How I Built a Marketing Team with One AI Agent and No Code (Free n8n Template)

[00:01] how I built this AI marketing team with just one AI agent. So as you can see this agent has access to these six tools. It can create videos, LinkedIn those images, and also search through its image database. And working from

[00:14] workflows look like. So this is the creating videos. This is LinkedIn posts. This is blog posts. This is creating an image. This is editing an image. And database. So there are a lot of resources that you'll need if you want

[00:27] all these different workflows, and I'll show you guys how to download all that of the video. But first, let's get into a live demo, and then we'll break down touch on pricing and what it actually costs to run this whole system. So, as

[00:41] agent here through Telegram, and that can be either voice or text. So, I'm here. I'm going to open up my Telegram. As you can see, here's a video that we going to ask it to create an image for us. Create an image for a flyer for cat

[00:56] food. And there's a flash sale. Okay, so we just sent that off. You can see it's marketing team agent, and it's going to hit its create image tool. And right I'll check back in a sec when we get that image. Okay, looks like it just

[01:10] our message. Here's your flyer image for it's a pretty cool image of a cat. Now, make it a little more realistic. So before we ask it to edit that quickly,

[01:22] let's just take a look at first of all in our log. We now have a new image Here's the prompt and here's the link that it got where we can click into it and it's the image that's in our Google Drive. But anyways, let's have it make

[01:34] that a little more realistic. So can you edit that image and make it more use its image database to grab the to go edit that. And I'll check back in when we get that edited image. Okay, so

[01:48] guys are aware, it takes about a minute per image and per edit. So, let's take a updated, more realistic flyer. It's a very realistic looking orange cat eating we can also see that if we go to our marketing team log, we have the new

[02:02] picture right here. And it's a type edit. We have the prompt. And then we also have the link for our Google Drive with the new image. Okay. Now, let's try one of these content creation ones. Can you please create a blog post about the

[02:14] effect of sleep on productivity? All right. We'll send that off. So anyways, this workflow was inspired by a actual full workflow video that I did a few days back. I'll tag that right up here. And I basically had that doing LinkedIn

[02:26] research, creating the post, and then it's creating an image prompt, and then we're logging it, sending the content, all that kind of stuff. So, if you want to see that full breakdown, watch the video up here. Okay, so the agent just

[02:38] Telegram, and we'll see what we got. Here's our graphic. Well-rested minds get more done. You can see it has some up arrows and stuff like that. And then bottom, it includes some references for us. And if we click into our log, we can

[02:51] see that we have our actual blog post. And you can see I did the same example looked like. So this one was a different type of graphic that had some more statistics in there. But then this one that we just did looks like this. And

[03:03] that I would definitely feel confident posting with a blog. But as you can see, title, we have a type, which was post, and then we have the actual post content over here. And then finally, let's just do a quick video. So, can you please

[03:17] create a video of a beaver building a house? So, we'll send that off and we'll running, I just wanted to explain something to you guys here. So, it's calling the video workflow, which looks like this. And this run takes

[03:30] little more. We have these weights because we have to wait for the images be generated. And so, what actually happens is the actual marketing agent right here, as you can see, it's waiting. this guy times out and then we

[03:44] what I've done is we actually get the response in the form of this workflow. video, send it to us on telegram and then also log the outputs. So it's just the way n kind of works right now with subflows having weight steps where right

[03:59] rather than the tool level. So it's going to error right here, but you'll see we still get the actual video in our telegram. So I'll just show you guys that once this run finishes up. Okay, looks like that just finished up. And as

[04:12] you can see, the Telegram node here failed because the agent aired out, but the video. So, let's click into this and watch it real watch it real quick. We got some nice sound

[04:25] effects. Okay, looks like he's building a dam, not a house. I was kind of hoping for more of an actual house, but either

[04:38] finish up. I'll switch over to executions of the faceless video tab. can see that this thing succeeded and the message that came in was a beaver gathering sticks, blah blah blah. And then it goes through the process of

[04:52] effects. So, anyways, I know it's not perfect because of this aspect, which I'm sure is currently working on, as well as the fact that the beaver doesn't clips, but that can be kind of worked on with prompting and other aspects. But,

[05:06] you started. Okay, so now that we've seen a demo and how some of these tools breaking down all the workflows. But before that, just wanted to mention if free, you can do so by joining my free school community. Link for that's down

[05:19] the title of the video or click on YouTube resources and you'll find the post associated with this video. And when you click on it, you can have the there'll also be the link to the Google sheet template you'll need and the

[05:32] need. And more towards the end of the video, I'll actually show you like what everything up because you'll have to download these six different workflows well. But now, let's get into the actual breakdown. Okay, so just to kick us off

[05:45] here, let's talk about the actual marketing team agent and what it's getting as well as what we're telling it to do. So it can get voice or text. So what we did is we standardized both of these outputs here and it gets fed into

[05:57] the agent in a field called text. So we just reference that as JSON.ext. basically just meaning if it's voice or if we type the agent will still be able to look at it as if it's the same. And then we can see the actual system prompt

[06:09] which is not too difficult for how many tools it has. We said overview you are a help the users create edit images based on the request or create content. The it what tools it has and when to use each one. And as you can see this is

[06:24] very very simply laid out. Create image. Use this to create an image. Edit image. Use this to edit an image. The user might also say make rather than edit. So create an image for me and then I said, you know, make that more realistic, it

[06:36] would know to edit. Then we gave it the image database. Use this to search in the image database. Blog post, LinkedIn post, video, and then also the think that is, it's just this think tool down here. I made a video. I'll tag it right

[06:48] it what tools it has and when to use each one very simply. And then just a few instructions like if the user asks something this indicates that they want to edit the last image in the database,

[07:01] the most recent one. So we can either use its memory to grab that image ID to search the database and just grab the last one. And finally, if the user requests a blog post or a LinkedIn post, use the tools and then output here's the

[07:14] post you requested. Hope you enjoy it because we have that actual workflow we just don't want it to get sent over twice. Then finally, we just wanted to return everything as a clickable link in our Telegram. And as you can see, we're

[07:26] our Telegram, whether it's an image, if it's an edit, whatever it is, we just it. Cool. So, that's basically all that's going on here. Um, you know, it's responding in Telegram. But then where the magic really happens are these six

[07:40] with the create image tool. Obviously, we called it create image. Call this tool to create an image. And then what we did is we linked our workflow. So workflow, you'll basically just have to choose from a list and search, you know,

[07:53] create image and then all you have to do is search for that and then just make sure you link the correct workflow to call. So from there, we set up workflow inputs. So we basically just told this this main agent, what do you send over

[08:05] to this sub workflow? And the way that you specify that is in this create image subworkflow, it has to start with a when executed by another workflow trigger because it's being called by a main agent. And in this trigger, you can

[08:18] choose the input. So rather than doing accept all data, we defined three fields we want, which are the image title, the image prompt, and then the Telegram chat ID. So we can actually send it back to Telegram. So when you actually define

[08:30] those three fields in this trigger, then you'll have these workflow input options pop up in your call nadn workflow tool for your agent. And then what we did is lets the model define sort of how to fill in this parameter. We told it to

[08:44] the title of the image. Should be no more than four words. We told it to fill requested by the user. And then for the chat ID, we're just using a dynamic chat trigger. So when we talk to this Telegram, I'll just I'll just do

[08:59] something real quick. I'm just going to say hi. We'll see that it gets the input even though it was text. It responds to us and says, you know, hi. But then what it does is it basically has this chat ID right here in our Telegram. And then

[09:12] that's what we pass over to this workflow right here. You can see now passed over and we can reference that later in this workflow to actually send Anyways, let's just click into the execution that we did for this live demo

[09:26] so we can see data moving through. The first thing is an image prompt agent. food/ flash sale. So we give it the image prompt from that main workflow. which basically is just telling it how to make a text image prompt. Not going

[09:41] download the template, you'll obviously have this in there. We basically just wanted it to be, you know, very descriptive, have the main subject, the details like that. And then you can see over here, it spits out a cheerful,

[09:54] appeal to pet owners, blah blah blah. And it just tells the image model, which we're using Chatchabt's new image model, which is insane, how to make the image. And so this is where we format that HTTP request to OpenAI's image generation

[10:09] the weeds of setting up this request and how that all works, but I've made a tag that right up here if you want to go check that out. But anyways, we're prompt right here. And you can see this is where we're using the new image

[10:21] model. But we're passing over that image prompt from that previous agent. And what it spits out is basically a really huge like base 64 JSON string. And my And all we want to do is we want to take that JSON string and turn that into a

[10:35] binary data that's actually a picture. So that's why we use this convert to field right here. All I did was drag that in like that. And then we're called data which we get right here. And if I click on view, we can see the

[10:49] actual image that it generated for us. And then because we have binary and we split the paths. So the first thing it's going to do because n when you split bottom. The first thing that it does is send the picture back to us in Telegram.

[11:03] And then what we do is we want to write to our drive and our Google sheet. So here's our chat ID. This is what we pulled in from that trigger." As you can see, we're referencing the when executed by another workflow trigger. And then we

[11:17] binary file. It's in the field called data. As you can see, data is right here. And then we just send that over. Simple as that. And then what we do is upload it to our Google Drive. So, the file name, I basically just pulled in

[11:30] the title that that the main agent sent over. I put the title.png. I chose the having trouble setting up your Google credentials, um, check out this tutorial I made right up here. Anyways, this got put in our drive as cat food/sale. And

[11:44] I can see cat food/ flash sale flyer. And I click into it and it is our cat illusion. You can also see the other one right here, which is the edited one, is the one that we asked it to make it more realistic. Cool. So after it's put into

[11:57] our Google Drive, we then just want to log that output in our sheet. So once are referencing the document called marketing team log, the first sheet. And then we're just mapping what columns we want to send over. So we have title,

[12:11] type, request, ID, link, and post. For this one, I just left the post blank, from that first trigger. All I would do is I scroll all the way down on this lefth hand side to the trigger, and I just dragged in image title right here.

[12:25] The type I fixed in here that this is always going to be type image. For the request, I dragged in the image prompt. For the ID, I went to the Google Drive ID, which is what we need in order to later edit it. And I dragged in the ID

[12:37] right here down there. And then for the link, I grabbed the web view link from found right here, which basically lets us actually view it. And that's what the and say, "Hey, here's the image. You can click on this link if you want to look

[12:51] image workflow. Now let's take a look at the edit image workflow. So it's very referencing a workflow. We're defining certain inputs which are the image, the request, the chat ID, and the picture ID. And this is important because with

[13:06] the OpenAI's endpoint for editing an image, you have to send one in, and then like here's a good example of making sure that they stay sort of like it to make a crocodile lifting weights. And you can see it has like, you know,

[13:20] this one's a little messed up. It's got these signs in the background. And then that image and it's going to keep it, you know, sort of similar. So you can see here it was an edit. The request was add headphones on the crocodile's head.

[13:33] pretty much except for now he just has headphones. So that's how you can do editing an image rather than just creating a new one. Anyways, this is the open up this workflow and we'll look at the live run and break it down. Okay, so

[13:47] here's the run that you guys saw in the demo. It was the cat food flash sale realistic. And if you remember in the demo, it was able to pass over the correct picture ID because what it did is it searched the database first and

[14:00] then it sent over the correct picture ID. Anyways, now we have that right ID that file from our Google Drive. So it sends in that ID. We click view. We can this is the one that we can send to OpenAI to edit. So that's why the next

[14:15] step is we're hitting OpenAI's endpoint. This time instead of using the edit endpoint. And then all we're doing is we're sending over the binary file right here called data. We're sending over the prompt which says make the

[14:28] flyer more realistic with a lifelike photograph of a cat enjoying delicious was able to say, "Okay, here's the picture. Here's the request. Um, here's your new image basically." And it does the exact same thing. It spits out a

[14:41] nasty string of B 64. So then we convert it once again. As you can see, we convert it. We get the actual new edit image which is right here. And then we do the exact same thing in the sense of we send it to Telegram and then we write

[14:54] to drive and we write to sheets. Okay. So now let's take a look at this search images tool which is an interesting one. So the idea here is there's there's two just quickly open this up. So we'll take a look at it. The idea here was that you

[15:08] I made a while ago about the crocodile? ask, you know, get my crocodile picture and it would send it over to you. Or the edit on an image and all you need is that image ID. So, let me explain what I

[15:23] mean by that. When this workflow gets called, it's going to send over the the user want to get an image or does the user want to edit an image? Because workflow works. Then, of course, we have the image title that gets passed over.

[15:37] editing, you still need to know what image you're going to get or edit. And passing over that Telegram chat ID so we can send stuff back to the user. But anyways, hopping back into the workflow. Let's open up the live execution real

[15:51] quick that we saw in the demo, which this one basically was just getting an image and passing that um picture ID back to the main workflow. So when this workflow triggered, we got the intent was edit. The image was the cat food/

[16:05] ID. And then we pass it into an image search agent where we say, "Hey, here's the title of the image." And all you need to do is you are an image retrieval agent. You need to look in your image database tool and return the name, ID,

[16:19] can't find one, you would output not found. And so over here, it outputs the name, ID, and and the image, and then a status. The status would be not found if database. And that's why we have a structured output down here to actually

[16:32] define, hey, here's how you should output information. And then here is the we're basically just giving it access to that sheet. And actually, it's called but it would just be searching through it. And then it would have all the

[16:45] basically just output whatever we're looking for. And then we have two if checks here. So the first one basically says is the status not found. If that's return to the main agent and say image wasn't found in the database. But if the

[17:01] to this next check, which is basically saying, what's the intent of the user? If the intent is just to get an image, then you're going to go down this branch where you'll download that file and just send the content over. But if the intent

[17:13] need to send the photo. We just need to send the the image ID back to that main workflow. So in this case, the intent was edit. It passes down this way. And then what we send back over to the main agent is the image ID as well as the

[17:27] image name. And then it can basically say, "Okay, here's the image ID. I'm image tool." And now it has all the information it needs to actually go make that edit. Okay, cool. Let's just keep chugging along here. I'm only going to

[17:40] basically the exact same thing. The prompting is just a little different. course, and the full video about the LinkedIn post one I'll I'll plug up here. But anyways, let's look at the blog posting one. So, very similar. We

[17:53] in. We are sending over a blog topic which is defined by the model. It says the topic of the blog. We're defining the target audience. So the target content will be written in a way that's kind of tailored towards this target

[18:06] sending over the chat ID. So here's what this workflow looks like. And as you can things over here. Same as the other ones. And let's click into the execution we saw in the demo. So what got passed over here was the effect of sleep on

[18:20] working professionals and students. and then our chat ID. And first we hit a blog posting agent which has access to Tavali to do web search. And let's take a look real quick at what it's getting

[18:32] in its system prompt. So we're passing it in the topic of the blog and the target audience. So as you can see right here, this is what it's looking at. And post, it has to read through its system prompt which says, "You are an AI agent

[18:45] specialized in creating professional, educational, and engaging blog articles based on the topic provided by the user. The objective is to always begin by conducting real-time web search using Tavali. The blog article should be

[18:57] written to appeal towards the provided target audience. So anyways, we just to start the blog, all this kind of stuff. And then we make sure that it clickable link and then just a quick example workflow of like order of

[19:11] you can see, it spits out a full blog post for us. You can see there's a few sources down here at the bottom and a little reminder which is a well-rested mind is a productive mind. So it basically takes this blog post and we're

[19:23] agent which is a image prompt agent. So we're saying hey here's the blog post. As you can see it's getting the entire blog post and then we say using that post make a sort of a graphic or a visual that will go really well with

[19:36] examples you'd see there's like statistics in the visual or you know it really aligns with the actual content and that's the whole goal, right? So, basically we're just telling it to read and analyze the post, identify the main

[19:50] highlights, and then make a text image pretty much you're going to output that that image prompt right here. But we prompt because we want to use a title later. So, anyways, we're doing that

[20:05] where we're saying output a title and output a prompt. And that's exactly what it did. So, then we can use them later. You'll see what I'm talking about down Anyways, this next step is the exact same as creating an image. We're hitting

[20:18] We're just passing in the prompt that came from the previous one. And if you see these little functions in here where you see like the variable with the dotreplace, I I need to change this so all of them have that. But basically, in

[20:31] an HTTP request, if you're passing over double quotes, it's going to fail. So had this function, it would have made double quotes here and it probably would check to replace those with single quotes. So anyways, obviously we're

[20:47] converting that to binary, right? As you can see right here, here is our graphic for that blog. We're sending that in. We're sending the photo and then we're also sending the blog content. So this is two different steps. The first one is

[20:59] the second one is we're sending the actual text content. They're both this one is the output from the blog post agent. And the previous one is just of course we're writing to drive into sheets because we want to have those

[21:14] images to access later. So same thing we're uploading to drive. We're using the title right here to title that picture. So this is why we wanted to have the image prompt agent spit out a title and a prompt. So there's the title

[21:26] when we're logging that back into sheets. So like I said, this is the exact same flow for the LinkedIn one. The only difference is that we have for LinkedIn posts and then we have different writing style here for the

[21:40] LinkedIn graphics are a little bit different than like a more of a general blog graphic. Cool. And then the last one we have is the video generation. So what we're passing over. This one's a little simpler on this front, but the

[21:54] complicated. But we're just passing over a topic of the video as well as the chat ID. So here's what the workflow looks like. This was inspired by another one I faceless shorts. So, if you want to check that one out, I'll tag that right

[22:09] up here. But anyways, let's click into the execution from the demo and we'll watch that data move through. So, what we got sent over was a beaver building a sticks, arranging them by a riverbank, creating a dam, all this kind of stuff,

[22:21] and the chat ID. And what we wanted to do is can see, there's four items passing through. We're basically creating four images and four 5-second long videos and rendering it all together in a 20 second

[22:34] rendering it all together in a 20 second clip. So the image prompt agent we are and then we told it this long system prompt here is basically you're a master visual story structuring agent. Your task is to take a given short video

[22:47] topic which is about the beaver building a house and you need to create four parts that are cohesive and you're going to turn those four parts into a text image generation prompt. But also we had to give it a output parser. So whenever

[23:00] little box right here that says um require specific output format. And we three, part four. And you can see that's how it outputs over here. And so even still only one item. So that's why we use the split out to turn in, you know,

[23:15] these four parts and one item into actually four different items so that we videos for each of these little image prompts. So that's exactly what happens next. We're hitting PI API's endpoint in order to generate those images. So, like

[23:28] faceless shorts, you'll see a little bit more in depth about how I set this up, but basically we're just hitting the endpoint for Flux image generation. We as you can see, this is going to send over four requests. So, the first one is

[23:41] a stocky beaver. The second one is the same detailed beaver. The third one is a placing sticks. And then the fourth one is he has the actual home. And so, images. You can see that right here. What happens is it spits out, you know,

[23:55] takes a little bit to actually generate them. And so that's why we wait for 90 seconds and then we're just basically making a request to a different endpoint as a get where we're going to just go check on the status um of these

[24:07] all completed and we have like a link to the actual picture, which would be right this, you can see this is one of the images about our beaver. And so one probably want to have this set up to be polling where you're not just waiting 90

[24:22] seconds and just taking a guess because if it's not done after 90 seconds, the workflow wouldn't be complete and you'd pass over, you know, maybe three of the isn't and you just would be missing one. So polling basically just means you'd be

[24:34] loop back, wait a little bit, and check again. And that just kind of ensures until all four images are done. If you want to see a quick example of that, I video. I'll tag that right up here. Anyways, from there we have those images

[24:48] and they come through as URLs. And then we're hitting runway to turn those URL images into videos. So what we're passing over here in the body request is we're sending over the URL. And there's going to be four different URLs. And

[25:01] which helps the model turn the images into a video. And this prompt text is the same one that we used in the text image generation. And now we're just generation because obviously like this kind of has like a little story

[25:14] involved. Anyways, what this one does is it spits out four IDs and then later actually be generated and then we're going to check if those IDs are done yet. And so when they are done after about 60 to 90 seconds, you can see all

[25:28] then we get the outputs in the form of a link which if I clicked into this one, this would be an actual video. But of course there's no sound effects yet. So that's kind of the next step is we have to make a sound effect for each clip and

[25:41] together. Okay. So the way we do that is with a sound agent. So what I did here is I passed over each scene individually. So scene one, scene two, we told it in its system prompt is that you're a master sound prompt generator.

[25:55] Your task is to create vivid immersive sound prompts based on the given scene. background sound effects. And as you can see, it spits out these four different outputs, which are all just like different sound effect prompts. and

[26:07] we're going to feed those in to 11 Labs to turn this text into a 5-second clip of sound. So that's why right here we are sending a request to 11 Labs. We're that you're going to turn into a sound effect." And then right here you can see

[26:20] we have four different clips and they're all 5 seconds long of sound effects. So fast, but don't want this video to be too long. And I have a full breakdown of So definitely check that out if you want to understand a little bit more of

[26:34] step down here where we're rendering it and then logging it back. So we're merging everything together so we can have those four items with, you know, And so that's what we get right here. You can see we have four items and all

[26:49] links we need. And then we just basically wanted to clean it up and split it out. So now we have here's item one and basically here's the audio for item one and here's the video for item one. And then we have all four of those

[27:01] so that we can really easily pass it into this create a mate template right passing over the four variables down here. So once you have my Cremate creatate, all you have to do is go to the source editor up here. You'll paste

[27:14] in that script right here. And then you'll click on um use template API curl command. And then all you have to do is when you're in Nitn, you'll just want to import your curl to this HTTP request and paste that in there. And

[27:27] go. And then you would just basically drag in, you know, I need the four video sources right here and the four audio sources right here, which are the web content link sources. So, as you can see, they just

[27:39] which means we have all four and then we have pretty much everything we need to mate. And so, this is like very similar videos where it comes through and says, "Hey, okay, like basically we're

[27:52] 25 seconds and then we're just going to go hit that endpoint to download the URL that it gives us and we get the URL from that previous step and then it comes back as you can see with a full video that actually has the sound effects

[28:07] rendered there. So, then because we have the video right here as binary, we're Telegram. And then we create a title real quick and then we put that title and everything else we need in the log. Okay, so that was kind of how all of

[28:21] apologies if I went a little fast. I just didn't want this video to go too long. But now let's talk about pricing real quick and then how you can actually set this up in your own NAN. Okay, so here's a little doc I threw together

[28:34] about the pricing of the system. So obviously Naden is where we're pretty the cloud, which in this video I'm using the cloud, so it's about, you know, the is. Um, so you've got that cost. The first thing I'll touch on is the image

[28:48] creation. So, we're using pretty much the new OpenAI, which is the GPT image one, which is the pretty much the same generation model as in the chat GPT40. And so, this is about 19 to 20 cents per image, as well as 19 to 20 cents per

[29:01] image edit. And we also are using this model in the um LinkedIn and blog posting agents as well. So, just keep that in mind. That's going to be about 20 cents per run for those. But in that create image endpoint, you also have the

[29:14] option to use the different models like Dolly 2 or Dolly 3, which are a little here. Um, and as you can see, like if we go into the request, there's a parameter where you could switch out that model if you wanted it to be cheaper. You could

[29:27] your agent to say, okay, you know, like what type of thing are we doing? Let's use a cheaper model, whatever it is. But then, anyways, in the video creation workflow, when we're creating those images, we're using Flux through PI API,

[29:39] which is about 1 and a half cents per image. So, that's like obviously a much cheaper route. Okay. Now, for video creation, we're using Runway, which is about 25 cents per 5-second video clip. I think it's 50 cents for a 10-second

[29:51] clip, but we're making four 5second long clips. So, it's about a dollar per run there. And you also have to add on the cost of the um images. Okay. And then for Creative, I'm currently on the free trial, which gives you 50 free credits.

[30:03] And if you see here, I've got, you know, three successful renders, and I've used three of my 50 credits. So, I I'm assuming that means like one credit per um, when I did some research, it said 2,000 credits was suitable for

[30:17] approximately 200 videos. Actually, if I click back into create a mate and I go to upgrade my plan, you can see this is, uh, 2,000 credits and 200 plus videos or 2,000 images. So, not exactly sure there, but so far I've done three

[30:29] for sound effects, we're using 11 Labs. And on the starter plan of five bucks a amount of credits. You probably won't even run through that. I haven't had to five bucks a month. And then for all of the text generation and reasoning for

[30:43] all the different workflows and agents, I've been using 4.1 mini and 4.1 for this entire workflow. I've obviously been doing that through Open Router, but they're not too bad. So 41 is $2 for a million input tokens, eight bucks for a

[30:56] million output tokens, and 41 mini is 40 cents for a million input tokens, and $1.60 for a million output tokens. Anyways, I'll have this document with all the other resources you need. Okay, so when it actually comes to

[31:09] setting all this up, first step is you'll go to the Free School community and you'll have to get the resources. So you'll join the Free School community. the video or if you click on YouTube resources and look for the post

[31:21] associated with this video, you can find all of the JSON templates in here and there'll be seven total workflows, you know, the main agent and the six tools. into an individual workflow. So you'll come up here, click import from file.

[31:34] Once you click on that workflow, the JSON workflow, it will pop up like this have like a setup guide somewhere over here which will tell you the different keys or whatever it is. And then you have to like configure the workflows. So

[31:47] when you import the search images workflow, you'll have to come into here one that you want. So you'll have to make sure like whatever you name it in images, you'll just have to make sure you click on it and that it's linked.

[31:59] And you can always check by clicking on this to open up that workflow in a new different API keys to plug in like your OpenAI, your Open Router, your Google Drive, your Google Sheets, all that. But the next step really would be to do the

[32:12] link to this template in the community and you'll make a copy and then you can just like have that in your own Google You know, the names will always be consistent. So, when you run it, it

[32:25] automatically and you'll be good to go there. And then, of course, you'll have to get the creatate template which will look like this. I'll post that in there template when you're in CreativeMate, start from scratch, open up the source

[32:38] then you can click on use template API integration, and then you'll import that curl into the faceless video step down here where you want to actually render curl command, and it will have everything filled out. You'll just need

[32:51] could do is leave all the variables here and you just need to switch out the and you just need to switch out the template ID from your creatate. And then plug in is like your Telegram credential. Um, hopefully I'm not

[33:03] have like a setup guide and then you should be good to go to start running this thing and using this agent. And so if you're a complete beginner or you little further, then definitely check out my paid community. The link for that

[33:16] a great community of over 1100 members who are using Nen and building classroom section with a full course on building agents, different deep dive topics like vector databases and APIs and HTTP requests, as well as some

[33:29] have five live calls per week, which are always recorded to make sure you're working on a few new courses that will help someone that's a complete beginner then work their way up to automating processes. Anyways, would love to see

[33:43] see you on a live call. But that's going to do it for this video. If you enjoyed definitely give it a like. Helps me out a ton. And as always, appreciate you guys making it to the end of this one. I'll see you in the next video. Thanks.