TL;DR Want to build an AI agent but don’t know where to start? I built a production-ready boilerplate that gets you from zero to chatting with AI in under 60 seconds. Grab it here , and steal whatever you need. That’s literally what it’s for. The AI Agent Gold Rush (And Why You’re Not Late) We’re in the middle of an AI agent revolution. Every day, someone’s launching a new chatbot, assistant, or “AI-powered something” that promises to change everything. And honestly? A lot of them actually are pretty cool. \ But here’s the thing most developers don’t realize: building AI agents is way simpler than it looks . \ I know, I know. When you first hear “AI agent,” your brain conjures images of complex neural networks, distributed systems, and PhD-level mathematics. But the reality? Most modern AI development boils down to one thing: making smart API calls to Large Language Models (LLMs) . \ That’s it. That’s the secret sauce. The Developer’s Dilemma: Knowledge vs. Access There’s a weird gap in the AI developer ecosystem right now. On one side, you have people who understand NestJS, React, TypeScript — all the standard web dev tools. On the other side, you have LLM APIs that can do incredibly smart things. \ The problem? These two worlds don’t always speak the same language. \ Many developers I’ve talked to are intimidated by the “AI” part. They think they need to understand transformers, attention mechanisms, and backpropagation. But here’s the truth bomb: you don’t need to know how the sausage is made to make a great sandwich . What Actually Goes Into an AI Agent? Let me demystify this for you. A basic AI agent setup involves: Picking an LLM provider (OpenAI, Anthropic, Google, etc.) Getting an API key (usually free to start) Creating a streaming endpoint (so responses feel real-time) Sometimes installing a Node module (the provider’s SDK) Wiring it to a UI (chat interface, usually) \ That’s… basically it. Sure, you can get fancy with RAG, function calling, embeddings, and all that jazz. But at its core? Five simple steps. \ And here’s the beautiful part: these steps are completely language and framework-agnostic . Python, JavaScript, Go, Rust - does not matter . Express, FastAPI, Spring Boot - does not matter . The concepts remain exactly the same. The LLM providers expose HTTP APIs that speak JSON. Your job is to call them and handle the responses. \ That is it. The API Key Dance Every LLM provider follows roughly the same pattern: 1. Sign up for their platform 2. Navigate to some “API Keys” section 3. Click “Create New Key” 4. Copy that key (you’ll only see it once, so don’t mess up) 5. Stick it in your .env file 6. Pick a model name (gpt-4, claude-3-opus, gemini-pro, whatever) \ It’s almost boring in its simplicity. Almost. The Streaming Part (Where It Gets Slightly Interesting) Nobody likes waiting 30 seconds for a response to appear all at once. That’s why modern LLM APIs support streaming - they send tokens as they’re generated, word by word, like a human typing. \ Setting this up is usually: const stream = await llmProvider.createChatCompletion({ model: ‘your-model-name’, messages: […], stream: true }); for await (const chunk of stream) { // Send chunk to frontend } \ Different providers have different APIs, but the concept is identical: you get chunks of text and push them to your UI in real-time. Enter the Boilerplate After building a few AI projects from scratch, I got tired of copy-pasting the same setup code. So I built a boilerplate that handles all the boring stuff: Monorepo structure (backend + frontend in one place) TypeScript everywhere (because we’re not savages) NestJS backend (clean, maintainable, scalable) React frontend (with a chat UI that doesn’t look like it’s from 2005) Shared types (so your API and UI speak the same language) Pre-configured streaming (real-time responses out of the box) \ The current version uses Google Gemini as the LLM provider, but here’s the cool part: it’s designed to be swapped out. Don’t like Gemini? Cool, use OpenAI. Want Claude instead? Go for it. The architecture doesn’t care. Why Gemini? (And Why It Doesn’t Really Matter) I chose Google Gemini for the default branch for a few reasons: Free tier that’s actually usable (not some “10 requests per month” nonsense) Simple API (dead simple to work with) Good performance (fast responses, decent quality) No credit card required to get started \ But honestly, the provider choice is like picking between pizza toppings. Everyone has their favorite, and switching is trivial once you have the infrastructure in place. \ Not feeling Gemini? Check out Groq - it might be even simpler. They run Llama models blazingly fast (like, seriously fast), have a generous free tier, and their API is nearly identical to OpenAI’s. Sometimes, the best choice is the one that gets you started fastest. \ The long-term vision? Add integrations for every major LLM provider out there. OpenAI, Anthropic, Groq, Cohere, Mistral, and local models via Ollama - the goal is to eventually cover them all. Each one in its own branch, clean and focused, so you can grab exactly what you need without wading through code for providers you’ll never use. The Frontend: Not Just an Afterthought Let’s talk about the UI for a second because this is where a lot of “developer-built” AI tools fall flat. \ You know the type: black terminal-style interfaces with green text that scream “I’m a backend developer who thinks CSS is black magic.” \ Not here. The boilerplate includes a clean, modern React chat interface with: Message streaming (words appear as they’re generated) Markdown support (code blocks, formatting, the works) Conversation history (because context matters) Responsive design (looks good on your phone, not just your 27“ monitor) \ Is it the most beautiful chat UI ever created? No. But it’s professional, functional, and a solid starting point. More importantly, it’s your starting point - fork it, style it, make it pink with Comic Sans if that’s your jam. The Monorepo Structure: Everything in Its Place One of my favorite parts of this setup is the monorepo organization: apps/ server/ # Backend API + agent logic web/ # Frontend UI \ Each app has its own: Environment variables (no config bleeding) Dependencies (install only what you need) Lifecycle (dev, build, test independently) \ Simple, clean, focused. The backend handles the AI logic and API endpoints. The frontend handles the user interface. No unnecessary abstraction layers, no over-engineering. So, What’s Next? Here’s where it gets exciting. More LLM Providers (Coming Soon™) I’m adding support for other providers as separate branches: openai branch: GPT-4, GPT-4 Turbo, GPT-4o anthropic branch: Claude 3.5 Sonnet, Claude 3 Opus ollama branch: Local models (Llama, Mistral, run it on your laptop) \ Why separate branches instead of one mega-config? Because each provider has its own quirks, dependencies, and setup patterns. Branches keep things clean — you pick the one you need, no bloat from providers you’ll never use. MCP Integration (The Really Cool Stuff) MCP (Model Context Protocol) is where things get spicy. It’s Anthropic’s open standard for connecting AI models to external data sources and tools. \ Imagine an AI agent that can: Query your company’s database Read your Google Drive documents Check your calendar Pull from internal APIs Access specialized knowledge bases \ That’s MCP. And it’s coming to the boilerplate soon. \ The architecture is already set up to support it - the agent layer is designed to be pluggable. Adding MCP tools will be a natural extension, not a complete rewrite. The “Steal This” Philosophy Here’s the part where I’m supposed to ask you to star the repo, follow me on Twitter, and join my newsletter about AI trends. \ Nah. \ Just take the code. Fork it. Copy-paste the parts you like. Delete the parts you don’t. Build something cool with it. \ That’s the whole point. \ This boilerplate exists because I got tired of rebuilding the same foundation over and over. Maybe you’re in the same boat. Maybe you just want to prototype something quickly. Maybe you’re learning how modern AI apps are structured. \ Whatever your reason, the code is there. Use it. Abuse it. Make it better. Or make it completely different — I’m not your boss. The Actual Getting Started (If You Skipped to the End) Okay, fine, here’s the ultra-condensed version: \ # Clone it git clone https://github.com/your-username/ai-agent-nest-react-boilerplate.git # Get a Gemini API key from https://ai.google.dev/ # Add it to your .env cp apps/server/.env.example apps/server/.env # Edit apps/server/.env and add: GEMINI_API_KEY=your_key_here # Install & run pnpm install pnpm dev # Open http://localhost:5173 Final Thoughts Building AI agents in 2026 isn’t about being a machine learning expert. It’s about understanding modern web architecture, knowing how to integrate APIs, and not being afraid to wire things together. \ The hard part — training massive language models — has already been done by teams with billions of dollars and warehouses full of GPUs. Your job is to use those tools to build something useful, interesting, or just plain fun. \ So, stop overthinking it. Grab the boilerplate, pick an LLM, and start building. The AI revolution isn’t coming - it’s here. And the barrier to entry is way lower than you think. \ Now, go make something cool. 🚀 \ Questions? Issues? Want to contribute? The repo is open. The issues are open. The PRs are welcome. Or just fork it and never speak to me again - that’s cool too. \ Happy hacking.