I run a generative AI infra company, unified API for 600+ models. Our team started deploying AI agents for our marketing and lead gen ops: content, engagement, analytics across multiple X accounts.

OpenClaw worked fine for single agents. But at ~14 agents across 6 accounts, the problem shifted from "how do I build agents" to "how do I manage them."

Deployment, monitoring, team isolation, figuring out which agent broke what at 3am. Classic orchestration problem.

So I built klaw, modeled on Kubernetes: Clusters — isolated environments per org/project Namespaces — team-level isolation (marketing, sales, support) Channels — connect agents to Slack, X, Discord Skills — reusable agent capabilities via a marketplace

CLI works like kubectl: klaw create cluster mycompany klaw create namespace marketing klaw deploy agent.yaml

I also rewrote from Node.js to Go — agents went from 800MB+ to under 10MB each.

Quick usage example: I run a "content cluster" where each X account is its own namespace. Agent misbehaving on one account can't affect others. Adding a new account is klaw create namespace [account] + deploy the same config. 30 seconds.

The key differentiator vs frameworks like CrewAI or LangGraph: those define how agents collaborate on tasks. klaw operates one layer above — managing fleets of agents across teams with isolation and operational tooling. You could run CrewAI agents inside klaw namespaces.

Happy to answer questions.