DEV Community: Asad Hussain

Introduction to OpenAI Agent SDK

Asad Hussain — Sun, 26 Apr 2026 16:19:47 +0000

What is an AI Agent?

An AI Agent is a system that can think, decide, and perform actions on behalf of a user.

Unlike a normal chatbot that only responds with text, an AI agent can:

Understand a user’s request
Decide what action is needed
Use external tools (like APIs, databases, or functions)
Return useful, real-world results

Think of it like this:

A chatbot talks.
An AI Agent works.

For example:

A chatbot may tell you how to check GitHub followers
An AI Agent can actually fetch the follower count for you automatically

This makes AI agents far more powerful for real applications.

What is the OpenAI Agent SDK?

The OpenAI Agent SDK helps developers build AI agents easily using JavaScript or TypeScript.

Instead of manually handling prompts, tool calling, and decision-making, the SDK provides a clean structure to:

Create agents
Define tools
Let agents use those tools automatically
Run user queries through the agent

This makes development faster, cleaner, and easier to scale.

How Does an AI Agent Work?

The flow usually looks like this:

User Query → Agent → Tool Selection → Tool Execution → Final Answer

Example Flow

Suppose the user says:

Hey agent, my GitHub ID is Asad-bot07

The agent will:

Understand that GitHub information is needed
Decide to use a GitHub tool
Call the GitHub API
Fetch the follower count
Return the final answer to the user

This is exactly what your project does.

What Are Tools?

A Tool is a function the agent can use to perform actions.

Tools help agents interact with the outside world.

Examples of tools:

Fetch weather data
Search GitHub profiles
Send emails
Read databases
Process payments
Generate reports

Without tools, agents are limited to conversation.

With tools, agents become useful assistants.

Project Example: GitHub Stats Agent

In this example, we are building a simple AI agent that checks a GitHub user’s follower count.

The user provides a GitHub username, and the agent automatically fetches the follower count using the GitHub API.

Installation

Before building your first AI agent using the OpenAI Agent SDK, you need to install the required packages and set up your environment.

Step 1: Create a New Node.js Project

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

This creates a fresh Node.js project with a default package.json file.

Step 2: Install Required Dependencies

npm install @openai/agents axios zod dotenv

What each package does:

@openai/agents → Used to create AI agents and tools
axios → Used for making API requests
zod → Used for validating tool parameters
dotenv → Used for securely loading environment variables

Step 3: Create a `.env` File

Create a file named:

.env

and add your OpenAI API key inside it:

OPENAI_API_KEY=your_openai_api_key_here

This helps keep your API key secure and prevents exposing sensitive information directly in your code.

Step 4: Basic Project Structure

Your project should look something like this:

openai-agent-sdk/
│
├── node_modules/
├── .env
├── package.json
├── package-lock.json
└── index.js

Your main agent code will be written inside index.js.

Code Breakdown

Step 1: Import Required Packages

import { Agent, run, tool } from "@openai/agents";
import axios from "axios";
import z from "zod";
import 'dotenv/config'

What these packages do:

`@openai/agents`

Used for creating agents and tools.

`axios`

Used to make API requests. Here, we use it to call the GitHub API.

`zod`

Used for validating tool parameters. It ensures the tool receives the correct input.

`dotenv`

Used for loading environment variables securely.

Step 2: Creating a Tool

const getGitStats = tool({
  name: "get git stats",
  description: "returns the git following of the user",

Here we define a tool called:

get git stats

This tool is responsible for fetching GitHub follower data.

Step 3: Defining Tool Parameters

parameters: z.object({
  userName: z.string().describe("Name of the user"),
})

This tells the agent:

The tool requires a userName as input.

Since we use Zod, the input is validated automatically. This prevents invalid tool usage.

Step 4: Tool Execution Logic

execute: async function ({ userName }) {
  const url = `https://api.github.com/users/${userName}`;
  const res = await axios.get(url);
  const user = res.data;
  return `The followers of ${userName} is : ${user.followers}`;
}

This is where the actual work happens.

What happens here:

A GitHub API URL is created
Axios sends a request
GitHub returns user data
We extract the followers value
The tool returns the final response

Example output:

The followers of Asad-bot07 is : 120

Step 5: Creating the Agent

const getGitAgent = new Agent({
  name: "get git stats agent",
  instructions:
    "Youre an agent who takes the github username of an user and returns the git stats of the username",
  tools: [getGitStats],
});

Now we create the actual AI agent.

This agent knows:

Its role
Its responsibility
Which tools it can use

The tools array connects the tool to the agent. This means the agent can now use getGitStats() automatically.

Step 6: Running the Agent

const query = "Hey agent, my github id is Asad-bot07";

run(getGitAgent, query).then((result) => {
  console.log(result.finalOutput);
});

This sends the user query to the agent.

The SDK handles:

understanding the request
choosing the correct tool
executing the tool
generating the final answer

Finally:

console.log(result.finalOutput)

prints the response.

Final Output

Run Your Project

node index.js

This will execute your AI agent and display the output in the terminal.

The followers of Asad-bot07 is : 120

Simple, clean, and powerful.

Why Use OpenAI Agent SDK?

Because it helps you build real AI applications faster.

Benefits:

Cleaner code
Automatic tool calling
Better architecture
Scalable projects
Real-world integrations
Production-ready systems

Instead of writing complex logic manually, the SDK handles most of it for you.

Real-World Use Cases

You can build agents for:

Customer support
GitHub assistants
Personal productivity apps
Research assistants
AI coding assistants
Business automation
Finance tracking
CRM systems
SaaS products

This is just the beginning.

Conclusion

AI Agents are the future of software. They are not just chatbots—they are systems that can think, decide, and act. With the OpenAI Agent SDK, building these agents becomes much easier. In this project, we created a GitHub Stats Agent that fetches follower counts using tools and API calls. This is a simple example, but the same concept can be scaled into powerful production systems. Once you understand Agents + Tools + Execution Flow, you can build almost anything. And that is where the real power begins.

Next Step

Try extending this project by adding:

Repository count
Following count
Public gists
Starred repositories
Latest commits

This will help you understand how powerful agent-based development can become.

Source Code

You can find the complete source code for this project in the GitHub repository below:

🔗 GitHub Repository: https://github.com/snackoverflowasad/Git-Agent

GitHub new space shooter README — Step-by-Step Guide

Asad Hussain — Mon, 02 Feb 2026 12:03:32 +0000

Hey folks!! In this post, I’ll show how I set up a GitHub Actions workflow that generates and updates a Space Shooter GIF no manual work required. The workflow runs on a schedule (daily) and can also be triggered manually, automatically committing the updated GIF back to the repository.

Setup Instructions (Step-by-Step)

STEP 1:

Create a Repository On GitHub (For those who don't have a README.md → New Repository → Name it (Same as your username) → Make it PUBLIC → Create it. Inside it, create README.md

STEP 2:

Create a folder named .github ( as shown above in the file structure ).
Inside it create another folder named workflows.
Inside it create a file name space-shooter.yml.
Follow the project structure given below :

.github/
└── workflows/space-shooter.yml
assets/
└── space-shooter.gif // will be created automatically
README.md // your original readme.md

Step 3:

Paste the code inside the space-shooter.yml file

name: Update Space Shooter GIF

on:
  schedule:
    - cron: "0 0 * * *"
  workflow_dispatch:

permissions:
  contents: write

jobs:
  generate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Create assets directory
        run: mkdir -p assets

      - uses: czl9707/gh-space-shooter@v1
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          output-path: assets/space-shooter.gif
          strategy: random

Save the file, commit the changes and push it to the main branch (recommended)

Step 4:

RUN the workflow

Open the repo -> click on the action tab -> select update space shooter GIF (On the left sidebar) -> click run workflow (On the right side)
Wait for it to complete.

Step 5:

Once the workflow is completed, paste this code in your READM.md file

<p align="center">
 <img src="assets/space-shooter.gif" />
</p>

Contact

I am open to discussing opportunities and collaborations. Connect with me:

Email: techie.asad.dev@gmail.com
GitHub: @asad-bot07
LinkedIn: Asad Hussain

Portfolio Link

Explore my work and professional journey: https://asadtechdev.vercel.app

Why NumPy and Pandas Are Essential: A Beginner’s Realization in AI/ML

Asad Hussain — Wed, 10 Dec 2025 19:49:40 +0000

Hello everyone, I am back after a very long time. I was busy with my end-semester exams, which went pretty good. You guys know how college exams go. Anyway, this fall I finally started learning about AI and ML since it has become an important part of computer science today.

For a long time, I wondered why AI and ML require NumPy and Pandas. I knew a little about NumPy from our curriculum, mostly the basics like converting data into arrays and performing simple operations. But once I actually started learning AI and ML, I understood the real power behind these libraries.

NumPy is way more than just 1D or 2D arrays. It gives precise control over big data sets and makes complex mathematical tasks easier. Features like random seeding, broadcasting, reshaping, vectorization, and efficient matrix operations changed my perspective completely. Coming from a Java background, starting with C in my first semester, operations like matrix multiplication or elementwise tasks always seemed difficult to operate. These are as simple as working with basic variables. The difference in speed is also huge because NumPy is optimized for numerical computation.

Then I started looking at Pandas. I had never worked with Pandas, and I always thought it was just like NumPy. Sure enough, when I started using it, I found out how powerful this library truly is when working with structured data. Reading files of types CSV, Excel, JSON, or even SQL outputs can easily be done. Functions like head(), tail(), iloc, and loc make filtering or selecting data very intuitive. The describe() method instantly gives you statistical information like mean, count, standard deviation, among others useful in getting a fast feel of a dataset.

Now, I dive into Pandas, where I will go through cleaning, preprocessing, handling missing values, grouping, data aggregation, and transformation. This is all very important because good quality, well-structured data is at the heart of every AI or ML model: a model is only as good as the data quality that it is trained on.

Conclusion

The starting AI and ML made me realize that NumPy and Pandas are not just optional tools, but a must-have for any data worker who wants to do the job efficiently and with meaning. NumPy handles the heavy mathematical lifting while Pandas does the organizing, cleaning, and preparing of data for modeling. Knowing these libraries has ironed my path into AI and ML so much, as well as smoother. I am looking forward to continued learning and exploration of more advanced concepts ahead.

How being lazy made me a better developer

Asad Hussain — Thu, 13 Nov 2025 17:41:51 +0000

Before I start lemme inform you that I don’t really have hobbies other than coding and gaming (which everyone loves anyway), and I’m honestly Z+++ lazy so lazy that my mom scolds me daily, but in a weird way this laziness pushes me to build things that make my own life easier. Me and my college friend (we’re in second year btw, and I probably have an exam next week idk whenever you’re reading this but yeah) always daydream about getting placed in some U.S. company or FAANG and earning in dollars, and every single time we end up Googling “convert USD to INR” to calculate our imaginary expenses and savings for our imaginary Lamborghini. I was too lazy to open Google and type it again and again, so I built a currency converter using React, Tailwind CSS, and a free API default set to USD -> INR so I don’t have to do anything. It’s not a big project, but it taught me API handling in React for the first time, and now I call APIs like they’re my girlfriend (I don’t have one). Another time, when boredom hit hard, I built a death calculator using TypeScript, Motion, and some basic JS logic pure brainrot but fun. These projects may look silly, but they help me learn and survive the boring days. And yes, I don’t only make dumb stuff; it’s just fun to share. I’m currently rebuilding another project purely out of laziness. All my projects are on my GitHub if you want to check them out see you soon with more chaotic builds.

Hey there, do you know me?

Asad Hussain — Sun, 09 Nov 2025 18:39:36 +0000

I’m Asad Hussain, currently doing my Bachelor of Computer Applications at IEM Kolkata. I’ve been into coding for a while now and honestly, it’s the only hobby that stuck 😭😭😭
I’m that kind of person who’s lazy in the smartest way possible. If there’s a long method to do something, I’ll find a shorter and smarter way to make it work. I love building stuff that solves problems or just makes life a little more fun.
When I get bored or tired of building complex projects for practice, I end up creating random things that may not change the world but definitely make me laugh. Like once, I built GrimTok(which i call atank) a totally useless (but hilarious) project that predicts my death. My mom thought it was real, and let’s just say… she wasn’t amused 💀🤣.
This is my first post here, and I’ll be sharing my projects, thoughts, and random coding experiments that keep me sane. If you’re into coding, memes, or just making weird but fun stuff you’re in the right place.

Thanks for reading :)

DEV Community: Asad Hussain

Introduction to OpenAI Agent SDK

What is an AI Agent?

What is the OpenAI Agent SDK?

How Does an AI Agent Work?

Example Flow

What Are Tools?

Project Example: GitHub Stats Agent

Installation

Step 1: Create a New Node.js Project

Step 2: Install Required Dependencies

What each package does:

Step 3: Create a .env File

Step 4: Basic Project Structure

Code Breakdown

Step 1: Import Required Packages

What these packages do:

@openai/agents

axios

zod

dotenv

Step 2: Creating a Tool

Step 3: Defining Tool Parameters

Step 4: Tool Execution Logic

What happens here:

Step 5: Creating the Agent

This agent knows:

Step 6: Running the Agent

Final Output

Run Your Project

Why Use OpenAI Agent SDK?

Benefits:

Real-World Use Cases

Conclusion

Next Step

Source Code

🔗 GitHub Repository: https://github.com/snackoverflowasad/Git-Agent

GitHub new space shooter README — Step-by-Step Guide

Hey folks!! In this post, I’ll show how I set up a GitHub Actions workflow that generates and updates a Space Shooter GIF no manual work required. The workflow runs on a schedule (daily) and can also be triggered manually, automatically committing the updated GIF back to the repository.

Setup Instructions (Step-by-Step)

STEP 1:

STEP 2:

Step 3:

Step 4:

RUN the workflow

Step 5:

Contact

Portfolio Link

Why NumPy and Pandas Are Essential: A Beginner’s Realization in AI/ML

Conclusion

How being lazy made me a better developer

Hey there, do you know me?

Step 3: Create a `.env` File

`@openai/agents`

`axios`

`zod`

`dotenv`