How it works
Connect a host (SSH or Kairo agent), link GitHub when needed, then chat intent — clone path, branch, runtime, and services. Kairo plans, executes with tools, and streams operator-facing progress until the system is healthy and reachable.
Architecture
GitHub and the operator client talk to the Kairo API. The AI decision engine coordinates knowledge retrieval, deployment execution, and monitoring against your infrastructure — with memory feeding future turns.
Deployment engine
Configure deploy keys or HTTPS for public repos, analyze the workspace, install only after path confirmation, and complete only with verified remote evidence — Docker preferred, PM2 when appropriate.
Knowledge memory
Failure signatures and successful remediations live in the knowledge layer. High-confidence historical fixes can auto-apply without re-asking for the same error pattern.
Decision engine
Planner and executor select tools, respect mutability gates, and synthesize plain-language outcomes. Provider failover keeps the loop alive; billing quota is never confused with upstream LLM limits.
AI workflow
Push → webhook or chat goal → analysis → deploy → monitor → detect → RCA → confidence → recommend → approve → heal → complete. Every stage is visible; none are simulated as success without proof.
Human approval
Yes (persist per server) or Read-only (prompt each time). No hard-block read-only errors — approval UI surfaces instead so operators stay in control.
Integrations
GitHub OAuth and App webhooks, Docker, Linux hosts, major app stacks, and agent-based outbound connectivity for locked-down networks.
Deployment lifecycle
First-time chat setup is separate from webhook auto-deploy quotas. Profiles store path and restart commands so each push reuses the same operating path.