> For the complete documentation index, see [llms.txt](https://lamatic.ai/llms.txt).

# Memory Add Node

<NodeOverview slug="memory-add-node" type="data" />

## 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.

![memory-add.png](../img/mem-add/mem-add.webp)

<NodeTypeInfo 
  batchTrigger={false}
  eventTrigger={false}
  action={true}
  description="This node is an **Action** node that stores data in memory for persistent use across workflow executions."
/>

## Features

<details>
  <summary>**Key Functionalities**</summary>

1. **Persistent Storage**: Store and manage contextual information that persists across workflow executions.

2. **User & Session Management**: Support for both user-level and session-specific memory storage with unique identifiers.

3. **Metadata Support**: Attach additional metadata to stored memories.

</details>

<details>
  <summary>**Benefits**</summary>
  1. **Persistence:** Maintain contextual information across multiple workflow executions.

2. **Scalability:** Efficiently store and retrieve large amounts of contextual data.

3. **Flexibility:** Support for both user-level and session-level memory storage.

</details>

## What Can I Build?

1. Chatbots that remember conversation history and user preferences.

2. Personalized recommendation systems based on user interactions.

3. Knowledge management systems with contextual information retrieval.

## Setup

### Configure the Memory Node

1. Select the Memory Add Node in your workflow.
2. Configure the unique user identifier.
3. Choose or create a memory collection.
4. Set up the embedding and generative models.
5. Define optional session ID and metadata.
6. Add the Memory Value
7. 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

```json
- 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

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

## Common Issues and Debugging

1. **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

1. **Check Node Configuration**

   - Verify all required fields are filled

2. **Monitor Workflow Logs**

   - Look for error messages in the execution logs
   - Check for timeout or rate limit errors

3. **Test Memory Operations**

   - Use the workflow testing feature to verify storage
   - Confirm immediate retrieval after storage
   - Test with simple data before complex implementations