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Node SDK Quickstart

Welcome Mem0 Platform Open Source Cookbooks Integrations API Reference Release Notes

Documentation

Getting Started

Self-Hosting Features

Configuration

Configure the OSS Stack
LLMs
Vector Databases
Embedding Models
Rerankers

Community & Support

On this page

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

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Install the SDK

npm install mem0ai

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Initialize the client

import { Memory } from "mem0ai/oss";

const memory = new Memory();

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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" } });

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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

Get one memory

Search memories

Update a memory

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

Disable history

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."
};