It feels like the AI goalposts are moving every single week. Not long ago, we were just getting used to chatbots that could hold a decent conversation. Now, the discussion has jumped ahead to AI agents that can actually do things, plan tasks, use tools, and see complex projects through on their own.
OpenAI is right in the middle of this shift. Their recent introduction of AgentKit, which is slated to replace the older Assistants API, is a pretty big deal.
This might sound like a niche update for developers, but it has a massive ripple effect on how businesses, especially customer support teams, think about automation. It all boils down to one question: what are the real-world differences between AgentKit vs Assistants API, and what does this change mean for anyone trying to build something that actually saves time and money?
Let's get into it.
What was the OpenAI Assistants API?
The Assistants API was OpenAI’s first real shot at helping developers build AI applications that could remember conversations and use tools. It was the foundation for creating something more capable than a simple Q&A bot. Its main features were "Threads," which handled conversation history so the AI had some memory, and "Tools" like Code Interpreter and Function Calling that let it take action.
It was powerful, for sure, but also a huge pain to work with.
Building anything useful with the Assistants API was a code-heavy project. Developers had to write a ton of "glue code" just to connect the different parts, manage what the agent was doing, and handle the outputs from its tools. It felt like trying to assemble a car engine from a box of unlabeled parts. It was complicated, slow, and frustrating.
Based on that developer feedback, OpenAI announced they were deprecating the Assistants API, with plans to sunset it completely by mid-2026. This cleared the path for AgentKit, a totally new approach designed to fix the very headaches the Assistants API caused.
What is OpenAI's AgentKit?
AgentKit isn't just a new name for the old API. It's a full-blown toolkit meant to simplify the whole process of building an agent. If the Assistants API gave you the raw materials, AgentKit is designed to give you the entire workshop. It's OpenAI's answer to the complexity that was holding everyone back.
It’s built on a few key ideas:
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Agent Builder: A visual canvas where you can drag and drop components to design, test, and tweak agent workflows without having to write lines and lines of code.
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Connector Registry: A central place to manage secure connections to other apps and data sources your agent might need, like Google Drive or SharePoint.
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ChatKit: A set of pre-built chat UI components you can embed to get a user-facing front end up and running fast.
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Evals & Guardrails: Built-in tools for testing how well your agent is performing, fine-tuning its prompts, and adding safety rules to keep it from going off the rails.
The objective is pretty obvious: take a project that used to take months of painful development and turn it into something you could potentially ship in a few days. The goal is to make agentic AI a lot more accessible.
A chart showing the relationship between Agent Builder, ChatKit, Evals, and Connectors to understand the OpenAI AgentKit pricing structure in the context of AgentKit vs Assistants API.Key differences: AgentKit vs Assistants API
When you look closer, the switch from the Assistants API to AgentKit is more than just a feature update; it’s a whole new way of thinking. Here’s how they compare in the areas that really count.
Ease of use and speed to deployment
The most glaring difference is the shift from a code-first to a visual-first approach. With the Assistants API, you had to write out every single step of your agent's logic in Python or JavaScript. AgentKit’s visual Agent Builder lets you map out that flow by connecting nodes on a canvas, which definitely makes it easier for more people to get started.
But "easier" doesn't mean it's suddenly a walk in the park.
AgentKit is still very much a tool for developers. You need technical know-how to manage the workflows, hook up custom tools, and figure out what went wrong when it inevitably breaks. Some early users have pointed out that monitoring an agent's execution is still a bit clunky, forcing you to jump between different screens just to see what a single step did. The core engineering challenges of building a reliable agent are still there, they've just been wrapped in a nicer interface.
Control, customization, and workflow orchestration
AgentKit's versioning system and visual canvas give you a much clearer view of how your agent operates compared to the tangled mess of scripts you needed for the Assistants API. That alone is a huge improvement for team collaboration and long-term maintenance.
But there’s a trade-off. By its nature, AgentKit locks you into using OpenAI's models. In a world where other models (like Claude or Gemini) might be better at certain tasks, that lack of flexibility can be a serious drawback. On top of that, AgentKit is a general-purpose tool. If you need to customize it for a specific business process, like a complex support ticket triage system with a bunch of different rules, you’re right back to needing custom code.
For a function as important as customer support, generic controls just don't cut it. You need control over the small details. This is where a specialized platform really makes a difference. With the workflow engine in eesel AI, you can set the AI's exact persona and tone, pick precisely which tickets it should handle based on your rules, and create custom actions that pull from your real-time data. This prevents a generic agent from going rogue and gives you the confidence to actually automate customer-facing work.
Ecosystem, integrations, and knowledge sources
With the Assistants API, connecting to external data was a completely manual, often painful process. AgentKit's new Connector Registry is a nice upgrade, giving you a central and more secure way to plug into common tools. But it's also a brand-new feature, so you're stuck with whatever connectors OpenAI decides to build and maintain.
More importantly, both frameworks overlook the single most valuable source of knowledge for any support team: its own history. They expect you to manually gather, clean, and upload knowledge base articles or documents. They can't learn from the thousands of real conversations where your team has already solved the exact same problems your customers are having.
Modern support teams don't just run on wikis. Their real knowledge is buried in past tickets, macros, and random documents. eesel AI is built to pull all of that together instantly. It automatically learns from your historical conversations in platforms like Zendesk, Freshdesk, or Intercom to nail your brand voice and solutions from day one. It also connects seamlessly to your other knowledge sources like Confluence, Google Docs, and Slack to give your AI a single, unified brain.
OpenAI's usage-based pricing model
Whether you were on the old Assistants API or are moving to the new AgentKit, the pricing is the same: you pay for what you use. This includes per-token costs (for both what you send and what you get back) and separate fees for things like tool usage. For example, File Search storage will set you back $0.10 per GB per day.
For a developer tinkering on a side project, that’s totally fine. But for a business trying to stick to a budget, it’s a nightmare. It creates total unpredictability. If your support volume doubles one month, your OpenAI bill will double right along with it. The model basically punishes you for growing and makes it impossible to forecast costs.
A screenshot of the OpenAI pricing page, providing a visual aid for the AgentKit vs Assistants API cost structure discussion.| Feature/Component | Pricing Model | Potential for Unpredictable Costs |
|---|---|---|
| Model Usage (Tokens) | Pay-per-token (input/output) | High |
| Tool Usage (e.g., File Search) | Per-query + Per-GB/day storage | Medium |
| AgentKit Components | Included in API usage | High (drives more token/tool use) |
Businesses need to know what they're spending. That’s why platforms like eesel AI offer clear, flat-rate monthly or annual plans. You get a set number of AI interactions, and we never charge per resolution. Your costs stay stable and predictable, even when things get busy.
A toolkit for developers, not a business solution
Let’s be clear: AgentKit is a massive step up from the Assistants API. It’s a more complete, powerful, and user-friendly toolkit for developers who want to build custom AI agents from scratch. It makes the idea of an AI workforce feel much more real.
But that’s the catch, it’s a developer framework. It’s not a ready-to-go solution for a business problem like automating customer support. AgentKit gives you a fantastic set of Lego bricks, but you still have to design the castle, build it, and fix it when it breaks. For most support teams, that’s a multi-quarter engineering project they just don’t have the time or people for.
For teams that need to solve problems today, like cutting down response times, boosting CSAT, and freeing up human agents for more important work, a dedicated, fully-managed platform is a much faster and more strategic way to get there.
Beyond AgentKit vs Assistants API: Build practical AI agents today with eesel AI
If you're looking for the quickest way to deploy an effective, autonomous AI in your support workflow, eesel AI was built for you. It’s designed to deliver business results, not engineering headaches.
Here’s what makes it different:
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Go live in minutes: It’s a truly self-serve platform with one-click integrations for your helpdesk. No sales calls, no required demos. Just sign up and go.
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You're in complete control: Use a powerful but intuitive workflow engine to define your AI's personality, actions, and exactly which tickets it should handle.
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It unifies all your knowledge: It learns from your past tickets, help centers, internal docs, and macros automatically. No manual uploads.
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Roll it out without risk: Use the powerful simulation mode to test your AI on thousands of your past tickets. You can prove its ROI before a single customer ever interacts with it.
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Predictable pricing: Our flat, transparent plans don't punish you for being successful. No surprises, ever.
Stop wrestling with developer frameworks. Start solving real business problems.
Start your free trial with eesel AI or book a demo to see how you can automate your support in minutes, not months.
Frequently asked questions
AgentKit is designed as a more complete, visual toolkit to simplify agent building, addressing the complexity and "glue code" issues of the older Assistants API. It aims to make agent development more accessible and faster through a new Agent Builder and other components.
AgentKit introduces a visual Agent Builder, replacing the code-heavy approach of the Assistants API, which significantly simplifies the design and testing of agent workflows. While still a developer tool, this shift is intended to reduce the time from concept to deployment.
OpenAI is deprecating the Assistants API with a full sunset planned by mid-2026. This means existing users will eventually need to migrate their projects to AgentKit or another solution, as AgentKit is designed to be its successor with improved functionality.
By its nature, AgentKit primarily locks you into using OpenAI's models. While it offers visual workflow orchestration, it doesn't inherently provide flexibility to easily switch to or integrate with other large language models like Claude or Gemini.
Both AgentKit and the Assistants API use a usage-based pricing model, charging per token and for tool usage. This can lead to unpredictable costs for businesses, as expenses scale directly with usage volume, making cost forecasting challenging.
AgentKit is positioned as a powerful developer framework and toolkit, not a ready-to-deploy business solution. While it greatly aids custom AI agent creation, it still requires significant engineering effort to tailor and manage for specific business needs like customer support automation.
AgentKit improves integrations with its Connector Registry, offering a more secure way to link to common tools compared to the manual process with Assistants API. However, both frameworks still generally expect manual gathering and uploading of knowledge, often overlooking the ability to automatically learn from historical support conversations.
