Helpers

Helper components provide utility functions to help manage data and perform simple tasks in your flow.

Calculator

The Calculator component performs basic arithmetic operations on mathematical expressions. It supports addition, subtraction, multiplication, division, and exponentiation operations.

For an example of using this component in a flow, see the Python Interpreter component.

Calculator parameters

Name	Type	Description
expression	String	Input parameter. The arithmetic expression to evaluate, such as 4*4*(33/22)+12-20.
result	Data	Output parameter. The calculation result as a Data object containing the evaluated expression.

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.

Current Date parameters

Name	Type	Description
timezone	String	Input parameter. The timezone for the current date and time.
current_date	String	Output parameter. The resulting current date and time in the selected timezone.

Message History

The Message History component provides combined chat history and message storage functionality. It can store and retrieve chat messages from either Langflow storage or a dedicated chat memory database like Mem0 or Redis.

It replaces the legacy Chat History and Message Store components.

important

The Language Model and Agent components have built-in chat memory that is enabled by default and uses Langflow storage.

This built-in chat memory functionality is sufficient for most use cases.

Use the Message History component only if you need to access chat memories outside the chat context, such as sentiment analysis flow that retrieves and analyzes recently stored memories, or you want to store memories in a specific database, separate from Langflow storage.

For more information, see Store chat memory.

Use the Message History component in a flow

The Message History component has two modes, depending on where you want to use it in your flow:

• Retrieve mode: The component retrieves chat messages from your Langflow database or external memory.
• Store mode: The component stores chat messages in your Langflow database or external memory.

This means that you need multiple Message History components in your flow if you want to both store and retrieve chat messages.

Use Langflow storage

The following steps explain how to create a chat-based flow that uses Message History components to store and retrieve chat memory from your Langflow installation's database:

1. Create or edit a flow where you want to use chat memory.

2. At the beginning of the flow, add a Message History component, and then set it to Retrieve mode.

3. Optional: In the Message History component's header menu, click Controls to enable parameters for memory sorting, filtering, and limits.

4. Add a Prompt Template component, add a {memory} variable to the Template field, and then connect the Message History output to the memory input.

   The Prompt Template component supplies instructions and context to LLMs, separate from chat messages passed through a Chat Input component. The template can include any text and variables that you want to supply to the LLM, for example:

   
   

   _10

   You are a helpful assistant that answers questions.

   _10

   _10

   Use markdown to format your answer, properly embedding images and urls.

   _10

   _10

   History:

   _10

   _10

   {memory}

   
   

   Variables ({variable}) in the template dynamically add fields to the Prompt Template component so that your flow can receive definitions for those values from elsewhere, such as other components, Langflow global variables, or runtime input.

   In this example, the {memory} variable is populated by the retrieved chat memories, which are then passed to a Language Model or Agent component to provide additional context to the LLM.

5. Connect the Prompt Template component's output to a Language Model component's System Message input.

   This example uses a Language Model component as the central chat driver, but you can also use an Agent component.

6. Add a Chat Input component, and then connect it to the Language Model component's Input input.

7. Connect the Language Model component's output to a Chat Output component.

8. At the end of the flow, add another Message History component, and then set it to Store mode.

   Configure any additional parameters in the second Message History component as needed, taking into consideration that this particular component will store chat messages rather than retrieve them.

9. Connect the Chat Output component's output to the Message History component's Message input.

   Each response from the LLM is output from the Language Model component to the Chat Output component, and then stored in chat memory by the final Message History component.

Use external chat memory

To store and retrieve chat memory from a dedicated, external chat memory database, use the Message History component and a provider-specific chat memory component.

Available provider-specific chat memory components include Cassandra Chat Memory component, Mem0 Chat Memory component, and Redis Chat Memory component. For all provider-specific chat memory components, see Bundles.

The following steps explain how to create a flow that stores and retrieves chat memory from Redis chat memory:

1. Create or edit a flow where you want to use chat memory.

2. At the beginning of the flow, add Message History and Redis Chat Memory components:

   1. Configure the Redis Chat Memory component to connect to your Redis database. For more information, see the Redis documentation.

   2. Set the Message History component to Retrieve mode.

   3. In the Message History component's header menu, click Controls, enable External Memory, and then click Close.

      In Controls, you can also enable parameters for memory sorting, filtering, and limits.

   4. Connect the Redis Chat Memory component's output to the Message History component's External Memory input.

3. Add a Prompt Template component, add a {memory} variable to the Template field, and then connect the Message History output to the memory input.

   The Prompt Template component supplies instructions and context to LLMs, separate from chat messages passed through a Chat Input component. The template can include any text and variables that you want to supply to the LLM, for example:

   
   

   _10

   You are a helpful assistant that answers questions.

   _10

   _10

   Use markdown to format your answer, properly embedding images and urls.

   _10

   _10

   History:

   _10

   _10

   {memory}

   
   

   Variables ({variable}) in the template dynamically add fields to the Prompt Template component so that your flow can receive definitions for those values from elsewhere, such as other components, Langflow global variables, or runtime input.

   In this example, the {memory} variable is populated by the retrieved chat memories, which are then passed to a Language Model or Agent component to provide additional context to the LLM.

4. Connect the Prompt Template component's output to a Language Model component's System Message input.

   This example uses a Language Model component as the central chat driver, but you can also use an Agent component.

5. Add a Chat Input component, and then connect it to the Language Model component's Input input.

6. Connect the Language Model component's output to a Chat Output component.

7. At the end of the flow, add another pair of Message History and Redis Chat Memory components:

   1. Configure the Redis Chat Memory component to connect to your Redis database.

   2. Set the Message History component to Store mode.

   3. In the Message History component's header menu, click Controls, enable External Memory, and then click Close.

      Configure any additional parameters in this component as needed, taking into consideration that this particular component will store chat messages rather than retrieve them.

   4. Connect the Redis Chat Memory component to the Message History component's External Memory input.

8. Connect the Chat Output component's output to the Message History component's Message input.

   Each response from the LLM is output from the Language Model component to the Chat Output component, and then stored in chat memory by passing it to the final Message History and Redis Chat Memory components.

Message History parameters

Many Message History component input parameters are hidden by default in the visual editor. You can toggle parameters through the Controls in the component's header menu.

Name	Type	Description
memory	Memory	Input parameter. Retrieve messages from an external memory. If empty, the Langflow tables are used.
sender	String	Input parameter. Filter by sender type.
sender_name	String	Input parameter. Filter by sender name.
n_messages	Integer	Input parameter. The number of messages to retrieve.
session_id	String	Input parameter. The session ID of the chat memories to store or retrieve. If omitted or empty, the current session ID for the flow run is used. Use custom session IDs if you need to segregate chat memory for different users or applications that run the same flow.
order	String	Input parameter. The order of the messages.
template	String	Input parameter. The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.
messages	Message	Output parameter. The retrieved memories as Message objects, including messages_text containing retrieved chat message text. This is the typical output format used to pass memories as chat messages to another component.
dataframe	DataFrame	Output parameter. A DataFrame containing the message data. Useful for cases where you need to retrieve memories in a tabular format rather than as chat messages.

Legacy Helper components

The following components are legacy components. You can use these components in your flows, but they are no longer maintained and may be removed in a future release. It is recommended that you replace legacy components with the recommended alternatives as soon as possible.

• Chat History: Replaced by the Message History component
• Message Store: Replaced by the Message History component

Create List

This component dynamically creates a record with a specified number of fields.

It accepts the following parameters:

Name	Type	Description
n_fields	Integer	Input parameter. The number of fields to be added to the record.
text_key	String	Input parameter. The key used as text.
list	List	Output parameter. The dynamically created list with the specified number of fields.

ID Generator

This component generates a unique ID.

It accepts the following parameters:

Name	Type	Description
unique_id	String	Input parameter. The generated unique ID.
id	String	Output parameter. The generated unique ID.

Output Parser

Replace the legacy Output Parser component with the Structured Output component and Parser component. The components you need depend on the data types and complexity of the parsing task.

The Output Parser component transforms the output of a language model into comma-separated values (CSV) format, such as ["item1", "item2", "item3"], using LangChain's CommaSeparatedListOutputParser. The Structured Output component is a good alternative for this component because it also formats LLM responses with support for custom schemas and more complex parsing.

Parsing components only provide formatting instructions and parsing functionality. They don't include prompts. You must connect parsers to Prompt Template components to create prompts that LLMs can use.

1. Open a flow that has a Chat Input, Language Model, and Chat Output components.

2. Add Output Parser and Prompt Template components to your flow.

3. Define your LLM's prompt in the Prompt Template component's Template, including all instructions and pre-loaded context. Make sure to include a {format_instructions} variable where you will inject the formatting instructions from the Output Parser component. For example:

   
   

   _10

   You are a helpful assistant that provides lists of information.

   _10

   _10

   {format_instructions}

   
   

   Variables in the template dynamically add fields to the Prompt Template component so that your flow can receive definitions for those values from other components, Langflow global variables, or fixed input.

4. Connect the Output Parser component's output to the Prompt Template component's format instructions input.

The Output Parser component accepts the following parameters:

Name	Type	Description
parser_type	String	Input parameter. Sets the parser type as "CSV".
format_instructions	String	Output parameter. Pass to a prompt template to include formatting instructions for LLM responses.
output_parser	Parser	Output parameter. The constructed output parser that can be used to parse LLM responses.