Your agent knows Kubernetes. It doesn't know your cluster. Radar (open-source) gives it context over MCP: ranked issues, a change timeline, logs and owners in one view. New: click Investigate on a failing workload. Claude Code, Codex or Cursor finds the root cause, read-only, with evidence for each claim. In our benchmark of 50 live faults across six tools, Radar found the most root causes. Radar Cloud investigates every alert and posts findings to Slack.
Hey Product Hunt, Nadav here, one of the people behind Radar.
We launched Radar here in May as an open-source Kubernetes UI. Unsurprisingly, more and more people have been telling us since then that they want to point their AI agents at their clusters. Agents have become really smart, but they burn a lot of time and tokens poking around the cluster with kubectl to understand what’s going on and piecing together the puzzle - and even then often miss the change that someone made ten minutes before things broke.
Radar's MCP server gives your agent (Claude Code, Codex, Cursor, Copilot, etc) the cluster already put together: issues detected, a history of events and changes to Kubernetes resources, topology and relationships between objects, logs and metrics.
We benchmarked it - same agent with Radar found root cause 3x (!) faster and 80% cheaper than with plain kubectl.
Click Investigate on any issue and Radar runs your own agent CLI, read-only, on your machine. You get the root cause, the evidence for each claim, and a fix that runs only if you approve it. Radar Cloud does the same when it detects an issue or an alert fires, and posts the answer to Slack.
We also ran six AI SRE tools on 50 live faults. Claude Code on Radar's MCP got 44 right, the most of the six, and was the fastest and cheapest. Method and every run: https://radarhq.io/benchmark/ai-sre-tools
Radar is free and open source, Apache 2.0:
brew install kubectl-radar
How do you debug your clusters today, and what would you want an agent to take on next?
About Radar on Product Hunt
“You don't need an AI SRE. Give your agent Radar.”
Radar launched on Product Hunt on October 29th, 2026 and earned 124 upvotes and 22 comments, placing #8 on the daily leaderboard. Your agent knows Kubernetes. It doesn't know your cluster. Radar (open-source) gives it context over MCP: ranked issues, a change timeline, logs and owners in one view. New: click Investigate on a failing workload. Claude Code, Codex or Cursor finds the root cause, read-only, with evidence for each claim. In our benchmark of 50 live faults across six tools, Radar found the most root causes. Radar Cloud investigates every alert and posts findings to Slack.
On the analytics side, Radar competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Radar performed against the three products that launched closest to it on the same day.
Who hunted Radar?
Radar was hunted by Nadav Erell. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Radar including community comment highlights and product details, visit the product overview.
Nadav Erell
Hey Product Hunt, Nadav here, one of the people behind Radar.
We launched Radar here in May as an open-source Kubernetes UI. Unsurprisingly, more and more people have been telling us since then that they want to point their AI agents at their clusters. Agents have become really smart, but they burn a lot of time and tokens poking around the cluster with kubectl to understand what’s going on and piecing together the puzzle - and even then often miss the change that someone made ten minutes before things broke.
Radar's MCP server gives your agent (Claude Code, Codex, Cursor, Copilot, etc) the cluster already put together: issues detected, a history of events and changes to Kubernetes resources, topology and relationships between objects, logs and metrics.
We benchmarked it - same agent with Radar found root cause 3x (!) faster and 80% cheaper than with plain kubectl.
Click Investigate on any issue and Radar runs your own agent CLI, read-only, on your machine. You get the root cause, the evidence for each claim, and a fix that runs only if you approve it. Radar Cloud does the same when it detects an issue or an alert fires, and posts the answer to Slack.
We also ran six AI SRE tools on 50 live faults. Claude Code on Radar's MCP got 44 right, the most of the six, and was the fastest and cheapest. Method and every run: https://radarhq.io/benchmark/ai-sre-tools
Radar is free and open source, Apache 2.0:
How do you debug your clusters today, and what would you want an agent to take on next?