mem0ai--mem0/docs/open-source/node-quickstart.mdx

---
title: Node SDK Quickstart
description: "Store and search Mem0 memories from a TypeScript or JavaScript app in minutes."
icon: "js"
---

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

<Steps>
<Step title="Install the SDK">
```bash
npm install mem0ai
```
</Step>

<Step title="Initialize the client">
```ts
import { Memory } from "mem0ai/oss";

const memory = new Memory();
```
</Step>

<Step title="Add a memory">
```ts
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" } });
```
</Step>

<Step title="Search memories">
```ts
const results = await memory.search("What do you know about me?", { filters: { userId: "alice" } });
console.log(results);
```

**Output**
```json
{
  "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"
    }
  ]
}
```
</Step>
</Steps>

<Snippet file="star-on-github.mdx" />

<Note>
By default the Node SDK uses local-friendly settings (OpenAI `gpt-5-mini`, `text-embedding-3-small`, in-memory vector store, and SQLite history). Pass a config to swap any of them.
</Note>

## Configure providers

Pass a config object to `new Memory()` to use your own LLM, embedder, and vector store:

```ts
import { Memory } from "mem0ai/oss";

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

For the full provider catalog, history stores, and every config option, see [Configuration](/open-source/configuration).

## What's next?

<CardGroup cols={3}>
  <Card title="Memory operations" icon="database" href="/core-concepts/memory-operations/add">
    Search, update, and manage memories with the full CRUD API.
  </Card>
  <Card title="Configure for production" icon="sliders" href="/open-source/configuration">
    Swap in your own LLM, embedder, and vector store.
  </Card>
  <Card title="Add to your framework" icon="plug" href="/integrations">
    Wire Mem0 into LangChain, CrewAI, LangGraph, and 20+ more.
  </Card>
</CardGroup>

If you have any questions, please feel free to reach out:

<Snippet file="get-help.mdx" />