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Multi-Agent Support

This is an example implementation of tracking operations from two separate agents First let’s install the required packages 
%pip install -U openai
%pip install -U agentops
%pip install -U python-dotenv
Then import them 
import agentops
from agentops.sdk.decorators import agent, operation
from openai import OpenAI
import os
from dotenv import load_dotenv
import logging
from IPython.display import display, Markdown
Next, we’ll set our API keys. There are several ways to do this, the code below is just the most foolproof way for the purposes of this notebook. It accounts for both users who use environment variables and those who just want to set the API Key here in this notebook. Get an AgentOps API key
  1. Create an environment variable in a .env file or other method. By default, the AgentOps init() function will look for an environment variable named AGENTOPS_API_KEY. Or…
  2. Replace <your_agentops_key> below and pass in the optional api_key parameter to the AgentOps init(api_key=...) function. Remember not to commit your API key to a public repo! 
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or "<your_openai_key>"
AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") or "<your_agentops_key>"
logging.basicConfig(
    level=logging.DEBUG
)  # this will let us see that calls are assigned to an agent

agentops.init(AGENTOPS_API_KEY, tags=["multi-agent-notebook"])
openai_client = OpenAI(api_key=OPENAI_API_KEY)
Now lets create a few agents! 
@agent(name="qa")
class QaAgent:
    def __init__(self):
        pass
        
    @operation
    def completion(self, prompt: str):
        res = openai_client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {
                    "role": "system",
                    "content": "You are a qa engineer and only output python code, no markdown tags.",
                },
                {"role": "user", "content": prompt},
            ],
            temperature=0.5,
        )
        return res.choices[0].message.content


@agent(name="engineer")
class EngineerAgent:
    def __init__(self):
        pass
        
    @operation
    def completion(self, prompt: str):
        res = openai_client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {
                    "role": "system",
                    "content": "You are a software engineer and only output python code, no markdown tags.",
                },
                {"role": "user", "content": prompt},
            ],
            temperature=0.5,
        )
        return res.choices[0].message.content

qa = QaAgent()
engineer = EngineerAgent()
Now we have our agents and we tagged them with the @agent decorator. Any LLM calls that go through this class will now be tagged as agent calls in AgentOps. Let’s use these agents! 
generated_func = engineer.completion("python function to test prime number")

display(Markdown("```python\n" + generated_func + "\n```"))

generated_test = qa.completion(
    "Write a python unit test that test the following function: \n " + generated_func
)

display(Markdown("```python\n" + generated_test + "\n```"))
Perfect! It generated the code as expected, and in the DEBUG logs, you can see that the calls were made by agents named “engineer” and “qa”! Let’s verify one more thing! If we make an LLM call outside of the context of a tracked agent, we want to make sure it gets assigned to the Default Agent. 
res = openai_client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are not a tracked agent"},
        {"role": "user", "content": "Say hello"},
    ],
)
res.choices[0].message.content
You’ll notice that we didn’t log an agent name, so the AgentOps backend will assign it to the Default Agent for the session!