OpenAI just killed Naden. Or did they

really? Because they just dropped their

agent kit. We may be seeing that

narrative right now online. But today, I

am here to actually compare Agent Kit

and Naden and talk about the future of

both of these agent builders. As always,

we're not just going to be talking about

it. I'm going to be comparing both of

these platforms across different

categories so we can highlight each of

the platform strengths and weaknesses.

So, to start off with a quick overview

of each of these tools, we've got Agent

Kit, which just dropped October 6th of

2025. It's OpenAI's agent builder that

looks like this. It's very very simple

to use and it's very drag and drop low

code visual interface. Here's a quote

from Sam Alman saying, "Agent Builder is

like Canva for building agents. It's a

fast visual way to design the logic,

steps, ideas." I'm assuming you guys

already know what Naden is, but if you

don't, it was released October 8th,

2019, so about 6 years ago. But in the

last year, it really started to take

off. As you can see by this Google

Trends chart right here, Naden's in the

blue. And then we have other AI

automation platforms like Make and

Zapier down below. And you can see it

just hockey stickked right past them.

You can plug AI into your own data and

there's over 500 integrations which

makes it really easy to connect to your

tools and this is what NN looks like. So

like I said today we're going to be

comparing agent kit with NN across

different categories. To answer the

question is Agent Kit going to kill

NAND? And in short my answer is no. When

we started hearing rumors about this

dropping everyone immediately ran to

it's going to kill NAND? I don't think

it's going to at all. And I also want to

be really clear that I don't think

OpenAI's goal was to compete with NAND.

I think that Agent Kit is built for

teams and consumers that are really

integrated into OpenAI's environment and

they're not looking to build super super

custom robust AI automations and

autonomous systems. They're looking to

build some really quick and easy

workflows that's going to save

themselves time and maybe even some

conversational agents internally for

their team. Now, NN of course was built

to be completely everything. It's super

powerful. It was honestly built for

developers. It's a little bit more

technical, but because of that, you have

the ability to truly do anything. Like

when we saw Google drop their Opal,

which was like very similar visual

interface. People said, "Oh, is this

going to kill any?" No. Real quick, why

should you trust my opinion on this? I

run an AI automation community where I'm

teaching over 150,000 people how to

build AI solutions to implement into

businesses. And all of that is being

done with NAN. Our AI company, True

Horizon, consults and implements custom

AI solutions. And pretty much all of

that is NADN. And we've been working

with enterprise companies as well. So

the categories that we're going to look

at today are ease of use, triggers,

agent tools, model support, UI chat

components, and deployment and control.

So starting off with the first category,

we have ease of use. So if I'm like a

complete beginner and I want to test out

agent kit, which is OpenAI agent

builder, I would come here and I would

search OpenAI agent builder. I could

look at the release notes or I could go

here to the OpenAI platform, click on

agent builder, and then I could open up

my agent builder right here. This is

pretty much available to everyone. You

just have to have an OpenAI API account.

You can see here we have the ability to

create a workflow. And what's key here

in my mind is it says to build a chat

agent. Now a chat agent or a

conversational agent in my mind is very

different to like a fully autonomous

tools agent because the chat agent is

going to be looking up some information

for you, helping you get your questions

answered, but not as much like take

action on your behalf autonomously. So

anyways, we have some templates down

here that we could look at, but I'm just

going to go ahead and create a new flow

so we can get familiarized with this

environment. What you'll notice here is

that we have a start and I actually

cannot delete this start because every

workflow needs a trigger. You can see

that once you have this start, you have

to drag the circle to say where does

this go next just like end where you'd

connect your nodes together. And we have

an AI agent right here which we're able

to name. We're able to give it

instructions. We can include chat

history. We can choose the model, the

reasoning, we can give it tools. And we

have an output format section where we

can do text, JSON or widget, which we'll

talk a little bit about later. But what

I'm immediately seeing off the bat is

this whole interface is a lot less

intimidating than nitn. I can easily

choose my model, give it instructions,

and I can also on the lefthand side see

everything I have available to me, which

is an agent. I can end it. I can do a

file search. I can do guard rails, MCP

servers. I've only got five logic and

data operations to choose from. If,

else, while, human approval, transform,

or set state. And this is a lot less

intimidating to me as a complete

beginner with no coding background to

get into compared to when I jump into

something like niten create a new

workflow and then when I open up this

first step I have tons of different

things in here. Let's say I want to pull

in an AI agent and I can click on that

agent right here. I have this input

where I'm looking at this variable. I

have all these options down here to

choose from and I might not know exactly

what they mean. You can also see I have

an error already because I didn't

connect to chat model. I also have the

option to add memory and all these

different tools. And this just initially

is going to be a lot more intimidating

than hopping into the agent builder by

OpenAI. One of the first things that I

wanted to do when I built my first agent

ever is I wanted to connect it to the

internet so it could look up things for

me. And with OpenAI's agent builder, all

I have to do is click on this plus for

tool and I can just do web search. And

that's basically going to search the way

that you can do web search when you're

in chatbt. You can also specify to only

look through certain websites and you

can also do location-based searches. So

just like that, I already have an AI

agent that can search the web and I can

also connect a chat model right here

without ever having to go and get any

API key. But in any if we wanted to

search the web, we'd have to give it a

tool. We'd have to choose from all of

these hundreds of different tools. We

may even need to set up our own custom

HTTP request and we would also have to

go get an API key for both the web

search and also the chat model, which is

just a lot more intimidating and

definitely raises the barrier to entry a

little bit compared to the agent

builder. So if I was to think about the

time that it would take for a complete

beginner to build one automation system

in OpenAI agent builder or the exact

same system in Naden, they would do it

much quicker and feel a lot less

overwhelmed in agent builder hands down.

Which is why for this first category,

agent kit gets an 8 out of 10. Naden

gets a 6 out of 10 and that is our new

total for the scores right now. Anyways,

let's move on to the next category which

is triggers. So remember earlier when we

were looking at this workflow section

and it says to build a chat agent. Now

when we look at the triggers, you can

kind of see why I am drawn to that or

not drawn to that fixated on that

because when you have your AI agent and

you have just this start option, there's

no other triggers available as you can

see which means that if we want to

communicate with this agent, the only

way we can actually trigger it is by

talking to it as you can see over here

in this preview. So if I want to test my

workflow, I can chat with it right here

and I can say search the web for NDN. We

can see visually that the agent right

now is using its web search function and

it's using its reasoning to search the

web for Naden. And that's really cool

because we spun up a web agent in matter

of seconds. But the issue here is I

don't see a great way to have web- based

events or scheduled triggers. Even if we

go up here to publish our AI agent,

let's just say we're going to call this

test web agent or text, I guess. And

when we publish this, it said right

there that it could be used in API

calls. I don't know if you guys caught

that, but what we're able to do is

either embed it as chatkit, which I will

talk about a little bit later, or we

could trigger this agent from an agent's

SDK, and it gives us the TypeScript or

the Python. So, what this tells me is

you technically could be able to trigger

this agent from uh an HTTP request or

from like an action in an app, but it's

not super intuitive or easy. It would be

really nice if OpenAI had scheduled

triggers or app events, stuff like that.

For instance, if you wanted to make an

open AAI agent to respond to your

emails, it would not be as easy as an

NAN because there's not like a Gmail

trigger on new message received trigger.

Whereas an NAN and you want to look at

your triggers, we could go to Gmail

right here and then we could see our

trigger is on new message received. So

we could easily have this agent go off

whenever we get a new Gmail. We also

have eight triggers in Slack. So on any

event, on bot mentioned, on file shared,

on new user, on a reaction added,

there's so many triggers that are

already natively baked into Nitn. We we

could of course have a web hook. So our

CRM could send data, we could have

Google Drive, we could have Twilio, we

can have pretty much anything. And in my

mind, the systems that are most valuable

when it comes to automation or AI

automation are the ones that the human

doesn't have to manually trigger. So

real quick example, a lot of businesses

reach out to us and they want to

integrate this personal assistant that I

had built. However, when we're trying to

actually communicate what would be the

best quick win to show them ROI really

fast, I never would recommend a personal

assistant. What I would recommend is

something that nurtures leads or

responds to leads quicker and stuff like

that because those type of systems help

grow the business and as the business

grows, those systems will get used more

and more. So, the throughput of the

system increases as the business grows,

which is just a scalable compoundable

ROI of the system. And it's not the

exact same with something like a

personal assistant because as the

business grows, it doesn't necessarily

mean that you're going to be using the

system more. So, in a nutshell, the most

powerful automations in my mind are ones

that run in the background when you even

know they're running. And because in

OpenAI's agent builder, we're kind of

limited on our triggers and being able

to schedule or have things go off in the

background. That's why I think any

definitely takes the cake here, coming

in at a 10 out of 10 and agent kit comes

at a 5 out of 10, which makes our new

score agent kit 13 and 16. All right,

moving on to agent tools, which is

really, really important because this is

what gives our agents the ability to

actually do things on our behalf. So,

we're back in our OpenAI agent builder

flow where we already gave our agent the

tool of web search, which was super

easy. No API key, really quick. I love

it. But now, when we go to add more

tools, you can see we only have a few

options. We have a client tool, which is

the ability to send data back to our

chat kit widget, which I will say that

is very cool. We can do MCP servers. So,

this is nice because you can connect

really easily to Gmail, Google Calendar,

Drive, Outlook. You can see these other

integrations right here, but there

aren't a ton here and they all have to

be MCP servers. You also could connect

to a different MCP server, which is cool

because there's, you know, tons in

Zapier. You can even connect to your own

custom end workflow and there's also

lots of other MCP servers that you could

connect to. And it's not as powerful as

how many APIs there are out there and

how many you could connect to with a

standard HTTP request. And in Nitn when

you come in here to connect tools you

can first of all also call on an NIDN

workflow which is super powerful and

then of course you have all of these

native integrations which is just

there's literally hundreds and hundreds

I think it's over 500 native

integrations that they have. So almost

any tool that you could think of you can

connect to with your AI agents here. And

if Naden doesn't have a native

integration, you can use this HTTP

request tool which is the most dynamic

and powerful tool in the world because

you can literally talk to any service

that has API documentation. And like I

mentioned with being able to call on

subworkflows, you can see here with the

ultimate personal assistant, for

example, it has an email agent. And if I

open up this email agent workflow, what

we see is a completely separate AI agent

that I built in Naden. And this agent in

itself has tons of other tools that it

can use in here. So you can build some

really really powerful orchestration

agent systems in NN. And all of these

are super modular and reusable because

now if I ever need an email agent, I can

just hook it up to this one. Even if

this agent is hooked up to like 100, it

doesn't matter. And yes, I did say that

you can hook up an OpenAI agent to an NN

agent, which does work, but you would

need to use this, which is the MCP

server trigger right here. And you would

be able to hook up this MCP server to a

bunch of different tools. So, you do

have the ability to talk to some custom

agents and stuff in these workflows down

here. It's just not as powerful as the

sub workflow as a tool option. So, for

agent tools, agent kit's going to come

in once again at a five. N's going to

come in at a 10. And our new total is

agent kit 18. N 26. So, let's move into

the next category, which is model

support. So, this one's pretty simple

and it's pretty easy because in OpenAI,

of course, you only have access to the

OpenAI models. And that's really not a

huge problem. As you can see, we have

tons and tons of models. You've got

heavy ones, fast ones, reasoning ones.

It's really not that big of a deal. And

you also in here can change the

reasoning effort really quickly. You can

change the verbosity, the summary, the

tool choice, all of this kind of stuff.

And once again, because it's all baked

into your OpenAI environment, you

already have everything right here.

You're also able to easily toggle on or

off if you want chat history to be

included or not. But in NN, what you can

do is choose your own chat model. So,

Anthropic, Azure, Bedrock, Coher, any of

these. Or you could also just go to open

router. And now you can choose pretty

much any of the hundreds of models that

are on Open Router. So you have way more

freedom here. And honestly, I think that

is very important because I don't always

only just use OpenAI's models. I do a

lot of the times, but for certain use

cases, I really want to use a Google or

I really want to use anthropic. And when

you choose any of these models, you

still have the option to change things

like your top P, your frequency, your

sampling temperature, all that kind of

stuff as well in NAND, which is why

NIAND comes in at a 10 out of 10 because

you can also use local models because

you can host NAND locally. And agent

kit's going to come in at a 6 out of 10

here, pushing our new total to agent kit

24 and N36. Now, this one's really

interesting. We are moving on to UI chat

components. So earlier in this video, I

alluded a few times to something called

Chatkit, which is like Agent Kit's

built-in tool to help you create like

website widgets and embed your OpenAI

agents really easily and really cleanly

onto your own website. Users can add

slick branded chat interfaces to their

apps without extra coding. Everything

looks polished and works with OpenAI

agents out of the box. And that is

actually a really big value ad for

OpenAI's agent kit. And it end on the

other hand focuses way more on the

backend workflows. So, it has no fancy

or easy way to embed chat interfaces by

default. You would typically need to

embed your agent into some sort of front

end if you want it to actually look

nice. Here's a quick example of all of

the different widgets you could create

and you could have your OpenAI agents

power these different widgets. And

that's where this output thing comes

into play. If I change this to widget,

we could basically set up the type of

widget we want and what little things

that our agents could change based on

its output in that widget. And so when I

click on create widget here, you can see

that we could either describe and start

to mock up what we want it to look like.

You can go to the gallery and see these

are types of widgets and this is what

you could have your agent actually

interface with and change. And so that's

why I think this is really cool for

their front end. But with Naden, you

have the option with the chat message

trigger to open up a chat interface and

embed it, but it just looks like this.

It's really hard to customize and it

just doesn't look great. So here's a

cool quote from HubSpot. Chatkit saved

us weeks of custom front-end work,

making it easy for us to prototype

enhancements to the UI of HubSpot's

breeze assistant and agents. With the

custom response widget, our agent can

deliver interactive guided solutions

instead of static replies. So, it is

very cool, which is why agent kit is

going to get a 9 out of 10 and Naden's

going to get a 5 out of 10, moving the

total to agent kit 33 and end 41.

Hopefully, I've done all this math

right. Let me know if I have not. And

now to talk about our final section, we

have deployment and control, which is

obviously a big deal, especially if you

want to do this for your own business or

help other businesses implement AI

automations. One of the biggest reasons

in my mind that blew up besides being

easy to use and visual is because it was

open- source. I think they prefer to

actually use the term code available or

something like that, but meaning you can

host it in the cloud, you can host it on

your own private server, you can host it

locally on your own computer, and you

can have full control over all of the

data, especially if you're locally

hosting your LLM as well. You know where

everything's going. Agent Kit, on the

other hand, is deployed and managed

entirely in OpenAI's cloud, so users

don't have to worry about the technical

setup, which is good. Everything's

hosted for them, but also OpenAI has

full control over where the data and

agents are living. And once again, if

your company and your workflows are

already completely baked into OpenAI's

environment, then that's probably not a

huge deal for you, and you could easily

spin up some really helpful automations

with OpenAI's Agent Kit. But Naden, once

again, you can have full control,

although sometimes that does require a

little bit more technical setup or

expertise at the beginning. So for

deployment and control, agent kit gets a

7 out of 10. Naden gets a 10 out of 10,

moving the total to a final agent kit 40

and NADN 51, which means that NADN is

our winner today. But once again, this

is very relative. If I was approaching

this from a standpoint of a different

type of consumer, maybe not as much of a

developer standpoint, I would probably

enjoy Agent Kit more. So anyways,

there's a few more miscellaneous things

I wanted to hit on real quick about the

two platforms. So like pricing,

evaluation tools, and community support.

This is still not a finished product by

Agent Kit, and they're capable of doing

a ton of cool stuff here. So their

pricing is not fully finalized right

now. You can just basically access it

for free, but I believe you'd be paying

for the usage of the AI chat models.

Agent Kit also has some really cool

evaluation tools, but there's also some

aspects of the evaluation that I don't

love. So, let me show you what I mean by

that real quick. Okay, so this is one of

OpenAI's templates. And what I want to

do real quick is just preview it and

just do a quick conversation. So, I

first of all say hello. You can see

what's going on over here. The triage

agent is going to figure out what to

output. And then it basically sends me

back some information and it says to get

started, could you share the following?

You can see I responded to that. You can

see we saw the path that this workflow

took. And now the launch helper agent is

basically creating us a guide on how to

launch this. So regardless of the

output, what I don't love here is I

can't see how the data moved through

each of these steps. So if I close out

of this preview and I click on, you

know, that we want to see what happened

in this condition, it's kind of hard to

see what data just passed through. And

if I click on evaluate, this is where we

could actually kind of, you know, see

those logs, but it's a little bit

confusing. Like you can see the timing

of each of these steps. You can see what

happened. If I click on this triage

agent, we can see that it used GPT5. We

can see the system prompts. We can see

the input right here. And we can see the

reasoning and the actions, but it's just

a lot harder to see the data moving

through the nodes in the way that NIDAN

makes it a lot easier to do because in

NIN with this workflow, you can see

everything's green. You can see how many

items are moving through. When you click

into a node, you can see the input on

the left, you can see the configuration

in the middle, and you can see the

output on the right. So, it's really

easy to follow the data trail and

understand exactly where something went

wrong and what you need to change. But

in my mind, in this OpenAI agent kit

environment, it's just hard for me to

understand what's coming in and what's

going out because you can see here we

have a variable which is input output

parsed blah blah blah. But I don't

exactly know where that variable is

coming from. I would have to come into

here and I would have to try to figure

out, okay, output format, we have JSON,

we have this response schema, and is

this where it's outputting the

information? So anyways, not trying to

get too technical here, but I really

like in Nitn how you can see the outputs

of the nodes. You can see data flow. You

can see the input configuration and the

output. And you can also click on

executions and you can go see all of the

different runs very clearly what

happened in a much more visual interface

than going into the agent builder,

looking at the evaluations, and looking

at all of the logs and having to kind of

look through it like this. It's just a

bit more confusing, which surprises me

because it's so much easier to use if I

was a complete beginner. But they also

do have some really cool evaluation

capabilities with data sets, trace

grading, and prompt optimization. As you

can see here, there's different ways

that you can grade your runs and then

like pass prompts through to see if

they're passing or failing. You can

optimize your prompts, and of course,

you can trace grade. The last thing I

wanted to hit on here was community

support. OpenAI obviously just released

their agent kit yesterday, and we all

know what that company is capable of.

They're it's just really cool what

they're working on. So, I imagine we're

going to see Agent Kit continue to

evolve and evolve. But because Eniden's

been around for 6 years and has blown up

in the past year, there's so much

content, there's so many courses,

there's so many free templates, like

5,000 plus templates, whereas in Open

AI's Agent Kit, you're a little bit more

on your own right now. Anyways, overall,

we have Agent Kit at 40, we have Naden

at 51. And I wanted to talk about real

quick when you would choose each of

these tools. So, I would choose OpenAI

agent kit if I wanted to prioritize

rapid deployment with minimal technical

complexity. If I wanted a polished chat

user interface or some widgets that

could be dynamic, if I'm looking to do

some really quick and easy comprehensive

agent evaluation and testing. And of

course, if I have everything that I know

about my business already in OpenAI's

ecosystem, it's going to make it really

easy to plug in these custom workflows.

Now, Nen, they definitely were targeting

more of the developers. Flexibility

across multiple AI providers, complex

workflow automation beyond simple chat,

cost control through self-hosting,

complete data and infrastructure

control. You obviously have the ability

to connect to literally anything,

trigger your workflows by literally

anything, and do any type of data

manipulation or transformation that you

need. But making this video was really

fun for me because I think a lot of

people tend to get caught up on tools.

You know, what's going to drop? What's

going to be relevant? What do I learn?

Am I taking a huge risk or gamble? The

answer is no. as long as you're

approaching it from the mindset of I

want to learn when AI is valuable, when

it's not, how to save time, and how to

solve a core problem that is actually

facing the business. Because if you're

trying to work with businesses, they're

probably not going to choose you because

of your specific tool. They're going to

choose you because you know how to

deliver them results that save them

money, save them time, and increase

their focus. So, real quick, the mindset

should be become tool agnostic. Solve

what the problem is at its core. Doesn't

really matter how you get there. So, if

I apologize for the ramble there, but

hopefully you guys understand where I'm

coming from. That's the type of stuff I

love to talk about in this space because

there is a lot of hype. And if you're

looking to connect with a ton of other

people who enjoy talking about that kind

of stuff as well, then definitely check

out my plus community. The link for this

is down in the description. We just hit

3,000 members, which is super exciting,

but it's full of people that are

building with NAND every day or

different platform. It's a community

full of AI problem solvers. Tons of

people in here are building businesses

with AI or using AI to help their own

businesses. It's a really cool space to

be. And we also have three full courses

right now. Agent zero which is the

foundations of AI automation for

beginners. We have 10 hours to 10

seconds where we dive into naden and

identifying how to save time with

automations. And then we have a new

course for our annual members called one

person AI automation agency where we lay

the foundation for building a scalable

AI automation business. I also run one

live call a week in here which have been

really fun lately. Been getting a lot of

cool questions and cool discussions. So

I'd love to see you guys in this

community. But that's going to do it for

today's video. If you enjoyed you

learned something new, please give it a

like. It definitely helps me out a ton.

And as always, I appreciate you guys

making it to the end of the video. I'll

see you on the next one.