Tag: n8n

Date: 04/12/2025

Watch the Video

Okay, this video is exactly what I’ve been looking for! It tackles a pain point I’ve definitely felt: n8n’s built-in chat interface for AI agents is…basic. It’s fine for quick tests, but falls apart when you need history, customization, or a more user-friendly experience. The video shows how to hook up your n8n AI agents to Open WebUI, giving you a full ChatGPT-like interface with persistent conversations and a slick frontend – something that significantly elevates the end-user experience.

What makes this valuable is the bridge it builds between low-code automation (n8n) and a more sophisticated UI. Think about it: We can build complex workflows and AI agents in n8n, then provide a real conversational interface to our clients or internal users via Open WebUI. Imagine building a lead qualification agent, and giving your sales team a dedicated, branded chat interface to interact with it. Or think about a customer service bot that runs in n8n but presents a familiar chat experience. This video basically gives you the keys to creating these kinds of polished, production-ready AI applications, and it looks relatively straightforward to implement.

I’m particularly excited about the “n8n Agent Template” and “Open WebUI + n8n Pipeline” resources. Having those pre-built starting points drastically reduces the ramp-up time. I’m definitely going to experiment with this over the next few days. The idea of packaging powerful n8n agents with a user-friendly chat interface? That’s a huge win for both internal automation and client-facing applications! Plus, the video addresses security, which is always top-of-mind when dealing with webhooks and external services. Worth a watch and a weekend project for sure!

Date: 04/10/2025

Watch the Video

Okay, this video on n8n’s new MCP (Model Context Protocol) support is seriously exciting and a total game-changer for how we integrate AI into our workflows. Basically, it shows you how to build custom AI tools that directly hook into things like Claude and Cursor, using n8n’s no-code platform as the glue. Think to-do list management, email handling, or even content generation, all powered by AI and automated without writing a single line of code.

For someone like me who’s been diving headfirst into AI-enhanced development, this is gold. Instead of wrestling with APIs and SDKs, we can leverage n8n to create MCP servers and clients, effectively building custom AI tools tailored to our specific needs. The video walks you through setting up the server, integrating it with AI apps, and even using n8n as an MCP client to access external services. Imagine automating the tedious parts of your development lifecycle with custom AI agents responding to your instructions in Claude or Cursor.

The real kicker is the potential for practical applications. We could build automated testing workflows, generate documentation from code comments, or even create AI-powered code review assistants. The video touches on connecting to-do lists and other services, which is just scratching the surface. And let’s be real, the thought of creating these kinds of custom integrations without getting bogged down in code is incredibly appealing and efficient. I’m particularly intrigued by the MCP client node. It basically unlocks a whole new level of automation. I’m already thinking of how I can use this to connect my internal tools with LLMs, and honestly, that’s an experiment worth diving into.

Date: 04/09/2025

Watch the Video

Okay, this video looks like gold for where I’m trying to go with my workflow! It’s all about building a local AI agent using n8n for automation, Ollama for the LLM, and PostgreSQL for vector storage. The beauty is that it’s entirely self-hosted, which means no hefty API bills or privacy concerns. The video walks you through the entire process, from setting up Ollama and PostgreSQL to orchestrating everything within n8n. They even tackle common troubleshooting issues.

This is exactly the kind of thing I need to dive deeper into. For the past year, I have been looking at self-hosted AI for cost reasons and privacy, but found it daunting to integrate it into actual workflows. Right now, I still use OpenAI for all my jobs, but it would be great to use this at least for local testing or for clients who have compliance issues. It seems possible I could create a RAG workflow that does not leave the customer premises. Imagine automating report generation, content summarization, or even personalized customer service bots, all running locally!

The video shows how to add RAG (Retrieval Augmented Generation) and tools into the workflow, which opens up huge possibilities for automating complex tasks. It’s worth experimenting with because it gives you a practical, hands-on approach to building AI solutions without being locked into external services. I’m always looking for ways to streamline development and cut costs, and this seems like a very promising avenue to explore.

Date: 04/05/2025

Watch the Video

Okay, this video on Agentic RAG with N8N is seriously inspiring, especially for someone like me who’s been diving deep into AI-powered workflows. It’s all about building a no-code system that goes way beyond basic RAG. Instead of just querying a single source and hoping for the best, this setup uses an AI agent to intelligently plan its research, pull data from multiple sources (web scraping with Spider Cloud, documents in Google Drive, databases in NocoDB), and even leverage tools like Perplexity and Jina for deep search. The end result? Fully researched blog posts generated automatically. Think of it as a research assistant that doesn’t sleep!

For us Laravel devs exploring AI, this is huge. We can apply these principles to automate so many tasks: from generating documentation and analyzing user feedback to creating personalized content and even automating code audits. The beauty of using N8N is that it makes these complex workflows accessible without getting bogged down in code. Imagine integrating this with a Laravel backend to automate content creation or knowledge base updates. Instead of manually researching and writing, we can build intelligent agents that do the heavy lifting, freeing us up to focus on strategy and fine-tuning.

Honestly, the idea of seeing an article go from title to publish in minutes, all thanks to a no-code Agentic RAG system, is incredibly compelling. I’m already brainstorming how to adapt this approach to automate report generation for my clients. It’s a game-changer and definitely worth experimenting with. I think the key is to start small, maybe with a simple content summarization workflow, and then gradually expand into more complex scenarios.

Date: 04/04/2025

Watch the Video

This video is all about automating faceless YouTube video creation using n8n, JSON2Video, and ElevenLabs. You feed it a topic, and it scrapes data for “Top 10” style content, automatically generates the visuals, creates a realistic voiceover, and publishes the video to YouTube. Pretty slick!

For a dev like me who’s knee-deep in integrating AI into my workflows, this is gold. It shows a practical, end-to-end example of how to leverage no-code tools (n8n) and AI services (ElevenLabs) to completely automate a content creation pipeline. Instead of manually coding every step, you’re orchestrating AI and APIs. I can immediately see how this approach could be adapted to automate other content-heavy tasks like generating documentation, creating marketing materials, or even building personalized learning experiences.

What really grabs me is the potential for rapid prototyping and iteration. Think about it: I could build a similar workflow to automatically generate product demos based on a JSON spec, or even automatically create training videos for new features! The JSON2Video aspect is especially interesting, as it offers a declarative way to define video content, which feels very aligned with how we define UI in modern frameworks. It’s definitely got me thinking about how I can offload tedious tasks to AI and focus on the higher-level logic and creative direction. Time to experiment!

Date: 04/01/2025

Watch the Video

Okay, so this video is gold for us devs diving into the AI space. It’s all about Cache-Augmented Generation (CAG), which is like RAG’s smarter, faster cousin. Instead of hitting the database every time, it leverages server-side memory from the big players like OpenAI, Anthropic, and Google Gemini. The video then pits CAG against traditional RAG in a head-to-head comparison focusing on speed, cost, and accuracy. It demos the implementation using n8n, showing how to set up workflows with different LLMs and how to upload documents to Gemini’s cache. Super practical stuff.

Why’s it valuable? Well, as we’re transitioning into AI-enhanced workflows, RAG is becoming a foundational piece for building AI tools that actually know something beyond their training data. This video takes it a step further. The comparison between CAG and RAG is key – it helps us understand when it’s worth investing in a more sophisticated caching mechanism. Plus, the n8n demo is killer because it provides a tangible, no-code approach to integrating these techniques. Instead of abstract theory, you see real workflows.

Think about it: We’re building more and more complex applications that rely on LLMs. The ability to reduce latency and lower costs while maintaining (or even improving) accuracy is HUGE. Imagine using CAG for customer support chatbots, internal knowledge bases, or even code generation tools that need to quickly access and recall vast amounts of information. Honestly, what I find most inspiring is the practical, hands-on approach. It’s not just about the “what,” but the “how.” I’m definitely eager to experiment with CAG to see how it stacks up against our current RAG implementations. Plus, n8n makes it super easy to prototype and test these ideas, so why not give it a shot?

Date: 03/26/2025

Watch the Video

Okay, this video on building a restaurant reservation system with N8N and VAPI is seriously cool and right up our alley! It’s basically about creating an AI voice receptionist using no-code tools. Think about it: instead of a human answering the phone, an AI handles booking reservations, potentially managing multiple calls simultaneously.

For us devs diving into AI and no-code, this is gold. The video breaks down how to build the entire workflow in N8N, from setting up the initial call flow to extracting reservation details using VAPI. It’s not just theoretical; it walks you through creating the tools, testing the process, and even talks about enhancements. It is incredibly powerful to extract structured data using AI instead of Regex. This is a must have to be able to connect LLMs to databases. Imagine automating all those tedious tasks with AI.

What makes this worth experimenting with is the tangible application. We can apply these concepts to automate customer support, appointment scheduling, or even lead qualification processes. Plus, the potential cost savings and efficiency gains are huge. I am excited to try out building my own AI powered voice assistant for my web apps. It’s a great way to see how these new tools can revolutionize how we build and deploy solutions.