Building AI Agents with Llama 3: A Step-by-Step Guide for Beginners

In the swiftly evolving world of artificial intelligence, staying ahead of the curve is essential, and building your own AI agents is a key step in harnessing this potential. With the introduction of Llama 3, an accessible and powerful language model, even those with minimal programming experience or limited hardware can participate in this technological revolution. In this article, we’ll explore how anyone, including non-programmers with less-than-ideal computers, can build AI agents using the Llama 3 model, guided by resources like AMA, VS Code, and Gro for optimized performance.

What You Need to Begin

Setting Up Your Environment

To kick things off, you’ll need to set up your development environment:

1. Download AMA: AMA acts as the execution environment for running models locally. It’s user-friendly and integrates seamlessly with VS Code.
2. Install Visual Studio Code: This free code editor is available at Visual Studio’s website. It is essential for writing and executing your code.
3. Choose and Install the Llama 3 Model: Navigate to the Llama models page and select the version that suits your needs. If you’re unsure, the 8 billion parameter model offers a solid balance of performance and resource efficiency.

Installing Required Tools

• AMA: Once VS Code and AMA are ready, you can download the Llama 3 model directly via the command line in VS Code.
• Gro: For enhancing performance and speed, integrating Gro can significantly boost your processing times, essential for real-time applications.

Constructing Your First AI Agents

Beginner-Friendly Programming

Once your tools are installed, creating AI agents is surprisingly straightforward. Begin by opening VS Code, creating a new Python file, and setting up your environment by importing necessary libraries from AMA and Gro.

Building and Connecting AI Agents

Define the purposes of different AI agents. For instance, you could create:

• An Email Classifier Agent: This agent can categorize emails into spam or important messages.
• A Response Writer Agent: Based on the classification by the previous agent, this one crafts appropriate responses.

Leverage AMA and Gro’s capabilities to connect these agents, allowing them to communicate and perform tasks sequentially or as required.

Execution and Monitoring Performance

Running Your Agents Locally

After coding your agents, the next step is to run them. You can execute the script in VS Code’s integrated terminal. Monitor the processing speed and resource utilization to ensure everything performs efficiently. Adjustments may be necessary depending on your system’s capabilities.

Troubleshooting and Optimization

Performance issues might arise, especially when integrating multiple systems like AMA and Gro. Debugging is facilitated through verbose outputs in the terminal, allowing you to trace and fix issues.

Scaling Your AI Projects

Utilizing Gro Cloud API

For those with less powerful computers, leveraging the Gro API can offload some of the computational load to the cloud, ensuring faster processing times without heavy hardware investments.

API Integration

Acquiring an API key from Gro Cloud and integrating it into your project can dramatically enhance performance, making real-time AI interactions feasible even on less capable machines.

Joining a Community of Innovators

Why Community Matters

Learning and building in isolation can be challenging and limiting. By joining a community of AI enthusiasts and experts, you can gain insights, shared resources, and support, accelerating your learning curve and leading to more successful projects.

Continuous Learning and Improvement

AI and technology landscapes are constantly changing. Staying engaged with a community helps you keep up with the latest developments, ensuring your skills and projects remain relevant.

Conclusion: Embracing the AI Revolution

Building AI agents with Llama 3 offers a unique opportunity for both beginners and experienced developers to engage with cutting-edge technology. With the right tools and community support, you can go from a novice to an AI innovator, contributing to and benefiting from the AI revolution. Don’t miss out—start building today and see where AI can take you.

[h3]Watch this video for the full details:[/h3]


If you’re serious about AI, and want to learn how to build Agents, join my community: https://www.skool.com/new-society

Follow me on Twitter – https://x.com/DavidOndrej1

Please Subscribe.

In this video I will show you how to build AI Agents powered by Llama 3.

[h3]Transcript[/h3]
my name is David Andre and I’ll show you how to build AI agents using the new llama fre model even if you have a bad computer and know nothing about programming now we’re going to be using AMA to run the models locally vs code to write the code and Gro to achieve super fast performance and look at this and now boom 216 tokens per second now this is the big model let’s try the smaller one Lama free 8 billion and let’s do tell me a long joke like that was instant so at the end of this video I’ll show you how to connect Gro to your team of agents that way you can get the benefits of this insane speed now check this out this is the llm arena where language models are ranked llama 370b an open source model is better than gbd4 the future is here if you aren’t doing anything about it if you aren’t building AI agents you’re falling behind so I urge you to take the first step and build something to make this as easy as possible for you I’ve built an entire Workshop that will teach you step by step how to build AI agents even if you’re not a programmer you can get it inside of my community the link will be in the description so let’s get to building agents I was planning on building on top of a previous project but then I realized I think you’ll get more value if I build everything from scratch so the basics right go to ama.com and hit download then you need vs code go to code. visual studio.com and download it obviously the next thing you need is the model so so if we go to all Lama click on models and select llama 3 obviously I would recommend going with 8 billion model which is already really good so copy this command just click on here copy it go to your terminal and if you don’t have u k that’s okay in vs code just click on Terminal and do new terminal and you should be able to run the AMA right here right yeah exactly the first time you run this obviously you won’t be able to run the LM you cannot just be like hey llama because you haven’t downloaded the model yet so the first time do this it will start downloading the model now 4.7 GB the smaller one so it will take like 20 minutes the bigger one though if you have a really good PC this one is 40 GB so this will take like 3 hours probably either way you have to download it the first time you use it right which is fine because you only have to do this once by the way quick tip whenever you’re in a chat and you want to end it just go SL bu and you don’t have to kill the terminal each time so we run this and this is just to chat with the llm locally and that’s already pretty good I mean most people can’t do that but honestly we’re not interested in that this is kind of a basic we want to build AI agents with Lama fre so let’s go into vs Studio by the way if you’re not subscribed Please Subscribe and I created just an empty file you know clicking right clicking new file naming it main.py just an empty python file literally as simple as that so what we need to do is we need to import AMA so go from Lang chain community. llms import or Lama and as you can see it’s highlighted meaning we have not installed it I have to be in the correct um environment okay so yeah if you have not installed this package yet don’t worry just go in here go into your terminal by the way again you can do this straight in vs code just go into the terminal make sure you’re in the right environment I mean you can just use the base environment if you want and just type in PIP install crew AI literally just type that and it will install everything and as you can see I already have it if you don’t have it it will take like 20 seconds so don’t worry inside of crei um you’ll get the L chain Community package which is what we’ll use to import o Lama next we need crew AI so from crew AI we need to import some things we need to import agent task crew and process that’s it those are the two Imports we need next thing we need to uh decide the model so I’m just going to use a variable and O Lama we have to do model equals and put the name of the model as it named in ama if you’re using the default version this the 8 billion just type in Lama 3 if you want to use the 70b version um then do llama free 70b so I’m just going to do llama free because um faster it’s faster so yeah and now I need to decide what type of Agents I want to build and I’ll just keep this simple that way all of you can follow along and what I came up with is a simple email classifier so we’ll go the first agent will receive receive a random email and classify it and based on the classification the second agent will write a response so let’s create a new variable named email and this is just going to be a simple string and I’m going to do a random one uh let’s do the classic Nigerian PS sending some gold let’s see if the llms are smart enough to recognize this is a spam by the way all of the code and prompts from this video will be linked below the module so another reason to join the community anyways let’s create our first agent and this is going to be classifier and we’re going to do agent like this beautiful and first we need to give it a role which is basically just a second name right and this is going to be email classifier next we need to give it a goal so what is what will this agent do what’s his main goal goal equals accurately classify emails based on their importance give every email one of these ratings important casual SP or spam and then we need to give it a backstory and I’m just going to keep it super simple that way this is not a 2hour video the next thing I need to do is set verbos so ver both equals true this will let us see the flow of the messages inside of the terminal next we do allow delegation equals false because we don’t want this agent delegating this task to somebody else else and lastly and this is super important we need to assign the llm which is model if we don’t do this it will automatically default to gp4 which will not work because we haven’t provided the API for open AI but we don’t want gbd4 we want llama free and that’s our agent done crew AI makes this super simple next agent is going to be responder so responder equals agent and then we basically do the same but for an email responder and again kab copal just Sav me some time based on the importance of the email write a concise and simple response all right we’re both true beautiful all of this is the same so now we have our agents created super simple now we need to define the task so first task I’m just going to name classify email equals task and creating tasks inside of crei is even simpler than creating agents first we do a description description equals and then I’m going to do an F string and the reason for that is because I want to enter the variable we’ve created right for the email so F strings in Python just allow you to insert the variable inside of a string agent equals classifier and then expected output one of these three options import on casual or spam yes beautiful so that’s our first task created then we’re going to create a second task respond to email okay exactly beautiful description respond to the email now we have created both of the tasks next step is to define the crew crew equals crew and first we need to assign the agents exactly a list of Agents so just create a list and enter all of the agents you have can be two can be three can be 10 it’s up to you then we do tasks and be careful because I used to put task here and this will not cause an error but it will cause your crew not to work verbos is different here it’s either one or two I’m going to do two and then process equals process. sequential and the last thing we need to do is output equals crew. kickoff okay and then obviously I need to print it out to see it and now this should work fingers crossed let’s see it do we have a team of llama free agents working or not okay this is promising let’s look at the activity monitor so we have memory used 36 it went up from like 30 which is means that the llamas 3 8 billion uses like 3 to 6 GB of RAM which is very good news because most of you can run it but this is bad news because it’s probably going to include a bunch of Chunk it should not be this slow it should be much faster and I’m suspecting that we’re going to see a bunch of hallucinations oh my God what is this so I don’t know why this is happening but inside of crew AI llama 3 does not seem to be working well it works perfectly in the terminal but some like somehow through crew AI it’s uh not really working well I mean I can try verbose false it’s not the issue of the model clearly like look at this this is perfect right look at this the model said a classic rating spam the Nigerian pen is a welln example of a fishing scam yeah so perfect I mean it gave it the rating exactly as I wanted but somehow running it as a agent through crew AI is causing troubles I have no clue why so we tested this now I’m going to show you how to add the gro API so if you have a bad computer and you want to use the API I’m going to show you how so okay let’s go Gro and let’s go Gro cloud and let’s go API Keys create API key I’m just going to do llama 3 submit and I’m going to delete this before posting the video you know never share API keys with anybody I’m going to do Gro API key equals let’s go into documentation see how we connect it let me duplicate this rename cuz we want to do Gro so now this is for the gro right so we delete AMA and okay let’s follow along with this we can just probably delete this all together honestly following the GitHub official GitHub from crei might be the move right so we need to assign these three variables and just make a default right so open a API base always that environment yes so we need to import OS we copy this we need these three lines and we actually need them not commented out beautiful let’s go to Gro documentation and open a compatibility and the base URL copy this API base equals model name let’s copy this again and the API key test Moment of Truth oh my god it works it works and look at the speed though let’s try the different email and let’s do it look at the speed okay what am I doing okay so I’m just going to explain what I did so I’m I was completely trolling the answer was right in front of my eyes right here inside of the main GitHub from crew AI I just needed to assign these three variables right and the reason why I deleted the llm is because crew AI is automatically made to use the default one so like this is like basically hidden right something like this is hidden and you don’t have to do that if you’re using the open endo and guys let’s just appreciate the speed one more time like this is an API look at this insane this is the AI Revolution we are watching the the AI Revolution happened right in front of our eyes unfortunately 99% of people will miss out on the AI Revolution and I don’t want you to be one of them the only way to stay ahead is to surround yourself with people who are at The Cutting Edge of AI which is exactly why I created the community I’m looking for people who want to take AI seriously so if that sounds like you make sure to join for this link in the description