---
title: 'How to Build AI Agents in Python'
source: 'https://youtube.com/watch?v=OKwDzKY_WN8'
video_id: 'OKwDzKY_WN8'
date: 2026-06-14
duration_sec: 895
---

# How to Build AI Agents in Python

> Source: [How to Build AI Agents in Python](https://youtube.com/watch?v=OKwDzKY_WN8)

## Summary

This video provides a strategic overview of building autonomous AI agents in Python, covering core components, popular frameworks, and design patterns. It emphasizes understanding the building blocks and trade-offs rather than step-by-step coding.

### Key Points

- **LLM Backbone** [01:49] — Every agent needs a large language model (LLM) as its reasoning engine, such as OpenAI's GPT-4, Anthropic's Claude, or local models via Ollama.
- **Prompt Templates and Reasoning Strategies** [02:28] — Prompt templates guide LLM responses. Reasoning strategies include ReAct (reasoning then action), plan and execute, and reflection.
- **Tools and Actions** [03:29] — Tools enable agents to interact with the world: web access, file operations, code execution, API calls. Without tools, an agent is just a chatbot.
- **Memory and State Management** [04:08] — Memory systems store past interactions for context. Simple buffer memory keeps recent conversation; advanced systems use vector search or JSON for structured data.
- **Control Loop** [04:47] — The control loop is the decision-making process that cycles through observing state, deciding action, executing, and observing results.
- **Langchain** [05:39] — A modular Python framework for building LLM applications with tools, memory, chains, and agents. Offers full programmatic control but has a learning curve.
- **Langraph** [06:18] — A stateful graph-based framework built on Langchain for complex multi-step or multi-agent workflows with precise state management.
- **Langflow** [07:04] — A visual drag-and-drop interface for building agents with minimal coding, ideal for quick prototyping and beginners.
- **Llama Index** [07:37] — A framework for connecting external data sources (PDFs, websites, databases) to LLMs with indexing, retrieval, and query routing. Best for data-centric RAG applications.
- **Crew AI** [08:16] — A teamwork framework for defining roles, assigning tasks, and enabling collaboration among multiple specialized agents.
- **ReAct Pattern** [09:51] — Reasoning and then action: agent thinks through what it knows, selects an action, observes the result, and repeats. Good for tool-using agents.
- **Plan and Execute Pattern** [10:37] — Divides work into a planner agent (develops step-by-step plan) and an executor agent (follows plan). Useful for complex, costly tasks.
- **Multi-Agent Collaboration** [11:31] — Teams of specialized agents (e.g., project manager, architect, developer, QA) work together on complex problems, mimicking human team dynamics.
- **Retrieval Augmented Generation (RAG)** [12:19] — Agent searches a knowledge base before generating a response to ground answers in factual information. Essential for domain-specific or proprietary data.
- **Choosing Your Stack** [13:16] — Start simple: one agent, one goal, no complex memory. Scale up as needed. Choose frameworks based on priorities: control (Langchain/Langraph), collaboration (Crew AI), quick demo (Streamlit/Langflow), privacy (local models with Ollama).

### Conclusion

Building AI agents in Python requires understanding five core components: LLM backbone, prompt templates/reasoning, tools, memory, and control loop. Start simple, choose the right framework for your needs, and iterate.

## Transcript

Today, we're diving deep into one of the
hottest topics in AI right now. Building
actual agents in Python. Not just
chatbots to respond to your queries, but
autonomous 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 agentic AI
certification. This one proves you can
actually design and deploy advanced
multi- aent systems. And the second is
the professional generative AI LLM
certification focused on fine-tuning and
optimizing large language models for
realworld use. These certifications
aren't just a piece of paper. They're
backed by NVIDIA and actually provide
realworld value. Whether you're a
student, developer, are 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. Going
to go through all of the things that are
probably going to make up every AI
agent. Now, first, every agent needs an
LLM backbone. LLM 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 GPT4 which powers
chat GPT or Entropics Claude. There are
also many open- source options like
models that come from Olama that you can
run locally on your own computer. Now
think of the LLM 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
LLM, which acts as the backbone. Now,
second, you need prompt templates and a
reasoning strategy. Prompt templates are
pre-esigned text structures that help
guide your LLM's 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, hm, 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 chatbot. 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 Langchain, a modular Python
framework that's become the go-to for
building LLM applications with tools,
memory, chains, and agents. You should
consider Langchain 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
Langraph. This is essentially a stateful
graph-based framework built on top of
Langchain. Think of it as a structured
way to model your agent workflows,
giving you a lot more control. Now, you
should use Langraphph when you want
precise control over how your agent
moves through tasks or states when
you're building complex multi-step or
multi- aent 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
Lang Chain. If you want much more
control and something that has a lot of
different paths it needs to follow, then
go with Langraph. 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 Langflow. This is
pretty much a visual lang 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 lang chain or lang graph
and when you prefer a visual nodebased
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.
Now, 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, Llama
Index is another standout framework.
It's designed specifically to connect
external data sources like PDFs,
websites, and databases to LLM with
powerful indexing, retrieval, and query
routing capabilities. So you should
consider Llama 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
datacentric AI agent applications. So if
you're using a lot of data, then check
out Llama 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-roll
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 Streamlin. 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 rack.
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 tooling
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 at 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 executor 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 executor 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- aent
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,
and maybe 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. Can be hard to set up but
when done well it works very well. Next
we have retrieval augmented generation
or rag. Now this has become an essential
pattern for knowledge inensive
applications. Now in rag 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 LLM's internal knowledge
which could be out ofd 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 LLM 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 Langchain
or Langraph. 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 Langflow 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
Olama, 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 to leave a like, subscribe to the
channel, and I will see you in the next
one.
[Music]