CrewAI Multi-Agent System for Writing Article from YouTube Movies

Introduction

Within the period of an evolving digital world, automation redefines how people strategy on a regular basis duties, together with analysis, content material creation and far more. A brand new and thrilling software rising on this discipline is CrewAI, which permits a number of AI brokers to work collectively, fixing complicated issues with enhanced creativity and decision-making by prioritising collaborative intelligence.

On this article, we are going to dive deep into understanding the whole CrewAI multi-agent system and the way this technique operates whereas creating a mission to write down articles from YouTube movies. This hands-on mission will assist you to acquire worthwhile insights and leverage AI to generate and streamline content material creation whereas sustaining a human contact. Let’s get began.

Undertaking Goal

Image this: You might be a part of Analytics Vidhya, a platform identified for its intensive library of articles on Information Science, Machine Studying, AI, and extra. When you’re doubtless aware of these sources, Analytics Vidhya additionally produces a variety of YouTube content material: informative movies, podcasts with AI leaders, information updates, and extra. Isn’t it an incredible thought to have articles summarising the content material from these movies as properly? Nonetheless, the method of manually writing such articles is resource-intensive and complicated. It requires a website knowledgeable first to look at and validate the movies, adopted by a content material author to construction and draft the articles. Think about scaling this course of for 1000’s of movies: communication breakdowns, delays, and potential errors grow to be inevitable.

To beat this, AI brokers can streamline the complete workflow. These brokers can effectively analyze video content material, validate it, and generate articles—quick, dependable, and with minimal human intervention. Let’s discover how this may be achieved utilizing AI!

Additionally learn: Construct an AI Analysis Assistant Utilizing CrewAI and Composio

What’s an AI Agent?

An agent is a software program system that understands its atmosphere, processes knowledge and takes actions to realize its aims. It doesn’t at all times should be clever; as an example, a movement sensor gentle acts as an agent by mechanically turning on when it detects motion.

However, an AI Agent is a extra superior type of an agent that includes Synthetic intelligence particularly designed to automate duties, enabling it to function with out direct human intervention. The AI Agent additionally capabilities by observing its atmosphere, processing the acquired data, and executing actions or making choices based mostly on predefined aims. Nonetheless, it additionally learns from its atmosphere and improves its decision-making over time. AI Brokers are engineered to duplicate human-like decision-making and deal with extra complicated duties by studying from knowledge, figuring out patterns, and deriving insights or making predictions to perform assigned duties effectively.

You possibly can understand it as an clever assistant on which you don’t have to have fixed supervision. For instance, an AI agent can course of massive quantities of information, deal with buyer queries, do analysis duties, write blogs, and create content material. Probably the most fascinating factor about these brokers is that they will work repeatedly with none breaks, analyze bigger quantities of information than people, and simply adapt to altering environments and conditions based mostly on the outlined guidelines or ML algorithms they’re designed with. By combining a number of AI brokers, as we are going to do on this article, the system’s collective intelligence will increase considerably, permitting it to deal with much more complicated duties easily and successfully.

If you wish to know concerning the Agentic AI design sample then discover this: 4 Agentic AI design sample

What’s CrewAI?

CrewAI is a brand new, rising, and progressive platform designed to allow the collaboration of a number of AI brokers effectively and seamlessly (CrewAI Multi-Agent System). With its framework, it permits customers to make use of the facility of collective intelligence, permitting AI brokers to work collectively on complicated duties, which might be difficult and costly for a single agent to deal with. With CrewAI you possibly can create your system with specialised brokers, every with its personal experience, to carry out varied duties. This collaborative strategy enhances decision-making and creativity and in addition streamlines workflows, making it simpler to realize desired outcomes.

Additionally learn: Constructing an Agentic Workflow with CrewAI and Groq

How are we utilizing Agent for this Undertaking?

The core idea of this mission entails deciding on a YouTube video, extracting all related content material from it, processing that data, and writing a structured article based mostly on the perceived content material.

To create a a number of AI-Agent system, we usually divide it into three elements: Brokers, Duties, and Instruments.

• Brokers: Clever entities designed to carry out particular roles throughout the system. Every agent possesses distinctive capabilities, permitting for environment friendly dealing with of varied elements of the mission.
• Duties: Clearly outlined aims assigned to brokers. These duties make sure that every agent understands its duties, optimizing workflow and productiveness.
• Instruments: Sources and applied sciences that help brokers in finishing their duties. These could embody software program purposes, knowledge sources, APIs, and custom-designed options geared toward enhancing the brokers’ effectivity, effectiveness, and workflow.

On this system, we are going to implement two brokers: one will function a website knowledgeable agent, tasked with researching the YouTube video and gathering content material utilizing an assigned software, whereas the opposite agent will operate as an article content material author, answerable for making a structured article based mostly on the data supplied by the area knowledgeable agent. This knowledge circulate from one agent to a different is known as a sequential course of. Whereas we are going to deal with this strategy, different processes, equivalent to hierarchical processes, can be mentioned in a later article.

Circulate of the Undertaking

Under is the whole circulate for the mission:

Additionally learn: The right way to Construct an AI Pair Programmer with CrewAI?

Let’s Begin Writing Article from YouTube Movies

To construct CrewAI Multi-Agent System, set up Python in your system. It’s beneficial that you just use Anaconda or Miniconda to handle environments. 

I’m creating this on a Linux system, utilizing Visible Studio Code (VS Code) as my IDE. VS Code is light-weight and versatile, helps a number of programming languages, and its built-in terminal simplifies improvement. It’s additionally a most well-liked alternative for a lot of builders—and actually, I believe it simply seems fairly cool. 

Now that you’ve got all the required instruments let’s dive into the article and get began!

Step 01: Setup the Setting and Necessities

On this step, we’ll begin by creating the necessities.txt file to specify all of the libraries crucial for the mission. After that, we’ll arrange the .env file, which can maintain atmosphere variables equivalent to API keys and different confidential data. Lastly, we are going to create a digital atmosphere. It is a greatest observe in improvement as a result of it locks the variations of the libraries and applied sciences used, stopping future errors as a consequence of model mismatches.

1. Create a Folder
Create a folder anyplace in your system, for instance, identify it Multi_AI_Agent_Article_Project.

2. Open in VS Code
Open VS Code, then open the folder you simply created within the VS Code Explorer.

3. Create necessities.txt
Contained in the folder, create a file named necessities.txt and paste the next textual content inside it, then save the file:

crewai
crewai_tools
load_dotenv
langchain-huggingface

4. Generate OpenAI API Key
For this mission, we’ll use an LLM from OpenAI, so you might want to generate an OpenAI API Key. You possibly can comply with the hyperlink: The right way to get your individual OpenAI API Key to get directions. The price of executing this mission is round $0.04, making it fairly cheap. If you have already got an API key, be happy to proceed to the following step.

5. Create .env File
Create one other file named .env and save your OpenAI API key on this format, then save the file:

OPENAI_API_KEY= paste_your_api_key_here

6. Create Digital Setting
Open the terminal in VS Code, guaranteeing it’s opened contained in the Multi_AI_Agent_Article_Project folder. Then sort the next command to create a digital atmosphere:

conda create -p venv python=3.10

This command will create a Conda digital atmosphere within the venv listing throughout the mission folder as an alternative of the default Conda listing. The -p flag specifies the trail the place the atmosphere can be created (on this case, the venv folder) and the Python model is ready to three.10.

7. Activate Digital Setting
Use this command to activate the digital atmosphere:

conda activate venv/

8. Set up Required Libraries
To put in all the required libraries from the necessities.txt file, run the next command:

pip set up -r necessities.txt

As soon as all of the steps above are full, you’re able to proceed with constructing the precise Multi AI Agent system!

Step 02: Create Instruments

Create a file named instruments.py and paste the code beneath, then save the file:

from crewai_tools import YoutubeChannelSearchTool

Code Clarification:

Right here, we’re importing the YoutubeChannelSearchTool from CrewAI. This software permits an AI agent to look and collect video knowledge, titles, descriptions, and metadata from a specified YouTube channel, enabling environment friendly content material analysis and evaluation.

#Initialize the software with a particular YouTube channel deal with to focus on your search
yt_channel_tool = YoutubeChannelSearchTool(youtube_channel_handle="@SystemDesignSchool")

Code Clarification:

Subsequent, we initialize an object for the software and supply the YouTube deal with for the channel SystemDesignSchool. We’re utilizing this channel as a result of it has solely 6 movies, which helps save time and sources when the system processes the movies.

You possibly can be taught extra about this software from the next hyperlink.

Step 03: Create Brokers

Now, create one other file named brokers.py and paste the next code into it, then save the file:

from crewai import Agent, LLM
from instruments import yt_channel_tool
import os

from dotenv import load_dotenv
load_dotenv() # Load atmosphere variables from the .env file

#Organising atmosphere variables for OpenAI API entry
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["OPENAI_MODEL_NAME"] = "gpt-4-0125-preview"

Code Clarification:

Right here, we import the required modules to create AI brokers and configure atmosphere variables. We use dotenv to load the OPENAI_API_KEY from the .env file we created earlier. We additionally set the mannequin identify to gpt-0125-preview.

# Creating the Area Professional Agent, specialised in extracting related content material from YouTube movies
domain_expert = Agent(
	function="Area Professional content material researcher from Youtube Movies",
	purpose="To get all of the related Content material from the Youtube Video for the subject: {matter}",
	verbose=True,
	reminiscence=True,
	backstory=(
    	'Area Professional in understanding and greedy movies from Youtube that are about Synthetic Intelligence,'
    	'Information Science, Machine Studying, Deep Studying, and Generative AI and offering content material.'
	),
	software = [yt_channel_tool],
	allow_delegation=True
)

# Creating the Content material Author Agent, answerable for writing a technical article based mostly on YouTube video content material
content_writer = Agent(
	function="Article Author",
	purpose="Write a structured and informative technical article concerning the Youtube Video matter: {matter}",
	verbose=True,
	reminiscence=True,
	backstory=(
    	'Expert at breaking down complicated technical content material into clear, participating tales that educate readers, whereas presenting discoveries in an accessible and easy-to-understand format.'
	),
	software = [yt_channel_tool],
	allow_delegation=False
)

Code Clarification:

Right here, we create two brokers. The Area Professional agent is designed to extract related content material from YouTube movies on matters like AI and Machine Studying utilizing the yt_channel_tool. The Content material Author agent focuses on writing structured, technical articles based mostly on the supplied content material.

Each brokers have reminiscence=True, permitting them to retain data and recollect it when wanted. verbose=True permits detailed logging to assist perceive the agent’s actions. allow_delegation=True for the Area Professional agent permits activity delegation, whereas it’s set to False for the Content material Author agent, as the author doesn’t have to delegate duties. Defining their roles, targets, and backstory ensures that the brokers deal with their particular duties effectively.

You possibly can be taught extra about brokers from the next hyperlink.

Step 04: Create Duties

Create one other file named duties.py and paste the next code into it, then save the file:

from crewai import Job
from instruments import yt_channel_tool
from brokers import domain_expert, content_writer

Code Clarification:

This code imports the Job class from CrewAI for creating duties, the yt_channel_tool from the instruments.py file, and the domain_expert and content_writer brokers from the brokers.py file, which can carry out particular roles within the duties.

# Area Professional - ReseaAIrch Job
research_task = Job(
	description=(
    	"Get and establish the Youtube Video {matter}."
    	"Get a completely detailed, knowledgeable stage content material/details about the video from the channel."
	),
	expected_output="An in depth 6 paragraphs lengthy structured report based mostly on the {matter} of video content material.",
	instruments = [yt_channel_tool],
	agent=domain_expert
)


# Content material Author - Article Writing Job
article_writing_task = Job(
	description=(
    	"Get content material/data from the Youtube Channel's Video on the subject: {matter}."
	),
	expected_output="Summarize the content material type the Youtube Channel and write an in depth lengthy structured Article from the Youtube Chanel Video based mostly on the {matter}",
	instruments = [yt_channel_tool],
	agent=content_writer,
	async_execution=False,
	output_file="new-article.md"
)

Code Clarification:

This part defines two duties: the area knowledgeable analysis activity and the content material author’s article writing activity. The analysis activity focuses on figuring out a YouTube video on the desired matter from the given channel and gathering detailed data to create a structured report. The article writing activity summarizes the video’s content material and creates a structured article based mostly on that data. Each brokers are assigned the yt_channel_tool to entry YouTube knowledge. Within the article_writing_task, async_execution=False ensures that the duty is accomplished totally earlier than shifting to the following step, stopping parallel execution of duties, and the output can be saved within the “new-article.md” file.

You possibly can be taught extra about duties from the next hyperlink.

Step 05: Execution of the Code

Now, as the ultimate step, create one other file named crew.py and paste the next code into it, then save the file:

from crewai import Crew,Course of
from brokers import domain_expert, content_writer
from duties import research_task, article_writing_task

Code Clarification:

This code imports the Crew and Course of lessons from crewai to handle a number of brokers together with the outlined brokers and their duties created in earlier steps, enabling them to work collectively on the mission.

# Making a Crew occasion with specified brokers, duties, and configuration
crew = Crew(
	brokers=[domain_expert, content_writer],
	duties=[research_task, article_writing_task],
	course of=Course of.sequential,
	reminiscence=True,
	cache=True,
	max_rpm=100,
	share_crew=True
)

# Kicking Off the Job
consequence = crew.kickoff(inputs={'matter': 'File Storage VS Object Storage'})
print(consequence)

Code Clarification:

This code creates a Crew occasion that mixes the 2 brokers, the Area Professional and the Content material Author, with their particular duties. Now we have outlined the method as sequential, that means the duties will run one after the opposite. The reminiscence and cache settings are each set to True to recollect previous data and enhance effectivity. The max_rpm parameter units the utmost variety of requests the crew can deal with per minute, guaranteeing environment friendly processing with out overwhelming the system. The share_crew possibility permits the crew to be shared for collaboration or reuse, permitting different processes or customers to entry and make the most of the identical crew configuration for various duties.

The crew is then initiated with the desired matter, which the brokers will seek for on the YouTube channel, carry out their duties, and print the consequence.

You possibly can be taught extra from the next hyperlink.

Closing Output

Screenshot of the output file “new-article.md”:

To be taught extra about What brokers are, go to the next Article: AI Agent.

Conclusion

Utilizing CrewAI Multi-Agent system considerably improves the effectivity and high quality of content material era from YouTube movies. We optimize the workflow of the mission by breaking it into brokers, instruments and duties which considerably reduces the effort and time required to create high-quality articles. The Area Professional agent gathers key data from the YouTube movies, whereas the Content material Author processes this data and writes well-organized articles.

This collaborative strategy reduces the necessity for guide effort whereas guaranteeing constant outcomes that keep the core message of the unique content material. As automation applied sciences progress, embracing these instruments permits content material creators to focus extra on strategic and artistic elements of their work.

To remain forward on this evolving discipline of Agentic AI, enroll in our Agentic AI Pioneer Program in the present day!