OpenKite - Opensource AWS DevOps AI Agent

I built an opensource cloud DevOps AI agent thst has more than 30 tools built using boto3 to manage, audit and analyse AWS services. OpenKite collapses that into a single interface: ask in plain english, get a well-researched plan and an agent that takes actions (Approved by human ofcourse) openkite ask "audit cost waste in us-east-1" → 5 parallel analyzers, 11 findings, $143/mo identified openkite ask "what changed in the last hour?" → CloudTrail lookup, slim rows, no 5KB JSON blobs in context openkite ask "delete stale EBS services" → \\\[confirm\\\] Delete EBS volume vol-0abc1234 in us-east-1? (yes/no) Production posture, by design: • Reasoning between tool calls : OpenKite is a ReAct agent — every tool result feeds back into the model before the next call. Ambiguous question? It clarifies. Empty result? It tries a different surface. A finding worth drilling into mid-audit? It chases it without being asked. The plan adapts to what AWS actually returns; you don't write the runbook, the agent runs one. • Read-only by default. Mutations are explicit, separately declared tools that pause for human confirmation before any boto3 write. • Auditable by construction. Every tool call — arguments and result — is persisted in LangGraph's SQLite checkpointer. Operations are replayable; "what did the agent do at 02:14?" is answerable from the log. • Cost-aware routing. Narrow questions take one LLM call; broad audits fan out in parallel. Haiku 4.5 is the default — fractions of a cent per query — Sonnet for the gnarly ones. Under the hood: LangGraph's create\\\_react\\\_agent over a typed boto3 toolbox. Per-tool interrupt() for human-in-the-loop. \\\~75 lines of agent code, every line auditable.