Quickstart: Build a Chat App with OpenRouter

Goal: Learn the fundamentals of OpenRouter by building a TypeScript chat app that sends messages and streams responses through OpenRouter. Outcome: A working multi-turn conversation loop that can talk to any of the 600+ models available on the platform by changing a single string.

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Prerequisites

Node.js 18+ installed
An OpenRouter API key. Create one at openrouter.ai/settings/keys or set up a new Stripe project

1. Create a project and install the SDK

Set up a new Node.js project and add the OpenRouter client SDK. The SDK is ESM-only, so set the package type to module. Install tsx so you can run the TypeScript examples directly.

mkdir openrouter-chat && cd openrouter-chat
npm init -y
npm pkg set type=module
npm install @openrouter/sdk
npm install --save-dev tsx

2. Send your first message

Create chat.ts with a client instance and a single Responses API request. The apiKey reads from the environment so you never hard-code credentials.

import { OpenRouter } from '@openrouter/sdk';

const client = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY,
});

const response = await client.responses.send({
  responsesRequest: {
    model: 'google/gemini-3.1-flash-lite',
    input: 'Say hello in one sentence.',
  },
});

if (!('output' in response)) {
  throw new Error('Expected a non-streaming response');
}

const message = [...response.output].reverse().find((item) => item.type === 'message');
const text = message?.content.find((part) => part.type === 'output_text');
console.log(text?.text);
console.log({
  inputTokens: response.usage?.inputTokens,
  outputTokens: response.usage?.outputTokens,
});

Run it with your API key:

OPENROUTER_API_KEY=sk-or-v1-... npx tsx chat.ts

You should see a single text response printed to the console. The text is in the output_text part of the message item in response.output, and the SDK returns token usage in camelCase fields such as inputTokens and outputTokens. The response.output array follows the same shape as the Responses API response. Without stream: true, client.responses.send is typed to return the full response, so the 'output' in response check is only a runtime safety guard.

3. Stream the response

Streaming returns text as it is generated instead of waiting for the full response. Pass stream: true and iterate over the returned event stream. Each response.output_text.delta event contains a delta with the new text fragment.

import { OpenRouter } from '@openrouter/sdk';

const client = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY,
});

const stream = await client.responses.send({
  responsesRequest: {
    model: 'google/gemini-3.1-flash-lite',
    input: 'Explain how routers work in three sentences.',
    stream: true,
  },
});

if ('output' in stream) {
  throw new Error('Expected a streaming response');
}

for await (const event of stream) {
  if (event.type === 'response.output_text.delta') {
    process.stdout.write(event.delta);
  }
}
console.log();

Text now prints incrementally. See the Streaming reference for the full SSE event format.

4. Add multi-turn conversation

Multi-turn works by sending the full conversation history with each request. The model uses all previous turns as context. Append each user input and assistant response to an input array of { role, content } items before the next call.

import { OpenRouter } from '@openrouter/sdk';
import * as readline from 'node:readline';

const client = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY,
});

const input: { role: 'user' | 'assistant'; content: string }[] = [];

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
});

function ask(): void {
  rl.question('You: ', async (line) => {
    if (line.toLowerCase() === 'exit') {
      rl.close();
      return;
    }

    input.push({ role: 'user', content: line });

    const stream = await client.responses.send({
      responsesRequest: {
        model: 'google/gemini-3.1-flash-lite',
        input,
        stream: true,
      },
    });
    if ('output' in stream) {
      throw new Error('Expected a streaming response');
    }

    let response = '';
    let completed = false;
    process.stdout.write('Assistant: ');
    for await (const event of stream) {
      if (event.type === 'response.output_text.delta') {
        process.stdout.write(event.delta);
        response += event.delta;
      }
      if (event.type === 'response.completed') {
        completed = true;
      }
    }
    console.log();

    // A stream that ends with response.failed or response.incomplete leaves a
    // partial answer; keep only completed turns in the history.
    if (completed) {
      input.push({ role: 'assistant', content: response });
    } else {
      input.pop();
      console.log('(The response did not complete, so that turn was not saved.)');
    }
    ask();
  });
}

ask();

Run the file and type messages. The model remembers prior turns because the full input array is sent with each request. Type exit to quit.

5. Swap models

OpenRouter gives you access to hundreds of models through one API. Change the model string to switch providers, no other code changes needed.

// Use OpenAI's latest chat model
model: 'openai/gpt-chat-latest',

// Use Anthropic Claude Sonnet latest
model: '~anthropic/claude-sonnet-latest',

// Use a free model
model: 'openrouter/free',

Browse all available models at openrouter.ai/models or query the Models API programmatically.

Check your work

npx tsx chat.ts prints a streamed response to the console
A multi-turn conversation maintains context across turns (ask a follow-up that references a previous answer)
Changing the model string switches to a different provider with no other code changes
The non-streaming response includes a usage object with inputTokens and outputTokens

Next steps

Connect a coding agent to OpenRouter
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Quickstart: Build a Chat App

Prerequisites

1. Create a project and install the SDK

2. Send your first message

3. Stream the response

4. Add multi-turn conversation

5. Swap models

Check your work

Next steps

​Prerequisites

​1. Create a project and install the SDK

​2. Send your first message

​3. Stream the response

​4. Add multi-turn conversation

​5. Swap models

​Check your work

​Next steps

Prerequisites

1. Create a project and install the SDK

2. Send your first message

3. Stream the response

4. Add multi-turn conversation

5. Swap models

Check your work

Next steps