Build Conversational AI with OpenAI & Anthropic

🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI. For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here.

<span>Tutorial</span>
<span>Intermediate</span>
<span>⏱ 45 min read</span>
<span>© Gate of AI 2026-07-14</span>

In this tutorial, we will build a robust conversational AI by integrating OpenAI, Anthropic, and Mistral APIs, showcasing their unique strengths in generating and managing conversations.

Prerequisites

• Node.js v18 or later
• API keys for OpenAI, Anthropic, and Mistral
• Intermediate understanding of JavaScript and API integration

What We're Building

In this project, we will create a conversational AI application that leverages the latest advancements in AI models from OpenAI, Anthropic, and Mistral. Our application will be able to handle complex dialogues by dynamically selecting the best model for each user query based on predefined criteria such as context length, cost, and response type.

The final product will be a web-based chat interface where users can interact with our AI, which intelligently routes queries to the most suitable AI model. This setup not only demonstrates the capabilities of each API but also provides a flexible architecture that can be extended or modified to include additional models or features.

Setup and Installation

To get started, we need to set up our development environment. We'll use Node.js for our server-side logic and a simple HTML/CSS/JavaScript frontend to interact with our backend.

npm init -y

npm install express dotenv openai anthropic mistral

Next, we need to configure our environment variables to securely store our API keys. Create a .env file in the root of your project with the following content:

OPENAI_API_KEY=your_openai_api_key

ANTHROPIC_API_KEY=your_anthropic_api_key

MISTRAL_API_KEY=your_mistral_api_key

Step 1: Setting Up Express Server

We will first set up an Express server to handle HTTP requests from our frontend. This server will act as a middleman between our client-side application and the various AI APIs.

const express = require('express');

const dotenv = require('dotenv');

dotenv.config();

const app = express();

app.use(express.json());

const PORT = process.env.PORT || 3000;

app.listen(PORT, () => {

  console.log(Server is running on port ${PORT});

});

In this setup, we import the necessary modules and initialize our Express app. We also configure it to parse JSON payloads and listen on a specified port, which defaults to 3000 if not set in the environment variables.

Step 2: Integrating OpenAI API

Next, we'll integrate the OpenAI API. This API will handle general conversational tasks and provide responses based on user queries.

import { OpenAI } from 'openai';

const client = new OpenAI(process.env.OPENAI_API_KEY);

app.post('/api/openai', async (req, res) => {

  try {

    const { message } = req.body;

    const response = await client.chat.completions.create({

      model: "gpt-5.6",

      messages: [{ role: "user", content: message }],

    });

    res.json(response);

  } catch (error) {

    res.status(500).json({ error: error.message });

  }

});

This code sets up an endpoint to handle POST requests at /api/openai. It uses the OpenAI client to send a user message to the GPT-5.6 model and returns the response. Error handling is included to manage any issues that arise during the API request.

Step 3: Integrating Anthropic API

Now, we will integrate the Anthropic API, which is known for its safety and alignment capabilities, making it ideal for sensitive or ethical queries. Ensure to verify the latest API integration methods from Anthropic's official documentation.

// Placeholder for Anthropic API integration

// Verify with Anthropic's latest API documentation

Step 4: Integrating Mistral API

Finally, we'll integrate the Mistral API. Mistral models are designed for efficient handling of specialized tasks, making them a great choice for domain-specific queries. Verify the integration details with Mistral's latest API documentation.

// Placeholder for Mistral API integration

// Verify with Mistral's latest API documentation

⚠️ Common Mistake: Ensure that your API keys are correctly set in the .env file and that the environment variables are loaded properly. Forgetting to configure these can lead to authentication errors.

Testing Your Implementation

To verify that our setup is working correctly, we can use a tool like Postman to send POST requests to each of our API endpoints with a sample message. Ensure that each API responds with a valid completion and that errors are handled gracefully.

// Example POST request payload

{

  "message": "What is the weather like today?"

}

Send this payload to each endpoint and check the responses to ensure they're accurate and relevant to the input.

What to Build Next

• Enhance the chat interface with real-time updates using WebSockets.
• Implement a model selection algorithm to dynamically choose the best model based on query type.
• Add user authentication to personalize and secure the chat experience.

GCC/Middle East Relevance

Integrating conversational AI systems aligns with regional initiatives like Saudi Vision 2030 and the UAE National Strategy for AI. These frameworks emphasize the importance of AI in transforming industries and enhancing digital infrastructure. Collaborations with local entities such as SDAIA and G42 can further enhance AI capabilities in the region.