Memory Add Node

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Overview

The Memory Add Node is a data storage component that stores and manages information in memory for use across workflow executions. This node enables persistent data storage and retrieval within workflows.

Node Type Information

Type	Description	Status
Batch Trigger	Starts the flow on a schedule or batch event. Ideal for periodic data processing.	❌ False
Event Trigger	Starts the flow based on external events (e.g., webhook, user interaction).	❌ False
Action	Executes a task or logic as part of the flow (e.g., API call, transformation).	✅ True

This node is an **Action** node that stores data in memory for persistent use across workflow executions.

Features

Key Functionalities

Persistent Storage: Store and manage contextual information that persists across workflow executions.
User & Session Management: Support for both user-level and session-specific memory storage with unique identifiers.
Metadata Support: Attach additional metadata to stored memories.

Benefits

Persistence: Maintain contextual information across multiple workflow executions.
Scalability: Efficiently store and retrieve large amounts of contextual data.
Flexibility: Support for both user-level and session-level memory storage.

What can I build?

Chatbots that remember conversation history and user preferences.
Personalized recommendation systems based on user interactions.
Knowledge management systems with contextual information retrieval.

Setup

Configure the Memory Node

Select the Memory Add Node in your workflow.
Configure the unique user identifier.
Choose or create a memory collection.
Set up the embedding and generative models.
Define optional session ID and metadata.
Add the Memory Value
Deploy the project.

Configuration Reference

Parameter	Description	Example Value
Unique Id	Identifier for the user whose memory is being stored	`29fcd52e-0813-4fa2-821c-1428a05636ba`
Session Id	Optional identifier for specific user sessions	`2 (Defaults to Global)`
Memory Store	The collection where memories will be stored	`MemoryStore`
Memory Value	The actual data from which facts to be retrieved and updated in memory	`I like cats`
Embedding Model Name	Model used for converting text to vectors	`text-embedding-ada-002`
Generative Model Name	Model used for processing stored information	`gpt-3.5-turbo`
Metadata	Additional JSON data to store with the memory	`{"source": "chat"}`

Low-Code Example

- nodeId: memoryNode_858
    nodeType: memoryNode
    nodeName: Memory Add
    values:
      uniqueId: '{{triggerNode_1.output.userId}}'
      sessionId: ''
      memoryValue:
        - role: user
          content: User likes to watch Pokemon
      memoryCollection: finalTest
      embeddingModelName:
        type: embedder/text
        model_name: default
        credentialId: some-id
        provider_name: openai
        credential_name: NEW PROVIDER
      generativeModelName:
        type: generator/text
        model_name: default
        credentialId: Some Credential Id
        provider_name: openai
        credential_name: Testing OpenAI

Output

-memoryActions: An array of objects representing actions or operations performed on the memory, which may be empty if no actions are recorded. -extractedFacts: An array of strings containing facts or insights derived from the input data and added to the memory.

Example Output

{
    "memoryActions": [],
    "extractedFacts": [
      "User expressed a positive opinion about lamatic."
    ]
  }

Common Issues and Debugging

Memory Not Being Stored
- Verify the Unique Id is properly configured and not empty
- Check that the Memory Collection name matches exactly across nodes
- Ensure the Memory Value contains valid content.
- Confirm the embedding model is accessible and running

Troubleshooting Steps

Check Node Configuration
- Verify all required fields are filled
Monitor Workflow Logs
- Look for error messages in the execution logs
- Check for timeout or rate limit errors
Test Memory Operations
- Use the workflow testing feature to verify storage
- Confirm immediate retrieval after storage
- Test with simple data before complex implementations