klawsh

klawsh / klaw.sh

Public

kubectl for AI Agents

klaw.sh agents ai-agents orchestration
557
34
100% credibility
Found Feb 17, 2026 at 421 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

klaw.sh is a single-binary tool that lets users manage, monitor, schedule, and scale AI agents across teams with kubectl-like commands and Slack integration.

How It Works

1
🔍 Discover klaw

You hear about klaw, the easy way to manage your team of AI helpers like a pro.

2
📥 Install in seconds

Run one simple command and klaw is ready on your computer, no hassle.

3
🔗 Connect your AI brain

Link your favorite AI service so your helpers can think and respond smartly.

4
🤖 Create your first helper

Build an AI worker for tasks like scoring leads or watching competitors with a few details.

5
⏰ Set automatic schedules

Tell it when to run, like every hour or daily, so it works while you sleep.

6
💬 Chat or use Slack control

Talk to your helpers directly or command them from Slack like @klaw status.

🚀 Your AI team thrives

Watch your helpers run smoothly, see logs and results, scale as needed - your work gets done effortlessly.

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AI-Generated Review

What is klaw.sh?

klaw.sh brings Kubernetes-style ops to AI agents via a single Go binary—think `klaw get agents` to list running lead-scorers or ticket-handlers, `klaw describe agent` for details, and `klaw logs` for real-time output. It solves production chaos: no more scattered cron jobs or manual scaling; get namespaces for isolation, built-in scheduling, and Slack control for quick status checks or runs. Installs in seconds with `curl -fsSL https://klaw.sh/install.sh | sh`, like grabbing a kubectl github binary.

Why is it gaining traction?

The kubectl CLI familiarity hooks Kubernetes users instantly—no learning curve for core ops like `kubectl get agents` or logs. Single-binary deploy beats multi-service alternatives like OpenClaw, with Slack integration letting teams trigger agents without CLI. Distributed mode scales by adding nodes via `klaw node join`, plus 300+ models via routers, making it dead simple for prod AI workflows.

Who should use this?

DevOps running agent fleets for sales lead scoring, support ticket automation, or research monitoring—teams already using kubectl github actions for deploys. Small ops groups needing quick visibility into idle/running agents without full K8s overhead.

Verdict

Grab it for prototyping AI ops (361 stars shows early buzz), but 1.0% credibility flags immaturity—solid docs and tests, yet watch for edge cases in scale. Worth a spin if you're tired of agent babysitting.

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