diff --git a/src/backend/base/langflow/components/helpers/memory.py b/src/backend/base/langflow/components/helpers/memory.py
index 3f3ceb6226b6..8ef9c516f169 100644
--- a/src/backend/base/langflow/components/helpers/memory.py
+++ b/src/backend/base/langflow/components/helpers/memory.py
@@ -19,10 +19,10 @@ class MemoryComponent(Component):
     documentation: str = "https://docs.langflow.org/components-helpers#message-history"
     icon = "message-square-more"
     name = "Memory"
-    default_keys = ["mode", "memory"]
+    default_keys = ["mode", "memory", "session_id"]
     mode_config = {
         "Store": ["message", "memory", "sender", "sender_name", "session_id"],
-        "Retrieve": ["n_messages", "order", "template", "memory"],
+        "Retrieve": ["n_messages", "order", "template", "memory", "session_id"],
     }
 
     inputs = [
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json
index 3b3a634d6e75..152d15870cbf 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json	
@@ -287,7 +287,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any, cast\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.memory import aget_messages, astore_message\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\nfrom langflow.utils.component_utils import set_current_fields, set_field_display\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n    documentation: str = \"https://docs.langflow.org/components-helpers#message-history\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n    default_keys = [\"mode\", \"memory\"]\n    mode_config = {\n        \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\"],\n        \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\"],\n    }\n\n    inputs = [\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Retrieve\", \"Store\"],\n            value=\"Retrieve\",\n            info=\"Operation mode: Store messages or Retrieve messages.\",\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"message\",\n            display_name=\"Message\",\n            info=\"The chat message to be stored.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender_type\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender\",\n            display_name=\"Sender\",\n            info=\"The sender of the message. Might be Machine or User. \"\n            \"If empty, the current sender parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            value=\"\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True),\n        Output(display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True),\n    ]\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n        if field_name == \"mode\":\n            # Start with empty outputs\n            frontend_node[\"outputs\"] = []\n            if field_value == \"Store\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Stored Messages\",\n                        name=\"stored_messages\",\n                        method=\"store_message\",\n                        hidden=True,\n                        dynamic=True,\n                    )\n                ]\n            if field_value == \"Retrieve\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Messages\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True\n                    ),\n                    Output(\n                        display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True\n                    ),\n                ]\n        return frontend_node\n\n    async def store_message(self) -> Message:\n        message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n        message.session_id = self.session_id or message.session_id\n        message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n        message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n        stored_messages: list[Message] = []\n\n        if self.memory:\n            self.memory.session_id = message.session_id\n            lc_message = message.to_lc_message()\n            await self.memory.aadd_messages([lc_message])\n\n            stored_messages = await self.memory.aget_messages() or []\n\n            stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n            if message.sender:\n                stored_messages = [m for m in stored_messages if m.sender == message.sender]\n        else:\n            await astore_message(message, flow_id=self.graph.flow_id)\n            stored_messages = (\n                await aget_messages(\n                    session_id=message.session_id, sender_name=message.sender_name, sender=message.sender\n                )\n                or []\n            )\n\n        if not stored_messages:\n            msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n            raise ValueError(msg)\n\n        stored_message = stored_messages[0]\n        self.status = stored_message\n        return stored_message\n\n    async def retrieve_messages(self) -> Data:\n        sender_type = self.sender_type\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender_type == \"Machine and User\":\n            sender_type = None\n\n        if self.memory and not hasattr(self.memory, \"aget_messages\"):\n            memory_name = type(self.memory).__name__\n            err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n            raise AttributeError(err_msg)\n        # Check if n_messages is None or 0\n        if n_messages == 0:\n            stored = []\n        elif self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages first\n\n            if order == \"DESC\":\n                stored = stored[::-1]  # Then reverse if needed\n\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender_type:\n                expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            # For internal memory, we always fetch the last N messages by ordering by DESC\n            stored = await aget_messages(\n                sender=sender_type,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=10000,\n                order=order,\n            )\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages\n\n        # self.status = stored\n        return cast(\"Data\", stored)\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        # self.status = stored_text\n        return Message(text=stored_text)\n\n    async def retrieve_messages_dataframe(self) -> DataFrame:\n        \"\"\"Convert the retrieved messages into a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the message data.\n        \"\"\"\n        messages = await self.retrieve_messages()\n        return DataFrame(messages)\n\n    def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: Any,  # noqa: ARG002\n        field_name: str | None = None,  # noqa: ARG002\n    ) -> dotdict:\n        return set_current_fields(\n            build_config=build_config,\n            action_fields=self.mode_config,\n            selected_action=build_config[\"mode\"][\"value\"],\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n"
+                "value": "from typing import Any, cast\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.memory import aget_messages, astore_message\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\nfrom langflow.utils.component_utils import set_current_fields, set_field_display\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n    documentation: str = \"https://docs.langflow.org/components-helpers#message-history\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n    default_keys = [\"mode\", \"memory\", \"session_id\"]\n    mode_config = {\n        \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\"],\n        \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\"],\n    }\n\n    inputs = [\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Retrieve\", \"Store\"],\n            value=\"Retrieve\",\n            info=\"Operation mode: Store messages or Retrieve messages.\",\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"message\",\n            display_name=\"Message\",\n            info=\"The chat message to be stored.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender_type\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender\",\n            display_name=\"Sender\",\n            info=\"The sender of the message. Might be Machine or User. \"\n            \"If empty, the current sender parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            value=\"\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True),\n        Output(display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True),\n    ]\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n        if field_name == \"mode\":\n            # Start with empty outputs\n            frontend_node[\"outputs\"] = []\n            if field_value == \"Store\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Stored Messages\",\n                        name=\"stored_messages\",\n                        method=\"store_message\",\n                        hidden=True,\n                        dynamic=True,\n                    )\n                ]\n            if field_value == \"Retrieve\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Messages\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True\n                    ),\n                    Output(\n                        display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True\n                    ),\n                ]\n        return frontend_node\n\n    async def store_message(self) -> Message:\n        message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n        message.session_id = self.session_id or message.session_id\n        message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n        message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n        stored_messages: list[Message] = []\n\n        if self.memory:\n            self.memory.session_id = message.session_id\n            lc_message = message.to_lc_message()\n            await self.memory.aadd_messages([lc_message])\n\n            stored_messages = await self.memory.aget_messages() or []\n\n            stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n            if message.sender:\n                stored_messages = [m for m in stored_messages if m.sender == message.sender]\n        else:\n            await astore_message(message, flow_id=self.graph.flow_id)\n            stored_messages = (\n                await aget_messages(\n                    session_id=message.session_id, sender_name=message.sender_name, sender=message.sender\n                )\n                or []\n            )\n\n        if not stored_messages:\n            msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n            raise ValueError(msg)\n\n        stored_message = stored_messages[0]\n        self.status = stored_message\n        return stored_message\n\n    async def retrieve_messages(self) -> Data:\n        sender_type = self.sender_type\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender_type == \"Machine and User\":\n            sender_type = None\n\n        if self.memory and not hasattr(self.memory, \"aget_messages\"):\n            memory_name = type(self.memory).__name__\n            err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n            raise AttributeError(err_msg)\n        # Check if n_messages is None or 0\n        if n_messages == 0:\n            stored = []\n        elif self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages first\n\n            if order == \"DESC\":\n                stored = stored[::-1]  # Then reverse if needed\n\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender_type:\n                expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            # For internal memory, we always fetch the last N messages by ordering by DESC\n            stored = await aget_messages(\n                sender=sender_type,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=10000,\n                order=order,\n            )\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages\n\n        # self.status = stored\n        return cast(\"Data\", stored)\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        # self.status = stored_text\n        return Message(text=stored_text)\n\n    async def retrieve_messages_dataframe(self) -> DataFrame:\n        \"\"\"Convert the retrieved messages into a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the message data.\n        \"\"\"\n        messages = await self.retrieve_messages()\n        return DataFrame(messages)\n\n    def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: Any,  # noqa: ARG002\n        field_name: str | None = None,  # noqa: ARG002\n    ) -> dotdict:\n        return set_current_fields(\n            build_config=build_config,\n            action_fields=self.mode_config,\n            selected_action=build_config[\"mode\"][\"value\"],\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n"
               },
               "memory": {
                 "_input_type": "HandleInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json
index 02b3f9df93b3..8ede37ca887c 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json	
@@ -1505,7 +1505,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any, cast\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.memory import aget_messages, astore_message\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\nfrom langflow.utils.component_utils import set_current_fields, set_field_display\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n    documentation: str = \"https://docs.langflow.org/components-helpers#message-history\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n    default_keys = [\"mode\", \"memory\"]\n    mode_config = {\n        \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\"],\n        \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\"],\n    }\n\n    inputs = [\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Retrieve\", \"Store\"],\n            value=\"Retrieve\",\n            info=\"Operation mode: Store messages or Retrieve messages.\",\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"message\",\n            display_name=\"Message\",\n            info=\"The chat message to be stored.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender_type\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender\",\n            display_name=\"Sender\",\n            info=\"The sender of the message. Might be Machine or User. \"\n            \"If empty, the current sender parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            value=\"\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True),\n        Output(display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True),\n    ]\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n        if field_name == \"mode\":\n            # Start with empty outputs\n            frontend_node[\"outputs\"] = []\n            if field_value == \"Store\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Stored Messages\",\n                        name=\"stored_messages\",\n                        method=\"store_message\",\n                        hidden=True,\n                        dynamic=True,\n                    )\n                ]\n            if field_value == \"Retrieve\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Messages\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True\n                    ),\n                    Output(\n                        display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True\n                    ),\n                ]\n        return frontend_node\n\n    async def store_message(self) -> Message:\n        message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n        message.session_id = self.session_id or message.session_id\n        message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n        message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n        stored_messages: list[Message] = []\n\n        if self.memory:\n            self.memory.session_id = message.session_id\n            lc_message = message.to_lc_message()\n            await self.memory.aadd_messages([lc_message])\n\n            stored_messages = await self.memory.aget_messages() or []\n\n            stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n            if message.sender:\n                stored_messages = [m for m in stored_messages if m.sender == message.sender]\n        else:\n            await astore_message(message, flow_id=self.graph.flow_id)\n            stored_messages = (\n                await aget_messages(\n                    session_id=message.session_id, sender_name=message.sender_name, sender=message.sender\n                )\n                or []\n            )\n\n        if not stored_messages:\n            msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n            raise ValueError(msg)\n\n        stored_message = stored_messages[0]\n        self.status = stored_message\n        return stored_message\n\n    async def retrieve_messages(self) -> Data:\n        sender_type = self.sender_type\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender_type == \"Machine and User\":\n            sender_type = None\n\n        if self.memory and not hasattr(self.memory, \"aget_messages\"):\n            memory_name = type(self.memory).__name__\n            err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n            raise AttributeError(err_msg)\n        # Check if n_messages is None or 0\n        if n_messages == 0:\n            stored = []\n        elif self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages first\n\n            if order == \"DESC\":\n                stored = stored[::-1]  # Then reverse if needed\n\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender_type:\n                expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            # For internal memory, we always fetch the last N messages by ordering by DESC\n            stored = await aget_messages(\n                sender=sender_type,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=10000,\n                order=order,\n            )\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages\n\n        # self.status = stored\n        return cast(\"Data\", stored)\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        # self.status = stored_text\n        return Message(text=stored_text)\n\n    async def retrieve_messages_dataframe(self) -> DataFrame:\n        \"\"\"Convert the retrieved messages into a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the message data.\n        \"\"\"\n        messages = await self.retrieve_messages()\n        return DataFrame(messages)\n\n    def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: Any,  # noqa: ARG002\n        field_name: str | None = None,  # noqa: ARG002\n    ) -> dotdict:\n        return set_current_fields(\n            build_config=build_config,\n            action_fields=self.mode_config,\n            selected_action=build_config[\"mode\"][\"value\"],\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n"
+                "value": "from typing import Any, cast\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.memory import aget_messages, astore_message\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\nfrom langflow.utils.component_utils import set_current_fields, set_field_display\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n    documentation: str = \"https://docs.langflow.org/components-helpers#message-history\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n    default_keys = [\"mode\", \"memory\", \"session_id\"]\n    mode_config = {\n        \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\"],\n        \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\"],\n    }\n\n    inputs = [\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Retrieve\", \"Store\"],\n            value=\"Retrieve\",\n            info=\"Operation mode: Store messages or Retrieve messages.\",\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"message\",\n            display_name=\"Message\",\n            info=\"The chat message to be stored.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender_type\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender\",\n            display_name=\"Sender\",\n            info=\"The sender of the message. Might be Machine or User. \"\n            \"If empty, the current sender parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            value=\"\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True),\n        Output(display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True),\n    ]\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n        if field_name == \"mode\":\n            # Start with empty outputs\n            frontend_node[\"outputs\"] = []\n            if field_value == \"Store\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Stored Messages\",\n                        name=\"stored_messages\",\n                        method=\"store_message\",\n                        hidden=True,\n                        dynamic=True,\n                    )\n                ]\n            if field_value == \"Retrieve\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Messages\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True\n                    ),\n                    Output(\n                        display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True\n                    ),\n                ]\n        return frontend_node\n\n    async def store_message(self) -> Message:\n        message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n        message.session_id = self.session_id or message.session_id\n        message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n        message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n        stored_messages: list[Message] = []\n\n        if self.memory:\n            self.memory.session_id = message.session_id\n            lc_message = message.to_lc_message()\n            await self.memory.aadd_messages([lc_message])\n\n            stored_messages = await self.memory.aget_messages() or []\n\n            stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n            if message.sender:\n                stored_messages = [m for m in stored_messages if m.sender == message.sender]\n        else:\n            await astore_message(message, flow_id=self.graph.flow_id)\n            stored_messages = (\n                await aget_messages(\n                    session_id=message.session_id, sender_name=message.sender_name, sender=message.sender\n                )\n                or []\n            )\n\n        if not stored_messages:\n            msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n            raise ValueError(msg)\n\n        stored_message = stored_messages[0]\n        self.status = stored_message\n        return stored_message\n\n    async def retrieve_messages(self) -> Data:\n        sender_type = self.sender_type\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender_type == \"Machine and User\":\n            sender_type = None\n\n        if self.memory and not hasattr(self.memory, \"aget_messages\"):\n            memory_name = type(self.memory).__name__\n            err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n            raise AttributeError(err_msg)\n        # Check if n_messages is None or 0\n        if n_messages == 0:\n            stored = []\n        elif self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages first\n\n            if order == \"DESC\":\n                stored = stored[::-1]  # Then reverse if needed\n\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender_type:\n                expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            # For internal memory, we always fetch the last N messages by ordering by DESC\n            stored = await aget_messages(\n                sender=sender_type,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=10000,\n                order=order,\n            )\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages\n\n        # self.status = stored\n        return cast(\"Data\", stored)\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        # self.status = stored_text\n        return Message(text=stored_text)\n\n    async def retrieve_messages_dataframe(self) -> DataFrame:\n        \"\"\"Convert the retrieved messages into a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the message data.\n        \"\"\"\n        messages = await self.retrieve_messages()\n        return DataFrame(messages)\n\n    def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: Any,  # noqa: ARG002\n        field_name: str | None = None,  # noqa: ARG002\n    ) -> dotdict:\n        return set_current_fields(\n            build_config=build_config,\n            action_fields=self.mode_config,\n            selected_action=build_config[\"mode\"][\"value\"],\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n"
               },
               "memory": {
                 "_input_type": "HandleInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json
index cc89c0b789f7..c73fe6aed352 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json	
@@ -855,7 +855,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Any, cast\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.memory import aget_messages, astore_message\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\nfrom langflow.utils.component_utils import set_current_fields, set_field_display\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n    documentation: str = \"https://docs.langflow.org/components-helpers#message-history\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n    default_keys = [\"mode\", \"memory\"]\n    mode_config = {\n        \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\"],\n        \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\"],\n    }\n\n    inputs = [\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Retrieve\", \"Store\"],\n            value=\"Retrieve\",\n            info=\"Operation mode: Store messages or Retrieve messages.\",\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"message\",\n            display_name=\"Message\",\n            info=\"The chat message to be stored.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender_type\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender\",\n            display_name=\"Sender\",\n            info=\"The sender of the message. Might be Machine or User. \"\n            \"If empty, the current sender parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            value=\"\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True),\n        Output(display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True),\n    ]\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n        if field_name == \"mode\":\n            # Start with empty outputs\n            frontend_node[\"outputs\"] = []\n            if field_value == \"Store\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Stored Messages\",\n                        name=\"stored_messages\",\n                        method=\"store_message\",\n                        hidden=True,\n                        dynamic=True,\n                    )\n                ]\n            if field_value == \"Retrieve\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Messages\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True\n                    ),\n                    Output(\n                        display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True\n                    ),\n                ]\n        return frontend_node\n\n    async def store_message(self) -> Message:\n        message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n        message.session_id = self.session_id or message.session_id\n        message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n        message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n        stored_messages: list[Message] = []\n\n        if self.memory:\n            self.memory.session_id = message.session_id\n            lc_message = message.to_lc_message()\n            await self.memory.aadd_messages([lc_message])\n\n            stored_messages = await self.memory.aget_messages() or []\n\n            stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n            if message.sender:\n                stored_messages = [m for m in stored_messages if m.sender == message.sender]\n        else:\n            await astore_message(message, flow_id=self.graph.flow_id)\n            stored_messages = (\n                await aget_messages(\n                    session_id=message.session_id, sender_name=message.sender_name, sender=message.sender\n                )\n                or []\n            )\n\n        if not stored_messages:\n            msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n            raise ValueError(msg)\n\n        stored_message = stored_messages[0]\n        self.status = stored_message\n        return stored_message\n\n    async def retrieve_messages(self) -> Data:\n        sender_type = self.sender_type\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender_type == \"Machine and User\":\n            sender_type = None\n\n        if self.memory and not hasattr(self.memory, \"aget_messages\"):\n            memory_name = type(self.memory).__name__\n            err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n            raise AttributeError(err_msg)\n        # Check if n_messages is None or 0\n        if n_messages == 0:\n            stored = []\n        elif self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages first\n\n            if order == \"DESC\":\n                stored = stored[::-1]  # Then reverse if needed\n\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender_type:\n                expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            # For internal memory, we always fetch the last N messages by ordering by DESC\n            stored = await aget_messages(\n                sender=sender_type,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=10000,\n                order=order,\n            )\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages\n\n        # self.status = stored\n        return cast(\"Data\", stored)\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        # self.status = stored_text\n        return Message(text=stored_text)\n\n    async def retrieve_messages_dataframe(self) -> DataFrame:\n        \"\"\"Convert the retrieved messages into a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the message data.\n        \"\"\"\n        messages = await self.retrieve_messages()\n        return DataFrame(messages)\n\n    def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: Any,  # noqa: ARG002\n        field_name: str | None = None,  # noqa: ARG002\n    ) -> dotdict:\n        return set_current_fields(\n            build_config=build_config,\n            action_fields=self.mode_config,\n            selected_action=build_config[\"mode\"][\"value\"],\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n"
+                "value": "from typing import Any, cast\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs.inputs import DropdownInput, HandleInput, IntInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.memory import aget_messages, astore_message\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\nfrom langflow.utils.component_utils import set_current_fields, set_field_display\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Stores or retrieves stored chat messages from Langflow tables or an external memory.\"\n    documentation: str = \"https://docs.langflow.org/components-helpers#message-history\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n    default_keys = [\"mode\", \"memory\", \"session_id\"]\n    mode_config = {\n        \"Store\": [\"message\", \"memory\", \"sender\", \"sender_name\", \"session_id\"],\n        \"Retrieve\": [\"n_messages\", \"order\", \"template\", \"memory\", \"session_id\"],\n    }\n\n    inputs = [\n        TabInput(\n            name=\"mode\",\n            display_name=\"Mode\",\n            options=[\"Retrieve\", \"Store\"],\n            value=\"Retrieve\",\n            info=\"Operation mode: Store messages or Retrieve messages.\",\n            real_time_refresh=True,\n        ),\n        MessageTextInput(\n            name=\"message\",\n            display_name=\"Message\",\n            info=\"The chat message to be stored.\",\n            tool_mode=True,\n            dynamic=True,\n            show=False,\n        ),\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"Memory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender_type\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender\",\n            display_name=\"Sender\",\n            info=\"The sender of the message. Might be Machine or User. \"\n            \"If empty, the current sender parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n            show=False,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n            show=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            value=\"\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=True,\n            required=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n            show=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Message\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True),\n        Output(display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True),\n    ]\n\n    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:\n        \"\"\"Dynamically show only the relevant output based on the selected output type.\"\"\"\n        if field_name == \"mode\":\n            # Start with empty outputs\n            frontend_node[\"outputs\"] = []\n            if field_value == \"Store\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Stored Messages\",\n                        name=\"stored_messages\",\n                        method=\"store_message\",\n                        hidden=True,\n                        dynamic=True,\n                    )\n                ]\n            if field_value == \"Retrieve\":\n                frontend_node[\"outputs\"] = [\n                    Output(\n                        display_name=\"Messages\", name=\"messages_text\", method=\"retrieve_messages_as_text\", dynamic=True\n                    ),\n                    Output(\n                        display_name=\"Dataframe\", name=\"dataframe\", method=\"retrieve_messages_dataframe\", dynamic=True\n                    ),\n                ]\n        return frontend_node\n\n    async def store_message(self) -> Message:\n        message = Message(text=self.message) if isinstance(self.message, str) else self.message\n\n        message.session_id = self.session_id or message.session_id\n        message.sender = self.sender or message.sender or MESSAGE_SENDER_AI\n        message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI\n\n        stored_messages: list[Message] = []\n\n        if self.memory:\n            self.memory.session_id = message.session_id\n            lc_message = message.to_lc_message()\n            await self.memory.aadd_messages([lc_message])\n\n            stored_messages = await self.memory.aget_messages() or []\n\n            stored_messages = [Message.from_lc_message(m) for m in stored_messages] if stored_messages else []\n\n            if message.sender:\n                stored_messages = [m for m in stored_messages if m.sender == message.sender]\n        else:\n            await astore_message(message, flow_id=self.graph.flow_id)\n            stored_messages = (\n                await aget_messages(\n                    session_id=message.session_id, sender_name=message.sender_name, sender=message.sender\n                )\n                or []\n            )\n\n        if not stored_messages:\n            msg = \"No messages were stored. Please ensure that the session ID and sender are properly set.\"\n            raise ValueError(msg)\n\n        stored_message = stored_messages[0]\n        self.status = stored_message\n        return stored_message\n\n    async def retrieve_messages(self) -> Data:\n        sender_type = self.sender_type\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender_type == \"Machine and User\":\n            sender_type = None\n\n        if self.memory and not hasattr(self.memory, \"aget_messages\"):\n            memory_name = type(self.memory).__name__\n            err_msg = f\"External Memory object ({memory_name}) must have 'aget_messages' method.\"\n            raise AttributeError(err_msg)\n        # Check if n_messages is None or 0\n        if n_messages == 0:\n            stored = []\n        elif self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages first\n\n            if order == \"DESC\":\n                stored = stored[::-1]  # Then reverse if needed\n\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender_type:\n                expected_type = MESSAGE_SENDER_AI if sender_type == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            # For internal memory, we always fetch the last N messages by ordering by DESC\n            stored = await aget_messages(\n                sender=sender_type,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=10000,\n                order=order,\n            )\n            if n_messages:\n                stored = stored[-n_messages:]  # Get last N messages\n\n        # self.status = stored\n        return cast(\"Data\", stored)\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        # self.status = stored_text\n        return Message(text=stored_text)\n\n    async def retrieve_messages_dataframe(self) -> DataFrame:\n        \"\"\"Convert the retrieved messages into a DataFrame.\n\n        Returns:\n            DataFrame: A DataFrame containing the message data.\n        \"\"\"\n        messages = await self.retrieve_messages()\n        return DataFrame(messages)\n\n    def update_build_config(\n        self,\n        build_config: dotdict,\n        field_value: Any,  # noqa: ARG002\n        field_name: str | None = None,  # noqa: ARG002\n    ) -> dotdict:\n        return set_current_fields(\n            build_config=build_config,\n            action_fields=self.mode_config,\n            selected_action=build_config[\"mode\"][\"value\"],\n            default_fields=self.default_keys,\n            func=set_field_display,\n        )\n"
               },
               "memory": {
                 "_input_type": "HandleInput",
diff --git a/src/backend/tests/unit/components/processing/test_structured_output_component.py b/src/backend/tests/unit/components/processing/test_structured_output_component.py
index 33ab6a3407d2..055e485512ad 100644
--- a/src/backend/tests/unit/components/processing/test_structured_output_component.py
+++ b/src/backend/tests/unit/components/processing/test_structured_output_component.py
@@ -252,8 +252,8 @@ def model_dump(self, **__):
         mock_get_chat_result.assert_called_once()
 
     @pytest.mark.skipif(
-        "OPENAI_API_KEY" not in os.environ,
-        reason="OPENAI_API_KEY environment variable not set",
+        not (os.getenv("OPENAI_API_KEY") or "").strip(),
+        reason="OPENAI_API_KEY is not set or is empty",
     )
     def test_with_real_openai_model_simple_schema(self):
         # Create a real OpenAI model
@@ -284,7 +284,7 @@ def test_with_real_openai_model_simple_schema(self):
         assert result[0]["age"] == 30
 
     @pytest.mark.skipif(
-        "OPENAI_API_KEY" not in os.environ,
+        not (os.getenv("OPENAI_API_KEY") or "").strip(),
         reason="OPENAI_API_KEY environment variable not set",
     )
     def test_with_real_openai_model_multiple_patterns(self):
@@ -397,7 +397,7 @@ def model_dump(self, **__):
             assert 899.99 in prices
 
     @pytest.mark.skipif(
-        "OPENAI_API_KEY" not in os.environ,
+        not (os.getenv("OPENAI_API_KEY") or "").strip(),
         reason="OPENAI_API_KEY environment variable not set",
     )
     def test_with_real_openai_model_simple_schema_fail(self):
@@ -433,7 +433,7 @@ def test_with_real_openai_model_simple_schema_fail(self):
         ), f"Expected max_tokens error but got: {error_message}"
 
     @pytest.mark.skipif(
-        "OPENAI_API_KEY" not in os.environ,
+        not (os.getenv("OPENAI_API_KEY") or "").strip(),
         reason="OPENAI_API_KEY environment variable not set",
     )
     def test_with_real_openai_model_complex_schema(self):
@@ -482,7 +482,7 @@ def test_with_real_openai_model_complex_schema(self):
         assert result[0]["rating"] == 4.0
 
     @pytest.mark.skipif(
-        "OPENAI_API_KEY" not in os.environ,
+        not (os.getenv("OPENAI_API_KEY") or "").strip(),
         reason="OPENAI_API_KEY environment variable not set",
     )
     def test_with_real_openai_model_nested_schema(self):
@@ -547,7 +547,7 @@ def test_with_real_openai_model_nested_schema(self):
         assert result[0]["would_return"] is True
 
     @pytest.mark.skipif(
-        "NVIDIA_API_KEY" not in os.environ,
+        not (os.getenv("NVIDIA_API_KEY") or "").strip(),
         reason="NVIDIA_API_KEY environment variable not set",
     )
     def test_with_real_nvidia_model_simple_schema(self):