[README]

README.md

@@ -73,15 +73,17 @@ out = agent.run("Generate a 10,000 word blog on health and wellness.")
 - Integrate Agent's with various LLMs and Multi-Modality Models
 
 ```python
-from swarms.models import OpenAIChat, BioGPT, Anthropic
+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")
 
-# Example usage
-api_key = (
-    ""  # Your actual API key here
-)
 
 # Initialize the language agent
 llm = OpenAIChat(
@@ -90,32 +92,28 @@ llm = OpenAIChat(
     max_tokens=3000,
 )
 
-biochat = BioGPT()
-
-# Use Anthropic
-anthropic = Anthropic()
 
 # Initialize the agent with the language agent
-agent1 = Agent(llm=llm, max_loops=1, dashboard=False)
+agent1 = Agent(llm=llm, max_loops=1)
 
 # Create another agent for a different task
-agent2 = Agent(llm=llm, max_loops=1, dashboard=False)
+agent2 = Agent(llm=llm, max_loops=1)
 
 # Create another agent for a different task
-agent3 = Agent(llm=biochat, max_loops=1, dashboard=False)
-
-# agent4 = Agent(llm=anthropic, max_loops="auto")
+agent3 = Agent(llm=llm, max_loops=1)
 
 # 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)
+workflow.add(
+    agent1, "Generate a 10,000 word blog on health and wellness.", 
+)
 
 # 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)
+workflow.add(
+    agent2, "Summarize the generated blog",
+)
 
 # Run the workflow
 workflow.run()
@@ -124,6 +122,7 @@ workflow.run()
 for task in workflow.tasks:
     print(f"Task: {task.description}, Result: {task.result}")
 
+
 ```
 
 ## `Multi Modal Autonomous Agents`