All 72 Dependencies
Every package optio depends on, ranked by repo health score.
Optio is an open-source AI engineering platform that orchestrates AI coding agent swarms entirely within your own Kubernetes cluster. Unlike hosted alternatives such as Devin or Cursor background agents, Optio keeps your source code, secrets, and agent logs inside your own network while giving you full control over which AI model powers each task.
The platform organizes agent work into three tiers. Tasks represent the full ticket-to-PR lifecycle — Optio provisions an isolated pod, runs an AI agent, monitors CI, automatically resumes the agent on failures or review requests, and squash-merges the PR when everything passes. Jobs are reusable, parameterized agent runs with no repository checkout, ideal for operational work like generating reports, triaging alerts, or querying databases. Persistent Agents are long-lived, message-driven processes that wake on user messages, inter-agent messages, webhooks, cron ticks, or ticket events — enabling multi-agent team setups.
At its core, Optio is built around a Kubernetes-style reconciliation control plane: a pure-decision-plus-CAS-executor loop that reads a WorldSnapshot, derives a single Action, and applies it with a compare-and-swap guard on the database timestamp. This architecture keeps task runs from getting stuck on lost events and makes every state transition auditable. All agent execution happens in ephemeral Kubernetes pods with configurable security contexts, RBAC, and encrypted secrets at rest using AES-256-GCM.
Optio supports multiple AI agent backends — Claude Code, OpenAI Codex, GitHub Copilot, Google Gemini, and OpenCode — behind a single unified interface, letting engineering teams switch models per repository or A/B test agents on the same task. The Connections system lets you inject external services (Notion, Linear, Jira, Slack, PostgreSQL, Sentry, or any MCP-compatible server) into agent pods at runtime with fine-grained access control.
