We kept hitting the same problems: writing and iterating on prompts at scale, orchestrating API calls that fail unpredictably, tracking costs, testing non-deterministic code, building datasets from production data, organizing repos so coding agents perform well. And every piece of tooling was a different SaaS product that didn't talk to the others.
We built Output around three ideas:
1. Make it easy for devs and coding agents to create and modify workflows in one or a few shots.
Filesystem first. Everything your agent needs lives in self-contained folders, full context visible without hunting. TypeScript and Zod provide the first validation layer for whether your workflow is correct.
2. One framework, minimal tooling sprawl.
We got tired of scattering data across SaaS products that don't talk to each other. Prompt files, evals, tracing, cost tracking, credentials all live in one place.
Your data stays on your infrastructure. Under the hood, we built on Temporal for orchestration. It's a hard problem and we weren't going to reinvent the wheel they've perfected. Open source and self-hostable, or Temporal Cloud. We wrapped it so you don't need to learn Temporal upfront, but the full power is there underneath.
3. A flat learning curve.
Our team is web engineers at different levels. We didn't want anyone to learn Python, five different tools, or the nuances of workflow idempotency before they could ship. We baked in conventions: same folder structure, file names, patterns across every workflow. Advanced features like Temporal primitives, evals, LLM-as-a-judge stay out of the way until you reach for them.
We've been building production workflows this way for over a year.
We extracted it, cleaned it up, and wanted to put it in front of people who'd push on it.
Docs and a video building a HN AI digest newsletter from scratch: https://output.ai
Happy to answer questions.
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