Node SDK Quickstart

Store and search Mem0 memories from a TypeScript or JavaScript app in minutes.

Spin up Mem0 with the Node SDK in just a few steps. You’ll install the package, initialize the client, add a memory, and confirm retrieval with a single search.

Prerequisites

• Node.js 18 or higher
• (Optional) OpenAI API key stored in your environment when you want to customize providers

Install and run your first memory

1. Install the SDK

   npm install mem0ai
   

2. Initialize the client

   import { Memory } from "mem0ai/oss";
   
   const memory = new Memory();
   

3. Add a memory

   const messages = [
     { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
     { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
     { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
     { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." }
   ];
   
   await memory.add(messages, { userId: "alice", metadata: { category: "movie_recommendations" } });
   

4. Search memories

   const results = await memory.search("What do you know about me?", { userId: "alice" });
   console.log(results);
   

   Output

   {
     "results": [
       {
         "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
         "memory": "User is planning to watch a movie tonight.",
         "score": 0.38920719231944799,
         "metadata": {
           "category": "movie_recommendations"
         },
         "userId": "alice"
       }
     ]
   }
   

By default the Node SDK uses local-friendly settings (OpenAI gpt-4.1-nano-2025-04-14, text-embedding-3-small, in-memory vector store, and SQLite history). Swap components by passing a config as shown below.

Configure for production

import { Memory } from "mem0ai/oss";

const memory = new Memory({
  version: "v1.1",
  embedder: {
    provider: "openai",
    config: {
      apiKey: process.env.OPENAI_API_KEY || "",
      model: "text-embedding-3-small"
    }
  },
  vectorStore: {
    provider: "memory",
    config: {
      collectionName: "memories",
      dimension: 1536
    }
  },
  llm: {
    provider: "openai",
    config: {
      apiKey: process.env.OPENAI_API_KEY || "",
      model: "gpt-4-turbo-preview"
    }
  },
  historyDbPath: "memory.db"
});

Manage memories (optional)

Get all memories

const allMemories = await memory.getAll({ userId: "alice" });
console.log(allMemories);

// Audit history
const history = await memory.history("892db2ae-06d9-49e5-8b3e-585ef9b85b8e");
console.log(history);

// Delete specific or scoped memories
await memory.delete("892db2ae-06d9-49e5-8b3e-585ef9b85b8e");
await memory.deleteAll({ userId: "alice" });

// Reset everything
await memory.reset();

Use a custom history store

The Node SDK supports Supabase (or other providers) when you need serverless-friendly history storage.

Supabase provider

import { Memory } from "mem0ai/oss";

const memory = new Memory({
  historyStore: {
    provider: "supabase",
    config: {
      supabaseUrl: process.env.SUPABASE_URL || "",
      supabaseKey: process.env.SUPABASE_KEY || "",
      tableName: "memory_history"
    }
  }
});

Create the Supabase table with:

create table memory_history (
  id text primary key,
  memory_id text not null,
  previous_value text,
  new_value text,
  action text not null,
  created_at timestamp with time zone default timezone('utc', now()),
  updated_at timestamp with time zone,
  is_deleted integer default 0
);

Configuration parameters

Mem0 offers granular configuration across vector stores, LLMs, embedders, and history stores.

Vector store

Parameter	Description	Default
provider	Vector store provider (e.g., "memory")	"memory"
host	Host address	"localhost"
port	Port number	undefined

LLM

Parameter	Description	Provider
provider	LLM provider (e.g., "openai", "anthropic")	All
model	Model to use	All
temperature	Temperature value	All
apiKey	API key	All
maxTokens	Max tokens to generate	All
topP	Probability threshold	All
topK	Token count to keep	All
openaiBaseUrl	Base URL override	OpenAI

Graph store

Parameter	Description	Default
provider	Graph store provider (e.g., "neo4j")	"neo4j"
url	Connection URL	process.env.NEO4J_URL
username	Username	process.env.NEO4J_USERNAME
password	Password	process.env.NEO4J_PASSWORD

Embedder

Parameter	Description	Default
provider	Embedding provider	"openai"
model	Embedding model	"text-embedding-3-small"
apiKey	API key	undefined

General

Parameter	Description	Default
historyDbPath	Path to history database	"{mem0_dir}/history.db"
version	API version	"v1.0"
customPrompt	Custom processing prompt	undefined

History store

Parameter	Description	Default
provider	History provider	"sqlite"
config	Provider configuration	undefined
disableHistory	Disable history store	false

Complete config example

const config = {
  version: "v1.1",
  embedder: {
    provider: "openai",
    config: {
      apiKey: process.env.OPENAI_API_KEY || "",
      model: "text-embedding-3-small"
    }
  },
  vectorStore: {
    provider: "memory",
    config: {
      collectionName: "memories",
      dimension: 1536
    }
  },
  llm: {
    provider: "openai",
    config: {
      apiKey: process.env.OPENAI_API_KEY || "",
      model: "gpt-4-turbo-preview"
    }
  },
  historyStore: {
    provider: "supabase",
    config: {
      supabaseUrl: process.env.SUPABASE_URL || "",
      supabaseKey: process.env.SUPABASE_KEY || "",
      tableName: "memories"
    }
  },
  disableHistory: false,
  customPrompt: "I'm a virtual assistant. I'm here to help you with your queries."
};

What’s next?

Explore Memory Operations

Review CRUD patterns, filters, and advanced retrieval across the OSS stack.

Customize Configuration

Swap in your preferred LLM, vector store, and history provider for production use.

Automate Node Workflows

See a full Node-based workflow that layers Mem0 memories onto tool-calling agents.

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