AI Agents Monitoring and Analytics with AgentOps and AutoGen

This video is a step-by-step tutorial to install and integrate AgentOps with AutoGen to monitor, test and replay analytics.

Code:

conda create -n agentops python=3.11 -y && conda activate agentops

pip install pyautogen agentops

export AGENTOPS_API_KEY=""

export OPENAI_API_KEY=""

import agentops

import autogen

from autogen import ConversableAgent, UserProxyAgent, config_list_from_json

agentops.init(tags=["fahdmirza"])

import os

llm_config = {

    "config_list": [{"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}],

}

assistant = autogen.ConversableAgent("agent", llm_config=llm_config)

user_proxy = UserProxyAgent("user", code_execution_config=False)

agentops.end_session("Success")

================================

import agentops

import autogen

from typing import Annotated, Literal

from autogen import ConversableAgent, register_function

import os

agentops.start_session(tags=["agentictools3"])

Operator = Literal["+", "-", "*", "/"]

def calculator(a: int, b: int, operator: Annotated[Operator, "operator"]) -> int:

    if operator == "+":

        return a + b

    elif operator == "-":

        return a - b

    elif operator == "*":

        return a * b

    elif operator == "/":

        return int(a / b)

    else:

        raise ValueError("Invalid operator")

llm_config = {

    "config_list": [{"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}],

}

# Create the agent that uses the LLM.

assistant = ConversableAgent(

    name="Assistant",

    system_message="You are a helpful AI assistant. "

    "You can help with simple calculations. "

    "Return 'TERMINATE' when the task is done.",

    llm_config=llm_config,

)

# The user proxy agent is used for interacting with the assistant agent

# and executes tool calls.

user_proxy = ConversableAgent(

    name="User",

    llm_config=False,

    is_termination_msg=lambda msg: msg.get("content") is not None and "TERMINATE" in msg["content"],

    human_input_mode="NEVER",

)

assistant.register_for_llm(name="calculator", description="A simple calculator")(calculator)

user_proxy.register_for_execution(name="calculator")(calculator)

# Register the calculator function to the two agents.

register_function(

    calculator,

    caller=assistant,  # The assistant agent can suggest calls to the calculator.

    executor=user_proxy,  # The user proxy agent can execute the calculator calls.

    name="calculator",  # By default, the function name is used as the tool name.

    description="A simple calculator",  # A description of the tool.

)

# Let the assistant start the conversation.  It will end when the user types exit.

user_proxy.initiate_chat(assistant, message="What is (1423 - 123) / 3 + (32 + 23) * 5?")

agentops.end_session("Success")