example_async_vs_multithread.py

import os
import asyncio
import threading
from swarms import Agent
from swarm_models import OpenAIChat
import time
import psutil

from swarms.prompts.finance_agent_sys_prompt import (
    FINANCIAL_AGENT_SYS_PROMPT,
)
from dotenv import load_dotenv

load_dotenv()

# Get the OpenAI API key from the environment variable
api_key = os.getenv("OPENAI_API_KEY")

# Create an instance of the OpenAIChat class
model = OpenAIChat(
    openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)

# Initialize the agent
agent = Agent(
    agent_name="Financial-Analysis-Agent",
    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
    llm=model,
    max_loops=1,
    autosave=True,
    dashboard=False,
    verbose=True,
    dynamic_temperature_enabled=True,
    saved_state_path="finance_agent.json",
    user_name="swarms_corp",
    retry_attempts=1,
    context_length=200000,
    return_step_meta=False,
    output_type="string",
    streaming_on=False,
)

# Function to measure time and memory usage
def measure_time_and_memory(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        memory_usage = psutil.Process().memory_info().rss / 1024 ** 2
        print(f"Time taken: {end_time - start_time} seconds")
        print(f"Memory used: {memory_usage} MB")
        return result
    return wrapper

# Function to run the agent asynchronously
@measure_time_and_memory
async def run_agent_async():
    await asyncio.gather(
        agent.run(
            "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
        )
    )

# Function to run the agent on another thread
@measure_time_and_memory
def run_agent_thread():
    asyncio.run(run_agent_async())

# Run the agent asynchronously and on another thread to test the speed
asyncio.run(run_agent_async())
run_agent_thread()