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Created November 7, 2025 05:37
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CodeFlow.json
{
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"id": "Agent-RjPJv",
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"id": "ChatOutput-lWzu4",
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"id": "xy-edge__MCP-qUf3x{œdataTypeœ:œMCPœ,œidœ:œMCP-qUf3xœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-RjPJv{œfieldNameœ:œtoolsœ,œidœ:œAgent-RjPJvœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
"selected": false,
"source": "MCP-qUf3x",
"sourceHandle": "{œdataTypeœ:œMCPœ,œidœ:œMCP-qUf3xœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}",
"target": "Agent-RjPJv",
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"id": "xy-edge__MCP-i45b2{œdataTypeœ:œMCPœ,œidœ:œMCP-i45b2œ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-RjPJv{œfieldNameœ:œtoolsœ,œidœ:œAgent-RjPJvœ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
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"nodes": [
{
"data": {
"id": "ChatInput-awJ4d",
"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": [
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],
"frozen": false,
"icon": "MessagesSquare",
"key": "ChatInput",
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"lf_version": "1.6.5",
"metadata": {},
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"outputs": [
{
"allows_loop": false,
"cache": true,
"display_name": "Chat Message",
"group_outputs": false,
"method": "message_response",
"name": "message",
"selected": "Message",
"tool_mode": true,
"types": [
"Message"
],
"value": "__UNDEFINED__"
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],
"pinned": false,
"score": 0.0020353564437605998,
"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.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs.inputs import BoolInput\nfrom langflow.io import (\n DropdownInput,\n FileInput,\n MessageTextInput,\n MultilineInput,\n Output,\n)\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_USER,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n input_types=[],\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 temp_file=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Chat Message\", name=\"message\", method=\"message_response\"),\n ]\n\n async def message_response(self) -> Message:\n # Ensure files is a list and filter out empty/None values\n files = self.files if self.files else []\n if files and not isinstance(files, list):\n files = [files]\n files = [f for f in files if f is not None and f != \"\"]\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=files,\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": [
"csv",
"json",
"pdf",
"txt",
"md",
"mdx",
"yaml",
"yml",
"xml",
"html",
"htm",
"docx",
"py",
"sh",
"sql",
"js",
"ts",
"tsx",
"jpg",
"jpeg",
"png",
"bmp",
"image"
],
"file_path": "",
"info": "Files to be sent with the message.",
"list": true,
"list_add_label": "Add More",
"name": "files",
"placeholder": "",
"required": false,
"show": true,
"temp_file": true,
"title_case": false,
"trace_as_metadata": true,
"type": "file",
"value": ""
},
"input_value": {
"_input_type": "MultilineInput",
"advanced": false,
"copy_field": false,
"display_name": "Input Text",
"dynamic": false,
"info": "Message to be passed as input.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"multiline": true,
"name": "input_value",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": "Hello"
},
"sender": {
"_input_type": "DropdownInput",
"advanced": true,
"combobox": false,
"dialog_inputs": {},
"display_name": "Sender Type",
"dynamic": false,
"info": "Type of sender.",
"name": "sender",
"options": [
"Machine",
"User"
],
"options_metadata": [],
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": 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,
"list_add_label": "Add More",
"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": "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,
"list_add_label": "Add More",
"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,
"list_add_label": "Add More",
"name": "should_store_message",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
}
},
"tool_mode": false
},
"selected_output": "message",
"showNode": true,
"type": "ChatInput"
},
"dragging": false,
"id": "ChatInput-awJ4d",
"measured": {
"height": 204,
"width": 320
},
"position": {
"x": 982.3731404746123,
"y": 591.9609678515191
},
"selected": false,
"type": "genericNode"
},
{
"data": {
"id": "ChatOutput-lWzu4",
"node": {
"base_classes": [
"Message"
],
"beta": false,
"category": "outputs",
"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",
"clean_data"
],
"frozen": false,
"icon": "MessagesSquare",
"key": "ChatOutput",
"legacy": false,
"lf_version": "1.6.5",
"metadata": {},
"minimized": true,
"output_types": [],
"outputs": [
{
"allows_loop": false,
"cache": true,
"display_name": "Output Message",
"group_outputs": false,
"method": "message_response",
"name": "message",
"selected": "Message",
"tool_mode": true,
"types": [
"Message"
],
"value": "__UNDEFINED__"
}
],
"pinned": false,
"score": 0.003169567463043492,
"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 collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.helpers.data import safe_convert\nfrom langflow.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.template.field.base import Output\nfrom langflow.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\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 ]\n outputs = [\n Output(\n display_name=\"Output 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 # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, _icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\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\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n clean_data: bool = getattr(self, \"clean_data\", False)\n return \"\\n\".join([safe_convert(item, clean_data=clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\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,
"list_add_label": "Add More",
"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": "HandleInput",
"advanced": false,
"display_name": "Inputs",
"dynamic": false,
"info": "Message to be passed as output.",
"input_types": [
"Data",
"DataFrame",
"Message"
],
"list": false,
"list_add_label": "Add More",
"name": "input_value",
"placeholder": "",
"required": true,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "other",
"value": ""
},
"sender": {
"_input_type": "DropdownInput",
"advanced": true,
"combobox": false,
"dialog_inputs": {},
"display_name": "Sender Type",
"dynamic": false,
"info": "Type of sender.",
"name": "sender",
"options": [
"Machine",
"User"
],
"options_metadata": [],
"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,
"list_add_label": "Add More",
"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,
"list_add_label": "Add More",
"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,
"list_add_label": "Add More",
"name": "should_store_message",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
}
},
"tool_mode": false
},
"showNode": true,
"type": "ChatOutput"
},
"dragging": false,
"id": "ChatOutput-lWzu4",
"measured": {
"height": 166,
"width": 320
},
"position": {
"x": 2144.4020369529935,
"y": 590.5244173339355
},
"selected": false,
"type": "genericNode"
},
{
"data": {
"id": "Agent-RjPJv",
"node": {
"base_classes": [
"Message"
],
"beta": false,
"conditional_paths": [],
"custom_fields": {},
"description": "Define the agent's instructions, then enter a task to complete using tools.",
"display_name": "Agent",
"documentation": "",
"edited": false,
"field_order": [
"agent_llm",
"max_tokens",
"model_kwargs",
"json_mode",
"model_name",
"openai_api_base",
"api_key",
"temperature",
"seed",
"max_retries",
"timeout",
"system_prompt",
"n_messages",
"tools",
"input_value",
"handle_parsing_errors",
"verbose",
"max_iterations",
"agent_description",
"add_current_date_tool"
],
"frozen": false,
"icon": "bot",
"last_updated": "2025-11-07T05:32:15.288Z",
"legacy": false,
"lf_version": "1.6.5",
"metadata": {},
"minimized": false,
"output_types": [],
"outputs": [
{
"allows_loop": false,
"cache": true,
"display_name": "Response",
"group_outputs": false,
"method": "message_response",
"name": "response",
"options": null,
"required_inputs": null,
"selected": "Message",
"tool_mode": true,
"types": [
"Message"
],
"value": "__UNDEFINED__"
}
],
"pinned": false,
"template": {
"_type": "Component",
"add_current_date_tool": {
"_input_type": "BoolInput",
"advanced": true,
"display_name": "Current Date",
"dynamic": false,
"info": "If true, will add a tool to the agent that returns the current date.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "add_current_date_tool",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
},
"agent_description": {
"_input_type": "MultilineInput",
"advanced": true,
"copy_field": false,
"display_name": "Agent Description [Deprecated]",
"dynamic": false,
"info": "The description of the agent. This is only used when in Tool Mode. Defaults to 'A helpful assistant with access to the following tools:' and tools are added dynamically. This feature is deprecated and will be removed in future versions.",
"input_types": [
"Message"
],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"multiline": true,
"name": "agent_description",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": "A helpful assistant with access to the following tools:"
},
"agent_llm": {
"_input_type": "DropdownInput",
"advanced": false,
"combobox": false,
"dialog_inputs": {},
"display_name": "Model Provider",
"dynamic": false,
"external_options": {
"fields": {
"data": {
"node": {
"display_name": "Connect other models",
"icon": "CornerDownLeft",
"name": "connect_other_models"
}
}
}
},
"info": "The provider of the language model that the agent will use to generate responses.",
"input_types": [],
"name": "agent_llm",
"options": [
"Anthropic",
"Google Generative AI",
"OpenAI"
],
"options_metadata": [
{
"icon": "Anthropic"
},
{
"icon": "GoogleGenerativeAI"
},
{
"icon": "OpenAI"
}
],
"placeholder": "Awaiting model input.",
"real_time_refresh": true,
"refresh_button": false,
"required": false,
"show": true,
"title_case": false,
"toggle": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": "OpenAI"
},
"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": [],
"load_from_db": true,
"name": "api_key",
"password": true,
"placeholder": "",
"real_time_refresh": true,
"required": false,
"show": true,
"title_case": false,
"type": "str",
"value": ""
},
"code": {
"advanced": true,
"dynamic": true,
"fileTypes": [],
"file_path": "",
"info": "",
"input_types": [],
"list": false,
"load_from_db": false,
"multiline": true,
"name": "code",
"password": false,
"placeholder": "",
"required": true,
"show": true,
"title_case": false,
"type": "code",
"value": "import json\nimport re\n\nfrom langchain_core.tools import StructuredTool\nfrom pydantic import ValidationError\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.agents.events import ExceptionWithMessageError\nfrom langflow.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS_DICT,\n MODELS_METADATA,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers.current_date import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n ToolCallingAgentComponent,\n)\nfrom langflow.custom.custom_component.component import _get_component_toolkit\nfrom langflow.custom.utils import update_component_build_config\nfrom langflow.field_typing import Tool\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import (\n BoolInput,\n DropdownInput,\n IntInput,\n MultilineInput,\n Output,\n TableInput,\n)\nfrom langflow.logging import logger\nfrom langflow.schema.data import Data\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\nfrom langflow.schema.table import EditMode\n\n\ndef set_advanced_true(component_input):\n component_input.advanced = True\n return component_input\n\n\nMODEL_PROVIDERS_LIST = [\"Anthropic\", \"Google Generative AI\", \"OpenAI\"]\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 documentation: str = \"https://docs.langflow.org/agents\"\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 # Filter out json_mode from OpenAI inputs since we handle structured output differently\n openai_inputs_filtered = [\n input_field\n for input_field in MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"]\n if not (hasattr(input_field, \"name\") and input_field.name == \"json_mode\")\n ]\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=[*MODEL_PROVIDERS_LIST],\n value=\"OpenAI\",\n real_time_refresh=True,\n refresh_button=False,\n input_types=[],\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST],\n external_options={\n \"fields\": {\n \"data\": {\n \"node\": {\n \"name\": \"connect_other_models\",\n \"display_name\": \"Connect other models\",\n \"icon\": \"CornerDownLeft\",\n }\n }\n },\n },\n ),\n *openai_inputs_filtered,\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 IntInput(\n name=\"n_messages\",\n display_name=\"Number of Chat History Messages\",\n value=100,\n info=\"Number of chat history messages to retrieve.\",\n advanced=True,\n show=True,\n ),\n MultilineInput(\n name=\"format_instructions\",\n display_name=\"Output Format Instructions\",\n info=\"Generic Template for structured output formatting. Valid only with Structured response.\",\n value=(\n \"You are an AI that extracts structured JSON objects from unstructured text. \"\n \"Use a predefined schema with expected types (str, int, float, bool, dict). \"\n \"Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. \"\n \"Fill missing or ambiguous values with defaults: null for missing values. \"\n \"Remove exact duplicates but keep variations that have different field values. \"\n \"Always return valid JSON in the expected format, never throw errors. \"\n \"If multiple objects can be extracted, return them all in the structured format.\"\n ),\n advanced=True,\n ),\n TableInput(\n name=\"output_schema\",\n display_name=\"Output Schema\",\n info=(\n \"Schema Validation: Define the structure and data types for structured output. \"\n \"No validation if no output schema.\"\n ),\n advanced=True,\n required=False,\n value=[],\n table_schema=[\n {\n \"name\": \"name\",\n \"display_name\": \"Name\",\n \"type\": \"str\",\n \"description\": \"Specify the name of the output field.\",\n \"default\": \"field\",\n \"edit_mode\": EditMode.INLINE,\n },\n {\n \"name\": \"description\",\n \"display_name\": \"Description\",\n \"type\": \"str\",\n \"description\": \"Describe the purpose of the output field.\",\n \"default\": \"description of field\",\n \"edit_mode\": EditMode.POPOVER,\n },\n {\n \"name\": \"type\",\n \"display_name\": \"Type\",\n \"type\": \"str\",\n \"edit_mode\": EditMode.INLINE,\n \"description\": (\"Indicate the data type of the output field (e.g., str, int, float, bool, dict).\"),\n \"options\": [\"str\", \"int\", \"float\", \"bool\", \"dict\"],\n \"default\": \"str\",\n },\n {\n \"name\": \"multiple\",\n \"display_name\": \"As List\",\n \"type\": \"boolean\",\n \"description\": \"Set to True if this output field should be a list of the specified type.\",\n \"default\": \"False\",\n \"edit_mode\": EditMode.INLINE,\n },\n ],\n ),\n *LCToolsAgentComponent._base_inputs,\n # removed memory inputs from agent component\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 = [\n Output(name=\"response\", display_name=\"Response\", method=\"message_response\"),\n ]\n\n async def get_agent_requirements(self):\n \"\"\"Get the agent requirements for the agent.\"\"\"\n llm_model, display_name = await self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n if isinstance(self.chat_history, Message):\n self.chat_history = [self.chat_history]\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n return llm_model, self.chat_history, self.tools\n\n async def message_response(self) -> Message:\n try:\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n # Set up and run agent\n self.set(\n llm=llm_model,\n tools=self.tools or [],\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 result = await self.run_agent(agent)\n\n # Store result for potential JSON output\n self._agent_result = result\n\n except (ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n await logger.aerror(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n # Avoid catching blind Exception; let truly unexpected exceptions propagate\n except Exception as e:\n await logger.aerror(f\"Unexpected error: {e!s}\")\n raise\n else:\n return result\n\n def _preprocess_schema(self, schema):\n \"\"\"Preprocess schema to ensure correct data types for build_model_from_schema.\"\"\"\n processed_schema = []\n for field in schema:\n processed_field = {\n \"name\": str(field.get(\"name\", \"field\")),\n \"type\": str(field.get(\"type\", \"str\")),\n \"description\": str(field.get(\"description\", \"\")),\n \"multiple\": field.get(\"multiple\", False),\n }\n # Ensure multiple is handled correctly\n if isinstance(processed_field[\"multiple\"], str):\n processed_field[\"multiple\"] = processed_field[\"multiple\"].lower() in [\n \"true\",\n \"1\",\n \"t\",\n \"y\",\n \"yes\",\n ]\n processed_schema.append(processed_field)\n return processed_schema\n\n async def build_structured_output_base(self, content: str):\n \"\"\"Build structured output with optional BaseModel validation.\"\"\"\n json_pattern = r\"\\{.*\\}\"\n schema_error_msg = \"Try setting an output schema\"\n\n # Try to parse content as JSON first\n json_data = None\n try:\n json_data = json.loads(content)\n except json.JSONDecodeError:\n json_match = re.search(json_pattern, content, re.DOTALL)\n if json_match:\n try:\n json_data = json.loads(json_match.group())\n except json.JSONDecodeError:\n return {\"content\": content, \"error\": schema_error_msg}\n else:\n return {\"content\": content, \"error\": schema_error_msg}\n\n # If no output schema provided, return parsed JSON without validation\n if not hasattr(self, \"output_schema\") or not self.output_schema or len(self.output_schema) == 0:\n return json_data\n\n # Use BaseModel validation with schema\n try:\n processed_schema = self._preprocess_schema(self.output_schema)\n output_model = build_model_from_schema(processed_schema)\n\n # Validate against the schema\n if isinstance(json_data, list):\n # Multiple objects\n validated_objects = []\n for item in json_data:\n try:\n validated_obj = output_model.model_validate(item)\n validated_objects.append(validated_obj.model_dump())\n except ValidationError as e:\n await logger.aerror(f\"Validation error for item: {e}\")\n # Include invalid items with error info\n validated_objects.append({\"data\": item, \"validation_error\": str(e)})\n return validated_objects\n\n # Single object\n try:\n validated_obj = output_model.model_validate(json_data)\n return [validated_obj.model_dump()] # Return as list for consistency\n except ValidationError as e:\n await logger.aerror(f\"Validation error: {e}\")\n return [{\"data\": json_data, \"validation_error\": str(e)}]\n\n except (TypeError, ValueError) as e:\n await logger.aerror(f\"Error building structured output: {e}\")\n # Fallback to parsed JSON without validation\n return json_data\n\n async def json_response(self) -> Data:\n \"\"\"Convert agent response to structured JSON Data output with schema validation.\"\"\"\n # Always use structured chat agent for JSON response mode for better JSON formatting\n try:\n system_components = []\n\n # 1. Agent Instructions (system_prompt)\n agent_instructions = getattr(self, \"system_prompt\", \"\") or \"\"\n if agent_instructions:\n system_components.append(f\"{agent_instructions}\")\n\n # 2. Format Instructions\n format_instructions = getattr(self, \"format_instructions\", \"\") or \"\"\n if format_instructions:\n system_components.append(f\"Format instructions: {format_instructions}\")\n\n # 3. Schema Information from BaseModel\n if hasattr(self, \"output_schema\") and self.output_schema and len(self.output_schema) > 0:\n try:\n processed_schema = self._preprocess_schema(self.output_schema)\n output_model = build_model_from_schema(processed_schema)\n schema_dict = output_model.model_json_schema()\n schema_info = (\n \"You are given some text that may include format instructions, \"\n \"explanations, or other content alongside a JSON schema.\\n\\n\"\n \"Your task:\\n\"\n \"- Extract only the JSON schema.\\n\"\n \"- Return it as valid JSON.\\n\"\n \"- Do not include format instructions, explanations, or extra text.\\n\\n\"\n \"Input:\\n\"\n f\"{json.dumps(schema_dict, indent=2)}\\n\\n\"\n \"Output (only JSON schema):\"\n )\n system_components.append(schema_info)\n except (ValidationError, ValueError, TypeError, KeyError) as e:\n await logger.aerror(f\"Could not build schema for prompt: {e}\", exc_info=True)\n\n # Combine all components\n combined_instructions = \"\\n\\n\".join(system_components) if system_components else \"\"\n llm_model, self.chat_history, self.tools = await self.get_agent_requirements()\n self.set(\n llm=llm_model,\n tools=self.tools or [],\n chat_history=self.chat_history,\n input_value=self.input_value,\n system_prompt=combined_instructions,\n )\n\n # Create and run structured chat agent\n try:\n structured_agent = self.create_agent_runnable()\n except (NotImplementedError, ValueError, TypeError) as e:\n await logger.aerror(f\"Error with structured chat agent: {e}\")\n raise\n try:\n result = await self.run_agent(structured_agent)\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n RuntimeError,\n ) as e:\n await logger.aerror(f\"Error with structured agent result: {e}\")\n raise\n # Extract content from structured agent result\n if hasattr(result, \"content\"):\n content = result.content\n elif hasattr(result, \"text\"):\n content = result.text\n else:\n content = str(result)\n\n except (\n ExceptionWithMessageError,\n ValueError,\n TypeError,\n NotImplementedError,\n AttributeError,\n ) as e:\n await logger.aerror(f\"Error with structured chat agent: {e}\")\n # Fallback to regular agent\n content_str = \"No content returned from agent\"\n return Data(data={\"content\": content_str, \"error\": str(e)})\n\n # Process with structured output validation\n try:\n structured_output = await self.build_structured_output_base(content)\n\n # Handle different output formats\n if isinstance(structured_output, list) and structured_output:\n if len(structured_output) == 1:\n return Data(data=structured_output[0])\n return Data(data={\"results\": structured_output})\n if isinstance(structured_output, dict):\n return Data(data=structured_output)\n return Data(data={\"content\": content})\n\n except (ValueError, TypeError) as e:\n await logger.aerror(f\"Error in structured output processing: {e}\")\n return Data(data={\"content\": content, \"error\": str(e)})\n\n async def get_memory_data(self):\n # TODO: This is a temporary fix to avoid message duplication. We should develop a function for this.\n messages = (\n await MemoryComponent(**self.get_base_args())\n .set(\n session_id=self.graph.session_id,\n order=\"Ascending\",\n n_messages=self.n_messages,\n )\n .retrieve_messages()\n )\n return [\n message for message in messages if getattr(message, \"id\", None) != getattr(self.input_value, \"id\", None)\n ]\n\n async def get_llm(self):\n if not isinstance(self.agent_llm, str):\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\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\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except (AttributeError, ValueError, TypeError, RuntimeError) as e:\n await logger.aerror(f\"Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\n\n def _build_llm_model(self, component, inputs, prefix=\"\"):\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n return component.set(**model_kwargs).build_model()\n\n def set_component_params(self, component):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n # Filter out json_mode and only use attributes that exist on this component\n model_kwargs = {}\n for input_ in inputs:\n if hasattr(self, f\"{prefix}{input_.name}\"):\n model_kwargs[input_.name] = getattr(self, f\"{prefix}{input_.name}\")\n\n return component.set(**model_kwargs)\n return component\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 async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\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 = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\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 build_config[\"agent_llm\"][\"display_name\"] = \"Model Provider\"\n elif field_value == \"connect_other_models\":\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 info=\"The provider of the language model that the agent will use to generate responses.\",\n options=[*MODEL_PROVIDERS_LIST],\n real_time_refresh=True,\n refresh_button=False,\n input_types=[\"LanguageModel\"],\n placeholder=\"Awaiting model input.\",\n options_metadata=[MODELS_METADATA[key] for key in MODEL_PROVIDERS_LIST],\n external_options={\n \"fields\": {\n \"data\": {\n \"node\": {\n \"name\": \"connect_other_models\",\n \"display_name\": \"Connect other models\",\n \"icon\": \"CornerDownLeft\",\n },\n }\n },\n },\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 (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(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 = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def _get_tools(self) -> list[Tool]:\n component_toolkit = _get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n tools = component_toolkit(component=self).get_tools(\n tool_name=\"Call_Agent\",\n tool_description=description,\n callbacks=self.get_langchain_callbacks(),\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n return tools\n"
},
"format_instructions": {
"_input_type": "MultilineInput",
"advanced": true,
"copy_field": false,
"display_name": "Output Format Instructions",
"dynamic": false,
"info": "Generic Template for structured output formatting. Valid only with Structured response.",
"input_types": [
"Message"
],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"multiline": true,
"name": "format_instructions",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": "You are an AI that extracts structured JSON objects from unstructured text. Use a predefined schema with expected types (str, int, float, bool, dict). Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all. Fill missing or ambiguous values with defaults: null for missing values. Remove exact duplicates but keep variations that have different field values. Always return valid JSON in the expected format, never throw errors. If multiple objects can be extracted, return them all in the structured format."
},
"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?",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "handle_parsing_errors",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": 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,
"list_add_label": "Add More",
"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.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "json_mode",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": 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.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "max_iterations",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "int",
"value": 100
},
"max_retries": {
"_input_type": "IntInput",
"advanced": true,
"display_name": "Max Retries",
"dynamic": false,
"info": "The maximum number of retries to make when generating.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "max_retries",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "int",
"value": 5
},
"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.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "max_tokens",
"placeholder": "",
"range_spec": {
"max": 128000,
"min": 0,
"step": 0.1,
"step_type": "float"
},
"required": false,
"show": true,
"title_case": false,
"tool_mode": 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.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "model_kwargs",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_input": true,
"type": "dict",
"value": {}
},
"model_name": {
"_input_type": "DropdownInput",
"advanced": false,
"combobox": true,
"dialog_inputs": {},
"display_name": "Model Name",
"dynamic": false,
"external_options": {},
"info": "To see the model names, first choose a provider. Then, enter your API key and click the refresh button next to the model name.",
"input_types": [],
"name": "model_name",
"options": [
"gpt-4o-mini",
"gpt-4o",
"gpt-4.1",
"gpt-4.1-mini",
"gpt-4.1-nano",
"gpt-4-turbo",
"gpt-4-turbo-preview",
"gpt-4",
"gpt-3.5-turbo",
"gpt-5",
"gpt-5-mini",
"gpt-5-nano",
"gpt-5-chat-latest",
"o1",
"o3-mini",
"o3",
"o3-pro",
"o4-mini",
"o4-mini-high"
],
"options_metadata": [],
"placeholder": "",
"real_time_refresh": false,
"required": false,
"show": true,
"title_case": false,
"toggle": 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 Chat History Messages",
"dynamic": false,
"info": "Number of chat history messages to retrieve.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "n_messages",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": 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.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "openai_api_base",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"output_schema": {
"_input_type": "TableInput",
"advanced": true,
"display_name": "Output Schema",
"dynamic": false,
"info": "Schema Validation: Define the structure and data types for structured output. No validation if no output schema.",
"input_types": [],
"is_list": true,
"list_add_label": "Add More",
"name": "output_schema",
"placeholder": "",
"required": false,
"show": true,
"table_icon": "Table",
"table_schema": {
"columns": [
{
"default": "field",
"description": "Specify the name of the output field.",
"disable_edit": false,
"display_name": "Name",
"edit_mode": "inline",
"filterable": true,
"formatter": "text",
"hidden": false,
"name": "name",
"sortable": true,
"type": "str"
},
{
"default": "description of field",
"description": "Describe the purpose of the output field.",
"disable_edit": false,
"display_name": "Description",
"edit_mode": "popover",
"filterable": true,
"formatter": "text",
"hidden": false,
"name": "description",
"sortable": true,
"type": "str"
},
{
"default": "str",
"description": "Indicate the data type of the output field (e.g., str, int, float, bool, dict).",
"disable_edit": false,
"display_name": "Type",
"edit_mode": "inline",
"filterable": true,
"formatter": "text",
"hidden": false,
"name": "type",
"options": [
"str",
"int",
"float",
"bool",
"dict"
],
"sortable": true,
"type": "str"
},
{
"default": false,
"description": "Set to True if this output field should be a list of the specified type.",
"disable_edit": false,
"display_name": "As List",
"edit_mode": "inline",
"filterable": true,
"formatter": "boolean",
"hidden": false,
"name": "multiple",
"sortable": true,
"type": "boolean"
}
]
},
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"trigger_icon": "Table",
"trigger_text": "Open table",
"type": "table",
"value": []
},
"seed": {
"_input_type": "IntInput",
"advanced": true,
"display_name": "Seed",
"dynamic": false,
"info": "The seed controls the reproducibility of the job.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "seed",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "int",
"value": 1
},
"system_prompt": {
"_input_type": "MultilineInput",
"advanced": false,
"copy_field": 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,
"list_add_label": "Add More",
"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 helpful coding assistant. You have access to a directory to write files to and documentation. \n\nYou should first plan what you are going to do and then break that down into tasks. You should write those tasks to a TODO.md file in the project root. When you have completed a task, you can check it off or delete it from the list. Ensure that you have covered all your tasks before you finish.\n\nYou can work in the directory at this path: /path/to/your/project/directory"
},
"temperature": {
"_input_type": "SliderInput",
"advanced": true,
"display_name": "Temperature",
"dynamic": false,
"info": "",
"input_types": [],
"max_label": "",
"max_label_icon": "",
"min_label": "",
"min_label_icon": "",
"name": "temperature",
"placeholder": "",
"range_spec": {
"max": 1,
"min": 0,
"step": 0.01,
"step_type": "float"
},
"required": false,
"show": true,
"slider_buttons": false,
"slider_buttons_options": [],
"slider_input": false,
"title_case": false,
"tool_mode": false,
"type": "slider",
"value": 0.1
},
"timeout": {
"_input_type": "IntInput",
"advanced": true,
"display_name": "Timeout",
"dynamic": false,
"info": "The timeout for requests to OpenAI completion API.",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "timeout",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "int",
"value": 700
},
"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"
],
"list": true,
"list_add_label": "Add More",
"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": "",
"input_types": [],
"list": false,
"list_add_label": "Add More",
"name": "verbose",
"placeholder": "",
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "bool",
"value": true
}
},
"tool_mode": false
},
"selected_output": "response",
"showNode": true,
"type": "Agent"
},
"dragging": false,
"id": "Agent-RjPJv",
"measured": {
"height": 594,
"width": 320
},
"position": {
"x": 1635.5747903473043,
"y": 284.1657686053261
},
"selected": false,
"type": "genericNode"
},
{
"data": {
"id": "MCP-i45b2",
"node": {
"base_classes": [
"DataFrame"
],
"beta": false,
"category": "MCP",
"conditional_paths": [],
"custom_fields": {},
"description": "Connect to an MCP server to use its tools.",
"display_name": "MCP Tools",
"documentation": "https://docs.langflow.org/mcp-client",
"edited": false,
"field_order": [
"mcp_server",
"tool",
"tool_placeholder"
],
"frozen": false,
"icon": "Mcp",
"key": "mcp_context7",
"last_updated": "2025-11-07T05:32:15.295Z",
"legacy": false,
"lf_version": "1.6.5",
"mcpServerName": "context7",
"metadata": {},
"minimized": false,
"output_types": [],
"outputs": [
{
"allows_loop": false,
"cache": true,
"display_name": "Toolset",
"group_outputs": false,
"hidden": null,
"method": "to_toolkit",
"name": "component_as_tool",
"options": null,
"required_inputs": null,
"selected": "Tool",
"tool_mode": true,
"types": [
"Tool"
],
"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 __future__ import annotations\n\nimport asyncio\nimport uuid\nfrom typing import Any\n\nfrom langchain_core.tools import StructuredTool # noqa: TC002\n\nfrom langflow.api.v2.mcp import get_server\nfrom langflow.base.agents.utils import maybe_unflatten_dict, safe_cache_get, safe_cache_set\nfrom langflow.base.mcp.util import (\n MCPSseClient,\n MCPStdioClient,\n create_input_schema_from_json_schema,\n update_tools,\n)\nfrom langflow.custom.custom_component.component_with_cache import ComponentWithCache\nfrom langflow.inputs.inputs import InputTypes # noqa: TC001\nfrom langflow.io import DropdownInput, McpInput, MessageTextInput, Output\nfrom langflow.io.schema import flatten_schema, schema_to_langflow_inputs\nfrom langflow.logging import logger\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n# Import get_server from the backend API\nfrom langflow.services.database.models.user.crud import get_user_by_id\nfrom langflow.services.deps import get_settings_service, get_storage_service, session_scope\n\n\nclass MCPToolsComponent(ComponentWithCache):\n schema_inputs: list = []\n tools: list[StructuredTool] = []\n _not_load_actions: bool = False\n _tool_cache: dict = {}\n _last_selected_server: str | None = None # Cache for the last selected server\n\n def __init__(self, **data) -> None:\n super().__init__(**data)\n # Initialize cache keys to avoid CacheMiss when accessing them\n self._ensure_cache_structure()\n\n # Initialize clients with access to the component cache\n self.stdio_client: MCPStdioClient = MCPStdioClient(component_cache=self._shared_component_cache)\n self.sse_client: MCPSseClient = MCPSseClient(component_cache=self._shared_component_cache)\n\n def _ensure_cache_structure(self):\n \"\"\"Ensure the cache has the required structure.\"\"\"\n # Check if servers key exists and is not CacheMiss\n servers_value = safe_cache_get(self._shared_component_cache, \"servers\")\n if servers_value is None:\n safe_cache_set(self._shared_component_cache, \"servers\", {})\n\n # Check if last_selected_server key exists and is not CacheMiss\n last_server_value = safe_cache_get(self._shared_component_cache, \"last_selected_server\")\n if last_server_value is None:\n safe_cache_set(self._shared_component_cache, \"last_selected_server\", \"\")\n\n default_keys: list[str] = [\n \"code\",\n \"_type\",\n \"tool_mode\",\n \"tool_placeholder\",\n \"mcp_server\",\n \"tool\",\n ]\n\n display_name = \"MCP Tools\"\n description = \"Connect to an MCP server to use its tools.\"\n documentation: str = \"https://docs.langflow.org/mcp-client\"\n icon = \"Mcp\"\n name = \"MCPTools\"\n\n inputs = [\n McpInput(\n name=\"mcp_server\",\n display_name=\"MCP Server\",\n info=\"Select the MCP Server that will be used by this component\",\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"tool\",\n display_name=\"Tool\",\n options=[],\n value=\"\",\n info=\"Select the tool to execute\",\n show=False,\n required=True,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n info=\"Placeholder for the tool\",\n value=\"\",\n show=False,\n tool_mode=False,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Response\", name=\"response\", method=\"build_output\"),\n ]\n\n async def _validate_schema_inputs(self, tool_obj) -> list[InputTypes]:\n \"\"\"Validate and process schema inputs for a tool.\"\"\"\n try:\n if not tool_obj or not hasattr(tool_obj, \"args_schema\"):\n msg = \"Invalid tool object or missing input schema\"\n raise ValueError(msg)\n\n flat_schema = flatten_schema(tool_obj.args_schema.schema())\n input_schema = create_input_schema_from_json_schema(flat_schema)\n if not input_schema:\n msg = f\"Empty input schema for tool '{tool_obj.name}'\"\n raise ValueError(msg)\n\n schema_inputs = schema_to_langflow_inputs(input_schema)\n if not schema_inputs:\n msg = f\"No input parameters defined for tool '{tool_obj.name}'\"\n await logger.awarning(msg)\n return []\n\n except Exception as e:\n msg = f\"Error validating schema inputs: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n else:\n return schema_inputs\n\n async def update_tool_list(self, mcp_server_value=None):\n # Accepts mcp_server_value as dict {name, config} or uses self.mcp_server\n mcp_server = mcp_server_value if mcp_server_value is not None else getattr(self, \"mcp_server\", None)\n server_name = None\n server_config_from_value = None\n if isinstance(mcp_server, dict):\n server_name = mcp_server.get(\"name\")\n server_config_from_value = mcp_server.get(\"config\")\n else:\n server_name = mcp_server\n if not server_name:\n self.tools = []\n return [], {\"name\": server_name, \"config\": server_config_from_value}\n\n # Use shared cache if available\n servers_cache = safe_cache_get(self._shared_component_cache, \"servers\", {})\n cached = servers_cache.get(server_name) if isinstance(servers_cache, dict) else None\n\n if cached is not None:\n self.tools = cached[\"tools\"]\n self.tool_names = cached[\"tool_names\"]\n self._tool_cache = cached[\"tool_cache\"]\n server_config_from_value = cached[\"config\"]\n return self.tools, {\"name\": server_name, \"config\": server_config_from_value}\n\n try:\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching MCP tools.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n\n # Try to get server config from DB/API\n server_config = await get_server(\n server_name,\n current_user,\n db,\n storage_service=get_storage_service(),\n settings_service=get_settings_service(),\n )\n\n # If get_server returns empty but we have a config, use it\n if not server_config and server_config_from_value:\n server_config = server_config_from_value\n\n if not server_config:\n self.tools = []\n return [], {\"name\": server_name, \"config\": server_config}\n\n _, tool_list, tool_cache = await update_tools(\n server_name=server_name,\n server_config=server_config,\n mcp_stdio_client=self.stdio_client,\n mcp_sse_client=self.sse_client,\n )\n\n self.tool_names = [tool.name for tool in tool_list if hasattr(tool, \"name\")]\n self._tool_cache = tool_cache\n self.tools = tool_list\n # Cache the result using shared cache\n cache_data = {\n \"tools\": tool_list,\n \"tool_names\": self.tool_names,\n \"tool_cache\": tool_cache,\n \"config\": server_config,\n }\n\n # Safely update the servers cache\n current_servers_cache = safe_cache_get(self._shared_component_cache, \"servers\", {})\n if isinstance(current_servers_cache, dict):\n current_servers_cache[server_name] = cache_data\n safe_cache_set(self._shared_component_cache, \"servers\", current_servers_cache)\n\n except (TimeoutError, asyncio.TimeoutError) as e:\n msg = f\"Timeout updating tool list: {e!s}\"\n await logger.aexception(msg)\n raise TimeoutError(msg) from e\n except Exception as e:\n msg = f\"Error updating tool list: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n else:\n return tool_list, {\"name\": server_name, \"config\": server_config}\n\n async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n \"\"\"Toggle the visibility of connection-specific fields based on the selected mode.\"\"\"\n try:\n if field_name == \"tool\":\n try:\n if len(self.tools) == 0:\n try:\n self.tools, build_config[\"mcp_server\"][\"value\"] = await self.update_tool_list()\n build_config[\"tool\"][\"options\"] = [tool.name for tool in self.tools]\n build_config[\"tool\"][\"placeholder\"] = \"Select a tool\"\n except (TimeoutError, asyncio.TimeoutError) as e:\n msg = f\"Timeout updating tool list: {e!s}\"\n await logger.aexception(msg)\n if not build_config[\"tools_metadata\"][\"show\"]:\n build_config[\"tool\"][\"show\"] = True\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"value\"] = \"\"\n build_config[\"tool\"][\"placeholder\"] = \"Timeout on MCP server\"\n else:\n build_config[\"tool\"][\"show\"] = False\n except ValueError:\n if not build_config[\"tools_metadata\"][\"show\"]:\n build_config[\"tool\"][\"show\"] = True\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"value\"] = \"\"\n build_config[\"tool\"][\"placeholder\"] = \"Error on MCP Server\"\n else:\n build_config[\"tool\"][\"show\"] = False\n\n if field_value == \"\":\n return build_config\n tool_obj = None\n for tool in self.tools:\n if tool.name == field_value:\n tool_obj = tool\n break\n if tool_obj is None:\n msg = f\"Tool {field_value} not found in available tools: {self.tools}\"\n await logger.awarning(msg)\n return build_config\n await self._update_tool_config(build_config, field_value)\n except Exception as e:\n build_config[\"tool\"][\"options\"] = []\n msg = f\"Failed to update tools: {e!s}\"\n raise ValueError(msg) from e\n else:\n return build_config\n elif field_name == \"mcp_server\":\n if not field_value:\n build_config[\"tool\"][\"show\"] = False\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"value\"] = \"\"\n build_config[\"tool\"][\"placeholder\"] = \"\"\n build_config[\"tool_placeholder\"][\"tool_mode\"] = False\n self.remove_non_default_keys(build_config)\n return build_config\n\n build_config[\"tool_placeholder\"][\"tool_mode\"] = True\n\n current_server_name = field_value.get(\"name\") if isinstance(field_value, dict) else field_value\n _last_selected_server = safe_cache_get(self._shared_component_cache, \"last_selected_server\", \"\")\n\n # To avoid unnecessary updates, only proceed if the server has actually changed\n if (_last_selected_server in (current_server_name, \"\")) and build_config[\"tool\"][\"show\"]:\n if current_server_name:\n servers_cache = safe_cache_get(self._shared_component_cache, \"servers\", {})\n if isinstance(servers_cache, dict):\n cached = servers_cache.get(current_server_name)\n if cached is not None and cached.get(\"tool_names\"):\n cached_tools = cached[\"tool_names\"]\n current_tools = build_config[\"tool\"][\"options\"]\n if current_tools == cached_tools:\n return build_config\n else:\n return build_config\n\n # Determine if \"Tool Mode\" is active by checking if the tool dropdown is hidden.\n is_in_tool_mode = build_config[\"tools_metadata\"][\"show\"]\n safe_cache_set(self._shared_component_cache, \"last_selected_server\", current_server_name)\n\n # Check if tools are already cached for this server before clearing\n cached_tools = None\n if current_server_name:\n servers_cache = safe_cache_get(self._shared_component_cache, \"servers\", {})\n if isinstance(servers_cache, dict):\n cached = servers_cache.get(current_server_name)\n if cached is not None:\n cached_tools = cached[\"tools\"]\n self.tools = cached_tools\n self.tool_names = cached[\"tool_names\"]\n self._tool_cache = cached[\"tool_cache\"]\n\n # Only clear tools if we don't have cached tools for the current server\n if not cached_tools:\n self.tools = [] # Clear previous tools only if no cache\n\n self.remove_non_default_keys(build_config) # Clear previous tool inputs\n\n # Only show the tool dropdown if not in tool_mode\n if not is_in_tool_mode:\n build_config[\"tool\"][\"show\"] = True\n if cached_tools:\n # Use cached tools to populate options immediately\n build_config[\"tool\"][\"options\"] = [tool.name for tool in cached_tools]\n build_config[\"tool\"][\"placeholder\"] = \"Select a tool\"\n else:\n # Show loading state only when we need to fetch tools\n build_config[\"tool\"][\"placeholder\"] = \"Loading tools...\"\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"value\"] = uuid.uuid4()\n else:\n # Keep the tool dropdown hidden if in tool_mode\n self._not_load_actions = True\n build_config[\"tool\"][\"show\"] = False\n\n elif field_name == \"tool_mode\":\n build_config[\"tool\"][\"placeholder\"] = \"\"\n build_config[\"tool\"][\"show\"] = not bool(field_value) and bool(build_config[\"mcp_server\"])\n self.remove_non_default_keys(build_config)\n self.tool = build_config[\"tool\"][\"value\"]\n if field_value:\n self._not_load_actions = True\n else:\n build_config[\"tool\"][\"value\"] = uuid.uuid4()\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"show\"] = True\n build_config[\"tool\"][\"placeholder\"] = \"Loading tools...\"\n elif field_name == \"tools_metadata\":\n self._not_load_actions = False\n\n except Exception as e:\n msg = f\"Error in update_build_config: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n else:\n return build_config\n\n def get_inputs_for_all_tools(self, tools: list) -> dict:\n \"\"\"Get input schemas for all tools.\"\"\"\n inputs = {}\n for tool in tools:\n if not tool or not hasattr(tool, \"name\"):\n continue\n try:\n flat_schema = flatten_schema(tool.args_schema.schema())\n input_schema = create_input_schema_from_json_schema(flat_schema)\n langflow_inputs = schema_to_langflow_inputs(input_schema)\n inputs[tool.name] = langflow_inputs\n except (AttributeError, ValueError, TypeError, KeyError) as e:\n msg = f\"Error getting inputs for tool {getattr(tool, 'name', 'unknown')}: {e!s}\"\n logger.exception(msg)\n continue\n return inputs\n\n def remove_input_schema_from_build_config(\n self, build_config: dict, tool_name: str, input_schema: dict[list[InputTypes], Any]\n ):\n \"\"\"Remove the input schema for the tool from the build config.\"\"\"\n # Keep only schemas that don't belong to the current tool\n input_schema = {k: v for k, v in input_schema.items() if k != tool_name}\n # Remove all inputs from other tools\n for value in input_schema.values():\n for _input in value:\n if _input.name in build_config:\n build_config.pop(_input.name)\n\n def remove_non_default_keys(self, build_config: dict) -> None:\n \"\"\"Remove non-default keys from the build config.\"\"\"\n for key in list(build_config.keys()):\n if key not in self.default_keys:\n build_config.pop(key)\n\n async def _update_tool_config(self, build_config: dict, tool_name: str) -> None:\n \"\"\"Update tool configuration with proper error handling.\"\"\"\n if not self.tools:\n self.tools, build_config[\"mcp_server\"][\"value\"] = await self.update_tool_list()\n\n if not tool_name:\n return\n\n tool_obj = next((tool for tool in self.tools if tool.name == tool_name), None)\n if not tool_obj:\n msg = f\"Tool {tool_name} not found in available tools: {self.tools}\"\n self.remove_non_default_keys(build_config)\n build_config[\"tool\"][\"value\"] = \"\"\n await logger.awarning(msg)\n return\n\n try:\n # Store current values before removing inputs\n current_values = {}\n for key, value in build_config.items():\n if key not in self.default_keys and isinstance(value, dict) and \"value\" in value:\n current_values[key] = value[\"value\"]\n\n # Get all tool inputs and remove old ones\n input_schema_for_all_tools = self.get_inputs_for_all_tools(self.tools)\n self.remove_input_schema_from_build_config(build_config, tool_name, input_schema_for_all_tools)\n\n # Get and validate new inputs\n self.schema_inputs = await self._validate_schema_inputs(tool_obj)\n if not self.schema_inputs:\n msg = f\"No input parameters to configure for tool '{tool_name}'\"\n await logger.ainfo(msg)\n return\n\n # Add new inputs to build config\n for schema_input in self.schema_inputs:\n if not schema_input or not hasattr(schema_input, \"name\"):\n msg = \"Invalid schema input detected, skipping\"\n await logger.awarning(msg)\n continue\n\n try:\n name = schema_input.name\n input_dict = schema_input.to_dict()\n input_dict.setdefault(\"value\", None)\n input_dict.setdefault(\"required\", True)\n\n build_config[name] = input_dict\n\n # Preserve existing value if the parameter name exists in current_values\n if name in current_values:\n build_config[name][\"value\"] = current_values[name]\n\n except (AttributeError, KeyError, TypeError) as e:\n msg = f\"Error processing schema input {schema_input}: {e!s}\"\n await logger.aexception(msg)\n continue\n except ValueError as e:\n msg = f\"Schema validation error for tool {tool_name}: {e!s}\"\n await logger.aexception(msg)\n self.schema_inputs = []\n return\n except (AttributeError, KeyError, TypeError) as e:\n msg = f\"Error updating tool config: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n\n async def build_output(self) -> DataFrame:\n \"\"\"Build output with improved error handling and validation.\"\"\"\n try:\n self.tools, _ = await self.update_tool_list()\n if self.tool != \"\":\n # Set session context for persistent MCP sessions using Langflow session ID\n session_context = self._get_session_context()\n if session_context:\n self.stdio_client.set_session_context(session_context)\n self.sse_client.set_session_context(session_context)\n\n exec_tool = self._tool_cache[self.tool]\n tool_args = self.get_inputs_for_all_tools(self.tools)[self.tool]\n kwargs = {}\n for arg in tool_args:\n value = getattr(self, arg.name, None)\n if value is not None:\n if isinstance(value, Message):\n kwargs[arg.name] = value.text\n else:\n kwargs[arg.name] = value\n\n unflattened_kwargs = maybe_unflatten_dict(kwargs)\n\n output = await exec_tool.coroutine(**unflattened_kwargs)\n\n tool_content = []\n for item in output.content:\n item_dict = item.model_dump()\n tool_content.append(item_dict)\n return DataFrame(data=tool_content)\n return DataFrame(data=[{\"error\": \"You must select a tool\"}])\n except Exception as e:\n msg = f\"Error in build_output: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n\n def _get_session_context(self) -> str | None:\n \"\"\"Get the Langflow session ID for MCP session caching.\"\"\"\n # Try to get session ID from the component's execution context\n if hasattr(self, \"graph\") and hasattr(self.graph, \"session_id\"):\n session_id = self.graph.session_id\n # Include server name to ensure different servers get different sessions\n server_name = \"\"\n mcp_server = getattr(self, \"mcp_server\", None)\n if isinstance(mcp_server, dict):\n server_name = mcp_server.get(\"name\", \"\")\n elif mcp_server:\n server_name = str(mcp_server)\n return f\"{session_id}_{server_name}\" if session_id else None\n return None\n\n async def _get_tools(self):\n \"\"\"Get cached tools or update if necessary.\"\"\"\n mcp_server = getattr(self, \"mcp_server\", None)\n if not self._not_load_actions:\n tools, _ = await self.update_tool_list(mcp_server)\n return tools\n return []\n"
},
"mcp_server": {
"_input_type": "McpInput",
"advanced": false,
"display_name": "MCP Server",
"dynamic": false,
"info": "Select the MCP Server that will be used by this component",
"name": "mcp_server",
"placeholder": "",
"real_time_refresh": true,
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "mcp",
"value": {
"config": {
"args": [
"-y",
"@upstash/context7-mcp",
"--api-key",
"YOUR_CONTEXT7_API_KEY"
],
"command": "npx"
},
"name": "context7"
}
},
"tool": {
"_input_type": "DropdownInput",
"advanced": false,
"combobox": false,
"dialog_inputs": {},
"display_name": "Tool",
"dynamic": false,
"external_options": {},
"info": "Select the tool to execute",
"name": "tool",
"options": [
"resolve-library-id",
"get-library-docs"
],
"options_metadata": [],
"placeholder": "",
"real_time_refresh": true,
"required": true,
"show": false,
"title_case": false,
"toggle": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"tool_placeholder": {
"_input_type": "MessageTextInput",
"advanced": false,
"display_name": "Tool Placeholder",
"dynamic": false,
"info": "Placeholder for the tool",
"input_types": [
"Message"
],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "tool_placeholder",
"placeholder": "",
"required": false,
"show": false,
"title_case": false,
"tool_mode": true,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"tools_metadata": {
"_input_type": "ToolsInput",
"advanced": false,
"display_name": "Actions",
"dynamic": false,
"info": "Modify tool names and descriptions to help agents understand when to use each tool.",
"is_list": true,
"list_add_label": "Add More",
"name": "tools_metadata",
"placeholder": "",
"real_time_refresh": true,
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "tools",
"value": [
{
"args": {
"libraryName": {
"description": "Library name to search for and retrieve a Context7-compatible library ID.",
"title": "Libraryname",
"type": "string"
}
},
"description": "Resolves a package/product name to a Context7-compatible library ID and returns a list of matching libraries.\n\nYou MUST call this function before 'get-library-docs' to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.\n\nSelection Process:\n1. Analyze the query to understand what library/package the user is looking for\n2. Return the most relevant match based on:\n- Name similarity to the query (exact matches prioritized)\n- Description relevance to the query's intent\n- Documentation coverage (prioritize libraries with higher Code Snippet counts)\n- Trust score (consider libraries with scores of 7-10 more authoritative)\n\nResponse Format:\n- Return the selected library ID in a clearly marked section\n- Provide a brief explanation for why this library was chosen\n- If multiple good matches exist, acknowledge this but proceed with the most relevant one\n- If no good matches exist, clearly state this and suggest query refinements\n\nFor ambiguous queries, request clarification before proceeding with a best-guess match.",
"display_description": "Resolves a package/product name to a Context7-compatible library ID and returns a list of matching libraries.\n\nYou MUST call this function before 'get-library-docs' to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.\n\nSelection Process:\n1. Analyze the query to understand what library/package the user is looking for\n2. Return the most relevant match based on:\n- Name similarity to the query (exact matches prioritized)\n- Description relevance to the query's intent\n- Documentation coverage (prioritize libraries with higher Code Snippet counts)\n- Trust score (consider libraries with scores of 7-10 more authoritative)\n\nResponse Format:\n- Return the selected library ID in a clearly marked section\n- Provide a brief explanation for why this library was chosen\n- If multiple good matches exist, acknowledge this but proceed with the most relevant one\n- If no good matches exist, clearly state this and suggest query refinements\n\nFor ambiguous queries, request clarification before proceeding with a best-guess match.",
"display_name": "resolve-library-id",
"name": "resolve-library-id",
"readonly": false,
"status": true,
"tags": [
"resolve-library-id"
]
},
{
"args": {
"context7CompatibleLibraryID": {
"description": "Exact Context7-compatible library ID (e.g., '/mongodb/docs', '/vercel/next.js', '/supabase/supabase', '/vercel/next.js/v14.3.0-canary.87') retrieved from 'resolve-library-id' or directly from user query in the format '/org/project' or '/org/project/version'.",
"title": "Context7Compatiblelibraryid",
"type": "string"
},
"tokens": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Maximum number of tokens of documentation to retrieve (default: 5000). Higher values provide more context but consume more tokens.",
"title": "Tokens"
},
"topic": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Topic to focus documentation on (e.g., 'hooks', 'routing').",
"title": "Topic"
}
},
"description": "Fetches up-to-date documentation for a library. You must call 'resolve-library-id' first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.",
"display_description": "Fetches up-to-date documentation for a library. You must call 'resolve-library-id' first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.",
"display_name": "get-library-docs",
"name": "get-library-docs",
"readonly": false,
"status": true,
"tags": [
"get-library-docs"
]
}
]
}
},
"tool_mode": true
},
"showNode": true,
"type": "MCP"
},
"dragging": false,
"id": "MCP-i45b2",
"measured": {
"height": 284,
"width": 320
},
"position": {
"x": 1144.6101565430783,
"y": -39.52657554766786
},
"selected": false,
"type": "genericNode"
},
{
"data": {
"id": "MCP-qUf3x",
"node": {
"base_classes": [
"DataFrame"
],
"beta": false,
"category": "MCP",
"conditional_paths": [],
"custom_fields": {},
"description": "Connect to an MCP server to use its tools.",
"display_name": "MCP Tools",
"documentation": "https://docs.langflow.org/mcp-client",
"edited": false,
"field_order": [
"mcp_server",
"tool",
"tool_placeholder"
],
"frozen": false,
"icon": "Mcp",
"key": "mcp_filesystem",
"last_updated": "2025-11-07T05:32:15.297Z",
"legacy": false,
"lf_version": "1.6.5",
"mcpServerName": "filesystem",
"metadata": {},
"minimized": false,
"output_types": [],
"outputs": [
{
"allows_loop": false,
"cache": true,
"display_name": "Toolset",
"group_outputs": false,
"hidden": null,
"method": "to_toolkit",
"name": "component_as_tool",
"options": null,
"required_inputs": null,
"selected": "Tool",
"tool_mode": true,
"types": [
"Tool"
],
"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 __future__ import annotations\n\nimport asyncio\nimport uuid\nfrom typing import Any\n\nfrom langchain_core.tools import StructuredTool # noqa: TC002\n\nfrom langflow.api.v2.mcp import get_server\nfrom langflow.base.agents.utils import maybe_unflatten_dict, safe_cache_get, safe_cache_set\nfrom langflow.base.mcp.util import (\n MCPSseClient,\n MCPStdioClient,\n create_input_schema_from_json_schema,\n update_tools,\n)\nfrom langflow.custom.custom_component.component_with_cache import ComponentWithCache\nfrom langflow.inputs.inputs import InputTypes # noqa: TC001\nfrom langflow.io import DropdownInput, McpInput, MessageTextInput, Output\nfrom langflow.io.schema import flatten_schema, schema_to_langflow_inputs\nfrom langflow.logging import logger\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\n\n# Import get_server from the backend API\nfrom langflow.services.database.models.user.crud import get_user_by_id\nfrom langflow.services.deps import get_settings_service, get_storage_service, session_scope\n\n\nclass MCPToolsComponent(ComponentWithCache):\n schema_inputs: list = []\n tools: list[StructuredTool] = []\n _not_load_actions: bool = False\n _tool_cache: dict = {}\n _last_selected_server: str | None = None # Cache for the last selected server\n\n def __init__(self, **data) -> None:\n super().__init__(**data)\n # Initialize cache keys to avoid CacheMiss when accessing them\n self._ensure_cache_structure()\n\n # Initialize clients with access to the component cache\n self.stdio_client: MCPStdioClient = MCPStdioClient(component_cache=self._shared_component_cache)\n self.sse_client: MCPSseClient = MCPSseClient(component_cache=self._shared_component_cache)\n\n def _ensure_cache_structure(self):\n \"\"\"Ensure the cache has the required structure.\"\"\"\n # Check if servers key exists and is not CacheMiss\n servers_value = safe_cache_get(self._shared_component_cache, \"servers\")\n if servers_value is None:\n safe_cache_set(self._shared_component_cache, \"servers\", {})\n\n # Check if last_selected_server key exists and is not CacheMiss\n last_server_value = safe_cache_get(self._shared_component_cache, \"last_selected_server\")\n if last_server_value is None:\n safe_cache_set(self._shared_component_cache, \"last_selected_server\", \"\")\n\n default_keys: list[str] = [\n \"code\",\n \"_type\",\n \"tool_mode\",\n \"tool_placeholder\",\n \"mcp_server\",\n \"tool\",\n ]\n\n display_name = \"MCP Tools\"\n description = \"Connect to an MCP server to use its tools.\"\n documentation: str = \"https://docs.langflow.org/mcp-client\"\n icon = \"Mcp\"\n name = \"MCPTools\"\n\n inputs = [\n McpInput(\n name=\"mcp_server\",\n display_name=\"MCP Server\",\n info=\"Select the MCP Server that will be used by this component\",\n real_time_refresh=True,\n ),\n DropdownInput(\n name=\"tool\",\n display_name=\"Tool\",\n options=[],\n value=\"\",\n info=\"Select the tool to execute\",\n show=False,\n required=True,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n info=\"Placeholder for the tool\",\n value=\"\",\n show=False,\n tool_mode=False,\n ),\n ]\n\n outputs = [\n Output(display_name=\"Response\", name=\"response\", method=\"build_output\"),\n ]\n\n async def _validate_schema_inputs(self, tool_obj) -> list[InputTypes]:\n \"\"\"Validate and process schema inputs for a tool.\"\"\"\n try:\n if not tool_obj or not hasattr(tool_obj, \"args_schema\"):\n msg = \"Invalid tool object or missing input schema\"\n raise ValueError(msg)\n\n flat_schema = flatten_schema(tool_obj.args_schema.schema())\n input_schema = create_input_schema_from_json_schema(flat_schema)\n if not input_schema:\n msg = f\"Empty input schema for tool '{tool_obj.name}'\"\n raise ValueError(msg)\n\n schema_inputs = schema_to_langflow_inputs(input_schema)\n if not schema_inputs:\n msg = f\"No input parameters defined for tool '{tool_obj.name}'\"\n await logger.awarning(msg)\n return []\n\n except Exception as e:\n msg = f\"Error validating schema inputs: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n else:\n return schema_inputs\n\n async def update_tool_list(self, mcp_server_value=None):\n # Accepts mcp_server_value as dict {name, config} or uses self.mcp_server\n mcp_server = mcp_server_value if mcp_server_value is not None else getattr(self, \"mcp_server\", None)\n server_name = None\n server_config_from_value = None\n if isinstance(mcp_server, dict):\n server_name = mcp_server.get(\"name\")\n server_config_from_value = mcp_server.get(\"config\")\n else:\n server_name = mcp_server\n if not server_name:\n self.tools = []\n return [], {\"name\": server_name, \"config\": server_config_from_value}\n\n # Use shared cache if available\n servers_cache = safe_cache_get(self._shared_component_cache, \"servers\", {})\n cached = servers_cache.get(server_name) if isinstance(servers_cache, dict) else None\n\n if cached is not None:\n self.tools = cached[\"tools\"]\n self.tool_names = cached[\"tool_names\"]\n self._tool_cache = cached[\"tool_cache\"]\n server_config_from_value = cached[\"config\"]\n return self.tools, {\"name\": server_name, \"config\": server_config_from_value}\n\n try:\n async with session_scope() as db:\n if not self.user_id:\n msg = \"User ID is required for fetching MCP tools.\"\n raise ValueError(msg)\n current_user = await get_user_by_id(db, self.user_id)\n\n # Try to get server config from DB/API\n server_config = await get_server(\n server_name,\n current_user,\n db,\n storage_service=get_storage_service(),\n settings_service=get_settings_service(),\n )\n\n # If get_server returns empty but we have a config, use it\n if not server_config and server_config_from_value:\n server_config = server_config_from_value\n\n if not server_config:\n self.tools = []\n return [], {\"name\": server_name, \"config\": server_config}\n\n _, tool_list, tool_cache = await update_tools(\n server_name=server_name,\n server_config=server_config,\n mcp_stdio_client=self.stdio_client,\n mcp_sse_client=self.sse_client,\n )\n\n self.tool_names = [tool.name for tool in tool_list if hasattr(tool, \"name\")]\n self._tool_cache = tool_cache\n self.tools = tool_list\n # Cache the result using shared cache\n cache_data = {\n \"tools\": tool_list,\n \"tool_names\": self.tool_names,\n \"tool_cache\": tool_cache,\n \"config\": server_config,\n }\n\n # Safely update the servers cache\n current_servers_cache = safe_cache_get(self._shared_component_cache, \"servers\", {})\n if isinstance(current_servers_cache, dict):\n current_servers_cache[server_name] = cache_data\n safe_cache_set(self._shared_component_cache, \"servers\", current_servers_cache)\n\n except (TimeoutError, asyncio.TimeoutError) as e:\n msg = f\"Timeout updating tool list: {e!s}\"\n await logger.aexception(msg)\n raise TimeoutError(msg) from e\n except Exception as e:\n msg = f\"Error updating tool list: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n else:\n return tool_list, {\"name\": server_name, \"config\": server_config}\n\n async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n \"\"\"Toggle the visibility of connection-specific fields based on the selected mode.\"\"\"\n try:\n if field_name == \"tool\":\n try:\n if len(self.tools) == 0:\n try:\n self.tools, build_config[\"mcp_server\"][\"value\"] = await self.update_tool_list()\n build_config[\"tool\"][\"options\"] = [tool.name for tool in self.tools]\n build_config[\"tool\"][\"placeholder\"] = \"Select a tool\"\n except (TimeoutError, asyncio.TimeoutError) as e:\n msg = f\"Timeout updating tool list: {e!s}\"\n await logger.aexception(msg)\n if not build_config[\"tools_metadata\"][\"show\"]:\n build_config[\"tool\"][\"show\"] = True\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"value\"] = \"\"\n build_config[\"tool\"][\"placeholder\"] = \"Timeout on MCP server\"\n else:\n build_config[\"tool\"][\"show\"] = False\n except ValueError:\n if not build_config[\"tools_metadata\"][\"show\"]:\n build_config[\"tool\"][\"show\"] = True\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"value\"] = \"\"\n build_config[\"tool\"][\"placeholder\"] = \"Error on MCP Server\"\n else:\n build_config[\"tool\"][\"show\"] = False\n\n if field_value == \"\":\n return build_config\n tool_obj = None\n for tool in self.tools:\n if tool.name == field_value:\n tool_obj = tool\n break\n if tool_obj is None:\n msg = f\"Tool {field_value} not found in available tools: {self.tools}\"\n await logger.awarning(msg)\n return build_config\n await self._update_tool_config(build_config, field_value)\n except Exception as e:\n build_config[\"tool\"][\"options\"] = []\n msg = f\"Failed to update tools: {e!s}\"\n raise ValueError(msg) from e\n else:\n return build_config\n elif field_name == \"mcp_server\":\n if not field_value:\n build_config[\"tool\"][\"show\"] = False\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"value\"] = \"\"\n build_config[\"tool\"][\"placeholder\"] = \"\"\n build_config[\"tool_placeholder\"][\"tool_mode\"] = False\n self.remove_non_default_keys(build_config)\n return build_config\n\n build_config[\"tool_placeholder\"][\"tool_mode\"] = True\n\n current_server_name = field_value.get(\"name\") if isinstance(field_value, dict) else field_value\n _last_selected_server = safe_cache_get(self._shared_component_cache, \"last_selected_server\", \"\")\n\n # To avoid unnecessary updates, only proceed if the server has actually changed\n if (_last_selected_server in (current_server_name, \"\")) and build_config[\"tool\"][\"show\"]:\n if current_server_name:\n servers_cache = safe_cache_get(self._shared_component_cache, \"servers\", {})\n if isinstance(servers_cache, dict):\n cached = servers_cache.get(current_server_name)\n if cached is not None and cached.get(\"tool_names\"):\n cached_tools = cached[\"tool_names\"]\n current_tools = build_config[\"tool\"][\"options\"]\n if current_tools == cached_tools:\n return build_config\n else:\n return build_config\n\n # Determine if \"Tool Mode\" is active by checking if the tool dropdown is hidden.\n is_in_tool_mode = build_config[\"tools_metadata\"][\"show\"]\n safe_cache_set(self._shared_component_cache, \"last_selected_server\", current_server_name)\n\n # Check if tools are already cached for this server before clearing\n cached_tools = None\n if current_server_name:\n servers_cache = safe_cache_get(self._shared_component_cache, \"servers\", {})\n if isinstance(servers_cache, dict):\n cached = servers_cache.get(current_server_name)\n if cached is not None:\n cached_tools = cached[\"tools\"]\n self.tools = cached_tools\n self.tool_names = cached[\"tool_names\"]\n self._tool_cache = cached[\"tool_cache\"]\n\n # Only clear tools if we don't have cached tools for the current server\n if not cached_tools:\n self.tools = [] # Clear previous tools only if no cache\n\n self.remove_non_default_keys(build_config) # Clear previous tool inputs\n\n # Only show the tool dropdown if not in tool_mode\n if not is_in_tool_mode:\n build_config[\"tool\"][\"show\"] = True\n if cached_tools:\n # Use cached tools to populate options immediately\n build_config[\"tool\"][\"options\"] = [tool.name for tool in cached_tools]\n build_config[\"tool\"][\"placeholder\"] = \"Select a tool\"\n else:\n # Show loading state only when we need to fetch tools\n build_config[\"tool\"][\"placeholder\"] = \"Loading tools...\"\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"value\"] = uuid.uuid4()\n else:\n # Keep the tool dropdown hidden if in tool_mode\n self._not_load_actions = True\n build_config[\"tool\"][\"show\"] = False\n\n elif field_name == \"tool_mode\":\n build_config[\"tool\"][\"placeholder\"] = \"\"\n build_config[\"tool\"][\"show\"] = not bool(field_value) and bool(build_config[\"mcp_server\"])\n self.remove_non_default_keys(build_config)\n self.tool = build_config[\"tool\"][\"value\"]\n if field_value:\n self._not_load_actions = True\n else:\n build_config[\"tool\"][\"value\"] = uuid.uuid4()\n build_config[\"tool\"][\"options\"] = []\n build_config[\"tool\"][\"show\"] = True\n build_config[\"tool\"][\"placeholder\"] = \"Loading tools...\"\n elif field_name == \"tools_metadata\":\n self._not_load_actions = False\n\n except Exception as e:\n msg = f\"Error in update_build_config: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n else:\n return build_config\n\n def get_inputs_for_all_tools(self, tools: list) -> dict:\n \"\"\"Get input schemas for all tools.\"\"\"\n inputs = {}\n for tool in tools:\n if not tool or not hasattr(tool, \"name\"):\n continue\n try:\n flat_schema = flatten_schema(tool.args_schema.schema())\n input_schema = create_input_schema_from_json_schema(flat_schema)\n langflow_inputs = schema_to_langflow_inputs(input_schema)\n inputs[tool.name] = langflow_inputs\n except (AttributeError, ValueError, TypeError, KeyError) as e:\n msg = f\"Error getting inputs for tool {getattr(tool, 'name', 'unknown')}: {e!s}\"\n logger.exception(msg)\n continue\n return inputs\n\n def remove_input_schema_from_build_config(\n self, build_config: dict, tool_name: str, input_schema: dict[list[InputTypes], Any]\n ):\n \"\"\"Remove the input schema for the tool from the build config.\"\"\"\n # Keep only schemas that don't belong to the current tool\n input_schema = {k: v for k, v in input_schema.items() if k != tool_name}\n # Remove all inputs from other tools\n for value in input_schema.values():\n for _input in value:\n if _input.name in build_config:\n build_config.pop(_input.name)\n\n def remove_non_default_keys(self, build_config: dict) -> None:\n \"\"\"Remove non-default keys from the build config.\"\"\"\n for key in list(build_config.keys()):\n if key not in self.default_keys:\n build_config.pop(key)\n\n async def _update_tool_config(self, build_config: dict, tool_name: str) -> None:\n \"\"\"Update tool configuration with proper error handling.\"\"\"\n if not self.tools:\n self.tools, build_config[\"mcp_server\"][\"value\"] = await self.update_tool_list()\n\n if not tool_name:\n return\n\n tool_obj = next((tool for tool in self.tools if tool.name == tool_name), None)\n if not tool_obj:\n msg = f\"Tool {tool_name} not found in available tools: {self.tools}\"\n self.remove_non_default_keys(build_config)\n build_config[\"tool\"][\"value\"] = \"\"\n await logger.awarning(msg)\n return\n\n try:\n # Store current values before removing inputs\n current_values = {}\n for key, value in build_config.items():\n if key not in self.default_keys and isinstance(value, dict) and \"value\" in value:\n current_values[key] = value[\"value\"]\n\n # Get all tool inputs and remove old ones\n input_schema_for_all_tools = self.get_inputs_for_all_tools(self.tools)\n self.remove_input_schema_from_build_config(build_config, tool_name, input_schema_for_all_tools)\n\n # Get and validate new inputs\n self.schema_inputs = await self._validate_schema_inputs(tool_obj)\n if not self.schema_inputs:\n msg = f\"No input parameters to configure for tool '{tool_name}'\"\n await logger.ainfo(msg)\n return\n\n # Add new inputs to build config\n for schema_input in self.schema_inputs:\n if not schema_input or not hasattr(schema_input, \"name\"):\n msg = \"Invalid schema input detected, skipping\"\n await logger.awarning(msg)\n continue\n\n try:\n name = schema_input.name\n input_dict = schema_input.to_dict()\n input_dict.setdefault(\"value\", None)\n input_dict.setdefault(\"required\", True)\n\n build_config[name] = input_dict\n\n # Preserve existing value if the parameter name exists in current_values\n if name in current_values:\n build_config[name][\"value\"] = current_values[name]\n\n except (AttributeError, KeyError, TypeError) as e:\n msg = f\"Error processing schema input {schema_input}: {e!s}\"\n await logger.aexception(msg)\n continue\n except ValueError as e:\n msg = f\"Schema validation error for tool {tool_name}: {e!s}\"\n await logger.aexception(msg)\n self.schema_inputs = []\n return\n except (AttributeError, KeyError, TypeError) as e:\n msg = f\"Error updating tool config: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n\n async def build_output(self) -> DataFrame:\n \"\"\"Build output with improved error handling and validation.\"\"\"\n try:\n self.tools, _ = await self.update_tool_list()\n if self.tool != \"\":\n # Set session context for persistent MCP sessions using Langflow session ID\n session_context = self._get_session_context()\n if session_context:\n self.stdio_client.set_session_context(session_context)\n self.sse_client.set_session_context(session_context)\n\n exec_tool = self._tool_cache[self.tool]\n tool_args = self.get_inputs_for_all_tools(self.tools)[self.tool]\n kwargs = {}\n for arg in tool_args:\n value = getattr(self, arg.name, None)\n if value is not None:\n if isinstance(value, Message):\n kwargs[arg.name] = value.text\n else:\n kwargs[arg.name] = value\n\n unflattened_kwargs = maybe_unflatten_dict(kwargs)\n\n output = await exec_tool.coroutine(**unflattened_kwargs)\n\n tool_content = []\n for item in output.content:\n item_dict = item.model_dump()\n tool_content.append(item_dict)\n return DataFrame(data=tool_content)\n return DataFrame(data=[{\"error\": \"You must select a tool\"}])\n except Exception as e:\n msg = f\"Error in build_output: {e!s}\"\n await logger.aexception(msg)\n raise ValueError(msg) from e\n\n def _get_session_context(self) -> str | None:\n \"\"\"Get the Langflow session ID for MCP session caching.\"\"\"\n # Try to get session ID from the component's execution context\n if hasattr(self, \"graph\") and hasattr(self.graph, \"session_id\"):\n session_id = self.graph.session_id\n # Include server name to ensure different servers get different sessions\n server_name = \"\"\n mcp_server = getattr(self, \"mcp_server\", None)\n if isinstance(mcp_server, dict):\n server_name = mcp_server.get(\"name\", \"\")\n elif mcp_server:\n server_name = str(mcp_server)\n return f\"{session_id}_{server_name}\" if session_id else None\n return None\n\n async def _get_tools(self):\n \"\"\"Get cached tools or update if necessary.\"\"\"\n mcp_server = getattr(self, \"mcp_server\", None)\n if not self._not_load_actions:\n tools, _ = await self.update_tool_list(mcp_server)\n return tools\n return []\n"
},
"mcp_server": {
"_input_type": "McpInput",
"advanced": false,
"display_name": "MCP Server",
"dynamic": false,
"info": "Select the MCP Server that will be used by this component",
"name": "mcp_server",
"placeholder": "",
"real_time_refresh": true,
"required": false,
"show": true,
"title_case": false,
"trace_as_metadata": true,
"type": "mcp",
"value": {
"config": {
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/your/project/directory"
],
"command": "npx"
},
"name": "filesystem"
}
},
"tool": {
"_input_type": "DropdownInput",
"advanced": false,
"combobox": false,
"dialog_inputs": {},
"display_name": "Tool",
"dynamic": false,
"external_options": {},
"info": "Select the tool to execute",
"name": "tool",
"options": [
"read_file",
"read_text_file",
"read_media_file",
"read_multiple_files",
"write_file",
"edit_file",
"create_directory",
"list_directory",
"list_directory_with_sizes",
"directory_tree",
"move_file",
"search_files",
"get_file_info",
"list_allowed_directories"
],
"options_metadata": [],
"placeholder": "",
"real_time_refresh": true,
"required": true,
"show": false,
"title_case": false,
"toggle": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"tool_placeholder": {
"_input_type": "MessageTextInput",
"advanced": false,
"display_name": "Tool Placeholder",
"dynamic": false,
"info": "Placeholder for the tool",
"input_types": [
"Message"
],
"list": false,
"list_add_label": "Add More",
"load_from_db": false,
"name": "tool_placeholder",
"placeholder": "",
"required": false,
"show": false,
"title_case": false,
"tool_mode": true,
"trace_as_input": true,
"trace_as_metadata": true,
"type": "str",
"value": ""
},
"tools_metadata": {
"_input_type": "ToolsInput",
"advanced": false,
"display_name": "Actions",
"dynamic": false,
"info": "Modify tool names and descriptions to help agents understand when to use each tool.",
"is_list": true,
"list_add_label": "Add More",
"name": "tools_metadata",
"placeholder": "",
"real_time_refresh": true,
"required": false,
"show": true,
"title_case": false,
"tool_mode": false,
"trace_as_metadata": true,
"type": "tools",
"value": [
{
"args": {
"head": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "If provided, returns only the first N lines of the file",
"title": "Head"
},
"path": {
"title": "Path",
"type": "string"
},
"tail": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "If provided, returns only the last N lines of the file",
"title": "Tail"
}
},
"description": "Read the complete contents of a file as text. DEPRECATED: Use read_text_file instead.",
"display_description": "Read the complete contents of a file as text. DEPRECATED: Use read_text_file instead.",
"display_name": "read_file",
"name": "read_file",
"readonly": false,
"status": false,
"tags": [
"read_file"
]
},
{
"args": {
"head": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "If provided, returns only the first N lines of the file",
"title": "Head"
},
"path": {
"title": "Path",
"type": "string"
},
"tail": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "If provided, returns only the last N lines of the file",
"title": "Tail"
}
},
"description": "Read the complete contents of a file from the file system as text. Handles various text encodings and provides detailed error messages if the file cannot be read. Use this tool when you need to examine the contents of a single file. Use the 'head' parameter to read only the first N lines of a file, or the 'tail' parameter to read only the last N lines of a file. Operates on the file as text regardless of extension. Only works within allowed directories.",
"display_description": "Read the complete contents of a file from the file system as text. Handles various text encodings and provides detailed error messages if the file cannot be read. Use this tool when you need to examine the contents of a single file. Use the 'head' parameter to read only the first N lines of a file, or the 'tail' parameter to read only the last N lines of a file. Operates on the file as text regardless of extension. Only works within allowed directories.",
"display_name": "read_text_file",
"name": "read_text_file",
"readonly": false,
"status": true,
"tags": [
"read_text_file"
]
},
{
"args": {
"path": {
"title": "Path",
"type": "string"
}
},
"description": "Read an image or audio file. Returns the base64 encoded data and MIME type. Only works within allowed directories.",
"display_description": "Read an image or audio file. Returns the base64 encoded data and MIME type. Only works within allowed directories.",
"display_name": "read_media_file",
"name": "read_media_file",
"readonly": false,
"status": true,
"tags": [
"read_media_file"
]
},
{
"args": {
"paths": {
"items": {
"type": "string"
},
"title": "Paths",
"type": "array"
}
},
"description": "Read the contents of multiple files simultaneously. This is more efficient than reading files one by one when you need to analyze or compare multiple files. Each file's content is returned with its path as a reference. Failed reads for individual files won't stop the entire operation. Only works within allowed directories.",
"display_description": "Read the contents of multiple files simultaneously. This is more efficient than reading files one by one when you need to analyze or compare multiple files. Each file's content is returned with its path as a reference. Failed reads for individual files won't stop the entire operation. Only works within allowed directories.",
"display_name": "read_multiple_files",
"name": "read_multiple_files",
"readonly": false,
"status": true,
"tags": [
"read_multiple_files"
]
},
{
"args": {
"content": {
"title": "Content",
"type": "string"
},
"path": {
"title": "Path",
"type": "string"
}
},
"description": "Create a new file or completely overwrite an existing file with new content. Use with caution as it will overwrite existing files without warning. Handles text content with proper encoding. Only works within allowed directories.",
"display_description": "Create a new file or completely overwrite an existing file with new content. Use with caution as it will overwrite existing files without warning. Handles text content with proper encoding. Only works within allowed directories.",
"display_name": "write_file",
"name": "write_file",
"readonly": false,
"status": true,
"tags": [
"write_file"
]
},
{
"args": {
"dryRun": {
"anyOf": [
{
"type": "boolean"
},
{
"type": "null"
}
],
"default": false,
"description": "Preview changes using git-style diff format",
"title": "Dryrun"
},
"edits": {
"items": {
"$ref": "#/$defs/AnonModel0"
},
"title": "Edits",
"type": "array"
},
"path": {
"title": "Path",
"type": "string"
}
},
"description": "Make line-based edits to a text file. Each edit replaces exact line sequences with new content. Returns a git-style diff showing the changes made. Only works within allowed directories.",
"display_description": "Make line-based edits to a text file. Each edit replaces exact line sequences with new content. Returns a git-style diff showing the changes made. Only works within allowed directories.",
"display_name": "edit_file",
"name": "edit_file",
"readonly": false,
"status": true,
"tags": [
"edit_file"
]
},
{
"args": {
"path": {
"title": "Path",
"type": "string"
}
},
"description": "Create a new directory or ensure a directory exists. Can create multiple nested directories in one operation. If the directory already exists, this operation will succeed silently. Perfect for setting up directory structures for projects or ensuring required paths exist. Only works within allowed directories.",
"display_description": "Create a new directory or ensure a directory exists. Can create multiple nested directories in one operation. If the directory already exists, this operation will succeed silently. Perfect for setting up directory structures for projects or ensuring required paths exist. Only works within allowed directories.",
"display_name": "create_directory",
"name": "create_directory",
"readonly": false,
"status": true,
"tags": [
"create_directory"
]
},
{
"args": {
"path": {
"title": "Path",
"type": "string"
}
},
"description": "Get a detailed listing of all files and directories in a specified path. Results clearly distinguish between files and directories with [FILE] and [DIR] prefixes. This tool is essential for understanding directory structure and finding specific files within a directory. Only works within allowed directories.",
"display_description": "Get a detailed listing of all files and directories in a specified path. Results clearly distinguish between files and directories with [FILE] and [DIR] prefixes. This tool is essential for understanding directory structure and finding specific files within a directory. Only works within allowed directories.",
"display_name": "list_directory",
"name": "list_directory",
"readonly": false,
"status": true,
"tags": [
"list_directory"
]
},
{
"args": {
"path": {
"title": "Path",
"type": "string"
},
"sortBy": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": "name",
"description": "Sort entries by name or size",
"title": "Sortby"
}
},
"description": "Get a detailed listing of all files and directories in a specified path, including sizes. Results clearly distinguish between files and directories with [FILE] and [DIR] prefixes. This tool is useful for understanding directory structure and finding specific files within a directory. Only works within allowed directories.",
"display_description": "Get a detailed listing of all files and directories in a specified path, including sizes. Results clearly distinguish between files and directories with [FILE] and [DIR] prefixes. This tool is useful for understanding directory structure and finding specific files within a directory. Only works within allowed directories.",
"display_name": "list_directory_with_sizes",
"name": "list_directory_with_sizes",
"readonly": false,
"status": true,
"tags": [
"list_directory_with_sizes"
]
},
{
"args": {
"path": {
"title": "Path",
"type": "string"
}
},
"description": "Get a recursive tree view of files and directories as a JSON structure. Each entry includes 'name', 'type' (file/directory), and 'children' for directories. Files have no children array, while directories always have a children array (which may be empty). The output is formatted with 2-space indentation for readability. Only works within allowed directories.",
"display_description": "Get a recursive tree view of files and directories as a JSON structure. Each entry includes 'name', 'type' (file/directory), and 'children' for directories. Files have no children array, while directories always have a children array (which may be empty). The output is formatted with 2-space indentation for readability. Only works within allowed directories.",
"display_name": "directory_tree",
"name": "directory_tree",
"readonly": false,
"status": true,
"tags": [
"directory_tree"
]
},
{
"args": {
"destination": {
"title": "Destination",
"type": "string"
},
"source": {
"title": "Source",
"type": "string"
}
},
"description": "Move or rename files and directories. Can move files between directories and rename them in a single operation. If the destination exists, the operation will fail. Works across different directories and can be used for simple renaming within the same directory. Both source and destination must be within allowed directories.",
"display_description": "Move or rename files and directories. Can move files between directories and rename them in a single operation. If the destination exists, the operation will fail. Works across different directories and can be used for simple renaming within the same directory. Both source and destination must be within allowed directories.",
"display_name": "move_file",
"name": "move_file",
"readonly": false,
"status": true,
"tags": [
"move_file"
]
},
{
"args": {
"excludePatterns": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": [],
"title": "Excludepatterns"
},
"path": {
"title": "Path",
"type": "string"
},
"pattern": {
"title": "Pattern",
"type": "string"
}
},
"description": "Recursively search for files and directories matching a pattern. Searches through all subdirectories from the starting path. The search is case-insensitive and matches partial names. Returns full paths to all matching items. Great for finding files when you don't know their exact location. Only searches within allowed directories.",
"display_description": "Recursively search for files and directories matching a pattern. Searches through all subdirectories from the starting path. The search is case-insensitive and matches partial names. Returns full paths to all matching items. Great for finding files when you don't know their exact location. Only searches within allowed directories.",
"display_name": "search_files",
"name": "search_files",
"readonly": false,
"status": true,
"tags": [
"search_files"
]
},
{
"args": {
"path": {
"title": "Path",
"type": "string"
}
},
"description": "Retrieve detailed metadata about a file or directory. Returns comprehensive information including size, creation time, last modified time, permissions, and type. This tool is perfect for understanding file characteristics without reading the actual content. Only works within allowed directories.",
"display_description": "Retrieve detailed metadata about a file or directory. Returns comprehensive information including size, creation time, last modified time, permissions, and type. This tool is perfect for understanding file characteristics without reading the actual content. Only works within allowed directories.",
"display_name": "get_file_info",
"name": "get_file_info",
"readonly": false,
"status": true,
"tags": [
"get_file_info"
]
},
{
"args": {},
"description": "Returns the list of directories that this server is allowed to access. Subdirectories within these allowed directories are also accessible. Use this to understand which directories and their nested paths are available before trying to access files.",
"display_description": "Returns the list of directories that this server is allowed to access. Subdirectories within these allowed directories are also accessible. Use this to understand which directories and their nested paths are available before trying to access files.",
"display_name": "list_allowed_directories",
"name": "list_allowed_directories",
"readonly": false,
"status": true,
"tags": [
"list_allowed_directories"
]
}
]
}
},
"tool_mode": true
},
"showNode": true,
"type": "MCP"
},
"dragging": false,
"id": "MCP-qUf3x",
"measured": {
"height": 312,
"width": 320
},
"position": {
"x": 664.6357935091627,
"y": -45.38837740060024
},
"selected": false,
"type": "genericNode"
},
{
"data": {
"id": "note-Z1xK4",
"node": {
"description": "# The Chat Input\n\nThis is where your prompt will be fed into the system and sent on to the agent.",
"display_name": "",
"documentation": "",
"template": {}
},
"type": "note"
},
"dragging": false,
"height": 424,
"id": "note-Z1xK4",
"measured": {
"height": 424,
"width": 406
},
"position": {
"x": 948.0891342764786,
"y": 417.82517411679385
},
"resizing": false,
"selected": false,
"type": "noteNode",
"width": 406
},
{
"data": {
"id": "note-CgZBn",
"node": {
"description": "# The Chat Output\n\nThis returns messages from the agent to let the user know what is being done.",
"display_name": "",
"documentation": "",
"template": {}
},
"type": "note"
},
"dragging": false,
"height": 357,
"id": "note-CgZBn",
"measured": {
"height": 357,
"width": 370
},
"position": {
"x": 2117.639944682914,
"y": 416.34394104615865
},
"resizing": false,
"selected": false,
"type": "noteNode",
"width": 370
},
{
"data": {
"id": "note-Dmp7m",
"node": {
"description": "# The Filesystem MCP server\n\nThis MCP server makes filesystem operations available as tools to the agent so that it can read code, explore directories, and most importantly create files and write code. ",
"display_name": "",
"documentation": "",
"template": {
"backgroundColor": "blue"
}
},
"type": "note"
},
"dragging": false,
"height": 565,
"id": "note-Dmp7m",
"measured": {
"height": 565,
"width": 436
},
"position": {
"x": 612.6505410567553,
"y": -259.56757874115664
},
"resizing": false,
"selected": false,
"type": "noteNode",
"width": 436
},
{
"data": {
"id": "note-ToFVG",
"node": {
"description": "# The Context7 MCP Server\n\nThis MCP server connects to Context7 to provide documentation to the coding agent on libraries that it chooses to use.",
"display_name": "",
"documentation": "",
"template": {
"backgroundColor": "blue"
}
},
"type": "note"
},
"dragging": false,
"height": 526,
"id": "note-ToFVG",
"measured": {
"height": 526,
"width": 419
},
"position": {
"x": 1099.5473807787023,
"y": -240.05784286564344
},
"resizing": false,
"selected": false,
"type": "noteNode",
"width": 419
},
{
"data": {
"id": "note-yKXCG",
"node": {
"description": "# The Agent\n\nThis component uses an LLM to follow your instructions, use tools and generate code to build an application with you.\n",
"display_name": "",
"documentation": "",
"template": {
"backgroundColor": "lime"
}
},
"type": "note"
},
"dragging": false,
"height": 800,
"id": "note-yKXCG",
"measured": {
"height": 800,
"width": 398
},
"position": {
"x": 1597.9879370140502,
"y": 114.56275849926442
},
"resizing": false,
"selected": false,
"type": "noteNode",
"width": 398
}
],
"viewport": {
"x": -312.2261460546182,
"y": 225.6270037325944,
"zoom": 0.6026700725959424
}
},
"description": "An agent capable of using the file system to write code and the Context7 documentation service.",
"endpoint_name": null,
"id": "0adaf1ef-0f6f-4653-bff8-0948e76b40a3",
"is_component": false,
"last_tested_version": "1.6.5",
"name": "CodeFlow",
"tags": [
"assistants",
"agents"
]
}
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