A generative AI infrastructure founder has open-sourced klaw, a Kubernetes-inspired platform for managing fleets of AI agents at scale. The tool emerged from real operational challenges: after deploying 14 agents across 6 accounts, the team found themselves drowning in deployment, monitoring, and isolation problems rather than building new capabilities.

klaw brings container orchestration patterns to AI agents with clusters, namespaces, and channels mirroring Kubernetes concepts. Agents deploy in under 10MB (down from 800MB+) thanks to a Go rewrite, and the CLI works like kubectl with commands like `klaw create cluster` and `klaw deploy agent.yaml`. The platform supports 300+ models through an LLM router, multi-channel deployment (Slack, CLI, TUI), and scheduled tasks via cron.

The key differentiator isn't how agents collaborate—that's what frameworks like CrewAI and LangGraph handle—but rather how to operate them across teams with proper isolation and tooling. klaw sits one layer above, managing agent fleets with the same operational maturity Kubernetes brought to containers. Users can run CrewAI agents inside klaw namespaces, combining collaboration frameworks with enterprise-grade orchestration.