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Multi-Agent Support

This is an example implementation of tracking operations from two separate agents

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Installation

pip install -U openai agentops

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Setup

Import the necessary libraries:

import agentops
from agentops.sdk.decorators import agent, operation
from openai import OpenAI
import os
from dotenv import load_dotenv
from IPython.display import display, Markdown

Set up your API keys:

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>"

Initialize AgentOps at the beginning of your application:

agentops.init(AGENTOPS_API_KEY, tags=["multi-agent-example"])
openai_client = OpenAI(api_key=OPENAI_API_KEY)

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Creating Multiple Agents

Now let’s create a few agents using the @agent decorator:

@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

Instantiate your agents:

def get_qa_response(prompt):
    qa = QaAgent()
    return qa.completion(prompt)
    
def get_engineer_response(prompt):
    engineer = EngineerAgent()
    return engineer.completion(prompt)

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Using Different Agents

Now we can use these agents to complete different tasks. Any LLM calls that go through these classes will be tagged as agent calls in AgentOps.

# Get code from the engineer
generated_func = get_engineer_response("python function to test prime number")
display(Markdown("```python\n" + generated_func + "\n```"))

# Get a test from the QA agent
generated_test = get_qa_response(
    "Write a python unit test that test the following function: \n " + generated_func
)
display(Markdown("```python\n" + generated_test + "\n```"))

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Default Agent Behavior

If we make an LLM call outside of the context of a tracked agent, it gets assigned to the Default Agent.

# This will be 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"},
    ],
)
print(res.choices[0].message.content)

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Further Reading

Check out other advanced examples like OpenAI Assistants integration.