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Create custom components

Custom components are created within Langflow and extend the platform's functionality with custom, reusable Python code.

Since Langflow operates with Python behind the scenes, you can implement any Python function within a Custom Component. This means you can leverage the power of libraries such as Pandas, Scikit-learn, Numpy, and thousands of other packages to create components that handle data processing in unlimited ways. You can use any type as long as the type is properly annotated in the output methods (e.g., > list[int]).

Custom Components create reusable and configurable components to enhance the capabilities of Langflow, making it a powerful tool for developing complex processing between user and AI messages.

Create a custom component in Langflow​

Creating custom components in Langflow involves creating a Python class that defines the component's functionality, inputs, and outputs. The default code provides a working structure for your custom component.


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# from langflow.field_typing import Data
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from langflow.custom import Component
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from langflow.io import MessageTextInput, Output
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from langflow.schema import Data
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class CustomComponent(Component):
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display_name = "Custom Component"
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description = "Use as a template to create your own component."
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documentation: str = "http://docs.langflow.org/components/custom"
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icon = "custom_components"
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name = "CustomComponent"
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inputs = [
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MessageTextInput(name="input_value", display_name="Input Value", value="Hello, World!"),
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]
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outputs = [
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Output(display_name="Output", name="output", method="build_output"),
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]
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def build_output(self) -> Data:
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data = Data(value=self.input_value)
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self.status = data
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return data

You can create your class in your favorite text editor outside of Langflow and paste it in later, or just follow along in the code pane.

  1. In Langflow, from under Helpers, drag a Custom Component into the workspace.
  2. Open the component's code pane.
  3. Import dependencies. Your custom component inherits from the langflow Component class so you need to include it.

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from langflow.custom import Component
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from langflow.io import MessageTextInput, Output
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from langflow.schema import Data

  1. Define the Class: Start by defining a Python class that inherits from Component. This class will encapsulate the functionality of your custom component.

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class CustomComponent(Component):
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display_name = "Custom Component"
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description = "Use as a template to create your own component."
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documentation: str = "http://docs.langflow.org/components/custom"
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icon = "custom_components"
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name = "CustomComponent"

  1. Specify Inputs and Outputs: Use Langflow's input and output classes to define the inputs and outputs of your component. They should be declared as class attributes.

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inputs = [
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MessageTextInput(name="input_value", display_name="Input Value", value="Hello, World!"),
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]
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outputs = [
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Output(display_name="Output", name="output", method="build_output"),
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]

  1. Implement Output Methods: Implement methods for each output, which contains the logic of your component. These methods can access input values using self.<input_name> , return processed values and define what to be displayed in the component with the self.status attribute.

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def build_output(self) -> Data:
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data = Data(value=self.input_value)
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self.status = data
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return data

  1. Use Proper Annotations: Ensure that output methods are properly annotated with their types. Langflow uses these annotations to validate and handle data correctly. For example, this method is annotated to output Data.

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def build_output(self) -> Data:

  1. Click Check & Save to confirm your component works. You now have an operational custom component.

Add inputs and modify output methods​

This code defines a custom component that accepts 5 inputs and outputs a Message.

Copy and paste it into the Custom Component code pane and click Check & Save.


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from langflow.custom import Component
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from langflow.inputs import StrInput, MultilineInput, SecretStrInput, IntInput, DropdownInput
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from langflow.template import Output, Input
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from langflow.schema.message import Message
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class MyCustomComponent(Component):
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display_name = "My Custom Component"
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description = "An example of a custom component with various input types."
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inputs = [
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StrInput(
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name="username",
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display_name="Username",
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info="Enter your username."
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),
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SecretStrInput(
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name="password",
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display_name="Password",
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info="Enter your password."
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),
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MessageTextInput(
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name="special_message",
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display_name="special_message",
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info="Enter a special message.",
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),
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IntInput(
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name="age",
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display_name="Age",
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info="Enter your age."
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),
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DropdownInput(
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name="gender",
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display_name="Gender",
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options=["Male", "Female", "Other"],
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info="Select your gender."
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)
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]
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outputs = [
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Output(display_name="Result", name="result", method="process_inputs"),
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]
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def process_inputs(self) -> Message:
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"""
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Process the user inputs and return a Message object.
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Returns:
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Message: A Message object containing the processed information.
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"""
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try:
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processed_text = f"User {self.username} (Age: {self.age}, Gender: {self.gender}) " \
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f"sent the following special message: {self.special_message}"
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return Message(text=processed_text)
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except AttributeError as e:
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return Message(text=f"Error processing inputs: {str(e)}")

Since the component outputs a Message, you can wire it into a chat and pass messages to yourself.

Your Custom Component accepts the Chat Input message through MessageTextInput, fills in the variables with the process_inputs method, and finally passes the message User Username (Age: 49, Gender: Male) sent the following special message: Hello! to Chat Output.

By defining inputs this way, Langflow can automatically handle the validation and display of these fields in the user interface, making it easier to create robust and user-friendly custom components.

All of the types detailed above derive from a general class that can also be accessed through the generic Input class.

tip

Use MessageInput to get the entire Message object instead of just the text.

Input Types​

Langflow provides several higher-level input types to simplify the creation of custom components. These input types standardize how inputs are defined, validated, and used. Here’s a guide on how to use these inputs and their primary purposes:

HandleInput​

Represents an input that has a handle to a specific type (e.g., BaseLanguageModel, BaseRetriever, etc.).

  • Usage: Useful for connecting to specific component types in a flow.

DataInput​

Represents an input that receives a Data object.

  • Usage: Ideal for components that process or manipulate data objects.
  • Input Types: ["Data"]

StrInput​

Represents a standard string input field.

  • Usage: Used for any text input where the user needs to provide a string.
  • Input Types: ["Text"]

MessageInput​

Represents an input field specifically for Message objects.

  • Usage: Used in components that handle or process messages.
  • Input Types: ["Message"]

MessageTextInput​

Represents a text input for messages.

  • Usage: Suitable for components that need to extract text from message objects.
  • Input Types: ["Message"]

MultilineInput​

Represents a text field that supports multiple lines.

  • Usage: Ideal for longer text inputs where the user might need to write extended text.
  • Input Types: ["Text"]
  • Attributes: multiline=True

SecretStrInput​

Represents a password input field.

  • Usage: Used for sensitive text inputs where the input should be hidden (e.g., passwords, API keys).
  • Attributes: password=True
  • Input Types: Does not accept input types, meaning it has no input handles for previous nodes/components to connect to it.

IntInput​

Represents an integer input field.

  • Usage: Used for numeric inputs where the value should be an integer.
  • Input Types: ["Integer"]

FloatInput​

Represents a float input field.

  • Usage: Used for numeric inputs where the value should be a floating-point number.
  • Input Types: ["Float"]

BoolInput​

Represents a boolean input field.

  • Usage: Used for true/false or yes/no type inputs.
  • Input Types: ["Boolean"]

NestedDictInput​

Represents an input field for nested dictionaries.

  • Usage: Used for more complex data structures where the input needs to be a dictionary.
  • Input Types: ["NestedDict"]

DictInput​

Represents an input field for dictionaries.

  • Usage: Suitable for inputs that require a dictionary format.
  • Input Types: ["Dict"]

DropdownInput​

Represents a dropdown input field.

  • Usage: Used where the user needs to select from a predefined list of options.
  • Attributes: options to define the list of selectable options.
  • Input Types: ["Text"]

FileInput​

Represents a file input field.

  • Usage: Used to upload files.
  • Attributes: file_types to specify the types of files that can be uploaded.
  • Input Types: ["File"]

Generic Input​

Langflow offers native input types, but you can use any type as long as they are properly annotated in the output methods (e.g., -> list[int]).

The Input class is highly customizable, allowing you to specify a wide range of attributes for each input field. It has several attributes that can be customized:

  • field_type: Specifies the type of field (e.g., str, int). Default is str.
  • required: Boolean indicating if the field is required. Default is False.
  • placeholder: Placeholder text for the input field. Default is an empty string.
  • is_list: Boolean indicating if the field should accept a list of values. Default is False.
  • show: Boolean indicating if the field should be shown. Default is True.
  • multiline: Boolean indicating if the field should allow multi-line input. Default is False.
  • value: Default value for the input field. Default is None.
  • file_types: List of accepted file types (for file inputs). Default is an empty list.
  • file_path: File path if the field is a file input. Default is None.
  • password: Boolean indicating if the field is a password. Default is False.
  • options: List of options for the field (for dropdowns). Default is None.
  • name: Name of the input field. Default is None.
  • display_name: Display name for the input field. Default is None.
  • advanced: Boolean indicating if the field is an advanced parameter. Default is False.
  • input_types: List of accepted input types. Default is None.
  • dynamic: Boolean indicating if the field is dynamic. Default is False.
  • info: Additional information or tooltip for the input field. Default is an empty string.
  • real_time_refresh: Boolean indicating if the field should refresh in real-time. Default is None.
  • refresh_button: Boolean indicating if the field should have a refresh button. Default is None.
  • refresh_button_text: Text for the refresh button. Default is None.
  • range_spec: Range specification for numeric fields. Default is None.
  • load_from_db: Boolean indicating if the field should load from the database. Default is False.
  • title_case: Boolean indicating if the display name should be in title case. Default is True.

Create a Custom Component with Generic Input​

Here is an example of how to define inputs for a component using the Input class.

Copy and paste it into the Custom Component code pane and click Check & Save.


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from langflow.template import Input, Output
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from langflow.custom import Component
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from langflow.field_typing import Text
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from langflow.schema.message import Message
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from typing import Dict, Any
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class TextAnalyzerComponent(Component):
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display_name = "Text Analyzer"
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description = "Analyzes input text and provides basic statistics."
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inputs = [
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Input(
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name="input_text",
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display_name="Input Text",
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field_type="Message",
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required=True,
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placeholder="Enter text to analyze",
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multiline=True,
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info="The text you want to analyze.",
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input_types=["Text"]
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),
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Input(
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name="include_word_count",
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display_name="Include Word Count",
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field_type="bool",
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required=False,
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info="Whether to include word count in the analysis.",
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),
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Input(
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name="perform_sentiment_analysis",
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display_name="Perform Sentiment Analysis",
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field_type="bool",
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required=False,
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info="Whether to perform basic sentiment analysis.",
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),
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]
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outputs = [
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Output(display_name="Analysis Results", name="results", method="analyze_text"),
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]
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def analyze_text(self) -> Message:
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# Extract text from the Message object
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if isinstance(self.input_text, Message):
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text = self.input_text.text
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else:
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text = str(self.input_text)
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results = {
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"character_count": len(text),
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"sentence_count": text.count('.') + text.count('!') + text.count('?')
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}
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if self.include_word_count:
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results["word_count"] = len(text.split())
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if self.perform_sentiment_analysis:
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# Basic sentiment analysis
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text_lower = text.lower()
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if "happy" in text_lower or "good" in text_lower:
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sentiment = "positive"
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elif "sad" in text_lower or "bad" in text_lower:
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sentiment = "negative"
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else:
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sentiment = "neutral"
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results["sentiment"] = sentiment
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# Convert the results dictionary to a formatted string
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formatted_results = "\n".join([f"{key}: {value}" for key, value in results.items()])
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# Return a Message object
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return Message(text=formatted_results)
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# Define how to use the inputs and outputs
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component = TextAnalyzerComponent()

In this custom component:

  • The input_text input is a required multi-line text field that accepts a Message object or a string. It's used to provide the text for analysis.

  • The include_word_count input is an optional boolean field. When set to True, it adds a word count to the analysis results.

  • The perform_sentiment_analysis input is an optional boolean field. When set to True, it triggers a basic sentiment analysis of the input text.

The component performs basic text analysis, including character count and sentence count (based on punctuation marks). If word count is enabled, it splits the text and counts the words. If sentiment analysis is enabled, it performs a simple keyword-based sentiment classification (positive, negative, or neutral).

Since the component inputs and outputs a Message, you can wire the component into a chat and see how the basic custom component logic interacts with your input.

Create a Custom Component with Multiple Outputs​

In Langflow, custom components can have multiple outputs. Each output can be associated with a specific method in the component, allowing you to define distinct behaviors for each output path. This feature is particularly useful when you want to route data based on certain conditions or process it in multiple ways.

  1. Definition of Outputs: Each output is defined in the outputs list of the component. Each output is associated with a display name, an internal name, and a method that gets called to generate the output.
  2. Output Methods: The methods associated with outputs are responsible for generating the data for that particular output. These methods are called when the component is executed, and each method can independently produce its result.

This example component has two outputs:

  • process_data: Processes the input text (e.g., converts it to uppercase) and returns it.
  • get_processing_function: Returns the process_data method itself to be reused in composition.

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from typing import Callable
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from langflow.custom import Component
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from langflow.inputs import StrInput
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from langflow.template import Output
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from langflow.field_typing import Text
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class DualOutputComponent(Component):
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display_name = "Dual Output"
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description = "Processes input text and returns both the result and the processing function."
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icon = "double-arrow"
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inputs = [
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StrInput(
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name="input_text",
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display_name="Input Text",
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info="The text input to be processed.",
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),
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]
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outputs = [
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Output(display_name="Processed Data", name="processed_data", method="process_data"),
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Output(display_name="Processing Function", name="processing_function", method="get_processing_function"),
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]
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def process_data(self) -> Text:
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# Process the input text (e.g., convert to uppercase)
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processed = self.input_text.upper()
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self.status = processed
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return processed
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def get_processing_function(self) -> Callable[[], Text]:
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# Return the processing function itself
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return self.process_data

This example shows how to define multiple outputs in a custom component. The first output returns the processed data, while the second output returns the processing function itself.

The processing_function output can be used in scenarios where the function itself is needed for further processing or dynamic flow control. Notice how both outputs are properly annotated with their respective types, ensuring clarity and type safety.

Special Operations​

Advanced methods and attributes offer additional control and functionality. Understanding how to leverage these can enhance your custom components' capabilities.

  • self.inputs: Access all defined inputs. Useful when an output method needs to interact with multiple inputs.
  • self.outputs: Access all defined outputs. This is particularly useful if an output function needs to trigger another output function.
  • self.status: Use this to update the component's status or intermediate results. It helps track the component's internal state or store temporary data.
  • self.graph.flow_id: Retrieve the flow ID, useful for maintaining context or debugging.
  • self.stop("output_name"): Use this method within an output function to prevent data from being sent through other components. This method stops next component execution and is particularly useful for specific operations where a component should stop from running based on specific conditions.

Contribute Custom Components to Langflow​

See How to Contribute to contribute your custom component to Langflow.

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