How to Build AI Agents in Python

[00:00] Today, we're diving deep into one of the

[00:02] hottest topics in AI right now. Building

[00:05] actual agents in Python. Not just

[00:07] chatbots to respond to your queries, but

[00:10] autonomous systems with memory, goals,

[00:12] and the ability to take actions in the

[00:14] world. We're talking about personal

[00:16] assistants that schedule your meetings,

[00:18] research bots that gather information,

[00:21] development tools that can fix your

[00:22] code, web scrapers that can gather data,

[00:25] and much, much more. Now, this will not

[00:27] be a step-by-step coding tutorial.

[00:30] Instead, I'm going to give you a

[00:31] strategic overview of the landscape so

[00:33] you can choose the right tools for your

[00:35] specific project. But before we jump in,

[00:38] I want to give a quick thank you to

[00:39] Nvidia for sponsoring this video. Nvidia

[00:42] just launched two brand new

[00:44] certifications that I think are game

[00:46] changers if you're working with AI. The

[00:48] first is the professional agentic AI

[00:50] certification. This one proves you can

[00:53] actually design and deploy advanced

[00:54] multi- aent systems. And the second is

[00:57] the professional generative AI LLM

[00:59] certification focused on fine-tuning and

[01:02] optimizing large language models for

[01:04] realworld use. These certifications

[01:07] aren't just a piece of paper. They're

[01:08] backed by NVIDIA and actually provide

[01:11] realworld value. Whether you're a

[01:13] student, developer, are already working

[01:15] in the field, NVIDIA certifications are

[01:17] a great way to validate your skills and

[01:19] stand out in a crowded job market. And

[01:21] here's the great thing. You can get 20%

[01:23] off any certification exam with the code

[01:25] tech with Tim 20. I'll leave the link in

[01:28] the description so you can sign up and

[01:29] start leveling up your AI career. So,

[01:31] big thank you to Nvidia for sponsoring

[01:33] this video. Now, let's get into it and

[01:35] start by understanding the core blocks

[01:37] of any AI agent. Now, think of this like

[01:40] your mental checklist when you're

[01:41] planning your agent architecture. Going

[01:44] to go through all of the things that are

[01:45] probably going to make up every AI

[01:47] agent. Now, first, every agent needs an

[01:49] LLM backbone. LLM stands for large

[01:52] language model and this is the brain of

[01:54] your agent that handles understanding

[01:56] language and generating responses. Now

[01:59] you've probably heard of some popular

[02:00] options like OpenAI's GPT4 which powers

[02:03] chat GPT or Entropics Claude. There are

[02:06] also many open- source options like

[02:08] models that come from Olama that you can

[02:10] run locally on your own computer. Now

[02:12] think of the LLM as the reasoning engine

[02:15] that powers everything else. It's what

[02:17] enables your agent to understand tasks,

[02:19] make decisions, and communicate in

[02:21] natural language. So, that's the first

[02:23] thing that you need for any AI agent and

[02:25] LLM, which acts as the backbone. Now,

[02:28] second, you need prompt templates and a

[02:30] reasoning strategy. Prompt templates are

[02:33] pre-esigned text structures that help

[02:34] guide your LLM's responses. Think of

[02:37] these as the questions or instructions

[02:39] that you give to your AI to get useful

[02:41] answers. Now, a good prompt template

[02:43] clearly explains the task, provides

[02:46] context, and specifies the format that

[02:48] you want for the particular response.

[02:50] Now, as for reasoning strategies,

[02:52] there's several popular approaches, and

[02:53] we'll get into them in more detail later

[02:55] in the video. First though, we have

[02:57] react, which stands for reasoning and

[02:59] then action or act. It's where the agent

[03:02] thinks through a problem step by step

[03:03] before taking action. Imagine it like a

[03:06] person thinking, hm, I need to find the

[03:08] weather. First, I'll go access the

[03:09] weather API. Then, I'll look up the

[03:11] user's location, and so on and so forth.

[03:14] Next, we have plan and execute. This is

[03:17] similar, but it separates the planning

[03:19] phase from the execution phase. Lastly,

[03:21] we have reflection, and this is a newer

[03:23] approach that encourages the agent to

[03:25] reflect on its previous actions to

[03:27] improve its future performance. Now,

[03:29] after prompts and reasoning, the third

[03:31] thing that our agent needs is tools and

[03:33] actions that it can use to interact with

[03:36] the world. Now, without tools, your

[03:37] agent is just a chatbot. It can talk,

[03:40] but it can't really do anything. Tools

[03:42] give your agent the ability to take

[03:44] actions beyond just generating text.

[03:46] This can include web access so it can

[03:48] search for information online, file

[03:50] operations like reading or writing

[03:52] files, code execution like running

[03:54] Python code or doing calculations, and

[03:57] API calls like connecting to services

[03:59] like Google Cloud or Slack. Now, think

[04:01] of tools as like the hands of your

[04:03] agent. They let it reach out and

[04:05] actually do things in the digital world.

[04:08] Now, after tools, every agent needs

[04:10] memory and state management. Without

[04:12] memory, your agent would be like a

[04:14] goldfish, forgetting everything as soon

[04:16] as the conversation moves on. Now,

[04:18] memory systems store information from

[04:20] past interactions so your agent can

[04:23] maintain context over time. Now, the

[04:25] simplest form of this is something like

[04:27] buffer memory, which just keeps a record

[04:29] of the recent conversation history. More

[04:31] advanced systems use vector search

[04:33] capabilities to store and retrieve

[04:35] relevant information from a larger

[04:37] knowledge base. And some agents use JSON

[04:40] to store and keep track of structured

[04:42] data like user preferences or a task

[04:44] status. Now that's memory. And finally,

[04:47] at the heart of every AI agent is the

[04:49] control loop. This is the

[04:51] decision-making processes that determine

[04:53] what the agent does next. The control

[04:55] loop continuously cycles through

[04:57] observing the current state, deciding

[05:00] what action to take based on goals and

[05:02] available tools, executing that action,

[05:04] observing the result, and then repeating

[05:06] the process. It's like a thought process

[05:09] of your agent. Now, these are the five

[05:11] components, the backbone, prompt

[05:14] templates, reasoning strategies, tools,

[05:16] and actions, memory, and state

[05:18] management, and finally, the control

[05:20] loop that form the foundation of any AI

[05:23] agent. And by understanding each piece,

[05:25] you'll be better equipped to choose the

[05:26] right frameworks and design patterns for

[05:29] your specific project. So now what I

[05:31] want to do is dive into actual Python

[05:33] frameworks that make building these

[05:35] agents much easier. So let's get into

[05:37] them. All right, so now that we

[05:39] understand the core building blocks,

[05:41] let's break down the most popular Python

[05:43] frameworks for building AI agents. And

[05:45] I've actually used all of these, so I

[05:46] can really speak to them well. Now,

[05:48] first up is Langchain, a modular Python

[05:51] framework that's become the go-to for

[05:53] building LLM applications with tools,

[05:56] memory, chains, and agents. You should

[05:58] consider Langchain when you want full

[06:00] programmatic control. When you're

[06:02] building agents that need to call APIs,

[06:05] perform reasoning tasks, or maintain

[06:07] memory, and when you're comfortable

[06:08] working with Python logic to connect

[06:10] everything together. It's extremely

[06:12] flexible, but there is a little bit of a

[06:14] learning curve, and you do need to know

[06:16] some Python. Now, next on my list is

[06:18] Langraph. This is essentially a stateful

[06:21] graph-based framework built on top of

[06:23] Langchain. Think of it as a structured

[06:25] way to model your agent workflows,

[06:27] giving you a lot more control. Now, you

[06:29] should use Langraphph when you want

[06:30] precise control over how your agent

[06:33] moves through tasks or states when

[06:35] you're building complex multi-step or

[06:37] multi- aent workflows or when you need

[06:39] asynchronous or branching logic like

[06:42] retry mechanics or conditional paths.

[06:44] Now, it's great for more complex agent

[06:46] architectures that need clear state

[06:48] management, but it is a little bit

[06:50] overkill for a basic agent. So, if you

[06:52] want to go with something basic, go with

[06:53] Lang Chain. If you want much more

[06:55] control and something that has a lot of

[06:57] different paths it needs to follow, then

[06:59] go with Langraph. And of course, I have

[07:01] tutorials on both of these on the

[07:02] channel. Now, for those of you that

[07:04] prefer a more visual approach, there is

[07:06] another tool called Langflow. This is

[07:08] pretty much a visual lang chain that

[07:10] lets you drag and drop components to

[07:12] build agents and workflows with minimal

[07:15] coding. Now, this is perfect when you

[07:16] want to prototype something quickly

[07:18] without knowing lang chain or lang graph

[07:20] and when you prefer a visual nodebased

[07:23] interface. It's also great when you're

[07:24] experimenting with different chain

[07:26] configurations and you want to iterate

[07:28] quickly without writing a bunch of code.

[07:30] Now, this is an excellent tool for

[07:31] beginners or for quick proof of concept

[07:33] and of course I have tutorials on this

[07:35] on the channel. Now, when it comes to

[07:37] connecting your agents with data, Llama

[07:39] Index is another standout framework.

[07:41] It's designed specifically to connect

[07:43] external data sources like PDFs,

[07:46] websites, and databases to LLM with

[07:48] powerful indexing, retrieval, and query

[07:51] routing capabilities. So you should

[07:53] consider Llama Index when your agent

[07:55] needs to retrieve context from private

[07:57] data, when you want to build a rag

[07:59] retrieval augmented generation system,

[08:02] or when you need to structure fine-tuned

[08:04] access to files, APIs, or databases.

[08:07] Now, it's the go-to solution for

[08:09] datacentric AI agent applications. So if

[08:12] you're using a lot of data, then check

[08:13] out Llama Index. Now, for more complex

[08:16] scenarios involving multiple agents, you

[08:18] can check out something like Crew AI.

[08:20] Now, this offers a teamwork framework

[08:22] where you can define different roles,

[08:24] assign tasks, and enable collaboration.

[08:27] This is ideal when your use case

[08:28] requires multiple agents that are

[08:30] working together, and when you want

[08:32] agents to follow structured roles and

[08:34] task flows or when you're building

[08:36] simulations of team workflows. Now, this

[08:38] is particularly strong for multi-roll

[08:40] processes like writing, coding, and

[08:43] research projects where different

[08:44] specialized agents need to coordinate

[08:46] together. Now, of course, there are a

[08:48] lot of other tools and frameworks that

[08:49] you can use, but these are the ones that

[08:51] I'm familiar with and that really are

[08:53] the most popular and definitely are

[08:55] going to get you where you need to go

[08:56] when it comes to building advanced AI

[08:58] agents. Now, beyond these frameworks,

[09:00] there are several additional tools that

[09:02] are worth mentioning that will help you

[09:04] when you're writing Python code. Now,

[09:06] number one is Streamlin. This offers a

[09:08] fast way to build web interfaces for

[09:10] your agents. It's extremely simple to

[09:12] use and it's my go-to for user

[09:14] interfaces for AI applications. Next, we

[09:17] have data stacks and Chroma DB. These

[09:20] both provide vector database solutions

[09:22] for storing and retrieving embeddings,

[09:24] and they're good for building in rack.

[09:26] And of course, we have libraries like

[09:27] pandas, for example, which are essential

[09:29] for data manipulation and analysis

[09:31] within your agent workflow. So, consider

[09:34] picking up some of these tools and

[09:35] learning some additional Python modules

[09:37] because they go really nicely with the

[09:39] frameworks I mentioned before. Anyways,

[09:41] let's now explore some common design

[09:43] patterns for AI agents with practical

[09:46] examples to help you understand and how

[09:48] to use each one. Now, first let's talk

[09:51] about the react pattern which stands for

[09:53] reasoning and then action. Now, this

[09:55] originated in academic research and has

[09:57] become the standard approach for tooling

[10:00] agents. In React, your agent first

[10:03] thinks through what it knows and what it

[10:04] needs to find out. It then selects an

[10:07] appropriate action, observes the result,

[10:09] and repeats that for what it needs to

[10:11] do. So, for example, if it was asked to

[10:13] find the population of Tokyo and compare

[10:15] it to New York, a React agent would

[10:17] first reason, I need the population data

[10:20] for two cities. It would then decide to

[10:22] search for Tokyo's population, observe

[10:24] the result, search for New York's

[10:26] population, observe the result, and then

[10:28] finally compare at the numbers. Now,

[10:30] this pattern excels when your agent

[10:32] needs to use tools strategically and

[10:34] explore information in a methodical way.

[10:37] Next, we have the plan and execute

[10:39] pattern. Now, this takes a more

[10:41] structured approach by dividing work

[10:43] between two specialized components.

[10:46] First, you have a planner agent. This

[10:48] develops a comprehensive step-by-step

[10:50] plan to achieve a particular goal. Then

[10:52] you have an executor agent which

[10:54] meticulously follows each step handling

[10:57] any complications that arrive during the

[10:59] implementation. Now think of this like

[11:01] an architect drawing a blueprint before

[11:03] the construction begins. So this pattern

[11:05] shines for complex tasks where mistakes

[11:08] will be costly. For example, if you're

[11:10] writing complex Python script with

[11:12] multiple API integrations, the planner

[11:15] might first outline all of the necessary

[11:16] imports, function definitions, and API

[11:19] calls, well, the executor would write

[11:21] all of the actual code following this

[11:23] blueprint. Now, this separation of

[11:25] concerns leads to more reliable outcomes

[11:27] for sophisticated tasks, and it's

[11:29] definitely something worth considering.

[11:31] Next, we have the multi- aent

[11:33] collaboration. Now, this expands on

[11:35] these foundations by creating teams of

[11:37] specialized agents that work together on

[11:39] complex problems. So, rather than having

[11:41] one agent handle everything, you assign

[11:44] specific roles based on different

[11:46] expertise. For instance, a coding

[11:48] project might involve a project manager

[11:50] agent that defines requirements, a

[11:52] solutions architect that designs the

[11:54] overall structure, and then multiple

[11:56] developer agents that write the code,

[11:58] and maybe a QA agent that tests this for

[12:01] bugs. Now these agents communicate with

[12:03] each other passing information and

[12:05] results between them as the project

[12:07] progresses. Now this approach mimics

[12:09] human team dynamics and works

[12:11] exceptionally well for projects

[12:12] requiring diverse skills and

[12:14] perspectives. Can be hard to set up but

[12:16] when done well it works very well. Next

[12:19] we have retrieval augmented generation

[12:21] or rag. Now this has become an essential

[12:24] pattern for knowledge inensive

[12:26] applications. Now in rag before your

[12:28] agent generates a response it first

[12:30] searches a knowledge base could be

[12:32] something like documents websites maybe

[12:34] a database for relevant information.

[12:37] This is to inform its answer. So for

[12:39] example if you're building a customer

[12:41] support agent the rag pattern would

[12:43] enable it to search through product

[12:44] documentation previous support tickets

[12:47] maybe information about the company

[12:49] before answering a customer's question.

[12:51] Now, this dramatically improves the

[12:53] accuracy by grounding the responses in

[12:55] factual information rather than relying

[12:58] solely on the LLM's internal knowledge

[13:00] which could be out ofd or just not

[13:02] relevant to the particular problem. Now,

[13:04] rag is very valuable when you're working

[13:06] with domain specific information,

[13:09] proprietary data, or rapidly changing

[13:11] knowledge that might not be in the

[13:13] existing LLM training data. So, how do

[13:16] you actually choose your stack and your

[13:18] architecture? Well, my advice is to

[13:20] start simple. One agent, one clear goal,

[13:23] no complex memory requirements, and as

[13:26] you understand the problem better, you

[13:28] can scale up by adding tools, memory

[13:30] systems, planning capabilities,

[13:32] team-based approaches, etc. Now, in

[13:34] terms of your choice of framework, you

[13:36] should be guided by your priorities. If

[13:38] you need full control, go with Langchain

[13:40] or Langraph. If collaboration between

[13:42] agents is key, then use something like

[13:44] Crew AI. And if you need to demo

[13:46] something quickly, you could use

[13:48] Streamlit and something like Langflow to

[13:50] get something up and running quickly.

[13:51] Lastly, if something like Privacy is a

[13:53] major concern, then definitely consider

[13:55] using some local models with tools like

[13:58] Olama, for example, which allow you to

[14:00] run models locally on your own computer,

[14:02] assuming that you have sufficient

[14:04] hardware. Now, the beauty of this field

[14:06] is that it's evolving rapidly with new

[14:08] tools and patterns every single day. So

[14:10] what matters most is understanding the

[14:12] fundamental building blocks and the

[14:14] trade-offs between different approaches.

[14:16] So start with a clear problem statement.

[14:18] Choose the simplest stack that addresses

[14:20] your needs and iterate from there. All

[14:23] right guys, so that's going to wrap up

[14:24] this video. I know that was a lot of

[14:26] information, but I wanted to provide a

[14:28] high-level kind of structured guide that

[14:30] goes over the key components of AI

[14:32] agents, some important frameworks that

[14:34] you might want to be aware of, and then

[14:36] of course the different design patterns

[14:37] so you have somewhere to start and you

[14:39] understand what's possible in this

[14:41] field. If you enjoyed the video, make

[14:43] sure to leave a like, subscribe to the

[14:45] channel, and I will see you in the next

[14:46] one.

[14:49] [Music]