AgentKit vs Gemini API: A 2025 guide for support teams

Let's be real, the hype around AI agents is everywhere. They’re supposed to automate tricky tasks, streamline workflows, and maybe even figure out the office coffee run one day. With big names like OpenAI and Google dropping powerful developer toolkits, it feels like that future is right around the corner.

But if you’re a busy support or IT manager, this all brings up a pretty practical question: are these tools actually useful for a team that needs to solve real problems today? Building a custom agent sounds impressive, but what happens when you don't have a team of AI engineers just waiting for a new project?

This guide is here to cut through the jargon. We're going to compare OpenAI's AgentKit and Google's Gemini API (via its Agent Development Kit) from a business point of view. We’ll look at what they do, where they come up short, and help you figure out which approach, if any, makes sense for your team.

Understanding OpenAI AgentKit

Think of OpenAI's AgentKit as a fancy LEGO set for building AI agents. It gives developers a framework to piece together agents that can think through problems, use different tools, and chat with users. The whole point is to make the building process a bit smoother than coding everything from the ground up.

It’s made up of a few main components:

• Agent Builder: This is a visual, drag-and-drop canvas where you can map out how the agent "thinks." It makes designing the agent's logic much easier than just staring at a screen full of code.

• Connector Registry: A central spot for managing how your agent connects to different tools and data sources. This could be your company’s internal APIs or other services you rely on.

• ChatKit: A ready-made chat interface you can plug into your website or app. It saves your developers the headache of having to build one from scratch.

A workflow diagram showing the various components of OpenAI's AgentKit, such as the Agent Builder and Connector Registry, which is a key topic in the AgentKit vs Gemini API comparison.

So, what's the takeaway? AgentKit is a definite step toward making agent development more approachable. But let’s be clear: it's still a toolkit for developers. It gives you the engine and the frame, but your team is still on the hook for assembling the car, running all the tests, and handling the tune-ups. It’s not a solution that's ready to drive off the lot for a specific job like customer support.

Understanding the Gemini API

While AgentKit tries to be more visual, Google goes all-in on a traditional, code-heavy framework. Gemini is the family of powerful AI models from Google, and the Agent Development Kit (ADK) is the software library developers use to build agents with them.

The ADK is a classic tool for coders, through and through. It’s built for engineers who want maximum flexibility and control, offering robust command-line tools and the ability to work in languages they already know, like Python. Developers tend to like that it isn’t locked into one specific model and can even work with LLMs from OpenAI or Anthropic.

But all that power has a downside: a pretty steep learning curve. If you browse developer communities, you’ll see the ADK described as complex and over-engineered for simple tasks. Juggling things like conversation history, memory, and making sure different processes run smoothly takes a lot of technical know-how. It's a fantastic toolkit if you're building something highly custom, but it’s a long way from being a simple plug-and-play tool.

AgentKit vs Gemini API: Key differences for business leaders

Instead of getting tangled up in the technical details, let’s compare these platforms on the things that actually matter when you’re trying to make a smart business decision.

Ease of use and implementation

Sure, AgentKit’s visual builder is more intuitive than writing Python code in the ADK. But "low-code" doesn't mean "no work." You still need a technical person to set up the logic, manage different versions, handle deployments, and configure every single integration through its Connector Registry. It makes the "how" a bit simpler, but you still need someone dedicated to figuring out the technical "what" and "where."

The Gemini API and ADK, on the other hand, don't pretend to be for anyone but software engineers. Building a working agent means writing and maintaining a codebase, managing servers, and hunting down tricky bugs. It gives you the most power, but it's the least friendly option for a team without developers.

This is where a purpose-built platform like eesel AI really stands out. eesel is designed so that anyone can use it. A support manager can connect their Zendesk or Intercom account with one click and have a working AI agent up and running in minutes. The focus shifts completely from building an agent to just configuring one that’s already been designed for the job.

Ecosystem and flexibility

AgentKit is built to keep you cozied up inside the OpenAI ecosystem. While you technically could connect to other models like Claude or Gemini, it’s not built to do that easily and requires custom work. This can lead to vendor lock-in, which is a big red flag for any business adopting a new technology at its core.

The ADK is more of an open playground, letting developers plug in different LLMs. That gives you more choice, but it also adds more complexity. Your team suddenly has to manage multiple API keys, make sure performance is consistent across different models, and deal with errors if one of them has an outage.

But maybe we should rethink what "flexibility" really means here. For a support team, the most important kind of flexibility isn't about swapping out AI models. It's about bringing all your scattered company knowledge together in one place. This is where eesel AI shines. Instead of being stuck with a few connectors, you can instantly pull in knowledge from everywhere. eesel learns from old tickets in your help desk, articles in your help center, and internal wikis like Confluence or Google Docs to create one single source of truth for your support team.

Features and capabilities for support teams

Developer toolkits are built to be generalists. They can do a little bit of everything, but they aren't specialized for anything in particular. That becomes pretty clear when you compare their features to a solution that was designed from day one for support teams.

Feature	OpenAI AgentKit	Google ADK / Gemini API	eesel AI
Setup Approach	Low-code, visual builder	Code-first (Python/TS)	No-code, self-serve
Implementation Time	Days to weeks	Weeks to months	Minutes to hours
Required Expertise	Developer / Technical PM	Software Engineer	Support/IT Manager
Knowledge Sources	Custom via API connectors	Custom via code	100+ one-click integrations
Pre-built for Support?	No, it's a general toolkit	No, it's a general SDK	Yes, purpose-built
Testing & Simulation	Basic preview runs	Requires custom test suites	Bulk simulation on past tickets
Reporting	Basic logs in OpenAI dash	Requires custom setup	Actionable ROI & gap reports

While AgentKit and ADK are impressive frameworks, they're missing the features you actually need to run a support automation program well. A platform like eesel AI comes with essentials already built-in, like the ability to simulate how the AI would have handled thousands of your past tickets and reports that tell you exactly where your knowledge gaps are.

AgentKit vs Gemini API: Pricing and accessibility

Both AgentKit and the Gemini API run on a usage-based pricing model. You pay for the number of "tokens" (which are basically pieces of words) your agent processes. This can lead to some wild and unpredictable monthly bills. If you have a sudden flood of customer questions or a seasonal rush, your AI costs could shoot up without any warning.

A screenshot of the AgentKit pricing page, highlighting the usage-based model in the AgentKit vs Gemini API debate.

On top of that, Google has tucked its most user-friendly tool, the "Agent Builder," behind its pricey Gemini Business or Enterprise plans. These can run you up to $30 per user per month, which is a pretty big hurdle for teams who just want to try the tech out and see if it’s a good fit.

This is a huge contrast to eesel AI's pricing model, which is all about being clear and predictable.

• No per-resolution fees: You never get punished for your AI being successful and helping more customers.

• Predictable tiers: Plans are based on a flat number of monthly AI interactions, so budgeting is simple and you always know what you’re paying.

• Easy to get started: You can sign up for a free plan and get going right away without ever having to talk to a salesperson. It completely removes the barrier to just giving it a try.

The better alternative for support automation: eesel AI

AgentKit and the ADK are powerful tools for building AI from scratch. They're like being handed a car engine, a chassis, and a toolbox and told, "Go build a vehicle." That's great if your goal is to build a one-of-a-kind race car, but it's complete overkill if you just need a reliable way to get to work every day.

eesel AI is that fully assembled, road-tested car designed specifically for support and internal help teams. It’s built using the same powerful AI technology but is packaged into a solution that solves real business problems from the moment you turn it on.

Here’s what makes it different:

1. Go live in minutes: Connect your help desk and knowledge bases in a few clicks and launch your AI agent the very same day. No developers needed.

2. Unify your existing knowledge: eesel instantly learns from the tools and processes you already use, making sure its answers are accurate and sound like they’re coming from your team.

3. Test with confidence: The simulation mode lets you test your AI on thousands of your own past tickets before it ever talks to a real customer. This gives you a clear forecast of its performance and takes the risk out of the whole process.

4. Total control: A simple dashboard lets you manage exactly which tickets get automated, how the AI should respond, and what actions it can take, whether that’s escalating a ticket or looking up a customer's order info.

Final thoughts on AgentKit vs Gemini API

For technical teams with plenty of developer time who want to build highly custom, general-purpose AI agents, AgentKit and the ADK are definitely exciting. They offer a peek into a future where custom AI is part of every aspect of a business.

However, for customer support, ITSM, and internal help desk teams who need to lower ticket volume, help agents be more efficient, and give better answers today, a dedicated, no-code platform is the faster and smarter choice. You get all the power of a custom-built agent without the engineering costs, unpredictable bills, and months-long projects.

Ready to see how an AI support agent can work for you? Start your free trial of eesel AI or book a demo to see it for yourself.