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How to Build AI Agents with OpenAI in Node.js 2025 Tutorial

How to Build AI Agents with OpenAI in Node.js 2025 Tutorial

Create AI agents with node.js MYGOM guide

Building AI Agents with OpenAI in Node.js (2025)

In 2025, AI agents are transforming how software (opens in new tab) interacts with users and automates workflows. In this guide you’ll learn how to build AI agents with the OpenAI Node.js SDK, using Node.js and modern frameworks to create systems that reason, plan and act.

Node.js is great for AI development - it’s fast, flexible, and easy to integrate. With OpenAI’s latest SDKs, you can quickly build AI agents that think, act, and connect to real-world tools for automation.

By the end of this guide, you’ll know how to:

Whether you’re a developer or AI enthusiast, this guide step-by-step shows how to build AI agents using OpenAI APIs.

Model Landscape in Late 2025

This guide uses GPT-4.1 and GPT-4.1-mini, which remain widely adopted, stable, and cost-effective models in late 2025, known for their strong reasoning and multitasking abilities, including multimodal inputs. While OpenAI released GPT-5 earlier this year, featuring more advanced capabilities, GPT-4.1 variants continue to provide excellent performance and affordability for many AI agent applications. For the latest updates on available models, please consult OpenAI’s official model documentation (opens in new tab).

Why Build AI Agents in 2025?

Picture a digital teammate that never sleeps, remembers everything and scales with your business. That’s exactly what happens when you develop OpenAI agents in Node.js (opens in new tab) - your applications don't just process data, they reason, plan, and act autonomously.

This isn't science fiction. Companies are automating research, handling customer requests, and streamlining operations in hours instead of weeks. Most developers can build their first working AI agent in Node.js in just a few hours - function calling and API integration included.

What Makes 2025 Different?

The OpenAI ecosystem has reached a new level:

What you'll build in this tutorial:

By the end, you’ll know how to connect the dots - from handling user requests to letting AI agents act on their own.

This tutorial will show you how to build AI agents step by step, from setup to production-ready automation.

You'll see where things break (and how to fix them).

Ready to build software that thinks for itself?

Let’s roll up our sleeves and start building your first OpenAI-powered AI agent in Node.js.

What You Need to Build AI Agents in Node.js

Essential Knowledge and Skills

To follow this guide, you should be comfortable with:

If you’ve ever built a simple Express server or connected to an API, you’re ready. If you’ve built a basic Node.js project before, you’re ready for this. It’s easier than it looks.

Tools & Setup

Here’s what you’ll need to set up your development environment:

Quick setup checklist:

Estimated setup time: 10–15 minutes if you’re starting fresh.

Part 1: Node.js Project Setup and OpenAI Connection

By the end of this section, you'll have a working Node.js project connected to OpenAI's API, understand how to secure credentials properly, and verify everything works.

Step 1: Initialize Your Node.js Project

Open your terminal and create a new project directory:

bash

mkdir openai-agent-2025
cd openai-agent-2025
npm init -y

You just created a folder and a package.json file. Think of package.json as your project's blueprint, it tracks dependencies and configuration.

Enable ES6 modules: Edit your package.json and add this line:

json

{
"name": "openai-agent-2025",
"version": "1.0.0",
"type": "module",
"main": "index.js",
"scripts": {
"start": "node index.js",
"dev": "node --watch index.js"
}
}

The "type": "module" line enables modern import syntax instead of require().

Step 2: Install Required Dependencies

Install the OpenAI SDK and environment variable handler:

bash

npm install openai dotenv

What these packages do:

For development, also install:

bash

npm install --save-dev jest @types/node

Step 3: Secure Your API Keys

Create a .env file in your project root:

env

OPENAI_API_KEY=sk-proj-your-actual-key-here
SERPER_API_KEY=your-serper-key-here

Critical security step: Add .env to .gitignore:

bash

echo ".env" >> .gitignore
echo "node_modules/" >> .gitignore

Why environment variables? Imagine leaving your house key under the doormat - terrible idea! API keys belong in .env files, never hardcoded in your source code. This is industry standard security practice.

⚠️ Security note: Never share your `.env` file - it contains sensitive API keys. Treat it like a password and make sure it’s listed in your `.gitignore`.

What happens if you skip this? Your API keys could end up on GitHub, get scraped by bots, and rack up thousands in unauthorized charges within hours.

Step 4: Test Your OpenAI Connection

Create index.js with a simple connection test:

javascript

import 'dotenv/config';
import OpenAI from 'openai';

const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY
});

async function testConnection() {
try {
console.log('Testing OpenAI connection...');

const response = await openai.chat.completions.create({
model: "gpt-4.1-mini", // Current mainstream, cost-effective model in late 2025. Consider GPT-5 for cutting-edge features.
messages: [
{
role: "user",
content: "Say hi and confirm you're working!"
}
],
max_tokens: 50 // Limit tokens to control costs
});

console.log('✅ Connection successful!');
console.log('Response:', response.choices[0].message.content);
console.log('Tokens used:', response.usage.total_tokens);

} catch (error) {
console.error('❌ Connection failed:');
console.error('Error:', error.message);

if (error.status === 401) {
console.error('Check your API key in .env file');
} else if (error.status === 429) {
console.error('Rate limit exceeded or insufficient credits');
}
}
}

testConnection();

Run your test:

bash

npm start

Expected output:

Testing OpenAI connection...
✅ Connection successful!
Response: Hi! I'm working perfectly and ready to help you.
Tokens used: 23

💡 Pro tip: Always test your API connection before moving to function-calling agents.

⚠️ Security note: Never share your .env file - it contains sensitive API keys.

Side-by-side image showing a code snippet on the left and terminal output on the right. The left panel contains OpenAI code using GPT-4 to test a database connection, while the right panel shows the terminal confirming “Database connection successful!” with a green check mark.
Example of how a simple OpenAI code snippet in Node.js produces a response in the terminal - verifying that the connection to the API works correctly.

Troubleshooting common errors:

Error

Cause

Fix

OPENAI_API_KEY is not defined

Missing .env file or wrong key name

Verify .env exists and has correct key name

401 Unauthorized

Invalid API key

Re-copy key from OpenAI dashboard

429 Too Many Requests

Out of credits or rate limited

Add credits or wait a few minutes

Module not found

Dependencies not installed

Run npm install again

Step 5: Understanding the Response Object

Let's examine what OpenAI returns:

javascript

{
id: 'chatcmpl-abc123',
object: 'chat.completion',
created: 1699999999,
model: 'gpt-4.1-mini',
choices: [
{
index: 0,
message: {
role: 'assistant',
content: 'Hi! I'm working perfectly...'
},
finish_reason: 'stop'
}
],
usage: {
prompt_tokens: 15,
completion_tokens: 8,
total_tokens: 23
}
}

Key fields explained:

Cost calculation: With gpt-4.1-mini at $0.15 per 1M input tokens and $0.60 per 1M output tokens, this test cost approximately $0.000007 (less than a penny).

💡 Pro tip: Always check usage.total_tokens - it helps you track costs and optimize performance early.

What's Next?

At this point, your Node.js project is properly configured and talking to OpenAI's API. You've verified authentication works and understand the basic request/response pattern.

In Part 2, we'll build a real agent that can use tools, starting with web search capabilities using actual API integrations.

AI Agents vs Chatbots - What’s the Difference?

Before diving into code, let's clarify what makes an AI agent different from a basic chatbot. This distinction is crucial for knowing when to use which approach.

Simple Chatbot (Chat Completions API)

What it does: Gives direct responses, like texting someone who replies instantly.

Example interaction:

You: "What's the weather in Vilnius?"
Bot: "I don't have access to real-time weather data,
but typically Vilnius in November averages 3-5°C..."

Characteristics:

When to use chat completions:

Comparison chart titled “Chatbots vs. Function-Calling AI Agents.” On the left, a chatbot robot icon with checkmark for “Answers Questions” and red cross for “Performs Actions.” On the right, an AI agent with a brain icon has green checkmarks for both “Answers Questions” and “Performs Actions (Uses Tools),” with a small note “Thinks → Decides → Acts.”
Chatbots can only answer questions, while AI agents can both think and act - using tools, APIs, and reasoning to perform real tasks autonomously.

AI Agent (Function Calling + Tools)

What it does: Plans, acts, and uses tools autonomously to accomplish goals.

Example interaction:

You: "What's the weather in Vilnius?"
Agent: [Thinks: I need current weather data]
[Action: Calls weather API with coordinates]
[Processes: Receives forecast data]
[Responds: "Currently 4°C in Vilnius with light rain.
Tomorrow: 6°C, partly cloudy."]

Characteristics:

When to use AI agents:

The OpenAI Assistants API (Advanced Agents)

What it adds: Persistent memory, built-in tools, and thread management.

Example use case:

Monday: "Analyze this sales spreadsheet" [uploads file]
Tuesday: "Compare it to last week's data" [agent remembers previous file]
Wednesday: "What's the trend?" [agent recalls entire conversation history]

Characteristics:

When to use Assistants API:

Quick Decision Guide

Your Need

Use This

Simple Q&A, no memory needed

Chat Completions

Need real-time data or tools

Function Calling (Agent)

Need memory across sessions

Assistants API

Cost is primary concern

Chat Completions

Building complex automation

Assistants API

In this tutorial, we'll build both:

This gives you the complete toolkit for any AI automation project.

Part 2: Building a Function-Calling Agent with Web Search

By the end of this section, you'll have a working AI agent that can search the web, process results, and answer questions with current information. No mocks - real API integration.

Understanding the Agent Loop

Before writing code, let's understand how (opens in new tab) agents think.

Imagine a chef preparing a meal:

This is the agent loop:

User Request → Agent Thinks → Needs Tool?
↓ Yes ↓ No
Execute Tool Return Answer
↓
Get Result → Agent Thinks → Loop continues

Key insight: The agent doesn't execute linearly. It loops, deciding at each step whether it has enough information or needs more tools.

A circular diagram titled “How an AI Agent Thinks” showing the agent loop: a user request enters the GPT model, which decides whether to use a tool (web search or API). The agent processes results, formats an answer, and returns it to the user in a continuous reasoning cycle.
An AI agent receives a user request, decides whether to use external tools (like web search or APIs), processes results, and refines its answer -repeating this loop until it reaches a complete response.

Agent Types in 2025

1. Reactive Agents

2. Planning Agents

3. Tool-Using Agents (What we're building)

4. Hybrid Agents

Step 1: Define Your Agent's Tools

Tools are functions your AI can call to accomplish tasks it can't do alone, like searching the web or querying databases.

Create tools.js:

javascript

// tools.js
export const tools = [
{
type: "function",
function: {
name: "search_web",
description: "Search the web for current information. Use this when you need up-to-date data, news, or facts that happened after your knowledge cutoff.",
parameters: {
type: "object",
properties: {
query: {
type: "string",
description: "The search query. Be specific and include relevant keywords."
},
num_results: {
type: "number",
description: "Number of results to return (1-10)",
default: 3
}
},
required: ["query"]
}
}
},
{
type: "function",
function: {
name: "calculate",
description: "Perform mathematical calculations. Use for any arithmetic or math operations.",
parameters: {
type: "object",
properties: {
expression: {
type: "string",
description: "Mathematical expression to evaluate (e.g., '2 + 2' or '15 * 23')"
}
},
required: ["expression"]
}
}
}
];

Why structure tools this way? The AI needs clear descriptions to decide when to use each tool. Good descriptions = better tool selection.

Bad description example:

javascript

description: "Search stuff" // Too vague

Good description example:

javascript

description: "Search the web for current information. Use this when you need up-to-date data, news, or facts that happened after your knowledge cutoff."

Step 2: Implement Tool Execution (Real APIs)

Now we'll create actual working implementations - no mocks.

Create tool-executor.js:

javascript

// tool-executor.js
import 'dotenv/config';

/**
* Executes web search using Serper API (Google results)
*/
async function searchWeb(query, numResults = 3) {
try {
const response = await fetch('https://google.serper.dev/search', {
method: 'POST',
headers: {
'X-API-KEY': process.env.SERPER_API_KEY,
'Content-Type': 'application/json'
},
body: JSON.stringify({
q: query,
num: numResults
})
});

if (!response.ok) {
throw new Error(`Serper API error: ${response.status}`);
}

const data = await response.json();

// Extract and format results
const results = data.organic?.slice(0, numResults).map(item => ({
title: item.title,
snippet: item.snippet,
link: item.link
})) || [];

return {
query,
results,
searchTime: new Date().toISOString()
};

} catch (error) {
console.error('Search error:', error.message);
return {
error: `Failed to search: ${error.message}`,
query
};
}
}

/**
* Safely evaluates mathematical expressions
*/
function calculate(expression) {
try {
// Basic safety: only allow numbers and operators
if (!/^[\d\s+\-*/().]+$/.test(expression)) {
throw new Error('Invalid expression: only numbers and basic operators allowed');
}

// Using Function constructor as a safer alternative to eval
const result = new Function(`return ${expression}`)();

return {
expression,
result,
calculated: true
};

} catch (error) {
return {
expression,
error: `Calculation failed: ${error.message}`,
calculated: false
};
}
}

/**
* Routes tool calls to their implementations
*/
export async function executeFunction(functionName, functionArgs) {
console.log(`🔧 Executing tool: ${functionName}`);
console.log(`📝 Arguments:`, functionArgs);

switch (functionName) {
case 'search_web':
return await searchWeb(
functionArgs.query,
functionArgs.num_results
);

case 'calculate':
return calculate(functionArgs.expression);

default:
throw new Error(`Unknown function: ${functionName}`);
}
}

What's happening here?

Security note: The calculate() function validates input to prevent code injection. Never use eval() directly with user input!

A diagram titled “AI Agent Architecture: Connecting APIs.” It shows an AI agent in the center linked to user input, OpenAI API, Serper Search, and databases. Arrows illustrate how the agent exchanges commands, feedback, and data between these tools to process information and return responses.
The AI agent sits at the center, connecting multiple APIs and data sources - from user input and web search to databases and OpenAI’s models — exchanging commands, results, and feedback in real time.

Step 3: Build the Complete Agent Loop

Now let's tie everything together with the agent's decision-making logic.

Create agent.js:

javascript

// agent.js
import 'dotenv/config';
import OpenAI from 'openai';
import { tools } from './tools.js';
import { executeFunction } from './tool-executor.js';

const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY
});

/**
* Agent loop - keeps running until task is complete
*/
export async function runAgent(userMessage, options = {}) {
const {
model = 'gpt-4.1-mini', // Cost-effective for most tasks
maxIterations = 5, // Prevent infinite loops
verbose = true // Log agent thinking
} = options;

const messages = [
{
role: 'system',
content: 'You are a helpful AI assistant with access to web search and calculation tools. Use tools when you need current information or need to perform calculations. Always provide clear, accurate answers.'
},
{
role: 'user',
content: userMessage
}
];

let iterationCount = 0;

while (iterationCount < maxIterations) {
iterationCount++;

if (verbose) {
console.log(`\n🤔 Agent thinking (iteration ${iterationCount})...`);
}

try {
// Call OpenAI with tools available
const response = await openai.chat.completions.create({
model,
messages,
tools,
tool_choice: 'auto', // Let AI decide when to use tools
});

const message = response.choices[0].message;
messages.push(message);

// Log token usage for cost tracking
if (verbose) {
console.log(`💰 Tokens used: ${response.usage.total_tokens}`);
}

// If no tool calls, we have final answer
if (!message.tool_calls || message.tool_calls.length === 0) {
if (verbose) {
console.log('✅ Agent finished thinking\n');
}
return {
answer: message.content,
iterations: iterationCount,
tokensUsed: response.usage.total_tokens
};
}

// Execute each tool call
if (verbose) {
console.log(`🔧 Agent needs to use ${message.tool_calls.length} tool(s)`);
}

for (const toolCall of message.tool_calls) {
const functionName = toolCall.function.name;
const functionArgs = JSON.parse(toolCall.function.arguments);

// Execute the tool
const result = await executeFunction(functionName, functionArgs);

if (verbose) {
console.log(`✓ Tool result:`, JSON.stringify(result, null, 2));
}

// Add tool result back to conversation
messages.push({
role: 'tool',
tool_call_id: toolCall.id,
content: JSON.stringify(result)
});
}

// Loop continues - agent will think about tool results

} catch (error) {
console.error('❌ Agent error:', error.message);
throw error;
}
}

// Hit max iterations without finishing
throw new Error(`Agent exceeded maximum iterations (${maxIterations})`);
}

What's the agent doing?

Safety features:

Step 4: Test Your Agent

Update index.js to test the agent:

javascript

// index.js
import { runAgent } from './agent.js';

async function main() {
console.log('🚀 Starting OpenAI Agent Demo\n');
console.log('=' .repeat(50));

// Test 1: Simple question (no tools needed)
console.log('\n📝 Test 1: Simple question');
console.log('Question: "What is 15 multiplied by 23?"');

const result1 = await runAgent('What is 15 multiplied by 23?');
console.log('\n💬 Answer:', result1.answer);
console.log('📊 Stats:', `${result1.iterations} iterations, ${result1.tokensUsed} tokens`);

// Test 2: Requires web search
console.log('\n' + '='.repeat(50));
console.log('\n📝 Test 2: Current information');
console.log('Question: "What are the latest developments in AI agents?"');

const result2 = await runAgent(
'What are the latest developments in AI agents?',
{ verbose: true }
);
console.log('\n💬 Answer:', result2.answer);
console.log('📊 Stats:', `${result2.iterations} iterations, ${result2.tokensUsed} tokens`);

// Test 3: Multi-step reasoning
console.log('\n' + '='.repeat(50));
console.log('\n📝 Test 3: Complex task');
console.log('Question: "Search for Node.js best practices and calculate 100 * 12"');

const result3 = await runAgent(
'Search for Node.js best practices and calculate 100 * 12'
);
console.log('\n💬 Answer:', result3.answer);
console.log('📊 Stats:', `${result3.iterations} iterations, ${result3.tokensUsed} tokens`);
}

main().catch(console.error);

Run your agent:

bash

npm start

Expected output:

🚀 Starting OpenAI Agent Demo

==================================================

📝 Test 1: Simple question
Question: "What is 15 multiplied by 23?"

🤔 Agent thinking (iteration 1)...
🔧 Agent needs to use 1 tool(s)
🔧 Executing tool: calculate
📝 Arguments: { expression: '15 * 23' }
✓ Tool result: {
"expression": "15 * 23",
"result": 345,
"calculated": true
}
💰 Tokens used: 156

🤔 Agent thinking (iteration 2)...
💰 Tokens used: 187
✅ Agent finished thinking

💬 Answer: 15 multiplied by 23 equals 345.
📊 Stats: 2 iterations, 187 tokens

Congratulations!

You've built a working AI agent that:

What you learned:

Next in Part 3: We'll upgrade to the Assistants API for persistent memory across sessions, turning your agent into a long-term digital teammate.

Part 3: Add Persistent Memory with the OpenAI Assistants API

Ready to level up? In this section, you'll build an advanced AI assistant that remembers conversations, manages multi-session workflows, and handles complex tasks over time.

Why Use the Assistants API?

The function-calling agent from Part 2 has a limitation: No memory between runs. Every conversation starts fresh.

Real-world problem:

Monday 9 AM:
You: "Analyze this sales spreadsheet" [uploads file]
Agent: [Analyzes] "Revenue increased 15% in Q4..."

Tuesday 10 AM:
You: "What was that revenue number again?"
Agent: "I don't have access to any spreadsheet."

The agent forgot! This makes it unsuitable for:

The Assistants API solves this with:

Comparison diagram showing two panels. Left: “Memoryless: Chat Completions API” — user request leads to direct response; conversation ends and context is lost. Right: “Memory-Aware: Assistants API” — user request is stored in thread history, future requests build on previous context, producing richer, contextual responses.
Traditional chat completions handle one request at a time with no memory, while the Assistants API stores conversation history, allowing future interactions to build on previous context.

Chat Completions vs Assistants API

Feature

Chat Completions (Part 2)

Assistants API (Part 3)

Memory

❌ None (stateless)

✅ Persistent across sessions

Setup Complexity

Low (simple)

Moderate (threads required)

Cost

Lower per request

Higher (storage + compute)

Built-in Tools

❌ Manual integration

✅ Code interpreter, file search

Use Case

Single-turn tasks

Multi-session workflows

Best For

Quick automation, chatbots

Customer support, analysis

Step 1: Create Your First Assistant

Create assistant.js:

javascript

// assistant.js
import 'dotenv/config';
import OpenAI from 'openai';

const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY
});

/**
* Creates a new OpenAI Assistant with specified tools and capabilities
*/
export async function createAssistant(options = {}) {
const {
name = 'Business Assistant',
instructions = 'You are a helpful business assistant. You can analyze data, search files, and help with calculations. Always provide clear, actionable insights.',
tools = [
{ type: 'code_interpreter' }, // Run Python code
{ type: 'file_search' } // Search uploaded files
],
model = 'gpt-4.1' // Use full GPT-4.1 for assistant features
} = options;

console.log('🔧 Creating assistant...');

const assistant = await openai.beta.assistants.create({
name,
instructions,
tools,
model,
temperature: 0.7
});

console.log('✅ Assistant created:', assistant.id);
return assistant;
}

/**
* Lists all your existing assistants
*/
export async function listAssistants() {
const assistants = await openai.beta.assistants.list({
limit: 20,
order: 'desc'
});

return assistants.data;
}

/**
* Retrieves a specific assistant by ID
*/
export async function getAssistant(assistantId) {
return await openai.beta.assistants.retrieve(assistantId);
}

/**
* Deletes an assistant (cleanup)
*/
export async function deleteAssistant(assistantId) {
console.log(`🗑️ Deleting assistant: ${assistantId}`);
await openai.beta.assistants.del(assistantId);
console.log('✅ Assistant deleted');
}

What's happening here?

Code Interpreter tool:

Advanced Capabilities - The Code Interpreter Tool

The code interpreter tool (opens in new tab) is a game changer for AI agents, enabling them to perform complex data analysis, manipulate spreadsheets, generate visualizations, and execute custom computations seamlessly, all within a secure environment. This lets developers and teams automate complex workflows and uncover insights - no manual coding required.

As AI agents continue to evolve, combining natural language understanding with powerful code execution and multi-tool orchestration, the possibilities for autonomous decision-making and intelligent automation expand exponentially. Whether you’re building assistants for research, customer support, content creation, or business automation, leveraging these advanced tools ensures your solutions remain adaptable, scalable, and ready for the complexities of real-world applications.

Conclusion - The Future of AI Agents in Node.js

Now you know how to build AI agents with OpenAI - from connecting APIs to giving them memory and autonomy.

Learning how to build AI agents with OpenAI’s SDK and persistent memory features is changing how software gets built in 2025. These AI agents don’t just respond - they reason, integrate with external systems, and autonomously perform complex tasks. Whether your goal is research automation, customer support, or advanced business workflows, these tools empower you to create scalable, intelligent solutions.

Curious how AI can speed up business analytics and data processing? Check out our project Agentic AI Business Intelligence (opens in new tab)

With the Node.js OpenAI SDK, you have a powerful and flexible platform to build robust AI agents quickly and efficiently. As you’ve seen throughout this tutorial, combining API integrations, error handling, cost optimization (opens in new tab), and multi-step reasoning unlocks new possibilities for AI automation.

Ready to harness the full power of AI agents? Start building your own AI automation with OpenAI APIs today and unlock new possibilities for your Node.js applications.

Start your project today, experiment with function calling and memory, and see how fast you can build AI agents that truly work for you.

Now that you’ve learned how to build AI agents with OpenAI in Node.js, here are answers to the most common developer questions.

FAQ

How do I secure my OpenAI API keys?

Securing your OpenAI API keys is critical to preventing unauthorized access and unexpected charges. Best practices include:

Following these measures forms a multi-layered defense that keeps your API access safe while enabling seamless development and deployment.

Difference Between Chat Completions and Function-Calling AI Agents

Function calling AI agents enable richer, scalable, and more intelligent applications beyond simple text exchange.

How can I optimize costs when using OpenAI APIs?

To manage and reduce OpenAI API usage costs:

Smart usage patterns ensure you benefit from powerful AI functionality without overspending.

Can I use persistent memory between sessions with OpenAI agents?

Yes, persistent memory is supported through the OpenAI Assistants API which:

Without persistent memory, each interaction is stateless, limiting functionality to single-turn or reactive agents. Using persistent memory transforms agents into true digital teammates capable of ongoing assistance and context-aware decision-making.

What about GPT-5?

GPT-5 represents OpenAI’s latest generation model, available to select customers and developers with advanced requirements. It offers improvements in reasoning, creativity, and multimodal understanding but at a higher computational cost. The GPT-4.1 series, including GPT-4.1-mini, remains widely used for reliable, cost-efficient AI agent building in 2025.

Ready to build your own AI agent?

At MYGOM, we help companies create real, production-ready OpenAI solutions - from simple chat agents to advanced systems that can think and act autonomously.
Get in touch with our team (opens in new tab) and let’s turn your idea into a working AI product.

Justas Česnauskas - CEO | Founder

Justas Česnauskas

CEO | Founder

Builder of things that (almost) think for themselves

Connect on LinkedIn

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