Kubeshark
eBPF-powered network observability for Kubernetes. Indexes L4/L7 traffic with full K8s context, decrypts TLS without keys. Queryable by AI agents via MCP and humans via dashboard.
安装配置
暂未识别到可直接复制的 MCP 配置，请查看 GitHub README。后台管理员可以补充配置。
README 摘要
Network Observability for SREs & AI Agents Live Demo · Docs --- 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.  --- ## Get Started ```bash 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](https://docs.kubeshark.com/en/ingress) instead of port-forward. **Connect an AI agent** via MCP: ```bash brew install kubeshark claude mcp add kubeshark -- kubeshark mcp ``` [MCP setup guide →](https://docs.kubeshark.com/en/mcp) --- ### Network Data for AI Agents Kubeshark exposes cluster-wide network data via [MCP](https://docs.kubeshark.com/en/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"* > *"Trace request abc123 through all services"* Works with Claude Code, Cursor, and any MCP-compatible AI.  [MCP setup guide →](https://docs.kubeshark.com/en/mcp) ### AI Skills Open-source, reusable skills that teach AI agents domain-specific workflows on top of Kubeshark's MCP tools: | Skill | Description | |-------|-------------| | **[Network RCA](skills/network-rca/)** | Retrospective root cause analysis — snapshots, dissection, PCAP extraction, trend comparison | | **[KFL](skills/kfl/)** | KFL (Kubeshark Filter Language) expert — writes, debugs, and optimizes traffic filters | Install as a Claude Code plugin: ``` /plugin marketplace add kubeshark/kubeshark /plugin install kubeshark ``` Or clone and use directly — skills trigger automatically based on conversation context. [AI Skills docs →](https://docs.kubeshark.com/en/mcp/skills) --- ### Query with API, Kubernetes, and Network Semantics Kubeshark indexes cluster-wide network traffic by parsing it according to protocol specifications, with support for HTTP, gRPC, Redis, Kafka, DNS, and more. A single [KFL query](https://docs.kubeshark.com/en/v2/kfl2) can combine all three semantic layers — Kubernetes identity, API context, and network attributes — to pinpoint exactly the traffic you need. No code instrumentation required.  [KFL reference →](https://docs.kub...
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