Technology Stack
Built With — Our AI Agent Technology Stack
Every AI agent system I build is powered by a carefully selected stack of technologies chosen for reliability, performance, and long-term maintainability. I don't chase hype. I pick tools that have proven themselves in production, integrate well with each other, and give my clients the flexibility to evolve their systems as their needs change.
Below you'll find every major technology in the OpenClaw stack. Click into any technology to learn what it does, why I use it, and how it contributes to building AI agents that actually work in real business environments.
The Philosophy
Production-Proven. Not Hype-Driven.
The AI space moves fast, and there's a new “must-use” framework or model every week. I've been building AI agent systems long enough to know that most of the hype fades, and what survives is the technology that actually works when your business depends on it. My stack reflects that philosophy: every tool earned its place by performing reliably in production deployments across dozens of client projects.
The stack combines best-in-class LLMs like GPT-4 and Claude for intelligence, proven frameworks like LangChain and CrewAI for agent orchestration, production-grade infrastructure like Supabase for data and RAG, and automation platforms like n8n for connecting everything together. Each component handles its domain exceptionally well and integrates cleanly with the others.
Importantly, this isn't a locked-in stack. Different projects call for different combinations. A simple customer support agent might use GPT-4o mini, LangChain, and Supabase. A complex multi-agent workforce might use Claude, LangGraph, CrewAI, and custom Python orchestration. I choose the right tools for each project based on the specific requirements, not based on a one-size-fits-all template.