How I Made AI Assistants Do My Work For Me: CrewAI

Summary

In this insightful video, the presenter explores the limitations of current AI language models, highlighting their reliance on fast, automatic thinking. By introducing innovative methods like 'tree of thought' prompting and custom AI agents, viewers learn how to enhance AI's problem-solving capabilities. The video also demonstrates practical applications, including creating a startup concept and generating content from real-time data sources like Reddit. Ultimately, it showcases the potential of AI in automating tasks while discussing the challenges and costs associated with using advanced models, encouraging experimentation and local model deployment.

Takeaways

• 😀 System 1 thinking is fast and automatic, while System 2 thinking is slow and rational, as described by Daniel Kahneman in 'Thinking, Fast and Slow.'
• 🤖 Current large language models (LLMs) operate primarily on System 1 thinking, lacking deep, rational processing capabilities.
• 🧠 Tree of Thought prompting allows LLMs to consider multiple perspectives for decision-making, simulating rational thinking.
• 🛠️ Custom agent systems like CreAI enable users to build AI agents that collaborate to solve complex problems.
• 👥 The tutorial illustrates how to set up a team of AI agents with distinct roles: a marketer, a technologist, and a business development expert.
• 📊 Each AI agent is assigned specific tasks, and the output from one agent serves as the input for the next, forming a sequential process.
• 🔍 Enhancing agents' intelligence involves integrating real-time data through built-in tools and custom scraping tools like a Reddit scraper.
• 📈 The quality of output can vary significantly based on the sources of information used by the agents.
• ⚙️ Local AI models are explored as alternatives to API calls, with varying levels of performance observed among different models.
• 💬 Viewers are encouraged to share their experiences with CreAI and other AI tools, fostering a community of knowledge sharing.

Q & A

What is the main focus of the video?

The video discusses the limitations of current large language models (LLMs) in performing complex problem-solving, and introduces methods to enhance their capabilities by creating teams of AI agents.

What are the two types of thinking described in the video?

The video describes two types of thinking: System 1, which is fast and automatic, and System 2, which is slow and requires conscious effort. Current LLMs primarily operate using System 1 thinking.

What is 'Tree of Thought' prompting?

'Tree of Thought' prompting is a method that forces LLMs to consider an issue from multiple perspectives or experts before arriving at a conclusion, simulating System 2 thinking.

How does CreAI allow users to build custom AI agents?

CreAI enables users, even non-programmers, to create custom agents that can collaborate on complex tasks by utilizing APIs or local models, making the process accessible and flexible.

What are the roles of the three AI agents in the startup example?

In the startup example, the three AI agents are assigned the roles of a market researcher expert, a technologist, and a business development expert, each with specific tasks to contribute to the business plan.

What was the outcome of the business plan created by the AI agents?

The business plan included ten bullet points, five business goals, a time schedule, and suggestions for using technologies like 3D printing, machine learning, and sustainable materials.

What tools can be added to make AI agents smarter?

Users can enhance the intelligence of AI agents by integrating built-in tools that provide access to real-time data, such as text-to-speech capabilities, Google search results, and various APIs.

How does the video suggest improving the quality of information generated by AI agents?

To improve the quality of information, the video suggests using better sources, such as local subreddits, and creating custom tools for scraping relevant data, thus ensuring the agents produce more accurate and relevant outputs.

What were some limitations of the local models tested?

The local models faced challenges in understanding tasks effectively, often producing generic outputs and failing to grasp specific instructions, indicating variability in performance among different models.

What was the overall conclusion regarding the use of local models versus API models?

The conclusion highlighted that while local models can avoid API costs and maintain privacy, they often struggle with task comprehension, and performance varies significantly across different models.

Outlines

00:00

😀 The Dilemma of Decision-Making

In this part, the speaker explores the complex thought processes behind making purchasing decisions, likening it to the concept of 'system two thinking' as discussed in Daniel Kahneman's book 'Thinking, Fast and Slow.' They describe the contrast between 'system one' (fast, subconscious thinking) and 'system two' (slow, deliberate thinking). The speaker emphasizes that current large language models (LLMs) primarily operate on system one thinking, lacking the capability for deep, rational analysis. To address this limitation, they introduce two methods for simulating rational thinking in AI: tree of thought prompting and the use of collaborative AI agent systems like Crew AI. The speaker sets the stage for demonstrating how to build a team of AI agents to tackle complex problems effectively.

05:01

🛠️ Building Your Team of AI Agents

This part outlines the process of creating a team of AI agents using Crew AI. The speaker demonstrates how to set up three specific agents: a market researcher, a technologist, and a business development expert. Each agent is assigned a clear role and goal to analyze and refine a startup concept involving innovative plugs for Crocs. The speaker explains the importance of defining tasks with specific outcomes and how agents collaborate sequentially. After instantiating the team and running the script, they observe results that provide a business plan, emphasizing the potential of AI agents in generating meaningful insights.

10:01

📈 Enhancing AI Agents with Real-Time Data

In this segment, the speaker discusses how to make AI agents smarter by providing them access to real-world data through built-in tools and custom solutions. They illustrate how to implement a Google scraping tool to gather information for a blog about the latest AI and machine learning innovations. Despite achieving the required format for the newsletter, the initial quality of the information is subpar. The speaker emphasizes the need for better data sources and introduces the concept of building a custom Reddit scraping tool to fetch relevant posts and comments from the local llama subreddit, showcasing the effectiveness of their approach.

15:03

🔍 Fine-Tuning AI Outputs with Custom Tools

This part delves into the creation of a custom scraping tool for Reddit to improve the quality of information fed into the AI agents. The speaker explains the code and methodology behind the tool, which compiles data from the subreddit. Despite initial setbacks with various local models, the speaker finds success with a regular llama model that provides meaningful output from the subreddit. They share insights on the performance of different models tested, revealing the challenges faced when working with local AI solutions and the importance of customizing tools to enhance output quality.

💰 Cost-Effective AI Solutions and Performance Insights

In the final part, the speaker reflects on the costs associated with running scripts using various AI models, emphasizing the need for budget-friendly options. They discuss their experiences with different local models, noting that while many failed to produce satisfactory results, some, like the regular llama model, showed promise. The speaker concludes with a summary of their findings, offering guidance on selecting the best-performing local models and inviting viewers to share their own experiences with AI tools. They provide links to their code and further resources for those interested in exploring AI agent systems.

Keywords

💡Aptitude

Aptitude refers to an individual's natural ability or talent to learn or excel in a particular area. In the context of the video, it highlights the importance of recognizing one's strengths and interests, which can guide personal and professional development. For example, the script emphasizes that understanding one's aptitude can lead to more informed career choices and enhance job satisfaction.

💡Learning

Learning is the process of acquiring knowledge or skills through experience, study, or teaching. The video underscores that learning is not just a formal process; it can occur in various environments, including personal experiences and social interactions. The script mentions how continuous learning is crucial for adapting to changing circumstances and improving one's aptitude.

💡Career Development

Career development refers to the ongoing process of managing life, learning, and work in a way that progresses one's career. The video stresses that understanding your aptitude is vital for effective career development, enabling individuals to pursue paths that align with their skills and interests. An example from the script illustrates how individuals who follow their aptitudes tend to achieve greater success and fulfillment in their careers.

💡Personal Growth

Personal growth involves activities that develop a person's capabilities and potential, fostering a sense of self-awareness and identity. The video connects personal growth to the concept of aptitude by suggesting that recognizing and cultivating one’s strengths can lead to enhanced self-esteem and a clearer sense of purpose. The script indicates that personal growth often requires stepping outside of one’s comfort zone and embracing new challenges.

💡Self-Discovery

Self-discovery is the process of gaining insight into one’s character, values, and motivations. The video emphasizes that self-discovery is essential for understanding one’s aptitude, as it helps individuals identify their passions and areas of interest. An example in the script refers to the importance of reflection and exploration in uncovering hidden talents and preferences.

💡Education

Education is the systematic instruction or training to impart knowledge and skills. The video points out that education plays a critical role in shaping an individual’s aptitude by providing the necessary knowledge and skills to thrive in various fields. The script discusses how formal education, combined with self-directed learning, can significantly enhance one’s opportunities for success.

💡Motivation

Motivation is the internal drive that prompts an individual to take action toward achieving a goal. In the context of the video, motivation is closely linked to aptitude, as individuals are more likely to pursue activities aligned with their innate talents. The script includes examples of how finding motivation can lead to perseverance and resilience, particularly in challenging situations.

💡Passion

Passion refers to a strong enthusiasm or interest in a particular subject or activity. The video posits that when individuals engage in activities that resonate with their aptitudes, they often develop a passion for those pursuits. The script provides examples of how following one’s passion can lead to a more fulfilling career and greater overall happiness.

💡Skills

Skills are the abilities acquired through practice, training, or experience. The video makes a distinction between natural aptitudes and learned skills, arguing that while some skills may come easily, others require significant effort to develop. The script highlights that recognizing one’s natural skills can guide effective skill-building and professional development.

💡Adaptability

Adaptability is the ability to adjust to new conditions and changes in the environment. The video stresses the importance of adaptability in today's rapidly changing world, particularly in relation to career paths. The script mentions that individuals who are aware of their aptitudes are better equipped to adapt and thrive in diverse situations.

Highlights

The inner dialogue illustrates the difference between System 1 and System 2 thinking, as described by Daniel Kahneman in 'Thinking, Fast and Slow'.

Current large language models (LLMs) primarily operate on System 1 thinking, providing rapid, automatic responses.

To achieve System 2 thinking, which involves more rational and deliberate processing, two innovative prompting methods have been developed.

The first method, 'Tree of Thought' prompting, encourages the LLM to consider various perspectives before arriving at a consensus.

The second method utilizes platforms like Crew AI to build custom agents that collaborate to tackle complex tasks.

Demonstration of setting up a team of AI agents to refine a startup concept, illustrating the practical application of the discussed methods.

Agents in Crew AI can have specific roles and goals, enhancing the collaborative nature of AI problem-solving.

Using Crew AI, a detailed business plan was generated by three specialized agents, each focusing on different aspects of the startup.

The process of defining tasks for agents is critical, with tasks needing specific descriptions and expected outcomes.

Incorporating real-world data tools into agents can significantly enhance their outputs and overall intelligence.

Implementation of custom tools, such as a Reddit scraper, to gather timely and relevant information for generating content.

A demonstration of how to build a custom tool for scraping the latest Reddit posts highlights the flexibility of AI agents.

Local models were tested to reduce dependency on paid API calls while maintaining privacy and reducing costs.

Different local models showed varying levels of performance, emphasizing the importance of model selection for task completion.

Despite challenges, one local model successfully produced relevant outputs based on Reddit data, showcasing its utility.

The session concluded with a reflection on personal experiences with Crew AI, inviting viewer engagement for shared experiences.

Transcripts

have you ever found yourself on the

verge of making a controversial purchase

just as you're about to click on that

buy button an unexpected thought

suddenly crosses your mind wait a minute

they look a little bit like soy cheese

don't they no no no no no they're

absolutely beautiful and Kanye West

loves them he wears them all the time

but if I like things that Kanye likes is

that really a good thing okay I need to

relax everything is fine and buying

these makes me a Visionary a trend

Setter do these holes exist for

ventilation purposes oh okay time for a

break I need to urgently distress from

all this thinking with some Pringles

wait is you think this like really

unhealthy so that inner dialogue you

just witness is what Daniel conman

author of Book Thinking Fast and Slow

calls system to thinking it's a slow

conscious type of thinking that requires

deliberate effort and time the opposite

of that is system one or fast thinking

system one is subconscious and automatic

for example when you effortlessly

recognize a familiar face in a a crowd

but why am I talking about this in a

video about AI assistance well in order

to understand that I have to mention an

amazing YouTube video posted by Andre

karpati a great engineer at open AI in

that video Andre clarifies that right

now all large language models are only

capable of system one thinking they're

like Auto predict on steroids none of

the current llms can take let's say 40

minutes to process a request think about

a problem from various angles

and then offer a very rational solution

to a complex problem and this rational

or system to thinking is what we

ultimately want from AI but some smart

people found a way to work around this

limitation actually they came up with

two different methods the first and

simpler way to simulate this type of

rational thinking is with tree of

thought prompting you might have heard

of it so this involves forcing the llm

to consider an issue from multiple

perspectives or from perspectives of

various experts these experts then make

a final decision together by respecting

everyone's contribution the second

method utilizes platforms like crew aai

and agent systems crei allows anyone

literally anyone even non-programmers to

build their own custom agents or experts

that can collaborate with each other

thereby solving complex tasks you can

tap into any model that has an API or

run local models through AMA another

very cool platform

and in this video I want to show you how

to assemble your own team of smart AI

agents to solve tricky complex problems

and I'll also demonstrate how to make

them even more intelligent by giving

them access to real world data like

emails or redit conversations and

finally I'll explain how to avoid paying

fees to companies and exposing your

private info by running models locally

instead and speaking of local models

I've actually made some really

surprising discoveries and I'm going to

talk about it a little bit later so

let's build an agent team I'll guide you

through getting started in a way that's

simple to follow along even if you're

not a programmer in my first example

I'll set up three agents to analyze and

refine my startup concept okay so let's

begin first open vs code and open a new

terminal I've already created and

activated my virtual environment and I

recommend you do the same and once

that's done you can actually install

crew AI by typing the following in the

terminal Next Step will be to import

necessary modules and packages and

you're going to need an open API key so

in this case I'm going to need the

standard module and I need to import

agent task processing crew from crew AI

you can set the open AI as the

environmental variable so by default

crew AI is going to use GPT 4 and if you

want to use use GPT 3.5 you have to

actually specify that but I don't think

that you're going to get amazing results

with 3.5 I actually recommend use GPT 4

now let's define three agents that are

going to help me with my startup there's

no actual coding here this is just good

old prompting so let's instantiate three

agents like this each agent must have a

specific role and I want one of my

agents to be a market researcher expert

so I'm going to assign it or this

specific role also each agent should

have a clearly defined goal in my case I

want this research expert agent to help

me understand if there is a substantial

need for my products and provide

guidance on how to reach the widest

possible target audience and finally I

need a backstory for my agent something

that's going to additionally explain to

the agent what this role what this role

is about lastly you can set verbos to

True which will enable agents to create

detailed outputs and by setting this

parameter to true I'm allowing my agents

to collaborate with each other so I will

save this agent as a marketer and I'm

going to do the same for two other

agents so overall I I'll have a marketer

a technologist and a business

development expert on my team of AI

agents so once this part is done it's

time to Define tasks tasks are always

specific and results um in this case it

can be let's say a detailed business

plan or market analysis for example

agents should be defined as Blueprints

and they should be reused for different

goals but tasks should always be defined

as specific results that you want to get

in the end and tasks should have a

description always something that

describes what the task is about

and they should also always have an

agent that's going to be assigned to

every specific test so in my case I want

to have three specific tasks my business

idea is to create elegant looking plugs

for Crocs so this iconic Footwear looks

less like Swiss chees I will assign the

first task to a marketer agent and this

agent will analyze the potential demand

for these super cool plugs in advis on

how to reach the largest possible

customer base another task is going to

be given to a technologist and this

agent will provide the analysis and

suggestions for how to make these plugs

and the final task will be given to a

business cons consultant who's going to

take into consideration everyone's

reports and write a business plan now

that I have defined all the agents and

all the tasks as a final step I'm going

to instantiate the crew or the team of

Agents I'm going to include all the

agents and tasks and I'm going to define

a

process process defines how these agents

work together and right now it's only

possible to have a sequential process

which means output of the first agent

will be the input for the second agent

and then that's going to be the input

for the third agent and now I'm going to

make my crew work with this final line

of code I also want to to see all the

results printed in the console so that's

the most basic possible example and it's

the best way to understand actually how

crew AI works and I expect these results

to be far from impressive I actually

believe that the results are going to be

just a little bit better than just

asking Char with to write a business

plan but let's

see okay so now I have the results I

have business plan with 10 build points

I have five business goals and a time

schedule and so I should have a 3D

printing technology and injection molds

laser Cuts apply machine learning

algorithms to analyze custom preferences

and predict future buying Behavior so I

guess this agent really took very

seriously my business idea and I even

have sustainable or recycled materials

that's great so there you go so how to

make a team of Agents even smarter

making agents smarter is very easy and

straightforward with tools by adding

these tools you're giving agents access

to real world realtime data and there

are two ways to go about this first and

easier option is to add built-in tools

that are part of L train and I will

include a link to a complete list of

Lang chain tools but some of my personal

favorites are 11 Labs text to speech

which generates the most realistic AI

voices then there are tools that allow

access to YouTube and all kinds of

Google data and Wikipedia so now I'll

change my crew and in this next example

I'll have three agents researcher

technical writer and writing critic

everyone will have their own task but in

the end I want to have a detailed report

in form of a blog or a newsletter about

the latest Ai and machine learning

Innovation the blog must absolutely have

10 paragraphs it has to have all the

names of all the projects tools written

in bold and every paragraph has to have

a link to the project I'll use Lang

chain Google seral tool which will fetch

Google search results but first I'll

send it for free API key through serer

Dev I'm going to include the link to all

the code and all the prompts in the

description box as usual so let's begin

by importing necessary modules and let's

initialize Sur API tool with API key so

I'll instantiate the tool I'll name the

tool a Google scraper tool and I'll give

it a functionality which is to execute

search queries and along with

description to indicate the use case as

a last step before running the script I

should assign this tool to my agent

that's going to run first and once I run

the script I can see all the scrape data

in blue letters green letters show agent

processing this information and white

letters are going to be the final output

of each agent so this is what my

newsletter looks like right now and I

have 10 paragraphs as requested each

paragraph has a link and around two to

three sentences so the form it is fine

it's exactly what I was looking for but

there is a big problem so the quality of

information in the newsletter is not

really the best none of these projects

are really in the news at this moment

and my newsletter is only as good as the

information that goes into it so let's

fix that how do I improve the quality of

the newsletter

well it's actually quite simple I just

need to find a better source of

information and that brings me to custom

made tools but before I dive into that

it's worth mentioning that there is one

more cool and very useful pre-built tool

that people might Overlook and that is

human inal lope this tool will ask you

for input if it runs into conflicting

information okay so back to fixing the

newsletter my favorite way to get

information is local llama subreddit the

community is amazing and they Shir an

incredible amount of cool exciting

projects and I just don't have enough

time to sit and read through all of it

so instead I'm going to write a custom

tool that scrapes latest 10 hot posts as

well as five comments per each post

there is a preil tool through length

chain a Reddit scraper but I don't

really like using it my own custom tool

gives me a lot more control and

flexibility here's a quick look at the

code so import Pro and Tool from link

chain and I'm going to create a new

class that's called browser tools which

is how I'm going to create this Custom

Tool then I'm going to need a decorator

and a single line dog string that

describes what the tool is for the

scrape Reddit method starts by

initializing the pro rdit object with

client ID client secret and user agent

it then selects the subreddit local

llama to scrape data from then the

method iterates through 12 hotest posts

on the Reddit extracting the post title

URL and up to seven top level comments

it handles API exceptions by pausing the

scraping process for 60 seconds before

continuing and the scrape data is

compiled into a list of dictionaries

each containing details of a post and

its comments which is returned in the

end and the rest of the code is the same

so I'm just going to copy it from the

previous tool with the exception of this

time I'm going to assign a Custom Tool

uh from the browser tool class and this

is the result that I'm getting with jp4

I'm just going to copypaste the output

into my notion notebook so that you can

see it better I have to say that I'm

more than pleased with the results it

would take me at least an hour to read

latest posts on Lo and Lama then to

summarize them and take notes but CI

agents did all of this in less than a

minute this is a type of research that I

need to do a few times a day and also

this is the first time that I managed to

completely automate part of my work with

agents one thing that I noticed is that

sometimes even GPT 4 doesn't really

follow my instructions there are no

links to these projects in this output

and I asked for them but when I run the

script yesterday the agent successfully

included all the links and these outputs

were made on the same day but they're

formatted differently so output varies

and agents can act a little bit flaky

from time to time I also test the Gemini

Pro which offer offers a free API key

you can request it through a link that

I'm going to include in the description

box essentially you just need to import

special package from L chain you need to

load Gemini with this line and then

you're going to need to assign this llm

to every agent so Gemini output was a

little bit underwhelming the model

didn't understand the task instead it

wrote a bunch of generic text from its

training data which is really

unfortunate so let me know if you run

into different results I'm I'm really

curious and now let's talk about price I

rent The Script many times and as part

of my experiments but on this particular

day 11th of January I remember that I

ran the script four times which means

that I paid around 30 cents every time I

ran it so as you can tell it adds up

pretty quickly and of course this is gp4

how to avoid paying for all these pricey

API calls and how to keep your team of

agents and conversation private yes

local model mod so let's talk about that

right now I've tested 13 open source

models in total and only one was able to

understand the Tas and completed in some

sense all the other models failed which

was a little bit surprising to me

because I expected a little bit more I

guess from these local models and I'll

reveal which ones perform the best and

the worst but first let me show you how

to run local models through all llama

the most important thing to keep in mind

is that you should have at least 8 GB of

RAM available to run models with 7

billion parameters 16 GB for 13 billion

and 32 GB to run 33 billion parameter

models having said that even though I

have a laptop with 16 GB of RAM I

couldn't run Falcon that only has 7

billion parameters and vuna with 13

billion parameters whenever I try to run

these two models my laptop would freeze

and crash so something to keep in mind

if you already installed a llama and you

downloaded a specific model you can very

easily instruct crew AI to use local

model instead of openi with this line

just import a llama from Lang chain and

set set the open source model that you

previously downloaded once you do that

you should also pass that model to all

the agents otherwise they're going to

default to CH GPD among 30 models that I

experimented with the worst performing

ones were llama 2 Series with seven b

parameters and another model that

performed poorly was 52 the smallest of

all of them latu was definitely

struggling to produce any type of

meaningful output and Fu was just losing

it it was painful to watch the best

performing model with seven bilder

parameters in my opinion was open chat

which produced an output that sounds

very newsletter the only downside was

that it didn't actually contain any data

from from local llama subreddit which

was the whole point obviously the model

didn't understand what the task is

similarly but with a lot more emojis

mistol produced a generic but fine

newsletter this is basically Mistro's

training data none of these projects are

companies were part of local subred

discussions which means that mistal

agents didn't understand what the task

is and open hermis and new hermis had a

similar output all of these outputs are

are the best attempts they were even

worst outputs since the results weren't

really that great I played with

different prompts variations of prompts

but that didn't really achieve anything

also I changed the model file that comes

with local models played with parameters

for each of the models and I added a

system PRT that specifically references

local llama but again no improvement my

agents still didn't understand what the

task is so the only remaining idea I had

was to run more modles with 13 billion

parameters which is the upper limit for

my laptop so I first ran llama 13

billion chat and text bottles not

quantized but full Precision models my

assumption was that these models are

going to be better at generating a

newsletter because they're bigger models

but I was wrong the output didn't look

better than let's say open chat or

mistro and the problem was still there

agents couldn't really understand what

the task is so I ended up with a bunch

of generic texts about self-driving cars

as usual again nothing even remotely

similar to actual Reddit conversations

on logal Lama so out of pure desperation

I tried a regular llama 13 billion

parameters model a model that is not

even fine-tuned for anything my

expectations were really low but to my

surprise it was the only model that

actually took into consideration this

great data from the subreddit it didn't

really sound like a newsletter or a Blog

but at least the names were there

together with some random free flung

thoughts which I found a little bit

surprising so there you have it you can

find my notes about which local modes to

avoid and which ones were okay together

with all the code on my GitHub which

I'll link down below and I'm curious

have you tried crew Ai and what were

your experiences like thank you for

watching and see you in the next one