Follow the tutorial notebook for an in-depth example. Note: you may need to adapt parts of the notebook's Phoenix setup to the Arize OTel setup described below.
AutoGen is a framework from Microsoft that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks.
Launch Arize
To get started, sign up for a free Arize account and get your Space ID and API Key.
Install
Install pyautogen, the necessary OpenInference instrumentors, and Arize OTel packages.
Configure the API key for the underlying LLM your AutoGen agents will use. For example, if using OpenAI:
export OPENAI_API_KEY='your_openai_api_key'
Setup Tracing
Connect to Arize using arize.otel.register and apply the instrumentors.
# Import Arize OTel registration
from arize.otel import register
# Import OpenInference instrumentors
from openinference.instrumentation.autogen import AutogenInstrumentor
from openinference.instrumentation.openai import OpenAIInstrumentor
# Setup OTel and Arize exporter
# set_global_tracer_provider=True can be helpful for complex agent interactions
tracer_provider = register(
space_id="YOUR_SPACE_ID",
api_key="YOUR_API_KEY",
project_name="my-autogen-app",
set_global_tracer_provider=True
)
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
AutogenInstrumentor().instrument(tracer_provider=tracer_provider)
Run AutoGen Example
Here's a basic example of setting up and running a simple AutoGen task. Ensure your OAI_CONFIG_LIST is correctly pointing to your LLM configuration (e.g., using the OPENAI_API_KEY environment variable).
import autogen
# Configuration for the LLM (e.g., OpenAI)
# Ensure OPENAI_API_KEY is set in your environment if model is not "stub"
config_list = autogen.config_list_from_json(
env_or_file="OAI_CONFIG_LIST", # Example: Set this env var to your config list
# Sample OAI_CONFIG_LIST content (save as a JSON file or set as ENV var):
# [
# {
# "model": "gpt-3.5-turbo",
# "api_key": "YOUR_OPENAI_API_KEY" # Can also be picked from env
# }
# ]
filter_dict={
"model": ["gpt-3.5-turbo", "gpt-4", "gpt-4o"] # Specify models you want to use
}
)
# Create agents
assistant = autogen.AssistantAgent(
name="assistant",
llm_config={
"config_list": config_list,
"temperature": 0
}
)
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=5,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config=False, # Set to a dict with work_dir if you need code execution
)
# Start a chat
user_proxy.initiate_chat(
assistant,
message="What is the capital of France? Reply TERMINATE when done.",
)
print("AutoGen chat initiated. Check Arize for traces.")
Observe
Traces from both the AutoGen framework interactions and the underlying LLM calls will be sent to your Arize account. This allows you to see the conversation flow between agents and the details of each LLM call.
