Description

@gladiator1st/n8n-nodes-mem0

!n8n Community Node
!License: MIT

An all-in-one n8n Community Node for Mem0 — the intelligent memory layer for personalized AI Agents, persistent user profiles, and continuous session context.

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⚡ Superpowers Included

                      ┌─────────────────────────────────────────────────────┐
                      │            @gladiator1st/n8n-nodes-mem0             │
                      └──────────────────────────┬──────────────────────────┘
                                                 │
                  ┌──────────────────────────────┴──────────────────────────────┐
                  ▼                                                             ▼
        🧠 Memory Operations                                           👤 User Operations
        • Add (Auto Fact & Preference Extraction)                       • Get User Profile & Metadata
        • Search (Semantic Natural Language Queries)                   • List Many Users
        • Get / Get Many (Batch Retrieval)                              • Delete User & Wipe Memory
        • Delete / Delete All (GDPR Compliance)                         • Multi-Tenant User Isolation

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📦 Key Capabilities

• 🧠 Auto Fact & Preference Extraction (Add): Feed conversational text or message transcripts, and Mem0’s AI engine automatically extracts, deduplicates, and stores atomic user facts and preferences.
• 🔍 Semantic Memory Search (Search): Query memories using natural language (e.g. “What are the user’s dietary preferences?”) to retrieve the most relevant context before generating LLM responses.
• 👤 Multi-User & Multi-Agent Isolation: Partition memories effortlessly by userId, agentId, or session runs to support multi-tenant chatbots and autonomous AI teams.
• 🤖 Autonomous AI Agent Tool (usableAsTool: true): Connect directly into LangChain AI Agents as an autonomous memory retrieval and storage tool.

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🚀 Installation

In n8n UI (Community Nodes)

1. In your n8n instance, go to Settings ➔ Community Nodes.
2. Click Install a community node.
3. Enter:

   @gladiator1st/n8n-nodes-mem0
   

4. Confirm the installation terms and click Install.

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🔑 Credentials Setup

1. Sign up or log in at app.mem0.ai.
2. Navigate to API Keys in your Mem0 dashboard and copy your API key (starts with m0-...).
3. In n8n, create a new credential for Mem0 API and paste your API key.

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📖 Step-by-Step Usage Examples

Example 1: Add a Memory (Automatic Fact & Preference Extraction)

This operation extracts user preferences and key facts automatically from free-form text or user messages.

┌──────────────────────────────┐
│ When Chat Message Received   │ ➔ User says: "I work as a software engineer in Seattle
└──────────────┬───────────────┘               and prefer oat milk in my latte."
               │
               ▼
┌──────────────────────────────┐
│ Mem0 (Resource: Memory)      │ ➔ Resource: Memory | Operation: Add
└──────────────┬───────────────┘   User ID: user_123
               │
               ▼
┌──────────────────────────────┐
│ Output: Extracted Fact IDs   │ ➔ Mem0 auto-extracts 2 distinct facts into vector memory!
└──────────────────────────────┘

#### Node Parameters:

• Resource: Memory
• Operation: Add
• User ID: user_123
• Memory Content / Text:

  I am a senior AI software engineer living in Seattle. I prefer dark mode on all apps and only drink oat milk lattes.
  

#### Sample Output Returned by the Node:

{
  "results": [
    {
      "id": "mem_89f1a23c-4b5d-4e6f-a1b2-c3d4e5f6a7b8",
      "memory": "Works as a senior AI software engineer in Seattle",
      "event": "ADD",
      "userid": "user123"
    },
    {
      "id": "mem_45e6b78a-9c0d-1e2f-3a4b-5c6d7e8f9a0b",
      "memory": "Prefers dark mode on all applications",
      "event": "ADD",
      "userid": "user123"
    },
    {
      "id": "mem_12a3b4c5-6d7e-8f9a-0b1c-2d3e4f5a6b7c",
      "memory": "Drinks only oat milk lattes",
      "event": "ADD",
      "userid": "user123"
    }
  ]
}

—

Example 2: Semantic Memory Search (Context Retrieval for AI Chatbot)

Search stored user memories using natural language before invoking an LLM, giving the AI instant recall of user preferences.

┌──────────────────────────────┐
│ When Chat Message Received   │ ➔ User asks: "Can you recommend a breakfast place?"
└──────────────┬───────────────┘
               │
               ▼
┌──────────────────────────────┐
│ Mem0 (Resource: Memory)      │ ➔ Search Query: "What food and beverage does the user like?"
└──────────────┬───────────────┘   User ID: user_123 | Limit: 5
               │
               ▼
┌──────────────────────────────┐
│ AI Agent / OpenAI Model      │ ➔ System Prompt injected with retrieved memory:
└──────────────────────────────┘   "The user only drinks oat milk lattes..."

#### Node Parameters:

• Resource: Memory
• Operation: Search
• User ID: user_123
• Search Query: What are the user's drink and workspace preferences?
• Limit: 5

#### Sample Output Returned by the Node:

[
  {
    "id": "mem_12a3b4c5-6d7e-8f9a-0b1c-2d3e4f5a6b7c",
    "memory": "Drinks only oat milk lattes",
    "score": 0.892,
    "userid": "user123",
    "created_at": "2026-09-01T10:15:30Z",
    "updated_at": "2026-09-01T10:15:30Z"
  },
  {
    "id": "mem_45e6b78a-9c0d-1e2f-3a4b-5c6d7e8f9a0b",
    "memory": "Prefers dark mode on all applications",
    "score": 0.741,
    "userid": "user123",
    "created_at": "2026-09-01T10:15:30Z",
    "updated_at": "2026-09-01T10:15:30Z"
  }
]

—

Example 3: Personalized AI Chatbot with Continuous Memory Loop

Connect Mem0 to an AI Agent to build a continuous memory loop where the assistant recalls past context and writes new memories after every conversation.

                  ┌─────────────────────────────────────────────────┐
                  │           Chat Trigger (Webhook / Slack)        │
                  └────────────────────────┬────────────────────────┘
                                           │
                                           ▼
                  ┌─────────────────────────────────────────────────┐
                  │      Mem0 Node: Search Memories                 │
                  │      (Query: {{ $json.message }}, User ID: 123) │
                  └────────────────────────┬────────────────────────┘
                                           │
                                           ▼
                  ┌─────────────────────────────────────────────────┐
                  │      AI Agent / OpenAI Node                     │
                  │      Prompt: Answer user taking into account:   │
                  │      Context: {{ $json.memory }}                │
                  └────────────────────────┬────────────────────────┘
                                           │
                                           ▼
                  ┌─────────────────────────────────────────────────┐
                  │      Mem0 Node: Add Memory                      │
                  │      (Content: {{ $json.message }}, UID: 123)   │
                  └─────────────────────────────────────────────────┘

#### Step-by-Step Flow:
1. Search Context: When a message arrives from user_123, the first Mem0 node searches relevant past memories with Operation: Search.
2. Personalized Response: The AI Agent receives the retrieved memories in its system prompt and answers with full personal context.
3. Save New Learnings: A downstream Mem0 node executes Operation: Add on the new user message so future conversations remember newly stated facts.

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Example 4: User Profile & Memory Lifecycle Management (GDPR Compliance)

Easily fetch all memories for a user or wipe a user’s entire memory store upon request.

• Retrieve All Memories for User: Set Resource: Memory, Operation: Get Many, User ID: user_123, Return All: true.
• Delete Single Memory: Set Resource: Memory, Operation: Delete, Memory ID: mem_12a3b4c5....
• Wipe All User Data (GDPR Delete): Set Resource: Memory, Operation: Delete All, User ID: user_123.

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🛠️ Operations & Parameters Reference

| Resource | Operation | Description | Required Parameters |
| :— | :— | :— | :— |
| Memory | Add | Automatically extract and store facts from text | Memory Content / Text |
| Memory | Search | Semantic vector search for relevant memories | Search Query |
| Memory | Get | Retrieve a specific memory item by ID | Memory ID |
| Memory | Get Many | List all memories for a user or agent | — (Optional User ID, Agent ID, Limit) |
| Memory | Delete | Delete a specific memory item by ID | Memory ID |
| Memory | Delete All | Delete all memories for a user or agent | — (Optional User ID, Agent ID) |
| User | Get Many | List all tracked users in Mem0 | — |
| User | Get | Retrieve user profile metadata | User ID |
| User | Delete | Delete a user and wipe all associated memories | User ID |

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👨‍💻 Author

Muhammad Qasim

• GitHub: @Gladiator1st
• Email: qasimasif958@gmail.com

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📄 License

MIT © Muhammad Qasim