NEW Sim AI DESTROYS N8N and AgentKit? (FREE!) 🤯

New Sim AI destroys N8 and agent kit for free. I just tested the newest AI workflow builder that everyone's talking about. And what I found blew my mind. I gave it one prompt, just one, and it built entire business workflows in minutes that would take hours in other tools. Today, I'm showing you exactly how Simai compares to N8N and OpenAI's agent kit and why this might be the biggest game changer for automating your business this year. And before we dive in, I want to show you something important. And as you can see, I'm using the free account only. No paid plans, no special access, just the free version. So everything I'm about to show you, you can do too right now without spending a dime. So OpenAI just dropped Agent Kit. N8N has been around for a while, and now there's this new player called Simai. Everyone's asking me which one is best. So I did what I always do. I tested them, all three, and I'm going to show you exactly what happened. Here's what I tested. Three real business workflows, the kind you actually need in your business, not some fake demo nonsense. Test one, SEO, keyword research workflow. Test two, lead generation workflow. Test three, client onboarding workflow. Each one saves hours of manual work. Each one makes you money. And each one shows you which tool actually works. Let me start with the big difference, the thing nobody's talking about. With Simai, I gave it one prompt. That's it. One sentence. create a workflow that automates keyword research and SEO content planning using AI and watch what happened. The AI copilot inside SIM started building. It asked me a few questions. What trigger do I want? What integrations do I need? Where should the output go? I said go ahead and it built the entire workflow automatically. Now, let me walk you through exactly what it created. It started with an input form trigger. This form has fields for seed topics, local, market, domain, and how many keywords you want per seed. So, you can type in something like smart home security or dog training and tell it you want 200 keywords per se. Then, it created an AI agent called expand seeds that takes your seed topics and uses GPT to generate hundreds of longtail keyword variations. This agent outputs structured JSON with all the keyword ideas. But here's where it gets smart. It added a normalization and dduplication function because when you generate hundreds of keywords, you'll have duplicates, variations, similar phrases. This function cleans everything up automatically. Then it created a loop called keyword loop that processes each keyword individually. Inside that loop, there's another AI agent called cluster keyword that assigns each keyword to a topic cluster and determines the search intent. Is itformational, commercial, transactional? The AI figures it out after the loop finishes. It added an aggregation function that groups all the keywords by cluster. So instead of having 500 random keywords, you now have them organized into topics, maybe 30 or 40 distinct clusters. Then it created another loop called cluster loop that takes each cluster and runs it through yet another AI agent called generate brief. This agent creates a full content brief for that cluster. It generates H2 and H3 headings, FAQ questions, related entities to mention, internal linking suggestions, everything a content writer needs. Finally, it created three separate functions to build data tables. One for all the keywords with their clusters and intent, one for the cluster summaries showing how many keywords are in each group, and one for all the content briefs. Then it connected all three tables to Google Sheets. One tab for keywords, one tab for clusters, one tab for briefs. All automatically formatted and ready to use. And remember, I didn't configure any of this. I didn't set up the loops. I didn't write the agent prompts. I didn't create the functions. The AI co-pilot built the entire thing from my one sentence. Now, here's where it gets interesting. I didn't write a single line of code. I didn't drag and drop anything. The AI built it for me. And get this. It even added a preview mode so I could test without API keys. Try doing that in N8. You'd be dragging nodes for an hour. configuring every single connection. Writing custom code for the clustering logic in agent kit. Forget it. You're locked into OpenAI only. No Google Sheets connector out of the box. No way to build loops that easily. But wait, let me show you test two because this one really shows the power. I said build a lead generation workflow that uses AI to find, score, and follow up with prospects. One prompt again. The co-pilot came back with a plan. lead discovery using Exer AI, lead scoring with custom criteria, email generation, automatic follow-up, everything stored in Google Sheets, emails sent through Gmail, and it built the whole thing. Let me break down what it actually created. First, it built an input form with fields for search query, industry, company size, range, and number of prospects you want to find. So, you could say B2B SAS companies in healthcare, and it knows what to look for. Then it created an AI agent called lead discovery agent that uses the ex search tool to actually scrape the web and find companies matching your criteria. This agent extracts structured data, company name, website URL

industry, employee count, business focus, contact titles, everything you need for outreach. Then here's where it gets interesting. It created a processing loop that handles each prospect. But it's not a regular loop. It's a parallel processing loop that can handle up to three prospects at the same time. So instead of processing one lead, waiting, then the next one, it's doing three simultaneously. Way faster. Inside that loop, it added a lead qualification agent that scores each prospect on multiple criteria. Company fit gets 30% weight. Market opportunity gets 25%. Engagement likelihood gets 25%. Contact accessibility gets 20%. The agent gives each lead a score from 1 to 100 and categorizes them as high priority, medium priority, low priority, or not qualified. But it doesn't stop there. After scoring, it feeds each qualified lead into another AI agent called email generator. This agent creates a completely personalized email for that specific prospect. It writes a compelling subject line based on the company's business focus. It generates body copy that references specific things about their company. Includes a clear call to action. Every email is unique, not a template with variables filled in. Actually, custom written by AI for each prospect. Then it stores everything in Google Sheets. Company name, industry, size, website, contact info, business focus, qualification score, priority tier, and the generated email, all organized in columns. And finally, it added a Gmail sender block that actually sends the personalized follow-up email to each prospect. It tracks all the outreach automatically. The entire pipeline from finding leads to sending personalized emails built from one sentence for AI agents. Two loops with parallel execution, Google Sheets integration, Gmail integration, all connected and working together. Now, let me tell you something important. This isn't just about speed. It's about actually getting it right. The co-pilot understood what I needed. It knew I'd want to ddup leads. parallel processing for speed. It knew I'd want the data stored somewhere. It knew I'd want to send emails. It thought through the entire workflow better than I would have planned it myself. And here's the kicker. It gave me execution logs, detailed monitoring, token usage, cost tracking, error traces. You know what? N gives you basic logs. That's it. Agent kit, no execution logs at all. Zero. Good luck debugging when something breaks. But with SIM, you get real enterprisegrade logging. Every workflow run gets an execution ID. You can click into any run and see exactly what happened at each step. You see how long each block took to execute. You see the exact input and output data for every single agent. You see how many tokens were used by each AI call. You see the cost per execution broken down by block. If something fails, you get a detailed error trace showing exactly where it broke and why. You can even reference these execution logs in the copilot. You can say why did this workflow fail and it will analyze the logs and tell you this level of observability is critical for production. You can't fix what you can't see and SIM shows you everything. Let me show you test three client onboarding. I said build a business automation workflow that manages client onboarding and task assignment. The co-pilot asked smart questions. What's your intake method? Where do you store client data? What task management tool? How do you communicate with clients? I said you decide. And it made smart choices. Google Forms for intake, Google Sheets for data storage, automatic task creation, email notifications, calendar booking for kickoff meetings. It even added validation, error handling, roundrobin assignment, SLA reminders. This is a real business workflow. The kind agencies charge thousands to build and SIM built it in minutes. Let me show you the specific components it created. First, it built an input form called onboarding intake with all the fields you need. company name, website, primary contact name and email, phone number, package tier, which could be standard or premium, start date, time zone, region, special notes or requirements, everything you'd ask a new client. Then it added a validation block with rejects patterns that checks if all required fields are filled out and if the email address is actually valid. If something's wrong, it stops the workflow and returns a validation error. No bad data gets through. After validation passes, it creates a record in Google Sheets. But before storing the data, it added a function called build task plan that generates a custom task list based on which package the client selected. Standard package gets one set of tasks. Premium gets more comprehensive tasks. Each task has a title, description, due date calculated from the start date and an assigned owner. The owner assignment uses roundroin logic so tasks get distributed evenly across your team. nobody gets overloaded. Then it created a loop called for each task that processes every task in that plan. For each task, it sends an internal email via Gmail to the assigned team member with the task title, due date, and client details. So, everyone knows

exactly what they need to do and when. At the same time, it creates a Google calendar event for the client kickoff meeting. It automatically schedules it on the start date, invites both the assigned owner and the client to the meeting. It includes a description with the client's company name and package tier. Everything is set up automatically. After the kickoff event is created, it sends a welcome email to the client. This email goes through another AI agent that generates personalized welcome copy. It includes next steps. It confirms their package. It tells them about the kickoff meeting. It CC's the assigned owner so they're in the loop. And finally, it returns a summary response with the company name, package assigned owner, and a link to the kickoff calendar event. The entire onboarding workflow from intake to task assignment to meeting scheduling to client communication fully automated built from one prompt with validation error handling roundroin assignment and Gmail and calendar integrations all working together. Now let me break down why this matters. First, it's actually open source. Apache 2. 0 license. You can self-host it. You own your data. N8 question mark fair use license with restrictions. Agent Kit, not open source at all. Second, it works with any AI provider. Open AI, Anthropic, Google, Grokes, Cerebras, localama models, agent kit, open AI only. That's it. You're locked in. Third, 80 plus integrations out of the box. Gmail, Slack, Notion, Air Table, GitHub, Google Sheets, and it supports MCP protocol. So, you can add custom integrations. N8N has more integrations total, but SIM has everything you need for AI workflows and it's growing fast. Fourth, the knowledge base. This is huge. SIM has built-in vector search. Upload your documents, PDFs, Word files, Excel, markdown. It processes them automatically, generates embeddings, makes them searchable. You can build rams in minutes. No external vector database needed. Agent Kit doesn't have this. N8N doesn't have this. Let me explain why this matters. The knowledge base is powered by PG Vector is a PostgreSQL extension that does semantic search, not just keyword matching. It understands meaning in context. So you can upload your company's documentation, your sales playbooks, your product specs, your customer support articles, and your AI agents can search through all of it intelligently. You can ask natural language questions. You can search by concept. It supports multiple languages. You can configure chunk sizes from 100 to 4,000 characters depending on your use case. And it's all built in. You don't need to set up pine cone. configure embeddings manually. You don't need to manage a separate vector database. It just works. This makes building rag agents trivial. Your AI can access your organization's entire knowledge base with contextaware precision. That's powerful. Fifth, team collaboration, real-time editing, multiple people working together, comments, permissions. Perfect for agencies, perfect for teams. Sixth, that AI co-pilot. This is the secret weapon. You can reference workflows, reference documentation, reference execution logs, reference your knowledge base, and it helps you build. It explains concepts. It suggests improvements. It can even edit your workflows when you approve. I've used a lot of AI tools. This is different. This actually understands workflow building. Let me explain how powerful this is. The co-pilot lives right in the editor. You can use the at context menu to reference specific things. You can at mention your workflows and ask questions about them. You can mention blocks and get explanations of how they work. You can mention your knowledge bases to give the co-pilot access to your documentation. You can at mention templates from the library. You can even mention execution logs to debug failures. The co-pilot has full context of everything in your workspace. And it's not just for answering questions. It can actually make changes. You can tell it add error handling to this workflow and it will insert the appropriate blocks. You can say optimize this for parallel processing and it will restructure the flow. You can ask why is this workflow slow and it will analyze your execution logs and suggest specific improvements. It explains complex concepts in simple terms. It suggests best practices. It catches mistakes before you deploy. It's like pair programming with an expert who knows the platform inside and out. And that dramatically accelerates development. Instead of reading documentation and figuring things out, you just ask and it shows you or it builds it for you. That's the difference. Now, here's what I want you to understand. Building AI workflows isn't about the tool. It's about solving real problems. Can you automate your keyword research? Can you generate leads automatically? Can you onboard clients without manual work? That's what matters. And from my testing, SIM makes it easiest. One prompt to start. I builds it for you. You tweak what you need. You deploy. You're done. No dragging nodes for hours. No writing custom code unless you want to. No getting stuck in configuration hell, just results. Let me show you something else. The workflows I built, they're production ready. The SEO workflow finds

keywords, clusters them by intent, generates content briefs, outputs to Google Sheets, ready to use today. You could literally run this tomorrow for your business. Type in your seed topics, hit run, come back in 10 minutes to a spreadsheet full of keyword clusters and content briefs. No manual research needed. The lead genen workflow finds prospects, scores them, sends personalized emails, tracks everything, ready to generate leads today. You could set this up to run weekly. Every Monday, it finds 50 new prospects in your target market, scores them, sends personalized outreach, all while you're doing other work. You just check the Google sheet to see who responded. The onboarding workflow captures client info, creates tasks, books meetings, sends welcome emails, ready to onboard clients today. The moment someone fills out your intake form, the entire onboarding sequence fires automatically. Tasks get assigned, meeting gets scheduled, welcome email goes out, your team is notified, no manual coordination needed, no steps forgotten, not demos, not prototypes, actual working systems, and I built them in less time than it takes to watch a tutorial on N. Each workflow has real error handling, real data validation, real integrations with tools you already use. They're not proof of concepts. They're production systems you can deploy right now. So, here's my challenge to you. Pick one workflow you do manually, one thing that takes hours every week, one process you know should be automated. Go to sim, give it one prompt, see what it builds. I bet you'll be surprised. Not because it's magic, because it's actually designed to solve this problem. And that makes all the difference. Now, if you want to go deeper on this stuff, I've got something for you. Inside the AI money lab, we have the exact SOP and process for building these workflows. Over 100 different use cases, step-by-step tutorials, 28,000 plus members getting value every single day. You can grab all the video notes and resources there, too. Link in the comments and description. Julian reads every single comment. So, let us know what you're working on. I'll see you in the next