Full transcript
0:00Welcome everybody to the first
0:02installment of the n8n webinar week. And
0:06in this session we're going to cover the
0:08foundations and basics of n8n. And also
0:12by the end of this session you're going
0:13to walk away with a live n8n agent
0:16regardless of whether you had any
0:17experience
0:19with n8n.
0:23So quick intro about me.
0:26My name is Marconi Don Rowan and I'm the
0:28director of revenue operations at Code
0:30Cloud and Code Cloud is a subscription
0:32based platform
0:33for DevOps engineering more of IT
0:36professional enablement. I'm also an n8n
0:39trainer and facilitator at Code Cloud.
0:41So I do have a n8n zero to hero course
0:45at codecloud.com if you guys want to go
0:47and check it out. Apart from that I also
0:50run a YouTube channel called automate
0:52with Mark. Where I teach a step-by-step
0:54guide to non-technical folks most of the
0:57time
0:58you know on how to use powerful AI
1:01automations with none of the technical
1:02jargon, right? So if you want to get in
1:05touch you know I can find me on YouTube
1:07LinkedIn Instagram
1:08scan the QR code and all my resources
1:11and contacts are there.
1:15So jumping to the goal for today is
1:17really to do a basic introduction to n8n
1:20what is n8n why we're using n8n
1:23and of course the first part of it first
1:2510-15 minutes or so we're going to cover
1:27some of the theoretical basics to n8n
1:29and don't worry we're just going to
1:31cover those that you actually really
1:33need to build your first workflow. And
1:35those are important concepts for you to
1:37understand how it all works together.
1:38But right after that it's going to be
1:40very hands-on right? So we're going to
1:41make sure the session hands-on. So I
1:43hope that you guys are in front of your
1:44laptop or your system so you can get
1:46signed up
1:47grab you know all the necessary tools to
1:50build your first live agent
1:52on n8n even if again Uh, if you
1:55completely new and never heard of n8n
1:57before, you're going to walk away with
1:58that today.
2:01All right, just a quick poll before we
2:02start. I wanted to understand the level
2:04of familiarity. Uh, I see a lot of
2:06people from uh, various different places
2:09um,
2:09on how familiar you are with n8n, right?
2:11From a scale of 1 to 5. 1 being, you
2:13know, you've either never heard of it or
2:15heard of it but never signed up, never
2:17really worked on any workflows before.
2:19And 5, you know, you're really advanced
2:20and technically you shouldn't have been
2:22here cuz these are uh, we're going over
2:23things that you already know. So, yeah,
2:25if you can just type into the chat to
2:27see
2:28uh, you know, what that looks like.
2:30It'd be great. Okay. So, 1 2 3.
2:36Someone with a 4. Okay. Okay. A lot of
2:381s.
2:40All right, somebody with a 5. So,
2:43cool.
2:45All right. So, um, a lot of 1s and 2s 1s
2:47and 2s. So, um, I think these sessions
2:48are uh, going to be pretty useful for
2:51for a lot of people.
2:52Um, but of course, we're going to cover
2:54the basics first and then we're going to
2:55show a couple of more advanced flow um,
2:57on what you can build with uh, different
2:59types of tools and stuff like that. But
3:01the ones that we're going to build
3:02together are going to be the ones that,
3:04you know, have all the free tools that
3:06you can grab, you know, stuff that you
3:08don't need to put in your credit card to
3:09pay. I want to make sure that everyone
3:11can just follow along um, you know, to
3:13build. So,
3:14let's jump right in.
3:18All right. So, what is n8n, right? Um,
3:21so, n8n, if you don't already know, is
3:24basically uh, in simple terms is an open
3:26and uh, free workflow platform, right?
3:29So, what it does is just connects uh,
3:30one process to another process, one app
3:32to another app, different services
3:34together in a very visual way. So, it's
3:36very visual. That's why there's there's
3:38this huge uptake about it because you
3:39can actually see it in that work. So,
3:41um, it has a huge selling point just
3:43because of that. People can actually see
3:44what it's doing. Um,
3:46in technical terms it actually runs
3:47under Node.js. Uh, with with Node.js
3:49under hood, you don't really need to
3:50know what that is, Um, but essentially
3:53that's to run the JavaScript and it can
3:55either be cloud or self-hosted. In this
3:57session, we're going to talk about the
3:59cloud-hosted one, specifically N8N Cloud
4:01because that's the easiest one to set up
4:03and also the easiest one to maintain. If
4:05there's any update from N8N, that's
4:06going to get updated first as opposed to
4:09self-hosted. If you self-hosted,
4:11technically you can self-host it on, you
4:13know, your own system or VPS, but you
4:15got to make sure that the maintenance
4:17are being taken care of if you're doing
4:19that, all right?
4:22And you might be asking, you know, with
4:24all the tools that are out there, why do
4:26we choose N8N, right? Of course, as any
4:29automation platform, orchestration is
4:31the one of the most important, if not
4:33the most important thing that an
4:35automation platform does. But of course,
4:36apart from that, N8N comes with its
4:38reliability, right? So, because you're
4:40building the node yourself or, you know,
4:43even with the help of an AI, you are
4:45able to see the processes in each node,
4:47which makes it really reliable and, you
4:49know, the control to that is very high.
4:52It's also a very scalable. If you have
4:54the workflow configured, it's very easy
4:56to scale it, very easy to connect new
4:58apps into it.
5:00Flexibility-wise, of course, you can
5:01build basically anything, all right? So,
5:02I think one of the
5:04tagline of N8N is you can automate and
5:06pretty much anything.
5:08So, if you have third-party apps,
5:10third-party tools, as long as they have
5:11API exposed, apps to be exposed, you
5:13pretty much can connect them to the
5:15workflow and essentially that gives you
5:18all the control and flexibility that you
5:20need.
5:22And with all the tools out there, you
5:23know, Cloud, Zapier,
5:26Open Claw, Manifold, you've probably
5:28heard of that.
5:29You're probably asking, why should we
5:31use N8N? Why should we consider N8N,
5:33right? So,
5:34I don't want to get into the weeds of
5:35comparing N8N, which is an automation
5:37platform, with something like Open Claw,
5:39where it is more of a 24/7 always-on
5:41agent because structurally they're
5:42different. But with all of these
5:44automation tools, you technically can
5:46put them uh position them in this
5:48sliding scale of the spectrum between
5:50control and reliability to independence
5:53and power and flexibility. So, for
5:54example, if you're using Claude uh
5:56sorry, Open Claude and Claude, um it
5:58comes with a lot of power flexibility.
6:00You can tell it something and and they
6:01could, you know, uh connect it with
6:03certain tools, etc. But, the steps that
6:06it's taking is not necessarily visible
6:08to you. And that's why, you know, you
6:09probably hear some stories about how it
6:11could go wrong, etc. and, you know, how
6:14it could actually use certain tools in
6:15the way that you didn't anticipate
6:17before.
6:18And um and N8N and Zapier, uh basically,
6:22being the automation platform, is the
6:23ones that actually you build uh from
6:26ground up. So, you actually know what it
6:28does. The guardrails are there. So, it's
6:29more deterministic than actually um
6:32tools, you know, like Open Claude and
6:33Claude is. So, so yeah, so that's
6:36probably how you want to view, you know,
6:38where these tools fall in terms of uh
6:40where these spectrums is. So, all right.
6:42So, diving into the foundations and uh
6:45the theoretical side, the concepts, the
6:46important concepts that we're going to
6:47cover today, right? So, again, I'm going
6:50to try to keep this as brief and as
6:51concise as possible. Only the
6:53need-to-know basis type of stuff, right?
6:55So, we're going to do a quick
6:56introduction to nodes, right? The types
6:57of nodes that there are in N8N. Uh we're
7:00going to also explore some AI agent
7:02architecture. Uh what it all means. How
7:03do you How do you connect it uh to an AI
7:05agent on N8N. Um and then, what are the
7:08input formats? What is the input? What
7:10is the output? What are the expressions?
7:12What is the fixed format? Um and then,
7:14also understand how N8N handle uh the
7:16data types. And these four things are
7:17super important uh because without this,
7:19even if you can automate the build, you
7:21wouldn't understand what's going on. So,
7:23N8N, uh if you don't already know, is a
7:26short form for node automation, right?
7:28So, essentially, what it is is just a
7:29bunch of nodes connected together uh to
7:31create a workflow. So, nodes are really
7:33the building component of N8N. So, in
7:35N8N, there are two types of nodes,
7:36right? You've got the trigger nodes,
7:37which there is, as the name suggests,
7:39the nodes that kickstarts the workflow,
7:41right? So, um it's an event that
7:43kickstarts the entire workflow. And then
7:45you have the action nodes, which is the
7:47node that performs a task, either send
7:49an email, store data, you know, have
7:51certain maybe even wait a certain period
7:54and stuff like that. So, those are the
7:55action nodes. So, 95% of the time you're
7:58working with action nodes, but the
7:59trigger node is usually the the first
8:00one that's going to start the entire
8:02workflow. And this is basically the
8:03typical basic AI agent architecture that
8:06we're going to work with today.
8:08Uh it comes Yeah, everything starts with
8:09a trigger node in N10. So, there's a
8:11trigger event that we define. And the
8:13trigger event can be on schedule, it
8:14could be let's say in every day 9:00
8:16a.m. or every week on a certain day, or
8:18it could be a chat trigger, right? You
8:20chat with N10 through Telegram or the
8:22native chat interface within N10, or
8:25form subscription, for example, right?
8:27So, all that data goes into what we call
8:29the AI agent node.
8:31And in N10, the AI agent node will then
8:34have an output into the next node. And
8:37this next next node could be another
8:39loop back to, you know, how you sent the
8:41message to the AI agent. Let's say if
8:43you sent it with Telegram, it may loop
8:45it back to Telegram as a reply. Or if
8:48you don't want it to do that, if you
8:49want it to do a certain action, that
8:50could be the case as well, right?
8:53But an AI agent node in N10 is actually
8:55you can
8:56you can think about it like an empty
8:58shell, right? If you don't connect it to
9:00three things, it's not going to be an AI
9:01agent. So, these three things are the
9:03LLM, sorry, which is you can view that
9:05as the brain of the AI agent. So, AI
9:07agent needs a brain.
9:09Um and that could be OpenAI, that could
9:11be Claude, that could be Ollama, could
9:14be Google Gemini, right? But all of
9:16these require API access token to
9:18connect it to the AI agent. And the
9:21second thing that you need to connect to
9:23an AI agent is the memory, right? So,
9:24this creates the persistent memory that
9:27an AI agent needs to be able to
9:28understand, contextualize the
9:30conversations or the flow events that's
9:32going into the AI agent.
9:35And third, you're going to connect it
9:36with the tools, right? That's really
9:37what's making the AI and AI agent is
9:40being able to leverage a tool, right?
9:42So, that tool can be anything from Gmail
9:44to Google Docs to, you know, Tavily to
9:48Firecrawl scraper, all sorts of stuff.
9:50So, again, like I said, in N8N, as long
9:53as the tool is exposed with API MCP, you
9:56can actually connect it to the AI agent.
9:57Although, a lot of tools these days
10:00already have their own community notes
10:02created within N8N. And again, to
10:04connect this uh to, you know, all these
10:06tools, you need either an OAuth or some
10:08form of API access to connect them to
10:11the tools, okay? So,
10:12the uh basically the architecture that
10:15we talked about, this is the way that is
10:17represented within N8N, right? Almost
10:19visually similar to what we just talked
10:21about, having a trigger node, which is
10:24right over here.
10:26Let me just turn on the laser pointer.
10:28Having a trigger node,
10:29oops.
10:30Having a trigger node going into the AI
10:32agent, and the AI agent having connected
10:34to a certain type of model, a memory,
10:37a tool, and then having that response
10:39set to another node. Now, quick one on
10:41the default run logic, right? So, no
10:44matter how extensive your workflow is,
10:47the way you can think about it is that
10:49it's going to do operate or it's going
10:50to process node by node, right? So, each
10:52node is going to be processed, it's
10:53going to run before the next node,
10:56you know, fetches the input and then
10:58process it and then
11:00kicks out the output. And we'll take a
11:01look at what that input and output looks
11:03like in a little bit. But I just wanted
11:05to clarify that if you have a branch in
11:08a workflow like this,
11:09the logic to run this is always first
11:11it'll it'll run the one at the top, and
11:13then it'll run the one at the second,
11:15always in that order, and they will
11:17always try to run both of them,
11:20you know, one after the other. Okay, so,
11:22with every node, when you open it up,
11:23and I know this is an AI agent node, but
11:25with every node, the concept is the
11:27same. We have an input
11:29and an output, right? So, the input is
11:31basically the data that's going into the
11:33node.
11:34And that is uh represented in three
11:36different format. And also the output is
11:39also represented in in three different
11:41format in terms of schema, table, and
11:43JSON, right? So,
11:46the schema uh template looks something
11:48like this. So, you have a variable key,
11:50and then you have a value, right? So, uh
11:53it's a key-value pair, and there's a
11:55reason why you want to keep this in mind
11:57because later on I'll show why that's
11:59important when it's uh when you're using
12:01it to drag and drop into the next node.
12:04And then there is a second version of
12:06that, which is a table format. And this
12:08is basically representing the same
12:09information, right? So, the information
12:11that you're seeing here on the left,
12:12schema, is just represented in a table
12:14format here. As you can see the the
12:16information or the data is exactly the
12:18same, uh which is chat input hello over
12:21here.
12:22And then
12:23the raw format is actually JSON, uh
12:25which is what's happening behind all of
12:26this. Uh in fact, the JSON format is
12:29basically the basis for that for any
12:30end-to-end workflows, right? Um so, you
12:32can toggle on any and you can toggle
12:34between these three, but essentially
12:35just to note that uh these are
12:37representing the same thing uh when
12:39going into the input and and output
12:41panel.
12:44So, in the input panel, as you can see
12:45here, you can later on uh we'll we'll do
12:48this live, but you can drag um you know,
12:51a certain variable into
12:53uh a prompt into the AI agent, for
12:55example. And the reason why this is
12:57powerful is because uh you can see see
12:59here there's a fixed and expression
13:01toggle.
13:02If you toggle this to the uh to a fixed,
13:04what happens is that uh and you know,
13:06and you type in hello, what happens is
13:08that this is going to be hardcoded every
13:10time uh when the AI agent runs. So, the
13:12AI agent's going to take hello as a
13:14prompt every time there's a run of the
13:15workflow. And that's why most of the
13:17time you don't want it to be a a fixed
13:19value unless you intentionally want that
13:21to be. Uh what you want to do is drag
13:23the chat input variable, drop it into
13:25here, and uh what that does is basically
13:27makes it a dynamic value. So, every time
13:30it runs, let's say the second
13:32uh thread of the conversation is no
13:34longer hello, it's how are you, this
13:35will change into how are you um
13:37automatically. All right, so I promise
13:39this is the last few slides on the
13:42conceptual side and then and then we'll
13:44jump right into the the hands-on
13:46session. But, data types are super
13:47important in n8n and in n8n there are
13:50five different data types, right? So,
13:51we've got the strings, we've got the
13:52numbers, we've got boolean, arrays, and
13:54object. And what are these things?
13:56Right, so string is basically a string
13:58of characters or alphabets, right? Like
14:00hello there. And then numbers are
14:02basically numbers, right? They could be
14:04They could be whole numbers like these
14:06or decimals or negative. And boolean is
14:08just either true or false, it's kind of
14:10just an expression. And arrays is
14:13basically a series of uh data values,
14:16right? This could be uh in strings or in
14:18numbers.
14:19And objects are basically next nested
14:21data and nested values, um which is a
14:24combination of all of that, all right?
14:26So, um you don't need to know about that
14:29too deeply, but just try to understand
14:31it uh because there are different data
14:33formats, a lot of the times the errors
14:34that you encounter while building n8n
14:36workflows because uh if a certain node
14:38is expecting a string input and you're
14:41inserting an object, that's when
14:43typically the error happens. So, it's
14:45important to note um you know, the
14:47different data types as we go along uh
14:49building these workflows.
14:52Okay, uh last but not least, uh
14:54community nodes. These are nodes that
14:56are uploaded and maintained by the tool
14:58providers. So, for example, this is, you
15:00know, a Firecrawl node, which is uh not
15:04uh which is um you know, uh created and
15:06maintained by Firecrawl, uh which is a
15:09uh
15:10uh tool that crawls the, you know,
15:11internet internet and scrape a certain
15:13page. Um you know, actually very useful,
15:16so
15:16um just to note that with n8n cloud, the
15:19community nodes is automatically
15:20uploaded, but if you are self-hosting,
15:22then there are a couple of steps to do
15:23uh for you to set it up. Cool. All
15:25right, so those are the concepts, the
15:28important concepts that you need to know
15:30to build your first end-to-end workflow
15:32and agent, right? So, there are two
15:34things that you need to have in order to
15:36build your first end-to-end
15:38workflow. And what you need is you need
15:40to sign up on n8n.
15:42And the second thing that you need to do
15:43is you need a Google AI Studio. And the
15:46reason for that is we're going to grab
15:47the LLM API keys from Google AI Studio.
15:50And it's one of the
15:52you know, it's one of the rare ones that
15:53gives you
15:54free credits without signing in putting
15:56into putting in your credit card into
15:58the system. So,
16:01I'm just going to give you guys maybe 2
16:04minutes to sign up and
16:06I think to recommend I'm not sure if the
16:08link has been shared with the audience.
16:13For n8n, if not, you can you guys can go
16:16to n8n.io and
16:19um
16:19you basically go to that page. All
16:21right, okay.
16:23So,
16:24I am going to walk through step-by-step
16:27on how to get signed up. I know most of
16:29you already probably signed up before or
16:31maybe you're already in the process of
16:32signing up, but if you haven't, go to
16:35n8n.io or the link that's been shared.
16:38And hit get started. And Tarek, I just
16:40want to make sure that I'm sharing the
16:41n8n page. Can or somebody can somebody
16:44from the CXL side confirm that
16:47I'm sharing that. Okay.
16:49Yeah, I can see it now. All good.
16:52All good. Okay. So, here it says, you
16:54know, company email, but technically you
16:56don't need to. It can be
16:58your Gmail.
16:59Doesn't matter, right?
17:03Submit that and then it's going to send
17:04you an email
17:06to so to your inbox. So, what you want
17:08to do is just uh
17:11make sure that
17:13you grab that. You can either can the
17:15link or just
17:17copy it
17:19and go back to
17:21the page, paste that here.
17:25on
17:26and
17:29Let's do random one there and then here.
17:35Okay, so the full name, password, you
17:38know, you guys I can fill that in
17:39accordingly, but account name, uh again,
17:41you can just name it based on whatever
17:43you prefer. Um click on verify that
17:47you're human and start the free 14-day
17:49trial. Again,
17:50um no credit card will be necessary.
17:53And just give it
17:55a couple seconds and it'll ask you some
17:58onboarding question there, right, which
18:01you know, you can answer accordingly.
18:03Team you're on, let's say marketing.
18:07Configuring API authentication.
18:11How did you hear about
18:12YouTube.
18:15Okay, so it's going to ask you to invite
18:17team members to your workspace and you
18:18can skip that for now.
18:23And just give it maybe 30 seconds or so
18:26um to spin up the workspace for you.
18:31All right, there we go. That's ready.
18:32So, I'm going to hit
18:34start automating. There we go. So, it's
18:37automatically spin spinning up a work
18:39flow for me immediately. But, if you are
18:43stuck at the overview page, what you can
18:45do is you can just hit the plus sign
18:47here
18:48and you're going to go to workflow and
18:50we're going to hit personal.
18:53And here, you're going to spin up your
18:55first workflow, right? So, we're going
18:56to add the first step here.
18:59All right, and I'm just going to pause
19:00here for a moment, just make sure that
19:02everyone that's signing up has
19:04successfully signed up.
19:05And if you have can you just
19:09type in yes in the chat and make sure
19:11that you know
19:13we're all on the same page.
19:16Great. All right.
19:18Cool. I see a lot of yeses.
19:20All right. So, going to go ahead and
19:22here you're going to hit the plus sign
19:23in the middle. So, this is where you're
19:25going to add your first trigger note,
19:26right? So, again, there are a bunch of
19:28trigger notes here they can pick from.
19:29You can trigger manually which means you
19:31it's just only good for you know test
19:33execution when you have to click on it.
19:35Again, you can hit on schedule which is
19:37you know scheduling every day, every
19:39hour. But, what we want to do here,
19:41we're going to connect it with a chat
19:42trigger, right? So, here if you type in
19:44chat
19:45and click on chat there's under trigger,
19:48there's an on new chat event, right? So,
19:50we're going to choose that
19:52and this is going to pop up, right? So,
19:55we can hit test trigger here.
19:58And this panel should pop up right
20:00below. This is a panel that is available
20:02if you are doing the N and N native
20:05chat. So, here we just going to type
20:07hello, all right?
20:08The reason why we not do that is we want
20:10to populate this node, right? So, what
20:11does populate mean? It basically
20:13fills in the input of the node and also
20:16in this case because it's the first
20:18node, it only shows the output which is
20:20the chat input hello.
20:22All right. Okay. So, now this is the
20:24first node, the very first node in your
20:26N and N workflow. So, what you want to
20:27do next is to build the next node. So,
20:29in the next node, what we want to do is
20:31hit the plus sign here and we're going
20:33to choose the AI agent node here. Sorry.
20:36We're going to hit AI agent.
20:40And this should pop up
20:42on your monitor.
20:44And here it knows already that you're
20:46connected to a chat trigger node. In
20:48this case, it is a chat trigger. So,
20:49we're going to leave it as that. And
20:51under prompt, it also knows that it also
20:54knows to take the chat input. So, you
20:55don't even need to drag and drop
20:56anymore, right? So, just to show you
20:59guys, you know, if let's say you want to
21:00define it yourself, you can hit that and
21:03now you can actually hardcode, you know,
21:05hello or you can toggle it to
21:07expression,
21:08and then you can drag the chat input and
21:10drop it over here. And you can see that
21:12below it says hello, which is reflective
21:14of what the value is here, right? So,
21:17this is how then you can dynamically
21:20put in any value that's coming in, and
21:22you can pick and choose which input data
21:24that you want to put in here, all right?
21:26So, I'm going to toggle this back to
21:27connected to chat trigger node, because
21:29it's the same, which is the chat input
21:30here,
21:31right? And then you see this three plus
21:34signs down below, the chat model, the
21:36memory, and tools. So, those are the
21:37three things that we need to make this
21:39an AI agent, right? So, we're going to
21:41hit chat model.
21:43And here, we're going to pick Gemini,
21:45right? So, literally type in Google
21:47Gemini,
21:49and we're going to click on that.
21:52And here,
21:53we're going to fill in the credential
21:54and the model, right? So, under
21:56credential, as you can see, there's no
21:57credential set up yet because it's a
21:59fresh account. So, I'm going to hit set
22:01up credential.
22:02Okay?
22:03So, there are a couple things that I
22:04need to fill in. We're going to leave
22:06the host URLs the same, right? So,
22:08everything we're going to leave it as as
22:10that.
22:11But under API key, this is where we want
22:12to fetch our API key, right? So, API key
22:15is just like a key for you to be able to
22:17be able to access the LLM. So, if you're
22:19not familiar with the term API,
22:23think of it as a way of communicating
22:26with your LLMs, right? So, how does
22:28Google know that you're authorized to
22:30use the LLMs? Because every use of LLMs,
22:33they you use tokens, right? So, tokens
22:35are our money, tokens are compute. So,
22:39you know, they need an API. So, the API
22:41key is basically the key to reflect that
22:42you are actually the authorized user
22:45for that particular,
22:46you know, to use that particular
22:47account. So, where are we going to get
22:49this API key? This where you want to go
22:51to
22:52Google Studio. So,
22:56go AI Studio.
23:02I'm sorry. So, it's aistudio.google.com.
23:07Right here, we're going to hit
23:08get started.
23:11Okay, I'm already signed in here. But,
23:13if you haven't, what you can do, and if
23:15you have a Google account, it should be
23:16a simple
23:18um
23:19it's a a simple sign-in with your Google
23:20account. And uh where you can go, once
23:23you're logged in to Google AI Studio, is
23:26to the left-hand panel here.
23:28And you can click get API
23:30key. All right. Again, I just want to
23:32pause here for a second. Just want to
23:34make sure that everybody's following.
23:36So, um again, I saw a great explanation
23:39about too fast. Okay. So, I'm going to
23:40slow down. So, just want to make sure
23:42that everyone
23:44is on the same page with the
23:45aistudio.google.com.
23:50So, if you are, uh please type in yes.
23:52If you're not, um yeah, type in no.
23:54Okay. Okay. Okay. A lot of yeses. Couple
23:57of nos. So,
23:59just want to Okay. So, a couple of uh
24:01requests to go back to the workflow
24:04and to repeat. So, I will do that. One
24:06second.
24:07So, in the workflow,
24:09the first thing you want to do, and I'm
24:10going to I'm going to just start on top
24:12here, right? Just to make sure that
24:13everyone everybody's following. If you
24:15are, I'm assuming that the chat message
24:17uh sorry, the the chat note
24:20has been, you know, you you managed to
24:22set that up.
24:23So, the AI agent note, once you have
24:25this on, you want to choose connected
24:27chat trigger note.
24:28And then, uh in the prompt, you're going
24:30to leave everything as is, right? The
24:32moment you create the AI agent note, um
24:34you're going to leave everything as is.
24:36And if you're
24:37wondering where you can get the chat the
24:39AI agent note, you can hit the plus sign
24:41here.
24:42And under AI,
24:44pick AI agent, and that note's going to
24:46pop up, right? So, this is a new one.
24:47I'm going to close this.
24:50And yeah, I'm going to delete that. All
24:52right. Um
24:54So, how I created the uh uh called the,
24:57you know, Gemini chat model is under
24:59chat model there's there was a plus sign
25:00if you click that it's going to open up
25:03the Google Gemini chat model.
25:06And right now there's no credential set
25:07up so you want to hit set up credential.
25:09This is where it's going to ask for an
25:10API key. All right, so that's the recap
25:13there.
25:15Okay, so under API keys what you want to
25:17do is you want to hit create API key.
25:20Right, so you can name your API key but
25:23I'm going to keep it as Gemini API key.
25:25Typically you can maybe name it okay,
25:27maybe name it edited and memo, right?
25:31And you're going to need to select a
25:33project. If you don't have a project
25:35you can create a project
25:37and hit create project.
25:40And some of the interfaces I've noticed
25:42that are different. Sometimes you
25:44already have a default if that was your
25:45first time creating it you may have a
25:47default API key over there.
25:49So if you already if you already have a
25:52default API key you can actually copy it
25:53and use it. Right, but uh otherwise you
25:56can create it the way I did and just hit
25:58copy API key
26:00over here with this button.
26:02And then go back to your workflow
26:04and paste the API key.
26:06And what you want to do now
26:08is hit is hit save. Sorry, I'm going to
26:10toggle this to expression so everyone
26:11can see the API key. I'm going to revoke
26:12it afterwards of course. So you want to
26:14be you want to keep this safe, right? So
26:16API key is always keep it safe because
26:17it um once someone has your API key
26:20basically can't access
26:22uh your Nello Labs all this stuff,
26:24right? So uh you want to keep that safe
26:26and then you want to hit save.
26:30Okay, so connection tested successfully
26:33means connection is live and we're good
26:35to go there.
26:38Cool.
26:39Okay, so now
26:41what we want to do the second thing let
26:43me just check to why this is showing
26:45that.
26:45Okay, so once you connect uh once you've
26:48set up the API account
26:51what you want to do is you can choose
26:53the model that you want to use, right?
26:54So, under Gemini, there's a couple of
26:56models, but in this case, I'm just going
26:58to keep it as Gemini 2.5 Flash. Uh
27:01that's good enough for the stuff that we
27:02want to do. So, we're going to leave it
27:04as that. We're not going to change
27:05anything, so we're going to toggle out,
27:06right?
27:07Now, the second thing you want to do is
27:08to connect the persistent memory, right?
27:10Because an AI agent with a brain but
27:13with no memory is not going to be able
27:15to remember what you just told it,
27:16right? So, let me just give it for
27:18example, if you type in hello now,
27:23All right. So, you can see the input is
27:24hello. The output is, "How can I help
27:26you today?" Which means it's like, all
27:28right, I can tell it my name
27:31is Marconi, and it's going to say,
27:33"Hello Marconi, it's good to meet you.
27:34How can I help you today?" I'm going to
27:35ask it again,
27:37"What is my name?"
27:45Right? So, it says, "I don't know your
27:46name. As an AI, I don't have access to
27:48personal information about you unless
27:49you choose to tell me." Right? So, it
27:51just completely forgot what I just told
27:53it. So, that's why uh the memory is a
27:55very core important part of building a
27:58uh you know,
27:59uh an effective AI agent. So, sorry, let
28:01me just redo that because I was too
28:03quick. So, what you want to do is to hit
28:05the plus sign,
28:07and there are a couple of memories that
28:09you can choose from, right? Uh typically
28:11Postgres is is a good one, but here,
28:13because we want to keep it simple, uh
28:14this is the first workflow you're
28:16building. So, we're going to hit simple
28:17memory because this is the native uh
28:20simple memory that N den Cloud has, um
28:23you know, and it's simple for you to use
28:25and it's quite reliable as well. So,
28:27it's actually pretty good and you know,
28:29to
28:30uh for you to be able to even build
28:32something that's pretty complex. So, um
28:34we're going to choose simple memory,
28:36and context window length is basically
28:38how many past interactions that you had
28:40with the particular AI agent, right? So,
28:41in this case, we're going to leave it as
28:43five. We're not going to change anything
28:44here,
28:45um and then we're going to toggle out.
28:49Okay? So, now you've got the LLM and the
28:52simple memory connected. The last thing
28:53we want to connect is the tool. So,
28:55we're going to hit the plus sign here
28:57under tool.
28:58And we're going to search for Gmail, all
29:01right?
29:02And here the two Gmails, we want to make
29:04sure we are choosing the right one.
29:06Um, so human in the loop is if we want
29:08to ask approval through Gmail, but what
29:11we want to choose now is not human in
29:12the loop, it is the Gmail tool. So, this
29:15is the second uh one if you type in
29:17Gmail. So, just make sure you choose the
29:18second one here.
29:20And once you choose that, this is going
29:22to pop up.
29:23And uh with Gmail now, it's super easy
29:26if you are on a Google Chrome, you can
29:27hit sign in with Google.
29:34Okay? And then it's going to ask you
29:37for, you know, a credential pop-up is
29:39going to show.
29:42And then you're going to select all the
29:45um, permissions that it's asking for.
29:47And you're going to hit continue.
29:52So, that's pretty much it, right? So, it
29:54it says here credential created
29:55successfully, your account has been
29:57connected successfully.
29:58So, now we're not going to do anything
30:00because we know that we are connecting
30:03this Gmail because we want the agent to
30:05be able to use the Gmail to send emails
30:07out. So, the operation send is correct.
30:09We want everything else is set up
30:11correctly, we're going to leave it as
30:12that. Now, to is basically who we're
30:15going to send this email to. This we're
30:17going to let the AI
30:19be able to decide this by just clicking
30:21on this blue button here, this blue
30:23sparkly button. Once you click that, it
30:25will say define automatically by model.
30:27So, the model is going to decide who to
30:29send it to. Now, that may sound scary,
30:31but it's going to follow your strict
30:33instruction. If you tell it,
30:34um, you know, send it to uh
30:36automatewithmark@gmail.com,
30:37it's only going to send it to
30:38automatewithmark@gmail.com. It's not
30:40going to send it to uh anybody else,
30:42right? Same with subject, we're going to
30:44let it be able to decide what the
30:45subject line is. And then message, we're
30:48also going to let it decide what the
30:50message is. All right, so we're going to
30:52you know, click on the blue button for
30:55these three here. Email type we're going
30:56to leave it as that.
30:58And that's it. We're going to toggle
30:59out, right?
31:01Okay.
31:02So, now that we have all these tools
31:05connected, I'm going to take a pause
31:07here again. Just want to make sure that
31:08everyone is following.
31:10Um I see somebody saying that they don't
31:13see the pop-up. So, it might be because
31:15I'm the screen share thing, but it's
31:17basically just a simple Google
31:18credential authentication pop-up.
31:21So, yeah. So, if you guys are on the
31:23same page, please type in yes.
31:26Um
31:26and then I'll move forward. Cool.
31:28Awesome. All right, seems like everyone
31:29is on the same page or most most
31:31everyone. So, here once we have all
31:34these three connected, now we can
31:35actually ask it to do something, right?
31:37So, we can ask it, "Hey, can you
31:41come up with
31:44a 300-word
31:46blog
31:48post
31:49or blog for let's say CXL,
31:53a
31:55premium
31:56B2B
31:58marketing
32:00um and education
32:02company
32:03and send it
32:06to my email at automate with
32:11Mark
32:12at gmail
32:15.com. All right, we're going to add a
32:16plus sign here just so that it does a
32:18differentiation, but
32:20All right, so we can tell it that. So,
32:21essentially I'm just telling it
32:24Uh
32:25but I cannot Okay, it's telling me I
32:26cannot write the blog 300 posts for you.
32:28Okay. So, maybe I need to connect some
32:30tools, right, to be able to
32:32come up with a good blog. Sometimes it
32:35will go ahead and write it for you. In
32:37this case, you know, it's it's refusing
32:39to. Maybe the model that I chose is not
32:41up for it. But what I can tell it, for
32:43example,
32:44can you
32:46instead, can you send an email
32:49about my appointment
32:52with Mark at 2:30 p.m.
32:56to
32:58automate
32:59with Mark.
33:01Hello.
33:03And Miller, oh.
33:05All right. So here you can see that the
33:07green sign is actually going and using
33:11the LLM twice. So it's referring to
33:13that. It's actually,
33:14you know, referring to the memory as
33:16well as it's also utilizing the
33:20the Gmail tool here to send the email.
33:22All right. So here, let's go to our
33:23email and see if we actually receive
33:25anything.
33:26So here, I actually received, "Hi Mark,
33:28this is a reminder about our appointment
33:30today at 2:30 p.m." All right. So
33:34So yeah, so right now we have
33:35successfully created an AI agent that's
33:37capable of sending email on your behalf
33:40to anybody
33:41as long as you tell it to send it to
33:43them, right?
33:44Of course, you know,
33:46when you look at this, this is a very
33:48simple workflow and you say,
33:50you know, some of my cloud agent or chat
33:52chat GPT agent might be able to do that
33:54as it is. So why are we doing that?
33:56Well, the reason is we're going to build
33:58the The reason is today we're we're
34:01building the basic foundation of a
34:02workflow of an AI agent.
34:05And later on I'll show you, you know,
34:06two or three other use cases where it
34:09gets a little bit more advanced, a
34:10little bit more complex,
34:12where it then it kind of makes sense how
34:14you know, all these things can be
34:16automated when put together. But it's
34:18just to give you an introduction, give
34:20me a feel of
34:21as to how these AI agents actually
34:23leverage tools, right? Like for example,
34:25earlier I said, you know, create
34:28a 300-word blog post and it's not
34:30confident in being able to use that
34:32or to do that. So, you can connect tool
34:34like for example, you know, Google Doc
34:35is another tool that you can connect,
34:37right? And the same thing with Google
34:39Doc, you can sign in with Google and be
34:41able to connect it, and then it will
34:43it'll be able to, you know, draft those
34:44blog posts into the Google Docs and be
34:47able to share that Google link to
34:50you know, via email, via Telegram and
34:52stuff like that, right?
34:53Um
34:54So, before I move on to, you know,
34:57demoing the other workflow, I just want
34:58to point out the difference between
35:00adding a tool here and adding a Gmail
35:04tool or Gmail node over here, right? So,
35:07you may be wondering, "Hey, you know,
35:09what's the difference between adding it
35:10as a tool to the agent
35:12uh versus adding it over here?" So, for
35:13example, I can add this node, right? I
35:15can add a send message node
35:17over here, and then it will
35:19it will it will still run that, right?
35:21So, the difference is that if I attach
35:23it as a tool, the agent has a choice on
35:26whether or not to to use the tool. So,
35:27it's going to infer from my request
35:29whether or not I'm asking it to send an
35:31email, right? So, if I say send an
35:32email, then it will infer that, "Okay, I
35:34actually need to use a tool to fulfill
35:36that request." But if not,
35:38uh you know, if I wire it up this way,
35:41what happens is that whether it doesn't
35:43have a choice, right? The workflow
35:44doesn't have a choice uh whether or not
35:45to send the email. It will always run
35:47the agent node and then subsequently run
35:49the send message. So, if you want it to
35:51always send a message via email, this is
35:53what you're going to do. You're going to
35:55connect it to the Gmail node this way.
35:57But if you want it to be
36:00uh multi-agentic, multi-tools, so, you
36:02know, not always all the requests are
36:04going to be involving sending email, you
36:06would connect it this way as a tool so
36:07that it has a choice
36:09uh to use it or not.
36:12Okay?
36:15All right.
36:16So, that is the uh I hope everyone is
36:19was able to get that up and running.
36:22Um
36:23Yeah, if you were able to get it up and
36:25running on your end, please type in yes.
36:27Just want to make sure that um
36:29Yeah, somebody also Yeah, somebody
36:31received a 300 word word blog post, so
36:33that's great. Awesome. A lot of yeses
36:35there. All right.
36:37Great. Yep, so um that was an easy one,
36:41uh you know, to get started um so that,
36:43you know, you guys have a feel on how it
36:45all works.
36:46Uh of course, you know, there are more
36:47advanced build that we can go ahead and
36:49build, but you know, this being a
36:5045-minute session, I can't squeeze all
36:52of that uh into one session, but don't
36:55worry, the next few sessions it's going
36:57to get progressively more advanced and
36:58and it's going to uh get you there uh by
37:01the end of it, right? Uh but here I just
37:03want to demo some of the few
37:04possibilities that we can we can do and
37:06we can come up with, right? Like for
37:07example, this particular uh
37:10uh workflow, and I'm going to explain
37:11each and every node uh to let you know
37:13what's happening here, right? Um this is
37:15a inbound sales, so this is a sales use,
37:18and later on I'll I'll show you another
37:19marketing usage as well. So, this is a
37:21sales usage where it will uh respond or
37:24the trigger event is actually an on form
37:26submission, right? So, this on form
37:27submission
37:28uh it can uh it's actually a form that
37:31you can embed uh to your website or to
37:33your landing page. So, I'm going to
37:35execute workflow here just to show you
37:37guys what it looks like, right? So, this
37:39is an inbound form. Again, this can be
37:40embedded into your web page uh into your
37:44landing page. So, for example, I'm just
37:45going to fill in, let's say, John Doe
37:48um
37:48email address. Again, I want to put my
37:50own email address here.
37:52The reason for that is I don't want it
37:54to randomly send it to other people,
37:56right?
37:57And then phone number, I'm going to
37:58leave it blank.
38:00So, this is a similar to
38:03let's say any inbound lead form that you
38:05have uh you know, as a business. You
38:08know, you want to know, you know, what
38:10their businesses are and stuff like
38:11that. In this case, I am uh acting as
38:14Kode Kloud, right? So, this is, let's
38:15say, how Kode Kloud inbound form is. You
38:18know, we sell to engineering teams,
38:20DevOps, um IT departments um who wants
38:23to upskill. So, let's say how can
38:25Co-cloud help? Let's say upskilling in
38:27AI skills.
38:28And let's say the person submits this
38:29form on your webpage, right? So, once
38:32they submit the form, it goes into an AI
38:34agent similar to what we've just built.
38:36But in this case, you see I didn't
38:38attach any memory, I didn't attach any
38:39tool. Why? Because this agent, I just
38:41wanted to come up with a prompt, right?
38:43So, I've given it some system prompts
38:46as a as a video prompt agent.
38:48And the reason the reason why
38:51uh why I want to I want it to create a
38:52prompt is because that prompt is going
38:54to get passed on to HeyGen, right? So,
38:57what HeyGen is
38:59I'm going to
39:00toggle it to here.
39:01Uh if you guys are not familiar with
39:03HeyGen, it is one of the tools that
39:04generate uh lifelike videos, uh
39:07AI-generated videos based on your
39:10similarities, based on uh based on your
39:12your videos, right? So, it can actually
39:14clone you or impersonate you. Uh so, in
39:16this case, I have a few avatars that uh
39:18I've created of myself. Um so, in in
39:21basically, once you've done that, you do
39:23need to be on a pay plan. Uh but once
39:25you've done that, you can grab the API
39:26keys uh in similar fashion that we did.
39:29Uh slightly more complex, which we won't
39:31get into the weeds of today. Uh but once
39:33you've done that, you basically call the
39:35HTTP request to post a request to make
39:37to make the video, right? So, HeyGen's
39:39actually processing the video.
39:41And then we're going to wait, you know,
39:43a certain period of time because it
39:44needs time to process. So, you know,
39:46it's processing the video based on the
39:47prompt that this agent comes up with.
39:49And then it goes to get the video,
39:51right? Why do I want to get the videos?
39:53Because hey, the video's been generated,
39:55right? Um so, we want to get the video.
39:57And this loop is just to make sure that
39:58if the video is not ready, it's going to
40:00go into a loop and it's going to try to
40:02get the video again and again until
40:04um you know, the video is ready. And
40:06then the if gate is if the video ready
40:08is ready, it goes into uh um another
40:12open AI model. And the reason why we
40:15want to feed it to that is because this
40:17is a caption model, right? So, if we
40:19look at if we look at at the content
40:21here, so it's coming up with the caption
40:23or the content based on what we've
40:25filled up on the form earlier here,
40:27right? So, it's taking inference from
40:29the form submission. So, what the uh
40:32prompt agent and the or the content
40:35caption agent is doing
40:37is uh sorry, not the caption agent, the
40:39email writing agent is doing is taking
40:41reference uh this guy called John Doe um
40:44based you know, from CXL and coming up
40:47with the email copies and and video
40:49contents and scripts uh in order uh to
40:53send them a a an email with a short
40:56video inside uh
40:58uh which is an intro video to get them
41:00to join or come into a demo call uh with
41:03your sales person, right? So, in this
41:04case, it's already sent an email to
41:07automate with mark@gmail. So, we're
41:08going to toggle over to
41:11automate with mark and
41:15and let's look at the
41:17and see what the email looks like,
41:19right? All right, cool. So, I should be
41:21seeing the email now. So, this is what
41:23the email looks like, right? So, um I
41:25received it 2 minutes ago, right? So,
41:28based on what I filled in, right? I
41:29filled in John Doe. Go, "Hey John, I
41:31took a quick look at CXL because I put
41:33in that my business URL is CXL and
41:36thought you might appreciate a brief
41:37intro on how Quick Cloud helps teams
41:39ramp up DevOps skills with hands-on
41:41training." Exactly how we would talk to
41:43engineering team, right? So, hands-on
41:44labs, uh real cloud sandboxes with
41:46Kubernetes, Docker, Terraform, AWS, etc.
41:49So, these are all these technical terms
41:51that the engineering team would like to
41:53hear and want to upskill in, and then it
41:55created a quick 10-second intro video
41:58here uh with the thumbnail so that, you
42:01know, people can click on it and it
42:02actually downloads the video. Okay, so
42:05that is a video that's generated based
42:07on I don't know if the audio is coming
42:08through, Tarek. It's
42:10I know you have to share screen with the
42:12audio. It happens a lot with Zoom. It's
42:14annoying.
42:17Did the Did the audio come through or
42:18no? No, they didn't come through. Like
42:20you do when you share your screen, you
42:21have to share and you have to enable a
42:23share sound as well.
42:27Okay, I don't know if
42:29But I believe you. But I trust you. It
42:31has a I know like I I faced the problem
42:33this problem so many times. Yeah, yeah.
42:35Yeah, yeah, yeah. So anyway, um
42:38assume that there was an audio. Uh you
42:40guys can see the movement and then the
42:42video, right? So I did not say anything
42:44or move like that or or or or said any
42:46any of those things in terms of the
42:48script, right? So this is all AI
42:49generated of myself. All I did was take
42:52took a 1 and 1/2 minute video on HeyGen
42:54and that generated my avatar. So now
42:57I have a workflow Okay.
42:59So basically with this workflow, every
43:00single lead that comes through the
43:02inbound form, it's going to immediately
43:04get an email that is personalized to
43:06them with a video that is talking to
43:08directly to them to their pain points,
43:10knowing what the business
43:12you know, pain points are. And you can
43:14add even layers to this, right? So right
43:15now it's very generic, you know, I built
43:17this in maybe 15 20 minutes, right?
43:20It is currently very generic. What you
43:21can do, you can add an Apollo layer, you
43:23know, data enrichment layer to find out
43:25who their managers are, you know, who
43:26their uh co-workers are and stuff like
43:29that, decision-makers, etc. and have a
43:30multi-thread channel. So there there are
43:33lots of potential that you can build in
43:34terms of workflow here
43:36uh in terms of sale, right?
43:39Cool. All right. I am mindful of the
43:40time.
43:41Uh so I'm just going to show
43:43my last uh workflow here with the
43:46marketing one. This is uh
43:48a bit more complex, but um basically
43:50what this workflow is trying to achieve,
43:52it is trying to create uh reels for
43:54Instagram, right? So um you have a
43:56folder and an edited folder with
43:59currently
44:00you know, some random pictures of uh
44:02gym,
44:04some coffee, and some croissant. So I'm
44:06assuming, you know, this is a coffee
44:08shop or a gym that is trying to create
44:10social media post, social media reel.
44:12Uh so, it goes through this node, which
44:14is a randomizer. So, it randomly picks
44:16an an image and then uploads it to a
44:18database, uh gets the real idea and the
44:21prompt, and then it posts it to Wave
44:23Speed. Again, Wave Speed generates
44:25videos from the particular image. Same
44:27thing here with what you saw earlier,
44:29wait for the image Oh, sorry, for the
44:31video to be generated. Um and then try
44:33to get the video. That's a loop there,
44:36and then goes to this thing called a Sub
44:37Magic. So, Sub Magic is, you know, uh
44:40it's an auto caption tool. Again, there
44:42are many tools that I'm covering here,
44:44but not all of them have alternatives,
44:46too, right? This is by no means like
44:47they're the best tools out there. Um so,
44:50same thing with calling any tools that
44:52that needs time to generate, you need to
44:53wait, and it goes into a loop uh before
44:55sending a message for approval.
44:57And uh once you do that, I'm going to
44:59run the workflow as I'm talking here. Um
45:01it's going to upload the reels into uh
45:05what we call Botato. Botato is a tool uh
45:08that automates your social media post.
45:10And then that creates an Instagram post
45:13at the end of it, right? So,
45:15um
45:16I'm going to fast forward on this. Um
45:18you know, if we go to our Instagram now,
45:21I don't know if it's posted yet.
45:22Typically, you need to wait,
45:24you know, uh like 30 seconds or so, and
45:27uh it will show up. Basically, again,
45:29just to recap, it's taking
45:32randomly one of these images and trying
45:34to create a social media reel
45:37uh to be posted to the to my Instagram,
45:40right? Uh again, because I don't run
45:43cafes and and gyms, I will probably
45:45delete it immediately.
45:47Uh let's see. Okay.
45:49So, yeah, so it's got a So, it picked
45:51the croissant. So, let's open that up.
45:54And
46:01Okay, it's a short one. I don't know if
46:02you guys can hear the audio, but there
46:04was an audio
46:05and the caption is important because
46:07some people on Instagram they're they're
46:09not turning on their audio, right? So
46:12So yeah, so it comes with the captions
46:13as well.
46:15Holiday vibes with a taste of festive
46:16magic etc. So yeah, okay, I'm going to
46:19delete this.
46:20Again, because this
46:22not typically what I would post on my
46:24Instagram. If you do the same demo in
46:26all of your presentation, like your
46:27followers will be like, why are you
46:28obsessed with with croissant? with
46:30croissant. So I'm hoping you're deleting
46:33Yeah, yeah, yeah, so it's
46:35I I should probably create a different
46:38Instagram account to run my demos.
46:40>> like maybe a burger like differentiate
46:42that croissant every time.
46:46That's true, that's true. Yeah, so yeah,
46:48that's a quick rundown on how it could
46:49possibly be used for you know, your
46:51Instagram Reels marketing etc. And
46:54that's all to say
46:56you know, because you are the one
46:57building this, you know what's happening
46:59on each node. You know, it's very
47:00deterministic, right? So they got rails
47:02around, it's not going to go completely
47:04off rails, completely wrong. So that is
47:06to me the power of n8n, right? All
47:09right. Yeah,
47:11conclusion.
47:12You know, what's next, right? So today
47:14you learned the very basic of n8n, how
47:17to sign up, how to set things up, how to
47:19run your first workflow. But to me I
47:21think that's where the magic is, right?
47:22Running your first AI agency, how it all
47:24works, seeing how it all connects
47:25together, seeing how it fetches, goes
47:27into your Gmail, helps you send message.
47:29I think that's the first spark that's
47:31going to help you you know, continuously
47:33learn on n8n and and by no means is
47:35super easy to learn n8n. I'm not going
47:37to sit here and sugarcoat everything,
47:38right? So but with the knowledge you
47:41actually learn also how the fundamentals
47:44of this is actually very much applicable
47:46to other types of tools, right? So the
47:48next thing you can do is n8n has an AI
47:51builder feature, right? So if you were
47:54to go to your workflow, I'm not going to
47:56toggle back, that, if you want to go to
47:57your workflow app on the right-hand
47:59corner, you'll see a little sparkly
48:01thing, and if you click on that, that is
48:02actually
48:03uh the AI help. You can chat with it.
48:05You can ask it, but there's also a
48:06builder version
48:07uh where you can ask it to build uh you
48:09a workflow for certain use cases and
48:11stuff like that, right? However, if you
48:13use that, you'd still need to understand
48:15how it all comes together. You still
48:16need to understand um
48:18you know, where to put the API keys, how
48:20to connect it to certain tools because
48:22they're not going to be able to do that
48:23for you, right? It can build you a
48:24template. It can build you a structure
48:26based on what you need, but you still
48:28need to understand, right? And the
48:29second thing is uh there's a huge
48:31ecosystem of n8n templates and creators
48:33in the marketplace. So, you can go to uh
48:35n8n templates. Some of it is paid, some
48:37of it is free.
48:38Um and uh I I also happen to be one of
48:40the creators there. Uh so, you can go
48:42check out um you know, some of the
48:44workflows that I build. They're all
48:45free, uh and some of them actually come
48:47with, you know, um a step-by-step guide
48:49on on how to put them together. Um so,
48:52yeah, go and check them out if you want.
48:55Um
48:56and then uh you know, some of the
48:58questions that you guys might have is
49:00in today's age with OpenClaw, with
49:02Claude Code, with uh Claude Co-Work, you
49:05know, do we really need n8n anymore,
49:07right? Uh the answer is yes, right? Uh
49:10the reason for that is there are some
49:11workflows that you probably want some
49:14pretty strict guardrails or guardrails
49:15or uh right? So, that's where you want
49:17to use n8n. So, for example, some of the
49:19workflows that I've used uh or that I
49:21showcase in terms of social media
49:22posting and stuff like that, I can
49:24connect that with OpenClaw. I can
49:25connect that to Claude Co-Work or Claude
49:27Code uh to be able to call that
49:29particular workflow. And that that way,
49:32I know for a fact that, you know, Claude
49:34can go ahead to my Instagram channel and
49:36and post something that I didn't approve
49:38because the only thing it can call is
49:40the n8n workflow, which requires my uh
49:43my approve my approval, right? Uh
49:45because I built it that way. So, that's
49:46where n8n could come hand in hand with
49:49all these new tools, all all new agents,
49:51um and uh you know, it's just uh
49:53extremely uh
49:55versatile tool, especially when, you
49:57know, connecting it to third-party apps
49:59that may or may not have built it off
50:01the shelf.
50:03And, um yeah, that's the new future of
50:05automation,
50:06uh you know, connecting it to somebody
50:08gigantic tools that people are rolling
50:10out. I know,
50:12you know, we are all overwhelmed. Every
50:14week there seems to be a new tool that's
50:15coming out. Um
50:17but, yeah, any then I think it's here to
50:19stay just because of how clear it is,
50:22how visible it is in terms of uh the
50:24step-by-step process that it's going
50:25through.
50:27All right, so that's the end of my time.
50:30Um and thank you all for sitting through
50:32that. I hope that was valuable, but
50:34yeah, just opening up the floor for any
50:36questions.
50:37Um you know, please uh go ahead.
50:40Awesome, man. That was like really nice,
50:42and I saw in the chat how everyone was
50:44following along. I think most of them
50:46were able to follow along and, you know,
50:48build their their first agent. In case
50:50you weren't able to catch up, uh we're
50:52going to share the recording with
50:54everyone. If you are signed up to the
50:56CXL platform, it will be available there
50:59within 24 hours. And then we're going to
51:01share the recording via email.
51:03Uh if you registered to this webinar,
51:04you'll receive an email, I think, early
51:06next week with all of the recordings
51:08from all the sessions in the upcoming
51:09few days.
51:11We are obviously out of time, but we're
51:13going to add just few minutes so maybe
51:15you can answer the question or two. Uh
51:17for the rest, you can connect with
51:18Marcony on LinkedIn, and in case we
51:20cannot answer your questions now, he's
51:23more than happy I'm uh speaking on your
51:25behalf. Are you happy
51:26>> No, feel free. Feel free to send him a
51:29LinkedIn message with your question if
51:30we're if we're not able to do so.
51:32Just few things from my my end before we
51:35answer two questions. Sorry about that,
51:37guys, but
51:38uh few things.
51:40Uh speaking of automations, speaking of
51:42AI,
51:43uh at CXL, we have lots of upcoming live
51:46sessions if this is something you want
51:48to upskill in. So from June until
51:50September we have 12 sessions around AI.
51:54So n8n, Claude Code, even vibe coding
51:57tools like Replit and lovable. So each
52:00week, each Tuesday we have a session
52:02talking about the digital marketing use
52:03cases. So you can see in June we talk
52:06about how you use n8n and Claude Code
52:08together as Marconi mentioned that one
52:10does not replace the other. You can use
52:12them hand in hand for content creation
52:15uh for SEO automation. In July it's more
52:17about paid advertising. In August it's
52:20all about uh
52:21vibe coding, satellite apps to get more
52:24leads. And in September we have a course
52:26for agencies on how you can adapt to
52:28this AI age. So you can get all of them
52:31for $499
52:33or if not all of them are relevant, you
52:36want one of them that will be $150 per
52:38course. But obviously being biased, I
52:40think if the full program would benefit
52:42you the most. Uh but if you want to get
52:45either one of them, just make sure to do
52:47it fast because after May 31st the
52:50prices will increase. So we going to put
52:53the link of this uh landing page in the
52:55chat. So feel free after this webinar to
52:57check it out. Look at the programs, see
52:59if it's a good fit for you and hopefully
53:01we can see you there.
53:03But yeah, now back to our uh normal
53:06scheduling. I'm just going to go through
53:08we have 18, 16 questions in the Q&A. I
53:11doubt we're going to be able to answer
53:13half of them even. So I'm going to pick
53:15a question or two and for the rest, like
53:17I said, feel free to reach out to
53:18Marconi.
53:20I think the most
53:22uh frequent one here is like what's the
53:23difference between n8n and Make and
53:25other automation tools like Zapier.
53:29That's a great question actually. And um
53:31not too long ago I made a video about
53:33that. Um so so with Make and uh Zapier
53:37uh well
53:38if simply put, I think uh with Zapier a
53:41lot of things and with Make to a certain
53:43extent, right? A lot of connections with
53:45third-party apps need to be off the
53:47shelf. So, if they have built a
53:49connection
53:50within Zapier, that's when you could
53:52choose
53:53that particular tool to be connected to.
53:55Meanwhile, n8n gives you absolute
53:57flexibility. Like I said, anything with
53:59API MCP exposed, you technically
54:02can connect that to n8n. For example, in
54:05one of the workflows that I've In both
54:07of the examples that I've shown, it was
54:09actually through a HTTP request node,
54:12right? So, that is a very That's a
54:13generic node to connect it to anything
54:15that has API or MCP exposed.
54:18Meanwhile, with Zapier and Make, I want
54:20to say that you know, it's easier to
54:23learn in a way. The learning curve is a
54:26lot less.
54:27So, if you're not thinking of something
54:29too complex,
54:30you know, Zapier and Make is a
54:32a good option to go with. However, you
54:35do trade that simplicity with some of
54:37the control. Like I said, with some of
54:39the
54:40So, for more sophisticated workflows
54:42that you might have in mind.
54:44For example, like, you know, having
54:45human in the loop and stuff like that,
54:47which
54:48to be fair, Zapier and Make can do, but
54:51it's not as
54:53as advanced as how n8n built it for. Cuz
54:56you can have human in the loop, you
54:57know, in Slack on
54:59Telegram on, you know, any other
55:01platforms that you want, and also be
55:03able to have another agent maybe check
55:05the previous agent's work and be able to
55:08call, you know, sub workflows, for
55:09example, right? Don't want to get too
55:11ahead of myself here, but maybe you'll
55:12hear this term, you know, in the next
55:14few webinars where you can have a
55:17workflow calling another workflow. So,
55:18having almost like a multi-agent
55:21orchestration layer, right? So, those
55:24are the type of things that may be a
55:25little bit difficult to build in Zapier
55:27and and Make.
55:29But yeah, so to me, that's the biggest
55:31difference between n8n and Zapier and
55:34Make.
55:36All right. So, I think there's also lots
55:37of questions around the pricing
55:39regarding any n there's no free plan as
55:42far as I know they give you a free trial
55:45and then you're hooked and you they take
55:49your money
55:50at least like for me I'm using the basic
55:52one which is $20 per per month. I think
55:55it with this one at least we're running
55:57lots of workflows. It depends if you're
55:59doing lots of workflows maybe you want a
56:01more advanced plan but I think as a
56:03start the basic one is more than enough.
56:05I don't know Marconi if you have other
56:08suggestions. No absolutely. I think the
56:10basic plan will get you a long way. It
56:13is 20 bucks a month but it comes with
56:15again the cloud infrastructure. Again
56:17like I said it's totally open open
56:19source right? So technically you could
56:21have GitHub and
56:22you know if you want to go through the
56:23technical setup you can set it up on
56:25your even your own system even on your
56:27own laptop right? But again as soon as
56:30you close your laptop
56:32you turn off your laptop it's not going
56:33to work because it's depending on your
56:34system to run. Right? So you probably
56:36want to run it in a VPS for example like
56:38hosting a right? So you probably see
56:40hosting a trying to you know
56:42peach their their any n instances and
56:45stuff like that is also a possibility.
56:47But again you need to maintain it. You
56:49need to spend time looking into the
56:51infrastructure. So really depending on
56:53your comfort level and
56:55what you're looking for there.
56:57Yeah and also there are some questions
56:58regarding the API costs. Also it depends
57:01on I would say which LLM you're using
57:03which model. So like if it's for example
57:06GPT-5 mini like it costs maybe few cents
57:09to run it. If you use maybe a different
57:11model it's more expensive. So definitely
57:13keep an eye on the API costs when you're
57:16running these workflows. Once you create
57:18the API key whether it's AI Google
57:20Studio or the platform.openai you can
57:23always see how much you're spending on a
57:25daily basis. But if you're using the
57:27basic modules it should be fine like few
57:29cents there and there.
57:32Yeah. Yeah. I would say, you know,
57:35just from experience that
57:37kind of again, it depends on
57:39what you're running, right? Like if
57:41you're generating video for every
57:42inbound lead that's coming in. It also
57:43depends on the traffic. I mean, inbound
57:45leads that you're getting, right? So, it
57:46can go up pretty quickly also.
57:49But there are ways to kind of fine-tune
57:50that. There are ways to kind of be
57:52selective with the model that you're
57:54using, which is what Anyscale is great
57:56for. As you've seen, you can actually
57:58choose the model the exact model that
57:59you want to choose for certain
58:00operations, right? So, if you think that
58:02for example, the operations of coming up
58:04with the email copies don't really need
58:06such a complex model,
58:08well, just put GPT-3 or whatever in it,
58:10right? And it's going to cost you like
58:120.001 cent for for every run, right? So,
58:15so yeah.
58:17Yeah, exactly. And even for me now, like
58:19we're going to see throughout the this
58:20week, but in some task we use Gemini and
58:23other task we use GPT. Like that's the
58:25cool thing about Anyscale is having that
58:27flexibility to like select which model
58:29to use for which task. Uh there's lots
58:32of more questions that unfortunately we
58:34cannot answer, but feel free to connect
58:36with Marconi after this session.
58:38Guys, we still have Anyscale webinars
58:40tomorrow, Wednesday, Thursday, and
58:42Friday. For tomorrow specifically, it's
58:44going to be more focused on creating
58:46workflows
58:47to for content creation. So, it's going
58:51to be a fun one. We're going to share
58:52lots of demos, live demos so you can see
58:54how you can use them yourself. Uh so,
58:57it's the same time as today. It's going
58:59to be around 11:00 a.m. CT time or 6:00
59:02p.m. CET time. Either way, you'll
59:04receive an email 1 hour before with the
59:08with the joining link. Uh Marconi, man,
59:11it was awesome having you. It was I
59:12think everyone in the chat
59:14enjoyed it. Super nice introduction
59:17session for Anyscale. I think people now
59:19have a good foundation of what they can
59:21do with it. And hopefully for the
59:23remaining of the week, they can see how
59:24they can build on that skills that
59:26they've learned today.
59:28Yeah. Thank you Zerex for having me and
59:30again, thank you everyone for uh sitting
59:33through that and I hope it was valuable
59:35and um yeah, please feel free to reach
59:37out uh if you have any questions. Um
59:40yeah, I'm happy to chat with everyone.
59:43And um have a good one, guys.
59:45Awesome. See you tomorrow, guys. Have a
59:46good one. Thanks, man. Enjoy your uh I
59:49think it's 1:00 a.m. your time or
59:50something.
59:51It is Yeah, it is. I'm going to hit hit
59:53the sack pretty soon.
59:55>> good night, man. All right, good night.