klag just got a bunch better — here’s what’s new.

Source: Dev.to

Over half year ago I’ve open sourced klag, a lightweight Kafka consumer lag exporter. I’ve been working in data streaming systems and data infrastructure for over a decade and it’s an ultimate tool, I wish I had sooner. Klag (pronounced “kay-lag”) docker image has been downloaded over 2,000 times, repo has passed 70 ⭐️, contributions started flowing in. So what has been added recently?

klag now ships an MCP server. Point your AI agent at it and just ask: “which consumer groups are falling behind right now?” There’s now a native image build. Same exporter, compiled ahead-of-time, near-instant startup, smaller footprint. You can now exclude groups, not just include them. Want myapp-* but not the noisy canary groups? Done. Comma-separated globs, includes and excludes, exactly where you’d expect them. The Helm chart is on ArtifactHub and the Grafana dashboard is public and ready to drop in. No git clone, no hardcoded datasource UIDs. helm install and you’ve got lag metrics flowing into Grafana in minutes. People are showing up with PRs that fix real edges in real clusters — full KAFKA_* passthrough, older-broker compatibility, more accurate time-based lag, and more. That’s exactly the feedback loop I wanted.