Merge pull request #1147 from aparekh02/mvstream

[FEAT] [TEST] [DOCS] Streaming Callback for Majority Voting

docs/swarms/structs/majorityvoting.md

@@ -854,3 +854,66 @@ for i, result in enumerate(concurrent_results, 1):
     else:
         print(f"Result: {result}")
 ```
+### Example 4: Majorty Voting with Custom Streaming Features
+
+This example demonstrates streaming callback with a custom streaming function.
+
+```python
+from swarms import Agent
+from swarms.prompts.finance_agent_sys_prompt import (
+    FINANCIAL_AGENT_SYS_PROMPT,
+)
+from swarms.structs.majority_voting import MajorityVoting
+from dotenv import load_dotenv
+
+def streaming_callback(agent_name: str, chunk: str, is_final: bool):
+    # Chunk buffer static per call (reset each session)
+    if not hasattr(streaming_callback, "_buffer"):
+        streaming_callback._buffer = ""
+        streaming_callback._buffer_size = 0
+
+    min_chunk_size = 512  # or any large chunk size you want
+
+    if chunk:
+        streaming_callback._buffer += chunk
+        streaming_callback._buffer_size += len(chunk)
+    if streaming_callback._buffer_size >= min_chunk_size or is_final:
+        if streaming_callback._buffer:
+            print(streaming_callback._buffer, end="", flush=True)
+            streaming_callback._buffer = ""
+            streaming_callback._buffer_size = 0
+    if is_final:
+        print()
+
+load_dotenv()
+
+
+# Initialize the agent
+agent = Agent(
+    agent_name="Financial-Analysis-Agent",
+    agent_description="Personal finance advisor agent",
+    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+    max_loops=1,
+    model_name="gpt-4.1",
+    dynamic_temperature_enabled=True,
+    user_name="swarms_corp",
+    retry_attempts=3,
+    context_length=8192,
+    return_step_meta=False,
+    output_type="str",  # "json", "dict", "csv" OR "string" "yaml" and
+    auto_generate_prompt=False,  # Auto generate prompt for the agent based on name, description, and system prompt, task
+    max_tokens=4000,  # max output tokens
+    saved_state_path="agent_00.json",
+    interactive=False,
+    streaming_on=True,  #if concurrent agents want to be streamed
+)
+
+swarm = MajorityVoting(agents=[agent, agent, agent])
+
+swarm.run(
+    "Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
+    streaming_callback=streaming_callback,
+
+)
+
+```

swarms/structs/majority_voting.py

@@ -13,6 +13,7 @@ from swarms.utils.history_output_formatter import (
 )
 from swarms.utils.loguru_logger import initialize_logger
 from swarms.utils.output_types import OutputType
+from typing import Callable, Optional
 
 logger = initialize_logger(log_folder="majority_voting")
 
@@ -65,9 +66,16 @@ def default_consensus_agent(
     system_prompt: str = None,
     description: str = "An agent that uses consensus to generate a final answer.",
     model_name: str = "gpt-4.1",
+    streaming_callback: Optional[Callable[[str], None]] = None,
     *args,
     **kwargs,
 ):
+    # If streaming_on is not None, force it to True; else, set to False
+    if streaming_callback is not None:
+        streaming_on_value = True
+    else:
+        streaming_on_value = False
+
     return Agent(
         agent_name=name,
         agent_description=description,
@@ -76,6 +84,7 @@ def default_consensus_agent(
         system_prompt=system_prompt,
         dynamic_context_window=True,
         dynamic_temperature_enabled=True,
+        streaming_on=streaming_on_value,
         *args,
         **kwargs,
     )
@@ -159,7 +168,7 @@ class MajorityVoting:
             title="Majority Voting",
         )
 
-    def run(self, task: str, *args, **kwargs) -> List[Any]:
+    def run(self, task: str, streaming_callback: Optional[Callable[[str, str, bool], None]] = None, *args, **kwargs) -> List[Any]:
         """
         Runs the majority voting system with multi-loop functionality and returns the majority vote.
 
@@ -179,6 +188,7 @@ class MajorityVoting:
         )
 
         for i in range(self.max_loops):
+
             output = run_agents_concurrently(
                 agents=self.agents,
                 task=self.conversation.get_str(),
@@ -190,10 +200,31 @@ class MajorityVoting:
                     role=agent.agent_name,
                     content=output,
                 )
-
-            # Now run the consensus agent
+            
+            # Set streaming_on for the consensus agent based on the provided streaming_callback
+            self.consensus_agent.streaming_on = streaming_callback is not None
+
+            # Instead of a simple passthrough wrapper, match the callback invocation pattern from the provided reference for the consensus agent:
+            consensus_agent_name = self.consensus_agent.agent_name
+
+            if streaming_callback is not None:
+                def consensus_streaming_callback(chunk: str):
+                    """Wrapper for consensus agent streaming callback."""
+                    try:
+                        if chunk is not None and chunk.strip():
+                            streaming_callback(consensus_agent_name, chunk, False)
+                    except Exception as callback_error:
+                        if self.verbose:
+                            logger.warning(
+                                f"[STREAMING] Callback failed for {consensus_agent_name}: {str(callback_error)}"
+                            )
+            else:
+                consensus_streaming_callback = None
+
+            # Run the consensus agent with the streaming callback, if any
             consensus_output = self.consensus_agent.run(
                 task=(f"History: {self.conversation.get_str()}"),
+                streaming_callback=consensus_streaming_callback,
             )
 
             self.conversation.add(

tests/structs/test_majority_voting.py

@@ -199,21 +199,63 @@ def test_majority_voting_different_output_types():
         max_loops=1,
     )
 
-    # Test different output types
-    for output_type in ["dict", "string", "list"]:
-        mv = MajorityVoting(
-            name=f"Output-Type-Test-{output_type}",
-            description=f"Testing output type: {output_type}",
-            agents=[
-                security_expert,
-                compliance_officer,
-                privacy_advocate,
-            ],
+    # Assert majority vote is correct
+    assert majority_vote is not None
+
+def test_streaming_majority_voting():
+    """
+    Test the streaming_majority_voting with logging/try-except and assertion.
+    """
+    logs = []
+    def streaming_callback(agent_name: str, chunk: str, is_final: bool):
+        # Chunk buffer static per call (reset each session)
+        if not hasattr(streaming_callback, "_buffer"):
+            streaming_callback._buffer = ""
+            streaming_callback._buffer_size = 0
+
+        min_chunk_size = 512  # or any large chunk size you want
+
+        if chunk:
+            streaming_callback._buffer += chunk
+            streaming_callback._buffer_size += len(chunk)
+        if streaming_callback._buffer_size >= min_chunk_size or is_final:
+            if streaming_callback._buffer:
+                print(streaming_callback._buffer, end="", flush=True)
+                logs.append(streaming_callback._buffer)
+                streaming_callback._buffer = ""
+                streaming_callback._buffer_size = 0
+        if is_final:
+            print()
+     
+    try:
+        # Initialize the agent
+        agent = Agent(
+            agent_name="Financial-Analysis-Agent",
+            agent_description="Personal finance advisor agent",
+            system_prompt="You are a financial analysis agent.",  # replaced missing const
             max_loops=1,
-            output_type=output_type,
+            model_name="gpt-4.1",
+            dynamic_temperature_enabled=True,
+            user_name="swarms_corp",
+            retry_attempts=3,
+            context_length=8192,
+            return_step_meta=False,
+            output_type="str",  # "json", "dict", "csv" OR "string" "yaml" and
+            auto_generate_prompt=False,  # Auto generate prompt for the agent based on name, description, and system prompt, task
+            max_tokens=4000,  # max output tokens
+            saved_state_path="agent_00.json",
+            interactive=False,
+            streaming_on=True,  #if concurrent agents want to be streamed
         )
-
-        result = mv.run(
-            "What are the key considerations for implementing GDPR compliance in our data processing systems?"
+        
+        swarm = MajorityVoting(agents=[agent, agent, agent])
+        
+        result = swarm.run(
+            "Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
+            streaming_callback=streaming_callback,
         )
         assert result is not None
+    except Exception as e:
+        print("Error in test_streaming_majority_voting:", e)
+        print("Logs so far:", logs)
+        raise