src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json

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          "description": "Create a prompt template with dynamic variables.",
          "display_name": "Prompt",
          "id": "Prompt-u7GZR",
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            "base_classes": [
              "Message"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {
              "template": [
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            },
            "description": "Create a prompt template with dynamic variables.",
            "display_name": "Prompt",
            "documentation": "",
            "edited": false,
            "field_order": [
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            "frozen": false,
            "icon": "prompts",
            "legacy": false,
            "lf_version": "1.0.19.post2",
            "metadata": {},
            "output_types": [],
            "outputs": [
              {
                "cache": true,
                "display_name": "Prompt Message",
                "method": "build_prompt",
                "name": "prompt",
                "selected": "Message",
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
              },
              "previous_response": {
                "advanced": false,
                "display_name": "previous_response",
                "dynamic": false,
                "field_type": "str",
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "input_types": [
                  "Message",
                  "Text"
                ],
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "previous_response",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "type": "str",
                "value": ""
              },
              "template": {
                "_input_type": "PromptInput",
                "advanced": false,
                "display_name": "Template",
                "dynamic": false,
                "info": "",
                "list": false,
                "load_from_db": false,
                "name": "template",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "type": "prompt",
                "value": "\n\nRESEARCH PLAN: {previous_response}\n\nUse Tavily Search to investigate the queries and analyze the findings.\nFocus on academic and reliable sources.\n\nSteps:\n1. Search using provided queries\n2. Analyze search results\n3. Verify source credibility\n4. Extract key findings\n\nFormat findings as:\n\nSEARCH RESULTS:\n[Key findings from searches]\n\nSOURCE ANALYSIS:\n[Credibility assessment]\n\nMAIN INSIGHTS:\n[Critical discoveries]\n\nEVIDENCE QUALITY:\n[Evaluation of findings]"
              },
              "tool_placeholder": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Tool Placeholder",
                "dynamic": false,
                "info": "A placeholder input for tool mode.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "tool_placeholder",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": true,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              }
            },
            "tool_mode": false
          },
          "type": "Prompt"
        },
        "dragging": false,
        "height": 347,
        "id": "Prompt-u7GZR",
        "position": {
          "x": 1803.2315476328304,
          "y": 839.0423490089254
        },
        "positionAbsolute": {
          "x": 1803.2315476328304,
          "y": 839.0423490089254
        },
        "selected": false,
        "type": "genericNode",
        "width": 320
      },
      {
        "data": {
          "id": "ChatInput-Mzp4f",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "category": "inputs",
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Get chat inputs from the Playground.",
            "display_name": "Chat Input",
            "documentation": "",
            "edited": false,
            "field_order": [
              "input_value",
              "should_store_message",
              "sender",
              "sender_name",
              "session_id",
              "files",
              "background_color",
              "chat_icon",
              "text_color"
            ],
            "frozen": false,
            "icon": "MessagesSquare",
            "key": "ChatInput",
            "legacy": false,
            "lf_version": "1.0.19.post2",
            "metadata": {},
            "output_types": [],
            "outputs": [
              {
                "cache": true,
                "display_name": "Message",
                "method": "message_response",
                "name": "message",
                "selected": "Message",
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "background_color": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Background Color",
                "dynamic": false,
                "info": "The background color of the icon.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "background_color",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "chat_icon": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Icon",
                "dynamic": false,
                "info": "The icon of the message.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "chat_icon",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "code": {
                "advanced": true,
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                "file_path": "",
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                "list": false,
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                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\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        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
              },
              "files": {
                "_input_type": "FileInput",
                "advanced": true,
                "display_name": "Files",
                "dynamic": false,
                "fileTypes": [
                  "txt",
                  "md",
                  "mdx",
                  "csv",
                  "json",
                  "yaml",
                  "yml",
                  "xml",
                  "html",
                  "htm",
                  "pdf",
                  "docx",
                  "py",
                  "sh",
                  "sql",
                  "js",
                  "ts",
                  "tsx",
                  "jpg",
                  "jpeg",
                  "png",
                  "bmp",
                  "image"
                ],
                "file_path": "",
                "info": "Files to be sent with the message.",
                "list": true,
                "name": "files",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "file",
                "value": ""
              },
              "input_value": {
                "_input_type": "MultilineInput",
                "advanced": false,
                "display_name": "Text",
                "dynamic": false,
                "info": "Message to be passed as input.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "input_value",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "Research the effectiveness of different prompt engineering techniques in controlling AI hallucinations, with focus on real-world applications and empirical studies."
              },
              "sender": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "display_name": "Sender Type",
                "dynamic": false,
                "info": "Type of sender.",
                "name": "sender",
                "options": [
                  "Machine",
                  "User"
                ],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "User"
              },
              "sender_name": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Sender Name",
                "dynamic": false,
                "info": "Name of the sender.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "sender_name",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "User"
              },
              "session_id": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Session ID",
                "dynamic": false,
                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "session_id",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "should_store_message": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Store Messages",
                "dynamic": false,
                "info": "Store the message in the history.",
                "list": false,
                "name": "should_store_message",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              },
              "text_color": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Text Color",
                "dynamic": false,
                "info": "The text color of the name",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "text_color",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              }
            }
          },
          "type": "ChatInput"
        },
        "dragging": false,
        "height": 234,
        "id": "ChatInput-Mzp4f",
        "position": {
          "x": 756.0075981758582,
          "y": 756.7423476254241
        },
        "positionAbsolute": {
          "x": 756.0075981758582,
          "y": 756.7423476254241
        },
        "selected": false,
        "type": "genericNode",
        "width": 320
      },
      {
        "data": {
          "description": "Display a chat message in the Playground.",
          "display_name": "Chat Output",
          "id": "ChatOutput-mWv8X",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Display a chat message in the Playground.",
            "display_name": "Chat Output",
            "documentation": "",
            "edited": false,
            "field_order": [
              "input_value",
              "should_store_message",
              "sender",
              "sender_name",
              "session_id",
              "data_template",
              "background_color",
              "chat_icon",
              "text_color"
            ],
            "frozen": false,
            "icon": "MessagesSquare",
            "legacy": false,
            "lf_version": "1.0.19.post2",
            "metadata": {},
            "output_types": [],
            "outputs": [
              {
                "cache": true,
                "display_name": "Message",
                "method": "message_response",
                "name": "message",
                "selected": "Message",
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "background_color": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Background Color",
                "dynamic": false,
                "info": "The background color of the icon.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "background_color",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "chat_icon": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Icon",
                "dynamic": false,
                "info": "The icon of the message.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "chat_icon",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\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        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
              },
              "data_template": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Data Template",
                "dynamic": false,
                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "data_template",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "{text}"
              },
              "input_value": {
                "_input_type": "MessageInput",
                "advanced": false,
                "display_name": "Text",
                "dynamic": false,
                "info": "Message to be passed as output.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "input_value",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "sender": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "display_name": "Sender Type",
                "dynamic": false,
                "info": "Type of sender.",
                "name": "sender",
                "options": [
                  "Machine",
                  "User"
                ],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "Machine"
              },
              "sender_name": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Sender Name",
                "dynamic": false,
                "info": "Name of the sender.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "sender_name",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "AI"
              },
              "session_id": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Session ID",
                "dynamic": false,
                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "session_id",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "should_store_message": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Store Messages",
                "dynamic": false,
                "info": "Store the message in the history.",
                "list": false,
                "name": "should_store_message",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              },
              "text_color": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Text Color",
                "dynamic": false,
                "info": "The text color of the name",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "text_color",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              }
            },
            "tool_mode": false
          },
          "type": "ChatOutput"
        },
        "dragging": false,
        "height": 234,
        "id": "ChatOutput-mWv8X",
        "position": {
          "x": 3200.774558432761,
          "y": 853.9881404769172
        },
        "positionAbsolute": {
          "x": 3200.774558432761,
          "y": 853.9881404769172
        },
        "selected": false,
        "type": "genericNode",
        "width": 320
      },
      {
        "data": {
          "description": "Create a prompt template with dynamic variables.",
          "display_name": "Prompt",
          "id": "Prompt-yDDjW",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {
              "template": [
                "search_results",
                "input_value"
              ]
            },
            "description": "Create a prompt template with dynamic variables.",
            "display_name": "Prompt",
            "documentation": "",
            "edited": false,
            "field_order": [
              "template"
            ],
            "frozen": false,
            "icon": "prompts",
            "legacy": false,
            "lf_version": "1.0.19.post2",
            "metadata": {},
            "output_types": [],
            "outputs": [
              {
                "cache": true,
                "display_name": "Prompt Message",
                "method": "build_prompt",
                "name": "prompt",
                "selected": "Message",
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
              },
              "input_value": {
                "advanced": false,
                "display_name": "input_value",
                "dynamic": false,
                "field_type": "str",
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "input_types": [
                  "Message",
                  "Text"
                ],
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "input_value",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "type": "str",
                "value": ""
              },
              "search_results": {
                "advanced": false,
                "display_name": "search_results",
                "dynamic": false,
                "field_type": "str",
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "input_types": [
                  "Message",
                  "Text"
                ],
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "search_results",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "type": "str",
                "value": ""
              },
              "template": {
                "_input_type": "PromptInput",
                "advanced": false,
                "display_name": "Template",
                "dynamic": false,
                "info": "",
                "list": false,
                "load_from_db": false,
                "name": "template",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "type": "prompt",
                "value": "RESEARCH FINDINGS: {search_results}\nORIGINAL QUERY: {input_value}\n"
              },
              "tool_placeholder": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Tool Placeholder",
                "dynamic": false,
                "info": "A placeholder input for tool mode.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "tool_placeholder",
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        "data": {
          "description": "**Tavily AI** is a search engine optimized for LLMs and RAG,         aimed at efficient, quick, and persistent search results. It can be used independently or as an agent tool.\n\nNote: Check 'Advanced' for all options.\n",
          "display_name": "Tavily AI Search",
          "id": "TavilyAISearch-rI4aD",
          "node": {
            "base_classes": [
              "Data",
              "Tool"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {},
            "description": "**Tavily AI** is a search engine optimized for LLMs and RAG,         aimed at efficient, quick, and persistent search results. It can be used independently or as an agent tool.\n\nNote: Check 'Advanced' for all options.\n",
            "display_name": "Tavily AI Search",
            "documentation": "https://docs.tavily.com/",
            "edited": false,
            "field_order": [
              "api_key",
              "query",
              "search_depth",
              "topic",
              "max_results",
              "include_images",
              "include_answer"
            ],
            "frozen": false,
            "icon": "TavilyIcon",
            "legacy": false,
            "lf_version": "1.0.19.post2",
            "metadata": {},
            "output_types": [],
            "outputs": [
              {
                "cache": true,
                "display_name": "Data",
                "method": "run_model",
                "name": "api_run_model",
                "required_inputs": [
                  "api_key"
                ],
                "selected": "Data",
                "types": [
                  "Data"
                ],
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              },
              {
                "cache": true,
                "display_name": "Tool",
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                "name": "api_build_tool",
                "required_inputs": [
                  "api_key"
                ],
                "selected": "Tool",
                "types": [
                  "Tool"
                ],
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              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "api_key": {
                "_input_type": "SecretStrInput",
                "advanced": false,
                "display_name": "Tavily API Key",
                "dynamic": false,
                "info": "Your Tavily API Key.",
                "input_types": [
                  "Message"
                ],
                "load_from_db": true,
                "name": "api_key",
                "password": true,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "str",
                "value": ""
              },
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from enum import Enum\n\nimport httpx\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 BoolInput, DropdownInput, IntInput, MessageTextInput, SecretStrInput\nfrom langflow.schema import Data\n\n\nclass TavilySearchDepth(Enum):\n    BASIC = \"basic\"\n    ADVANCED = \"advanced\"\n\n\nclass TavilySearchTopic(Enum):\n    GENERAL = \"general\"\n    NEWS = \"news\"\n\n\nclass TavilySearchSchema(BaseModel):\n    query: str = Field(..., description=\"The search query you want to execute with Tavily.\")\n    search_depth: TavilySearchDepth = Field(TavilySearchDepth.BASIC, description=\"The depth of the search.\")\n    topic: TavilySearchTopic = Field(TavilySearchTopic.GENERAL, description=\"The category of the search.\")\n    max_results: int = Field(5, description=\"The maximum number of search results to return.\")\n    include_images: bool = Field(default=False, description=\"Include a list of query-related images in the response.\")\n    include_answer: bool = Field(default=False, description=\"Include a short answer to original query.\")\n\n\nclass TavilySearchToolComponent(LCToolComponent):\n    display_name = \"Tavily AI Search\"\n    description = \"\"\"**Tavily AI** is a search engine optimized for LLMs and RAG, \\\n        aimed at efficient, quick, and persistent search results. It can be used independently or as an agent tool.\n\nNote: Check 'Advanced' for all options.\n\"\"\"\n    icon = \"TavilyIcon\"\n    name = \"TavilyAISearch\"\n    documentation = \"https://docs.tavily.com/\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"Tavily API Key\",\n            required=True,\n            info=\"Your Tavily API Key.\",\n        ),\n        MessageTextInput(\n            name=\"query\",\n            display_name=\"Search Query\",\n            info=\"The search query you want to execute with Tavily.\",\n        ),\n        DropdownInput(\n            name=\"search_depth\",\n            display_name=\"Search Depth\",\n            info=\"The depth of the search.\",\n            options=list(TavilySearchDepth),\n            value=TavilySearchDepth.ADVANCED,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"topic\",\n            display_name=\"Search Topic\",\n            info=\"The category of the search.\",\n            options=list(TavilySearchTopic),\n            value=TavilySearchTopic.GENERAL,\n            advanced=True,\n        ),\n        IntInput(\n            name=\"max_results\",\n            display_name=\"Max Results\",\n            info=\"The maximum number of search results to return.\",\n            value=5,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"include_images\",\n            display_name=\"Include Images\",\n            info=\"Include a list of query-related images in the response.\",\n            value=True,\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"include_answer\",\n            display_name=\"Include Answer\",\n            info=\"Include a short answer to original query.\",\n            value=True,\n            advanced=True,\n        ),\n    ]\n\n    def run_model(self) -> list[Data]:\n        # Convert string values to enum instances with validation\n        try:\n            search_depth_enum = (\n                self.search_depth\n                if isinstance(self.search_depth, TavilySearchDepth)\n                else TavilySearchDepth(str(self.search_depth).lower())\n            )\n        except ValueError as e:\n            error_message = f\"Invalid search depth value: {e!s}\"\n            self.status = error_message\n            return [Data(data={\"error\": error_message})]\n\n        try:\n            topic_enum = (\n                self.topic if isinstance(self.topic, TavilySearchTopic) else TavilySearchTopic(str(self.topic).lower())\n            )\n        except ValueError as e:\n            error_message = f\"Invalid topic value: {e!s}\"\n            self.status = error_message\n            return [Data(data={\"error\": error_message})]\n\n        return self._tavily_search(\n            self.query,\n            search_depth=search_depth_enum,\n            topic=topic_enum,\n            max_results=self.max_results,\n            include_images=self.include_images,\n            include_answer=self.include_answer,\n        )\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"tavily_search\",\n            description=\"Perform a web search using the Tavily API.\",\n            func=self._tavily_search,\n            args_schema=TavilySearchSchema,\n        )\n\n    def _tavily_search(\n        self,\n        query: str,\n        *,\n        search_depth: TavilySearchDepth = TavilySearchDepth.BASIC,\n        topic: TavilySearchTopic = TavilySearchTopic.GENERAL,\n        max_results: int = 5,\n        include_images: bool = False,\n        include_answer: bool = False,\n    ) -> list[Data]:\n        # Validate enum values\n        if not isinstance(search_depth, TavilySearchDepth):\n            msg = f\"Invalid search_depth value: {search_depth}\"\n            raise TypeError(msg)\n        if not isinstance(topic, TavilySearchTopic):\n            msg = f\"Invalid topic value: {topic}\"\n            raise TypeError(msg)\n\n        try:\n            url = \"https://api.tavily.com/search\"\n            headers = {\n                \"content-type\": \"application/json\",\n                \"accept\": \"application/json\",\n            }\n            payload = {\n                \"api_key\": self.api_key,\n                \"query\": query,\n                \"search_depth\": search_depth.value,\n                \"topic\": topic.value,\n                \"max_results\": max_results,\n                \"include_images\": include_images,\n                \"include_answer\": include_answer,\n            }\n\n            with httpx.Client() as client:\n                response = client.post(url, json=payload, headers=headers)\n\n            response.raise_for_status()\n            search_results = response.json()\n\n            data_results = [\n                Data(\n                    data={\n                        \"title\": result.get(\"title\"),\n                        \"url\": result.get(\"url\"),\n                        \"content\": result.get(\"content\"),\n                        \"score\": result.get(\"score\"),\n                    }\n                )\n                for result in search_results.get(\"results\", [])\n            ]\n\n            if include_answer and search_results.get(\"answer\"):\n                data_results.insert(0, Data(data={\"answer\": search_results[\"answer\"]}))\n\n            if include_images and search_results.get(\"images\"):\n                data_results.append(Data(data={\"images\": search_results[\"images\"]}))\n\n            self.status = data_results  # type: ignore[assignment]\n\n        except httpx.HTTPStatusError as e:\n            error_message = f\"HTTP error: {e.response.status_code} - {e.response.text}\"\n            logger.debug(error_message)\n            self.status = error_message\n            raise ToolException(error_message) from e\n        except Exception as e:\n            error_message = f\"Unexpected error: {e}\"\n            logger.opt(exception=True).debug(\"Error running Tavily Search\")\n            self.status = error_message\n            raise ToolException(error_message) from e\n        return data_results\n"
              },
              "include_answer": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Include Answer",
                "dynamic": false,
                "info": "Include a short answer to original query.",
                "list": false,
                "name": "include_answer",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              },
              "include_images": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Include Images",
                "dynamic": false,
                "info": "Include a list of query-related images in the response.",
                "list": false,
                "name": "include_images",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              },
              "max_results": {
                "_input_type": "IntInput",
                "advanced": true,
                "display_name": "Max Results",
                "dynamic": false,
                "info": "The maximum number of search results to return.",
                "list": false,
                "name": "max_results",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "int",
                "value": 5
              },
              "query": {
                "_input_type": "MessageTextInput",
                "advanced": false,
                "display_name": "Search Query",
                "dynamic": false,
                "info": "The search query you want to execute with Tavily.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "query",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "search_depth": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "display_name": "Search Depth",
                "dynamic": false,
                "info": "The depth of the search.",
                "load_from_db": false,
                "name": "search_depth",
                "options": [
                  "basic",
                  "advanced"
                ],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "advanced"
              },
              "topic": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "display_name": "Search Topic",
                "dynamic": false,
                "info": "The category of the search.",
                "load_from_db": false,
                "name": "topic",
                "options": [
                  "general",
                  "news"
                ],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "general"
              }
            },
            "tool_mode": false
          },
          "type": "TavilyAISearch"
        },
        "dragging": false,
        "height": 481,
        "id": "TavilyAISearch-rI4aD",
        "position": {
          "x": 1802.2291194402355,
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      },
      {
        "data": {
          "description": "Generates text using OpenAI LLMs.",
          "display_name": "OpenAI",
          "id": "OpenAIModel-Rc3MO",
          "node": {
            "base_classes": [
              "LanguageModel",
              "Message"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {},
            "description": "Generates text using OpenAI LLMs.",
            "display_name": "OpenAI",
            "documentation": "",
            "edited": false,
            "field_order": [
              "input_value",
              "system_message",
              "stream",
              "max_tokens",
              "model_kwargs",
              "json_mode",
              "output_schema",
              "model_name",
              "openai_api_base",
              "api_key",
              "temperature",
              "seed",
              "output_parser"
            ],
            "frozen": false,
            "icon": "OpenAI",
            "legacy": false,
            "lf_version": "1.0.19.post2",
            "metadata": {},
            "output_types": [],
            "outputs": [
              {
                "cache": true,
                "display_name": "Text",
                "method": "text_response",
                "name": "text_output",
                "required_inputs": [],
                "selected": "Message",
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              },
              {
                "cache": true,
                "display_name": "Language Model",
                "method": "build_model",
                "name": "model_output",
                "required_inputs": [],
                "selected": "LanguageModel",
                "types": [
                  "LanguageModel"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "api_key": {
                "_input_type": "SecretStrInput",
                "advanced": false,
                "display_name": "OpenAI API Key",
                "dynamic": false,
                "info": "The OpenAI API Key to use for the OpenAI model.",
                "input_types": [
                  "Message"
                ],
                "load_from_db": true,
                "name": "api_key",
                "password": true,
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "type": "str",
                "value": "OPENAI_API_KEY"
              },
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
              },
              "input_value": {
                "_input_type": "MessageInput",
                "advanced": false,
                "display_name": "Input",
                "dynamic": false,
                "info": "",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "input_value",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "json_mode": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "JSON Mode",
                "dynamic": false,
                "info": "If True, it will output JSON regardless of passing a schema.",
                "list": false,
                "name": "json_mode",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": false
              },
              "max_tokens": {
                "_input_type": "IntInput",
                "advanced": true,
                "display_name": "Max Tokens",
                "dynamic": false,
                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
                "list": false,
                "name": "max_tokens",
                "placeholder": "",
                "range_spec": {
                  "max": 128000,
                  "min": 0,
                  "step": 0.1,
                  "step_type": "float"
                },
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "int",
                "value": ""
              },
              "model_kwargs": {
                "_input_type": "DictInput",
                "advanced": true,
                "display_name": "Model Kwargs",
                "dynamic": false,
                "info": "Additional keyword arguments to pass to the model.",
                "list": false,
                "name": "model_kwargs",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "type": "dict",
                "value": {}
              },
              "model_name": {
                "_input_type": "DropdownInput",
                "advanced": false,
                "combobox": false,
                "display_name": "Model Name",
                "dynamic": false,
                "info": "",
                "name": "model_name",
                "options": [
                  "gpt-4o-mini",
                  "gpt-4o",
                  "gpt-4-turbo",
                  "gpt-4-turbo-preview",
                  "gpt-4",
                  "gpt-3.5-turbo",
                  "gpt-3.5-turbo-0125"
                ],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "gpt-4o-mini"
              },
              "openai_api_base": {
                "_input_type": "StrInput",
                "advanced": true,
                "display_name": "OpenAI API Base",
                "dynamic": false,
                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
                "list": false,
                "load_from_db": false,
                "name": "openai_api_base",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "output_parser": {
                "_input_type": "HandleInput",
                "advanced": true,
                "display_name": "Output Parser",
                "dynamic": false,
                "info": "The parser to use to parse the output of the model",
                "input_types": [
                  "OutputParser"
                ],
                "list": false,
                "name": "output_parser",
                "placeholder": "",
                "required": false,
                "show": true,
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                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
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                "name": "openai_api_base",
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            "description": "# Research Agent \n\nWelcome to the Research Agent! This flow helps you conduct in-depth research on various topics using AI-powered tools and analysis.\n\n## Instructions\n1. Enter Your Research Query\n   - Type your research question or topic into the Chat Input node.\n   - Be specific and clear about what you want to investigate.\n\n2. Generate Research Plan\n   - The system will create a focused research plan based on your query.\n   - This plan includes key search queries and priorities.\n\n3. Conduct Web Search\n   - The Tavily AI Search tool will perform web searches using the generated queries.\n   - It focuses on finding academic and reliable sources.\n\n4. Analyze and Synthesize\n   - The AI agent will review the search results and create a comprehensive synthesis.\n   - The report includes an executive summary, methodology, findings, and conclusions.\n\n5. Review the Output\n   - Read the final report in the Chat Output node.\n   - Use this information as a starting point for further research or decision-making.\n\nRemember: You can refine your initial query for more specific results! 🔍📊",
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          "description": "Define the agent's instructions, then enter a task to complete using tools.",
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                "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 (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\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 (\n    ToolCallingAgentComponent,\n)\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=\"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 = await 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    async 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 await 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 (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\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",
                "advanced": true,
                "display_name": "Handle Parse Errors",
                "dynamic": false,
                "info": "Should the Agent fix errors when reading user input for better processing?",
                "list": false,
                "name": "handle_parsing_errors",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              },
              "input_value": {
                "_input_type": "MessageTextInput",
                "advanced": false,
                "display_name": "Input",
                "dynamic": false,
                "info": "The input provided by the user for the agent to process.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "input_value",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": true,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "json_mode": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "JSON Mode",
                "dynamic": false,
                "info": "If True, it will output JSON regardless of passing a schema.",
                "list": false,
                "name": "json_mode",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": false
              },
              "max_iterations": {
                "_input_type": "IntInput",
                "advanced": true,
                "display_name": "Max Iterations",
                "dynamic": false,
                "info": "The maximum number of attempts the agent can make to complete its task before it stops.",
                "list": false,
                "name": "max_iterations",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "int",
                "value": 15
              },
              "max_tokens": {
                "_input_type": "IntInput",
                "advanced": true,
                "display_name": "Max Tokens",
                "dynamic": false,
                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
                "list": false,
                "name": "max_tokens",
                "placeholder": "",
                "range_spec": {
                  "max": 128000,
                  "min": 0,
                  "step": 0.1,
                  "step_type": "float"
                },
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "int",
                "value": ""
              },
              "memory": {
                "_input_type": "HandleInput",
                "advanced": true,
                "display_name": "External Memory",
                "dynamic": false,
                "info": "Retrieve messages from an external memory. If empty, it will use the Langflow tables.",
                "input_types": [
                  "BaseChatMessageHistory"
                ],
                "list": false,
                "name": "memory",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "other",
                "value": ""
              },
              "model_kwargs": {
                "_input_type": "DictInput",
                "advanced": true,
                "display_name": "Model Kwargs",
                "dynamic": false,
                "info": "Additional keyword arguments to pass to the model.",
                "list": false,
                "name": "model_kwargs",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "type": "dict",
                "value": {}
              },
              "model_name": {
                "_input_type": "DropdownInput",
                "advanced": false,
                "combobox": false,
                "display_name": "Model Name",
                "dynamic": false,
                "info": "",
                "name": "model_name",
                "options": [
                  "gpt-4o-mini",
                  "gpt-4o",
                  "gpt-4-turbo",
                  "gpt-4-turbo-preview",
                  "gpt-4",
                  "gpt-3.5-turbo",
                  "gpt-3.5-turbo-0125"
                ],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "gpt-4o-mini"
              },
              "n_messages": {
                "_input_type": "IntInput",
                "advanced": true,
                "display_name": "Number of Messages",
                "dynamic": false,
                "info": "Number of messages to retrieve.",
                "list": false,
                "name": "n_messages",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "int",
                "value": 100
              },
              "openai_api_base": {
                "_input_type": "StrInput",
                "advanced": true,
                "display_name": "OpenAI API Base",
                "dynamic": false,
                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
                "list": false,
                "load_from_db": false,
                "name": "openai_api_base",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "order": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "display_name": "Order",
                "dynamic": false,
                "info": "Order of the messages.",
                "name": "order",
                "options": [
                  "Ascending",
                  "Descending"
                ],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "Ascending"
              },
              "output_parser": {
                "_input_type": "HandleInput",
                "advanced": true,
                "display_name": "Output Parser",
                "dynamic": false,
                "info": "The parser to use to parse the output of the model",
                "input_types": [
                  "OutputParser"
                ],
                "list": false,
                "name": "output_parser",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "other",
                "value": ""
              },
              "output_schema": {
                "_input_type": "DictInput",
                "advanced": true,
                "display_name": "Schema",
                "dynamic": false,
                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled. [DEPRECATED]",
                "list": true,
                "name": "output_schema",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_input": true,
                "type": "dict",
                "value": {}
              },
              "seed": {
                "_input_type": "IntInput",
                "advanced": true,
                "display_name": "Seed",
                "dynamic": false,
                "info": "The seed controls the reproducibility of the job.",
                "list": false,
                "name": "seed",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "int",
                "value": 1
              },
              "sender": {
                "_input_type": "DropdownInput",
                "advanced": true,
                "combobox": false,
                "display_name": "Sender Type",
                "dynamic": false,
                "info": "Filter by sender type.",
                "name": "sender",
                "options": [
                  "Machine",
                  "User",
                  "Machine and User"
                ],
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_metadata": true,
                "type": "str",
                "value": "Machine and User"
              },
              "sender_name": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Sender Name",
                "dynamic": false,
                "info": "Filter by sender name.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "sender_name",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "session_id": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Session ID",
                "dynamic": false,
                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "session_id",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              },
              "system_prompt": {
                "_input_type": "MultilineInput",
                "advanced": false,
                "display_name": "Agent Instructions",
                "dynamic": false,
                "info": "System Prompt: Initial instructions and context provided to guide the agent's behavior.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "system_prompt",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "You are a research analyst with access to Tavily Search."
              },
              "temperature": {
                "_input_type": "FloatInput",
                "advanced": true,
                "display_name": "Temperature",
                "dynamic": false,
                "info": "",
                "list": false,
                "name": "temperature",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "float",
                "value": 0.1
              },
              "template": {
                "_input_type": "MultilineInput",
                "advanced": true,
                "display_name": "Template",
                "dynamic": false,
                "info": "The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "template",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": "{sender_name}: {text}"
              },
              "tools": {
                "_input_type": "HandleInput",
                "advanced": false,
                "display_name": "Tools",
                "dynamic": false,
                "info": "These are the tools that the agent can use to help with tasks.",
                "input_types": [
                  "Tool",
                  "BaseTool",
                  "StructuredTool"
                ],
                "list": true,
                "name": "tools",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "other",
                "value": ""
              },
              "verbose": {
                "_input_type": "BoolInput",
                "advanced": true,
                "display_name": "Verbose",
                "dynamic": false,
                "info": "",
                "list": false,
                "name": "verbose",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "trace_as_metadata": true,
                "type": "bool",
                "value": true
              }
            },
            "tool_mode": false
          },
          "type": "Agent"
        },
        "dragging": false,
        "height": 658,
        "id": "Agent-9E8IU",
        "position": {
          "x": 2156.60686936856,
          "y": 439.4579572266066
        },
        "positionAbsolute": {
          "x": 2156.60686936856,
          "y": 439.4579572266066
        },
        "selected": true,
        "type": "genericNode",
        "width": 320
      },
      {
        "data": {
          "description": "Create a prompt template with dynamic variables.",
          "display_name": "Prompt",
          "id": "Prompt-T4lL6",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
            "conditional_paths": [],
            "custom_fields": {
              "template": []
            },
            "description": "Create a prompt template with dynamic variables.",
            "display_name": "Prompt",
            "documentation": "",
            "edited": false,
            "field_order": [
              "template"
            ],
            "frozen": false,
            "icon": "prompts",
            "legacy": false,
            "lf_version": "1.0.19.post2",
            "metadata": {},
            "output_types": [],
            "outputs": [
              {
                "cache": true,
                "display_name": "Prompt Message",
                "method": "build_prompt",
                "name": "prompt",
                "selected": "Message",
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
              },
              "template": {
                "_input_type": "PromptInput",
                "advanced": false,
                "display_name": "Template",
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                "name": "template",
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                "value": "You are an expert research assistant.\n\nCreate a focused research plan that will guide our search.\n\nFormat your response exactly as:\n\nRESEARCH OBJECTIVE:\n[Clear statement of research goal]\n\nKEY SEARCH QUERIES:\n1. [Primary academic search query]\n2. [Secondary search query]\n3. [Alternative search approach]\n\nSEARCH PRIORITIES:\n- [What types of sources to focus on]\n- [Key aspects to investigate]\n- [Specific areas to explore]"
              },
              "tool_placeholder": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Tool Placeholder",
                "dynamic": false,
                "info": "A placeholder input for tool mode.",
                "input_types": [
                  "Message"
                ],
                "list": false,
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                "trace_as_input": true,
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                "type": "str",
                "value": ""
              }
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            "tool_mode": false
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          "type": "Prompt"
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          "description": "Create a prompt template with dynamic variables.",
          "display_name": "Prompt",
          "id": "Prompt-f4xQ5",
          "node": {
            "base_classes": [
              "Message"
            ],
            "beta": false,
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            "custom_fields": {
              "template": []
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            "description": "Create a prompt template with dynamic variables.",
            "display_name": "Prompt",
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            "edited": false,
            "field_order": [
              "template"
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            "frozen": false,
            "icon": "prompts",
            "legacy": false,
            "lf_version": "1.0.19.post2",
            "metadata": {},
            "output_types": [],
            "outputs": [
              {
                "cache": true,
                "display_name": "Prompt Message",
                "method": "build_prompt",
                "name": "prompt",
                "selected": "Message",
                "types": [
                  "Message"
                ],
                "value": "__UNDEFINED__"
              }
            ],
            "pinned": false,
            "template": {
              "_type": "Component",
              "code": {
                "advanced": true,
                "dynamic": true,
                "fileTypes": [],
                "file_path": "",
                "info": "",
                "list": false,
                "load_from_db": false,
                "multiline": true,
                "name": "code",
                "password": false,
                "placeholder": "",
                "required": true,
                "show": true,
                "title_case": false,
                "type": "code",
                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
              },
              "template": {
                "_input_type": "PromptInput",
                "advanced": false,
                "display_name": "Template",
                "dynamic": false,
                "info": "",
                "list": false,
                "load_from_db": false,
                "name": "template",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": false,
                "trace_as_input": true,
                "type": "prompt",
                "value": "You are a research synthesis expert.\n\nCreate a comprehensive synthesis and report of our findings.\n\nFormat your response as:\n\nEXECUTIVE SUMMARY:\n[Key findings and implications]\n\nMETHODOLOGY:\n- Search Strategy Used\n- Sources Analyzed\n- Quality Assessment\n\nFINDINGS & ANALYSIS:\n[Detailed discussion of discoveries]\n\nCONCLUSIONS:\n[Main takeaways and insights]\n\nFUTURE DIRECTIONS:\n[Suggested next steps]\n\nIMPORTANT: For each major point or finding, include the relevant source link in square brackets at the end of the sentence or paragraph. For example: \"Harvard has developed a solid-state battery that charges in minutes. [Source: https://example.com/article]\"\n"
              },
              "tool_placeholder": {
                "_input_type": "MessageTextInput",
                "advanced": true,
                "display_name": "Tool Placeholder",
                "dynamic": false,
                "info": "A placeholder input for tool mode.",
                "input_types": [
                  "Message"
                ],
                "list": false,
                "load_from_db": false,
                "name": "tool_placeholder",
                "placeholder": "",
                "required": false,
                "show": true,
                "title_case": false,
                "tool_mode": true,
                "trace_as_input": true,
                "trace_as_metadata": true,
                "type": "str",
                "value": ""
              }
            },
            "tool_mode": false
          },
          "type": "Prompt"
        },
        "dragging": false,
        "height": 260,
        "id": "Prompt-f4xQ5",
        "position": {
          "x": 2498.9482347755306,
          "y": 889.7491088138673
        },
        "positionAbsolute": {
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        "selected": false,
        "type": "genericNode",
        "width": 320
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      {
        "data": {
          "id": "note-86Le6",
          "node": {
            "description": "# 🔑 Tavily AI Search Needs API Key\n\nYou can get 1000 searches/month free [here](https://tavily.com/) ",
            "display_name": "",
            "documentation": "",
            "template": {
              "backgroundColor": "lime"
            }
          },
          "type": "note"
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        "dragging": false,
        "height": 325,
        "id": "note-86Le6",
        "position": {
          "x": 1797.5781951055678,
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        "positionAbsolute": {
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    "viewport": {
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  },
  "description": "starterProjects.researchAgent.description",
  "endpoint_name": null,
  "gradient": "5",
  "icon": "TextSearchIcon",
  "id": "67b16861-1344-465b-963a-c1c338623438",
  "is_component": false,
  "last_tested_version": "1.0.19.post2",
  "name": "starterProjects.researchAgent.name",
  "tags": [
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}