WEBVTT 00:00:00.000 --> 00:00:02.720 Welcome, curious minds, to another deep dive. 00:00:03.299 --> 00:00:04.860 Have you ever found yourself using a powerful 00:00:04.860 --> 00:00:07.839 tool, feeling pretty productive, but you get 00:00:07.839 --> 00:00:09.740 that nagging thought, is there like a secret 00:00:09.740 --> 00:00:12.560 advanced mode I'm missing? Something that could 00:00:12.560 --> 00:00:16.260 unlock even more. Today, we're basically on a 00:00:16.260 --> 00:00:18.820 mission to uncover exactly that kind of hidden 00:00:18.820 --> 00:00:21.410 power within NEN. you know, the incredible workflow 00:00:21.410 --> 00:00:23.489 automation tool. You're probably already using 00:00:23.489 --> 00:00:25.449 their AI nodes for all sorts of things, sentiment 00:00:25.449 --> 00:00:28.010 analysis, content generation, finding them indispensable, 00:00:28.210 --> 00:00:29.910 right? But what if I told you there's a hidden 00:00:29.910 --> 00:00:32.509 gem, maybe just a single node, that can utterly 00:00:32.509 --> 00:00:35.270 transform how you approach AI automation? You're 00:00:35.270 --> 00:00:38.590 spot on. Many inating users, they know nodes, 00:00:38.950 --> 00:00:41.469 like... the standard AI agent. And yeah, they're 00:00:41.469 --> 00:00:43.710 great for getting started quickly. But what often 00:00:43.710 --> 00:00:46.509 goes unnoticed, kind of behind the scenes, is 00:00:46.509 --> 00:00:49.170 that these are often simplified interfaces. They're 00:00:49.170 --> 00:00:52.130 built on top of a much more robust underlying 00:00:52.130 --> 00:00:56.310 technology. the Lang chain framework. So our 00:00:56.310 --> 00:00:58.189 deep dive today isn't just about finding this 00:00:58.189 --> 00:01:01.609 powerful Lang chain code node within NAN. It's 00:01:01.609 --> 00:01:03.509 about understanding why it's there, what makes 00:01:03.509 --> 00:01:06.290 it so incredibly effective, and crucially, how 00:01:06.290 --> 00:01:08.609 it can give you a really significant competitive 00:01:08.609 --> 00:01:11.049 advantage. We're talking about building highly 00:01:11.049 --> 00:01:13.950 intelligent, precisely tailored AI agents that 00:01:13.950 --> 00:01:16.209 move far beyond the limits of those pre -built 00:01:16.209 --> 00:01:18.489 solutions. This is where the real customization 00:01:18.489 --> 00:01:20.930 starts. Okay, let's untack this then. So we're 00:01:20.930 --> 00:01:22.969 talking about a hidden powerful node built on 00:01:22.969 --> 00:01:24.819 Lang chain. Before we get to the node itself, 00:01:24.859 --> 00:01:27.060 maybe we should back up. What exactly is Langchain? 00:01:27.140 --> 00:01:29.040 Why should you care about this framework? Right. 00:01:29.359 --> 00:01:31.680 Well, Langchain isn't just another tool you download. 00:01:32.000 --> 00:01:34.959 It's more like a comprehensive framework. It's 00:01:34.959 --> 00:01:37.640 engineered specifically for developing applications 00:01:37.640 --> 00:01:40.819 powered by large language models, LLMs. Think 00:01:40.819 --> 00:01:43.180 of it this way. Standard LLMs can think, right? 00:01:43.519 --> 00:01:45.939 But Langchain gives them like hands and eyes. 00:01:46.140 --> 00:01:48.420 It's the framework that lets these powerful AIs 00:01:48.420 --> 00:01:51.379 not just generate text, but actually act in the 00:01:51.379 --> 00:01:54.560 world, querying databases, making web searches, 00:01:54.840 --> 00:01:57.659 even controlling IoT devices, potentially. You 00:01:57.659 --> 00:01:59.840 know, leading tech companies, Replet, Klarna, 00:01:59.900 --> 00:02:01.560 they use it to build their own sophisticated 00:02:01.560 --> 00:02:04.439 AI assistance and co -pilots. So, it's serious 00:02:04.439 --> 00:02:06.900 stuff. Ah, okay. So, it's handling all the complex 00:02:06.900 --> 00:02:09.020 plumbing, letting us focus purely on the intelligence 00:02:09.020 --> 00:02:12.550 side. Sounds like a dream, really. Does that 00:02:12.550 --> 00:02:14.909 abstraction ever cause issues, or is it mostly 00:02:14.909 --> 00:02:17.789 just flexibility? It's overwhelmingly about flexibility, 00:02:17.909 --> 00:02:19.969 yeah. And that's what's really fascinating here. 00:02:20.189 --> 00:02:22.229 LangChain is incredibly versatile. You can work 00:02:22.229 --> 00:02:23.909 with loads of different language models. You 00:02:23.909 --> 00:02:26.789 can switch them, combine models from OpenAI, 00:02:27.150 --> 00:02:30.169 Anthropic, Google AI, you name it, all within 00:02:30.169 --> 00:02:33.830 the same workflow. Plus, it provides really sophisticated 00:02:33.830 --> 00:02:36.909 memory management. That's absolutely key for 00:02:36.909 --> 00:02:39.710 agents to maintain context across conversations, 00:02:40.009 --> 00:02:42.729 build seamless natural dialogues. Right, so it 00:02:42.729 --> 00:02:46.229 remembers things. Exactly. Imagine an agent that 00:02:46.229 --> 00:02:49.449 actually recalls your past interactions. And 00:02:49.449 --> 00:02:51.669 agents can also execute custom code snippets, 00:02:52.069 --> 00:02:54.669 follow complex conditional logic, make autonomous 00:02:54.669 --> 00:02:56.969 decisions. And for those really tricky scenarios, 00:02:57.069 --> 00:03:00.069 you can build these chains and graphs of logic, 00:03:00.469 --> 00:03:03.379 creating incredibly complex multi -stage workflows 00:03:03.379 --> 00:03:06.080 where, you know, the output of one step becomes 00:03:06.080 --> 00:03:08.240 the input for the next, like a domino effect 00:03:08.240 --> 00:03:10.780 of intelligence. That's a huge difference. Are 00:03:10.780 --> 00:03:13.180 you saying that many of the NE and AI nodes we 00:03:13.180 --> 00:03:15.219 might already be using, the ones that look simple 00:03:15.219 --> 00:03:17.199 on the surface, they're actually just a friendly 00:03:17.199 --> 00:03:19.759 front for this super powerful Lang chain framework? 00:03:20.280 --> 00:03:22.400 Exactly. Yeah, this is where it gets really interesting, 00:03:22.400 --> 00:03:24.960 I think. If you ever peek under the hood, like 00:03:24.960 --> 00:03:27.419 if you look at the underlying JSON configuration 00:03:27.419 --> 00:03:30.500 that defines how NNN workflows are built, you'll 00:03:30.500 --> 00:03:33.460 actually see the standard AI agent node identified 00:03:33.460 --> 00:03:38.759 as nnnodes -langchain .agent. No way. Yeah. It's 00:03:38.759 --> 00:03:41.139 like finding the blueprint, you know? It reveals 00:03:41.139 --> 00:03:43.960 the advanced engineering underneath. So when 00:03:43.960 --> 00:03:46.840 you're dragging, dropping, configuring that AI 00:03:46.840 --> 00:03:50.020 agent, you are, in fact, interacting with a simplified 00:03:50.020 --> 00:03:52.400 interface for Langchain. So it's a bit of a wizard 00:03:52.400 --> 00:03:54.300 -behind -the -curtain situation then. Yeah. And 00:03:54.300 --> 00:03:55.979 it's not just the AI agent node you're saying. 00:03:56.159 --> 00:03:58.740 No, definitely not. It holds true for a whole 00:03:58.740 --> 00:04:01.300 range of other AI nodes you might use every day, 00:04:01.719 --> 00:04:04.759 the basic LLM chain, information extractor, Q 00:04:04.759 --> 00:04:07.560 &A, sentiment analysis, summarization. Yeah, 00:04:07.680 --> 00:04:09.460 they're all essentially simplified interfaces. 00:04:09.680 --> 00:04:11.819 Now, this simplification is incredibly convenient 00:04:11.819 --> 00:04:13.979 for rapid development, getting started quickly, 00:04:14.439 --> 00:04:17.189 but it comes with a trade -off. Limited customization. 00:04:17.870 --> 00:04:19.970 By finding and using the lang chain code node 00:04:19.970 --> 00:04:22.050 directly, you can just bypass these limitations 00:04:22.050 --> 00:04:24.709 completely and harness the full unbridled power 00:04:24.709 --> 00:04:26.970 of the lang chain framework itself. Fascinating. 00:04:27.310 --> 00:04:30.829 Okay, so if this lang chain code node is so powerful, 00:04:31.410 --> 00:04:34.089 why isn't it front and center? Is it really a 00:04:34.089 --> 00:04:38.850 secret or just less obvious? For those of us 00:04:38.850 --> 00:04:42.069 wanting to unlock this power, Where do we even 00:04:42.069 --> 00:04:44.230 find it? Well, it's intentionally a bit tucked 00:04:44.230 --> 00:04:46.209 away, almost like a secret level for people who 00:04:46.209 --> 00:04:47.970 know where to look. It's not, you know, right 00:04:47.970 --> 00:04:50.490 there when you open the AI section. It guides 00:04:50.490 --> 00:04:52.949 new users to the simpler, more abstract nodes 00:04:52.949 --> 00:04:55.290 first, which makes sense. But once you know it's 00:04:55.290 --> 00:04:56.629 pretty straightforward, you'll find it within 00:04:56.629 --> 00:04:59.029 the AI category in the nodes panel. Usually you 00:04:59.029 --> 00:05:01.050 got to scroll down to other AI nodes and then 00:05:01.050 --> 00:05:04.610 click on miscellaneous. Yeah, the placement itself 00:05:04.610 --> 00:05:07.149 kind of hints at its advanced, maybe more behind 00:05:07.149 --> 00:05:08.930 the scenes capabilities. Right. And when you 00:05:08.930 --> 00:05:11.699 pull it onto your canvas, it looks well. Deceptively 00:05:11.699 --> 00:05:14.579 simple, just a blank box, no obvious inputs or 00:05:14.579 --> 00:05:17.759 outputs. So how do you even start setting up 00:05:17.759 --> 00:05:21.259 this powerful yet empty node? Ugh, this is where 00:05:21.259 --> 00:05:23.519 its power really lies, because it forces you 00:05:23.519 --> 00:05:26.019 to be explicit about what you need. It's not 00:05:26.019 --> 00:05:29.259 pre -configured. You add inputs and outputs specifically 00:05:29.259 --> 00:05:31.220 for your agents' requirements. So you'll add 00:05:31.220 --> 00:05:33.319 a main input, right, to connect your trigger 00:05:33.319 --> 00:05:36.800 or data source, and a main output to pass results 00:05:36.800 --> 00:05:39.740 along. Crucially, though, you'll also add specific 00:05:39.740 --> 00:05:41.560 connections for language model. That's where 00:05:41.560 --> 00:05:43.899 you link up OpenAI, Anthropic, whatever you're 00:05:43.899 --> 00:05:46.660 using. Got it. And optionally, memory for context, 00:05:46.740 --> 00:05:49.420 maybe connecting simple memory or even post -gresql, 00:05:49.560 --> 00:05:52.939 and tool that's vital for agents needing external 00:05:52.939 --> 00:05:55.500 tools like HTTP requests or... database nodes. 00:05:55.920 --> 00:05:57.500 It's like building your agent from the ground 00:05:57.500 --> 00:06:00.100 up piece by piece. OK, so you custom wire all 00:06:00.100 --> 00:06:02.680 its connections. But then what? The real core, 00:06:02.819 --> 00:06:05.540 you said, is this add code and execute section 00:06:05.540 --> 00:06:08.360 where you write custom JavaScript. Now, for some 00:06:08.360 --> 00:06:10.639 listeners, that might sound a bit intimidating, 00:06:11.240 --> 00:06:13.519 writing code. It's true. It does grant you complete 00:06:13.519 --> 00:06:16.040 control. And yes, it requires code. But here's 00:06:16.040 --> 00:06:18.259 a fantastic trick, almost like a cheat code itself. 00:06:18.279 --> 00:06:20.980 You can use other AI models like Claude or ChatGTT 00:06:20.980 --> 00:06:23.319 to help you write the lang chain code. Yeah. 00:06:23.449 --> 00:06:25.750 Just describe what you want your agent to do, 00:06:25.750 --> 00:06:28.750 you know, in plain English, and the AI can generate 00:06:28.750 --> 00:06:31.329 the starter code for you. Then you copy paste 00:06:31.329 --> 00:06:33.850 and refine it inside the lang chain code node. 00:06:33.970 --> 00:06:37.209 It lowers the barrier quite a bit. And this code 00:06:37.209 --> 00:06:39.730 section, this is where the magic really happens. 00:06:40.069 --> 00:06:43.129 It lets you define truly custom workflows, create 00:06:43.129 --> 00:06:46.790 complex conditional logic, decision trees, implement 00:06:46.790 --> 00:06:50.310 loops. You can even build teams of agents that 00:06:50.310 --> 00:06:52.569 collaborate on a task. Teams of agents. Yeah. 00:06:52.759 --> 00:06:55.160 And you can switch between different LLMs for 00:06:55.160 --> 00:06:57.100 different parts of a task, you know, use the 00:06:57.100 --> 00:06:59.420 best model for the job, and even create more 00:06:59.420 --> 00:07:01.300 autonomous agents that can plan their own steps 00:07:01.300 --> 00:07:03.639 and adapt. That's just incredible control. Yeah. 00:07:03.660 --> 00:07:05.459 I mean, it's totally clear why this is a hidden 00:07:05.459 --> 00:07:08.740 gem. To really grasp the leap in power here, 00:07:09.079 --> 00:07:10.839 let's think about the components of an AI agent. 00:07:11.399 --> 00:07:14.480 There's input, language model, memory, tools, 00:07:14.720 --> 00:07:16.819 instructions, logic, and output, right? Seven 00:07:16.819 --> 00:07:19.420 things. With the standard AI agent node, if I 00:07:19.420 --> 00:07:21.199 remember right, You can configure maybe five 00:07:21.199 --> 00:07:23.319 of those, but the logic part is prefixed. Am 00:07:23.319 --> 00:07:24.759 I right in thinking the line chain code node 00:07:24.759 --> 00:07:27.019 just blows that wide open? You're absolutely 00:07:27.019 --> 00:07:29.420 right. That fixed logic is the key limitation 00:07:29.420 --> 00:07:32.670 of the standard nodes. The Lang Chang code node, 00:07:32.689 --> 00:07:35.509 however, gives you full explicit control over 00:07:35.509 --> 00:07:38.209 all seven components. It really paves the way 00:07:38.209 --> 00:07:42.250 for exponentially more sophisticated, nuanced, 00:07:42.350 --> 00:07:44.550 and powerful agents. It's the difference between 00:07:44.550 --> 00:07:47.370 using a preset template and, well, designing 00:07:47.370 --> 00:07:49.509 your own architecture from scratch. OK, so let's 00:07:49.509 --> 00:07:51.769 be really clear about the trade -offs. For someone 00:07:51.769 --> 00:07:53.889 just starting out maybe with simpler automation 00:07:53.889 --> 00:07:57.129 needs, is the standard AI agent node still a 00:07:57.129 --> 00:07:59.009 good choice? Or should everyone just jump straight 00:07:59.009 --> 00:08:01.410 to this code node? Oh, absolutely. The standard 00:08:01.410 --> 00:08:04.350 AI agent node still has its place, for sure. 00:08:04.769 --> 00:08:07.470 It's incredibly easy to use, offers super quick 00:08:07.470 --> 00:08:10.430 setup, and it's perfect for straightforward logic. 00:08:11.250 --> 00:08:13.050 If your needs are simple, or you need a fast 00:08:13.050 --> 00:08:15.269 prototype, or maybe you just prefer a no -code 00:08:15.269 --> 00:08:17.810 approach, it's definitely your go -to. But yeah, 00:08:18.209 --> 00:08:20.029 customization is limited. You're generally stuck 00:08:20.029 --> 00:08:22.629 with one model per node, and it only offers pretty 00:08:22.629 --> 00:08:25.629 basic agent autonomy. Complex workflows often 00:08:25.629 --> 00:08:27.470 mean stringing together lots of these nodes, 00:08:27.470 --> 00:08:30.480 which can get messy. chain code node on the other 00:08:30.480 --> 00:08:32.659 hand yes it's more complex to set up it takes 00:08:32.659 --> 00:08:34.419 more development time because like we said it 00:08:34.419 --> 00:08:37.899 involves code but the benefits are huge unlimited 00:08:37.899 --> 00:08:40.320 customization orchestrating multiple models in 00:08:40.320 --> 00:08:43.220 one node advanced agent autonomy with real planning 00:08:43.220 --> 00:08:46.100 and reflection handling entire complex workflows 00:08:46.100 --> 00:08:49.379 in one spot plus highly customizable error handling 00:08:49.379 --> 00:08:51.580 and debugging this is where you get the true 00:08:51.580 --> 00:08:54.259 power the real efficiency and frankly a more 00:08:54.259 --> 00:08:56.519 future proof design right and connecting this 00:08:56.519 --> 00:08:58.700 to the bigger picture you mentioned the underlying 00:08:58.480 --> 00:09:00.500 Lanking's structure allows for advanced stuff 00:09:00.500 --> 00:09:02.600 like Lanksmith integration, that's Lanking's 00:09:02.600 --> 00:09:05.279 own monitoring platform, right? What can that 00:09:05.279 --> 00:09:07.409 actually tell you? Yeah, LangSmith gives you 00:09:07.409 --> 00:09:10.029 incredible visibility. You can track crucial 00:09:10.029 --> 00:09:13.110 metrics like token usage, those little bits of 00:09:13.110 --> 00:09:15.549 text, the LLM processes, which directly hit your 00:09:15.549 --> 00:09:18.169 costs. Ah, important. Very important. And monitor 00:09:18.169 --> 00:09:20.909 response times. It provides these detailed logs 00:09:20.909 --> 00:09:23.129 of the agent's whole reasoning process. So you 00:09:23.129 --> 00:09:25.730 can see its internal thought process, step -by 00:09:25.730 --> 00:09:29.070 -step execution of tool calls makes it much easier 00:09:29.070 --> 00:09:31.769 to spot errors or performance bottlenecks. It's 00:09:31.769 --> 00:09:34.529 basically like an x -ray for your AI agent. Just 00:09:34.529 --> 00:09:37.000 a keynote here. LangSmith integration currently 00:09:37.000 --> 00:09:40.340 only works with self -hosted NaN instances. So 00:09:40.340 --> 00:09:42.100 that's something to keep in mind. That level 00:09:42.100 --> 00:09:44.399 of insight sounds invaluable, especially for 00:09:44.399 --> 00:09:46.879 debugging complex agents. How does this whole 00:09:46.879 --> 00:09:49.139 Langchain approach in NaN stack up against other 00:09:49.139 --> 00:09:51.580 things people might know, like say the OpenAI 00:09:51.580 --> 00:09:54.259 Assistance API? That's a good question. Both 00:09:54.259 --> 00:09:57.019 approaches can work fine within Na10 and they 00:09:57.019 --> 00:09:59.830 each have their strengths. Langchain, its big 00:09:59.830 --> 00:10:02.190 plus, is model flexibility. You can use pretty 00:10:02.190 --> 00:10:04.870 much any LLM provider, so you avoid vendor lock 00:10:04.870 --> 00:10:07.690 -in. You get full control over every aspect of 00:10:07.690 --> 00:10:10.070 the agent's behavior. There's a self -hosting 00:10:10.070 --> 00:10:11.950 option, which is great for data sovereignty, 00:10:12.250 --> 00:10:14.970 broader tool integration generally, and you benefit 00:10:14.970 --> 00:10:18.169 from that big open source community. The OpenAI 00:10:18.169 --> 00:10:20.669 Assistance API, on the other hand, it really 00:10:20.669 --> 00:10:23.870 prioritizes simplicity. Less code involved. It's 00:10:23.870 --> 00:10:26.610 obviously optimized for OpenAI models, includes 00:10:26.610 --> 00:10:28.970 some handy built -in features. like file handling 00:10:28.970 --> 00:10:31.470 and code execution. And it's a managed service, 00:10:31.570 --> 00:10:34.570 so less operational headache for you. But I'd 00:10:34.570 --> 00:10:37.610 say for most NAA use cases where you really need 00:10:37.610 --> 00:10:39.889 that ultimate control and flexibility, Langchain 00:10:39.889 --> 00:10:42.070 usually comes out on top. OK, that makes sense. 00:10:42.370 --> 00:10:45.210 So where does this advanced power become truly 00:10:45.210 --> 00:10:47.169 indispensable? Can you give us some specific, 00:10:47.210 --> 00:10:49.129 maybe real -world examples of things you could 00:10:49.129 --> 00:10:50.889 build with these sophisticated Langchain agents 00:10:50.889 --> 00:10:52.950 that you just couldn't do with a standard node? 00:10:53.100 --> 00:10:57.820 Absolutely. Imagine building multi -agent systems, 00:10:58.399 --> 00:11:01.399 like actual teams of specialized agents collaborating. 00:11:01.799 --> 00:11:04.299 Maybe a research agent gathers info, passes it 00:11:04.299 --> 00:11:06.500 to an analysis agent, which then hands off to 00:11:06.500 --> 00:11:08.440 a writing agent to synthesize the results all 00:11:08.440 --> 00:11:11.340 working together autonomously. Or you can implement 00:11:11.340 --> 00:11:14.240 dynamic reasoning, where an agent plans its actions, 00:11:14.519 --> 00:11:16.419 reflects on the outcome, and then adapts its 00:11:16.419 --> 00:11:18.440 approach on the fly. Like, it tries one tool, 00:11:18.539 --> 00:11:20.759 realizes, hmm, that's not working well, and then 00:11:20.759 --> 00:11:22.419 autonomously tries a different tool or method. 00:11:22.379 --> 00:11:24.980 This lets you build really complex workflows 00:11:24.980 --> 00:11:27.600 with sophisticated decision trees or iterative 00:11:27.600 --> 00:11:29.940 processing where agents keep refining results 00:11:29.940 --> 00:11:32.559 until they meet some criteria. And that model 00:11:32.559 --> 00:11:34.720 switching, we talked about using different LLMs 00:11:34.720 --> 00:11:36.480 for different parts of a task based on their 00:11:36.480 --> 00:11:38.919 strengths. You can even build in custom safety 00:11:38.919 --> 00:11:41.539 checks for sensitive operations. Just think of 00:11:41.539 --> 00:11:43.919 a simple but powerful research agent you could 00:11:43.919 --> 00:11:46.360 build with this node. It takes a topic you give 00:11:46.360 --> 00:11:49.019 it, uses a connected search tool to find info 00:11:49.019 --> 00:11:52.120 online, then uses an LLM to analyze and summarize 00:11:52.120 --> 00:11:54.559 the key points. That alone shows how the lane 00:11:54.559 --> 00:11:56.820 chain code node lets you implement multi -step 00:11:56.820 --> 00:11:59.379 intelligent logic that interacts with external 00:11:59.379 --> 00:12:01.519 tools. That's something that's either incredibly 00:12:01.519 --> 00:12:04.080 hard or just plain impossible with only the standard 00:12:04.080 --> 00:12:06.799 AI agent node. OK, so the big question then, 00:12:07.080 --> 00:12:09.039 for you listening, the learner, after hearing 00:12:09.039 --> 00:12:12.240 all this, should I actually invest the time? 00:12:12.379 --> 00:12:14.580 to learn how to use this lane chain code node? 00:12:14.879 --> 00:12:17.000 When does it really become essential? Yeah, that's 00:12:17.000 --> 00:12:19.419 the key question. And the answer, honestly, it 00:12:19.419 --> 00:12:22.279 depends on your specific needs and maybe your 00:12:22.279 --> 00:12:24.639 ambitions. You should absolutely stick with the 00:12:24.639 --> 00:12:28.360 standard AI agent node if you need a quick, simple 00:12:28.360 --> 00:12:31.179 solution. If your agent's logic is pretty straightforward, 00:12:31.480 --> 00:12:33.440 if you're not yet comfortable with coding or 00:12:33.440 --> 00:12:36.960 don't want to be, if you only need one AI model, 00:12:37.000 --> 00:12:39.320 and if your workflow doesn't need complex multi 00:12:39.320 --> 00:12:41.519 -branch decisions, it's excellent for all those 00:12:41.519 --> 00:12:44.279 scenarios. No question. However, you should definitely 00:12:44.279 --> 00:12:46.500 dive into the Langtian code node if you find 00:12:46.500 --> 00:12:48.779 yourself craving advanced customization and full 00:12:48.779 --> 00:12:51.220 control. If your agent needs to make complex, 00:12:51.220 --> 00:12:53.659 adaptive decisions with lots of different paths, 00:12:53.879 --> 00:12:56.440 if you want to seamlessly use and orchestrate 00:12:56.440 --> 00:12:58.659 multiple AI models together, if you're building 00:12:58.659 --> 00:13:00.940 something that needs to be really scalable and 00:13:00.940 --> 00:13:03.299 robust, and crucially, if you are willing to 00:13:03.299 --> 00:13:05.639 invest a bit of time in learning a more powerful, 00:13:06.720 --> 00:13:09.200 more developer -centric solution, looking ahead, 00:13:09.220 --> 00:13:11.110 understanding frameworks like Langtian, likely 00:13:11.110 --> 00:13:13.250 to become an increasingly valuable skill. AI 00:13:13.250 --> 00:13:15.250 is getting deeper into everything, right? The 00:13:15.250 --> 00:13:18.529 ability to build these sophisticated, truly autonomous 00:13:18.529 --> 00:13:22.320 systems that can reason, plan, adapt. That's 00:13:22.320 --> 00:13:24.240 going to be a significant competitive advantage 00:13:24.240 --> 00:13:26.299 in the years to come, I think. Yeah, this deep 00:13:26.299 --> 00:13:29.240 dive has really pulled back the curtain on a 00:13:29.240 --> 00:13:31.779 hidden layer of power within NADAN, hasn't it? 00:13:32.080 --> 00:13:34.320 From that friendly drag and drop surface down 00:13:34.320 --> 00:13:36.480 to the powerful Lang chain framework underneath. 00:13:36.960 --> 00:13:38.860 We've seen how you can move from simple automation 00:13:38.860 --> 00:13:42.039 to building truly custom intelligent AI agents 00:13:42.039 --> 00:13:45.519 tailored exactly to what you need. The possibilities 00:13:45.519 --> 00:13:48.590 this Lang chain code node opens up. They're just 00:13:48.590 --> 00:13:50.950 not there with the standard nodes. It gives you 00:13:50.950 --> 00:13:53.409 unparalleled flexibility and control over your 00:13:53.409 --> 00:13:56.429 AI workflows. Exactly. And as AI keeps evolving 00:13:56.429 --> 00:13:58.870 so rapidly, the ability to build these custom 00:13:58.870 --> 00:14:00.629 sophisticated agents will just become more and 00:14:00.629 --> 00:14:03.210 more vital. By understanding and using the line 00:14:03.210 --> 00:14:05.169 chain code node, you're not just learning an 00:14:05.169 --> 00:14:07.090 NEA feature, you're developing skills that are 00:14:07.090 --> 00:14:10.269 valuable across the entire AI landscape, which 00:14:10.269 --> 00:14:12.029 raises an important question for you to think 00:14:12.029 --> 00:14:15.230 about. What kind of truly sophisticated adaptive 00:14:15.230 --> 00:14:17.370 AI agent could you build if you had that complete 00:14:17.370 --> 00:14:20.169 control over its logic and its tools? My advice, 00:14:20.509 --> 00:14:23.070 start small. Maybe even just peek under the hood, 00:14:23.309 --> 00:14:25.190 look at that JSON for the standard nodes like 00:14:25.190 --> 00:14:27.710 you mentioned, see how they work, and then gradually 00:14:27.710 --> 00:14:29.809 work your way up to building your own custom 00:14:29.809 --> 00:14:31.990 agents. The journey might be a bit challenging, 00:14:32.190 --> 00:14:34.690 sure, but the results could be incredibly rewarding. 00:14:34.909 --> 00:14:38.169 Yeah, definitely. As next steps, we really encourage 00:14:38.169 --> 00:14:41.590 you experiment. Set up a test workflow with the 00:14:41.590 --> 00:14:44.009 LangChain code note. Try building a simple agent 00:14:44.009 --> 00:14:46.309 that uses, say, two different language models 00:14:46.309 --> 00:14:49.570 or calls an external API tool. Dive into the 00:14:49.570 --> 00:14:51.769 official LangChain documentation. Seriously, 00:14:51.929 --> 00:14:54.590 it's a gold mine of information. And if you do 00:14:54.590 --> 00:14:57.470 have a self -hosted NNN instance, really consider 00:14:57.470 --> 00:14:59.710 integrating LangSmith for that amazing monitoring 00:14:59.710 --> 00:15:02.009 and debugging capability. Oh, and don't forget 00:15:02.009 --> 00:15:04.149 to join the NNN community, share what you build, 00:15:04.330 --> 00:15:06.110 ask questions, learn from others. It's a great 00:15:06.110 --> 00:15:08.360 resource. Thank you so much for joining us on 00:15:08.360 --> 00:15:10.500 this deep dive into the hidden AI power of an 00:15:10.500 --> 00:15:13.759 ADN. Until next time, keep exploring, keep learning, 00:15:14.120 --> 00:15:15.120 and keep diving deep.