[{"id":"2026/04/build-ai-agent-workflow-n8n-guide","title":"Build Your First AI Agent Workflow with n8n","description":"Build AI agent workflows in n8n 2.0 with LangChain integration. Two practical projects: a research agent and a RAG-powered support bot.","date":"2026-04-14","tags":["n8n","ai agents","langchain","workflow automation","rag","no code ai","self hosted ai"],"authors":["nomadlab"],"url":"/blog/2026/04/build-ai-agent-workflow-n8n-guide","content":"Most AI agent tutorials start with fifty lines of Python and a vague promise about \"autonomous systems.\" By the end, you've got a script that calls an API and prints to the terminal. Not exactly the autonomous workflow you were picturing. n8n takes a different approach. Since version 2.0 dropped in January 2026, it ships with native LangChain integration, 70+ AI-specific nodes, and the ability to wire up agents that actually do things — send emails, query databases, update spreadsheets, post to Slack. The whole point of an AI agent is that it takes action, and n8n gives you the connective tissue to make that happen without writing a custom integration for every tool. This guide walks through two real workflows you can build today. The first is a research agent that pulls web content and produces structured summaries. The second is a customer support agent with RAG and persistent memory. Both are practical enough to use in production, not just demos to show off at a meetup. Why n8n for AI Agents (and Not Just Another Framework) There are a dozen ways to build AI agents right now. LangGraph, CrewAI, AutoGen, raw API calls with your own orchestration. So why drag a workflow automation tool into this? Three reasons, and they're all practical. You can see what's happening. When an agent makes a decision in a Python script, you're reading logs. In n8n, you're looking at a visual graph where each step shows its input and output. When something goes wrong — and with agents, something always goes wrong — you click the node that failed and see exactly what the LLM received and what it returned. This alone saves hours of debugging. Connecting to external services is trivial. n8n has 400+ built-in integrations. Want your agent to check a Google Sheet, send a Slack message, and create a Jira ticket based on what it finds? That's three nodes, no API wrappers needed. With LangGraph you'd be writing custom tool definitions for each of those. Self-hosting keeps costs predictable. Th"},{"id":"2026/04/google-gemma-4-local-setup-ai-agent","title":"Google Gemma 4: Run It Locally and Build Your First AI Agent","description":"A practical guide to running Google's Gemma 4 locally with Ollama, choosing the right model size, and building an AI agent with native function calling.","date":"2026-04-13","tags":["gemma4","ollama","open source llm","ai agents","google ai","local ai","function calling"],"authors":["nomadlab"],"url":"/blog/2026/04/google-gemma-4-local-setup-ai-agent","content":"Google dropped Gemma 4 on April 2, 2026, and it immediately shook up the open-weight model rankings. Four models, Apache 2.0 license, multimodal from the ground up, and — the part that caught my attention — native function calling that actually works well enough to build real agents with. I've been running Gemma 4 locally for the past week and a half. Here's what you need to know if you want to do the same, from picking the right model size to building a simple agent that can call tools on your behalf. What Makes Gemma 4 Different from Every Other Open Model Gemma 4 is built from the same research that powers Gemini 3, Google's flagship commercial model. That lineage shows. The benchmark jumps from Gemma 3 are frankly absurd: AIME 2026 math scores went from 20.8% to 89.2%, LiveCodeBench coding jumped from 29.1% to 80.0%, and GPQA science scores climbed from 42.4% to 84.3%. But benchmarks are benchmarks. What actually matters for local use is the combination of three things: the Apache 2.0 license means you can do whatever you want commercially (no MAU caps like Llama's 700M limit, no EU restrictions), the architecture is optimized for efficient inference on consumer hardware, and the function calling capability is baked into the model rather than bolted on as an afterthought. The 256K context window across all model sizes is another quiet advantage. Most open models either cap out at 32K or charge you dearly in compute for longer contexts. Gemma 4 handles long documents natively, which matters a lot when you're feeding tool outputs back into an agent loop. The Four Model Sizes (and Which One You Actually Want) Google released four Gemma 4 variants, each targeting a different use case. Here's the honest breakdown: Gemma 4 E2B — The Phone Model 2.3 billion parameters. This one runs on smartphones and edge devices. It's impressively capable for its size, but unless you're building a mobile app, skip it. You'll be frustrated by its reasoning limitations for anything bey"}]