Tencent EdgeOne
Deliver Fastest and Most Secure Performance at the Edge
219 followers
Deliver Fastest and Most Secure Performance at the Edge
219 followers
Tencent EdgeOne is a cutting-edge global edge network platform engineered to optimize traffic routing, accelerate content delivery, and strengthen security across distributed environments. By seamlessly integrating CDN, DNS, WAF, DDoS protection, and intelligent route optimization, it delivers comprehensive security safeguards, superior network and application acceleration, advanced edge computing capabilities, and robust monitoring and operational analytics.
This is the 2nd launch from Tencent EdgeOne. View more
Tencent EdgeOne Makers
Launching today
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.
Free
Launch Team / Built With
Tencent EdgeOne
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!
Triforce Todos
Congrats on the launch!
For framework-agnostic support, does the sandboxed tool execution behave the same across Claude SDK, LangGraph, and CrewAI, or do some frameworks get more native support than others?
Tencent EdgeOne
@abod_rehman Thanks! The key design choice here is that sandboxed tool execution lives at the runtime layer, not inside any specific framework — so it behaves consistently whether you're on Claude SDK, LangGraph, or CrewAI. The framework handles your agent logic; the sandbox, isolation and tool execution sit underneath and work the same for all of them. That said, we do ship starter templates for each so you get a smooth zero-config start.
Tencent EdgeOne
@abod_rehman
Where framework-awareness kicks in is at the tool interface layer (context.tools) and memory layer (context.store). Both adapt automatically based on a framework field in edgeone.json — so Claude SDK, LangGraph, and CrewAI each get their tools and conversation history pre-wrapped in the format they natively expect, without any glue code on your side.
Since this is polyglot (JS + Python), can a single agent mix both, or is it one language per deployment?
Tencent EdgeOne
@boyuan_deng1 Great question! Today it's one language per deployment — each agent runs in either a JS or Python runtime. But since Web and agents share the same project, you can absolutely have, say, a Python agent and a JS service running side by side and talking to each other. Mixing both within a single agent isn't supported yet — curious, what's the use case you had in mind? It'd help us think about where to take this.
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?
Tencent EdgeOne
@kitty_lee1 @thea5
Thanks! The sandbox is the part we're most proud of too. Each agent runs in an isolated environment, and you get control over things like which tools it can call (web search, code execution, browser, shell, file access) plus guardrails like execution timeouts and interrupt control — so a misbehaving or long-running task can't run away with your resources. Happy to go deeper on the permission model if it's useful — what's your use case?
Tencent EdgeOne
@thea5 Thanks! The sandbox is honestly the part I personally pushed hardest on. When we started, our view was simple: if you're going to let an agent execute code, browse, or touch files on behalf of real users, isolation can't be an afterthought — it has to be the default.
So today each agent runs in its own isolated environment, and you control exactly which tools it can reach — code execution, browser, shell, file access — with timeouts and interrupt controls so nothing runs away with your resources. Happy to walk you through the permission model in more depth — what are you looking to build?
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?
Tencent EdgeOne
@artstavenka1 Great question — and you're right that edge V8 is tuned for short requests. So we don't run agents on the edge.
Agents run on a dedicated cloud runtime built for long, stateful work: a single run goes up to 30 min, with a sandbox (for tools/code) up to 1 hour.
Runs for minutes → sticky-routed by conversation_id, so in-memory state is reused, plus built-in conversation memory that persists across runs (native to Claude / OpenAI Agents / LangGraph / CrewAI). Step retries live in your framework loop.
Picked up later → native cron (schedules in edgeone.json) can trigger an agent on a schedule, and since state is keyed by conversation_id, it resumes from the prior context.
The edge bet is really about distribution + the fast path — the heavy work runs where it should. Curious what you're building!
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?
Tencent EdgeOne
@orman_canida Thanks — the sandbox is my favorite part too.
Isolation: every conversation gets its own sandbox instance (Tencent Cloud isolated instances), keyed by conversation_id and physically isolated from other conversations. It auto-recycles on timeout, and you can extendTimeout() or kill() it yourself.
Tool permissions: you decide exactly which tools the LLM can see — context.tools.all() for everything, grouped subsets like context.tools.files() / context.tools.browser(), or hand-pick with context.tools.get(name). So you can hand an agent only file access, without shell or browser.
On the developer side you also get the raw atomic APIs (context.sandbox.commands / files / browser / runCode), with per-command control over user / env / cwd / timeout.
Happy to point you at a template if you tell me what you're building!
KnowU
Congrats on shipping this. Agent demos are easy now, but production is still messy. Memory, tracing, scaling, storage, all the boring stuff is exactly where most projects slow down.
Tencent EdgeOne
@carlvert You just described our entire reason for building this. The "boring stuff" is exactly where projects stall — and it's boring precisely because everyone has to rebuild it from scratch every time. We figured that should just come with the platform, so you can spend your time on the agent, not the plumbing. Thanks for getting it so precisely!