cleaned up comments

examples/multi_agent/test_sequentialworkflow_streaming.py

@@ -1,17 +1,13 @@
 from swarms.structs.agent import Agent
 from swarms.structs.sequential_workflow import SequentialWorkflow
 
-def run_workflow_with_streaming_callback(task, streaming_callback):
+def streaming_callback(token):
-    """
+    buffer.append(token)
-    Run a sequential workflow with two agents and a streaming callback.
+    if len(buffer) >= 20 or token.endswith("\n"):
-
+        print("".join(buffer), end="", flush=True)
-    Args:
+        buffer.clear()
-        task (str): The task to process through the workflow.
-        streaming_callback (callable): Function to handle streaming output.
 
-    Returns:
+def run_workflow_with_streaming_callback(task, streaming_callback):
-        The final result from the workflow.
-    """
 
     agent1 = Agent(
         name="Research Agent",
@@ -55,34 +51,9 @@ def run_workflow_with_streaming_callback(task, streaming_callback):
 
 if __name__ == "__main__":
 
-# ## REGULAR STREAMING CALLBACK
+    buffer = []
-#     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,
+        streaming_callback=streaming_callback,
     )

swarms/structs/agent_rearrange.py

@@ -386,7 +386,6 @@ class AgentRearrange:
 
                         for agent_name in agent_names:
                             agent = self.agents[agent_name]
-                            # Set agent.streaming_on if no streaming_callback
                             if self.streaming_callback is not None:
                                 agent.streaming_on = True
                             result = agent.run(
@@ -398,7 +397,6 @@ class AgentRearrange:
                             result = any_to_str(result)
 
 
-                            # Call streaming callback with the result if provided
                             if self.streaming_callback:
                                 self.streaming_callback(result)
 
@@ -422,7 +420,6 @@ class AgentRearrange:
 
                         agent = self.agents[agent_name]
 
-                        # Add sequential awareness information for the agent
                         awareness_info = (
                             self._get_sequential_awareness(
                                 agent_name, tasks
@@ -436,7 +433,6 @@ class AgentRearrange:
                                 f"Added sequential awareness for {agent_name}: {awareness_info}"
                             )
 
-                        # Set agent.streaming_on if no streaming_callback
                         if self.streaming_callback is not None:
                             agent.streaming_on = True
                         current_task = agent.run(
@@ -447,7 +443,6 @@ class AgentRearrange:
                         )
                         current_task = any_to_str(current_task)
 
-                        # Call streaming callback with the result if provided
                         if self.streaming_callback:
                             self.streaming_callback(current_task)