My First Workflow with n8n + AI

In the rapidly evolving world of digital tools and automation, maintaining control over your data and the processes you build is paramount—at least, it certainly is for me. This fundamental need for sovereignty over my digital operations is why I was immediately captivated by n8n. It’s a remarkably versatile automation tool that stands out not just for its ability to connect a vast ecosystem of applications but also because it offers a source-available, self-hostable version. This crucial feature meant I could keep all my data securely on my own infrastructure, manage every aspect of my workflows exactly as I needed, and, significantly, integrate seamlessly with my locally hosted AI model (Llama 3.1 running via Ollama). The choice to adopt n8n felt intuitive and empowering, and it swiftly became the cornerstone of my inaugural automation endeavor: a workflow designed to generate a unique short story every single day, powered by AI.

Embarking on this journey required setting up n8n locally first. If you haven’t done that yet and are using a Windows machine, I’ve previously documented a detailed, step-by-step guide on how to get n8n up and running in your own environment. I recommend following that guide first to establish your local n8n instance, and then you can return here to delve into the fascinating process of creating the workflow itself.

This article chronicles my experience, detailing the steps, challenges, and discoveries involved in building that specific workflow. It’s a project that has genuinely transformed my morning routine, injecting a dose of creativity and technological wonder into the start of each day. Join me as I unpack the creation process, hoping to inspire and guide you in your own automation explorations.

Why n8n Was the Right Choice for My Automation Needs

Before diving into the workflow specifics, let’s elaborate on why n8n emerged as the ideal platform for this project. In a landscape filled with various automation tools, many of which operate solely in the cloud, n8n’s philosophy resonated deeply with my priorities. The primary draw was its commitment to data ownership and control. By offering a self-hostable version, n8n empowers users to run their workflows on their own servers, whether that’s a home computer, a local server, or a private cloud instance. This was non-negotiable for me, especially when dealing with potentially sensitive or proprietary information, and critical for integrating with local AI models without sending data externally.

Furthermore, n8n’s node-based visual interface simplifies the process of building complex workflows. Each node represents a specific action or application integration, and connecting them is intuitive. This visual approach lowers the barrier to entry for those less experienced with coding while still offering immense power and flexibility for seasoned developers. The ability to seamlessly switch between the visual editor and writing custom code (like JavaScript or Python within specific nodes) provides the best of both worlds.

The integration capabilities are vast. n8n boasts hundreds of built-in nodes for popular apps and services, covering everything from databases and APIs to communication tools and file management. Crucially for this project, its HTTP Request node is incredibly flexible, allowing connection to almost any API, including the Ollama API serving my local Llama 3.1 model. This extensibility ensures that even if a specific pre-built node doesn’t exist, you can likely still connect to the desired service.

Finally, the active community and transparent development process are significant advantages. Finding support, sharing solutions, and even contributing back to the project are all part of the n8n ecosystem. This collaborative spirit makes tackling challenges less daunting and fosters continuous learning.

Setting Up the Workflow: A Step-by-Step Breakdown

With the decision firmly made to use n8n, I embarked on constructing the workflow to generate my daily AI-powered short story. The goal was clear: automate the entire process from idea generation to final posting on Blogger. Here’s a detailed look at how the different pieces, represented by n8n nodes, came together:

1. Triggering the Workflow: The Daily Kick-Off

Every automated process needs a starting point, a trigger. For this daily task, the Schedule Trigger node (often referred to conceptually as a cron job in scheduling contexts) was the perfect fit. I configured it to activate the workflow every morning at precisely 4:00 AM. Why so early? Primarily, this timing ensures that the story generation process, which involves calls to my local AI model, runs when my computer’s resources, particularly the GPU powering the AI, are least likely to be in demand. I’m typically not using the computer actively at that hour, guaranteeing smoother and potentially faster execution. This proactive scheduling means the finished story is ready and waiting by the time I start my day.

2. Generating the Creative Spark: The Initial AI Prompt

The heart of the creative process begins here. The first active node in the workflow is a Basic LLM node. This node is configured to connect directly to my locally hosted Llama 3.1 model, accessed via its Ollama API endpoint. Its specific task is to generate the foundational elements for the story. I instructed it, through a carefully crafted prompt, to devise:

• A compelling story prompt or concept.
• An appropriate title for the story.
• A suitable genre (e.g., Sci-Fi, Fantasy, Mystery).
• A central theme (e.g., Courage, Discovery, Betrayal).
• Crucially, it also generates a descriptive search query designed for finding a relevant image on Unsplash.

This initial AI step sets the creative direction for the entire workflow.

3. Structuring the Chaos: The Info Organizer Node

The output from the first LLM node is raw text containing all the generated elements. To make this information usable by subsequent nodes in a structured way, I introduced another AI-powered step: an Info Organizer node. This node also connects to my local Llama 3.1 model. Its function is to take the unstructured text output from the previous step and organize it neatly into a predefined JSON (JavaScript Object Notation) schema. This JSON object cleanly separates the title, genre, theme, the detailed story instructions (based on the initial prompt), and the Unsplash image search query into distinct fields. This structured data is much easier for the following nodes to parse and utilize reliably.

4. Avoiding Repetition: The Uniqueness Check

To ensure each day brings a genuinely new story idea, I implemented a uniqueness check. This involves several steps:

1. Fetching Past Prompts: The workflow queries a local Postgres database where I store the core prompts of previously generated stories.
2. Comparison Logic: A Code node, running custom JavaScript, takes the newly generated prompt (from the structured JSON) and compares it against the list of past prompts retrieved from the database. The comparison logic aims to detect significant similarities, preventing near-duplicate story ideas.
3. Decision Point: If the Code node determines the new prompt is too similar to a previous one, it triggers a loop back to the beginning (the prompt generation step) to create a new idea. This loop continues until a sufficiently unique prompt is generated.
4. Storing the New Prompt: Once a unique prompt passes the check, its core details are stored in the Postgres database using another database node. This ensures it will be part of the comparison set for future workflow runs.

This uniqueness loop is vital for maintaining the novelty and interest of the daily stories.

5. Weaving the Narrative: The Main Story Generation

With a unique and structured prompt confirmed, the workflow proceeds to the main event: writing the short story. Another Basic LLM node, again connected to Llama 3.1, takes the detailed story instructions (genre, theme, specific prompt elements) from the organized JSON object. It uses these inputs to generate the full text of the short story, aiming for a length suitable for a quick morning read.

6. Visual Accompaniment: Fetching an Image from Unsplash

A story often feels more complete with a relevant image. Using the image search query generated way back in step 2 and structured in step 3, the workflow employs an HTTP Request node. This node sends a request to the Unsplash API using the generated query terms. Unsplash returns a selection of relevant images, and the workflow typically selects the first or a randomly chosen high-quality image that matches the story’s theme or mood.

My ideal scenario involves generating a unique AI image for each story using something like Stable Diffusion locally. However, my initial attempts to integrate this reliably into the n8n workflow faced some technical hurdles I couldn’t immediately overcome. For the sake of getting the workflow operational, I decided to stick with Unsplash for now. If anyone has successfully integrated local AI image generation (like Stable Diffusion via API) into an n8n workflow, I’d be very interested in hearing about your approach in the comments! This remains a planned future enhancement.

7. Formatting Finesses: Preparing the Blogger Payload

Getting the generated content ready for posting presented its own set of challenges, particularly with formatting. Blogger’s API expects data in a specific format, and JSON’s handling of special characters (like newlines `\n` or quotes `”` which require escaping `\\n`, `\\”` ) caused significant issues when trying to post the story text directly. The story often ended up as one large, unreadable block of text on the blog.

To address this, I introduced a crucial Code node just before the final posting step. This node uses JavaScript to meticulously compile all the necessary data—the title, the formatted story text, and the selected image URL—into a single, precisely structured JSON payload conforming to Blogger API’s requirements. This allowed me to handle dynamic data injection and complex string manipulations involving escape characters in one consolidated step. However, achieving perfect paragraph spacing within the story text remains an ongoing challenge due to the nuances of JSON string formatting and how different platforms interpret escaped newline characters. If any JSON experts have tips for preserving line breaks and paragraph spacing reliably when passing text through JSON payloads to APIs like Blogger’s, your insights would be invaluable!

8. Publishing the Story: The Final Step

With the payload perfectly prepped (or as perfectly as possible), the final action is executed by another HTTP Request node. This node sends a POST request to the Blogger API endpoint for creating new posts. It includes the carefully crafted JSON payload containing the story title, the main story content (body), and any relevant metadata like labels or the image. If everything is configured correctly, the new short story appears automatically on my designated Blogger blog, ready for the world (or at least, me) to read.

Navigating the Hurdles: Challenges and Insights

Building this first complex workflow was an enlightening but certainly not friction-free experience. My background is primarily in Python, so transitioning to the JavaScript-centric environment of n8n’s Code nodes and grappling with the intricacies of JSON manipulation presented a significant learning curve. Debugging asynchronous operations and correctly formatting data for API calls, especially the Blogger API with its specific requirements, took considerable trial and error.

One of the trickiest parts was handling the flow of data between nodes, ensuring the output of one step correctly matched the expected input format of the next. The visual nature of n8n helps, but understanding the underlying data structures being passed is crucial. Debugging often involved examining the JSON output of each node meticulously to pinpoint where data was malformed or missing.

Throughout this process, I developed a personal strategy for tackling persistent coding bugs, particularly when using AI assistance, which I’ve affectionately dubbed “ByteMage’s Three Strike Rule”. If I presented the same coding problem or error message to a particular Large Language Model (like ChatGPT, Claude, or my local Llama 3.1) three times without getting a working solution, I would switch to a different LLM. Often, a fresh perspective or a different model’s training data would yield the insight needed to break through the roadblock. Furthermore, I found that providing the LLMs with relevant context, such as snippets of official documentation or links to forum posts discussing similar issues, dramatically improved the quality and relevance of their suggestions.

Despite the challenges, the process was incredibly rewarding. Each hurdle overcome solidified my understanding of n8n’s capabilities and my own ability to wield them. By the end of the project, I felt substantially more confident navigating n8n’s interface, working with JavaScript within its ecosystem, and integrating various services, especially local AI models. A key factor in mitigating frustration was n8n’s excellent integration of documentation – nearly every node has a direct link to its relevant documentation page, providing quick access to parameters, examples, and explanations whenever I felt stuck.

Reflecting on the Experience and Future Plans

Looking back on the entire process, from initial concept to daily operational workflow, I am incredibly pleased with how **My First Workflow with n8n + AI** turned out. It successfully achieves its primary goal: delivering a fresh, AI-generated short story to my Blogger site every morning. This creative output has become a genuinely enjoyable part of my daily routine, sparking imagination right at the start of the day. I fully intend to keep this workflow running indefinitely.

However, the journey doesn’t end here. This project has sparked numerous ideas for enhancements and refinements. As mentioned earlier, the most significant planned upgrade is to replace the Unsplash image fetching with local AI image generation. Creating a truly unique image tailored specifically to each story’s content would elevate the entire project. Another key area for improvement is tackling the persistent text formatting issue – figuring out how to ensure proper paragraph breaks and spacing survive the journey through JSON encoding and the Blogger API remains a top priority.

More broadly, this experience has profoundly expanded my understanding of what’s achievable with modern automation tools, particularly when combined with the power of locally hosted AI. The ability to orchestrate complex tasks involving multiple applications and AI models, all while maintaining control over the data and execution environment, is truly transformative. Since completing this story generator, I’ve already leveraged my newfound skills to build other practical workflows, such as a system that automatically generates daily task lists based on my calendar and priorities, and an email sorter that intelligently categorizes incoming messages. The possibilities feel boundless, and I’m genuinely excited to continue exploring, iterating, and automating various aspects of my digital life.

Getting Started with Your Own n8n + AI Workflow

Inspired to build your own automated workflows with n8n and AI? Getting started might be easier than you think. Begin with a simple, clearly defined task you’d like to automate. Perhaps it’s summarizing articles, categorizing emails, generating social media post ideas, or translating text snippets. Connect n8n to an AI model (either a cloud service like OpenAI or a local one like Llama 3.1 via Ollama) using the appropriate LLM nodes or the HTTP Request node.

Start small, focusing on getting a basic two or three-node workflow running. Test each step individually, examining the input and output data carefully. Don’t be afraid to experiment and consult the documentation frequently. The beauty of n8n lies in its visual interface and the immediate feedback loop it provides, allowing you to iterate quickly. Remember the power of self-hosting for data control and local AI integration – it’s a key differentiator that platforms like n8n offer.

Let’s Continue the Journey Together

Automation and AI are fields that thrive on shared knowledge and collaboration. Have you experimented with building workflows in n8n, perhaps integrating AI elements? Or are you contemplating your first automation project and looking for ideas or guidance? I’m keen to hear about your own experiences, challenges, and successes.

If you’re working on a particular n8n workflow and need some help troubleshooting, or if you simply want to brainstorm potential automation ideas, feel free to reach out. Share your thoughts, questions, or project details in the comments section below. Let’s foster a community where we can learn from each other and collectively push the boundaries of what’s possible with tools like n8n.

If you found this walkthrough of my first n8n + AI workflow insightful, consider exploring more content on the LifetimeSoftwareHub blog. We regularly publish tips, tutorials, software reviews, and insights into the world of automation, AI, and lifetime software deals designed to empower creators, entrepreneurs, and tech enthusiasts like you.