My 2026 Tech Stack for Building AI Applications

Everyone loves a good tech stack post. But most of them read like shopping lists—tools without context, choices without reasoning. That's not useful.

So here's the stack I actually use to build AI applications in 2026, with the real reasons behind each choice. Some of these decisions took months of trial and error. Hopefully I can save you some of that pain.

Why Stack Choices Matter

Before we dive in: why does this even matter?

The AI tooling landscape is genuinely overwhelming. New frameworks drop weekly. Every tool promises to be the "last one you'll ever need." And because this space moves so fast, yesterday's best practice is tomorrow's antipattern.

Bad stack choices in AI development hurt more than in traditional software. You're not just fighting bad abstractions—you're fighting compounding costs, unpredictable latency, and debugging issues in systems that are already hard to reason about.

The stack I've landed on optimizes for three things: debuggability, cost control, and speed to production. Not theoretical elegance. Practical outcomes.

The Core Stack

LangGraph for Agent Orchestration

Here's a take that might be controversial: I think vanilla LangChain is usually the wrong choice in 2026.

LangChain was revolutionary. It gave us a shared vocabulary for LLM applications when we desperately needed one. But as I built more complex agents, I kept fighting against its abstractions rather than working with them. Chains felt too rigid. The magic was too magical.

LangGraph changed that for me. It's built by the same team, but with a fundamentally different philosophy. Instead of chaining, you're building state machines. Your agent is a graph of nodes with explicit transitions. When something goes wrong—and it will—you can actually see where in the graph execution failed.

The killer feature? Persistence and time-travel debugging. I can pause an agent mid-execution, inspect its state, modify it, and resume. When you're building agents that make decisions over multiple steps, this is invaluable. I've lost count of how many hours this has saved me.

It's also more honest about what agents actually are: not linear chains of operations, but complex state machines that branch, loop, and recover from failures.

Claude API for Reasoning

I've used GPT-4, Gemini, Claude, and a half-dozen open-source models. For the work I do—building agents that need to reason about complex tasks and maintain coherence over long interactions—Claude has consistently performed best.

A few specific reasons I stick with Anthropic:

Instruction following. Claude handles nuanced system prompts better than alternatives I've tested. When I say "do X except in cases Y and Z," it actually remembers the exceptions. This matters enormously for production agents.

Longer context without degradation. The 200K context window isn't just a number—it's actually usable. I've thrown massive documents at Claude and asked questions about specific details near the end. It works. Other models often "forget" earlier context as the window fills.

The API is boring. I mean this as a compliment. It's stable. Breaking changes are rare and well-communicated. The pricing is predictable. When you're building production systems, boring is beautiful.

I still use other models situationally. GPT-4o-mini for high-volume, lower-stakes tasks. Local models for anything with strict data residency requirements. But Claude is my default.

Prefect for Workflow Scheduling

"Why not Airflow?"

Because I want to build AI applications, not manage Airflow.

Look, Airflow is battle-tested. It powers data infrastructure at massive scale. But for the kind of work I do—smaller teams, faster iteration, AI-heavy workflows—it's overkill and operational overhead.

Prefect hits a sweet spot. It's Pythonic (decorators, not XML). Local development actually works. And the cloud offering means I don't need to run my own scheduler infrastructure.

For AI workflows specifically, a few things stand out:

Retries with exponential backoff are first-class citizens. Essential when you're calling rate-limited APIs.
Caching based on inputs. If I've already processed a document, don't process it again.
Observability that doesn't require stitching together five different tools.

Most of my agents run on schedules—daily data refreshes, weekly reports, periodic monitoring. Prefect makes this easy without making everything else hard.

Python as the Glue

This barely needs explanation. Python won the AI/ML ecosystem years ago, and the gap has only widened.

Every model API has a Python SDK. Every orchestration framework is Python-first. Every data tool speaks Python fluently. Fighting this is fighting gravity.

I use uv for dependency management (finally, fast installs) and Pydantic for everything that touches data validation. The combination of type hints plus runtime validation has eliminated entire categories of bugs in my agent code.

Supporting Tools

The core stack handles the AI-heavy lifting. These tools handle everything around it.

Cursor for AI-Assisted Coding

My editor is Cursor, and at this point I can't imagine going back to vanilla VS Code.

The inline completions are good. The chat is good. But the real value is Composer—you describe what you want, and it edits multiple files coherently. For refactoring agent code, adding new capabilities, or debugging weird state machine issues, it's shockingly effective.

I write maybe 60% of my code by hand now. Cursor handles the rest. The time savings compound.

v0.dev for UI Prototyping

Most of my AI work is backend-focused. But sometimes I need a quick UI—a demo for a client, an internal tool, a proof of concept.

v0.dev lets me go from "I need a dashboard that shows X" to working React code in minutes. I describe what I want, it generates components using shadcn/ui, I tweak and ship. For someone who isn't primarily a frontend developer, this is a superpower.

I don't use it for production UIs that need heavy customization. But for 80% of use cases, it's faster than building from scratch.

Vercel for Hosting

Frontend from v0 goes straight to Vercel. The integration is seamless (they're the same company), but I'd use Vercel anyway.

Deploy previews for every PR. Edge functions when I need them. Generous free tier. It just works.

Google Sheets API for Client-Facing Output

This one might surprise you, but it's incredibly practical.

Many of my clients live in spreadsheets. They don't want to learn a new tool. They don't want a dashboard. They want their data in the same Google Sheet they've used for years, updated automatically.

So I build agents that write directly to Google Sheets. The API is solid, the authentication is (mostly) straightforward, and clients love it because they're already comfortable there.

Never underestimate the power of meeting people where they are.

What I Tried and Dropped

Sharing failures is more useful than pretending I got everything right the first time.

CrewAI: Promising multi-agent framework, but I found it too opinionated about agent roles. LangGraph gives me more control.

Haystack: Good for search-focused applications, but I was fighting the abstractions for general agent work.

Serverless-first everything: Lambda cold starts and API Gateway complexity weren't worth it for my use cases. I run most agents on simple VMs now.

Vector databases as the default: Not everything needs semantic search. Sometimes a SQLite FTS5 index is exactly right. I still use vector DBs, but more selectively.

How These Fit Together

Here's the typical architecture for an agent I build:

Prefect schedules the workflow—maybe daily, maybe triggered by an event.
LangGraph orchestrates the agent logic—gathering data, making decisions, taking actions.
Claude powers the reasoning at each decision point.
Results land in Google Sheets or a database, depending on the use case.
If there's a UI, it's built with v0/Cursor and deployed to Vercel.

It's not fancy. That's the point. Every component is understandable, debuggable, and replaceable.

Recommendations for Different Use Cases

Just starting with AI? Skip the orchestration frameworks entirely. Call the Claude API directly. Add complexity only when you feel the pain of not having it.

Building your first agent? LangGraph with a simple linear flow. Don't try to build a complex state machine on day one.

Need production reliability? Invest in Prefect (or similar). Scheduled workflows with proper retries will save you from 3 AM pages.

Scaling to multiple agents? Now you might need the full architecture. But even then, start simple and grow.

The Stack Keeps Evolving

I've rewritten this stack three times in the past two years. I'll probably rewrite it again. That's the reality of building in a fast-moving space.

What stays constant: choosing tools that are debuggable, boring enough to be reliable, and flexible enough to adapt as the landscape shifts.

I write weekly about building AI applications—the practical stuff, not the hype. If that's useful to you, subscribe to the newsletter for updates.