Twitter / X

Build AI-powered Twitter bots to automate posting, analyze trends, and interact with users seamlessly using KLGG’s modular templates.

KLGG simplifies the development of AI-powered tools for Twitter, enabling you to automate tasks like posting updates, replying to mentions, and analyzing content. Using modular Input, Processing, and Output blocks, you can integrate AI providers such as OpenAI, Claude, or Gemini to create smarter and more efficient bots.

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Step 1: Installation and Setup

Install KLGG and the Twitter API library:

bashCopy code# Install KLGG
pip install KLGG

# Install Twitter API wrapper (Tweepy)
pip install tweepy

# Install AI provider SDK (e.g., OpenAI)
pip install openai

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Step 2: Set Up Your Twitter API Keys

1. Go to the Twitter Developer Portal and create a new app.

2. Generate the following API credentials:

   • API Key

   • API Secret Key

   • Access Token

   • Access Token Secret

3. Save these keys securely for use in your bot.

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Step 3: Full Twitter AI Bot Code

This example will create a Twitter bot that listens to mentions, processes them using OpenAI’s GPT-4, and replies with an AI-generated response.

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Complete Code

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pythonCopy codeimport tweepy
from KLGG import InputBlock, ProcessingBlock, OutputBlock
import time

# Twitter API Credentials
API_KEY = "YOUR_API_KEY"
API_SECRET = "YOUR_API_SECRET"
ACCESS_TOKEN = "YOUR_ACCESS_TOKEN"
ACCESS_SECRET = "YOUR_ACCESS_SECRET"

# Initialize Tweepy Client
auth = tweepy.OAuth1UserHandler(API_KEY, API_SECRET, ACCESS_TOKEN, ACCESS_SECRET)
api = tweepy.API(auth)

# KLGG Workflow Blocks
class TwitterAgent:
    def __init__(self):
        # Input Block: Fetch mentions from Twitter
        self.input_block = InputBlock("twitter_mentions", api=api)

        # Processing Block: Use OpenAI GPT-4 to generate a reply
        self.processing_block = ProcessingBlock("openai", model="gpt-4", api_key="YOUR_OPENAI_API_KEY")

        # Output Block: Send replies back to Twitter
        self.output_block = OutputBlock("twitter_reply", api=api)

    def process_mentions(self):
        print("Checking for new mentions...")
        mentions = self.input_block.fetch()

        for mention in mentions:
            print(f"Processing mention: {mention.text} from {mention.user.screen_name}")

            # Input: User's tweet content
            self.input_block.data = {"prompt": f"Reply to this tweet: {mention.text}"}

            # Process: Generate AI-based reply
            response = self.processing_block.process()
            print(f"Generated Reply: {response}")

            # Output: Reply to the user's mention
            self.output_block.send(reply=response, tweet_id=mention.id)

# Main Loop
def main():
    twitter_agent = TwitterAgent()

    while True:
        try:
            twitter_agent.process_mentions()
            print("Waiting for 60 seconds...")
            time.sleep(60)  # Check for mentions every minute
        except Exception as e:
            print(f"Error: {e}")
            time.sleep(60)

if __name__ == "__main__":
    main()

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Step 4: How It Works

1. Input Block: Fetches mentions from Twitter.

2. Processing Block: Sends the input (tweet) to OpenAI’s GPT-4 and generates a response.

3. Output Block: Replies to the user on Twitter with the AI-generated content.

4. Main Loop: Runs the workflow continuously, checking for mentions every minute.

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Step 5: Run the Bot

Save the script as twitter_ai_bot.py and run it:

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bashCopy codepython twitter_ai_bot.py

Your bot will now monitor mentions on Twitter and respond with AI-generated replies.

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Extending the Template

Here’s how you can extend the Twitter bot for additional use cases:

1. Automate Posting Content Schedule tweets or post updates dynamically using AI-generated content:

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pythonCopy codecontent = ProcessingBlock("openai", model="gpt-4", api_key="YOUR_API_KEY", prompt="Generate a motivational quote.")
api.update_status(content.process())

1. Analyze Trending Topics Fetch trending hashtags and use AI to create context-aware posts:

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pythonCopy codetrends = api.get_place_trends(1)  # WOEID 1 = Worldwide
print(f"Trending: {trends[0]['name']}")

1. Monitor Keywords and Auto-Respond Track specific keywords and respond with relevant messages:

pythonCopy codetweets = api.search_tweets(q="KLGG", count=10)
for tweet in tweets:
    reply = ProcessingBlock("openai", model="gpt-4", prompt=f"Generate a reply for this tweet: {tweet.text}")
    api.update_status(f"@{tweet.user.screen_name} {reply.process()}", in_reply_to_status_id=tweet.id)

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Use Cases for Twitter Templates

• Customer Support: Provide real-time responses to queries and complaints.

• Content Automation: Schedule AI-generated posts for marketing, news, or updates.

• Trend Analysis: Use AI to generate engaging content around trending hashtags.

• Engagement Boosting: Reply to mentions, engage with followers, and build a stronger community presence.

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Conclusion

KLGG’s Twitter templates provide a flexible and modular approach to building AI-powered bots. Automate replies, generate posts, and analyze content with ease using KLGG workflows. Start creating smarter, faster Twitter tools—one block at a time.