AutoGen

import traceroot
from traceroot import Integration

# Initialize AutoGen alongside your LLM provider to capture tokens and costs
traceroot.initialize(integrations=[
    Integration.AUTOGEN,
    Integration.GOOGLE_GENAI  # Or OPENAI, ANTHROPIC, etc.
])

import os
import autogen
from typing import Annotated
from autogen import register_function

llm_config = {
    "config_list": [{
        "model": "gemini-2.5-flash",
        "api_key": os.environ["GEMINI_API_KEY"],
        "api_type": "google"
    }]
}

assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config=llm_config
)
user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=5
)

def get_weather(city: Annotated[str, "City name"]) -> str:
    """Get current weather for a city."""
    return f"The weather in {city} is 72 degrees and sunny."

def get_forecast(city: Annotated[str, "City name"], days: Annotated[int, "Number of days"]) -> str:
    """Get a multi-day weather forecast for a city."""
    return f"{days}-day forecast for {city}: mostly sunny with highs around 70-75°F."

# Register tools — caller decides when to use them, executor runs the function
register_function(
    get_weather,
    caller=assistant,
    executor=user_proxy,
    name="get_weather",
    description="Get current weather for a city",
)
register_function(
    get_forecast,
    caller=assistant,
    executor=user_proxy,
    name="get_forecast",
    description="Get a multi-day weather forecast for a city",
)

# The entire conversation hierarchy is automatically traced
user_proxy.initiate_chat(
    assistant,
    message="What's the weather in Tokyo today, and what does the 3-day forecast look like?",
)