Thanks! The main advantage is simplicity. klaw is a single binary with zero dependencies, no k8s cluster required. For teams already running Kubernetes your operator makes a lot of sense, but most teams we've talked to managing AI agents don't have or want a k8s setup just for that. klaw gives them the same mental model without the infrastructure overhead.
On the license, fair question. The SaaS restriction is narrow, it only prevents someone from reselling klaw as a hosted orchestration service. Internal use, building on it, extending it is all fine. We looked at what Elastic and HashiCorp did and felt this was the right balance for now. Could it slow adoption? Maybe. But we'd rather start here and open up further based on how the community evolves.
the binary is built from the repo, it's Go so we distribute compiled binaries like most Go CLI tools (kubectl, terraform, hugo). All agent communication goes through your own configured LLM provider, klaw doesn't proxy or store any prompts.
Right now the controller can see secrets across namespaces, so that level of isolation isn’t there yet. It’s on the roadmap though. Namespace-scoped secrets where a controller agent can spawn agents but can’t read their secrets is the right model.
No human approval flow yet either, agents create directly. Would you want something like klaw dispatch --approve that queues until a human confirms?
Makes sense, if your agents need full desktop and GUI automation VMs are the way to go. klaw is more on the headless side, agents talking to APIs, Slack, X, that kind of thing, so the lightweight binary model works. How many agents are you running on Proxmox?
Mostly the second, plus fleet management. Each agent runs in an isolated namespace with its own config, channels, and skills. You manage them declaratively like you would pods, but the unit of work is an AI agent instead of a container.
The Kubernetes analogy is about the operational model: clusters for org isolation, namespaces for team isolation, declarative deploys, central monitoring. Not about hardware scheduling.
I'll improve the README to make this clearer, good feedback.
Also worth noting, Gas Town and klaw solve different problems. Gas Town orchestrates coding agents on a codebase. klaw orchestrates operational agents (social media, support, sales) across teams and platforms. Different layer entirely.
Correct, it's not a k8s operator. Standalone binary, zero dependencies. Just uses the same mental model because clusters and namespaces map really well to multi-team agent management.
No, klaw is standalone. It borrows the mental model from Kubernetes (clusters, namespaces, declarative config) but doesn't depend on it. Single binary, runs anywhere.
Thanks! The "Kubernetes is scary" point is fair, that's why the CLI is designed to feel intuitive even if you've never touched kubectl. There's also a controller agent that manages the whole cluster from plain English.
On "what comes after", I think it's agents managing other agents. An AI SRE that watches load and spins up new agents automatically. The cluster/namespace model was designed with that direction in mind.
And yeah, not trying to replace OpenClaw, different layer.
OpenClaw defines what an agent does, klaw manages where and how many run. Complementary.