As an application developer in this era of AI, there are a bunch of different approaches you can

take to bring AI into your applications. You could possibly take a code-driven approach with

one of many various languages, or maybe a UI-driven one, and possibly

a bit of both. Maybe your company only allows a particular vendor, or you'd like to take a more

open path. Do you plan on using any observability tools or possibly MCP? So

these are all considerations we need to take into account. And if you're looking to quickly build

and iterate on AI on your apps, this is where Langflow really comes into play.

So, what is Langflow? Langflow is an open-source visual studio for developers that allows you to

build agentic RAG in gen AI workflows in a drag-and-drop, low code/no code way.

Visual workflow builders can really help simplify the process of chaining AI components together

without a lot of code. It is completely model and vector store-agnostic,

with support for many dozens of model and vector database providers. So, many of the

major model providers. If you want to use local models, you can do that. If you want to stick with

a particular model vendor, these are all options that you have available, and it's the same on the

vector store front. There are all sorts of options, dozens that are supported out of the box. And

what's really important is you can also edit any of the components as you wish. It is all Python

under the hood. So if you want to make custom components, maybe you have a vector or model

provider that isn't there by default, you can absolutely add those in with some Python code. And

because it's open source, you can inspect and modify any component or contribute your own back

to the community. Okay, so let's take a look at what it's like to build an agent with Langflow.

So as I mentioned earlier, Langflow is a visual studio that uses drag-and-drop components to

build out AI workflows. So I have some input and output components. I would drag these over, right.

I'm just going to go ahead and put an output here. And I do need these, right. In a case where I'm say

talking with an agent where I need to be able to communicate, both give agent, give the agent

instructions and receive data back from the agent. And then, I would pull over an agent component,

let's say agent here and then hook them up. And this is where some of the UI and visual aspects

really come into play. So you'll find in, Langflow uses this node-based architecture. And the nodes

have these nice little colors on them and they denote the type, right. So, in the case of like

these inputs and outputs, maybe they're a message or some other types of components that could be a

data, data frame. There are various types that you can find in Langflow. And if I want to get more

information on a particular type, I can just click on the little node, and it will tell me what kind

of type it is. And it'll also filter on the other components in the system to show me which ones

are compatible. Now, for now, let's get back to our agent. In the agent itself, once I have it in place,

I'm going to want to choose a model. All right. The model and the model provider. Now, this can also be

you know, again, as I mentioned before, there are dozens of various types of providers. You can use

local models. And if you want to change any of the base providers that are in there, again, you can

just bring a component over from the side. It would be some model. And again, this is where

you would then hook that up. And as long, in the case of an agentic case, as long as the model

has tool support, then you will be able to use whatever model you hook up with your agent. Now

once I have my basic agent in place, I can use the playground up here in the top corner. And this

allows me to start testing my agent directly well before I've even hooked up an

application, right? So I can start to work out the basic flow of what my agent is going to do, and

then I can worry about my application later. Now, this is just the most basic setup. If I really

want an agent to do something for me, I need to hook up tools. And the cool thing is that most

components in Langflow have a tool mode that you can just switch on. So I could bring, say, the URL

component over and up on the top there's this little, little radio button. I can just switch that

up, pop into tool mode. And now, I can hook it right into my agent. And what's super cool about this is

that once I'm in tool mode or I hook up a tool, the agent will auto discover it, right? It'll

actually auto discover what that tool is for, when to use it, and the agent can start using it right

away. Langflow also has native support for MCP, both in being able to add

MCP tools to your flows and expose your flows as MCP tools

themselves. This gives you access to possibly thousands of tools and allows you to easily

create MCP tools, curated with your own agent instructions and capabilities that you can use in

downstream agents like an AI code companion. And a tool for developers wouldn't mean much if you

couldn't easily hook it into your application. Every flow in Langflow has its own API access

that allows you to execute flows, pass in data, stream data, receive responses, and you can tweak

any of the parameters you see in components. And this gives you full programmatic control over

your flows. So you're not stuck with only what you see in the UI. And finally, Langflow is not limited to

basic flows like I've been drawing here. Here is a more complex example of a flow that uses database

access, agents, conditional logic and structured output to ensure my agents can conform exactly to

my application's requirements in a more deterministic way. Imagine trying to explain this

to a non-coder, or even another coder without the visual aid to help in seeing everything that's

going on. So there you have it. I hope you now have a much better idea of what Langflow is and how

you can use it to power your AI workflow.