Kodosumi
Hi Hacker News, I’m part of the team behind Kodosumi. We built it because deploying agentic services in production was much harder than building proof-of-concepts. Many frameworks let you build agents, but when you want them to run long tasks, scale across machines, or be observed/monitored reliably, things fall apart. Kodosumi is our answer to that. It’s an open-source runtime that uses Ray + FastAPI + Litestar to let you: Deploy “agents” and “flows” and expose APIs easily, with minimal config (just one YAML) Scale horizontally for bursty workloads and long-running tasks without losing…
Kodosumi launched on June 11, 2025 with 167 votes, #208 of 7,999 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Deploy and scale your AI agents with Kodosumi, the open-source runtime built for developers. Fast, scalable, and fully free to use.
Deploy, orchestrate, and scale AI agent services with Kodosumi. A distributed runtime built on Ray for agent execution, pipelines, and observability.
The distributed runtime environment that manages and executes agentic services at enterprise scale. Deploy locally, run pipelines in parallel, fine-tune. Kodosumi scales effortlessly thanks to Ray. Built-in observability through Ray dashboard gives full visibility into agent operations and resource usage. Kodosumi is open source, framework agnostic, and portable across platforms. Use any AI framework you prefer. A single YAML config file is all you need. Define dependencies, environment variables, and deploy. An autonomous object that can perform specific tasks or services. Agents are the fundamental building blocks in Kodosumi, each encapsulating specialized capabilities that can be…from kodosumi.io
In the maker’s words, at launch
Hi Hacker News, I’m part of the team behind Kodosumi. We built it because deploying agentic services in production was much harder than building proof-of-concepts. Many frameworks let you build agents, but when you want them to run long tasks, scale across machines, or be observed/monitored reliably, things fall…
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