kubeshark/kubeshark

SRE和AI代理的网络可观测性

现场演示 ·文档

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Kubeshark 利用 eBPF 在内核层级索引集群范围的网络流量——通过网络、API 和 Kubernetes 语义即时回答任何查询。

你能做什么：

• 下载回顾性PCAPs——按节点、时间、工作负载和IP过滤的集群范围数据包捕获。存储PCAP以供长期保存及后续调查。
• 可视化网络数据 — 通过实时仪表盘探索使用 API、Kubernetes 或网络语义的流量匹配查询。
• 请参见明文加密流量 — 使用 eBPF 自动解密 TLS/mTLS 流量，无需密钥管理或侧车。
• 与AI集成 — 连接你喜爱的AI助手（如Claude、Copilot），将网络数据纳入AI驱动的工作流程，如事件响应和根因分析。

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开始

在浏览器中打开http://localhost:8899。你正在捕获流量。

对于生产环境，我们建议使用入口控制器代替端口转发。

**通过MCP连接AI代理：

MCP设置指南→

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人工智能代理的网络数据

Network Observability for SREs & AI Agents

Live Demo · Docs

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Kubeshark indexes cluster-wide network traffic at the kernel level using eBPF — delivering instant answers to any query using network, API, and Kubernetes semantics.

What you can do:

• Download Retrospective PCAPs — cluster-wide packet captures filtered by nodes, time, workloads, and IPs. Store PCAPs for long-term retention and later investigation.
• Visualize Network Data — explore traffic matching queries with API, Kubernetes, or network semantics through a real-time dashboard.
• See Encrypted Traffic in Plain Text — automatically decrypt TLS/mTLS traffic using eBPF, with no key management or sidecars required.
• Integrate with AI — connect your favorite AI assistant (e.g. Claude, Copilot) to include network data in AI-driven workflows like incident response and root cause analysis.

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Get Started

helm repo add kubeshark https://helm.kubeshark.com
helm install kubeshark kubeshark/kubeshark
kubectl port-forward svc/kubeshark-front 8899:80

Open http://localhost:8899 in your browser. You’re capturing traffic.

For production use, we recommend using an ingress controller instead of port-forward.

Connect an AI agent via MCP:

brew install kubeshark
claude mcp add kubeshark -- kubeshark mcp

MCP setup guide →

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Network Data for AI Agents

Kubeshark exposes cluster-wide network data via MCP — enabling AI agents to query traffic, investigate API calls, and perform root cause analysis through natural language.

“Why did checkout fail at 2:15 PM?” “Which services have error rates above 1%?” “Show TCP retransmission rates across all node-to-node paths”