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Use Emby’s OpenAI-compatible API in your Node.js application for AI-powered features.

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Prerequisites

  • Node.js 18+ installed
  • An Emby account with an API key

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Installation

npm install openai

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Quick Start

1

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Set Environment Variables

Create a .env file in your project root:
EMBY_API_KEY=your-api-key-here
EMBY_BASE_URL=https://dev.emby.ai/v1
Install dotenv to load environment variables:
npm install dotenv
2

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Create the API Client

// emby.js
require('dotenv').config();
const OpenAI = require('openai');

const emby = new OpenAI({
  apiKey: process.env.EMBY_API_KEY,
  baseURL: process.env.EMBY_BASE_URL,
});

module.exports = emby;
Or with ES modules:
// emby.mjs
import 'dotenv/config';
import OpenAI from 'openai';

export const emby = new OpenAI({
  apiKey: process.env.EMBY_API_KEY,
  baseURL: process.env.EMBY_BASE_URL,
});
3

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Make Your First Request

// index.js
const emby = require('./emby');

async function main() {
  const completion = await emby.chat.completions.create({
    model: 'gpt-4o',
    messages: [
      { role: 'user', content: 'Hello! What can you help me with?' }
    ],
  });

  console.log(completion.choices[0].message.content);
}

main();

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Streaming Responses

For real-time output, use streaming: 
const emby = require('./emby');

async function streamChat(prompt) {
  const stream = await emby.chat.completions.create({
    model: 'gpt-4o',
    messages: [{ role: 'user', content: prompt }],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || '';
    process.stdout.write(content);
  }
  console.log(); // New line at the end
}

streamChat('Write a haiku about coding');

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Express.js Integration

Integrate Emby with an Express server: 
const express = require('express');
const emby = require('./emby');

const app = express();
app.use(express.json());

app.post('/api/chat', async (req, res) => {
  const { message } = req.body;

  try {
    const completion = await emby.chat.completions.create({
      model: 'gpt-4o',
      messages: [{ role: 'user', content: message }],
    });

    res.json({ reply: completion.choices[0].message.content });
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

app.listen(3000, () => {
  console.log('Server running on http://localhost:3000');
});

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Streaming with Express

app.post('/api/chat/stream', async (req, res) => {
  const { message } = req.body;

  res.setHeader('Content-Type', 'text/plain; charset=utf-8');
  res.setHeader('Transfer-Encoding', 'chunked');

  try {
    const stream = await emby.chat.completions.create({
      model: 'gpt-4o',
      messages: [{ role: 'user', content: message }],
      stream: true,
    });

    for await (const chunk of stream) {
      const content = chunk.choices[0]?.delta?.content || '';
      res.write(content);
    }
    res.end();
  } catch (error) {
    res.status(500).end(error.message);
  }
});

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TypeScript Support

The OpenAI SDK includes TypeScript types out of the box: 
// emby.ts
import OpenAI from 'openai';

export const emby = new OpenAI({
  apiKey: process.env.EMBY_API_KEY,
  baseURL: process.env.EMBY_BASE_URL,
});

// Type-safe usage
import type { ChatCompletionMessageParam } from 'openai/resources/chat';

const messages: ChatCompletionMessageParam[] = [
  { role: 'system', content: 'You are a helpful assistant.' },
  { role: 'user', content: 'Hello!' },
];

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Available Models

Use any Emby supported model: 
// Popular choices
const model = 'gpt-4o';           // Fast and capable
const model = 'gpt-5';            // Most powerful
const model = 'claude-sonnet-4-5'; // Anthropic's latest

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