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N8N Automated Twitter Reply Bot Workflow

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Created by: Max || dziura

Max

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Last update a month ago

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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

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

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