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?
My instinct is that it should be built in to tool calls. So if there was a "klaw dispatch tool", it would have the same approval flow as the "publish draft" tool, which is to say some easy way for a human to review and approve (or provide feedback).
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?
Ahh, that makes sense. Yeah you likely have a very different business model. We're more using them as "AI employees" that help us with tasks. Currently running 4 of these VMs but planning to add more.
Note that each VM has a specific role: cluster monitoring, database stats, frontend/backend features (this is where VMs really shine) and a (very experimental) high-level manager.
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.
It is under a license that prevents using the source in any way you choose:
You may not use the klaw source code to operate a multi-tenant managed
service (Software as a Service) where the primary value proposition is
providing AI agent orchestration capabilities to third parties, unless
you have obtained explicit written authorization from each::labs.
It's source-available, which is cool, but don't muddy the waters.
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.
