🤖 Agent Development Kits (ADK) Overview

🌿 Growing Concept

Label: Your Toolkit for Building AI Agents

An Agent Development Kit (ADK) is an open-source, code-first toolkit for building, evaluating, and deploying AI agents. ADKs provide the foundational components and patterns you need to create sophisticated multi-agent systems.

What is an ADK?

Core Components

┌─────────────────────────────────────────────┐
│         Agent Development Kit (ADK)         │
├─────────────────────────────────────────────┤
│                                             │
│  ┌─────────────┐      ┌─────────────┐     │
│  │   Agents    │      │   Tools     │     │
│  │  Framework  │      │  Ecosystem  │     │
│  └─────────────┘      └─────────────┘     │
│                                             │
│  ┌─────────────┐      ┌─────────────┐     │
│  │Orchestration│      │  Evaluation │     │
│  │   Engine    │      │  Framework  │     │
│  └─────────────┘      └─────────────┘     │
│                                             │
│  ┌─────────────┐      ┌─────────────┐     │
│  │ Deployment  │      │  Monitoring │     │
│  │   Tools     │      │  & Logging  │     │
│  └─────────────┘      └─────────────┘     │
└─────────────────────────────────────────────┘

💡 Why Use an ADK?

Multi-Agent by Design

ADKs are built for multi-agent systems from the ground up:

from adk import Agent, Workflow

# Define specialized agents
researcher = Agent(
    name="Researcher",
    role="Information Gatherer",
    tools=[web_search, database_query]
)

analyst = Agent(
    name="Analyst",
    role="Data Processor",
    tools=[data_analysis, visualization]
)

writer = Agent(
    name="Writer",
    role="Content Creator",
    tools=[document_generator, style_checker]
)

# Orchestrate them together
workflow = Workflow(agents=[researcher, analyst, writer])
result = workflow.run("Create a market analysis report")

Rich Tool Ecosystem

ADKs connect to existing tools and data:

Your Agent (via ADK)
       │
       ├──→ Productivity Tools (Notion, Slack, Email)
       │
       ├──→ Business Systems (HubSpot, Salesforce)
       │
       ├──→ Development Tools (GitHub, Jira)
       │
       ├──→ Tool Frameworks (LangChain, LlamaIndex)
       │
       └──→ Agent Frameworks (LangGraph, CrewAI)

🔬 Deep Dive: ADK Capabilities

1. Flexible Orchestration

ADKs support multiple orchestration patterns:

Sequential Execution

class SequentialWorkflow:
    """
    Execute agents one after another
    """
    def __init__(self, agents: List[Agent]):
        self.agents = agents

    async def execute(self, task: str) -> dict:
        """
        Each agent processes output from previous agent
        """
        result = {"input": task}

        for agent in self.agents:
            result = await agent.process(result)
            print(f"{agent.name} completed: {result['status']}")

        return result


# Example: Content creation pipeline
workflow = SequentialWorkflow([
    researcher,  # Gathers information
    analyst,     # Analyzes data
    writer       # Creates content
])

result = await workflow.execute("Write about AI trends")

Parallel Execution

class ParallelWorkflow:
    """
    Execute multiple agents simultaneously
    """
    def __init__(self, agents: List[Agent]):
        self.agents = agents

    async def execute(self, task: str) -> dict:
        """
        All agents work on same task in parallel
        """
        # Execute all agents concurrently
        results = await asyncio.gather(*[
            agent.process(task) for agent in self.agents
        ])

        # Combine results
        combined = self.merge_results(results)

        return combined


# Example: Multi-perspective analysis
workflow = ParallelWorkflow([
    technical_analyst,
    business_analyst,
    user_experience_analyst
])

analysis = await workflow.execute("Evaluate this product feature")

Dynamic Orchestration

class DynamicWorkflow:
    """
    Adapt execution based on intermediate results
    """
    def __init__(self, coordinator: Agent, specialists: dict[Agent]):
        self.coordinator = coordinator
        self.specialists = specialists

    async def execute(self, task: str) -> dict:
        """
        Coordinator decides which specialists to engage
        """
        # Coordinator analyzes task
        plan = await self.coordinator.create_plan(task)

        results = {}
        for step in plan['steps']:
            # Select appropriate specialist
            specialist = self.specialists[step['agent_type']]

            # Execute with context from previous steps
            result = await specialist.process(step['subtask'], context=results)
            results[step['id']] = result

            # Coordinator evaluates progress
            evaluation = await self.coordinator.evaluate(results)
            if evaluation['should_adjust']:
                plan = await self.coordinator.replan(task, results)

        return results


# Example: Adaptive project management
coordinator = Agent("Project Manager", tools=[planning, evaluation])
specialists = {
    "code": Agent("Developer", tools=[code_generation, testing]),
    "design": Agent("Designer", tools=[ui_design, prototyping]),
    "docs": Agent("Technical Writer", tools=[documentation])
}

workflow = DynamicWorkflow(coordinator, specialists)
result = await workflow.execute("Build user authentication feature")

2. Tool Integration

Connecting to External Services

from adk import Tool, Agent

# Define tools
@Tool
def search_notion(query: str) -> dict:
    """
    Search Notion workspace for documents

    Args:
        query: Search query string

    Returns:
        dict: Search results with page titles and content
    """
    notion_client = NotionClient(api_key=NOTION_API_KEY)
    results = notion_client.search(query)
    return {
        "results": results,
        "count": len(results)
    }

@Tool
def send_slack_message(channel: str, message: str) -> dict:
    """
    Send message to Slack channel

    Args:
        channel: Slack channel name
        message: Message content

    Returns:
        dict: Message delivery status
    """
    slack_client = SlackClient(token=SLACK_TOKEN)
    response = slack_client.chat_postMessage(
        channel=channel,
        text=message
    )
    return {"status": "sent", "timestamp": response['ts']}

@Tool
def update_hubspot_contact(contact_id: str, updates: dict) -> dict:
    """
    Update HubSpot contact record

    Args:
        contact_id: HubSpot contact identifier
        updates: Dictionary of fields to update

    Returns:
        dict: Updated contact information
    """
    hubspot_client = HubSpotClient(api_key=HUBSPOT_API_KEY)
    contact = hubspot_client.contacts.update(contact_id, updates)
    return contact


# Create agent with tools
assistant = Agent(
    name="ProductivityAssistant",
    tools=[search_notion, send_slack_message, update_hubspot_contact],
    instructions="""
    You are a productivity assistant that helps users:
    1. Find information in Notion
    2. Communicate via Slack
    3. Manage contacts in HubSpot

    Use the appropriate tool for each task.
    """
)

# Agent automatically uses tools as needed
response = await assistant.process(
    "Find the Q4 planning doc in Notion and share it in #strategy channel"
)

3. Integration with Agent Frameworks

Using LangChain Tools

from langchain.tools import Tool as LangChainTool
from adk import Agent, adapt_langchain_tool

# Adapt LangChain tools for ADK
langchain_tool = LangChainTool(
    name="Calculator",
    func=lambda x: eval(x),
    description="Performs mathematical calculations"
)

adk_tool = adapt_langchain_tool(langchain_tool)

# Use in ADK agent
calculator_agent = Agent(
    name="MathAssistant",
    tools=[adk_tool]
)

Integrating with CrewAI

from crewai import Crew, Task
from adk import Agent as ADKAgent

# Bridge ADK agents with CrewAI
def adk_to_crewai(adk_agent: ADKAgent):
    """Convert ADK agent to CrewAI agent"""
    return CrewAgent(
        role=adk_agent.role,
        goal=adk_agent.goal,
        backstory=adk_agent.backstory,
        tools=adk_agent.tools
    )

# Use both frameworks together
adk_researcher = ADKAgent("Researcher", tools=[web_search])
crew_writer = CrewAgent(role="Writer", tools=[content_gen])

crew = Crew(
    agents=[adk_to_crewai(adk_researcher), crew_writer]
)

4. Sharing via MCP and A2A

MCP Integration

from adk import Agent, MCPServer

# Create agent with MCP server
agent = Agent("DocumentExpert", tools=[doc_search, doc_summary])

# Expose agent via MCP
mcp_server = MCPServer(agent)
mcp_server.start(port=8080)

# Now any MCP-compatible client can use this agent

A2A Communication

from adk import Agent, A2AClient

class CollaborativeAgent(Agent):
    """
    Agent that can collaborate with other agents via A2A
    """
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.a2a_client = A2AClient()

    async def collaborate(self, task: str):
        """
        Delegate subtasks to specialist agents
        """
        # Analyze task
        subtasks = await self.break_down_task(task)

        # Find appropriate agents via A2A
        for subtask in subtasks:
            specialist = await self.a2a_client.find_agent(
                capability=subtask['required_skill']
            )

            # Delegate via A2A protocol
            result = await self.a2a_client.send_request(
                agent_id=specialist['id'],
                task=subtask['description'],
                context=subtask['context']
            )

            self.incorporate_result(result)


# Usage
agent = CollaborativeAgent("Coordinator")
result = await agent.collaborate("Create comprehensive market report")
# Agent automatically finds and delegates to research, analysis, and writing agents

🎯 Choosing the Right ADK

Decision Matrix

┌─────────────────────────────────────────────────┐
│         What's Your Priority?                   │
└─────────────────────────────────────────────────┘
         │
         ├─→ Quick Prototyping ──────→ LangChain
         │                             + Simple ADK wrapper
         │
         ├─→ Production Scale ────────→ Full-featured ADK
         │                             (Google ADK, etc.)
         │
         ├─→ Team Collaboration ──────→ CrewAI-compatible ADK
         │
         ├─→ Custom Workflows ────────→ LangGraph
         │                             + ADK integration
         │
         └─→ Research/Experimentation → Lightweight custom ADK

Popular ADK Choices

1. Google Agent Development Kit

Best for: Enterprise applications, production deployment

from google_adk import Agent, Workflow

agent = Agent.from_config("config.yaml")
workflow = Workflow.create(agents=[agent])

2. LangChain + Custom Orchestration

Best for: Rapid prototyping, rich tool ecosystem

from langchain.agents import create_agent
from custom_adk import Orchestrator

agent = create_agent(tools=[...])
orchestrator = Orchestrator([agent])

3. Custom Python ADK

Best for: Full control, specialized requirements

class CustomADK:
    def create_agent(self, config):
        # Your implementation
        pass

⚡ Quick Win: Build Your First Multi-Agent System

30-Minute Implementation

from adk import Agent, SequentialWorkflow

# Step 1: Define agents
task_breakdown_agent = Agent(
    name="Task Breakdown Agent",
    instructions="Analyze tasks and break them into subtasks"
)

execution_agents = [
    Agent(name="Code Agent", instructions="Generate code"),
    Agent(name="Design Agent", instructions="Create designs"),
    Agent(name="Docs Agent", instructions="Write documentation")
]

review_agent = Agent(
    name="Review Agent",
    instructions="Quality check all outputs"
)

# Step 2: Create workflow
workflow = SequentialWorkflow([
    task_breakdown_agent,
    *execution_agents,
    review_agent
])

# Step 3: Execute
result = await workflow.run(
    "Build a user authentication system with docs"
)

print(f"Project completed: {result['status']}")

🌳 Advanced: Custom ADK Architecture

Building Your Own ADK

class CustomADK:
    """
    Custom ADK with specialized features
    """
    def __init__(self):
        self.agents = {}
        self.workflows = {}
        self.tool_registry = ToolRegistry()

    def register_agent(self, agent: Agent):
        """Register an agent"""
        self.agents[agent.id] = agent
        agent.set_tool_registry(self.tool_registry)

    def create_workflow(self, workflow_type: str, config: dict):
        """Factory for creating workflows"""
        if workflow_type == "sequential":
            return SequentialWorkflow(config)
        elif workflow_type == "parallel":
            return ParallelWorkflow(config)
        elif workflow_type == "dynamic":
            return DynamicWorkflow(config)
        else:
            raise ValueError(f"Unknown workflow type: {workflow_type}")

    def deploy(self, agent: Agent, platform: str):
        """Deploy agent to production platform"""
        deployer = DeploymentManager(platform)
        endpoint = deployer.deploy(agent)
        return endpoint

🎯 Key Takeaways

• ADKs provide comprehensive toolkits for agent development
• Multi-agent orchestration is built-in and flexible
• Tool integration connects agents to real-world systems
• Framework compatibility lets you mix and match approaches
• MCP and A2A enable sharing and collaboration

Next Steps

Continue to Agent Types to learn about different agent architectures and when to use each.

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💡 Pro Tip: Start with an established ADK for your first project, then customize or build your own as you understand your specific needs.