optio

Hey HN! I shared Optio here back in March. Originally it was focused on getting swarms of coding agents (claude code/codex) to drive PRs towards a merge by automatically resuming them on CI failures or PR comments. Since then, I've reworked the project to be a general platform for running any kind of AI workflow on your infra by allowing users to configure each component of a possible workflow: When: On demand, a cron schedule, or triggered by external events such as a webhook, slack message, ticket (jira/linear/gh), pagerduty/datadog/alertsmanager/pylon/sentry. Where: In a k8s pod or any machine you have installed the optio daemon on (I use my laptop). Who: Any harness you prefer (Claude Code, Codex, Gemini, Pi, etc.) What/Why: You can format parameters from the triggers directly into the prompts and then configure if the agent should ex

Previously I've shared optio - my project for orchestrating coding agents. It ties into ticketing systems and when assigned a ticket, it launches a coding agent in k8s and works until the PR is ready, resuming for failed CI or PR feedback (https://news.ycombinator.com/item?id=47520220). Recently I've been trying to expand it from just coding focused to any kind of agent workflow. So now there are cron and webhook triggers, and more general agent tasks that aren't necessarily coding focused (https://github.com/jonwiggins/optio/blob/main/docs/persisten...). I think next I want to try and add features for long term memory for agents, but haven't decided on a good way to do it.

I'm working on Optio - an AI agent orchestration platform built on Kubernetes: https://github.com/jonwiggins/optio It's built around multiple different types of agents: - Coding Agents are placed into cloned repos with a ticket (Jira/Linear/Notion/GH), and work until they open a PR, are resumed on CI failures or github feedback, and work until they can merge the PR. - Standalone Agents are reusable, parameterized agent runs with no repo checkout. Generate reports, triage alerts, audit dependencies, query a database, post to Slack, etc. - Persistent Agents are long-lived, named, message-driven agent processes. Each has a stable slug, an inbox, and a cyclic state machine. Wake on user messages, agent messages, webhooks, cron ticks, or ticket events.

Thanks for considering it. So how are you doing it now ? Who is generating the plan inside of issue ex: https://github.com/jonwiggins/optio/issues/418 Do you just add the Issue Title like this "feat: CLI improvements — status dashboard, workflow commands, shell completions" and it generated the plan in issue body and started working on it OR is the plan generated by another ai agent and copied to issue body for pickup by optio ?

I think like many of you, I've been jumping between many claude code/codex sessions at a time, managing multiple lines of work and worktrees in multiple repos. I wanted a way to easily manage multiple lines of work and reduce the amount of input I need to give, allowing the agents to remove me as a bottleneck from as much of the process as I can. So I built an orchestration tool for AI coding agents: Optio is an open-source orchestration system that turns tickets into merged pull requests using AI coding agents. You point it at your repos, and it handles the full lifecycle: - Intake — pull tasks from GitHub Issues, Linear, or create them manually - Execution — spin up isolated K8s pods per repo, run Claude Code or Codex in git worktrees - PR monitoring — watch CI checks, review status, and merge readiness every 30s - Self-healing — auto-r