AgentKit vs GPTs vs Plugins: What’s the difference in 2025?

Trying to keep up with OpenAI's platform can feel like a full-time job. Just when we all got a handle on Plugins, GPTs became the next big thing. Now, with AgentKit and "Apps in ChatGPT" entering the scene, the ground has shifted yet again. It's easy to get confused about which tool is for what.

This guide is here to clear things up. We’ll break down the real differences between Plugins, GPTs, and the new AgentKit, looking at what each is good for and, just as importantly, where they fall short.

Because while these tools are impressive, they often require a ton of developer effort to become genuinely useful for a business. Let's get into it and figure out which one makes sense for you.

AgentKit vs GPTs vs Plugins: A quick definition of terms

Before we get into a side-by-side comparison, let's make sure we’re all talking about the same things. Understanding what each tool was originally built for is the first step in picking the right one.

What were ChatGPT Plugins?

Remember ChatGPT Plugins? They were OpenAI's first shot at letting ChatGPT talk to the live internet and other services. A developer could build a plugin, get it approved, and then users could manually enable it to do things like book flights or check restaurant reviews.

They were a decent first step, but the experience was a bit clunky for users. These days, they're mostly being replaced by the smoother "Apps in ChatGPT" model, making them more of a legacy feature than something you’d build with today.

What are GPTs?

GPTs really took off because they made it possible for anyone to create a custom chatbot, no coding needed. Using a simple chat-based setup, you can build your own specialized version of ChatGPT.

You can feed it your own files (like PDFs), give it custom instructions to shape its personality, and even connect it to outside tools through "actions." They're great for spinning up a personal research assistant, an internal Q&A bot for company policies, or just a fun chatbot for a niche hobby. But as easy as they are to create, they aren't built for the heavy lifting of business operations, like managing customer support queues.

What is AgentKit?

AgentKit is the newest and most heavy-duty option in the lineup. It's a full-blown toolkit made specifically for developers to build and manage production-ready AI agents from the ground up. Think of it less like a single tool and more like a whole workshop.

It’s made up of a few key pieces:

• Agent Builder: A visual, drag-and-drop canvas for mapping out complex, multi-step agent behaviors without writing a mountain of code.

• ChatKit: A UI kit with the building blocks you need to drop the chat experience into your own website or app.

• Connector Registry & Evals: Tools to help manage data connections and test your agent's performance to make sure it's reliable.

AgentKit is definitely the "pro" option for building complex, autonomous systems. The key thing to remember, though, is that it's a developer toolkit, not an out-of-the-box business solution. You still have to build, host, and maintain everything yourself.

This workflow illustrates the key components of OpenAI's AgentKit, showing how the Agent Builder, ChatKit, and other tools work together.

AgentKit vs GPTs vs Plugins: A head-to-head comparison

Lining them up side-by-side really clarifies the different roles they play. GPTs are for creators, while AgentKit is for coders.

Feature	ChatGPT Plugins (Legacy)	Custom GPTs	OpenAI AgentKit
Primary Audience	End-users (to enable)	Non-technical users & creators	Developers & technical teams
Primary Goal	Extend ChatGPT's capabilities	Create personalized chatbots	Build production-grade AI agents
Setup & Creation	Required developer submission	No-code, conversational setup	Visual builder & SDK (code)
Customization	Limited to predefined actions	Custom instructions, knowledge files	Full control over logic, tools, actions
Deployment	Within ChatGPT interface	Within ChatGPT, shareable link	Embeddable in any app/website
Best For	Basic, real-time data lookups	Quick prototypes, internal Q&A	Complex, autonomous workflows

Practical use cases and key limitations

Knowing what each tool is doesn't tell you when to use it. Let's walk through some real-world scenarios to see where each one works well and where it creates more problems than it solves.

When to use GPTs (and when not to)

GPTs are perfect for quick, specific tasks where you don't need a deeply integrated or scalable solution.

• Good for: Building a personal research assistant that knows all your saved articles, creating an internal HR bot that can answer questions from the employee handbook, or designing a fun AI that talks like a pirate. They are fantastic for personal projects and internal productivity hacks.

• The drawbacks:

  • They're stuck inside ChatGPT. You can't just drop a custom GPT onto your website and call it your new customer support bot. Your users would have to log into ChatGPT to use it, which isn't a realistic option for most businesses.

  • Your control is limited. You can give a GPT instructions, but you don't get precise control over its workflow, how it handles errors, or how it navigates multi-step problems. It’s a bit of a black box compared to a true agent framework.

  • They aren't built for scale. GPTs just aren't designed to handle the high-volume, critical demands of frontline customer service or other core business functions.

When to use AgentKit (and its hidden costs)

AgentKit is the way to go when you have a development team ready to build a completely custom, autonomous agent from scratch.

• Good for: Building a custom sales bot that finds leads and drafts personalized outreach, creating a financial analysis agent that processes market data, or developing a complex IT support workflow that can troubleshoot issues and create tickets automatically.

• The drawbacks and hidden costs:

  • It’s a toolkit, not a solution. You get all the parts, but you have to assemble the car yourself. You still need developers to design, build, deploy, and maintain the agent, which is a major commitment of time and money.

  • The user interface is your problem. ChatKit gives you the components, but you're on the hook for building, hosting, and managing the entire front-end experience. It's not a simple copy-paste; it takes real development to make it feel seamless for users.

  • Connecting to your business is complex. Getting an agent to talk to your core business systems (like a helpdesk or e-commerce platform) means writing custom API integrations. That work isn't just hard to set up; it also needs constant maintenance as your other systems evolve.

The alternative: An all-in-one AI agent platform

AgentKit is a fantastic set of tools for teams with the engineering firepower to build from the ground up. But let's be honest, most businesses just need a solution that works now, without kicking off a multi-month development project.

That's the gap a platform like eesel AI is designed to fill. It's built specifically for business needs like customer service and internal support, giving you the power of a sophisticated AI agent without the DIY headaches and engineering overhead.

Go live in minutes, not months

The difference in setup time is huge. Building a production-ready agent with AgentKit can easily take weeks or months. With eesel AI, you can be up and running the same day.

It starts with one-click integrations for your helpdesk. You can instantly connect eesel AI to platforms like Zendesk, Freshdesk, and Intercom, which means you don't have to write any custom API code.

Even better, eesel AI lets you test things out safely. Its powerful simulation mode runs your AI agent on thousands of your past support tickets in a sandbox environment. You can see exactly how it would have replied, get solid forecasts on resolution rates, and tweak its behavior before it ever talks to a real customer. That kind of risk-free testing is something you’d have to build entirely from scratch with a generic toolkit.

Unify your knowledge and control the workflow

An AI agent is only as smart as the information you give it. With AgentKit, you have to build data pipelines to feed your agent knowledge. On the other hand, eesel AI instantly connects to and learns from all your existing sources. It absorbs your past tickets, help articles, and internal wikis in places like Confluence or Google Docs to understand your business from day one.

You also get a fully customizable workflow engine, so you're always in the driver's seat. A simple interface lets you define which types of tickets the AI should handle, how it should respond, and what actions it can take, like tagging a ticket or escalating it to a human. This gives you the fine-grained control businesses need, without writing any code.

Comparing pricing and predictability

It’s easy to just look at subscription fees, but the total cost of ownership is much bigger. It includes development time, ongoing maintenance, and the wild card of usage-based pricing.

The unpredictable costs of OpenAI's AgentKit

AgentKit's pricing is based on API usage. You pay for every token the model uses, but you also get hit with per-tool fees (like a charge per session for the code interpreter) and data storage costs.

For any business, this model can be a budgeting headache. A busy support month could lead to a shockingly large bill. That lack of predictability makes it tough to forecast expenses.

A screenshot of the OpenAI AgentKit pricing page, highlighting the usage-based costs discussed in the article.

eesel AI's transparent, predictable pricing

eesel AI offers a much clearer path with its transparent pricing. Plans are based on features and a set number of AI interactions, and there are never any per-resolution fees. You know exactly what you’re paying each month.

This lets you budget with confidence and scale your support without worrying that your success will lead to runaway costs. The Team, Business, and Custom plans are straightforward, making it easy to see the value and choose the right fit.

AgentKit vs GPTs vs Plugins: Choose the right tool for the job

OpenAI has created an amazing ecosystem, but you have to match the tool to the task.

• Plugins are pretty much legacy tech at this point.

• GPTs are perfect for simple, no-code bots for personal use or quick internal tasks.

• AgentKit is a powerful toolkit for engineering teams that need to build a highly custom AI agent from scratch.

• eesel AI is the production-ready platform for businesses that want powerful AI agents for customer support and internal helpdesks, without the massive engineering project.

If you have a team of developers ready for a custom build, AgentKit is a great starting point. But if you need to automate support and see a return on your investment this week, an all-in-one platform is the smarter way to go.

Ready to see how fast you can get a powerful AI agent working for your team? Start a free trial of eesel AI or book a demo today.