Sectr
Serverless backend platform for easy deployment of AI agents
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Hey HN, I've been building Sectr, a backend-as-a-service for making it easy(ier) to develop and deploy AI agents. Deploying agents requires non-trivial systems design work. Consider a support agent: how do you stream messages to your frontend, how/where do you persist messages for frontend fetching later (or for auditing), and when the agent wants to issue a refund, do you spin-down the compute infra while it's waiting on human approval? Sectr aims to be the serverless abstraction and platform to provide these systems for you.No sign-up needed to try using the dev server: curl -fsSL https://sectr.dev/install.sh | sh # Install CLI sectr init my-agent && cd my-agent # Starter code sectr dev # Dev server, with UI to test chatting Sectr provides:- Streaming of every token, tool call and event, and persistence so they can be loaded fresh later.- Scaling up, and down to zero when the agent needs to wait, and resumption right to the suspended agent's state.- Human approved tools automatically use Sectr's suspension and resumption capabilities, no code needed from you.- AG-UI endpoint for any deployed agent so you can trivially hook up your frontend- Local dev server experience, so you can rapidly debug and iterate on your agent's behaviorAt the core, you can deploy any container image to Sectr as long as it implements a certain shaped HTTP endpoint. In practice, currently Sectr provides adapters to let you deploy your existing OpenAI Agents SDK, LangGraph, or Pydantic AI agents, with only 2 new lines of code: app = AgentApp(name="support") app.add_route("/chat", openai_agents(agent)) Certainly the hardest part of building Sectr was getting the resumption logic to be correct and invisible, across the Program State x Model Provider x Agent Framework matrix. For example, an earlier bug was when a model replied with text plus a tool call, Sectr would resume that as two separate messages, instead of one message with a text and tool part. This works, but breaks prompt caching (and might subtly change model behavior).Sectr is free during the beta (and can be tried locally without sign-up); I mainly want feedback, especially on dev & deployment experiences, any bugs, and what features would excite you.Introductory video: https://www.youtube.com/watch?v=H-y3wynkKc0Docs: https://docs.sectr.dev
HN · launched Oct 27, 2026 · 5 upvotes (top 90%) · 0 views
Niche: ai agent development platforms · 598 launches, 22 in the last 7 days ▼ -29% · Explore this niche →
Category and tags
- Horizontal
- Dev tools
- Developer
- AI-native
- Commercial product
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More like this: all 598 launches in ai agent development platforms →
Could you build this?
Partial The SDK, one-click deploy flow, and frontend integration are buildable, but a real serverless agent platform needs multi-tenant sandboxed execution, autoscaling, and durable streaming infrastructure that vibe coding alone won't produce.
What it would actually take: Start with a thin wrapper that packages a Python agent into a container and deploys it to an existing serverless platform such as Cloud Run or Lambda, with a streaming adapter for AG-UI. Building the real product means a multi-tenant control plane, isolation and sandboxing for untrusted agent code, persistent message and state storage, and cost controls at scale. That requires infrastructure and DevOps expertise.
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Competitors
Other products that read as similar to this one : 630 launches clear the similarity bar, closest 8 shown.
Attention rank: #616 of 631 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 355 days after the earliest competitor.
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- AgentClaw · ph · 2026-10-31 · 1 upvotes · similarity 0.43
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