diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json
index bffc330deba..1f1d40bc3eb 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json	
@@ -573,7 +573,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\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_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\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        ),\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            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        ),\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        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\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 == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = self.memory.messages\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = get_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n"
+                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\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_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\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        ),\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            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        ),\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        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\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 == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = self.memory.messages\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = get_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n"
               },
               "memory": {
                 "_input_type": "HandleInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json
index 9cca340f7fd..88bafceed20 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json	
@@ -1730,7 +1730,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json
index 4b00b907c59..46711b30263 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
@@ -2007,7 +2007,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
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 56f5e9291f9..1f41f171d0a 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	
@@ -1163,7 +1163,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\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_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\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        ),\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            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        ),\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        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\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 == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = self.memory.messages\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = get_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n"
+                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\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_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\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        ),\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            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        ),\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        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\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 == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = self.memory.messages\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = get_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n"
               },
               "memory": {
                 "_input_type": "HandleInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json
index a98a36c5514..438be2c51cb 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json	
@@ -2256,7 +2256,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json
index 63a11ca3092..ff51ef9d6fe 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json	
@@ -782,7 +782,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents .json b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents .json
index 264fd0d0bfa..c618f23a92e 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents .json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents .json	
@@ -753,7 +753,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.memories.memory import MemoryComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -1366,7 +1366,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.memories.memory import MemoryComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -3228,7 +3228,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.memories.memory import MemoryComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent .json b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent .json
index d0613171b1f..0ef9d121bde 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent .json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent .json	
@@ -253,7 +253,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -771,7 +771,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import ast\nimport pprint\nfrom enum import Enum\n\nimport yfinance as yf\nfrom langchain.tools import StructuredTool\nfrom langchain_core.tools import ToolException\nfrom loguru import logger\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import DropdownInput, IntInput, MessageTextInput\nfrom langflow.schema import Data\n\n\nclass YahooFinanceMethod(Enum):\n    GET_INFO = \"get_info\"\n    GET_NEWS = \"get_news\"\n    GET_ACTIONS = \"get_actions\"\n    GET_ANALYSIS = \"get_analysis\"\n    GET_BALANCE_SHEET = \"get_balance_sheet\"\n    GET_CALENDAR = \"get_calendar\"\n    GET_CASHFLOW = \"get_cashflow\"\n    GET_INSTITUTIONAL_HOLDERS = \"get_institutional_holders\"\n    GET_RECOMMENDATIONS = \"get_recommendations\"\n    GET_SUSTAINABILITY = \"get_sustainability\"\n    GET_MAJOR_HOLDERS = \"get_major_holders\"\n    GET_MUTUALFUND_HOLDERS = \"get_mutualfund_holders\"\n    GET_INSIDER_PURCHASES = \"get_insider_purchases\"\n    GET_INSIDER_TRANSACTIONS = \"get_insider_transactions\"\n    GET_INSIDER_ROSTER_HOLDERS = \"get_insider_roster_holders\"\n    GET_DIVIDENDS = \"get_dividends\"\n    GET_CAPITAL_GAINS = \"get_capital_gains\"\n    GET_SPLITS = \"get_splits\"\n    GET_SHARES = \"get_shares\"\n    GET_FAST_INFO = \"get_fast_info\"\n    GET_SEC_FILINGS = \"get_sec_filings\"\n    GET_RECOMMENDATIONS_SUMMARY = \"get_recommendations_summary\"\n    GET_UPGRADES_DOWNGRADES = \"get_upgrades_downgrades\"\n    GET_EARNINGS = \"get_earnings\"\n    GET_INCOME_STMT = \"get_income_stmt\"\n\n\nclass YahooFinanceSchema(BaseModel):\n    symbol: str = Field(..., description=\"The stock symbol to retrieve data for.\")\n    method: YahooFinanceMethod = Field(YahooFinanceMethod.GET_INFO, description=\"The type of data to retrieve.\")\n    num_news: int | None = Field(5, description=\"The number of news articles to retrieve.\")\n\n\nclass YfinanceToolComponent(LCToolComponent):\n    display_name = \"Yahoo Finance\"\n    description = \"Access financial data and market information using Yahoo Finance.\"\n    icon = \"trending-up\"\n    name = \"YahooFinanceTool\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"symbol\",\n            display_name=\"Stock Symbol\",\n            info=\"The stock symbol to retrieve data for (e.g., AAPL, GOOG).\",\n        ),\n        DropdownInput(\n            name=\"method\",\n            display_name=\"Data Method\",\n            info=\"The type of data to retrieve.\",\n            options=list(YahooFinanceMethod),\n            value=\"get_news\",\n        ),\n        IntInput(\n            name=\"num_news\",\n            display_name=\"Number of News\",\n            info=\"The number of news articles to retrieve (only applicable for get_news).\",\n            value=5,\n        ),\n    ]\n\n    def run_model(self) -> list[Data]:\n        return self._yahoo_finance_tool(\n            self.symbol,\n            self.method,\n            self.num_news,\n        )\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"yahoo_finance\",\n            description=\"Access financial data and market information from Yahoo Finance.\",\n            func=self._yahoo_finance_tool,\n            args_schema=YahooFinanceSchema,\n        )\n\n    def _yahoo_finance_tool(\n        self,\n        symbol: str,\n        method: YahooFinanceMethod,\n        num_news: int | None = 5,\n    ) -> list[Data]:\n        ticker = yf.Ticker(symbol)\n\n        try:\n            if method == YahooFinanceMethod.GET_INFO:\n                result = ticker.info\n            elif method == YahooFinanceMethod.GET_NEWS:\n                result = ticker.news[:num_news]\n            else:\n                result = getattr(ticker, method.value)()\n\n            result = pprint.pformat(result)\n\n            if method == YahooFinanceMethod.GET_NEWS:\n                data_list = [Data(data=article) for article in ast.literal_eval(result)]\n            else:\n                data_list = [Data(data={\"result\": result})]\n\n        except Exception as e:\n            error_message = f\"Error retrieving data: {e}\"\n            logger.debug(error_message)\n            self.status = error_message\n            raise ToolException(error_message) from e\n\n        return data_list\n"
+                "value": "import ast\nimport pprint\nfrom enum import Enum\n\nimport yfinance as yf\nfrom langchain.tools import StructuredTool\nfrom langchain_core.tools import ToolException\nfrom loguru import logger\nfrom pydantic import BaseModel, Field\n\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.field_typing import Tool\nfrom langflow.inputs import DropdownInput, IntInput, MessageTextInput\nfrom langflow.schema import Data\n\n\nclass YahooFinanceMethod(Enum):\n    GET_INFO = \"get_info\"\n    GET_NEWS = \"get_news\"\n    GET_ACTIONS = \"get_actions\"\n    GET_ANALYSIS = \"get_analysis\"\n    GET_BALANCE_SHEET = \"get_balance_sheet\"\n    GET_CALENDAR = \"get_calendar\"\n    GET_CASHFLOW = \"get_cashflow\"\n    GET_INSTITUTIONAL_HOLDERS = \"get_institutional_holders\"\n    GET_RECOMMENDATIONS = \"get_recommendations\"\n    GET_SUSTAINABILITY = \"get_sustainability\"\n    GET_MAJOR_HOLDERS = \"get_major_holders\"\n    GET_MUTUALFUND_HOLDERS = \"get_mutualfund_holders\"\n    GET_INSIDER_PURCHASES = \"get_insider_purchases\"\n    GET_INSIDER_TRANSACTIONS = \"get_insider_transactions\"\n    GET_INSIDER_ROSTER_HOLDERS = \"get_insider_roster_holders\"\n    GET_DIVIDENDS = \"get_dividends\"\n    GET_CAPITAL_GAINS = \"get_capital_gains\"\n    GET_SPLITS = \"get_splits\"\n    GET_SHARES = \"get_shares\"\n    GET_FAST_INFO = \"get_fast_info\"\n    GET_SEC_FILINGS = \"get_sec_filings\"\n    GET_RECOMMENDATIONS_SUMMARY = \"get_recommendations_summary\"\n    GET_UPGRADES_DOWNGRADES = \"get_upgrades_downgrades\"\n    GET_EARNINGS = \"get_earnings\"\n    GET_INCOME_STMT = \"get_income_stmt\"\n\n\nclass YahooFinanceSchema(BaseModel):\n    symbol: str = Field(..., description=\"The stock symbol to retrieve data for.\")\n    method: YahooFinanceMethod = Field(YahooFinanceMethod.GET_INFO, description=\"The type of data to retrieve.\")\n    num_news: int | None = Field(5, description=\"The number of news articles to retrieve.\")\n\n\nclass YfinanceToolComponent(LCToolComponent):\n    display_name = \"Yahoo Finance\"\n    description = \"\"\"Uses [yfinance](https://pypi.org/project/yfinance/) (unofficial package) \\\nto access financial data and market information from Yahoo Finance.\"\"\"\n    icon = \"trending-up\"\n    name = \"YahooFinanceTool\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"symbol\",\n            display_name=\"Stock Symbol\",\n            info=\"The stock symbol to retrieve data for (e.g., AAPL, GOOG).\",\n        ),\n        DropdownInput(\n            name=\"method\",\n            display_name=\"Data Method\",\n            info=\"The type of data to retrieve.\",\n            options=list(YahooFinanceMethod),\n            value=\"get_news\",\n        ),\n        IntInput(\n            name=\"num_news\",\n            display_name=\"Number of News\",\n            info=\"The number of news articles to retrieve (only applicable for get_news).\",\n            value=5,\n        ),\n    ]\n\n    def run_model(self) -> list[Data]:\n        return self._yahoo_finance_tool(\n            self.symbol,\n            self.method,\n            self.num_news,\n        )\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"yahoo_finance\",\n            description=\"Access financial data and market information from Yahoo Finance.\",\n            func=self._yahoo_finance_tool,\n            args_schema=YahooFinanceSchema,\n        )\n\n    def _yahoo_finance_tool(\n        self,\n        symbol: str,\n        method: YahooFinanceMethod,\n        num_news: int | None = 5,\n    ) -> list[Data]:\n        ticker = yf.Ticker(symbol)\n\n        try:\n            if method == YahooFinanceMethod.GET_INFO:\n                result = ticker.info\n            elif method == YahooFinanceMethod.GET_NEWS:\n                result = ticker.news[:num_news]\n            else:\n                result = getattr(ticker, method.value)()\n\n            result = pprint.pformat(result)\n\n            if method == YahooFinanceMethod.GET_NEWS:\n                data_list = [Data(data=article) for article in ast.literal_eval(result)]\n            else:\n                data_list = [Data(data={\"result\": result})]\n\n        except Exception as e:\n            error_message = f\"Error retrieving data: {e}\"\n            logger.debug(error_message)\n            self.status = error_message\n            raise ToolException(error_message) from e\n\n        return data_list\n"
               },
               "method": {
                 "_input_type": "DropdownInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json
index b02312ecc43..858024ec123 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json	
@@ -1379,7 +1379,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.memories.memory import MemoryComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -1992,7 +1992,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.memories.memory import MemoryComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -2605,7 +2605,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.memories.memory import MemoryComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/YouTube Transcript Q&A.json b/src/backend/base/langflow/initial_setup/starter_projects/YouTube Transcript Q&A.json
index cd8fb2d9a68..64cb38eb158 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/YouTube Transcript Q&A.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/YouTube Transcript Q&A.json	
@@ -255,7 +255,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Add tool Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix), display_name\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",