diff --git a/python/packages/core/agent_framework/_workflows/_agent.py b/python/packages/core/agent_framework/_workflows/_agent.py
index 3fb83803c4..9893024ee7 100644
--- a/python/packages/core/agent_framework/_workflows/_agent.py
+++ b/python/packages/core/agent_framework/_workflows/_agent.py
@@ -213,6 +213,14 @@ def run(
             )
         return self._run_impl(messages, response_id, session, checkpoint_id, checkpoint_storage, **kwargs)
 
+    def _filter_messages(self, chat_messages: list[Message]) -> list[Message]:
+        """From a list[Message] output, return only the last meaningful assistant message."""
+        for msg in reversed(chat_messages):
+            if msg.role != "user" and msg.text and msg.text.strip():
+                return [msg]
+        # fallback: last non-user message
+        return [m for m in reversed(chat_messages) if m.role != "user"][:1]
+
     async def _run_impl(
         self,
         messages: AgentRunInputs,
@@ -476,7 +484,7 @@ def _convert_workflow_events_to_agent_response(
                     messages.append(data)
                     raw_representations.append(data.raw_representation)
                 elif is_instance_of(data, list[Message]):
-                    chat_messages = cast(list[Message], data)
+                    chat_messages = self._filter_messages(cast(list[Message], data))
                     messages.extend(chat_messages)
                     raw_representations.append(data)
                 else:
@@ -593,7 +601,7 @@ def _convert_workflow_event_to_agent_response_updates(
                 ]
             if is_instance_of(data, list[Message]):
                 # Convert each Message to an AgentResponseUpdate
-                chat_messages = cast(list[Message], data)
+                chat_messages = self._filter_messages(cast(list[Message], data))
                 updates = []
                 for msg in chat_messages:
                     updates.append(
diff --git a/python/packages/core/tests/workflow/test_workflow_agent.py b/python/packages/core/tests/workflow/test_workflow_agent.py
index b2fbded39b..d0d96c659d 100644
--- a/python/packages/core/tests/workflow/test_workflow_agent.py
+++ b/python/packages/core/tests/workflow/test_workflow_agent.py
@@ -469,15 +469,20 @@ async def raw_yielding_executor(
         assert updates[2].raw_representation.value == 42
 
     async def test_workflow_as_agent_yield_output_with_list_of_chat_messages(self) -> None:
-        """Test that yield_output with list[Message] extracts contents from all messages.
+        """Test that yield_output with list[Message] surfaces only the last assistant message.
 
-        Note: Content items are coalesced by _finalize_response, so multiple text contents
-        become a single merged Content in the final response.
+        When a workflow executor yields a list[Message] (as GroupChat orchestrators
+        do with self._full_conversation on termination), _filter_messages returns
+        only the last meaningful assistant message to avoid re-emitting user input
+        and replaying the full conversation history across turns. See #4261.
+
+        Users who need intermediate agent responses can opt in via
+        intermediate_outputs=True in GroupChatBuilder.
         """
 
         @executor
         async def list_yielding_executor(messages: list[Message], ctx: WorkflowContext[Never, list[Message]]) -> None:
-            # Yield a list of Messages (as SequentialBuilder does)
+            # Yield a list of Messages (as GroupChat orchestrator does with _full_conversation)
             msg_list = [
                 Message(role="user", text="first message"),
                 Message(role="assistant", text="second message"),
@@ -491,25 +496,24 @@ async def list_yielding_executor(messages: list[Message], ctx: WorkflowContext[N
         workflow = WorkflowBuilder(start_executor=list_yielding_executor).build()
         agent = workflow.as_agent("list-msg-agent")
 
-        # Verify streaming returns the update with all 4 contents before coalescing
+        # Streaming: _filter_messages returns only the last meaningful assistant message
         updates: list[AgentResponseUpdate] = []
         async for update in agent.run("test", stream=True):
             updates.append(update)
 
-        assert len(updates) == 3
+        # Only the last assistant message should be surfaced (user messages filtered,
+        # earlier assistant messages treated as conversation history replay)
+        assert len(updates) == 1
         full_response = AgentResponse.from_updates(updates)
-        assert len(full_response.messages) == 3
-        texts = [message.text for message in full_response.messages]
-        # Note: `from_agent_run_response_updates` coalesces multiple text contents into one content
-        assert texts == ["first message", "second message", "thirdfourth"]
+        assert len(full_response.messages) == 1
+        assert full_response.messages[0].text == "thirdfourth"
 
-        # Verify run()
+        # Non-streaming: same filtering applies
         result = await agent.run("test")
 
         assert isinstance(result, AgentResponse)
-        assert len(result.messages) == 3
-        texts = [message.text for message in result.messages]
-        assert texts == ["first message", "second message", "third fourth"]
+        assert len(result.messages) == 1
+        assert result.messages[0].text == "third fourth"
 
     async def test_session_conversation_history_included_in_workflow_run(self) -> None:
         """Test that messages provided to agent.run() are passed through to the workflow."""
@@ -1296,3 +1300,96 @@ def test_merge_updates_function_result_no_matching_call(self):
 
         # Order: text (user), text (assistant), function_result (orphan at end)
         assert content_types == ["text", "text", "function_result"]
+
+
+class TestWorkflowAgentUserInputFilteringRegression:
+    """Regression tests for #4261: user input must not compound across successive turns.
+
+    When a GroupChat orchestrator terminates, it yields self._full_conversation
+    (a list[Message]) containing the entire conversation history. Without filtering,
+    user inputs and earlier assistant messages accumulate in the response across turns.
+
+    The _filter_messages fix returns only the last meaningful assistant message from
+    list[Message] output, which aligns with the default GroupChatBuilder behavior
+    (intermediate_outputs=False) where only the orchestrator's final summary is surfaced.
+
+    Users who need intermediate agent responses can opt in via
+    intermediate_outputs=True in GroupChatBuilder.
+    """
+
+    async def test_streaming_compounding_not_observed_across_turns(self):
+        """Regression: turn 1's user input must not appear in turn 2's streamed response.
+
+        Reproduces the escalating symptom from #4261 where each new turn accumulated
+        all previous user messages in the streamed response text.
+        """
+
+        @executor
+        async def groupchat_like_executor(messages: list[Message], ctx: WorkflowContext[Never, AgentResponse]) -> None:
+            input_text = messages[-1].text or ""
+            response = AgentResponse(
+                messages=[
+                    Message(role="user", text=input_text),
+                    Message(
+                        role="assistant",
+                        contents=[Content.from_text(text=f"Answer to: {input_text}")],
+                        author_name="Principal",
+                    ),
+                ],
+            )
+            await ctx.yield_output(response)
+
+        workflow = WorkflowBuilder(start_executor=groupchat_like_executor).build()
+        agent = workflow.as_agent("groupchat-agent")
+
+        # Turn 1
+        updates1: list[AgentResponseUpdate] = []
+        async for chunk in agent.run("first_question", stream=True):
+            updates1.append(chunk)
+
+        # Turn 2: "first_question" must NOT bleed into turn 2's streamed output
+        updates2: list[AgentResponseUpdate] = []
+        async for chunk in agent.run("second_question", stream=True):
+            updates2.append(chunk)
+
+        text2 = " ".join(u.text or "" for u in updates2 if u.text)
+        assert "first_question" not in text2, (
+            "Turn 1 user input should not appear in turn 2 streaming output (compounding regression)"
+        )
+        assert "Answer to: second_question" in text2
+
+    async def test_nonstreaming_compounding_not_observed_across_turns(self):
+        """Regression: turn 1's user input must not appear in turn 2's response.
+
+        Reproduces the escalating symptom from #4261 where each new turn accumulated
+        all previous user messages in the response text.
+        """
+
+        @executor
+        async def groupchat_like_executor(messages: list[Message], ctx: WorkflowContext[Never, AgentResponse]) -> None:
+            input_text = messages[-1].text or ""
+            response = AgentResponse(
+                messages=[
+                    Message(role="user", text=input_text),
+                    Message(
+                        role="assistant",
+                        contents=[Content.from_text(text=f"Answer to: {input_text}")],
+                        author_name="Principal",
+                    ),
+                ],
+            )
+            await ctx.yield_output(response)
+
+        workflow = WorkflowBuilder(start_executor=groupchat_like_executor).build()
+        agent = workflow.as_agent("groupchat-agent")
+
+        # Turn 1
+        await agent.run("first_question")
+
+        # Turn 2: "first_question" must NOT bleed into turn 2's response
+        result2 = await agent.run("second_question")
+        text2 = " ".join(m.text or "" for m in result2.messages)
+        assert "first_question" not in text2, (
+            "Turn 1 user input should not appear in turn 2 response (compounding regression)"
+        )
+        assert "Answer to: second_question" in text2