In this video, I break down n8n’s new instance-level MCP and show you why it is a big upgrade from running your own MCP Server Trigger inside a workflow. I explain what it is, how it works, and the key differences you need to know so you can get the most out of it. I also walk through how to connect the new MCP to tools like ChatGPT, Claude, and Lovable so your AI agents can take real actions inside your n8n instance. You’ll see practical examples, simple use cases, and a full demo of how to think about using this in your daily life to automate tasks, speed up your workflow, and make your AI tools much more useful. If you want to understand how to unlock the full power of AI agents inside n8n, this video will show you exactly how to get started.

Hello community! 👋 The AI automation landscape is moving at breakneck speed. One of the current keys is equipping our agents with specific knowledge (RAG) without dying trying. Until recently, this required setting up complex pipelines with Pinecone, Supabase, etc. But recently, both 𝗚𝗼𝗼𝗴𝗹𝗲 (𝗚𝗲𝗺𝗶𝗻𝗶) and 𝗢𝗽𝗲𝗻𝗔𝗜 have launched solutions that promise to drastically simplify this process. I've been analyzing both options thoroughly, comparing costs, ease of use, and integration (especially thinking about workflows like n8n), and here is the final verdict. 👇 🔥 𝗧𝗵𝗲 𝗠𝗶𝗹𝗹𝗶𝗼𝗻 𝗗𝗼𝗹𝗹𝗮𝗿 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: W𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝗶𝘀 𝗯𝗲𝘁𝘁𝗲𝗿? The short answer is: It depends on your priority. Are you looking for maximum economy and speed, or the most elegant integration into your workflow? 🥇 𝗚𝗲𝗺𝗶𝗻𝗶 𝗙𝗶𝗹𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗣𝗜 (𝗧𝗵𝗲 𝗞𝗶𝗻𝗴 𝗼𝗳 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 & 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆) If your priority is low cost and getting it running fast, Gemini is unbeatable. - 💸 𝗔𝗹𝗺𝗼𝘀𝘁 𝗭𝗲𝗿𝗼 𝗖𝗼𝘀𝘁: Storage is currently free. Indexing a 120-page PDF costs less than $0.15. It's ridiculously cheap compared to the competition. - 🚀 𝗡𝗼 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: Forget about managing external vector databases. Google takes care of chunking, embedding, and storage. Just upload the file and you're done. 🥈 𝗢𝗽𝗲𝗻𝗔𝗜 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗲 (𝗧𝗵𝗲 𝗞𝗶𝗻𝗴 𝗼𝗳 𝗡𝗮𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻) If you value a clean workflow and integrated tools within the OpenAI ecosystem. - 🧠 𝗡𝗮𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: It shines especially if you use n8n. You access the vector base directly via the OpenAI API, without weird extra nodes. - 🛠️ "𝗕𝘂𝗶𝗹𝘁-𝗶𝗻" 𝗧𝗼𝗼𝗹𝘀: It allows adding file or web search directly in the agent configuration, eliminating the need for external tools like Perplexity in many cases. It's a much more elegant design. 📊 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝘁𝗵𝗲 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 (𝗘𝘅𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝗜𝗻𝗳𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰𝘀) I have prepared detailed infographics for the community that visually summarize: - The quick verdict. - The brutal difference in prices. - How they differ from traditional RAG and their limitations (watch out, they aren't magic!).

Tired of AI generating code without context or having to review every single line? 𝗞𝗶𝗿𝗼 changes the game. W𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗞𝗶𝗿𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁? It's not just another code assistant. It's an 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗜𝗗𝗘 that manages the entire development lifecycle, from initial concept to production. 🧠 𝟭. 𝗔𝗜 𝘁𝗵𝗮𝘁 𝘁𝗿𝘂𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 - Analyzes your existing code or initial concept - Generates automatic documentation - Understands the complete context of your application 📋 𝟮. "𝗦𝗽𝗲𝗰" 𝗙𝗹𝗼𝘄: 𝗙𝗿𝗼𝗺 𝗶𝗱𝗲𝗮 𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗮𝗯𝗹𝗲 𝗽𝗹𝗮𝗻 You say: "𝘈𝘥𝘥 𝘎𝘰𝘰𝘨𝘭𝘦 𝘭𝘰𝘨𝘪𝘯" Kiro delivers: 📄 Requirements documents (what and why) 🗺️ Detailed design plans (architecture + UI) ✅ Interactive task list ready to execute ⚡ 𝟯. 𝗔𝗴𝗲𝗻𝘁 𝗛𝗼𝗼𝗸𝘀: 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 - Save a component → Kiro automatically creates unit tests - Customizable to your workflow - Saves you hours of repetitive work 🛑 𝟰. 𝗠𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁𝗹𝘆: 𝗬𝗢𝗨 𝘀𝘁𝗮𝘆 𝗶𝗻 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 Kiro doesn't deploy anything without your approval: - Review generated code - Modify as needed - Approve before production 𝗥𝗲𝘀𝘂𝗹𝘁: Quality code, AI speed, expert developer control. 𝗜𝘀 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗵𝘂𝗺𝗮𝗻𝘀 𝗮𝗻𝗱 𝗔𝗜? Share your thoughts 👇

Level up your n8n AI agents effortlessly! In this video, discover how to utilize OpenAI's Responses API as a powerful chat model to enhance your agents. I show you the quicker, easier method to bake in essential tools like web search and file search directly into your agents, bypassing the need for connecting other tools, messing with prompts, and building data pipelines for you knowledge base. Learn exactly why this new approach is a significant upgrade and how to set it up in n8n without writing any code, unlocking a new level of power and capability for your AI agents.

I’ve been testing 𝗟𝗼𝘃𝗮𝗯𝗹𝗲, 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗜 𝗦𝘁𝘂𝗱𝗶𝗼, 𝗮𝗻𝗱 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗻𝘁𝗶𝗴𝗿𝗮𝘃𝗶𝘁𝘆 these days, and I had to share this because… wow. People compare them as if they were the same thing, but each one plays a completely different sport. Here’s my honest take — no fluff. 💙 𝗟𝗼𝘃𝗮𝗯𝗹𝗲: 𝗽𝘂𝗿𝗲 𝘀𝗽𝗲𝗲𝗱 𝗳𝗼𝗿 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗱𝗼𝗻’𝘁 𝗰𝗼𝗱𝗲 Lovable is that friend who tells you: “Relax, just describe what you want and I’ll build the app for you.” If you don’t have a tech background or you need to validate an idea fast, it’s perfect. Why it shines: - ridiculously fast MVPs - simple apps without touching code - you can export the project later for real dev work - Its limit: when logic gets complex, Lovable starts to sweat. 👉 𝗜𝗳 𝘆𝗼𝘂𝗿 𝗴𝗼𝗮𝗹 𝗶𝘀 𝘁𝗼 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗼𝗿 𝘀𝗵𝗶𝗽 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘀𝗶𝗺𝗽𝗹𝗲 𝗳𝗮𝘀𝘁, 𝗟𝗼𝘃𝗮𝗯𝗹𝗲 𝘀𝗮𝘃𝗲𝘀 𝘆𝗼𝘂. 🤖 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗜 𝗦𝘁𝘂𝗱𝗶𝗼: 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗵𝗲 𝗔𝗜 𝘁𝗼 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘂𝘀𝗲𝗳𝘂𝗹 AI Studio feels like: “I want to build something with AI, but I don’t want to jump into a full IDE yet.” Great for people who have a bit of technical background and want more serious prototypes: chatbots, workflows, logic, small tools powered by AI. Where it shines: - experimenting with Google’s models - building AI-powered features - quick prototyping for ideas that depend heavily on AI 👉 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗺𝗶𝘅𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 + 𝗔𝗜, 𝗔𝗜 𝗦𝘁𝘂𝗱𝗶𝗼 𝗶𝘀 𝗮 𝘃𝗲𝗿𝘆 𝘀𝗼𝗹𝗶𝗱 𝗺𝗶𝗱𝗱𝗹𝗲 𝗴𝗿𝗼𝘂𝗻𝗱. 🚀 𝗔𝗻𝘁𝗶𝗴𝗿𝗮𝘃𝗶𝘁𝘆: 𝗚𝗼𝗼𝗴𝗹𝗲 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝗵𝗼𝘄 𝘄𝗲’𝗹𝗹 𝗯𝘂𝗶𝗹𝗱 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗶𝗻 𝟮𝟬𝟮𝟱 This is where my brain exploded. Antigravity is not an editor. It’s a 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗮𝗴𝗲𝗻𝘁𝘀 that actually work for you: they plan, generate structure, install deps, write code, debug, document, test… while you direct the whole thing. What you can do here: - complete apps with backend + frontend - multiple agents working in parallel (one builds, one fixes, one optimizes…) - integrated browser that tests your own app - support for Gemini 3 Pro, Claude Sonnet 4.5 and GPT-compatible models This is no longer “AI helps you”. This is 𝗔𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗶𝗻𝗴 𝘀𝗶𝗱𝗲-𝗯𝘆-𝘀𝗶𝗱𝗲 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂.