So, you’re looking to build a custom chat interface for an AI agent. You’ve probably realized that building a solid chat UI from scratch is a huge pain. You have to worry about state management, streaming responses, handling user input… the list goes on. It can feel like you're reinventing the wheel for the hundredth time.
OpenAI’s ChatKit swoops in to solve exactly this problem. It’s an embeddable, production-ready chat UI that promises to give you a beautiful, agent-like experience without all the frontend grunt work. But here’s the thing: while ChatKit might simplify the frontend, it brings its own set of challenges to the table, especially if you want real control over how it works.
This guide is for developers who are weighing their options. We'll walk through what ChatKit actually is, how its more advanced features like ChatKit Client Tools work, and, most importantly, what limitations you need to think about before you go all-in.
What exactly is OpenAI's ChatKit?
Think of ChatKit as a pre-built chat window component you can drop right into your website or app. It’s designed to be the face of an "agentic" AI, which is just a fancy way of saying an AI that can do things, run multi-step tasks, use tools, and give you more than just a block of text as a response. It saves your team from having to build the entire chat client from the ground up.
When you decide to use ChatKit, you have two main ways to set it up:
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The easy route (Recommended Integration): You embed the ChatKit UI on your site and let OpenAI handle hosting and scaling the backend agent. You build the agent’s logic using their visual Agent Builder. This is definitely the quickest way to get a prototype up and running, but you’re living entirely within OpenAI’s world.
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The DIY route (Advanced Integration): You run ChatKit on your own infrastructure. This gives you the freedom to connect it to any backend you want (like one built with LangGraph), but it also means you’re on the hook for building your own server, managing data storage, and figuring out authentication.
The easy route is tempting, but most businesses need the flexibility of the advanced path to connect to their own internal databases and private APIs. And that, my friend, is where the trade-offs really start to pile up.
What's cool about ChatKit's core features?
Before we get into the nitty-gritty of the hard parts, let's look at why developers are even talking about ChatKit in the first place. It comes with some genuinely useful features that can make building a polished chat UI much, much faster.
Embeddable UI and easy theming
At its heart, ChatKit is a web component. You can stick it in any HTML page, whether you’re using a framework like React or just plain old HTML. It’s pretty straightforward to make it match your brand’s look and feel by tweaking CSS variables for things like colors, fonts, and border radius. This helps the chat widget feel like a natural part of your application instead of a clunky, third-party add-on.
Interactive widgets for richer conversations
This is probably one of the most compelling features. Instead of your AI only responding with text, it can send back interactive components called widgets. These can be cards, forms, lists, you name it.
Imagine a travel bot that doesn’t just list flights but shows them in a "Card" widget with pictures and "Book Now" buttons. Or an e-commerce assistant that uses a "Form" widget to collect a user's shipping address right there in the chat window. This turns a simple Q&A bot into something that feels more like a mini-app, which is a big step up in user experience.
File uploads and chain-of-thought views
ChatKit also supports file attachments, so users can upload images or documents. This is a must-have for a lot of use cases. Think of a customer support bot that needs to see a screenshot of an error, or an internal HR assistant that needs to process a resume.
It also has a built-in way to show the agent's "chain of thought." This is a nice touch for transparency, as it can help users (and you, the developer) understand the steps the AI took to arrive at an answer. It helps build a bit of trust when the AI isn’t just a black box.
But a word of caution here: if you're self-hosting, you’re responsible for everything that happens with those files. You have to build the logic for storing them, securing them, and managing access. That’s a whole project in itself and adds a significant layer of work and security considerations.
Unpacking the power of ChatKit Client Tools
Alright, this is where ChatKit’s capabilities get really interesting and are a key reason you might consider it for a complex project. ChatKit Client Tools are a feature that lets your backend agent assign tasks back to the user's browser.
Think about it: your backend agent lives on a server somewhere. It has no idea what’s happening in the user’s browser. It can’t access local storage, see the user’s GPS location, or know the current state of your app’s UI.
This is where client tools come in. The agent can essentially pause, ask the frontend to run a piece of code, wait for the result, and then use that information to continue its task.
Here’s a simplified look at how that flow works:
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A user types a message and hits send.
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The ChatKit UI sends that message to your backend agent.
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Your agent processes the message and realizes it needs information from the browser. For example, it needs to know what items are currently in the user's shopping cart.
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The agent sends a "tool_call" event back to the ChatKit UI, telling it which tool to run (e.g., "get_shopping_cart") and any parameters.
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Your frontend code, which is listening for that specific tool name, runs the function.
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The browser sends the result (like a JSON object of cart items) back to your agent.
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The agent now has the cart info and uses it to craft its final, helpful response for the user.
When would you actually use ChatKit Client Tools?
Client tools are super handy for a few specific situations:
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Accessing browser-specific APIs: You can get data from "localStorage", "sessionStorage", or interact with hardware tokens.
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Reading or changing your app's UI state: The agent could ask to add an item to a shopping cart that’s managed entirely on the client side.
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Using client-side SDKs: If you have a third-party SDK that only runs in the browser, this is your bridge to let the agent use it.
While this is incredibly powerful, it’s not exactly plug-and-play. You have to define the tools with the exact same names on both your backend agent and in your frontend ChatKit setup. This creates a tight coupling between your frontend and backend. Any changes to a tool require a coordinated deployment across both parts of your stack, which can slow things down.
This level of custom coding is a big jump in complexity. It's worth noting that some platforms approach this differently by offering pre-built actions. For example, instead of writing custom code for an order lookup, a platform like eesel AI provides a one-click integration with tools like Shopify. This allows your AI to securely look up order details through a pre-built action you can configure in a simple UI, no coding required.
The hidden costs of ChatKit Client Tools: What you're really signing up for
ChatKit is a fantastic UI kit, but it’s just one piece of a much larger puzzle. If your goal is to launch a complete, reliable, and easy-to-manage AI support solution, relying only on ChatKit can lead to some serious hidden costs and headaches down the road.
The beast of the 'advanced integration'
If you want to connect ChatKit to your own knowledge bases or use a backend that isn't from OpenAI, you have to go down the "Advanced Integration" path. And "advanced" is putting it mildly. You become responsible for:
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Building a data store from scratch: You need to architect and implement the entire database layer to keep track of conversation threads, messages, and files. The official docs suggest using JSON blobs to make migrations easier, but that's still a massive engineering lift.
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Creating an attachment store: As we touched on earlier, you need to handle everything related to file uploads yourself. This includes storage, access control, and generating previews. If you get the security wrong here, you’re in for a world of hurt.
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Managing your own authentication: You have to build a secure endpoint that can issue short-lived tokens to authenticate users.
This isn't a "drop-in" solution by any stretch of the imagination. It requires dedicated developer time, continuous maintenance, and deep expertise in backend systems. It’s why many businesses end up choosing a fully managed platform instead. With a tool like eesel AI, you can be live in a few minutes, not a few months. It handles all the data storage, security, and integrations for you. You just connect your knowledge sources, like Zendesk, Confluence, or your team's Google Docs, and you’re ready to go.
It's just a UI, not a brain: A limitation of ChatKit
This is the most critical point to understand: ChatKit gives you the chat window, but it does absolutely nothing to make your AI agent intelligent. The quality of your agent's responses depends entirely on the knowledge it can access and the logic you build on the backend.
ChatKit’s job ends where the real work begins. You're still on the hook for building the RAG (Retrieval-Augmented Generation) pipelines, managing how your knowledge sources are ingested and updated, and creating the workflows your agent needs to follow to be useful.
This is the exact problem platforms like eesel AI are designed to solve. Eesel unifies all your company knowledge automatically, whether it’s in old helpdesk tickets, internal wikis, or scattered documents. It trains the AI on your specific business context so that the answers it gives are actually relevant and accurate from the very beginning.
The risk of being locked into an ecosystem
When you build with ChatKit, you’re building on OpenAI's specific protocols and SDKs. While you can technically connect it to other backends, all the documentation, helpers, and examples are designed for OpenAI's ecosystem.
What happens in a year if you want to switch to a different LLM provider or use a new orchestration framework? You could be looking at a complete rewrite of your chat infrastructure. That's a huge strategic risk, especially for something as core as your customer-facing chat. It locks you into one vendor's way of doing things, which might not be the best fit for you long-term.
What's the verdict on ChatKit Client Tools?
OpenAI's ChatKit is an impressive and powerful tool for developers who have the time, resources, and specific need to build a highly custom, agentic chat UI from the ground up. Features like interactive widgets and ChatKit Client Tools offer a degree of interactivity that’s difficult to replicate without a ton of effort.
However, it’s not a silver bullet. For most businesses, the main goal is to deploy an effective AI assistant quickly and reliably. ChatKit only solves the frontend piece of that puzzle. The backend complexity, the challenge of knowledge management, and the real risk of vendor lock-in are massive hurdles to overcome.
If your objective is to automate support, give your team better tools, and unify your company’s knowledge without kicking off a massive engineering project, a comprehensive platform is almost always a more direct path to getting value. Tools like eesel AI provide the complete, end-to-end solution, from the chat UI and knowledge integrations to automation workflows and analytics, letting you deploy a smart, helpful AI assistant in minutes, not months.
Frequently asked questions
ChatKit Client Tools allow your backend AI agent to request information or actions directly from the user's browser. They essentially bridge the gap between the server-side agent logic and client-side browser capabilities, enabling richer, more context-aware interactions.
Implementing ChatKit Client Tools in an advanced integration requires defining the tools with identical names on both your backend agent and the frontend ChatKit setup. You'll need to write custom frontend code to listen for these tool calls, execute the desired browser-side logic, and send the results back to your agent.
Certainly! They are beneficial for scenarios like accessing browser-specific APIs (e.g., localStorage, GPS), reading or modifying the client-side UI state (e.g., adding to a shopping cart), or interacting with third-party SDKs that only run in the browser.
The primary challenges include the tight coupling between your frontend and backend tool definitions, requiring coordinated deployments. You're also responsible for building all the logic for tool execution, error handling, and security on the client side, adding significant engineering complexity.
No, ChatKit Client Tools are focused on enabling interactive capabilities and data exchange with the client, not on enhancing the AI's core intelligence. The AI's ability to understand queries and generate relevant responses still depends entirely on your backend agent's knowledge, RAG pipelines, and orchestration logic.
Yes, there can be a risk. While ChatKit Client Tools can connect to various backends, they are designed within OpenAI's specific protocols. Shifting to a different LLM provider or orchestration framework might necessitate a significant rewrite of your client-side tool integration and backend agent logic.
