How to Build AI Agents in Python

Today we're diving deep into one of the hottest topics in AI right now. Building actual agents in Python, not just chat bots to respond to your queries, but at tournament systems with memory goals and the ability to take actions in the world. We're talking about personal assistants that schedule your meetings. Research bots that gather information. Development tools that can fix your code. Web scrapers that can gather data. And much, much more. Now, this will not be a step by step coding tutorial. Instead, I'm going to give you a strategic overview of the landscape so you can choose the right tools for your specific project. But before we jump in, I want to give a quick thank you to Nvidia for sponsoring this video. Nvidia just launched two brand new certifications that I think are game changers. If you're working with AI. The first is The Professional, a genetic AI certification. This one proves you can actually design and deploy advanced multi-agent systems. And the second is the professional generative AI certification, focused on fine tuning and optimizing large language models for real world use. These certifications aren't just a piece of paper. They're backed by Nvidia and actually provide real world value whether you're a student developer or already working in the field. Nvidia certifications are a great way to validate your skills and stand out in a crowded job market. And here's the great thing you can get 20% off any certification exam with the code tech with Tim 20. I'll leave the link in the description so you can sign up and start leveling up your AI career. So big thank you to Nvidia for sponsoring this video. Now let's get into it and start by understanding the core blocks of any AI agent. Now think of this like your mental checklist when you're planning your agent architecture. I'm going to go through all of the things that are probably going to make up every AI agent. Now, first, every agent needs an Lem backbone. A Lem stands for large language model and this is the brain of your agent that handles understanding language and generating responses. Now, you've probably heard of some popular options like OpenAI's GPT four, which powers ChatGPT or entropic Claude. There are also many open source options, like models that come from Alama that you can run locally on your own computer. Now think of the Lem as the reasoning engine that powers everything else. It's what enables your agent to understand tasks, make decisions, and communicate in natural language. So that's the first thing that you need for any AI agent and Lem, which acts as the backbone. Now second, you need prompt templates and a reasoning strategy. Prompt templates are pre-designed text structures that help guide your LMS responses. Think of these as the questions or instructions that you give to your AI to get useful answers. Now, a good prompt template clearly explains the task, provides context, and specifies the format that you want for the particular response. Now, as for reasoning strategies, there's several popular approaches and we'll get into them in more detail later in the video. First, though, we have react, which stands for reasoning and then action or act. It's where the agent thinks through a problem step by step before taking action. Imagine it like a person thinking, I need to find the weather first. I'll go access the weather API, then I'll look up the user's location and so on and so forth. Next we have Plan and execute. This is similar, but it separates the planning phase from the execution phase. Lastly, we have reflection. And this is a newer approach that encourages the agent to reflect on its previous actions to improve its future performance. Now, after prompts and reasoning, the third thing that our agent needs is tools and actions that it can use to interact with the world. Now, without tools, your agent is just a chat bot. It can talk, but it can't really do anything. Tools give your agent the ability to take actions beyond just generating text. This can include web access so it can search for information online file operations like reading or writing files. Code execution like running Python code or doing calculations and API calls like connecting to services like Google Cloud or Slack. Now think of tools as like the hands of your agent. They let it reach out and actually do things in the digital world. Now, after tools, every agent needs memory and state management. Without memory, your agent would be like a goldfish forgetting everything. As soon as the conversation moves on. Now, memory systems store information from past interactions so your agent can maintain context over time. Now, the simplest form of this is something like buffer memory, which just keeps a record of the recent conversation history. More advanced systems use vector search capabilities to store and retrieve relevant information from a larger knowledge base. And some agents use Json to store and keep track of structured data like user preferences or a task status. Now that's memory. And finally, at the heart of every AI agent is the control loop. This is the decision making processes that determine what the agent does next. The control loop continuously cycles through observing the current state, deciding what action to take based on goals and available tools, executing that action, observing the result, and then repeating the process. It's like a thought process of your agent. Now these are the five components. The backbone prompt, templates, reasoning strategies, tools and actions, memory and state management. And finally the control loop that form the foundation of any AI agent. And by understanding each piece, you'll be better equipped to choose the right frameworks and design patterns for your specific project. So now what I want to do is dive into actual Python frameworks that make building these agents much easier. So let's get into them. All right, so now that we understand the core building blocks, let's break down the most popular Python frameworks for building AI agents. And I've actually used all of these so I can really speak to them well. Now first up is Lang Chain, a modular Python framework that's become the go to for building applications with tools, memory chains and agents. You should consider lang chain when you want full programmatic control. When you're building agents that need to call APIs, perform reasoning tasks, or maintain memory. And when you're comfortable working with Python logic to connect everything together, it's extremely flexible. But there is a little bit of a learning curve, and you do need to know some Python. Now next on my list is Lane Graph. This is essentially a stateful graph based framework built on top of lang chain. Think of it as a structured way to model your agent workflows, giving you a lot more control. Now you should use Lane Graph when you want precise control over how your agent moves through tasks or states. When you're building complex, multi-step, or multi-agent workflows, or when you need asynchronous or branching logic like retry mechanics or conditional paths. Now, it's great for more complex agent architectures that need clear state management, but it is a little bit overkill for a basic agent. So if you want to go with something basic, go with Link Train. If you want much more control and something that has a lot of different paths it needs to follow. Then go with Lane Graph. And of course I have tutorials on both of these on the channel. Now, for those of you that prefer a more visual approach, there is another tool called Lane flow. This is pretty much a visual lane chain that lets you drag and drop components to build agents and workflows with minimal coding. Now this is perfect when you want to prototype something quickly without knowing lane chain or lane graph. And when you prefer a visual node based interface. It's also great when you're experimenting with different chain configurations and you want to iterate quickly without writing a bunch of code. And this is an excellent tool for beginners or for quick proof of concept. And of course I have tutorials on this on the channel. Now, when it comes to connecting your agents with data, Love Index is another standout framework. It's designed specifically to connect external data sources like PDFs, websites and databases to Lems with powerful indexing, retrieval, and query routing capabilities. So you should consider Love Index when your agent needs to retrieve context from private data. When you want to build a Rag retrieval augmented generation system, or when you need to structure fine tuned access to files, APIs, or databases. Now it's the go to solution for data centric AI agent applications. So if you're using a lot of data, then check out Lambda Index. Now for more complex scenarios involving multiple agents. You can check out something like crew AI. Now this offers a teamwork framework where you can define different roles, assign tasks, and enable collaboration. This is ideal when your use case requires multiple agents that are working together, and when you want agents to follow structured roles and task flows, or when you're building simulations of team workflows. Now, this is particularly strong for multi-role processes like writing, coding, and research projects where different specialized agents need to coordinate together. Now, of course, there are a lot of other tools and frameworks that you can use, but these are the ones that I'm familiar with and that really are the most popular and definitely are going to get you where you need to go when it comes to building advanced AI agents. Now, beyond these frameworks, there are several additional tools that are worth mentioning that will help you when you're writing Python code. Now number one is Streamlit. This offers a fast way to build web interfaces for your agents. It's extremely simple to use, and it's my go to for user interfaces for AI applications. Next we have data stacks and chroma DB. These both provide vector database solutions for storing and retrieving embeddings. And they're good for building in RAC. And of course we have libraries like pandas for example, which are essential for data manipulation and analysis within your agent workflow. So consider picking up some of these tools and learning some additional Python modules, because they go really nicely with the frameworks I mentioned before. Anyways, let's now explore some common design patterns for AI agents with practical examples to help you understand and how to use each one. Now first let's talk about the react pattern, which stands for reasoning and then action. Now this originated in academic research and has become the standard approach for tool using agents. In react, your agent first thinks through what it knows and what it needs to find out. It then selects an appropriate action, observes the result, and repeats that for what it needs to do. So, for example, if it was asked to find the population of Tokyo and compare it to New York, a react agent would. First reason I need the population data for two cities. It would then decide to search for Tokyo's population, observe the result, search for New York's population, observe the result, and then finally compare the numbers. Now, this pattern excels when your agent needs to use tools strategically and explore information in a methodical way. Next, we have the plan and execute pattern. Now this takes a more structured approach by dividing work between two specialized components. First, you have a planner agent. This develops a comprehensive step by step plan to achieve a particular goal. Then you have an executer agent which meticulously follows each step, handling any complications that arrive during the implementation. Now think of this like an architect drawing a blueprint before the construction begins. So this pattern shines for complex tasks where mistakes will be costly. For example, if you're writing complex Python script with multiple API integrations, the planner might first outline all of the necessary imports, function definitions, and API calls. Well, the Executer would write all of the actual code following this blueprint. Now, this separation of concerns leads to more reliable outcomes for sophisticated tasks, and it's definitely something worth considering. Next we have the multi-agent collaboration. Now this expands on these foundations by creating teams of specialized agents that work together on complex problems. So rather than having one agent handle everything, you assign specific roles based on different expertise. For instance, a coding project might involve a project manager agent that defines requirements, a solutions architect that designs the overall structure, and then multiple developer agents that write the code. It may be a QA agent that tests this for bugs. Now these agents communicate with each other, passing information and results between them as the project progresses. Now, this approach mimics human team dynamics and works exceptionally well for projects requiring diverse skills and perspectives. It can be hard to set up, but when done well, it works very well. Next, we have retrieval augmented generation or RAC. And this has become an essential pattern for knowledge intensive applications. Now, in drag, before your agent generates a response, it first searches a knowledge base. Could be something like documents, websites, maybe a database for relevant information. This is to inform its answer. So for example, if you're building a customer support agent, the Rag pattern would enable it to search through product documentation, previous support tickets, maybe information about the company before answering a customer's question. Now, this dramatically improves the accuracy by grounding the responses in factual information rather than relying solely on the LMS internal knowledge, which could be out of date or just not relevant to the particular problem. Now, Rag is very valuable when you're working with domain specific information, proprietary data, or rapidly changing knowledge that might not be in the existing LM training data. So how do you actually choose your stack and your architecture? Well, my advice is to start simple. One agent, one clear goal. No complex memory requirements. And as you understand the problem better, you can scale up by adding tools, memory systems, planning capabilities, team based approaches, etc.. Now, in terms of your choice of framework, you should be guided by your priorities. If you need full control, go with length chain or line graph. If collaboration between agents is key, then use something like crew AI. And if you need to demo something quickly, you could use Streamlit and something like Lang Flow to get something up and running quickly. Lastly, if something like privacy is a major concern, then definitely consider using some local models with tools like Alama for example, which allow you to run models locally on your own computer. Assuming that you have sufficient hardware. Now, the beauty of this field is that it's evolving rapidly with new tools and patterns every single day. So what matters most is understanding the fundamental building blocks and the trade offs between different approaches. So start with a clear problem statement. Choose the simplest stack that addresses your needs and iterate from there. All right guys. So that's going to wrap up this video. I know that was a lot of information, but I wanted to provide a high level kind of structured guide that goes over the key components of AI agents, some important frameworks that you might want to be aware of. And then of course the different design patterns. So you have somewhere to start and you understand what's possible in this field. If you enjoyed the video make sure leave a like subscribe to the channel and I will see you in the next one.