Google ADK with LiteLLM

Use Google ADK with LiteLLM Python SDK, LiteLLM Proxy

This tutorial shows you how to create intelligent agents using Agent Development Kit (ADK) with support for multiple Large Language Model (LLM) providers with LiteLLM.

Overview

ADK (Agent Development Kit) allows you to build intelligent agents powered by LLMs. By integrating with LiteLLM, you can:

• Use multiple LLM providers (OpenAI, Anthropic, Google, etc.)
• Switch easily between models from different providers
• Connect to a LiteLLM proxy for centralized model management

Prerequisites

• Python environment setup
• API keys for model providers (OpenAI, Anthropic, Google AI Studio)
• Basic understanding of LLMs and agent concepts

Installation

Install dependencies

pip install google-adk litellm

1. Setting Up Environment

First, import the necessary libraries and set up your API keys:

Setup environment and API keys

import os
import asyncio
from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm  # For multi-model support
from google.adk.sessions import InMemorySessionService
from google.adk.runners import Runner
from google.genai import types
import litellm  # Import for proxy configuration

# Set your API keys
os.environ["GOOGLE_API_KEY"] = "your-google-api-key"  # For Gemini models
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # For OpenAI models
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"  # For Claude models

# Define model constants for cleaner code
MODEL_GEMINI_PRO = "gemini-1.5-pro"
MODEL_GPT_4O = "openai/gpt-4o"
MODEL_CLAUDE_SONNET = "anthropic/claude-3-sonnet-20240229"

2. Define a Simple Tool

Create a tool that your agent can use:

Weather tool implementation

def get_weather(city: str) -> dict:
    """Retrieves the current weather report for a specified city.
    
    Args:
        city (str): The name of the city (e.g., "New York", "London", "Tokyo").
    
    Returns:
        dict: A dictionary containing the weather information.
              Includes a 'status' key ('success' or 'error').
              If 'success', includes a 'report' key with weather details.
              If 'error', includes an 'error_message' key.
    """
    print(f"Tool: get_weather called for city: {city}")
    
    # Mock weather data
    mock_weather_db = {
        "newyork": {"status": "success", "report": "The weather in New York is sunny with a temperature of 25°C."},
        "london": {"status": "success", "report": "It's cloudy in London with a temperature of 15°C."},
        "tokyo": {"status": "success", "report": "Tokyo is experiencing light rain and a temperature of 18°C."},
    }
    
    city_normalized = city.lower().replace(" ", "")
    
    if city_normalized in mock_weather_db:
        return mock_weather_db[city_normalized]
    else:
        return {"status": "error", "error_message": f"Sorry, I don't have weather information for '{city}'."}

3. Helper Function for Agent Interaction

Create a helper function to facilitate agent interaction:

Agent interaction helper function

async def call_agent_async(query: str, runner, user_id, session_id):
    """Sends a query to the agent and prints the final response."""
    print(f"\n>>> User Query: {query}")

    # Prepare the user's message in ADK format
    content = types.Content(role='user', parts=[types.Part(text=query)])
    
    final_response_text = "Agent did not produce a final response."
    
    # Execute the agent and find the final response
    async for event in runner.run_async(
        user_id=user_id, 
        session_id=session_id, 
        new_message=content
    ):
        if event.is_final_response():
            if event.content and event.content.parts:
                final_response_text = event.content.parts[0].text
            break
            
    print(f"<<< Agent Response: {final_response_text}")

4. Using Different Model Providers with ADK

4.1 Using OpenAI Models

OpenAI model implementation

# Create an agent powered by OpenAI's GPT model
weather_agent_gpt = Agent(
    name="weather_agent_gpt",
    model=LiteLlm(model=MODEL_GPT_4O),  # Use OpenAI's GPT model
    description="Provides weather information using OpenAI's GPT.",
    instruction="You are a helpful weather assistant powered by GPT-4o. "
                "Use the 'get_weather' tool for city weather requests. "
                "Present information clearly.",
    tools=[get_weather],
)

# Set up session and runner
session_service_gpt = InMemorySessionService()
session_gpt = session_service_gpt.create_session(
    app_name="weather_app",
    user_id="user_1",
    session_id="session_gpt"
)

runner_gpt = Runner(
    agent=weather_agent_gpt,
    app_name="weather_app",
    session_service=session_service_gpt
)

# Test the GPT agent
async def test_gpt_agent():
    print("\n--- Testing GPT Agent ---")
    await call_agent_async(
        "What's the weather in London?",
        runner=runner_gpt,
        user_id="user_1",
        session_id="session_gpt"
    )

# Execute the conversation with the GPT agent
await test_gpt_agent()

# Or if running as a standard Python script:
# if __name__ == "__main__":
#     asyncio.run(test_gpt_agent())

4.2 Using Anthropic Models

Anthropic model implementation

# Create an agent powered by Anthropic's Claude model
weather_agent_claude = Agent(
    name="weather_agent_claude",
    model=LiteLlm(model=MODEL_CLAUDE_SONNET),  # Use Anthropic's Claude model
    description="Provides weather information using Anthropic's Claude.",
    instruction="You are a helpful weather assistant powered by Claude Sonnet. "
                "Use the 'get_weather' tool for city weather requests. "
                "Present information clearly.",
    tools=[get_weather],
)

# Set up session and runner
session_service_claude = InMemorySessionService()
session_claude = session_service_claude.create_session(
    app_name="weather_app",
    user_id="user_1",
    session_id="session_claude"
)

runner_claude = Runner(
    agent=weather_agent_claude,
    app_name="weather_app",
    session_service=session_service_claude
)

# Test the Claude agent
async def test_claude_agent():
    print("\n--- Testing Claude Agent ---")
    await call_agent_async(
        "What's the weather in Tokyo?",
        runner=runner_claude,
        user_id="user_1",
        session_id="session_claude"
    )

# Execute the conversation with the Claude agent
await test_claude_agent()

# Or if running as a standard Python script:
# if __name__ == "__main__":
#     asyncio.run(test_claude_agent())

4.3 Using Google's Gemini Models

Gemini model implementation

# Create an agent powered by Google's Gemini model
weather_agent_gemini = Agent(
    name="weather_agent_gemini",
    model=MODEL_GEMINI_PRO,  # Use Gemini model directly (no LiteLlm wrapper needed)
    description="Provides weather information using Google's Gemini.",
    instruction="You are a helpful weather assistant powered by Gemini Pro. "
                "Use the 'get_weather' tool for city weather requests. "
                "Present information clearly.",
    tools=[get_weather],
)

# Set up session and runner
session_service_gemini = InMemorySessionService()
session_gemini = session_service_gemini.create_session(
    app_name="weather_app",
    user_id="user_1",
    session_id="session_gemini"
)

runner_gemini = Runner(
    agent=weather_agent_gemini,
    app_name="weather_app",
    session_service=session_service_gemini
)

# Test the Gemini agent
async def test_gemini_agent():
    print("\n--- Testing Gemini Agent ---")
    await call_agent_async(
        "What's the weather in New York?",
        runner=runner_gemini,
        user_id="user_1",
        session_id="session_gemini"
    )

# Execute the conversation with the Gemini agent
await test_gemini_agent()

# Or if running as a standard Python script:
# if __name__ == "__main__":
#     asyncio.run(test_gemini_agent())

5. Using LiteLLM Proxy with ADK

LiteLLM proxy provides a unified API endpoint for multiple models, simplifying deployment and centralized management.

Required settings for using litellm proxy

Variable	Description
LITELLM_PROXY_API_KEY	The API key for the LiteLLM proxy
LITELLM_PROXY_API_BASE	The base URL for the LiteLLM proxy
USE_LITELLM_PROXY or litellm.use_litellm_proxy	When set to True, your request will be sent to litellm proxy.

LiteLLM proxy integration

# Set your LiteLLM Proxy credentials as environment variables
os.environ["LITELLM_PROXY_API_KEY"] = "your-litellm-proxy-api-key"
os.environ["LITELLM_PROXY_API_BASE"] = "your-litellm-proxy-url"  # e.g., "http://localhost:4000"
# Enable the use_litellm_proxy flag
litellm.use_litellm_proxy = True

# Create a proxy-enabled agent (using environment variables)
weather_agent_proxy_env = Agent(
    name="weather_agent_proxy_env",
    model=LiteLlm(model="gpt-4o"), # this will call the `gpt-4o` model on LiteLLM proxy
    description="Provides weather information using a model from LiteLLM proxy.",
    instruction="You are a helpful weather assistant. "
                "Use the 'get_weather' tool for city weather requests. "
                "Present information clearly.",
    tools=[get_weather],
)

# Set up session and runner
session_service_proxy_env = InMemorySessionService()
session_proxy_env = session_service_proxy_env.create_session(
    app_name="weather_app",
    user_id="user_1",
    session_id="session_proxy_env"
)

runner_proxy_env = Runner(
    agent=weather_agent_proxy_env,
    app_name="weather_app",
    session_service=session_service_proxy_env
)

# Test the proxy-enabled agent (environment variables method)
async def test_proxy_env_agent():
    print("\n--- Testing Proxy-enabled Agent (Environment Variables) ---")
    await call_agent_async(
        "What's the weather in London?",
        runner=runner_proxy_env,
        user_id="user_1",
        session_id="session_proxy_env"
    )

# Execute the conversation
await test_proxy_env_agent()