diff --git a/templates/components/multiagent/python/app/api/routers/vercel_response.py b/templates/components/multiagent/python/app/api/routers/vercel_response.py
index fce55230..12082496 100644
--- a/templates/components/multiagent/python/app/api/routers/vercel_response.py
+++ b/templates/components/multiagent/python/app/api/routers/vercel_response.py
@@ -1,64 +1,50 @@
 import json
 import logging
+from abc import ABC
 from typing import AsyncGenerator, List
 
 from aiostream import stream
 from app.agents.single import AgentRunEvent, AgentRunResult
-from app.api.routers.events import EventCallbackHandler
 from app.api.routers.models import ChatData, Message
 from app.api.services.suggestion import NextQuestionSuggestion
 from fastapi import Request
 from fastapi.responses import StreamingResponse
-from llama_index.core.chat_engine.types import StreamingAgentChatResponse
 
 logger = logging.getLogger("uvicorn")
 
 
-class VercelStreamResponse(StreamingResponse):
+class VercelStreamResponse(StreamingResponse, ABC):
     """
-    Class to convert the response from the chat engine to the streaming format expected by Vercel
+    Base class to convert the response from the chat engine to the streaming format expected by Vercel
     """
 
     TEXT_PREFIX = "0:"
     DATA_PREFIX = "8:"
 
-    @classmethod
-    def convert_text(cls, token: str):
-        # Escape newlines and double quotes to avoid breaking the stream
-        token = json.dumps(token)
-        return f"{cls.TEXT_PREFIX}{token}\n"
+    def __init__(self, request: Request, chat_data: ChatData, *args, **kwargs):
+        self.request = request
 
-    @classmethod
-    def convert_data(cls, data: dict):
-        data_str = json.dumps(data)
-        return f"{cls.DATA_PREFIX}[{data_str}]\n"
+        stream = self._create_stream(request, chat_data, *args, **kwargs)
+        content = self.content_generator(stream)
 
-    def __init__(
-        self,
-        request: Request,
-        event_handler: EventCallbackHandler,
-        response: StreamingAgentChatResponse,
-        chat_data: ChatData,
-    ):
-        content = VercelStreamResponse.content_generator(
-            request, event_handler, response, chat_data
-        )
         super().__init__(content=content)
 
-    @staticmethod
-    async def _generate_next_questions(chat_history: List[Message], response: str):
-        questions = await NextQuestionSuggestion.suggest_next_questions(
-            chat_history, response
-        )
-        if questions:
-            return {
-                "type": "suggested_questions",
-                "data": questions,
-            }
-        return None
+    async def content_generator(self, stream):
+        is_stream_started = False
 
-    @classmethod
-    def content_generator(
+        async with stream.stream() as streamer:
+            async for output in streamer:
+                if not is_stream_started:
+                    is_stream_started = True
+                    # Stream a blank message to start the stream
+                    yield self.convert_text("")
+
+                yield output
+
+                if await self.request.is_disconnected():
+                    break
+
+    def _create_stream(
         self,
         request: Request,
         chat_data: ChatData,
@@ -108,3 +94,26 @@ def _event_to_response(event: AgentRunEvent) -> dict:
             "type": "agent",
             "data": {"agent": event.name, "text": event.msg},
         }
+
+    @classmethod
+    def convert_text(cls, token: str):
+        # Escape newlines and double quotes to avoid breaking the stream
+        token = json.dumps(token)
+        return f"{cls.TEXT_PREFIX}{token}\n"
+
+    @classmethod
+    def convert_data(cls, data: dict):
+        data_str = json.dumps(data)
+        return f"{cls.DATA_PREFIX}[{data_str}]\n"
+
+    @staticmethod
+    async def _generate_next_questions(chat_history: List[Message], response: str):
+        questions = await NextQuestionSuggestion.suggest_next_questions(
+            chat_history, response
+        )
+        if questions:
+            return {
+                "type": "suggested_questions",
+                "data": questions,
+            }
+        return None