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 ```bash showLineNumbers title="Install dependencies" uv add google-adk litellm ``` ## 1. Setting Up Environment First, import the necessary libraries and set up your API keys: ```python showLineNumbers title="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: ```python showLineNumbers title="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: ```python showLineNumbers title="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 ```python showLineNumbers title="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 ```python showLineNumbers title="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 ```python showLineNumbers title="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. | ```python showLineNumbers title="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() ``` --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/google-adk-2) · https://claudary.paisolsolutions.com