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Generate LinkedIn posts from Wikipedia with GPT-4 summaries and Ideogram images

IncrementorsIncrementors
599 views
2/3/2026
PostgreSQLMySQLDatabaseCSVData Import
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Wikipedia to LinkedIn AI Content Poster with Image via Bright Data

📋 Overview

Workflow Description: Automatically scrapes Wikipedia articles, generates AI-powered LinkedIn summaries with custom images, and posts professional content to LinkedIn using Bright Data extraction and intelligent content optimization.

🚀 How It Works

The workflow follows these simple steps:

  1. Article Input: User submits a Wikipedia article name through a simple form interface
  2. Data Extraction: Bright Data scrapes the Wikipedia article content including title and full text
  3. AI Summarization: Advanced AI models (OpenAI GPT-4 or Claude) create professional LinkedIn-optimized summaries under 2000 characters
  4. Image Generation: Ideogram AI creates relevant visual content based on the article summary
  5. LinkedIn Publishing: Automatically posts the summary with generated image to your LinkedIn profile
  6. URL Generation: Provides a shareable LinkedIn post URL for easy access and sharing

⚡ Setup Requirements

Estimated Setup Time: 10-15 minutes

Prerequisites

  • n8n instance (self-hosted or cloud)
  • Bright Data account with Wikipedia dataset access
  • OpenAI API account (for GPT-4 access)
  • Anthropic API account (for Claude access - optional)
  • Ideogram AI account (for image generation)
  • LinkedIn account with API access

🔧 Configuration Steps

Step 1: Import Workflow

  1. Copy the provided JSON workflow file
  2. In n8n: Navigate to Workflows → + Add workflow → Import from JSON
  3. Paste the JSON content and click Import
  4. Save the workflow with a descriptive name

Step 2: Configure API Credentials

🌐 Bright Data Setup

  • Go to Credentials → + Add credential → Bright Data API
  • Enter your Bright Data API token
  • Replace BRIGHT_DATA_API_KEY in all HTTP request nodes
  • Test the connection to ensure access

🤖 OpenAI Setup

  • Configure OpenAI credentials in n8n
  • Ensure GPT-4 model access
  • Link credentials to the "OpenAI Chat Model" node
  • Test API connectivity

🎨 Ideogram AI Setup

  • Obtain Ideogram AI API key
  • Replace IDEOGRAM_API_KEY in the "Image Generate" node
  • Configure image generation parameters
  • Test image generation functionality

💼 LinkedIn Setup

  • Set up LinkedIn OAuth2 credentials in n8n
  • Replace LINKEDIN_PROFILE_ID with your profile ID
  • Configure posting permissions
  • Test posting functionality

Step 3: Configure Workflow Parameters

Update Node Settings:

  • Form Trigger: Customize the form title and field labels as needed
  • AI Agent: Adjust the system message for different content styles
  • Image Generate: Modify image resolution and rendering speed settings
  • LinkedIn Post: Configure additional fields like hashtags or mentions

Step 4: Test the Workflow

Testing Recommendations:

  • Start with a simple Wikipedia article (e.g., "Artificial Intelligence")
  • Monitor each node execution for errors
  • Verify the generated summary quality
  • Check image generation and LinkedIn posting
  • Confirm the final LinkedIn URL generation

🎯 Usage Instructions

Running the Workflow

  1. Access the Form: Use the generated webhook URL to access the submission form
  2. Enter Article Name: Type the exact Wikipedia article title you want to process
  3. Submit Request: Click submit to start the automated process
  4. Monitor Progress: Check the n8n execution log for real-time progress
  5. View Results: The workflow will return a LinkedIn post URL upon completion

Expected Output

📝 Content Summary

  • Professional LinkedIn-optimized text
  • Under 2000 characters
  • Engaging and informative tone
  • Bullet points for readability

🖼️ Generated Image

  • High-quality AI-generated visual
  • 1280x704 resolution
  • Relevant to article content
  • Professional appearance

🔗 LinkedIn Post

  • Published to your LinkedIn profile
  • Includes both text and image
  • Shareable public URL
  • Professional formatting

🛠️ Customization Options

Content Personalization

  • AI Prompts: Modify the system message in the AI Agent node to change writing style
  • Character Limits: Adjust summary length requirements
  • Tone Settings: Change from professional to casual or technical
  • Hashtag Integration: Add relevant hashtags to LinkedIn posts

Visual Customization

  • Image Style: Modify Ideogram prompts for different visual styles
  • Resolution: Change image dimensions based on LinkedIn requirements
  • Rendering Speed: Balance between speed and quality
  • Brand Elements: Include company logos or brand colors

🔍 Troubleshooting

Common Issues & Solutions

⚠️ Bright Data Connection Issues

  • Verify API key is correctly configured
  • Check dataset access permissions
  • Ensure sufficient API credits
  • Validate Wikipedia article exists

🤖 AI Processing Errors

  • Check OpenAI API quotas and limits
  • Verify model access permissions
  • Review input text length and format
  • Test with simpler article content

🖼️ Image Generation Failures

  • Validate Ideogram API key
  • Check image prompt content
  • Verify API usage limits
  • Test with shorter prompts

💼 LinkedIn Posting Issues

  • Re-authenticate LinkedIn OAuth
  • Check posting permissions
  • Verify profile ID configuration
  • Test with shorter content

⚡ Performance & Limitations

Expected Processing Times

  • Wikipedia Scraping: 30-60 seconds
  • AI Summarization: 15-30 seconds
  • Image Generation: 45-90 seconds
  • LinkedIn Posting: 10-15 seconds
  • Total Workflow: 2-4 minutes per article

Usage Recommendations

Best Practices:

  • Use well-known Wikipedia articles for better results
  • Monitor API usage across all services
  • Test content quality before bulk processing
  • Respect LinkedIn posting frequency limits
  • Keep backup of successful configurations

📊 Use Cases

📚 Educational Content

Create engaging educational posts from Wikipedia articles on science, history, or technology topics.

🏢 Thought Leadership

Transform complex topics into accessible LinkedIn content to establish industry expertise.

📰 Content Marketing

Generate regular, informative posts to maintain active LinkedIn presence with minimal effort.

🔬 Research Sharing

Quickly summarize and share research findings or scientific discoveries with your network.

🎉 Conclusion

This workflow provides a powerful, automated solution for creating professional LinkedIn content from Wikipedia articles. By combining web scraping, AI summarization, image generation, and social media posting, you can maintain an active and engaging LinkedIn presence with minimal manual effort.

The workflow is designed to be flexible and customizable, allowing you to adapt the content style, visual elements, and posting frequency to match your professional brand and audience preferences.

For any questions or support, please contact:
info@incrementors.com
or fill out this form: https://www.incrementors.com/contact-us/

n8n Workflow: Generate LinkedIn Posts from Wikipedia with AI Summaries and Ideogram Images

This n8n workflow automates the creation and publishing of engaging LinkedIn posts. It leverages AI to summarize Wikipedia articles and can potentially integrate with an image generation service (like Ideogram, as suggested by the directory name, though not explicitly in the JSON) to create rich, informative content. The workflow is triggered manually via an n8n form submission, allowing for controlled content generation.

What it does

This workflow simplifies the process of creating LinkedIn posts by:

  1. Triggering Manually: It starts when an n8n form is submitted, allowing you to initiate the content generation on demand.
  2. Fetching Data: It makes an HTTP request, likely to an external API or service, to retrieve information.
  3. Conditional Logic: It uses an "If" node to introduce conditional branching, allowing different actions based on the data retrieved or processed.
  4. AI-Powered Summarization: It utilizes an AI Agent (likely powered by LangChain) and an OpenAI Chat Model (or Anthropic Chat Model) to process and summarize content, presumably from the data fetched in the HTTP request.
  5. Structured Output: It employs a Structured Output Parser, potentially with an Auto-fixing Output Parser, to ensure the AI-generated content adheres to a specific format, making it ready for LinkedIn.
  6. Posting to LinkedIn: It publishes the generated content to a LinkedIn account.
  7. Introducing Delays: It includes a "Wait" node, which can be used to space out posts or introduce pauses in the workflow, for example, to avoid API rate limits or to simulate human-like posting intervals.
  8. Code Execution: It incorporates a "Code" node for custom JavaScript logic, which can be used for data manipulation, formatting, or more complex conditional operations not covered by standard nodes.
  9. Notes: Includes a sticky note for documentation within the workflow.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • LinkedIn Account: Credentials configured in n8n for your LinkedIn account to allow posting.
  • OpenAI API Key (or Anthropic API Key): Credentials for either OpenAI or Anthropic to power the AI Chat Model and Agent.
  • External API/Service: Access to the API or service targeted by the "HTTP Request" node (e.g., Wikipedia API, a custom data source).
  • LangChain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance, as it uses AI Agent, OpenAI Chat Model, Anthropic Chat Model, Auto-fixing Output Parser, and Structured Output Parser nodes.

Setup/Usage

  1. Import the Workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your LinkedIn credentials within n8n.
    • Set up your OpenAI or Anthropic credentials within n8n.
  3. Configure HTTP Request: Adjust the "HTTP Request" node (Node ID: 19) to point to your desired data source (e.g., Wikipedia API endpoint) and configure any necessary parameters or authentication.
  4. Configure AI Agent and Output Parsers: Review the "AI Agent" (Node ID: 1119), "OpenAI Chat Model" (Node ID: 1153) or "Anthropic Chat Model" (Node ID: 1145), and "Structured Output Parser" (Node ID: 1179) nodes. You may need to customize the prompts for the AI Agent and the schema for the Structured Output Parser to fit your specific content generation needs.
  5. Customize LinkedIn Post: Modify the "LinkedIn" node (Node ID: 367) to define how the AI-generated content should be structured for your LinkedIn posts (e.g., post text, image URLs if integrated with an image service, etc.).
  6. Adjust Conditional Logic: If needed, modify the conditions in the "If" node (Node ID: 20) to control the workflow's branching behavior.
  7. Configure Form Trigger: The "n8n Form Trigger" (Node ID: 1225) will provide a URL once activated. You can use this URL to manually trigger the workflow by submitting data to it.
  8. Activate the Workflow: Once configured, activate the workflow in n8n. You can then trigger it via the form URL.

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