Built 3 AI agents in n8n without coding. How I did it.

🚀 Just built my first AI agent using n8n - and I didn't write a single line of code! As someone who's always been intimidated by the technical barriers of AI development, discovering n8n's visual workflow builder has been a game-changer. In just a few hours, I created a fully functional AI agent that can: ✅ Process incoming chat messages ✅ Leverage OpenRouter's chat models for intelligent responses ✅ Store conversation context in memory ✅ Integrate with external APIs seamlessly What amazes me most is how n8n democratizes AI development. The drag-and-drop interface makes complex integrations feel intuitive, and the visual flow helps you understand exactly how data moves through your system. This is just the beginning. I'm already brainstorming how to expand this agent with: • Multi-modal capabilities • Custom knowledge bases • Advanced workflow automation • Integration with business tools The no-code revolution isn't just about building websites anymore - it's about making AI accessible to creators, entrepreneurs, and businesses who have great ideas but may not have deep technical backgrounds. To anyone who's been curious about AI but felt overwhelmed by the technical complexity: tools like n8n prove that the barrier to entry is lower than ever. Your next breakthrough might just be a few workflow nodes away. What's your experience with no-code AI tools? Drop your thoughts in the comments! 👇 #AI #NoCode #n8n #Automation #Innovation #TechForEveryone #AIAgent #WorkflowAutomation

Many believe AI agent creation demands deep coding expertise and monolithic frameworks. This often leads to over-engineered solutions or no solution at all. We're consistently missing the point: practical, actionable agents don't always need rocket science. What if your next agent could be built visually, by connecting existing services? Enter n8n, a powerful workflow automation tool, uniquely positioned for rapid AI agent deployment. Its visual canvas allows orchestrating complex AI tasks by chaining APIs, services, and conditional logic. This isn't about writing another LLM; it's about giving an LLM structured tools and a clear operating environment. It shifts focus from 'building models' to 'building systems'. Consider agents for automated content curation, data enrichment, or intelligent decision routing. With n8n, you define the 'brain' via an LLM, then provide the 'limbs' through integrations to databases, CRMs, or messaging platforms. This drastically reduces development cycles and allows non-coders to conceptualize and deploy sophisticated automations. It's about empowering *anyone* to build functional agents, not just specialized ML engineers. This approach challenges the assumption that every agent needs a custom codebase from scratch. It posits that a significant portion of valuable AI agents are really just intelligent automation workflows. Are we overcomplicating AI agent development by always defaulting to complex coding frameworks, when a robust, visual orchestration tool might be more effective for many use cases? #AIagents #n8n #LowCode #Automation #WorkflowAutomation #SystemDesign

Morning check-in. ☕ Your team has a fresh Notion database—policies, FAQs, project docs all neatly logged. But here’s the catch: every new schema means hours of tinkering before an AI assistant can query it. ⚙️ *Some fields break. *JSON outputs collapse into [object Object]. And your “assistant” spends more time failing than helping. That’s the before. Now—picture this instead: *You drop a Notion DB URL into chat 💬 *Our workflow validates the schema, simplifies properties, and hands it to Anthropic + OpenAI agents 🤖 The system autogenerates a new ready-to-paste workflow JSON, retries if broken, and delivers it straight back to chat ✅ No manual debugging. No schema headaches. Just an AI agent tuned to your Notion knowledge base, in minutes. At Clepto.io, we call this automation with resilience—where compliance and adaptability come built-in. 💡 Ready to turn Notion into a queryable knowledge hub with zero friction? #CleptoIO #AIAutomation #WorkflowOrchestration #NotionAI #KnowledgeManagement #EnterpriseAutomation #ComplianceTech #DigitalOps #ProductivityTools

🚀 CREATE LLM-POWERED WORKFLOWS FOR FREE WITH OPENROUTER + N8N!   Here's how to get started (in <2 mins!):   1️⃣ SIGN UP AT OPENROUTER.AI   • Visit OpenRouter.ai   • Click "Sign Up" (no credit card needed)   2️⃣ CREATE HTTP REQUEST NODE IN N8N   • Install n8n (or use cloud version)   • Drag & drop HTTP REQUEST node   3️⃣ GET YOUR API KEY   • In OpenRouter dashboard: Click 🔑 "Keys"   • Copy your free API key (10K free tokens/month!)   4️⃣ CONFIGURE NODE SETTINGS   ```JSON URL: https://lnkd.in/evxek3JC   Method: POST   Headers:   - Authorization: Bearer YOUR_API_KEY   - Content-Type: application/json   Body (JSON):   {  "model": "google/palm-2",  "messages": [{"role": "user", "content": "{{your_input}}"}]   } ```   5️⃣ TEST & DEPLOY!   👉 Attach a Manual Trigger node for testing   👉 Execute workflow → Watch AI responses flow in!   PRO TIP: Use {{variables}} from previous nodes to create dynamic prompts!   🔥 BONUS: Pair this with GPT-4/Claude/Llama models (usage costs apply)   ⚠️ REMEMBER: Free tier = 10K tokens/mo. Track usage in OpenRouter dashboard.   👉 YOUR TURN! Which AI automation would YOU build first?   FOLLOW ME FOR MORE NO-CODE + AI MAGIC!   #LLM #AI #Automation #N8N #OpenRouter #NoCode #TechTips #Productivity #APIs #FreeTools

What happens if an AI call goes unanswered? Until recently: Nothing. The call just failed. Now: It waits, retries, and logs everything. I recently upgraded one of my client’s AI voice agents (built with n8n + Retell AI) to make it: Smarter (knows when to retry) More reliable (safe exits, no loops) Fully trackable (logs every call & outcome) Here’s what changed 👇 ✅ Google Sheets Logging Every call—answered, missed, failed, or skipped—is stored automatically. No more digging through console logs. ✅ Retry Logic If unanswered, the system: Waits 30 minutes Redials once Tracks if the retry worked ✅ Safe Exit Path No infinite loops. A call is never retried more than once. ✅ Skipped Calls Handled Even missing info (like no phone number in a calendar invite) gets logged with clear reasoning. ✅ Future-Ready Built modular, so new logic can be plugged in anytime without breaking things. 💡 Why this matters: It’s no longer “just an AI call.” It’s a trackable, testable, and dependable system—the kind businesses can actually rely on. 🚀 If you want your AI workflows to behave like this, let’s talk. Tasknova Aarav Varma Rajpal Rathore #nocode #automation #voiceAI #RetellAI #n8n #workflowautomation #productops #retrylogic #googleapps #aiintegration #builders

This SINGLE GitHub repo is one of the best hands-on Al agents repo you'll ever see :) From starter agents to advanced multi-agent teams, this covers everything: → Voice AI Agents with real-time conversation capabilities → MCP AI Agents for GitHub, Notion, and browser automation → Multi-agent Teams for recruitment, finance, legal, and design → RAG implementations with hybrid search and corrective approaches → Autonomous Game Playing Agents (Chess, 3D Pygame, Tic-Tac-Toe) → LLM Apps with persistent memory systems 📌 Link: https://lnkd.in/dey3xk95 💡 Save this post so you can dive back into it later—it’s a goldmine. 🔁 Repost to make sure more people see this! ➕ Follow for more Production AI | ML | Agents | Automation stuff. #ArtificialIntelligence #MachineLearning #AIagents #GenerativeAI #AIAgents

✨ Hello everyone! I’m excited to share that I’ve created my first AI Agent Workflow using n8n 🚀. This project was a great learning experience where I explored how AI and automation can work together to handle repetitive tasks, save time, and improve productivity 🚀 Project Highlight: AI Agent Workflow with n8n Over the past few days, I’ve been exploring how AI and automation can simplify complex tasks without writing heavy code. Using n8n (a powerful low-code/no-code automation tool), I built my first AI Agent project from scratch. 🔍 Problem I wanted to solve: Manually handling repetitive tasks like summarizing text, generating responses, or connecting different tools can be time-consuming and prone to error. ⚡ My Solution with n8n: I designed a workflow where: 1️⃣ User inputs are captured and processed inside n8n. 2️⃣ AI models are connected via API to analyze or generate text. 3️⃣ The results are automatically sent back or stored in connected apps (like Google Sheets, Slack, or email). 4️⃣ The workflow runs end-to-end with just a few nodes — no complex coding needed! 🎯 Outcome: The automation works smoothly, creating an AI-powered assistant that can handle tasks like drafting replies, summarizing information, or routing data across platforms — saving time and effort. 💡 Takeaway: This project proved how low-code AI automation is opening doors for anyone (even without a coding-heavy background) to build intelligent systems. I’m excited to explore more advanced AI + automation projects and share them here. #AI #Automation #n8n #NoCode #AIAgents #LearningJourney

🌟MCPMark: A New Benchmark for LLMs! 🌟 The MCPMark is designed to evaluate the multi-tool-use and code-related capabilities of large language models (LLMs). It features an associated leaderboard that ranks models based on their performance. 🔑 Key Aspects of MCPMark: Purpose: The benchmark aims to "stress-test" LLMs in complex contexts that require the use of multiple tools to accomplish tasks. This is especially relevant for agentic applications, where models must interact with external tools and environments to tackle real-world challenges. Developers: Created by Researchers in collaboration with Eval Sys and LobeHub. Test Data: Utilizes a high-quality dataset of 127 expert-created samples to rigorously test a model's abilities. 🎯 Focus Areas: Multi-tool Use (MCP): Evaluates how effectively models can interact with and orchestrate multiple tools to solve problems. Comprehensive Contexts: Assesses the ability to handle tasks that require more than a single-turn response. Coding: Measures performance on real-world coding tasks. 📊 How the Leaderboard Works: The MCPMark leaderboard ranks both proprietary and open-source models based on several metrics, providing a holistic evaluation: Success Rate: The percentage of tasks that the model completes successfully. Average Agent Time: The average time it takes for the model to complete the tasks. Per-Run Cost: The cost of running the model for a single task, highlighting efficiency for commercial applications and open-source development. 👇 https://lnkd.in/gpSpGpRJ #ai #agent #evals #mcp

🤖 Building AI agents is often misunderstood. It’s not about sprinkling some “magic AI” into an app — it’s about serious software engineering. If we had to put numbers on it: maybe 5% AI, 95% software architecture. Why? Because agents don’t live in isolation. They require the same foundations as any enterprise-grade system: identity and access management, governance over sensitive documents, schema mapping, human-in-the-loop oversight, scalable infrastructure across SQL and vector databases, and guardrails for cost, reliability, and security. Think of AI agents less like “mystical assistants” and more like APIs that can reason. They still demand fine-grained access control, storage that separates structured from unstructured data, orchestration flows, fallback routes, tracing, and compliance-grade auditability. 👉 Before tuning prompts or experimenting with clever hacks, the real work is in building solid foundations. Only then can agents operate at enterprise scale. #AI #SoftwareEngineering #EnterpriseAI #AIagents

An AI agent wiped prod during a code freeze then faked users to hide it. Backups saved it, CEO apologized. If agents have write without fences, you’re playing dodgeball with a steamroller. Do this: Separate tokens: read / propose / write / deploy Two-person approval for any prod DDL/DML Stage = prod, weekly restore drills, append-only logs Link for the full video in the comments. #ai #softwareengineering #devops #sre #datasafety #infosec #llm #aiagents