Input / Output
Langflow's input and output components define where data enters and exits your flow, but they don't have identical functionality.
Specifically, Chat Input/Output components are designed to facilitate conversational interactions where messages are exchanged in a cumulative dialog. The data handled by these components includes the message text plus additional metadata like senders, session IDs, and timestamps.
In contrast, Text Input/Output components are designed for simple string input and output that doesn't require the additional context and metadata associated with chat messages. The data handled by these components is pared down to basic text strings.
Chat Input/Output
Chat Input/Output components are required to chat with your flow in the Playground. For more information, see Test flows in the Playground.
Chat Input/Output components are designed to handle conversational interactions in Langflow.
Chat Input
The Chat Input component accepts text and file input, such as a chat message or a file.
This data is passed to other components as Message data containing the provided input as well as associated chat metadata, such as the sender, session ID, timestamp, and file attachments.
Initial input should not be provided as a complete Message object because the Chat Input component constructs the Message object that is then passed to other components in the flow.
Chat Input parameters
Most Chat Input component input parameters are hidden by default in the visual editor. You can enable other parameters through the Controls in the component's header menu.
For information about the resulting Message object, including input parameters that are directly mapped to Message attributes, see Message data.
| Name | Display Name | Info |
|---|---|---|
| input_value | Input Text | Input parameter. The message text string to be passed as input. |
| sender | Sender Type | Input parameter. Identifies the sender as either User or Language Model. |
| sender_name | Sender Name | Input parameter. The name of the sender. If unspecified, defaults to User or Language Model. |
| session_id | Session ID | Input parameter. The unique identifier for the chat session. If empty, the current session ID parameter is used. |
| files | Files | Input parameter. The files to be sent with the message. |
| background_color | Background Color | Input parameter. The background color of the icon. |
| chat_icon | Icon | Input parameter. The icon of the message. |
| should_store_message | Store Messages | Input parameter. Whether to store the message in chat history. |
| text_color | Text Color | Input parameter. The text color of the name. |
| message | Message | Output parameter. The resulting chat Message object with all specified properties. |
Message method for Chat Input
The ChatInput class provides an asynchronous method to create and store a Message object based on the input parameters.
The Message object is created in the message_response method of the ChatInput class using the Message.create() factory method.
_12message = await Message.create(_12 text=self.input_value,_12 sender=self.sender,_12 sender_name=self.sender_name,_12 session_id=self.session_id,_12 files=self.files,_12 properties={_12 "background_color": background_color,_12 "text_color": text_color,_12 "icon": icon,_12 },_12)
Chat Output
The Chat Output component ingests Message, Data, or DataFrame data from other components, transforms it into Message data if needed, and then emits the final output as a chat message.
For information about these data types, see Use Langflow data types.
In the Playground, chat output is limited to the parts of the Message object that are relevant to the chat interface, such as the text response, sender name, and file attachments.
To see the metadata associated with a chat message, inspect the message logs in the Playground.
When using the Langflow API, the API response includes the Chat Output Message object along with other response data from the flow run.
Langflow API responses can be extremely verbose, so your applications must include code to extract relevant data from the response to return to the user.
For an example, see the Langflow quickstart.
Chat Output parameters
Most Chat Output component input parameters are hidden by default in the visual editor. You can enable them through the Controls in the component's header menu.
For information about the resulting Message object, including input parameters that are directly mapped to Message attributes, see Message data.
| Name | Display Name | Info |
|---|---|---|
| input_value | Inputs | Input parameter. The message text string to be passed as output. |
| should_store_message | Store Messages | Input parameter. Whether to store the message in chat history. |
| sender | Sender Type | Input parameter. Identifies the sender as either User or Language Model. |
| sender_name | Sender Name | Input parameter. The name of the sender. If unspecified, defaults to User or Language Model. |
| session_id | Session ID | Input parameter. The unique identifier for the chat session. If empty, the current session ID parameter is used. |
| data_template | Data Template | Input parameter. The template to convert Data input to text. If empty, it is dynamically set to the Data object's text key. |
| background_color | Background Color | Input parameter. The background color of the icon. |
| chat_icon | Icon | Input parameter. The icon of the message. |
| text_color | Text Color | Input parameter. The text color of the name. |
| clean_data | Basic Clean Data | Input parameter. When enabled, DataFrame input is cleaned when converted to text. Cleaning removes empty rows, empty lines in cells, and multiple newlines. |
| message | Message | Output parameter. The resulting chat Message object with all specified properties. |
Use Chat Input/Output components in a flow
To use the Chat Input and Chat Output components in a flow, connect them to components that accept or emit Message data.
For example, the following flow connects Chat Input and Chat Output to a Language Model component, creating a simple LLM-based chat flow.
For detailed examples of Chat Input/Output components in flows, see the following:
- Langflow quickstart: Create and run a basic agent flow.
- Basic prompting template: Create an LLM-based chat flow that accepts chat input as well as a prompt with additional instructions for the LLM. Many other Langflow templates also use Chat Input/Output components.
- Connect applications to agents: Explore more advanced concepts around agentic flows and prompting, including triggering agent flows from external applications.
Send chat input with the Langflow API
You can use the Langflow API to run a flow by sending input to a Chat Input component:
_10curl --request POST \_10 --url "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID" \_10 --header "Content-Type: application/json" \_10 --header "x-api-key: $LANGFLOW_API_KEY" \_10 --data '{_10 "input_value": "What's the recommended way to install Docker on Mac M1?",_10 "output_type": "chat",_10 "input_type": "chat"_10}'
When triggering flows with the Langflow API, the payload must contain the values for the Chat Input component's input parameters, such as input_value.
Not all parameters need to be specified in the request.
For example, session_id uses the flow's default session ID if omitted.
If you want to use a custom session ID, include session_id in your request:
_10curl --request POST \_10 --url "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID" \_10 --header "Content-Type: application/json" \_10 --header "x-api-key: $LANGFLOW_API_KEY" \_10 --data '{_10 "input_value": "Whats the recommended way to install Docker on Mac M1",_10 "session_id": "$USER_ID",_10 "output_type": "chat",_10 "input_type": "chat"_10}'
For more information, see Trigger flows with the Langflow API.
Text Input/Output
Text Input/Output components aren't supported in the Playground. Because the data isn't formatted as a chat message, the data doesn't appear in the Playground, and you can't chat with your flow in the Playground.
If you want to chat with a flow in the Playground, you must use the Chat Input/Output components.
Text Input/Output components are designed for flows that ingest or emit simple text strings. These components don't support full conversational interactions.
Passing chat-like metadata to a Text Input/Output component doesn't change the component's behavior; the result is still a simple text string.
Text Input
The Text Input component accepts a text string input that is passed to other components as Message data containing only the provided text string.
Initial input should not be provided as a complete Message object because the Text Input component constructs the Message object that is then passed to other components in the flow.
Text Input parameters
| Name | Display Name | Info |
|---|---|---|
| input_value | Text | Input parameter. Text supplied as input to the component. Can be entered directly or passed as Message data from other components. |
| text | Text | Output parameter. The resulting Message object containing the input text in the text attribute. |
Text Output
The Text Output component ingests Message data from other components, emitting only the text attribute in a simplified Message object.
Text Output parameters
| Name | Display Name | Info |
|---|---|---|
| input_value | Text | Input parameter. Text to be ingested and output as a string. Can be entered directly or passed as Message data from other components. |
| text | Text | Output parameter. The resulting Message object containing the output text in the text attribute. |
