Every AI agent team rebuilds the same infrastructure from scratch. Sandboxing. Retries. Observability. Multi-agent orchestration. Every. Single. Team. Savine makes that disappear. Deploy your agent → get a live API endpoint. We handle the runtime, loop detection, real-time streaming, cost tracking, and multi-agent pipelines. One command. One endpoint. No DevOps.100+ LLM models. Free to start.→ savine.in
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Hey Product Hunt! 👋
I'm Sarthak, the founder of Savine.
I built this because I kept seeing the same thing — every team building AI agents was spending 2–3 months on deployment infrastructure before shipping a single feature to users.
Sandboxing. Retries. Observability. Multi-agent orchestration. From scratch. Every single time.
Savine makes that problem disappear.
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What makes Savine different from just "deploy your agent to a cloud VM":
↳ We OWN the execution loop — an XState state machine controls every agent run. Your agent cannot loop forever, run out of control, or crash silently.
↳ Real-time streaming — watch every THINK, ACT, TOOL CALL, and OBSERVE step as it happens. No more black box debugging.
↳ Multi-agent systems — wire multiple agents together with a config file. Get one API endpoint for the whole pipeline.
↳ 100+ LLM models — bring your own OpenAI, Anthropic, Groq, or Ollama key. Switch models per agent, per task.
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The platform is live and free to try at savine.in
I'll be here all day answering every question — ask me anything about the architecture, the decisions, the roadmap, or why I built this.
Would especially love to hear from anyone currently dealing with this exact infrastructure problem. I want to know what's broken for you.
Thanks for being here on day one 🙏
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How does the loop detection work when agents call each other in multi agent pipelines? Congrats on the launch!
Within each agent - SHA-256 hashes every reasoning step. Same output repeating in a sliding window = agent killed automatically.
Across agents - the pipeline is validated as a DAG before deployment. Cycles between agents are structurally impossible to create. If agent A → agent B → agent A exists in your config, deployment is rejected before anything runs.
Runtime detection + structural prevention. Both working together. 🙏
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impresive product , can make a huge difference as I had a product with 50+ agents , it can definitely stream line and add a observability and a lot more as well , will definitely use
How does the loop detection work when agents call each other in multi agent pipelines? Congrats on the launch!
@borrellr_ Great question!
Two layers of protection:
Within each agent - SHA-256 hashes every reasoning step. Same output repeating in a sliding window = agent killed automatically.
Across agents - the pipeline is validated as a DAG before deployment. Cycles between agents are structurally impossible to create. If agent A → agent B → agent A exists in your config, deployment is rejected before anything runs.
Runtime detection + structural prevention. Both working together. 🙏
impresive product , can make a huge difference as I had a product with 50+ agents , it can definitely stream line and add a observability and a lot more as well , will definitely use