Google Agent Development Kit (ADK) Guide

Overview

ADK is Google's open-source framework for building AI agents powered by Gemini models. It supports single-agent and multi-agent architectures with built-in tool integration, state management, callbacks, guardrails, and deployment options.

Critical Rules

1. Every agent package MUST have __init__.py that imports the agent module: from . import agent
2. Entry point MUST be root_agent -- a module-level variable in agent.py (Python). Not agent, not my_agent.
3. Set GOOGLE_API_KEY in .env or configure Vertex AI credentials before running.
4. Always set max_iterations on LoopAgent to prevent unbounded execution.
5. One agent = one responsibility. Split agents with 5+ tools into specialists.
6. Use output_key + output_schema for reliable data flow between agents -- not free text.

Documentation & Resources

For up-to-date API references and detailed guides beyond this skill, always consult:

• ADK Docs Index: https://google.github.io/adk-docs/llms.txt
• Official Samples: https://github.com/google/adk-samples (Python, Java, TypeScript)

Supported Languages

This guide shows Python examples. For Java, Go, and TypeScript patterns, see references/multi-language.md.

Quick Reference

Project Structure

Every ADK project follows this layout:

my_agent/
├── my_agent/
│   ├── __init__.py          # Must import agent module
│   ├── agent.py             # Defines root_agent (entry point)
│   ├── prompts.py           # Instruction strings (optional)
│   ├── tools.py             # Custom tool functions (optional)
│   ├── sub_agents/          # Sub-agent packages (optional)
│   └── shared_libraries/    # Callbacks, utilities (optional)
├── tests/
│   └── test_agent.py
├── pyproject.toml
└── .env                     # GOOGLE_API_KEY or GOOGLE_CLOUD_PROJECT

Critical: init.py

# my_agent/__init__.py
from . import agent

Critical: root_agent

The root_agent variable at module level is the framework entry point:

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

root_agent = Agent(
    name="my_agent",
    model="gemini-2.5-flash",
    description="Brief description for agent discovery",
    instruction="Detailed system prompt...",
    tools=[...],
)

pyproject.toml

Minimal config: requires-python = ">=3.10", dependencies = ["google-adk"], build-backend setuptools. For full example and environment config, see references/advanced-patterns.md.

Agent Types

1. LlmAgent (Single Agent)

The fundamental building block. Wraps a single LLM call with tools and instructions.

from google.adk.agents import LlmAgent

agent = LlmAgent(
    name="assistant",
    model="gemini-2.5-flash",
    description="General assistant",
    instruction="""You are a helpful assistant.
    Use the search tool when you need current information.""",
    tools=[search_tool],
    output_schema=ResponseModel,   # Optional structured output
    output_key="response",         # State key for output
    generate_content_config=types.GenerateContentConfig(
        temperature=0.7,
    ),
)

2. SequentialAgent

Runs sub-agents in order. Output of each flows to the next via shared state.

from google.adk.agents import SequentialAgent

pipeline = SequentialAgent(
    name="research_pipeline",
    description="Research then summarize",
    sub_agents=[
        researcher_agent,    # Step 1: writes to state["research"]
        summarizer_agent,    # Step 2: reads state["research"]
    ],
)

3. ParallelAgent

Runs sub-agents concurrently. Use for independent tasks.

from google.adk.agents import ParallelAgent

parallel = ParallelAgent(
    name="multi_channel",
    description="Send to all channels simultaneously",
    sub_agents=[
        email_agent,
        slack_agent,
        calendar_agent,
    ],
)

4. LoopAgent

Repeats sub-agents until termination. Two termination patterns:

Pattern A: Fixed iterations

from google.adk.agents import LoopAgent

loop = LoopAgent(
    name="refinement_loop",
    description="Iteratively refine output",
    sub_agents=[writer_agent, critic_agent],
    max_iterations=3,
)

Pattern B: Checker agent with escalate

# The checker agent uses tool_context.actions.escalate = True to stop
def check_quality(score: float, tool_context: ToolContext) -> str:
    """Check if quality meets threshold."""
    if score >= 0.9:
        tool_context.actions.escalate = True
        return "Quality threshold met, stopping loop."
    return "Quality below threshold, continuing refinement."

checker = Agent(
    name="checker",
    model="gemini-2.5-flash",
    instruction="Evaluate the output quality and call check_quality.",
    tools=[check_quality],
)

loop = LoopAgent(
    name="quality_loop",
    sub_agents=[generator_agent, checker],
)

5. Composing Agent Types

Nest agent types freely for complex workflows. Example: SequentialAgent containing a ParallelAgent containing LlmAgents. See references/advanced-patterns.md for hierarchical workflow examples.

Tools

Function Tools

Any Python function with type hints and a docstring becomes a tool:

def get_weather(city: str, units: str = "celsius") -> dict:
    """Get current weather for a city.

    Args:
        city: The city name to look up weather for.
        units: Temperature units - 'celsius' or 'fahrenheit'.

    Returns:
        dict with temperature, conditions, and humidity.
    """
    # Implementation
    return {"temperature": 22, "conditions": "sunny", "humidity": 45}

agent = Agent(
    name="weather_agent",
    model="gemini-2.5-flash",
    instruction="Help users check the weather.",
    tools=[get_weather],
)

Requirements:

• Type hints on all parameters
• Docstring with description and Args section
• Return type annotation

Tools with State Access

Use ToolContext to read/write session state:

from google.adk.tools import ToolContext

def add_to_cart(item: str, quantity: int, tool_context: ToolContext) -> dict:
    """Add an item to the shopping cart."""
    cart = tool_context.state.get("cart", [])
    cart.append({"item": item, "quantity": quantity})
    tool_context.state["cart"] = cart
    return {"status": "added", "cart_size": len(cart)}

AgentTool (Agent-as-Tool)

Wrap an agent to use it as a tool for another agent:

from google.adk.tools.agent_tool import AgentTool

specialist = Agent(
    name="code_reviewer",
    model="gemini-2.5-pro",
    instruction="Review code for bugs and best practices.",
)

coordinator = Agent(
    name="coordinator",
    model="gemini-2.5-flash",
    instruction="Coordinate tasks. Use code_reviewer for code reviews.",
    tools=[AgentTool(agent=specialist)],
)

Built-in Tools

from google.adk.tools import google_search

agent = Agent(
    name="researcher",
    tools=[google_search],
)

MCP Tools

from google.adk.tools.mcp_tool import MCPToolset, StdioConnectionParams
from mcp import StdioServerParameters

agent = Agent(
    name="db_agent",
    tools=[
        MCPToolset(
            connection_params=StdioConnectionParams(
                server_params=StdioServerParameters(
                    command="npx",
                    args=["-y", "some-mcp-server"],
                ),
            ),
        ),
    ],
)

For advanced tool patterns (FunctionTool, ToolboxToolset, long-running tools), see references/tools-reference.md.

Callbacks

Callbacks intercept the agent lifecycle. Return None to proceed, return a value to short-circuit.

def before_tool(tool, args, tool_context) -> dict | None:
    """Return dict to skip tool execution with that response."""
    if tool.name == "approve_discount" and args.get("value", 0) > 50:
        return {"status": "rejected", "reason": "Discount too large"}
    return None  # Proceed normally

agent = Agent(
    name="guarded_agent",
    model="gemini-2.5-flash",
    instruction="...",
    before_tool_callback=before_tool,
)

For rate limiting, input validation, and safety callbacks, see references/advanced-patterns.md.

State Management

State is a shared dictionary across agents, tools, and callbacks. Scopes: state["key"] (session), app:key (app-wide), user:key (user-wide).

Passing data between agents: Use output_key to write to state, read in next agent's instruction:

researcher = Agent(name="researcher", output_key="findings", output_schema=ResearchOutput, ...)
writer = Agent(name="writer", instruction="Write report based on state['findings'].", ...)
pipeline = SequentialAgent(name="pipeline", sub_agents=[researcher, writer])

Structured Output

Use Pydantic models for typed, validated agent output:

from pydantic import BaseModel

class AnalysisResult(BaseModel):
    summary: str
    key_findings: list[str]
    confidence: float
    recommendations: list[str]

agent = Agent(
    name="analyzer",
    model="gemini-2.5-flash",
    instruction="Analyze the provided data and return structured results.",
    output_schema=AnalysisResult,
    output_key="analysis",  # Stored in state["analysis"]
)

Running and Testing

Local Development

# Install
pip install google-adk

# Set API key
export GOOGLE_API_KEY="your-key"

# Run interactively
adk run my_agent

# Run with web UI
adk web my_agent

Testing with InMemoryRunner

import pytest
from google.adk.runners import InMemoryRunner
from google.genai import types

@pytest.mark.asyncio
async def test_agent():
    runner = InMemoryRunner(agent=root_agent, app_name="test")
    session = await runner.session_service.create_session(
        user_id="test_user", app_name="test",
    )
    content = types.Content(
        role="user", parts=[types.Part.from_text(text="Hello")],
    )
    events = []
    async for event in runner.run_async(
        user_id="test_user", session_id=session.id, new_message=content,
    ):
        events.append(event)
    assert "expected" in events[-1].content.parts[0].text.lower()

Evaluation

ADK provides built-in evaluation for tool correctness, response quality, and safety. Define eval cases in .test.json files and run with adk eval, pytest, or the web UI. See references/evaluation.md for eval data formats, all 8 metrics, and patterns.

Model Selection

Use gemini-2.5-flash for most agents (fast, cost-effective). Use gemini-2.5-pro for complex reasoning. Gemini 3 models (gemini-3-flash, gemini-3-pro) also supported. Non-Gemini models work too: Claude, Ollama, LiteLLM, vLLM. Configure via generate_content_config=types.GenerateContentConfig(temperature=0.2).

Design Patterns

Key design rules:

• Split agents with 5+ tools into focused specialists. One agent = one responsibility.
• Pass data between agents via output_key + output_schema (Pydantic) -- never rely on free text.
• Set max_iterations on every LoopAgent. No exceptions.
• Separate generation from evaluation -- use a different agent to critique (avoids self-review bias).
• Write precise sub-agent description fields -- they drive Auto-Flow routing decisions.
• Embed reasoning steps in instructions: "1. Analyze 2. Plan 3. Execute 4. Verify".
• Include a fallback route for unclear inputs -- ambiguous requests must not be silently misrouted.

See also: advanced-patterns.md (App config, plugins, deployment, AG-UI, session rewind) | design-patterns.md (chaining, reflection, planning, guardrails, memory, HITL) | a2a-protocol.md (A2A server/client, agent cards) | troubleshooting.md (debugging, performance).

Decision Guide

When to use which agent type:

Single task, one LLM call? → Agent / LlmAgent
Steps must run in order? → SequentialAgent
Steps are independent? → ParallelAgent
Need iteration/refinement? → LoopAgent
Need on-demand delegation? → AgentTool
Remote agent, different service? → RemoteA2aAgent (A2A)
Complex multi-stage? → Compose agent types

When to use which tool type:

Simple function? → Python function with type hints
Need state access? → Add ToolContext parameter
Delegate to another agent? → AgentTool
Remote agent over network? → RemoteA2aAgent (A2A protocol)
External MCP server? → MCPToolset
Database access? → ToolboxToolset
Web search? → google_search (built-in)
Modular skill package? → SkillToolset