[FIX] Streaming Example Function

examples/multi_agent/sequential_workflow/demo_sequential_workflow_streaming.py

@@ -1,62 +1,16 @@
-import time
 from typing import Callable
 from swarms.structs.agent import Agent
 from swarms.structs.sequential_workflow import SequentialWorkflow
 
 def create_streaming_callback() -> Callable[[str, str, bool], None]:
     """
-    Create a streaming callback that shows live paragraph formation.
+    Create a streaming callback that prints each chunk as it arrives, with no duplication or timestamps.
     """
-    agent_buffers = {}
-    paragraph_count = {}
-
     def streaming_callback(agent_name: str, chunk: str, is_final: bool):
-        timestamp = time.strftime("%H:%M:%S")
+        if chunk:
-
+            print(chunk, end="", flush=True)
-        # Initialize buffers for new agents
-        if agent_name not in agent_buffers:
-            agent_buffers[agent_name] = ""
-            paragraph_count[agent_name] = 1
-            print(f"\n🎬 [{timestamp}] {agent_name} starting...")
-            print("=" * 60)
-
-        if chunk.strip():
-            # Split chunk into tokens (words/punctuation)
-            tokens = chunk.replace("\n", " \n ").split()
-
-            for token in tokens:
-                # Handle paragraph breaks
-                if token == "\n":
-                    if agent_buffers[agent_name].strip():
-                        print(
-                            f"\n📄 [{timestamp}] {agent_name} - Paragraph {paragraph_count[agent_name]} Complete:"
-                        )
-                        print(f"{agent_buffers[agent_name].strip()}")
-                        print("=" * 60)
-                        paragraph_count[agent_name] += 1
-                        agent_buffers[agent_name] = ""
-                else:
-                    # Add token to buffer and show live accumulation
-                    agent_buffers[agent_name] += token + " "
-                    print(
-                        f"\r[{timestamp}] {agent_name} | {agent_buffers[agent_name].strip()}",
-                        end="",
-                        flush=True,
-                    )
-
         if is_final:
-            print()  # New line after live updates
-            # Print any remaining content as final paragraph
-            if agent_buffers[agent_name].strip():
-                print(
-                    f"\n✅ [{timestamp}] {agent_name} COMPLETED - Final Paragraph:"
-                )
-                print(f"{agent_buffers[agent_name].strip()}")
             print()
-            print(f"🎯 [{timestamp}] {agent_name} finished processing")
-            print(f"📊 Total paragraphs processed: {paragraph_count[agent_name] - 1}")
-            print("=" * 60)
-
     return streaming_callback
 
 def create_agents():
@@ -69,6 +23,7 @@ def create_agents():
             model_name="gpt-4o-mini",
             max_loops=1,
             streaming_on=True,
+            print_on=False,
         ),
         Agent(
             agent_name="Analysis_Agent",
@@ -77,6 +32,7 @@ def create_agents():
             model_name="gpt-4o-mini",
             max_loops=1,
             streaming_on=True,
+            print_on=False,
         ),
     ]
 
@@ -84,7 +40,6 @@ if __name__ == "__main__":
     print("🎯 SEQUENTIAL WORKFLOW STREAMING DEMO")
     print("=" * 50)
 
-    # Create agents and workflow
     agents = create_agents()
     workflow = SequentialWorkflow(
         id="research_analysis_workflow",
@@ -96,18 +51,15 @@ if __name__ == "__main__":
         multi_agent_collab_prompt=True,
     )
 
-    # Define task
     task = "What are the latest advancements in AI?"
 
     print(f"Task: {task.strip()}")
 
-    # Create streaming callback
     streaming_callback = create_streaming_callback()
 
     print("\n🎬 EXECUTING WITH STREAMING CALLBACKS...")
     print("Watch real-time agent outputs below:\n")
 
-    # Execute with streaming
     result = workflow.run(
         task=task,
         streaming_callback=streaming_callback,

swarms/structs/sequential_workflow.py

@@ -133,7 +133,6 @@ class SequentialWorkflow:
             agent_names = []
             for agent in self.agents:
                 try:
-                    # Try to get agent_name, fallback to name if not available
                     agent_name = (
                         getattr(agent, "agent_name", None)
                         or agent.name
@@ -187,7 +186,6 @@ class SequentialWorkflow:
             Exception: If any error occurs during task execution.
         """
         try:
-            # prompt = f"{MULTI_AGENT_COLLAB_PROMPT}\n\n{task}"
             return self.agent_rearrange.run(
                 task=task,
                 img=img,