swarms/sequential_workflow_example.py

import os 
from swarms.models import OpenAIChat
from swarms.structs import Agent
from swarms.structs.sequential_workflow import SequentialWorkflow
from dotenv import load_dotenv

load_dotenv()

# Load the environment variables
api_key = os.getenv("OPENAI_API_KEY")


# Initialize the language agent
llm = OpenAIChat(
    openai_api_key=api_key,
    temperature=0.5,
    max_tokens=3000,
)


# Initialize the agent with the language agent
agent1 = Agent(llm=llm, max_loops=1)

# Create another agent for a different task
agent2 = Agent(llm=llm, max_loops=1)

# Create another agent for a different task
agent3 = Agent(llm=llm, max_loops=1)

# agent4 = Agent(llm=anthropic, max_loops="auto")

# Create the workflow
workflow = SequentialWorkflow(max_loops=1)

# Add tasks to the workflow
workflow.add(
    "Generate a 10,000 word blog on health and wellness.", agent1
)

# Suppose the next task takes the output of the first task as input
workflow.add("Summarize the generated blog", agent2)

workflow.add(
    "Create a references sheet of materials for the curriculm", agent3
)

# Run the workflow
workflow.run()

# Output the results
for task in workflow.tasks:
    print(f"Task: {task.description}, Result: {task.result}")