How to Create AI Agents with LangGraph
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All right. Get ready, because today we're going to take a deep dive into the world of AI agents. Oh, yeah. The kind of AI that makes those chatbots we've all seen look like. Well, much in history. Exactly. Like imagine AI that can actually handle complex tasks. Yeah. Like having a personal assistant, but like super powered. Yes.

And we're going to break down how they're built using this thing called LandGraph. It's really cool because we're moving beyond just asking a question and getting an answer. Right. It's AI that can actually like gather info, make decisions. Oh, wow. And even take action all on its own. So it's like AI is finally graduating from basic tasks to... Really understanding how to navigate complex processes. Yeah. It's a big step. Huge. And to really get into how it works...

We're focusing on this LandGraph thing. It's the latest tool from the Langchain team. For anyone who's familiar with Langchain, this is like next level stuff. And it's all built around this graph structure. Oh, yeah. Graphs are cool. Which really opens up a ton of possibilities. Definitely. I mean, Langchain is really great for straightforward AI tasks. Sure. But if you want to build agents that can handle these things,

really intricate multi-step processes. Okay. LandGraph is where it's at. Got it. Instead of like a simple chain of actions. Right. LandGraph lets you create a roadmap with multiple paths, loops, all kinds of decision points. So it's giving AI options. Yes. And the smarts to choose the best route. Ah.

Based on what's happening. Like that dynamic. Yeah. So to really see it in action, we're going to walk through a real world example later. Definitely. Of building a Landgraf agent. But first, let's take a step back. Okay. Talk about AI agents in general. Sure. Because I think a lot of people are like- What is that? Yeah, what is that? Exactly. Is it just a chat bot or- It is and it isn't. Okay. An AI agent is basically a piece of software. Okay. That can do tasks without needing a human to tell it every single step. Oh, okay. Yeah.

But the key here is that we're talking about AI agents doing complex tasks. Gotcha. So not just. No, not just setting a timer or playing music. Right. This is next level stuff. Give me an example then of like. Okay. So let's say. A complex task. Healthcare. Okay. Imagine an AI agent that looks at a patient's medical records. Okay. Test results. Even data from wearables. Yes.

Oh, yeah. And then it can predict potential conditions and suggest treatment plans. Wow. Based on all that information. That's amazing. It could even like alert a doctor if something seems off. Wow. That's like. It's like having a. It's constant health guardian. Yeah, exactly. Personalized medicine to the max.

That's amazing. But how is this different from what we talked about before with RAG? Oh, good question. Where it's like AI using outside info to give better answers. Right. So RAG is great. Yeah. But AI agents go way beyond just retrieving information. Okay. They can learn from their interactions. Oh, wow. Adapt, juggle multiple things at once. Okay. So we've got these super smart AI agents. So where does Landgraf fit into all this?

Landgraf is like the toolkit for building these really sophisticated multi-actor applications. And it's built on Landchain. So you get all those benefits, the familiarity, but with all the advanced features that Landgraf offers. So what are some of the features that make Landgraf so special for building these agents? One of the best things is that you can create

Cycles and branching. Oh, cool. Within the AI's behavior. Okay. Remember that roadmap analogy? Yeah. So this means the AI can loop through processes. Okay. Make decisions based on new info, adapt its approach. So it's not just following a script? Nope. It's actually thinking. Wow. Strategizing. Mind-blowing. It's pretty cool. Yeah.

What else makes Landgraf stand out? Another crucial feature is something we call persistence. Persistence. So Landgraf, it automatically remembers past interactions. Oh, wow. Imagine you're working with an AI agent to plan a trip. Okay. And it remembers things like the hotels you liked, the types of activities you enjoyed. It makes it feel like... Personalized. Yes, exactly. That's cool. So for things like recommendations...

Or even just remembering where you left off in a complex task. Oh, yeah. That's huge. It's learning and growing with you. Exactly. It's really cool. Are there any other features that we should highlight? Absolutely. Landgraf also has human-in-the-loop interaction built right in. Okay. So that means you can have a human step in, approve, edit, whatever, override the AI if you need to. So it's like a safety net. Exactly. It's a great way to make sure things stay on track.

So, LandGraph sounds really powerful. It is. But how does it actually work in practice? So, remember that real world example we mentioned? Yeah, with the solar panels? Yeah. So, let's say you're a homeowner and you're thinking about going solar. Okay.

You want to see how much you could save? Mm-hmm. That's where a Landgraf agent could really come in handy. Okay. I'm following. How would it tackle this problem? First, it needs to gather some basic info from you. Like what? Like your monthly electricity costs. Okay. Stuff like that. Makes sense. Designed to be super user-friendly. Yeah. Because it's going to be easy to use. Exactly. If you want people to actually trust it with something like energy. It has to be smooth. And what happens next? So once it has that initial info. Okay.

It can start doing the calculations. Oh, okay. This is where things get interesting. Okay. It takes all its knowledge about solar panel efficiency, installation costs, and then it figures out how many panels you need, the potential savings over time. So it's not just spitting out numbers. No. It's understanding them. It has to make sense. Yeah, for someone who's not a solar panel expert. Right, exactly. That's cool. And that's a great example of how Landgraf's cycles and branching work.

Oh, okay. So it might need to ask you for more info. Like what? Like which direction your roof faces. Okay. Or if there's any shape, things like that. Adapting to the situation. Exactly. To give you the most accurate estimate. That's really impressive. What happens once it's crunched all the numbers? So then the agent presents its findings to you. Okay. In a clear way. Like what would it say? It might say something like, based on your usage, you could potentially save $10,000 over 10 years by switching to solar.

Okay. That's pretty compelling. Yeah, right. So how does Langraph compare to other AI frameworks out there? Good question. We talked about Langchain. Yeah. What about other tools? How does Langraph stack up? So one key difference is that Langraph is specifically designed for these more intricate

agent interactions. Oh, okay. Where you need to keep track of the conversation's context. Oh, okay. Like the state of things. Right. Traditional line chain is great. Yeah. But it's not as well suited for these complex tasks. So different tools for different jobs. Exactly. If you're building a birdhouse, you use a hammer and nails. But if you're building a skyscraper, you need something a bit more heavy duty. You need a land graph. Land graph. All right. So for someone who's not a developer, why should they care about all of this?

all of this. So this shift from basic RI to these more sophisticated AI agents, it has the potential to impact every aspect of our lives. Really? Think about customer service. Imagine an AI agent that remembers your entire history with a company.

Oh, wow. Understands your issue. Right. Offers solutions specific to you. Okay. Yeah, that would be nice. Way better than those frustrating phone menus. Definitely. Or chatbots that just don't get it. Right, right. But what about other areas? Yeah. Beyond customer service. Sure. Where else could these agents make an impact? So let's take data analysis. Okay. AI agents could...

sift through mountains of data uh-huh find those hidden patterns the insights yes and then present them in a way that's easy to understand oh wow so it's like having it's like a whole team of analysts yeah 24/7 yeah helping businesses make better decisions absolutely scientists make discoveries or even just helping us understand the world better that's amazing

And are there companies already using Langraph? Oh yeah. To do some cool stuff? Definitely. Like what? A great example is Replit. Right. Their platform used by millions of developers. Oh, cool. They've actually incorporated Langsmith. Langsmith. It works really well with Langraph. Okay. To build a multi-agent AI system. Okay. That's interesting. It's called Replit Agent. Replit Agent? Yeah. And it lets users create software applications. Okay. Without writing any code.

Wow. They have specialized agents for managing, editing, even verifying. Wow. So it's like. Like an AI pair programmer. Yeah, helping you out. Making sure your code is good. That's amazing. And it could really democratize software development. Right. Make it accessible to more people. Exactly. What kind of results have they seen? The results have been phenomenal. A 90% success rate in tool invocations. Wow. So that means the AI is really accurate.

that's awesome plus they've seen improvements in server costs startup times so it's not just cool it's actually making a difference that's impressive do you have any other examples another great one is podium codium they're a communication platform okay used by small businesses gotcha and they use langsmith as well oh cool to optimize their ai employee agent their ai employee agent yeah and it handles those important customer interactions

So an AI agent that can handle customer inquiries. Yeah, for small businesses. Especially those that might not have a big customer service team. Exactly. What kind of impact did that have? So Podium's AI agent got a 98.6% F1 score in response quality. Wow. Which

Which means it's really good at understanding and responding to inquiries. That's great. And they were able to reduce the need for human intervention by 90%. That's huge. Yeah, it frees up their staff to focus on other things. While still providing good service. It's a win-win. These success stories are really inspiring. They're pretty cool. It sounds like Landgraf and AI agents are already making big changes. They are. And it's not just limited to... It's not just Langstreet and Landgraf. Yeah. There's a whole world of tools and frameworks out there.

Like what? Remember we talked about small agents? Yeah. The one that's really user friendly. Yeah, exactly. And then you have all the research happening around multi-agent systems. Okay. Where multiple AI agents work together. Oh, wow. To solve even more complex problems. So it's like. Like a team of specialized agents. Yeah. Each with their own skills. Working together towards a common goal.

That's amazing. What about the language aspect? How are they becoming more human-like? Well, there's a lot of research and development happening in natural language processing, which is making AI agents more conversational. So it's becoming harder to tell if you're talking to a human. Exactly. Or an AI. It's getting really good. That's fascinating. But we've covered a lot today. Yeah, we have. What's the key takeaway for our listeners? Yeah, what's that one question we want them to think about?

So if we think about all this potential. Yeah. From handling simple tasks to tackling these really complex problems. Right. The big question becomes. What else can we do? Yeah. What else can we automate? Mm-hmm.

enhance using Landgraf agents. What problems could AI help you solve? That's a good question. It really makes you think. It does. How AI could really change the world. For the better, hopefully. Yeah, exactly. It's like an invitation to be a part of it. You know, help guide its future. We can shape it. Instead of being intimidated by AI, we can be empowered by it. Exactly. Thinking about how it can make things easier, more efficient. More fulfilling. Yeah, I like that.

It's exciting. We've only just scratched the surface. Oh, yeah, just the beginning. Of what's possible. There's so much more to come. Well, that's a great note to end on. Thanks for joining us for this deep dive into the world of AI agents and Lingraph. My pleasure. I know I learned a lot. Me too. Hopefully our listeners did too. I hope so. Until next time, keep exploring, keep learning. Keep asking those big questions. And we'll see you on the next deep dive. Really?