A developer’s guide to the ChatKit starter app configuration

OpenAI recently launched AgentKit, a new toolkit designed to help people build AI agents. At the front and center of this is ChatKit, the UI component that lets developers add a sleek, conversational chat interface to their apps. For a lot of us, the first stop is the "ChatKit Starter App", the official launchpad for getting a feel for what it can do.

But what’s the real story behind getting from a GitHub repository to a fully functioning chat agent? This guide is a straightforward look at the ChatKit Starter App Configuration process. We’ll walk through the technical steps, check out what it can do, and most importantly, talk about the practical stuff, like time, resources, and limitations, that every team should think about before jumping in.

What is OpenAI’s ChatKit?

ChatKit is a front-end UI toolkit for building AI-powered chat experiences. It’s framework-agnostic, which is nice because it means you can drop it into a React, Next.js, or even a plain old JavaScript project. Think of it as a box of pre-built, customizable Lego blocks for building a chat window.

It’s also important to get how it fits with AgentKit. AgentKit is the backend engine where you visually map out your AI agent’s logic and workflows. ChatKit is purely the user-facing part that your customers will actually see and talk to.

Here’s the catch, though: ChatKit is a developer’s tool, through and through. It isn’t a plug-and-play chatbot solution you can hand off to the business team. It gives you the components, APIs, and a starter template, but turning it into a smart agent that can actually help customers takes a good amount of coding and backend setup. Out of the box, it does offer some handy features for developers to build on, like response streaming, tool integration for agent actions, interactive widgets, and file attachment handling.

The core pieces of a ChatKit Starter App Configuration

Getting the starter app running isn’t just a front-end job. It’s a full-stack effort that involves getting a client, a server, and the AI workflow to all play nicely together. Here’s a look at the technical parts you’ll need to put together.

1. Setting up the front-end client

Your journey starts on GitHub, where you’ll clone the "openai-chatkit-starter-app" repository. This gives you a basic boilerplate, usually in React/Next.js or vanilla JavaScript, to start from.

Right away, you’re in a developer’s world. This code is designed to be rendered on the client side only. If you’re using a framework like Next.js, you have to be careful to sidestep Server-Side Rendering (SSR) issues, which can cause hydration mismatches and break the whole thing. This is the first sign that the setup process is really for people who are already comfortable with the quirks of modern web development frameworks.

2. Building a secure server-side token endpoint

The ChatKit UI can’t just exist on its own. To do anything useful, it needs to securely talk to OpenAI’s backend. And you definitely can’t just slap your secret API key into the front-end code where anyone with a browser inspector could find it.

This means you have to build a server-side API endpoint. The starter app tutorials show examples of creating a route in Next.js that acts as a go-between. When the chat widget loads, it calls your server. Your server then uses your secret key to ask OpenAI for a temporary client token and sends it back to the client. It’s a standard security practice, but it’s also a clear signal that you need someone with backend skills on your team just to get the chat window to load.

3. Connecting to an agent workflow

Once your chat UI is authenticated, it needs an AI "brain" to talk to. This brain is a workflow you build in OpenAI’s Agent Builder. Each workflow gets a unique ID, which you then have to plug into your starter app’s configuration.

This part involves managing environment variables like your "OPENAI_API_KEY" and the "NEXT_PUBLIC_CHATKIT_WORKFLOW_ID" in a ".env.local" file. It’s another technical step that requires making sure your front-end, your secure backend endpoint, and the Agent Builder workflow are all communicating correctly. If any single piece is misconfigured, the chat will either fail silently or throw some cryptic error, leaving you to debug the connections between all the moving parts.

Customization and features: What can you actually build?

After you’ve wrestled with the initial setup, ChatKit does give you a good amount of flexibility for customization. But it’s important to remember that this flexibility comes with a price: more development work.

Theming and branding

Making the chat widget look and feel like it belongs in your app is a big deal. ChatKit gives you options to change the theme from light to dark, set custom accent colors, and tweak UI elements like the roundness of corners.

You can also change the default text, like the placeholder in the composer, to give users a hint about what to ask. For new chats, you can set up a few starter prompts to get the conversation rolling. These are all nice touches, but every single customization is a code change that a developer has to implement, test, and ship.

Adding custom tools and widgets

Beyond looks, ChatKit lets you build more advanced, interactive features. You can turn on file attachments, add custom buttons to the header for certain actions, or even render rich, interactive widgets right inside the conversation.

For example, an agent could pop up a form for a user to fill out or show a card with their order details. This is where ChatKit really starts to show its power as a developer framework. But it’s also where the complexity shoots way up. Every custom widget or tool needs a lot of code on both the client side (to render the UI) and the server side (to handle the logic in your Agent Builder workflow).

The hidden costs and limits of a DIY ChatKit setup

The developer-first nature of ChatKit forces you to make a strategic choice. It offers a ton of control for teams with the right engineering resources, but it comes with trade-offs that can seriously slow down any plans for support automation.

The permanent developer tax

From the minute you clone the starter app, you’re signing up for a developer-led project. Every little change, whether it’s tweaking a prompt, adding a new knowledge source, or adjusting the chat widget’s color, needs an engineer to write, test, and deploy code.

For a support team that needs to move fast, this is a huge bottleneck. A support manager who spots a common customer problem can’t just go in and update the agent’s behavior. They have to file a ticket, get in the engineering queue, and wait. This dependency makes it incredibly difficult to iterate quickly and keep the agent up-to-date with what customers actually need.

The "blank canvas" problem

ChatKit gives you a chat window, but it doesn’t give you any of the logic that makes a support agent genuinely useful. You get the UI, but you’re left to build everything else from scratch.

This includes the often tedious work of:

• Connecting knowledge sources: You have to manually build the pipelines to feed data from your help center, internal wikis, and past support tickets into your agent.

• Defining workflows: All the logic for when to escalate, how to triage tickets, and rules for routing customers has to be designed and coded by hand.

• Integrating with your helpdesk: If you want your agent to do something as simple as tagging a ticket, updating a customer field, or closing a conversation in Zendesk or Intercom, you have to build those API connections yourself.

Missing key support features

Maybe most importantly, ChatKit is missing the enterprise-level features you need to manage and scale support automation with any real confidence. When you build with ChatKit, you’re on your own for:

• No simulation mode: You can’t test how your agent will perform on your actual past tickets before you set it loose on live customers. You’re flying blind, with no way to predict resolution rates or spot where it might go wrong.

• No knowledge gap analytics: The system won’t tell you what it doesn’t know. It can’t analyze failed conversations to show you where your documentation is weak or point out new issues customers are running into.

• No automated knowledge creation: It can’t learn from successful resolutions handled by your human agents and suggest new help articles, leaving your team to manually patch up the knowledge gaps.

eesel AI: The self-serve alternative to a custom ChatKit setup

The headaches of a DIY ChatKit setup really highlight the need for a different tool, one that’s built for the teams actually running support. eesel AI is a platform designed to solve these problems by letting support teams build, manage, and improve their own AI agents without writing a single line of code.

• Go live in minutes, not months: You can forget about the complex, multi-step ChatKit Starter App Configuration. eesel AI has one-click integrations with helpdesks like Zendesk, Freshdesk, and Gorgias. The setup is genuinely self-serve, meaning you can have a working AI agent trained on your knowledge sources in minutes.

• Total control for support teams: eesel AI gives you a no-code prompt editor and a powerful workflow engine. Support leads can define the AI’s personality, limit its knowledge to certain sources, and set up custom actions, like looking up an order in Shopify or creating a ticket in Jira, all from a simple dashboard.

• Test with confidence: This is a huge one. eesel AI’s simulation mode lets you test your AI on thousands of your past tickets in a safe environment. You can preview responses, get accurate forecasts on resolution rates, and fine-tune its performance without any risk before a customer ever talks to it.

• Transparent, predictable costs: The real cost of a ChatKit project isn’t just the sticker price; it’s the developer salaries, infrastructure costs, and unpredictable API bills. eesel AI offers clear pricing plans based on usage, with no surprise per-resolution fees. This makes budgeting easy and ties your costs directly to the value you’re getting.

Beyond the ChatKit Starter App Configuration, build vs. buy

At the end of the day, the choice between building a custom solution with ChatKit and using a platform like eesel AI comes down to your team’s resources, goals, and timeline.

The ChatKit Starter App Configuration can be a good path for companies with dedicated developers who need to build a deeply customized, one-of-a-kind chat UI and are ready for the ongoing maintenance that comes with it.

However, for most teams that just need a powerful, secure, and integrated support automation platform that can be managed by the support org itself, a ready-made solution is the far more practical choice. A platform like eesel AI removes the technical barriers, lets your support experts own their automation, and delivers results in a fraction of the time. Before you commit your engineering team to a long build cycle, it’s worth asking if a platform that’s ready to go today might be the smarter path to getting where you want to go.