="your-data-store-id",
        project_id="your-project"
    )]
)

Agent as Tool

Use specialized agents as tools for delegation:

from google.adk.tools import AgentTool

specialist = LlmAgent(
    name='data_analyst',
    description='Analyzes numerical data and creates visualizations',
    instruction='Perform statistical analysis on provided data'
)

coordinator = LlmAgent(
    name='coordinator',
    tools=[AgentTool(specialist)]  # Specialist available as tool
)

MCP Tools

Integrate Model Context Protocol servers as tools:

from google.adk.tools.mcp import MCPToolset

mcp_tools = MCPToolset(
    server_config={
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path"]
    },
    tool_filter=["read_file", "list_directory"]  # Optional: specific tools
)

agent = Agent(tools=[mcp_tools])

Multi-Agent Coordination

Coordinator Pattern

Central agent routes requests to specialists:

greeter = LlmAgent(
    name='greeter',
    description='Handles greetings, introductions, and farewells',
    instruction='Greet users warmly and professionally'
)

task_executor = LlmAgent(
    name='executor',
    description='Executes user tasks and provides solutions',
    instruction='Help users accomplish their requested tasks'
)

coordinator = LlmAgent(
    name='coordinator',
    description='Routes user requests to appropriate specialist',
    instruction="""Analyze the user's request and transfer to:
    - greeter: For greetings, hellos, goodbyes
    - executor: For task requests, questions, help
    
    Transfer immediately to the appropriate agent.""",
    sub_agents=[greeter, task_executor]
)

root_agent = coordinator

Pipeline Pattern

Sequential data processing with state passing:

validator = LlmAgent(
    name='validator',
    instruction='Validate the input data',
    output_key='validation_status'
)

processor = LlmAgent(
    name='processor',
    instruction='Process data if {validation_status} is valid',
    output_key='result'
)

reporter = LlmAgent(
    name='reporter',
    instruction='Generate report from {result}'
)

root_agent = SequentialAgent(
    name='data_pipeline',
    sub_agents=[validator, processor, reporter]
)

Fan-Out/Gather Pattern

Parallel execution with aggregation:

# Fan-out: Parallel research
parallel_research = ParallelAgent(
    name='researchers',
    sub_agents=[
        LlmAgent(name='tech_research', output_key='tech_data'),
        LlmAgent(name='market_research', output_key='market_data'),
        LlmAgent(name='competitor_research', output_key='competitor_data')
    ]
)

# Gather: Synthesize results
synthesizer = LlmAgent(
    name='synthesizer',
    instruction='Synthesize: {tech_data}, {market_data}, {competitor_data}'
)

root_agent = SequentialAgent(
    name='research_workflow',
    sub_agents=[parallel_research, synthesizer]
)

Session & State Management

Shared State

Primary communication mechanism between agents:

# Agent A writes to state
agent_a = LlmAgent(
    name='fetcher',
    output_key='raw_data'  # Saves response to state['raw_data']
)

# Agent B reads from state via instruction templating
agent_b = LlmAgent(
    name='processor',
    instruction='Process this data: {raw_data}'  # Reads state['raw_data']
)

State Namespaces:

• Persistent: state['key'] - survives across conversation turns
• Temporary: state['temp:key'] - cleared after current turn

Session Services

Development (InMemorySessionService):

from google.adk.sessions import InMemorySessionService
session_service = InMemorySessionService()  # Data lost on restart

Production (VertexAiSessionService):

from google.adk.sessions import VertexAiSessionService
session_service = VertexAiSessionService(
    project_id="your-project",
    location="us-central1"
)

Deployment

Local Testing

# Terminal CLI
adk run agent_module.root_agent "Your query"

# Web UI (interactive testing)
adk web

# API Server (REST endpoint)
adk api_server

Cloud Run

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8080
CMD ["adk", "api_server", "--host", "0.0.0.0", "--port", "8080"]

Deploy:

gcloud run deploy my-agent \
  --source . \
  --platform managed \
  --region us-central1

Vertex AI Agent Engine

from google.cloud import aiplatform

aiplatform.init(project="your-project", location="us-central1")

reasoning_engine = aiplatform.ReasoningEngine.create(
    reasoning_engine_source="agent_module",
    requirements=["google-adk"],
    display_name="my-agent"
)

Common Patterns & Best Practices

Effective Instructions

Good:

instruction="""You are a data analyst. When users provide data:
1. Use analyze_data tool to compute statistics
2. Use create_visualization tool for charts
3. Present findings with key insights and recommendations"""

Bad:

instruction="Help with data"  # Too vague

Tool Documentation

Excellent docstring:

def search_database(query: str, max_results: int = 10) -> dict:
    """
    Search company knowledge base for relevant information.
    
    Use when users ask about:
    - Company policies, procedures, guidelines
    - Product documentation or specifications
    - Historical project information
    
    Args:
        query: Natural language search (e.g., "vacation policy")
        max_results: Number of results (default 10, max 50)
    
    Returns:
        dict with results (list), total_count (int), search_time_ms (int)
    """

Error Handling in Tools

def call_api(endpoint: str) -> dict:
    """Call external API with robust error handling"""
    try:
        response = requests.get(f"https://api.example.com/{endpoint}")
        response.raise_for_status()
        return {"success": True, "data": response.json()}
    except requests.HTTPError as e:
        return {
            "success": False,
            "error": f"API error {e.response.status_code}",
            "suggestion": "Verify endpoint and retry"
        }
    except requests.RequestException as e:
        return {
            "success": False,
            "error": "Network error",
            "suggestion": "Check connectivity and retry"
        }

State Management

# Write state
context.state['user_preferences'] = {'theme': 'dark'}
context.state['temp:current_step'] = 'validation'  # Temporary

# Read state in instructions via templating
instruction="Process {user_preferences} for step {current_step}"

# Access in tools
def my_tool(tool_context):
    prefs = tool_context.state.get('user_preferences', {})

Documentation

Official ADK and A2A documentation cloned locally:

Core Documentation

Base: .claude/skills/adk/docs/adk-docs/docs/

Key Topics:

• Agents: agents/ - LlmAgent, workflow agents (Sequential, Parallel, Loop), multi-agent patterns, custom agents
• Tools: tools/ - Function tools, built-in tools, MCP tools, authentication, third-party integrations
• A2A Protocol: a2a/ - Exposing agents via A2A, consuming remote agents, quickstarts
• Streaming: streaming/ - Custom WebSocket streaming, bidirectional audio/video, configuration
• Sessions & State: sessions/ - State management, memory, session services, express mode
• Deployment: deploy/ - Cloud Run, Vertex AI Agent Engine, GKE deployment guides
• Get Started: get-started/ - Installation, quickstart, language-specific guides
• Context: context/ - Caching, compaction for managing token limits
• MCP: mcp/ - Model Context Protocol integration
• Safety: safety/ - Content filtering and safety configurations
• Observability: observability/ - Logging, monitoring, debugging

Use when: Need detailed API reference, advanced patterns, deployment strategies, or troubleshooting

A2A Protocol Specification

Base: .claude/skills/adk/samples/A2A/docs/

Key Topics:

• specification.md - Complete A2A protocol specification
• sdk/python/ - Python SDK documentation for A2A
• topics/ - Protocol design topics and patterns
• tutorials/ - Step-by-step A2A implementation guides

Use when: Implementing A2A protocol, multi-framework orchestration, custom agent communication

Sample Code

Cloned samples available locally for reference:

ADK Samples

Location: .claude/skills/adk/samples/adk-samples/python/agents/

Key examples:

• realtime-conversational-agent/ - Bidirectional audio/video streaming with Gemini Live API
• customer-service/ - Multi-agent coordinator pattern with specialists
• travel-concierge/ - Sequential pipeline with state passing
• RAG/ - Google RAG Engine integration
• data-science/ - Parallel agent execution patterns
• See README.md in agents directory for complete catalog

A2A Protocol Samples

Location: .claude/skills/adk/samples/a2a-samples/samples/python/

Key directories:

• agents/ - A2A server implementations (ADK, LangGraph, CrewAI, AG2, etc.)
  • adk_expense_reimbursement/ - ADK agent exposed via A2A
  • langgraph/ - LangGraph agent with A2A protocol
  • birthday_planner_adk/ - Multi-agent ADK system with A2A
• hosts/ - A2A client/orchestrator patterns
  • multiagent/ - Orchestrating multiple remote A2A agents
  • a2a_multiagent_host/ - Host coordinating remote agents
  • cli/ - Command-line A2A client

Use when: Need working code examples, want to see patterns in action

Quick Reference (Skill-Specific)

Condensed reference guides in this skill for quick lookup:

references/core_concepts.md

Condensed API reference covering key concepts:

• All agent types with parameters and examples
• Tool system architecture and implementation
• Multi-agent patterns and communication
• Session/state management essentials
• Model configuration and authentication
• Common architectural patterns

When to use: Quick lookup for API signatures, common patterns. For detailed API docs, see docs/adk-docs/docs/

references/deployment_best_practices.md

Production deployment essentials:

• Development workflow and project structure
• Deployment options (Cloud Run, Vertex AI, containers)
• Performance optimization strategies
• Security best practices
• Testing and debugging techniques
• Common pitfalls and solutions

When to use: Quick deployment guidance. For complete deployment guides, see docs/adk-docs/docs/deploy/

references/examples.md

Quick-start code snippets:

• Minimal agent setup
• Agents with custom tools
• Sequential pipelines
• Parallel data gathering
• Coordinator patterns
• Loop-based refinement
• Error handling patterns

When to use: Quick code snippets. For complete working examples, see samples/adk-samples/

Decision Guide

Choose ADK when:

• Building complex agentic workflows requiring multi-step reasoning
• Need multiple specialized agents coordinating on tasks
• Require production-grade deployment on Google Cloud
• Want flexibility to use Gemini, GPT, Claude, or other models
• Need persistent session management and conversation memory

Consider alternatives when:

• Simple single-prompt LLM calls (use SDK directly)
• No need for tools or multi-agent coordination
• Primarily using non-Google models (though ADK supports via LiteLLM)
• Seeking managed agentic platform (consider Vertex AI agents directly)

ADK vs Other Frameworks:

• LangChain: ADK is more code-first, less abstraction, tighter Google integration
• CrewAI: ADK offers more flexible orchestration, better Gemini integration
• AutoGen: ADK focuses on production deployment, modular architecture

Key Differences from Other Agent Frameworks

ADK's unique advantages:

• Code-first: Define agents in pure Python with minimal abstractions
• Flexible orchestration: Mix deterministic workflows with LLM-driven routing
• Production-ready: Native Vertex AI integration, session persistence, streaming
• Modular: Compose agents hierarchically with clear separation of concerns
• Tool ecosystem: Seamless integration with Google services, MCP, LangChain tools