Tencent EdgeOne Makers

curious how edgeone handles the growing wave of AI agent traffic specifically. traditional CDN caching works great for human browsing patterns but agent requests tend to be API-heavy, bursty, and less cacheable. is there anything in the stack tuned for that kind of workload or is the focus still primarily on conventional web delivery?

congratulations to the team! i appreciate the focus on reducing infrastructure complexity.how does pricing scale for teams that suddenly experience rapid user growth?

congratulations on the launch! supporting both Python and JavaScript is a huge plus.are there plans to expand support for additional languages in the future?

congrats on the launch! the idea of treating AI Agent deployment like web app deployment is really interesting.how does Makers handle long running agent workflows that may span hours or even days?

How do you handle cold starts for agents that haven't been invoked in a while — is there a warm-up penalty on the first request, and does that differ between the JS and Python runtimes?

Congrats on the launch 🚀 Tencent EdgeOne Makers looks like a platform I’d genuinely want to try — deploying AI agents with built-in runtime, sandboxed tools, and edge performance feels like it could save me tons of setup time.

Congrats! @tobias_lau

Congrats team! The observability piece is probably what I’d test first. Debugging agent behavior is still way too painful in most setups.

Congrats on the launch! Interesting direction. What does debugging look like when multi-step agent workflows fail across tools and runtimes in production?

Hey Product Hunt 👋 Kitty here, product lead for Tencent EdgeOne Makers. Over the past year, I've watched more and more people—including myself—start building their own AI Agents.Today, building an Agent has never been easier. A solid idea and a few hours gets you a working demo. But the real work starts after the demo ships.Suddenly, you're hit with a wall of production questions: How do you manage memory? How do you run tools securely in sandboxed environments? How do you trace and debug execution paths? How do you scale when a hundred users hit it at once? And how do you deploy it globally so it's actually fast?Most builders end up choosing between two painful paths: spend weeks building all of this boilerplate infrastructure from scratch, or lock themselves into a restrictive platform that dictates which framework, language, or model they have to use.We wanted a third option. That's why we built Tencent EdgeOne Makers.Tencent EdgeOne Makers is an edge platform for modern web apps and AI Agents. It fits into the workflows developers already know, with familiar CLI, Git, and CI/CD support. You get Agent runtime, sandboxed tools, memory, observability, model gateway support, serverless functions, and storage built in, without having to stitch together complex infrastructure yourself. In other words, you can deploy AI Agents the same way you deploy web apps. We kept the platform completely open. No vendor lock-in, no framework constraints:Framework agnostic: Works out of the box with Claude SDK, OpenAI SDK, LangGraph, CrewAI, and more.Polyglot: Full support for both JavaScript and Python.Flexibility: Use whatever model or tech stack makes sense for your application. Whether you’re looking to plug an Agent into an existing SaaS, website, or e-commerce flow, or you're building a brand-new AI application from scratch (like an AI recruiter, sales rep, data analyst, or fitness coach)—Tencent EdgeOne Makers is designed to let you spend your time on your product, not the plumbing.We're excited to share this with the Product Hunt community today. Give it a spin, ask us any tough questions, and let us know what you think! Feel free to join our Discord to chat with us.Thanks so much for the support!