AI Content Writing Tools for Automated Blog, LinkedIn, and Twitter Posts | Custom AI Agents & Automation
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Learn how to use AI content writing tools to effortlessly create LinkedIn posts, Twitter tweets, and blog articles! π In this video, we demonstrate how AI agents and automation can streamline your content creation process, from crafting engaging social media posts to writing compelling blog content.
Discover the power of AI content creation with Python and see how AI prompts can help you generate high-quality content quickly and efficiently. Perfect for marketers, content creators, and businesses looking to enhance their online presence with cutting-edge AI tools.
Donβt miss out on transforming your content strategy with AI!
---requirements.txt----
crewai[tools]
youtube-transcript-api
yt-dlp
crewai
python-dotenv
deprecated==1.2.13
--code--
from dotenv import load_dotenv
load_dotenv()
#load env variable
#import module
from crewai import Agent, Task
from crewai import Crew, Process
from crewai_tools import YoutubeChannelSearchTool
youtube_tool = YoutubeChannelSearchTool(youtube_channel_handle='@name')
#Agents
#Topic Researcher Agent
topic_researcher = Agent(role='Topic Researcher',
goal='Search for relevant videos on the topic {topic} from the provided YouTube channel',
verbose=True,
backstory='Expert in finding and analyzing relevant content from YouTube channels, specializing in SEO strategies, keyword optimization, content ranking, and search engine algorithm updates.',
tools=[youtube_tool],
allow_delegation=True)
#Linkedin_writer_agent
linkdin_post=Agent(role='LinkedIn Post Creator',
goal='Create a concise LinkedIn post summary from the transcription provided by the Topic Researcher.',
verbose=True,
memory=True,
backstory='Expert in crafting engaging LinkedIn posts that summarize complex topics and include trending hashtags for maximum visibility.',
allow_delegation=False)
#twitter_writer_agent
twitter_agent= Agent(role='Twitter Content Creator',
goal='Create a short tweet from the transcription provided by the Topic Researcher that capture key points and insights',
verbose=True,
memory=True,
backstory='Specializes in distilling complex information into concise, impactful tweets that resonate with a tech-savvy audience',
allow_delegation=False)
#blog_writer_agent
blog_writer = Agent(role='Blog Writer',
goal='Write a comprehensive blog post from the transcription provided by the Topic Researcher, covering all necessary sections',
verbose=True,
memory=True,
backstory='Experienced in creating in-depth, well-structured blog posts that explain technical concepts clearly and engage readers from introduction to conclusion.',
allow_delegation=False)
#Tasks
#research_task
research_task = Task(
description='Identify and analyze videos on the topic {topic} from the specified YouTube channel.',
expected_output='A complete word by word report on the most relevant video found on the topic {topic}.',
agent=topic_researcher,
tools=[youtube_tool]
)
#blog_task
blog_task = Task(
description=''' Write a comprehensive blog post based on the transcription provided by the Topic Researcher.
The article must include an introduction , step-by-step guides, and conclusion.
The overall content must be about 1200 words long.''',
expected_output='A markdown-formatted of the blog',
agent=blog_writer,
output_file='blog-post.md'
)
#linkedin_task
linkdin_task = Task(
description='Create a LinkedIn post summarizing the key points from the transcription provided by the Topic Researcher, including relevant hashtags.',
expected_output='A markdown-formatted of the LinkedIn post',
agent=linkdin_post,
output_file='linkdin-post.md'
)
#twitter_task
twitter_task = Task(
description='Create a tweet from the transcription provided by the Topic Researcher, including relevant hashtags.',
expected_output='A markdown-formatted of the Twitter post',
agent=twitter_agent,
output_file='tweet.md'
)
mycrew = Crew(
agents=[topic_researcher, linkdin_post, twitter_agent, blog_writer],
tasks=[research_task,blog_task, linkdin_task, twitter_task ],
verbose=True, process=Process.sequential,
memory=True,
cache=True,
max_rpm=100,
share_crew=True
)
topic = '6 Easy SEO Techniques (That Work)'
result = mycrew.kickoff(inputs={'topic': topic})
print('---'*20)
print(result)