How I Built My First OpenAI AgentKit Agent in an Afternoon (And You Can Too)

TL;DR:

• OpenAI AgentKit enables fast, beginner-friendly agent building with visual tools and ready-made SDKs.
• Prerequisites: Python or JS/TS, basic CLI skills, OpenAI API key, Node/Python installed.
• Step-by-step: Set up environment, install SDK, run a “hello agent,” add tools, debug issues, deploy, and monitor.
• Start simple, then add complexity (tools, networks, connectors, chat UIs).
• Use official docs, community guides, and visual builders for the fastest results.
• Building your first AI agent is now an afternoon project—not a multi-week ordeal.

Introduction

Building your own AI agent used to mean weeks of wrestling with fragmented tools, cryptic documentation, and endless trial-and-error. But with the release of OpenAI AgentKit, I went from absolute beginner to running a real, functioning agent in less than a day. If you’ve ever wondered how to build an agent—whether for fun, learning, or automating tasks—this guide is for you.

In this comprehensive walkthrough, I’ll show you, step by step, how I built my first OpenAI AgentKit agent in a single afternoon. Along the way, you’ll learn the essentials: what AgentKit is, how to set up your environment, write and run your first agent, add tools, debug common issues, and scale up to more advanced workflows. Plus, I’ll share the exact code I used, lessons learned, and how you can avoid the mistakes most beginners make.

Ready to build your first agent? Let’s dive in.

What Is OpenAI AgentKit (And Why Should You Care)?

OpenAI AgentKit is a new, all-in-one toolkit for building, deploying, and optimizing AI agents. Unlike the old days, where you had to glue together SDKs, custom connectors, manual prompt pipelines, and hacky frontends, AgentKit gives you:

• Agent Builder: A visual canvas to design agent workflows with drag-and-drop logic.
• Connector Registry: A central hub to manage data and tool connections.
• ChatKit: Tools to embed customizable, chat-based agent experiences in your apps.
• Automated Evaluation: Datasets, trace grading, and prompt optimization to improve your agents.
• Multi-agent Orchestration: Compose networks of specialized agents for complex tasks.

Why does this matter? Because it means you can go from idea to working AI agent—complete with custom tools and chat UIs—in hours, not weeks.

Prerequisites: What You Need Before You Start

Before you jump in, here’s what you’ll need:

• Basic familiarity with Python or JavaScript/TypeScript.
• Terminal/CLI knowledge: Installing packages, running scripts, setting environment variables.
• An OpenAI API key: Get one here.
• Node.js (18+) and npm (9+) or Python 3.9+ installed on your machine.

Optional but helpful: GitHub account (for cloning starter templates), code editor (VS Code recommended).

Step 1: Set Up Your Environment

For Python Users

1. Create a new project folder and virtual environment:

mkdir agentkit-hello && cd agentkit-hello
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate

1. Install the OpenAI Agents SDK:

pip install openai-agents

1. Set your OpenAI API key:

export OPENAI_API_KEY="sk-your-key" # macOS/Linux
# PowerShell (Windows)
$Env:OPENAI_API_KEY = "sk-your-key"

For JavaScript/TypeScript Users

1. Create a new Node project:

mkdir js-agent && cd js-agent
npm init -y
npm install @openai/agents zod@3

1. Set your API key as an environment variable:

export OPENAI_API_KEY="sk-your-key"

Step 2: Your First “Hello Agent” Run

Let’s write the simplest agent possible—a bot that responds with a two-line haiku.

Python Example

# hello_agent.py
from agents import Agent, Runner

agent = Agent(
name="Assistant",
instructions="You are a helpful assistant. Keep responses brief.",
)

result = Runner.run_sync(agent, "Write a two-line haiku about recursion.")
print(result.final_output)

Run it:

python hello_agent.py

If you see a haiku-style response, congrats! Your environment is ready.

JavaScript Example

// tool_agent.js
import { Agent, run, tool } from "@openai/agents";
import { z } from "zod";

// A simple, deterministic tool that adds two numbers
const addTool = tool({
name: "add",
description: "Add two numbers and return the sum.",
parameters: z.object({
a: z.number(),
b: z.number(),
}),
execute: async ({ a, b }) => {
const sum = a + b;
return `The sum of ${a} and ${b} is ${sum}.`;
},
});

const agent = new Agent({
name: "Math helper",
instructions: "When a calculation is needed, call the add tool.",
tools: [addTool],
});

const prompt = "Please add 7 and 13.";
const result = await run(agent, prompt);
console.log(result.finalOutput);

Run it:

node tool_agent.js

Step 3: Add Your First Tool/Action

Agents become powerful when you give them tools. Here’s how to add a deterministic “add” tool in JS (see above), or a function tool in Python.

Why tools? They let your agent call APIs, fetch data, take actions, or run custom logic—making it much more than just a chatbot.

Step 4: Debugging and Troubleshooting

Everyone hits snags on their first attempt. Here are the most common issues—and how to fix them:

• Authentication errors: Double-check your OPENAI_API_KEY is set in the same terminal session.
• Module not found: Ensure virtual environment is activated (Python), or packages are installed (JS).
• Tool schema errors: Verify parameter names and types match exactly.
• Model not found: Make sure your OpenAI account has access to the selected model.
• Rate limits/network errors: Retry or keep your demo lightweight.

If you’re stuck, check the Agents SDK GitHub repo or community forums.

Step 5: Moving Beyond “Hello World”—Building a Real Agent

Now that the basics work, let’s build a practical agent in AgentKit:

1. Define your agent with a purpose and system prompt.
2. Add one or more tools (fetch data, call APIs, trigger actions).
3. Test locally, then deploy with the Agent Builder’s visual interface.
4. Use the Connector Registry to link databases, APIs, and external systems.
5. Embed your agent in a chat UI with ChatKit.

Example: A Database Admin Agent

import { createAgent, anthropic } from '@inngest/agent-kit';

const dbaAgent = createAgent({
name: 'Database administrator',
description: 'Provides expert support for managing PostgreSQL databases',
system: 'You are a PostgreSQL expert database administrator. You only provide answers to questions related to PostgreSQL schema, indexes, and extensions.',
model: anthropic({
model: 'claude-3-5-haiku-latest',
defaultParameters: {
max_tokens: 1000,
},
}),
});

Add a tool:

import { createTool } from '@inngest/agent-kit';
import { z } from 'zod';

const saveAnswerTool = createTool({
name: "save_answer",
description: "Save the answer to the questions",
parameters: z.object({
z.string(), }), handler: async ({ answer }, { network }) => { network.state.data.dba_agent_answer = answer; }, });

Step 6: Experiment With Agent Networks

AgentKit lets you compose multiple agents (e.g., a database admin + security expert) into a network that can tackle complex, multi-step queries with routing and shared state.

import { createNetwork } from "@inngest/agent-kit";

const devOpsNetwork = createNetwork({
name: "DevOps team",
agents: [dbaAgent, securityAgent],
router: async ({ network }) => {
if (!network.state.data.security_agent_answer) {
return securityAgent;
} else if (network.state.data.security_agent_answer && network.state.data.dba_agent_answer) {
return;
}
return dbaAgent;
},
});

Step 7: Deploy and Monitor

Deploy your agent or agent network using AgentKit’s built-in HTTP server and Inngest platform. Set up environment variables, deploy to your preferred cloud (Vercel, AWS, GCP), and sync with the Inngest dashboard for live monitoring and debugging.

Deployment in a nutshell:

1. Install the Inngest SDK (npm install inngest).
2. Serve your AgentKit network over HTTP.
3. Configure environment variables for your cloud provider.
4. Sync your deployment in the Inngest dashboard.
5. Monitor runs, inspect traces, and optimize performance.

Lessons Learned: What I Wish I Knew Before I Started

• Start with a minimal agent and build up. Don’t try to create a giant, multi-tool agent on day one.
• Use environment variables for secrets. Never hardcode API keys.
• Check compatibility: Make sure you’re using the latest AgentKit, Node, or Python versions.
• Leverage the visual builder. It’s faster and more intuitive than wiring everything by hand.
• Read the official docs and community examples. Most issues have already been solved by someone else.

Conclusion: Build Your First Agent Today

If you’re even remotely curious about building AI agents, AgentKit makes it possible to go from zero to working agent in a single afternoon. The platform’s visual tools, ready-made connectors, and robust SDKs remove the friction and let you focus on what matters—solving real problems.

I built my first agent in a handful of hours. You can do it too.

What will your first agent do?

Metaflow AI: Business Results, Not Just Developer Tools

While OpenAI's AgentKit offers exciting possibilities for developers, Metaflow AI takes a fundamentally different approach—focusing on business outcomes first. Here's why this matters:

• Results-driven vs. developer-centric: Metaflow AI prioritizes actual business results rather than merely providing developer tools. It's about what you can achieve, not just how you build it.
• No-code/low-code advantage: While AgentKit requires significant technical expertise, Metaflow AI lets business users create powerful workflows with minimal coding—democratizing AI across your organization.
• Growth-ready from day one: Metaflow AI includes prebuilt, battle-tested agents and workflows for all aspects of Growth from inbound automation, to GTM engineering, fully furnished and ready to plug-n-play into your Martech stack.
• Focus on tangible ROI: Instead of emphasizing technological capabilities, Metaflow AI centers on measurable business impact—increased efficiency, revenue growth, and cost reduction.

Ready to focus on outcomes instead of development complexity? Try Metaflow AI and experience the difference between a business solution and a developer toolkit.

Frequently Asked Questions about AgentKit

🧭 Getting Started: Basics & Overview

Q1. What is OpenAI Agent Builder?

OpenAI Agent Builder is a new framework that lets developers and non-developers create AI agents that can perform multi-step tasks, call APIs, and connect to external tools—all powered by GPT models. It offers a visual and code-based interface for assembling autonomous workflows.

Q2. What is AgentKit, and how is it related to OpenAI Agent Builder?

AgentKit is the SDK (software development kit) behind OpenAI’s Agent Builder. It provides the core APIs and building blocks—like tools, actions, and workflows—that developers use to define agent logic programmatically. Think of AgentKit as the code layer; Agent Builder as the UI layer.

Q3. What can I build with OpenAI Agent Builder or AgentKit?

You can build autonomous agents that perform research, draft content, analyze data, manage tasks, or integrate with tools like Slack, Notion, or GitHub. Developers can extend them into full automation systems or integrate them into SaaS products.

Q4. Is OpenAI Agent Builder free to use?

The Agent Builder is expected to follow OpenAI’s standard pricing model—usage-based on GPT calls and API requests. Some developer tools may remain free for experimentation.

Q5. Who is AgentKit for?

AgentKit is built for developers, technical product managers, and automation engineers who want to design complex, multi-step AI workflows with API access and persistent context.

Q6. How do I get started with OpenAI Agent Builder?

You can start by signing into your OpenAI account, enabling the “Agents” or “Agent Builder” beta (if available), and following the walkthrough or template. For local development, install agentkit from npm or pip depending on your stack.

Q7. How do I install AgentKit?

Once you have OpenAI’s developer access, run:

npm install @openai/agentkit

or

pip install openai-agentkit

Then import the library and initialize it with your OpenAI API key.

Q8. What are the prerequisites for using AgentKit?

You’ll need an OpenAI API key, Node.js or Python installed, and basic familiarity with API calls and environment variables.

Q9. Where can I find the AgentKit GitHub repo or docs?

The official GitHub repository and documentation will be available at github.com/openai/agentkit once released, along with examples and starter templates.

🧩 Building & Tutorials

Q10. How do I build my first agent using AgentKit?

Start by defining your agent’s role, tools, and goals. Use the AgentKit API to register actions (e.g., “search web,” “summarize text”) and link them with a reasoning loop powered by GPT.

Example:

const agent = new Agent({ model: "gpt-4-turbo", tools: [searchTool, fetchData] });
await agent.run("Find and summarize the latest news about AI agents");

Q11. Is there a step-by-step tutorial for AgentKit?

Yes, most developers start with “Build Your First Agent” tutorials that walk you through environment setup, action creation, tool integration, and testing loops.

Q12. Can I connect my AgentKit agent to external APIs or tools?

Yes. AgentKit supports REST and GraphQL API integration, as well as OAuth-based connections for tools like Slack, GitHub, or Notion. You can define custom tools with authentication and rate limits.

Q13. How do I give my agent memory or context?

AgentKit supports persistent context using vector databases like Chroma, Pinecone, or OpenAI’s native memory API. You can embed and recall previous information to maintain continuity across interactions.

Q14. Can I use AgentKit with LangChain or Dust.tt?

Yes. AgentKit is designed to be modular. It can either replace or complement orchestration frameworks like LangChain, Dust.tt, or LlamaIndex, depending on your architecture.

Q15. How do I debug an AgentKit agent?

AgentKit includes a logging system for tracking thought steps, tool invocations, and errors. You can also use console outputs or connect to an observability tool like LangFuse or OpenDevin.

🚀 Deployment & Workflows

Q16. How do I deploy my agent to production?

You can deploy your agent on any Node.js or Python environment—Vercel, AWS Lambda, or Render. For enterprise-scale setups, containerize the agent and secure it behind an API gateway.

Q17. How do I connect multiple agents together?

You can orchestrate multi-agent workflows by passing outputs from one agent to another. AgentKit supports chaining through function calls or message queues.

Q18. Can I schedule or trigger my agent automatically?

Yes. You can connect your AgentKit setup to schedulers (like Inngest, Airplane.dev, or CRON jobs) or trigger it from webhooks.

Q19. How do I add authentication or user sessions to my agent?

Use JWT tokens or OAuth to manage user sessions. AgentKit’s SDK lets you restrict actions or memory access per user ID.

Q20. How do I monitor agent activity or logs?

AgentKit exposes hooks for logging, telemetry, and analytics.

🔍 Comparison & Ecosystem

Q21. How does OpenAI Agent Builder compare to n8n or Make.com?

Agent Builder focuses on AI reasoning and decision-making, not just automation. While tools like n8n connect APIs, Agent Builder can autonomously plan and decide which steps to take based on goals.

Q22. What’s the difference between OpenAI’s AgentKit and LangChain?

LangChain provides orchestration patterns for LLMs; AgentKit provides a first-party runtime and tool interface tied directly to OpenAI’s models and Model Context Protocol (MCP).

Q23. Can I self-host OpenAI Agent Builder or AgentKit?

Not initially. AgentKit SDKs are open-source, but the hosted Agent Builder interface will likely run on OpenAI’s infrastructure for now.

Q24. Is there a visual interface like a workflow editor?

Yes. Agent Builder provides a canvas-style visual builder to connect reasoning nodes, APIs, and workflows—similar to tools like Metaflow.life

Q25. Where can I find community examples or templates?

You can explore the OpenAI AgentKit GitHub examples or community projects shared on Reddit, Discord, or the OpenAI developer forum.

🧠 Advanced Topics

Q26. How do I create custom tools for my agent?

You can define custom functions with input schemas and attach them to your agent. These tools can perform API calls, database queries, or local computations.

Q27. How do I manage data security and privacy in AgentKit?

Use encrypted environment variables, anonymize sensitive data before sending to models, and ensure compliance with your region’s data policies (e.g., GDPR).

Q28. What is MCP (Model Context Protocol) and how does it relate to AgentKit?

MCP is a protocol for connecting LLMs with external data sources and tools safely. AgentKit leverages MCP to extend model capabilities securely and consistently.

Q29. Can I use OpenAI’s AgentKit for enterprise-grade automation?

Yes. The SDK is designed for scaling and integrates well with enterprise authentication, monitoring, and data pipelines.

Q30. What are best practices for building efficient agents?

Keep reasoning loops short, cache frequent responses, batch API calls, and store intermediate results. Treat your agent like a service with clear contracts and observability.