N8N Automated Twitter Reply Bot Workflow

For latest version, check: dziura.online/automation

Latest documentation can be find here

You must have Apify community node installed before pasting the JSON to your workflow. 

Overview

This n8n workflow creates an intelligent Twitter/X reply bot that automatically scrapes tweets based on keywords or communities, analyzes them using AI, generates contextually appropriate replies, and posts them while avoiding duplicates. The bot operates on a schedule with intelligent timing and retry mechanisms.

Key Features

• Automated tweet scraping from Twitter/X using Apify actors

• AI-powered reply generation using LLM (Large Language Model)

• Duplicate prevention via MongoDB storage

• Smart scheduling with timezone awareness and natural posting patterns

• Retry mechanism with failure tracking

• Telegram notifications for status updates

• Manual trigger option via Telegram command

Required Credentials & Setup

1. Telegram Bot

• Create a bot via @BotFather on Telegram

• Get your Telegram chat ID to receive status messages

• Credential needed: Telegram account (Bot token)

2. MongoDB Database

• Set up a MongoDB database to store replied tweets and prevent duplicates

• Create a collection (default name: collection_name)

• Credential needed: MongoDB account (Connection string)

• Tutorial: MongoDB Connection Guide

3. Apify Account

• Sign up at Apify.com

• Primary actors used:

  • Search Actor: api-ninja/x-twitter-advanced-search - For keyword-based tweet scraping (ID: 0oVSlMlAX47R2EyoP)

  • Community Actor: api-ninja/x-twitter-community-search-post-scraper - For community-based tweet scraping (ID: upbwCMnBATzmzcaNu)

• Credential needed: Apify account (API token)

4. OpenRouter (LLM Provider)

• Sign up at OpenRouter.ai

• Used for AI-powered tweet analysis and reply generation

• Model used: x-ai/grok-3 (configurable)

• Credential needed: OpenRouter account (API key)

5. Twitter/X API

• Set up developer account at developer.x.com

• Note: Free tier limited to ~17 posts per day

• Credential needed: X account (OAuth2 credentials)

Workflow Components

Trigger Nodes

1. Schedule Trigger

• Purpose: Runs automatically every 20 minutes

• Smart timing: Only active between 7 AM - 11:59 PM (configurable timezone)

• Randomization: Built-in probability control (~28% execution chance) to mimic natural posting patterns

2. Manual Trigger

• Purpose: Manual execution for testing

3. Telegram Trigger

• Purpose: Manual execution via /reply command in Telegram

• Usage: Send /reply to your bot to trigger the workflow manually

Data Processing Flow

1. MongoDB Query (Find documents)

• Purpose: Retrieves previously replied tweet IDs to avoid duplicates

• Collection: collection_name (configure to match your setup)

• Projection: Only fetches tweet_id field for efficiency

2. Data Aggregation (Aggregate1)

• Purpose: Consolidates tweet IDs into a single array for filtering

3. Keyword/Community Selection (Keyword/Community List)

• Purpose: Defines search terms and communities

• Configuration: Edit the JSON to include your keywords and Twitter community IDs

Format:{

  "keyword_community_list": [

    "SaaS",

    "Entrepreneur", 

    "1488663855127535616"  // Community ID (19-digit number)

  ],

  "failure": 0

}

4. Random Selection (Randomized community, keyword)

• Purpose: Randomly selects one item from the list to ensure variety

5. Routing Logic (If4)

• Purpose: Determines whether to use Community search or Keyword search

• Logic: Uses regex to detect 19-digit community IDs vs keywords

Tweet Scraping (Apify Actors)

Community Search Actor

• Actor: api-ninja/x-twitter-community-search-post-scraper

• Purpose: Scrapes tweets from specific Twitter communities

Configuration:{

  "communityIds": ["COMMUNITY_ID"],

  "numberOfTweets": 40

}

Search Actor

• Actor: api-ninja/x-twitter-advanced-search

• Purpose: Scrapes tweets based on keywords

Configuration:{

  "contentLanguage": "en",

  "engagementMinLikes": 10,

  "engagementMinReplies": 5,

  "numberOfTweets": 20,

  "query": "KEYWORD",

  "timeWithinTime": "2d",

  "tweetTypes": ["original"],

  "usersBlueVerifiedOnly": true

}

Filtering System (Community filter)

The workflow applies multiple filters to ensure high-quality replies:

• Text length: >60 characters (substantial content)

• Follower count: >100 followers (audience reach)

• Engagement: >10 likes, >3 replies (proven engagement)

• Language: English only

• Views: >100 views (visibility)

• Duplicate check: Not previously replied to

• Recency: Within 2 days (configurable in actor settings)

AI-Powered Reply Generation

LLM Chain (Basic LLM Chain)

• Purpose: Analyzes filtered tweets and generates contextually appropriate replies

• Model: Grok-3 via OpenRouter (configurable)

• Features:

  • Engagement potential scoring

  • User authority analysis

  • Timing optimization

  • Multiple reply styles (witty, informative, supportive, etc.)

  • <100 character limit for optimal engagement

Output Parser (Structured Output Parser)

• Purpose: Ensures consistent JSON output format

Schema:{

  "selected_tweet_id": "tweet_id_here",

  "screen_name": "author_screen_name", 

  "reply": "generated_reply_here"

}

Posting & Notification System

Twitter Posting (Create Tweet)

• Purpose: Posts the generated reply as a Twitter response

• Error handling: Catches API limitations and rate limits

Status Notifications

• Success: Notifies via Telegram with tweet link and reply text

• Failure: Notifies about API limitations or errors

• Format: HTML-formatted messages with clickable links

Database Storage (Insert documents)

• Purpose: Saves successful replies to prevent future duplicates

• Fields stored: tweet_id, screen_name, reply, tweet_url, timestamp

Retry Mechanism

The workflow includes intelligent retry logic:

Failure Counter (If5, Increment Failure Counter1)

• Logic: If no suitable tweets found, increment failure counter

• Retry limit: Maximum 3 retries with different random keywords

• Wait time: 3-second delay between retries

Final Failure Notification

• Trigger: After 4 failed attempts

• Action: Sends Telegram notification about unsuccessful search

• Recovery: Manual retry available via /reply command

Configuration Guide

Essential Settings to Modify

1. MongoDB Collection Name: Update collection_name in MongoDB nodes

2. Telegram Chat ID: Replace 11111111111 with your actual chat ID

3. Keywords/Communities: Edit the list in Keyword/Community List node

4. Timezone: Update timezone in Code node (currently set to Europe/Kyiv)

5. Actor Selection: Enable only one actor (Community OR Search) based on your needs

Filter Customization

Adjust filters in Community filter node based on your requirements:

• Minimum engagement thresholds

• Text length requirements

• Time windows

• Language preferences

LLM Customization

Modify the AI prompt in Basic LLM Chain to:

• Change reply style and tone

• Adjust engagement criteria

• Modify scoring algorithms

• Set different character limits

Usage Tips

1. Start small: Begin with a few high-quality keywords/communities

2. Monitor performance: Use Telegram notifications to track success rates

3. Adjust filters: Fine-tune based on the quality of generated replies

4. Respect limits: Twitter's free tier allows ~17 posts/day

5. Test manually: Use /reply command for testing before scheduling

Troubleshooting

Common Issues

1. No tweets found: Adjust filter criteria or check keywords

2. API rate limits: Reduce posting frequency or upgrade Twitter API plan

3. MongoDB connection: Verify connection string and collection name

4. Apify quota: Monitor Apify usage limits

5. LLM failures: Check OpenRouter credits and model availability

Best Practices

• Monitor your bot's replies for quality and appropriateness

• Regularly update keywords to stay relevant

• Keep an eye on engagement metrics

• Adjust timing based on your audience's activity patterns

• Maintain a balanced posting frequency to avoid appearing spammy

Documentation Links

• Full Documentation: Google Doc Guide

• Latest Version: dziura.online/automation

• MongoDB Setup Tutorial: YouTube Guide

This workflow provides a comprehensive solution for automated, intelligent Twitter engagement while maintaining quality and avoiding spam-like behavior.