Merge pull request #1681 from adichaudhary/feat/mixture-stop-pass

[feat] [MixtureOfAgents] workers receive task + prev-layer synthesis instead of full transcript

Author: kyegomez

swarms/structs/mixture_of_agents.py

@@ -20,10 +20,15 @@
 class MixtureOfAgents:
     """Run a layered Mixture-of-Agents workflow.
 
-    ``MixtureOfAgents`` sends the task and accumulated conversation
-    context to each worker agent concurrently for each configured layer.
-    After all layers complete, the aggregator agent receives the full
-    conversation and produces the final synthesized answer.
+    ``MixtureOfAgents`` runs workers in parallel across multiple layers,
+    then synthesises their outputs with an aggregator agent.
+
+    Worker context per layer:
+    - Layer 0: each worker receives only the original task.
+    - Layer 1+: each worker receives the original task plus the
+      concatenated outputs from the previous layer.
+
+    The aggregator always receives the full conversation transcript.
 
     Args:
         id: Optional identifier accepted for API compatibility.
@@ -158,8 +163,8 @@ def step(
         """Run one worker layer concurrently.
 
         Args:
-            task: Task or accumulated conversation context to send to each
-                worker agent.
+            task: On layer 0 this is the raw user task. On later layers it
+                is ``"Original task: …\\n\\nPrevious layer synthesis:\\n…"``.
             img: Optional image path, URL, or encoded image payload passed
                 through to each worker agent.
 
@@ -192,20 +197,31 @@ def _run(
 
         self.conversation.add(role="User", content=task)
 
-        full_context = self.conversation.get_str()
+        # Workers receive only the original task on the first layer, and
+        # task + previous-layer synthesis on subsequent layers. This avoids
+        # re-sending the full growing transcript to every worker on every layer.
+        worker_input = task
+        prev_layer_output: Optional[str] = None
 
         for i in range(self.layers):
-            # Pass the full context/history string to the step method
-            step_output = self.step(task=full_context, img=img)
+            if prev_layer_output is not None:
+                worker_input = (
+                    f"Original task: {task}\n\n"
+                    f"Previous layer synthesis:\n{prev_layer_output}"
+                )
+
+            step_output = self.step(task=worker_input, img=img)
 
-            # Log each agent's output with full context awareness
             for agent_name, agent_output in step_output.items():
                 self.conversation.add(
                     role=agent_name, content=agent_output
                 )
 
-            # Update the full_context with the latest conversation history
-            full_context = self.conversation.get_str()
+            # Summarise the layer as the concatenation of worker outputs so
+            # the next layer has a compact view of what was produced.
+            prev_layer_output = "\n\n".join(
+                f"{name}: {out}" for name, out in step_output.items()
+            )
 
         aggregator_output = self.aggregator_agent.run(
             task=self.conversation.get_str()