Build the LLM Agent with Google ADK and self deployed LLM Model

Here it shows how we build the LLM Agent with Google ADK(Agent Development Kit), and we integrate it with our own models.

Follow the quick start to setup the project

• https://google.github.io/adk-docs/get-started/quickstart/

And the project structure will be like

 tree
.
├── multi_tool_agent
│   ├── __init__.py
│   ├── __pycache__
│   │   ├── __init__.cpython-312.pyc
│   │   ├── __init__.cpython-39.pyc
│   │   ├── agent.cpython-312.pyc
│   │   └── agent.cpython-39.pyc
│   └── agent.py
└── requirements.txt

Install the requirements

cat requirements.txt
google-adk
litellm

pip install -r requirements.txt

Here is the agent.py

import datetime
from zoneinfo import ZoneInfo
from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm
from google.adk.agents import LlmAgent

def get_weather(city: str) -> dict:
    """Retrieves the current weather report for a specified city.

    Args:
        city (str): The name of the city for which to retrieve the weather report.

    Returns:
        dict: status and result or error msg.
    """
    if city.lower() == "new york":
        return {
            "status": "success",
            "report": (
                "The weather in New York is sunny with a temperature of 25 degrees"
                " Celsius (41 degrees Fahrenheit)."
            ),
        }
    else:
        return {
            "status": "error",
            "error_message": f"Weather information for '{city}' is not available.",
        }


def get_current_time(city: str) -> dict:
    """Returns the current time in a specified city.

    Args:
        city (str): The name of the city for which to retrieve the current time.

    Returns:
        dict: status and result or error msg.
    """

    if city.lower() == "new york":
        tz_identifier = "America/New_York"
    else:
        return {
            "status": "error",
            "error_message": (
                f"Sorry, I don't have timezone information for {city}."
            ),
        }

    tz = ZoneInfo(tz_identifier)
    now = datetime.datetime.now(tz)
    report = (
        f'The current time in {city} is {now.strftime("%Y-%m-%d %H:%M:%S %Z%z")}'
    )
    return {"status": "success", "report": report}


# Endpoint URL provided
api_base_url = "http://127.0.0.1:3001/v1"

# Model name as specified
model_name = "gpt-4o"

# API Key
api_key = "sk-or-v1-xxxx"


root_agent = LlmAgent(
    name="weather_time_agent",
    model=LiteLlm(
        model=model_name,
        api_base=api_base_url,
        api_key=api_key
    ),
    description=(
        "Agent to answer questions about the time and weather in a city."
    ),
    instruction=(
        "I can answer your questions about the time and weather in a city."
    ),
    tools=[get_weather, get_current_time],
)

adk debug

Start the Web Application

adk web
INFO:     Started server process [68331]
INFO:     Waiting for application startup.
+-----------------------------------------------------------------------------+
| ADK Web Server started                                                      |
|                                                                             |
| For local testing, access at http://localhost:8000.                         |
+-----------------------------------------------------------------------------+

INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Reference

1. https://google.github.io/adk-docs/get-started/quickstart/
2. https://google.github.io/adk-docs/get-started/tutorial/#step-2-going-multi-model-with-litellm
3. https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/