diff --git a/mem0/proxy/main.py b/mem0/proxy/main.py
index 8689ef3e1c..1fecc7b6a1 100644
--- a/mem0/proxy/main.py
+++ b/mem0/proxy/main.py
@@ -17,7 +17,9 @@
             subprocess.check_call([sys.executable, "-m", "pip", "install", "litellm"])
             import litellm
         except subprocess.CalledProcessError:
-            print("Failed to install 'litellm'. Please install it manually using 'pip install litellm'.")
+            print(
+                "Failed to install 'litellm'. Please install it manually using 'pip install litellm'."
+            )
             sys.exit(1)
     else:
         raise ImportError("The required 'litellm' library is not installed.")
@@ -96,6 +98,8 @@ def create(
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         model_list: Optional[list] = None,  # pass in a list of api_base,keys, etc.
+        # New parameter
+        add_to_memory: bool = True,
     ):
         if not any([user_id, agent_id, run_id]):
             raise ValueError("One of user_id, agent_id, run_id must be provided")
@@ -107,10 +111,17 @@ def create(
 
         prepared_messages = self._prepare_messages(messages)
         if prepared_messages[-1]["role"] == "user":
-            self._async_add_to_memory(messages, user_id, agent_id, run_id, metadata, filters)
-            relevant_memories = self._fetch_relevant_memories(messages, user_id, agent_id, run_id, filters, limit)
+            if add_to_memory:
+                self._async_add_to_memory(
+                    messages, user_id, agent_id, run_id, metadata, filters
+                )
+            relevant_memories = self._fetch_relevant_memories(
+                messages, user_id, agent_id, run_id, filters, limit
+            )
             logger.debug(f"Retrieved {len(relevant_memories)} relevant memories")
-            prepared_messages[-1]["content"] = self._format_query_with_memories(messages, relevant_memories)
+            prepared_messages[-1]["content"] = self._format_query_with_memories(
+                messages, relevant_memories
+            )
 
         response = litellm.completion(
             model=model,
@@ -151,7 +162,9 @@ def _prepare_messages(self, messages: List[dict]) -> List[dict]:
             return [{"role": "system", "content": MEMORY_ANSWER_PROMPT}] + messages
         return messages
 
-    def _async_add_to_memory(self, messages, user_id, agent_id, run_id, metadata, filters):
+    def _async_add_to_memory(
+        self, messages, user_id, agent_id, run_id, metadata, filters
+    ):
         def add_task():
             logger.debug("Adding to memory asynchronously")
             self.mem0_client.add(
@@ -165,9 +178,13 @@ def add_task():
 
         threading.Thread(target=add_task, daemon=True).start()
 
-    def _fetch_relevant_memories(self, messages, user_id, agent_id, run_id, filters, limit):
+    def _fetch_relevant_memories(
+        self, messages, user_id, agent_id, run_id, filters, limit
+    ):
         # Currently, only pass the last 6 messages to the search API to prevent long query
-        message_input = [f"{message['role']}: {message['content']}" for message in messages][-6:]
+        message_input = [
+            f"{message['role']}: {message['content']}" for message in messages
+        ][-6:]
         # TODO: Make it better by summarizing the past conversation
         return self.mem0_client.search(
             query="\n".join(message_input),
@@ -182,7 +199,9 @@ def _format_query_with_memories(self, messages, relevant_memories):
         # Check if self.mem0_client is an instance of Memory or MemoryClient
 
         if isinstance(self.mem0_client, mem0.memory.main.Memory):
-            memories_text = "\n".join(memory["memory"] for memory in relevant_memories["results"])
+            memories_text = "\n".join(
+                memory["memory"] for memory in relevant_memories["results"]
+            )
         elif isinstance(self.mem0_client, mem0.client.main.MemoryClient):
             memories_text = "\n".join(memory["memory"] for memory in relevant_memories)
         return f"- Relevant Memories/Facts: {memories_text}\n\n- User Question: {messages[-1]['content']}"