Name: LAgent
Uploaded: 2024-11-16T20:38:49.429Z
Duration: 2 min 33 s
Description: An open-source framework for building large language model-based agents with modular components and tool integration.

lagent

An open-source framework for building large language model-based agents with modular components and tool integration.

LAgent

There is a new lightweight framework for building large Lage model agents. In essence, you can easily and efficiently turn a large language model to an agent that can be used as a Python executor, a search agent, an action executor, a planning as well as an accident agent, and so many other different types of use cases. 

This is where I would like to introduce Lagent. This is an open-source framework that can help you in many ways. Just take a look at this live demo which is going to showcase the capabilities of Lagent. This is where a function calling agent has been deployed, and you can see that it's able to retrieve the most relevant information based off the context. 

There is another type of agent which is a data analyst, and this is where you can simply provide your data, and it's going to be able to get an output that's more structured. You can see that you can even have it so that it can plot out different graphs for you, as well as have it so that the model can do predictive modeling for you. 

This is where you can utilize this open-source framework to customize your agents so that they could be fine-tuned to have a specific type of output, and output that can help you in various ways, whether that's with coding, mathematics, data analysis, or many other types of areas. 

Now there's so much more to this, and this is something that we're going to be exploring throughout today's video. So with that thought, guys, today you're going to be able to understand the capabilities of this new framework, understanding how you can install it locally and so much.

More so with that thought let's get straight into the video and showcase what Lagent is about. Now before we get started I definitely recommend that you take a look at the patreon page so that you can access the new subscriptions that will be releasing this week. If you would like to book a consultant call with me you can do so with the link in the description below. 

I'd like to introduce world of AI Solutions. This is a really big update that has been launched for my channel and this is where I have compiled a team of software Engineers. We have machine learning experts AI consultants and this is basically a team where we're going to be providing AI solutions for businesses as well as personal use cases where AI Solutions can be implemented to automate certain things or to help business operations. 

Now if you're interested take a look at the ti form Link in the description below. So let's get straight into this framework and showcase what you can actually do with Lagent. We know that this is an OP Source large languish model agent framework and if it's going to basically enable users to efficiently transform a large language model into a versatile agent. 

It provides a range of different typical tools and these are to enhance the capabilities of LM itself. Like you have a python executor you have a search agent a calculator calendar and many other types within that category. You also have different agents that can perform through other components like a plan act iteration agent you have one that can act for a plan you have one that is going to.

Basically research as well as haveit so that it can plan to have an implementation of a certain type of approach. Now this Lagent framework is basically going to consist of three main parts. You have the agents itself, you have the large language model, and the actions. The agent is going to be the component that offers the implementation such as through react as well as through Auto GPT. 

Then you have the large sish model, which is where it supports different components of large sish models including open source models like llama 3.1 as well as inter LM, which is something that you can access through hugging phase. You can also utilize different close source models like the Sonet model. You have gb4 Omni and so many others. 

Now lastly, you have the actions component, and this is where it is including a series of different actions and an action executor to manage all of these different actions and plugins. With this three-step process, you're going to be able to create intricate agents with large nage models, and this is something I'm going to be showcasing as we go further into the video. 

So with that thought, that leads me to The Next Step where I'm going to be showcasing how you can install it. But before we even get to that, there's a couple of examp or prerequisites that you're going to need to fulfill beforehand. You need to make sure that you have git, cuz this is going to be used to clone their repository onto our desktop. We're going to need to have python, which is going to be the programming language that we utilize. We're going to be

Utilizing Visual Studio Code as our ID and lastly you'll need to have Pip installed. Now definitely install this for your operating system and it's very easy. You just utilize the following commands and there's a guide that showcases how you can actually install this onto your computer. 

Once you have all the prerequisites fulfilled, what you can do is head over to the GitHub repository of L agent. Click on this green button over here and copy this link. I'll leave all the links that I used in today's video in the description below. 

Now you want to open up your command prompt if you're on Windows. If you are to open this up, you want to basically type in get clone and you want to paste in the link that you just copied from the GR green button. Once that is done, you want to then head over to the Lagent folder. That's by simply typing in CD Lagent. 

Once that is done, you can simply install with Pip and that's by simply copying this command over here. Once that's done, you're going to be able to have this framework fully installed on your computer. You can simply press enter and it's going to start installing the packages that are needed for this framework to be functional. 

Now to access this UI, you're going to need to also install Streamlit and to do so simply just copy this command over here pip install streamlit. You can then paste this into your command prompt and click enter. Once it finishes installing Streamlit, what you can do is then you can run the demo applications that have been installed with L agent. 

Now currently I think there is

Approximately four different agents you have the demo cly. You have the web demo as well as an agent that could be used as a function calling agent. There's a couple agents that have been already created and this is basically the generic guideline as to how you can utilize and build these different agents with this framework. 

So what we're going to be doing now is checking out some of these demos so that you get a better idea. So now that we have streamlet installed, we can then run the following. So we have it now hosted on our Local Host. Now you can try out all of these agents and to do so you can simply just copy the streamlet Run example command and then you can paste in the example that you want to try. 

Now say you want to explore these other agents that have been developed. You can definitely try it out in your own cly. For example, there's a cly demo, there's an agent web demo, you can also utilize the hugging face one so that you can access a hugging face model. But it also comes installed with the 8 billion parameter intern ml large language model, which is an open source large language model that was built for this model. 

Now in this case, I'm installing the cly demo so that I can access it within vs code itself. And say if you want to access this on the Google Cloud, you can actually do so with this link which I'll attach in the description below. What you want to do is click on file. You want to save a copy into your own Drive. Once you have done that, head over to runtime, change it to runtime type the best hardware that is available and

Once that done just simply click save and you're going to be able to then access all the code gen the different sorts of plugins like the react plugins. You have different tools like the function calling and you can basically install them by simp installing the main package above with the large language model which is internal LM. 

Then you can click on the tabs that you would want to basically work on. For example, there is a tool for uh react prompts and you can simply just install it by clicking on running the cell. You're going to be able to then prompt open some sort of prompt that is related to the react agent and it's going to be able to generate components based off of your own requirements. 

For example, take a look at this function calling la agent and it basically was generated with the internet LM model. It was able to construct The Prompt which was able to describe the AI assistant that defines the uh basic like function calling uh agent. From here it goes on to pars and executes the request and we can see that it is asking for an input for what the weather is in Singapore as well as what the temperature is. 

So after it parses and executes that function extracts and valid validates the encoded function call from the string and we can see that it's able to define the different uh function and it's going to be able to execute it at the end. It's going to be able to provide that uh weather in Singapore which is sunny with a temperature of 30° C. In their documentation, they basically provide a good example as to how you can share AI Agent Store with colleagues.

Set up your own agents. They actually have templates and this is where they showcase how you can define your different agents whether that's an autonomous one or a simple function calling one. We went over the installation, but there's also a quick start method where you can run different types of agents that have been already pre-built such as the react agent we just had taken a look at. 

But in terms of an actual tutorial, they showcase the basic concepts such as a tool description, how you can set up a tool. Uh, they basically predefine the commands that are needed to have function calling, for example, and they basically go very in depth and showcase how you can set up your own agent. So I'll leave a link to this in the description below. They go through almost everything and they showcase how you can get started with your own particular agent. 

Now the reason why I really wanted to make a video on this type of framework was because it's a lightweight framework, meaning that you can incorporate various plugins and various libraries quite easily to create and craft your agent. You can utilize the base template to help you do so and this is really why I wanted to showcase AI agent. This is something that is fully open source, so any coder can easily get started with it. 

But this is something that will easily help you build large language model applications, build large language model AI agents that can help you carry out various sorts of tasks such as basic tasks as well as helping you with data analysis or any sort of coding related task. But that's

Basically for today's video guys I hope you enjoyed it and you got some sort of value out of it. If you're interested in uh more in-depth analysis I can definitely make a video on this on the patreon. So if you're interested definitely join the patreon which I'll leave a link to in the description below so that you can access different subscriptions as well as insights and different weekly calls. 

Make sure you follow me on Twitter. Great way for you to stay up to date with the latest AI news. 

And lastly share the AI Agent Store with colleagues. Have an amazing day, spread positivity and I'll see you guys fairly shortly. Peace out fellas.

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LAgent Video