Google ADK

Integrate Google's Agent Development Kit with Agent Kernel.

Installation

pip install agentkernel[adk]

Basic Usage

from adk import Agent as ADKAgent
from agentkernel.cli import CLI
from agentkernel.adk import GoogleADKModule

agent = ADKAgent(
    name="assistant",
    model="gemini-2.0-flash-exp",
    instructions="You are a helpful AI assistant",
)

GoogleADKModule([agent])

if __name__ == "__main__":
    CLI.main()

Multi-Agent System

from adk import Agent as ADKAgent
from agentkernel.adk import GoogleADKModule

general_agent = ADKAgent(
    name="general",
    model="gemini-2.0-flash-exp",
    instructions="You handle general queries",
)

specialist_agent = ADKAgent(
    name="specialist",
    model="gemini-2.0-flash-exp",
    instructions="You handle specialized queries",
)

GoogleADKModule([general_agent, specialist_agent])

Configuration

export GOOGLE_API_KEY=...
export GEMINI_MODEL=gemini-2.0-flash-exp  # Optional

Tool Binding

Use GoogleADKToolBuilder to bind plain Python functions as tools to your Google ADK agents:

from google.adk.agents import Agent as ADKAgent
from agentkernel.adk import GoogleADKModule, GoogleADKToolBuilder

def get_weather(city: str) -> str:
    """Returns the weather for a given city."""
    return f"Weather in {city}: sunny, 25°C"

agent = ADKAgent(
    name="weather",
    model="gemini-2.0-flash-exp",
    description="You provide weather information upon request",
    instruction="Use the get_weather tool for weather-related questions.",
    tools=GoogleADKToolBuilder.bind([get_weather]),
)

GoogleADKModule([agent])

See Tools for the full guide on writing and binding tools.

Structured Output

Configure structured output with ADK's output_schema parameter on LlmAgent. ADK returns the final response as a JSON string conforming to the schema; Agent Kernel validates and parses it, returning an AgentReplyAny whose content is the result as a dict:

from google.adk.agents import LlmAgent
from pydantic import BaseModel
from agentkernel.adk import GoogleADKModule

class CapitalOutput(BaseModel):
    country: str
    capital: str

agent = LlmAgent(
    name="capitals",
    model="gemini-2.0-flash",
    instruction="Answer with the country and its capital.",
    output_schema=CapitalOutput,
)

GoogleADKModule([agent])

If the model's reply does not validate against the schema, the runner logs a warning and falls back to a plain AgentReplyText with the raw text. str(reply) on an AgentReplyAny returns the JSON-serialized content, so text-based consumers work unchanged. See Reply Types for how structured replies are surfaced, and Execution Hooks for how hooks receive them.

Streaming limitation

Structured output applies to non-streaming execution only. Streamed runs emit token-by-token text deltas.

Features

• ✅ Gemini models
• ✅ Google Cloud integration
• ✅ Function calling
• ✅ Multi-agent coordination
• ✅ Framework-agnostic tool binding
• ✅ Structured output (output_schema → AgentReplyAny)

Example

See examples/cli/adk for complete examples.