Hey there, Eden here.
And in this video we'll be reviewing what we'll be building.
Now we'll be implementing a react agent executor, but with Landgraab.
Now, the reason I chose this project, because it emphasizes how easy it is to use graph to describe
agent flows, and specifically the react agent, which was kind of hard to understand, but with graph
it would be super easy to implement.
We're also going to dive deep into graph state and how to implement a custom state.
And by the end of this video, we'll have a working agent executor using the react algorithm, but implemented
with graph.
And we can provide this agent executor with tools.
And we're going to be using the search tool and a custom tool that we're going to write.
And then the agent is going to execute the graph which has a loop.
And it's going to decide whether or not to use our tools or not.
And eventually is going to answer us.
So the question we're going to be asking is what's the weather in San Francisco?
And please multiply it by three.
This is the hello world example of agents.
but we're going to be implementing all of this in Landgraaf.
So a quick update here.
This is me one year later with a little bit of less hair and some more pounds that I've gained, and
Linkchain is going and leaning towards Landgraaf when it comes to building and implementing agents.
So I recently refilmed this entire section to leverage the latest versions of Landgraaf, leveraging
something which is called a tool node and leveraging function calling, which is going to make our agents
much more robust and much more trustworthy.
So I hope you will enjoy this section.
And an important side note in the Genii space, everything is built on top of each other and understanding
how they react.
Algorithm works with the react prompt.
The old way is, in my opinion, super, super important to know because once you understand the basics
and where everything comes from, everything else just makes sense.
And if you implemented the react executor from the previous section, everything now would be much easier
because you know better the concepts and you know the ideas and you know how everything evolves and
it gives you much deeper understanding of agents.