LLM integration with OpenAI Responses API

Large language models (LLMs) understand and generate text from prompts. OpenAI exposes models through the Responses API . The official openai npm package is the practical way to call it from Node.js. This post covers common patterns beyond a single prompt string. Prerequisites OpenAI account Generated API key Enabled billing Node.js version 26 openai package installed ( npm i openai ) For Markdown output: marked , dompurify , and jsdom ( npm i marked dompurify jsdom ) Client setup Create a client with your API key (read from the environment in production). import OpenAI from ' openai ' ; const client = new OpenAI ({ apiKey : process . env . OPENAI_API_KEY }); The same SDK can target other hosts that implement a compatible API by setting baseURL and apiKey : const client = new OpenAI ({ apiKey : process . env . LLM_API_KEY , baseURL : ' https://your-gateway.example/v1 ' , }); Azure OpenAI uses AzureOpenAI instead. Many third-party gateways support Chat Completions only; the examples below use client.responses.* , so confirm your provider supports the Responses API (especially for tools like web search). Basic integration Pass a string as input and read output_text from the response. const response = await client . responses . create ({ model : ' gpt-5.5 ' , input : ' Write a one-sentence bedtime story about a unicorn. ' , }); console . log ( response . output_text ); System prompt Use top-level instructions for stable behavior (tone, format, role). They take precedence over casual wording in the user message. const response = await client . responses . create ({ model : ' gpt-5.5 ' , instructions : ' Reply in one short sentence. Use plain language. ' , input : ' Explain what an LLM is. ' , }); console . log ( response . output_text ); Few-shot prompting Pass prior turns as an input array with user and assistant roles, then the new user message. Keep task rules in instructions . const response = await client . responses . create ({ model : ' gpt-5.5 ' , instructions :