diff --git a/docs/swarms/structs/graph_workflow.md b/docs/swarms/structs/graph_workflow.md
index cfa70f81..d1154056 100644
--- a/docs/swarms/structs/graph_workflow.md
+++ b/docs/swarms/structs/graph_workflow.md
@@ -134,45 +134,44 @@ print("Execution results:", results)
 Below is a comprehensive example demonstrating the creation and execution of a workflow graph:
 
 ```python
-
-import os
-
-from dotenv import load_dotenv
-
-
 from swarms import Agent, Edge, GraphWorkflow, Node, NodeType
 
-from swarm_models import OpenAIChat
-
-load_dotenv()
-
-api_key = os.environ.get("OPENAI_API_KEY")
-
-llm = OpenAIChat(
-    temperature=0.5, openai_api_key=api_key, max_tokens=4000
+# Initialize two agents with GPT-4o-mini model and desired parameters
+agent1 = Agent(
+    model_name="gpt-4o-mini",
+    temperature=0.5,
+    max_tokens=4000,
+    max_loops=1,
+    autosave=True,
+    dashboard=True,
+)
+agent2 = Agent(
+    model_name="gpt-4o-mini",
+    temperature=0.5,
+    max_tokens=4000,
+    max_loops=1,
+    autosave=True,
+    dashboard=True,
 )
-agent1 = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
-agent2 = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
 
 def sample_task():
     print("Running sample task")
     return "Task completed"
 
+# Build workflow graph
 wf_graph = GraphWorkflow()
 wf_graph.add_node(Node(id="agent1", type=NodeType.AGENT, agent=agent1))
 wf_graph.add_node(Node(id="agent2", type=NodeType.AGENT, agent=agent2))
-wf_graph.add_node(
-    Node(id="task1", type=NodeType.TASK, callable=sample_task)
-)
+wf_graph.add_node(Node(id="task1", type=NodeType.TASK, callable=sample_task))
+
 wf_graph.add_edge(Edge(source="agent1", target="task1"))
 wf_graph.add_edge(Edge(source="agent2", target="task1"))
 
 wf_graph.set_entry_points(["agent1", "agent2"])
 wf_graph.set_end_points(["task1"])
 
+# Visualize and run
 print(wf_graph.visualize())
-
-# Run the workflow
 results = wf_graph.run()
 print("Execution results:", results)