Helpers

This Langflow feature is currently in public preview. Development is ongoing, and the features and functionality are subject to change. Langflow, and the use of such, is subject to the DataStax Preview Terms.

Helper components provide utility functions to help manage data, tasks, and other components in your flow.

Use a helper component in a flow

Chat memory in Langflow is stored either in local Langflow tables with LCBufferMemory, or connected to an external database.

The Store Message helper component stores chat memories as Data objects, and the Message History helper component retrieves chat messages as data objects or strings.

This example flow stores and retrieves chat history from an Astra DB Chat Memory component with Store Message and Chat Memory components.

Create list

This component is in Legacy, which means it is no longer in active development. Use the Structured output component instead.

This component takes a list of text inputs and converts each text into a data object. These data objects are then collected into a list, which is returned as the output.

Parameters

Inputs
Name	Display Name	Info
texts	Texts	Enter one or more texts. This input accepts multiple text entries.

Outputs
Display Name	Name	Info
Data List	list	A list of data objects created from the input texts.

Component code

create_list.py

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Current date

The Current date component returns the current date and time in a selected timezone. This component provides a flexible way to obtain timezone-specific date and time information within a Langflow pipeline.

Parameters

Inputs
Name	Display Name	Info
timezone	Timezone	Select the timezone for the current date and time.

Outputs
Name	Display Name	Info
current_date	Current Date	The resulting current date and time in the selected timezone.

Component code

current_date.py

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Custom component

This component is available by clicking New Custom Component in the Components menu.

Use this component as a template to create your custom component.

Component code

custom_component.py

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Hierarchical Task

This component has moved to the Bundles section of the components menu.

This component creates and manages hierarchical tasks for CrewAI agents in a Playground environment.

For more information, see the CrewAI documentation.

Parameters

Inputs
Name	Display Name	Info
task_description	Description	Descriptive text detailing task’s purpose and execution.
expected_output	Expected Output	Clear definition of expected task outcome.
tools	Tools	List of tools/resources limited for task execution. Uses the Agent tools by default.

Outputs
Name	Display Name	Info
task_output	Task	The built hierarchical task.

Component code

hierarchical_task.py

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ID generator

This component generates a unique ID.

Parameters

Outputs
Name	Display Name	Info
value	Value	Unique ID generated.

Component code

id_generator.py

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Message history

This component was named Chat Memory prior to Langflow version 1.1.0.

This component retrieves and manages chat messages from Langflow tables or an external memory.

Parameters

Inputs
Name	Display Name	Info
memory	External Memory	Retrieve messages from an external memory. If empty, it uses the Langflow tables.
sender	Sender Type	Filter by sender type.
sender_name	Sender Name	Filter by sender name.
n_messages	Number of Messages	Number of messages to retrieve.
session_id	Session ID	The session ID of the chat. If empty, the current session ID parameter is used.
order	Order	Order of the messages.
template	Template	The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.

Outputs
Name	Display Name	Info
messages	Messages (data object)	Retrieved messages as data objects.
messages_text	Messages (text)	Retrieved messages formatted as text.
lc_memory	Memory	The created LangChain-compatible memory object.

Component code

memory.py

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Output Parser

This component is in Legacy, which means it is no longer in active development as of Langflow version 1.3. Use the Structured output component instead.

This component transforms the output of a language model into a specified format. It supports CSV format parsing, which converts LLM responses into comma-separated lists using Langchain’s CommaSeparatedListOutputParser.

This component only provides formatting instructions and parsing functionality. It does not include a prompt. You’ll need to connect it to a separate Prompt component to create the actual prompt template for the LLM to use.

Both the Output Parser and Structured Output components format LLM responses, but they have different use cases. The Output Parser is simpler and focused on converting responses into comma-separated lists. Use this when you just need a list of items, for example ["item1", "item2", "item3"]. The Structured Output is more complex and flexible, and allows you to define custom schemas with multiple fields of different types. Use this when you need to extract structured data with specific fields and types.

To use this component:

Create a Prompt component and connect the Output Parser’s format_instructions output to it. This ensures the LLM knows how to format its response.
Write your actual prompt text in the Prompt component, including the {format_instructions} variable. For example, in your Prompt component, the template might look like:
```
{format_instructions}
Please list three fruits.
```
Connect the output_parser output to your LLM model.
The output parser converts this into a Python list: ["apple", "banana", "orange"].

Parameters

Inputs
Name	Display Name	Info
parser_type	Parser	Select the parser type. Currently supports "CSV".

Outputs
Name	Display Name	Info
format_instructions	Format Instructions	Pass to a prompt template to include formatting instructions for LLM responses.
output_parser	Output Parser	The constructed output parser that can be used to parse LLM responses.

Sequential task

This component has moved to the Bundles section of the components menu.

This component creates and manage sequential tasks for CrewAI agents. It builds a SequentialTask object with the provided description, expected output, and agent, allowing for the specification of tools and asynchronous execution.

For more information, see the CrewAI documentation.

Parameters

Inputs
Name	Display Name	Info
task_description	Description	Descriptive text detailing task’s purpose and execution.
expected_output	Expected Output	Clear definition of expected task outcome.
tools	Tools	List of tools/resources limited for task execution. Uses the Agent tools by default.
agent	Agent	CrewAI Agent that will perform the task.
task	Task	CrewAI Task that will perform the task.
async_execution	Async Execution	Boolean flag indicating asynchronous task execution.

Outputs
Name	Display Name	Info
task_output	Task	The built sequential task or list of tasks.

Component code

sequential_task.py

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Message store

This component stores chat messages or text into Langflow tables or an external memory.

It provides flexibility in managing message storage and retrieval within a chat system.

Parameters

Inputs
Name	Display Name	Info
message	Message	The chat message to be stored. (Required)
memory	External Memory	The external memory to store the message. If empty, it will use the Langflow tables.
sender	Sender	The sender of the message. Can be Machine or User. If empty, the current sender parameter will be used.
sender_name	Sender Name	The name of the sender. Can be AI or User. If empty, the current sender parameter will be used.
session_id	Session ID	The session ID of the chat. If empty, the current session ID parameter will be used.

Outputs
Name	Display Name	Info
stored_messages	Stored Messages	The list of stored messages after the current message has been added.

Component code

store_message.py

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Structured output

This component transforms LLM responses into structured data formats.

Use the structured output component in a flow

In this example from the Financial Support Parser template, the Structured Output component transforms unstructured financial reports into structured data.

Structured output component parsing output

The connected LLM model is prompted by the Structured Output component’s system_prompt parameter to extract structured output from the unstructured text.

In the Structured Output component, click the Open table button to view the output_schema table. The output_schema parameter defines the structure and data types for the model’s output using a table with the following fields:

Name: The name of the output field.
Description: The purpose of the output field.
Type: The data type of the output field. The available types are str, int, float, bool, list, or dict. Default: text.
Multiple: Set to True if you expect multiple values for a single field. For example, a list of features is set to true to contain multiple values, such as ["waterproof", "durable", "lightweight"]. Default: True.

The Parse DataFrame component parses the structured output into a template for orderly presentation in chat output. The template receives the values from the output_schema table with curly braces.

For example, the template EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT} presents the extracted values in the Playground as EBITDA: 900 million , Net Income: 500 million , GROSS_PROFIT: 1.2 billion.

Parameters

Inputs
Name	Display Name	Info
llm	Language Model	The language model to use to generate the structured output.
input_value	Input Message	The input message to the language model.
system_prompt	Format Instructions	Instructions to the language model for formatting the output.
schema_name	Schema Name	The name for the output data schema.
output_schema	Output Schema	The structure and data types for the model’s output.
multiple	Generate Multiple	[Deprecated] Always set to True.

Outputs
Name	Display Name	Info
structured_output	Structured Output	The structured output based on the defined schema.
structured_output_dataframe	DataFrame	The structured output converted to a DataFrame format.

Component code

structured_output.py

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Helpers

Use a helper component in a flow

Create list

Parameters

Component code

Current date

Parameters

Component code

Custom component

Component code

Hierarchical Task

Parameters

Component code

ID generator

Parameters

Component code

Message history

Parameters

Component code

Output Parser

Parameters

Sequential task

Parameters

Component code

Message store

Parameters

Component code

Structured output

Use the structured output component in a flow

Parameters

Component code

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