Tencent EdgeOne Makers is an edge platform for modern web apps and AI agents. Build with your preferred frameworks and deploy through familiar CLI, Git, and CI/CD workflows. Get built-in agent runtime, sandboxed tools, memory, observability, model gateway support, serverless functions, and storage—without stitching together complex infrastructure. Add AI agents to existing products or launch new AI applications in minutes. Deploy AI agents like web apps.
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.
This sounds very important like Cloudflare. I wish it was clearer whether the AI agents can use EdgeOne through a CLI! Congrats on the launch :>
Congrats on the launch! The no lock-in part is important. A lot of teams don’t want to bet their whole AI stack on one model or one framework.
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 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?
The fact that I can start from a working example instead of a blank repo is underrated for actually shipping.
Congrats Kitty and team. The sandboxed tools part is the most interesting to me. Do you have examples of what kind of tool permissions or isolation developers can control?
It's interesting how much of user experience depends on things people never see directly. Faster load times and reliability rarely make headlines, but users definitely notice when they're missing. Nice work.
The fact that I can start from a working example instead of a blank repo is underrated for actually shipping.
Web and agents in one project with unified deploy is a really smart move. Less context switching, less glue code.
Congrats on the great product! The edge angle is the interesting bet. Edge runtimes are usually tuned for short requests, but agents that actually do work run long, with memory and retries between steps. How do you guys handle an agent that runs for minutes or picks up a scheduled task later?
Built-in tracing is underrated. Debugging agent behavior in production is usually pure pain — happy to see it first-class here.
Congrats @kitty_lee1 and team. The sandboxed tools part is the most interesting to me. Do you have examples of what kind of tool permissions or isolation developers can control?
About Tencent EdgeOne Makers on Product Hunt
“Ship AI agents like web apps, in minutes.”
Tencent EdgeOne Makers launched on Product Hunt on June 24th, 2026 and earned 348 upvotes and 80 comments, earning #2 Product of the Day. Tencent EdgeOne Makers is an edge platform for modern web apps and AI agents. Build with your preferred frameworks and deploy through familiar CLI, Git, and CI/CD workflows. Get built-in agent runtime, sandboxed tools, memory, observability, model gateway support, serverless functions, and storage—without stitching together complex infrastructure. Add AI agents to existing products or launch new AI applications in minutes. Deploy AI agents like web apps.
Tencent EdgeOne Makers was featured in Website Builder (9.6k followers), Artificial Intelligence (471.8k followers) and Development (6k followers) on Product Hunt. Together, these topics include over 108k products, making this a competitive space to launch in.
Who hunted Tencent EdgeOne Makers?
Tencent EdgeOne Makers was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Tencent EdgeOne Makers stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Zac Zuo
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!