💥 The AI Tech Stack That Will Dominate 2026 (My 41-Tool List)

Hey there!

If you are building software in 2026, you are probably wondering what tools you should use. There are so many choices, and it can make you feel overwhelmed (confused and stressed) very fast.

But here is the thing: I am about to share with you a complete tech stack (set of tools) that I have been testing and using myself for over a year.

This is not just a random list of tools. It is a group of technologies I chose carefully. They all work smoothly together, and they focus on building with AI first.

Before we start, here is my biggest piece of advice: Find what works for you and stick with it. Don't keep jumping from tool to tool. My way of thinking is simple: What a tool can do is more important than the tool itself. Focus on solving problems, not on getting the newest, shiniest tool.

This article is organized by the type of app you are building. It doesn't matter if you are making AI agents, full-stack apps, web automation tools, or RAG systems. I’ve got you covered. Let’s start.

Part 1: Core Infrastructure - The Foundation For Everything

These are the tools that power almost everything I build. I don't use them 100% of the time, but they are useful for almost any software project.

1. Database: PostgreSQL (using Neon or Supabase)

• What it is: Think of a database like a giant, organized filing cabinet for your app. It stores all the information (user data, posts, etc.). PostgreSQL is my choice for almost everything. I run it using Neon or Supabase.

• Why I use it:

  • I have used Supabase for a longer time, but I am testing Neon more now because it scales (grows) very well.

  • Both platforms look similar and work the same way.

  • PostgreSQL is the industry standard for AI agents.

  • Large Language Models (LLMs) understand SQL (the language of this database) much better than NoSQL databases. This is very important. When you ask AI to write a query to get data, it does a great job with SQL.

  • It has better pricing and scaling compared to other options.

• Other options:

  • MongoDB: A popular NoSQL database.

  • Firestore: Another NoSQL option from Google.

  • I was a big fan of Firestore years ago, but I switched to SQL because it works much better with AI development.

• When to use it: For any project that needs to store structured data. This is basically everything, from AI agents to full-stack apps.

2. Caching: Redis (Or Valkey For Open-Source)

• What it is: Caching is like your app's short-term memory. Instead of always going to the "filing cabinet" (database) to get something you ask for often, the app keeps it in its "pocket" (cache) for faster access. Redis is a tool that does this.

• Why I use it:

  • Extremely fast performance.

  • It's the industry standard for caching.

  • Easy to set up and use.

• Open-source option:

  • Valkey: A completely open-source option that works just like Redis.

  • I include Valkey in my local AI package. So, you can use Redis when you want something convenient and hosted (managed for you), but you can switch to Valkey when you want to run everything locally (on your own computer).

• When to use it: When you need to make your app faster by caching data that you use a lot.

3. AI Coding Assistant: Claude (With Archon)

• What it is: Claude (from Anthropic) is probably the tool I have open most on my computer. It's an AI coding assistant that helps me write code faster and better.

• Why I use it:

  • I always use it with Archon, which is my own open-source project for managing knowledge and tasks for AI coding assistants.

  • It has the best features for slash commands, sub-agents, and the new Claude Skills.

  • Right now, it's considered one of the best AI coding assistants you can get.

• Other options:

  • Cursor 2.0: Very popular and improving fast.

  • Codex (from OpenAI): It's catching up, but not quite there yet.

• An example prompt (command): Instead of just asking "how to make a button?", I will ask:

"Claude, I'm building a React app with Tailwind CSS. Make me a React component named 'PrimaryButton'. It needs to take a 'text' prop and an 'onClick' prop. The button should have a blue background, rounded corners, a lighter hover effect, and white text."

• When to use it: For all your coding work. I use Claude with Archon for every type of software I create.

4. No-Code Prototyping: n8n

• What it is: n8n is a no-code tool. It helps me build prototypes (test versions) of AI agents and workflows by connecting visual blocks together, like building with Lego. No coding needed.

• Why I use it:

  • Perfect for quickly testing ideas.

  • Great for checking if the tools you want to give your agent will work.

  • Good for testing system prompts before you move to code.

  • Has tons of app integrations.

  • Very focused on AI with constant updates.

  • It's open-source and you can self-host it (run it on your own server).

• Other options:

  • Langflow: Another good option.

  • Flowise: I have talked about this on my channel before.

• My workflow: I will use n8n to quickly build a test AI agent, check everything, and then move to a coded solution once I am happy with my prototype.

• When to use it: When you want to quickly test an idea before you spend time writing code.

Part 2: Core Stack For AI Agents

Now let's talk about the tools I use for building AI agents specifically. These tools are built on top of the core infrastructure we just covered.

5. AI Agent Framework: Pydantic AI

• What it is: A framework is like a toolkit or a set of building blocks. It gives you ready-made parts so you don't have to build everything from scratch. Pydantic AI is my chosen framework for building individual AI agents.

• Why I use it over other options:

  • People always ask me why I use Pydantic AI when there are so many options. Here's why:

  • It's easier to build agents than using raw LLM calls.

  • It still gives me all the flexibility and control I need.

  • No "abstraction distraction." This just means I don't have to "fight" the tool. Many frameworks are too complex and make you learn their weird rules instead of helping you build.

  • It's always being updated to support new protocols (ways of communicating) like MCP, A2A, and AGUI.

  • It makes it easy to switch between different LLM providers (like OpenAI, Claude, etc.).

• Other options:

  • Raw LLM calls: Some people like this for maximum control.

  • Other agent frameworks: There are many, but most have that "abstraction distraction" problem.

• When to use it: For building any AI agent where you need it to be reliable and flexible.

6. Multi-Agent Framework: LangGraph

• What it is: If Pydantic AI builds one AI "worker," then LangGraph is the "manager" that helps multiple workers connect and work together on complex jobs.

• Why I use it:

  • Pydantic AI builds my individual agents.

  • LangGraph connects them together for multi-agent systems.

  • It's easy to manage the "state" (what's happening) across all agents.

  • It has "human-in-the-loop" features (this lets a human check the work) for complex workflows.

  • You can save and see the workflow as a graph.

  • It's the most mature (well-developed) option for managing many agents.

• Important note: I only use multi-agent systems when the project really needs it. Don't over-engineer (make it too complicated) - start simple!

• Other options:

  • Crew AI: Many people love this for multi-agent systems.

  • Pydantic AI Graphs: You can also use Pydantic AI for multi-agent systems.

• When to use it: Only when you need multiple agents working together on complex tasks. Start with a single agent first.

7. Tool Authorization: Arcade

• What it is: Arcade handles agent permission and tool security. This is very important when your agents need to access a user's account.

• Why this is important:

  • Let's say your AI agent needs to:

    • Read emails from a user's Gmail.

    • Send a message for them in Slack.

    • Post to their Reddit account.

  • Your agent needs permission to do these things. That's where Arcade comes in. It's like a security guard.

• What makes Arcade special:

  • It handles the secure OAuth (secure login) process automatically.

  • It's very secure with detailed permissions.

  • It recently released an MCP server SDK for building secure MCP servers.

  • There are no real alternatives that do it this way.

• When to use it: When your AI agents need to do things for individual users with their accounts.

8. Agent Observability: Langfuse

• What it is: "Observability" is a fancy word, but it just means "being able to see what's happening." Langfuse lets you watch and monitor your AI agents, especially when they are running for real users (in production).

• Why you can't skip this:

  • Without this, you have no idea what your agents are doing. Langfuse shows you:

    • How many tokens (small pieces of words) are used for every run.

    • The total cost for every time the agent runs.

    • How long it took (latency).

    • All the tool calls your agents make.

    • The different agents in a multi-agent system.

• What you can do with it:

  • Set up evaluations (check if the agent is doing a good job).

  • A/B test different system prompts.

  • Fix bugs (debug) in production.

  • Optimize costs and make the agent more reliable.

• Other options:

  • LangSmith: Popular, but not fully open-source.

  • Helicone: Another good option.

• Why Langfuse? It has the most features and is fully open-source and self-hostable.

• When to use it: Always. Seriously, use it for every AI agent project.

Part 3: Tools For RAG Agents

Quick Note: "RAG" stands for "Retrieval-Augmented Generation." This is a complex way of saying: "The agent can read documents before it answers you." It's like an "open-book" exam for the AI, so it can give you facts and not just "guess."

9. Data Extraction From Documents: Docling

• What it is: Docling is a framework for pulling data out of complex documents like PDFs with diagrams, Excel files, and more.

• Why I use it:

  • It makes getting data from complex documents so much easier.

  • It works with self-hosted models.

  • It's completely open-source.

  • One of the newest additions to my stack.

• Other options:

  • LlamaIndex: Has many extraction tools (but it's more of a full RAG framework).

  • Unstructured: Another option.

• When to use it: When you are working with files – PDFs, Word documents, Excel sheets, anything that is a document.

10. Data Extraction From Websites: Crawl4AI

• What it is: A fast and efficient web scraping tool made just for AI applications. "Scraping" means grabbing information from websites.

• Why I use it:

  • Very fast and efficient.

  • It automatically cleans up junk (like ads, menus, etc.) from websites and just gets the main content.

  • It has LLM integrations built-in.

  • You can ask the LLM to pull specific information from a page.

• My simple decision process:

  • Working with files? → Use Docling.

  • Working with websites? → Use Crawl4AI.

• When to use it: When you need to get information from websites for your RAG system.

11. Vector Database: PostgreSQL With pgvector

• What it is: A vector database is a special kind of database. A normal database searches for exact words. A vector database searches for similar meaning or concepts. I use PostgreSQL as my vector database by using the pgvector extension.

• Why I don't use dedicated vector databases:

  • Yes, dedicated vector databases like Pinecone and Qdrant are faster. But here's why I still use PostgreSQL:

  • Most RAG systems also need a normal SQL database (to store metadata, user info, etc.).

  • This gives me a simpler architecture - one database instead of two. This saves me a lot of time.

  • PostgreSQL scales (grows) extremely well.

• Other options:

  • Qdrant: A dedicated vector database, faster.

  • Pinecone: Another dedicated option, also faster.

• When to use it: For any RAG system where you need to store and search through embeddings (these are the number representations of meaning).

12. Long-Term Memory: Mem0

• What it is: Mem0 is a framework for adding long-term memory to AI agents.

• Why long-term memory is just RAG: Long-term memory is really just a type of RAG. Your agent saves "memories" (as text) and retrieves (reads) them when needed.

• Why I love Mem0:

  • It integrates with any database I want.

  • It works directly with pgvector (which I already use).

  • Super easy to add to any AI agent.

  • It can wrap around any agent framework.

• When to use it: When your AI agent needs to remember things between conversations (for example: "Remember that I don't like the color pink").

13. Knowledge Graphs: Neo4j + Graphiti

• What it is: Knowledge graphs store relationships. A Vector Database (item 11) is good at finding similar text. A Knowledge Graph is good at answering "Who worked with Steve Jobs at Apple?"

  • Neo4j: The graph database (the engine).

  • Graphiti: The library to add and search data (how you use the engine).

• Why I use Neo4j:

  • It has a beautiful UI (user interface) to see the graphs (relationships).

  • Easy to query (ask questions of) the data.

  • Supported by most knowledge graph libraries.

• Why I use Graphiti:

  • A knowledge graph needs an AI to read plain text and find the relationships. Graphiti does that.

  • It intelligently finds entities (people, places) and relationships (works for, founded) and stores them in Neo4j.

• When to use it: When you need to understand complex relationships between things in your data. This is an advanced technique.

14. RAG Evaluation: Ragas

• What it is: Ragas is a tool to grade or score your RAG system.

• Why evaluation is important:

  • You need to know if your RAG system is actually working well. Ragas gives you scores for things like:

  • Faithfulness: Is the agent's answer true to the information it read? (Or is it making things up?)

  • Relevance: Was the information it read related to the question?

• Why not just use Langfuse?

  • Langfuse (item 8) is great for general monitoring. But for RAG-specific scores like faithfulness, Ragas is specialized and better.

• When to use it: When you need to measure and improve your RAG system's performance.

15. Web Search: Brave Search API

• What it is: Brave Search API gives your agents the ability to search the entire web.

• Why this is part of RAG: RAG isn't just about searching your own knowledge - it's also about searching the web when needed.

• Why I use Brave:

  • It's privacy-focused and doesn't track you.

  • It has its own independent index (it's not just a copy of Google).

  • It has AI search features built-in.

• Other option:

  • Perplexity: More detailed but slower.

  • I use both. Brave for quick searches, Perplexity for more detailed research.

• When to use it: When your agent needs current information from the web.

Part 4: Tools For Web Automation Agents

This part is about agents that control a browser and interact with websites (clicking, filling forms). This is different from data extraction (Part 3), which is just reading information.

16. Live Web Data Extraction: Crawl4AI

• What it is: Same tool as before (item 10), but used in a different way.

• How I use it for automation:

  • Instead of pre-loading data, I give my agents a Crawl4AI tool so they can get information from websites in real-time during a conversation.

• When to use it: When your agent needs to pull information from websites while it's running, not beforehand.

17. Social Platform Data: Apify Or Bright Data

• What it is: Special tools for getting data from social platforms.

• Why you need this:

  • Crawl4AI does not work well on social platforms (LinkedIn, X/Twitter, Instagram). For those, you need special tools.

  • Note: Be careful. Scraping these sites might be against their rules (Terms of Service) and could get your account banned.

• When to use it: When your agent needs to work with social media platforms.

18. Deterministic Browser Automation: Playwright

• What it is: "Deterministic" means predictable or scripted. Playwright is for simpler, step-by-step browser automation.

• Why I use it:

  • You write a script: 1. Go to website. 2. Click "login." 3. Type username. 4. Type password. It does the exact same steps every time.

  • It supports multiple browsers.

  • The Playwright MCP server is amazing for AI coding assistants (it lets the AI see the website changes it makes).

• Other options:

  • Puppeteer: Another good option.

  • Selenium: I used this for years before I switched to Playwright.

• When to use it: For predictable web automation and testing tasks.

19. AI-Powered Browser Control: Browserbase

• What it is: Browserbase is where things get really powerful. This is for letting an AI agent control a browser for you using natural language.

• Why I love it:

  • Managed infrastructure (you don't worry about servers).

  • All sessions (what the agent did) are recorded and stored.

  • It has anti-bot detection built-in.

  • Very secure.

• Special features:

  1. Stagehand MCP Server: You describe what you want in natural language (e.g., "get today's weather"). It starts a secure browser, visits websites to find the info, and you can replay the session later.

  2. Director: Lets you give your agent any web task: "Get me the latest price for protein powder on Amazon." It shows you step-by-step what it did, including screenshots.

• When to use it: When you want your agent to control a browser and complete complex web tasks.

Part 5: Full-Stack Development Tech Stack

"Full-stack" just means building the entire application: both the backend (what runs on the server) and the frontend (what you see in your browser).

20. API Framework: FastAPI

• What it is: An API is how the frontend (website) talks to the backend (server). FastAPI is my choice for building APIs in Python.

• Why I use it:

  • I build my AI agents in Python.

  • I want my API framework to also be in Python.

  • It has more features than Flask (another Python option).

• When to use it: For any backend API that powers your AI agents or full-stack apps.

21. Database: PostgreSQL

Same as before – PostgreSQL is my standard database. I won't repeat myself here!

22. Simple Authentication: Supabase

• What it is: Authentication is the "login" process. Supabase provides simple, built-in authentication.

• Why I use it:

  • Super simple to set up.

  • Works perfectly with PostgreSQL (which I'm already using).

  • Row-Level Security is built-in.

• When to use it: For simple login needs in your apps (email/password login, Google login).

23. Enterprise Authentication: Auth0

• What it is: Auth0 handles more complex login needs.

• When you need Auth0 instead of Supabase:

  • Multi-factor authentication (MFA).

  • Login with more providers.

  • Enterprise Single Sign-On (SSO) with SAML or Active Directory.

  • More complex login requirements.

• Other options: Clerk, Okta.

• When to use it: For business-level login needs.

24. Frontend Library: React + Vite

• What it is: React is the library (toolkit) for building the frontend (the user interface). Vite is the build tool that makes React run fast.

• Why I use React:

  • It's very simple to use.

  • All AI coding assistants prefer React.

  • Frontend builders like Lovable (item 26) use React.

  • It makes light and fast applications.

• Other options:

  • Next.js: I used this a lot in the past, but it keeps changing between versions (12, 13, 14, 15), which breaks my code.

  • Vue: Another good option.

• When to use it: For any frontend application you are building.

25. Component Library & Styling: Shadcn + Tailwind CSS

• What it is:

  • Tailwind CSS: A library for "coloring" and "styling" your app.

  • Shadcn: A library of pre-built "blocks" (components) like buttons and pop-up boxes that you can copy and paste into your project. It uses Tailwind CSS for styling.

• Why I use them:

  • They are pretty standard these days.

  • They work great together.

  • They make building UIs (user interfaces) much faster.

• When to use it: For styling your React applications.

26. AI-Driven Frontend Builder: Lovable

• What it is: Lovable is an AI agent that builds beautiful user interfaces for you.

• Why use this instead of just Claude (item 3):

  • Claude is great at logic, but it doesn't make the most beautiful user interfaces. Lovable has special system prompts that are optimized for making beautiful designs.

• When to use it: When you need to create a beautiful frontend quickly.

27. Rapid UI Prototyping: Streamlit

• What it is: Streamlit lets you build user interfaces (UIs) directly in Python.

• Why this is powerful:

  • This is the easiest way you can possibly make a UI.

  • No need to build a separate frontend and backend.

  • Perfect for making prototypes (test versions).

• My workflow for building AI agents:

  1. Prototype the agent in n8n (no-code).

  2. Move to code, use Streamlit for a simple UI (like a chat box).

  3. Create a full React app with Lovable when I'm ready for my MVP (Minimum Viable Product).

• When to use it: For quick prototypes where you want a nice chat interface without building a full React app.

28. App Monitoring: Sentry

• What it is: Sentry provides real-time monitoring for your apps. It tells you when and why your app crashes or has an error.

• When to use it: For monitoring your live applications (in production).

29. Payments: Stripe

• What it is: Stripe handles payments in your applications.

• Why I use it:

  • The best developer experience (easy for coders to use).

  • Fantastic documentation (instructions).

  • It's the industry standard.

• When to use it: For any application that needs to accept payments.

Part 6: Deployment & Infrastructure

"Deployment" is the process of "putting your app on the internet" so other people can use it.

30. Simple Deployment: Render

• What it is: Render is a Platform-as-a-Service (PaaS). You just give them your code, and they do the rest (managing servers, etc.).

• Why I use it:

  • The simplest deployment option.

  • You can define your infrastructure as code (in a YAML file).

  • Git-based deployments (you push code to a branch, it deploys automatically).

  • Free to host frontends.

• Other options: Fly.io, Netlify.

• When to use it: For most of your deployment needs when you want simplicity.

31. Enterprise Cloud: Google Cloud Platform (GCP)

• What it is: Google's cloud infrastructure platform. This is the "heavy-duty" option.

• When I use this instead of Render:

  • For enterprise (big business) requirements for certain clients.

  • When I need SLAs (service guarantees) or specific compliance.

  • When I want serverless functions.

• Why GCP and not AWS:

  • I prefer GCP's serverless functions over AWS's. This is a personal preference.

• When to use it: For enterprise needs or when you need more control over your infrastructure.

32. GPU Hosting: RunPod

• What it is: GPUs are special computer chips that are very powerful for AI. You need them to run open-source AI models. RunPod provides GPU hosting (you can rent them).

• Why I use it:

  • The cheapest, reliable GPU hosting I have found.

  • No queue (no waiting) - GPUs are available instantly.

  • "Spot instances" are available for even lower cost (but they are less reliable).

• Other options: TensorDock, Lambda Labs.

• When to use it: When you need to run GPU-heavy tasks, like local AI models, in the cloud.

33. Virtual Machines: DigitalOcean

• What it is: DigitalOcean provides virtual machines (computers in the cloud) that you own and manage.

• Difference from Render:

  • Render manages the infrastructure for you.

  • DigitalOcean gives you the machine, and you manage it yourself.

• Why I use it:

  • Very reliable.

  • Predictable pricing.

  • Great AI integrations (app platform, managed databases, RAG features).

• When to use it: When you want to host something yourself, like a local AI package, in the cloud.

34. Containerization: Docker

• What it is: Docker creates isolated "containers" (boxes) to run your applications.

• Why this is important:

  • The "it works on my machine" problem: Sometimes your app works on your computer, but when you move it to a server, it breaks (because of missing files, different versions, etc.).

• How Docker solves this:

  • It "packages" your app and everything it needs into one "box". If it works in the "box" on your machine, it is guaranteed to work anywhere else that runs that "box".

• When to use it: For all your application deployments (except some frontends).

35. CI/CD: GitHub Actions

• What it is: CI/CD stands for "Continuous Integration / Continuous Deployment." It's an automation robot.

  • CI (Continuous Integration): When you push new code, the robot automatically runs all your tests.

  • CD (Continuous Deployment): If the tests pass, the robot automatically "pushes" your new code to the internet (deploys it).

• Why I use it:

  • It's built right into your GitHub repository.

  • Free for public repositories.

  • A huge marketplace of pre-built actions.

• When to use it: To automate testing and deployment in all your projects.

36. Testing Frameworks

• For Python: Pytest

• For TypeScript: Jest

• Why: Both are easy to use and are industry standards. They make testing reliable.

• When to use them: Always test your code, especially before you deploy to production.

37. AI Code Review: CodeRabbit

• What it is: CodeRabbit automatically reviews your pull requests (requests to merge code) using AI.

• Why I love it:

  • Completely free for open-source repositories.

  • Very thorough (detailed) reviews (sometimes too thorough!).

  • It includes security vulnerability detection.

• When to use it: For any open-source project on GitHub.

Part 7: Self-Hostable & Local Tools

These are open-source tools you can run completely on your own hardware (your computer). This is great for privacy and for experimenting.

38. Local LLM Chat: Open WebUI

• What it is: Open WebUI is like ChatGPT, but it runs completely on your machine.

• Why I use it:

  • You can add custom agents.

  • It has RAG (the ability to read documents) built-in.

  • Very rich in features.

• When to use it: When you want a ChatGPT-like interface for your local models.

39. Local Web Search: SearXNG

• What it is: A completely local web search that doesn't rely on external APIs. It gets results from many other search engines and mixes them together.

• When to use it: When you want web search abilities without sending data to external services.

40. Local LLM Serving: Ollama

• What it is: Ollama is the tool that serves (runs) open-source large language models on your machine. This tool was a game-changer for me.

• Why I use it:

  • It works with any open-source LLM (Llama 3, Mistral, etc.).

  • It's the easiest to use. Before Ollama, running these models was very hard. Ollama makes it simple.

  • It automatically uses multiple GPUs (if you have them).

• When to use it: For running any open-source language model on your machine.

41. HTTPS/TLS: Caddy

• What it is: When you see the "lock" icon in your browser, that is HTTPS. Caddy is the easiest way to get that "lock" for your self-hosted services.

• Why I use it:

  • The simplest option available.

  • It gives you HTTPS automatically.

  • Easy configuration.

• When to use it: When you need a domain name and HTTPS for things you are running yourself.

Part 8: Final Thoughts: Building Your Tech Stack

We’ve covered a lot of ground here. Let me summarize the most important points:

1. Stick with What Works: Find tools that work for you and generally stick with them. Don't keep jumping to the newest, shiniest thing.

2. Be Adaptable: With that said, be willing to try new tools when they solve a problem better than your current stack.

3. Capabilities Over Tools: Focus on being a problem solver, not an expert in specific tools. Use tools to solve problems; don't obsess over them.

4. AI-First is the Future: I don't see another way forward. AI-first development is not just a trend – it's how software will be built in the future.

5. Start Simple: Don't over-engineer. Start with a single agent before building multi-agent systems. Use Streamlit before building a full React app.

6. Use This as a Reference: This article is meant to be a resource. If you're ever unsure what technology to use for a part of your stack, come back to this guide and take my recommendation. Then get back to solving the actual problem.

Remember, this tech stack has been tested and proven for over a year. It's stable, reliable, and covers everything you need for AI-first development in 2026.

Now go build something amazing!

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