WEBVTT

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Hey there, Eden here.

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And in this video we'll be reviewing what we'll be building.

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Now we'll be implementing a react agent executor, but with Landgraab.

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Now, the reason I chose this project, because it emphasizes how easy it is to use graph to describe

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agent flows, and specifically the react agent, which was kind of hard to understand, but with graph

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it would be super easy to implement.

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We're also going to dive deep into graph state and how to implement a custom state.

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And by the end of this video, we'll have a working agent executor using the react algorithm, but implemented

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with graph.

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And we can provide this agent executor with tools.

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And we're going to be using the search tool and a custom tool that we're going to write.

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And then the agent is going to execute the graph which has a loop.

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And it's going to decide whether or not to use our tools or not.

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And eventually is going to answer us.

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So the question we're going to be asking is what's the weather in San Francisco?

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And please multiply it by three.

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This is the hello world example of agents.

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but we're going to be implementing all of this in Landgraaf.

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So a quick update here.

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This is me one year later with a little bit of less hair and some more pounds that I've gained, and

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Linkchain is going and leaning towards Landgraaf when it comes to building and implementing agents.

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So I recently refilmed this entire section to leverage the latest versions of Landgraaf, leveraging

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something which is called a tool node and leveraging function calling, which is going to make our agents

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much more robust and much more trustworthy.

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So I hope you will enjoy this section.

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And an important side note in the Genii space, everything is built on top of each other and understanding

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how they react.

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Algorithm works with the react prompt.

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The old way is, in my opinion, super, super important to know because once you understand the basics

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and where everything comes from, everything else just makes sense.

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And if you implemented the react executor from the previous section, everything now would be much easier

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because you know better the concepts and you know the ideas and you know how everything evolves and

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it gives you much deeper understanding of agents.