diff --git a/examples/multi_agent/test_sequentialworkflow_streaming.py b/examples/multi_agent/test_sequentialworkflow_streaming.py
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+++ b/examples/multi_agent/test_sequentialworkflow_streaming.py
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+from swarms.structs.agent import Agent
+from swarms.structs.sequential_workflow import SequentialWorkflow
+
+def run_workflow_with_streaming_callback(task, streaming_callback):
+    """
+    Run a sequential workflow with two agents and a streaming callback.
+
+    Args:
+        task (str): The task to process through the workflow.
+        streaming_callback (callable): Function to handle streaming output.
+
+    Returns:
+        The final result from the workflow.
+    """
+
+    agent1 = Agent(
+        name="Research Agent",
+        description="A research agent that can answer questions",
+        model_name="gpt-4o",
+        system_prompt=(
+            "You are a ResearchAgent. Your task is to research and gather "
+            "information about the given topic. Provide comprehensive research "
+            "findings and key insights."
+        ),
+        max_loops=1,
+        interactive=True,
+        verbose=True,
+    )
+
+    agent2 = Agent(
+        name="Analysis Agent",
+        description="An analysis agent that draws conclusions from research",
+        model_name="gpt-4o-mini",
+        system_prompt=(
+            "You are an AnalysisAgent. Your task is to analyze the research "
+            "provided by the previous agent and draw meaningful conclusions. "
+            "Provide detailed analysis and actionable insights."
+        ),
+        max_loops=1,
+        interactive=True,
+        verbose=True,
+    )
+
+    workflow = SequentialWorkflow(
+        id="research_analysis_workflow",
+        name="Research Analysis Workflow",
+        description="A sequential workflow that researches and analyzes topics",
+        agents=[agent1, agent2],
+        max_loops=1,
+        output_type="str",
+        streaming_callback=streaming_callback,
+        multi_agent_collab_prompt=True,
+    )
+    return workflow.run(task)
+
+if __name__ == "__main__":
+
+# ## REGULAR STREAMING CALLBACK
+#     def streaming_callback(token):
+#         print(token, end="", flush=True)
+
+#     run_workflow_with_streaming_callback(
+#         task="What are the latest advancements in AI?",
+#         streaming_callback=streaming_callback,
+#     )
+
+
+# ## CUSTOM BUFFERING STREAMING_CALLBACK BASED ON DEV PREFERED 
+#     buffer = []
+#     def streaming_callback(token):
+#         buffer.append(token)
+#         # Print in bigger chunks (e.g., every 20 tokens or on final flush)
+#         if len(buffer) >= 20 or token.endswith("\n"):
+#             print("".join(buffer), end="", flush=True)
+#             buffer.clear()
+#         # Optionally, you could add a flush at the end of the run if needed
+
+#     run_workflow_with_streaming_callback(
+#         task="What are the latest advancements in AI?",
+#         streaming_callback=streaming_callback,
+#     )
+
+
+## NO ADDED STREAMING_CALLBACK
+    run_workflow_with_streaming_callback(
+        task="What are the latest advancements in AI?",
+        streaming_callback=None,
+    )
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