AI Blog Post Generation from YouTube Video Transcripts to WordPress - n8n Workflow

Content Agencies and Marketers needing to repurpose video content rapidly.
Users seeking highly complex n8n templates utilizing AI chaining and local LLMs (like Ollama).
SEO specialists requiring automated content generation that includes external research (EEAT).
Developers looking for a detailed n8n workflow example covering text, data, and image manipulation.

Generating high-quality, long-form content consistently is challenging. This advanced n8n workflow solves this by fully automating the pipeline from a new YouTube video to a published WordPress blog post. Unlike simpler integrations, this solution uses a sophisticated chain of AI agents (powered by Ollama and LangChain) for deep analysis, structuring, writing, editing (EEAT compliance), and generating bespoke featured images via a service like Nano/Banana. The entire process is managed within a single, powerful n8n node sequence, ensuring the resulting content is both relevant to the video transcript and optimized for search engines. This is one of the most comprehensive n8n templates available for content repurposing.

The n8n workflow begins with the 'Check Youtube Channel' RSS Feed Read n8n trigger, monitoring for new videos, or alternatively, a Schedule Trigger/Google Sheets node providing input data.

1. Data Ingestion: The video URL is passed to an HTTP Request n8n node to 'Get Transcript', followed by a 'Clean Data' Code n8n node to prepare the text.


2. AI Planning: The 'Search intent and style' and 'Table of Contents' LangChain agents analyze the transcript to define the article's structure and tone. Structured Output Parsers ensure the AI output adheres to required formats.


3. Section Creation & Iteration: The 'Create the Sections' agent breaks the main article down. The workflow uses a 'Split Out1' n8n node to process content sections individually, which are then written by the 'Generate the Content' agent. These sections are collected and rebuilt using 'Merge' and 'Aggregate1' n8n nodes.


4. Refinement and EEAT: The aggregated content is passed through the 'EEAT' agent (Expertise, Authoritativeness, Trustworthiness) and subsequently the 'Content Editor' agent, often using the 'Search in Tavily' n8n node for real-time external research to enhance factual accuracy.


5. Metadata & Imagery: The 'Generate Blog Metadata' agent creates SEO titles and descriptions. The 'Generate Image Prompt + Alt Text' agent crafts a prompt used by the 'Nano 🍌' HTTP Request n8n node for AI image generation (likely calling a specialized image service). The image is processed by 'Convert to File' and 'Resize Image' n8n nodes.


6. Publishing: The workflow uploads the image via the 'Upload Image To WP' n8n node, creates the post using the dedicated WordPress n8n node ('Create a post'), updates it, and finally sets the featured image using another HTTP Request n8n node ('Set Featured Image').

1. Import the n8n workflow: Copy the provided JSON data and paste it into your n8n instance via the 'New' menu -> 'Import from JSON'.


2. Setup Credentials:

1. Configure Triggers: Adjust the 'Check Youtube Channel' RSS n8n trigger parameters (URL and interval) to monitor the desired channel. Alternatively, activate the 'Schedule Trigger' for time-based execution.


2. Activate: Ensure all nodes are connected and enable the n8n workflow.

Check Youtube Channel (RSS Feed Read Trigger): This n8n trigger starts the process when a new item (video) is detected in the configured YouTube channel RSS feed.
Get Transcript (HTTP Request): Fetches the transcript data for the video link obtained from the n8n trigger, a critical input for the AI agents.
Search intent and style / Table of Contents / Generate the Content (LangChain Agent): A series of advanced n8n node configurations that manage the complex AI logic, using LLMs (linked via the Ollama Chat Model n8n node) to plan and draft the article content based on the transcript.
Ollama Chat Model (Language Model Node): Serves as the backbone LLM for the LangChain agents. This n8n node provides efficient, locally managed AI processing.
Search in Tavily (Tavily Tool): A crucial n8n node for the EEAT process, allowing the AI agents to perform real-time web searches to verify facts and gather fresh information.
Split Out1 / Merge / Aggregate1 (Flow Control Nodes): These core logic n8n node types manage the iterative process, splitting the content creation task into smaller, manageable chunks for the AI and then recombining the outputs.
Nano 🍌 (HTTP Request): This n8n node is configured to send the image prompt to an external image generation API, demonstrating integration beyond standard n8n templates.
WordPress (WordPress Node): Handles the final steps: publishing the content, setting categories, and providing the Post ID necessary for setting the featured image.

• Set Featured Image (HTTP Request): Executes the final API call to link the newly uploaded image to the published post as the featured image, concluding this complex n8n workflow.