OpenAI AgentKit vs Anthropic API: Which is best for AI agents in 2025?

Everyone's talking about building AI agents, and if you're in that boat, you've probably noticed two big names popping up: OpenAI and Anthropic. They both offer toolkits to help you build, but they come at it from completely different angles. This isn't just a simple feature-by-feature comparison. We're talking about two distinct philosophies: a polished, all-in-one platform versus a flexible, code-first framework.

So, how do you decide which one is right for your project? I’m going to walk you through the practical differences in how they’re built, what they can do, and what they’ll actually cost you. By the end, you should have a much clearer idea of which path makes sense for your team, or if maybe there's another route worth considering.

AgentKit vs Anthropic API: What are we comparing?

Even though the big search term is "AgentKit vs Anthropic API," what we’re really looking at is OpenAI's integrated agent-building platform versus Anthropic's developer-focused tools that sit on top of their API. They both get you to a similar destination, an AI agent, but their starting points couldn't be more different.

What is OpenAI's AgentKit?

OpenAI's AgentKit is a set of tools aimed at getting you from a rough idea to a working AI agent as smoothly as possible. Think of it as a complete package that puts a visual, no-code experience front and center.

Its main components are:

• Agent builder: A visual, drag-and-drop canvas where you can map out your agent's logic without having to write any code.

• Connector registry: A library of ready-made integrations for connecting to other tools and services.

• ChatKit: A toolkit that helps you embed the finished agent right into your website or app.

The whole idea behind AgentKit is to be fast and convenient. It's built for teams that want to get from a prototype to a live product in record time, as long as they don't mind living and working entirely inside the OpenAI ecosystem.

A workflow diagram illustrating the key components of OpenAI's AgentKit, including the Agent Builder, ChatKit, and Connector Registry.

What is the Anthropic API for agents?

Anthropic went in a different direction. Their approach is all about the code, giving developers the Claude Agent SDK, which is a software library for building agents programmatically. When people talk about using the "Anthropic API" for agents, this is what they mean.

A couple of key ideas to grasp here are:

• SDK-first: The goal is to give developers total control. You're writing the code, which means you can weave the agent deeply into your existing systems.

• Model Context Protocol (MCP): This is an open protocol Anthropic is pushing to create a standard for how agents connect to tools. It's built with security and auditing in mind, giving you a clear view of what the agent is allowed to do.

Anthropic's whole setup is designed for control, flexibility, and the kind of security bigger companies need. It gives your team the power to run agents on your own servers, manage exactly how data flows, and keep full ownership over the entire environment.

The core philosophies in the AgentKit vs Anthropic API debate

This really gets to the heart of the difference between them. OpenAI is offering a beautiful, curated garden where everything is designed to work together perfectly. Anthropic, on the other hand, hands you a box of powerful Lego bricks and an open field to build whatever you want.

OpenAI's approach: Speed and convenience at a cost

You can't deny the appeal of AgentKit's "walled garden." The visual builder, built-in connectors, and managed infrastructure make getting started incredibly fast. You could genuinely have a working prototype up and running in a few hours instead of a few weeks.

But that ease comes with some pretty big trade-offs. The main limitations you'll run into are:

• Vendor lock-in: AgentKit is built to work with OpenAI's models. While you might be able to shoehorn others in, it's not a natural fit. If you decide to switch model providers later, it could be a difficult and expensive process.

• Less control: Since OpenAI manages everything behind the scenes, you have less say over your data, compliance, and the agent's logic. If you're in a regulated industry like finance or healthcare, this could be a deal-breaker.

• Rigid workflows: The visual builder is fantastic for straightforward tasks. But if you're building something with complex logic and a lot of 'if-this-then-that' steps, the visual canvas can turn into a tangled web that’s a nightmare to manage and fix.

Anthropic's approach: Control and flexibility for a price

The Claude Agent SDK puts you completely in charge. This "open playground" style means you can run your agents on your own machines, connect them to any internal tool using MCP, and keep total control over your data.

All that power, however, doesn't come for free:

• More upfront work: Building with a software development kit just takes more engineering time. Your team is on the hook for setting up servers, managing the agent's execution, and building out all the tool integrations.

• Complexity: Being explicit is great for control, but it can also make things more complicated and slow you down when you just want to build a simple prototype. A task that takes a few clicks in AgentKit might end up being a hundred lines of code.

The third way: A platform without the prison

Both of these options force you into a tough choice: give up control to move fast, or give up speed to have control. For most businesses just trying to solve a real-world problem, neither one feels quite right. You want the power of a custom solution without the pain of building it all from scratch.

But what if you need both speed and control? This is the gap that a purpose-built platform like eesel AI tries to fill. It gives you the simplicity of a platform with the flexibility of a framework. You get a self-serve, no-code experience that can be live in minutes, but you’re never boxed in by one model or a limited set of tools. You can connect all your existing knowledge sources and set up custom actions without needing a team of developers.

Feature showdown: AgentKit vs Anthropic API

Let's dig into the practical, day-to-day differences your team will actually experience when working with these tools.

Feature	OpenAI AgentKit	Anthropic Agent SDK	The eesel AI Advantage
Setup & onboarding	Fast and visual, driven by a user interface. Very little infrastructure needed. Best for product teams and quick prototyping.	Code-first and methodical. You'll need to install the SDK and set up an MCP server. Best for engineering teams that need control.	Set up in minutes. It’s a self-serve platform with one-click integrations for tools you already use, like Zendesk or Confluence, so you can get started right away.
Tool integration	Works with a curated Connector Registry and built-in tool nodes. Simpler for common integrations.	Uses the open Model Context Protocol (MCP). More flexible for custom or internal tools, but takes more work to set up.	Connect tools without the hassle. You can define custom API actions and link to internal tools through a straightforward UI, skipping the need to build and manage your own MCP servers.
Model flexibility	Primarily designed for OpenAI models. Support for other providers is limited.	Model-agnostic by design. You can use different models, but you have to manage the integration yourself.	Focuses on your knowledge. While it uses high-quality models, the real strength is how it trains on your specific data from past tickets, documents, and help centers. This means you get answers that are relevant to your business, no matter which LLM is under the hood.
Deployment & hosting	Runs on OpenAI's managed cloud. Easy to deploy, but gives you less control over your data.	Can be run on your own servers, on-premise, or in a private cloud. Gives you full control over your data and environment.	Managed security and hosting. You get options like EU data residency and strong security controls without having to manage the infrastructure yourself.
Testing & validation	Includes built-in evaluation tools (Evals) for developers to test agent performance.	Testing is up to the developer. You'll need to build your own testing setups to make sure the agent is behaving correctly.	Simulate before you launch. A simulation mode lets you test your agent on thousands of your past tickets before it goes live. This gives you a realistic preview of its performance and how many issues it can actually resolve.

Pricing and TCO: AgentKit vs Anthropic API

Neither OpenAI nor Anthropic charges a fee for the "kit" or "SDK" itself, but don't let that fool you. The total cost of ownership (TCO) is something you really have to think about, and it's way more than just the initial price tag.

The hidden costs of DIY

Both platforms charge you based on the number of tokens their language models (like GPT-4o or Claude 3.5 Sonnet) process. This usage-based pricing means your costs can be hard to predict and will grow with every single customer conversation.

But the biggest hidden cost is the developer time it takes to build, connect, test, and maintain the agent. This isn't a one-time setup; it's work that never really stops. For Anthropic's SDK, you also have to add in the costs of hosting and managing the servers for your agent and its tools. When you add it all up, the TCO for a DIY agent can easily climb into the tens of thousands of dollars per year.

A screenshot of the OpenAI API pricing page, relevant to the discussion on total cost of ownership in the AgentKit vs Anthropic API comparison.

The eesel AI alternative: Predictable cost and immediate ROI

eesel AI’s approach is different, focusing on predictable costs. The plans are based on a flat monthly or annual fee, so you aren't charged for every single resolution. This means you won’t get a nasty surprise on your bill after a busy month.

With eesel AI, there are no hosting, maintenance, or hidden infrastructure costs to worry about. The real saving, though, is time. Instead of your team spending months building an agent from scratch, you can have a working AI solution deployed in a few minutes. The benefits start showing up right away, not after a long development project.

Choosing the right path: AgentKit vs Anthropic API

So, how do you make the call? It really boils down to your priorities and what resources you have.

• Choose AgentKit if: You're already all-in on the OpenAI ecosystem, need to build a simple, customer-facing agent as fast as possible, and you're okay with the limitations of a closed system.

• Choose Anthropic's SDK if: You have a dedicated engineering team, need absolute control over your data and infrastructure, and you're prepared for a much longer and more expensive development project.

The real question for most businesses isn't just about which platform to use; it's about strategy. Do you want your team to become experts in AI infrastructure, or do you just want to solve a business problem? Because let’s be honest, building an agent from scratch with either of these frameworks is a huge commitment of time, money, and engineering effort.

For most companies, the goal is to improve customer support or make internal work easier, not to build a whole new AI tech stack from the ground up. eesel AI offers a more direct path. You get the benefits of a custom-trained AI agent without the headache, expense, and risk of building one from the ground up. It’s the best of both worlds: a powerful, flexible, and secure platform that you could launch this afternoon.

Ready to see what a production-ready AI agent can do for you? Start your free trial with eesel AI or book a demo to learn more.