Whenever I try to use LlmAgent with LiteLlm I am getting this error

'charmap' codec can't decode byte 0x81 in position 1980: character maps to

I have pasted the code below any help would be much appreciated

`import os
import asyncio
import warnings
import logging

from google.adk.agents import Agent
from google.adk.agents import LlmAgent
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 # For creating message Content/Parts

Ignore all warnings

warnings.filterwarnings("ignore")

Configure logging

logging.basicConfig(level=logging.ERROR)

Set API keys

os.environ['OPENAI_API_KEY'] = "My key here"

print("API Keys Set:")
print(f"Google API Key set: {'Yes' if os.environ.get('GOOGLE_API_KEY') and os.environ['GOOGLE_API_KEY'] != 'YOUR_GOOGLE_API_KEY' else 'No (REPLACE PLACEHOLDER!)'}")
print(f"OpenAI API Key set: {'Yes' if os.environ.get('OPENAI_API_KEY') and os.environ['OPENAI_API_KEY'] != 'YOUR_OPENAI_API_KEY' else 'No (REPLACE PLACEHOLDER!)'}")
print(f"Anthropic API Key set: {'Yes' if os.environ.get('ANTHROPIC_API_KEY') and os.environ['ANTHROPIC_API_KEY'] != 'YOUR_ANTHROPIC_API_KEY' else 'No (REPLACE PLACEHOLDER!)'}")

Configure ADK to use API keys directly (not Vertex AI for this multi-model setup)

os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "False"

Models

MODEL_GEMINI_2_0_FLASH = "gemini-2.0-flash"
MODEL_GPT_4O = "openai/gpt-4o"
MODEL_CLAUDE_SONNET = "anthropic/claude-3-sonnet-20240229"

print("\nEnvironment configured.")

Define tool

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., "Mumbai","Chennai","Delhi").

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} ---")

city_normalized = city.lower().replace(" ", "")  # Normalize input

mock_weather_db = {
    "delhi": {"status": "success", "report": "The weather in Delhi is sunny with a temperature of 35°C."},
    "mumbai": {"status": "success", "report": "It's humid in Mumbai with a temperature of 30°C."},
    "bangalore": {"status": "success", "report": "Bangalore is experiencing light showers and a temperature of 22°C."},
    "kolkata": {"status": "success", "report": "Kolkata is partly cloudy with a temperature of 29°C."},
    "chennai": {"status": "success", "report": "It's hot and humid in Chennai with a temperature of 33°C."},
}

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}'."}

Example tool usage

print(get_weather("Mumbai"))

Set up agent

AGENT_MODEL = MODEL_GPT_4O
weather_agent = LlmAgent(
name="weather_agent_v1",
model= LiteLlm(AGENT_MODEL),
description="Provides weather information for specific cities.",
instruction=(
"You are a helpful weather assistant. Your primary goal is to provide current weather reports. "
"When the user asks for the weather in a specific city, "
"you MUST use the 'get_weather' tool to find the information. "
"Analyze the tool's response: if the status is 'error', inform the user politely about the error message. "
"If the status is 'success', present the weather 'report' clearly and concisely to the user. "
"Only use the tool when a city is mentioned for a weather request."
),
tools=[get_weather],
)

print(f"Agent '{weather_agent.name}' created using model '{AGENT_MODEL}'.")
`