
Kubeshark
io.github.kubeshark由 GitHub 帳號 kubeshark 發布v53.6.0更新於 Oct 26, 2026
Real-time Kubernetes network traffic visibility and API analysis for HTTP, gRPC, Redis, Kafka, DNS.
概覽
Kubeshark MCP 讓 AI 助理取得整個 Kubernetes 叢集的網路流量資料,用於查詢 API、調查事故與根因分析。
- 可以做什麼
- 以結合 Kubernetes 身分、API 脈絡與網路屬性的 KFL 查詢過濾叢集範圍的網路流量
- 調查 API 呼叫並跨服務追蹤請求,包括錯誤率與 TCP 重傳趨勢
- 依時間範圍、節點、工作負載與 IP 下載回溯性 PCAP,供 Wireshark 等工具使用
- 處理 HTTP、gRPC、GraphQL、Redis、Kafka、DNS 等已索引的 L7 流量,包含解密後的 TLS 酬載
- 搭配開源 AI 技能進行網路根因分析與流量篩選器的撰寫與除錯
- 適用情境
- 適合助理需要回答 Kubernetes 即時或近期網路流量問題時使用,例如某個時間點結帳為何失敗、哪些服務錯誤率超過門檻、某個請求如何流經各服務。適用於已在叢集部署 Kubeshark 的 SRE 與事故應變流程。
- 執行需求
- 以本機程序在使用者電腦上執行(僅限桌面端),可透過 Homebrew 或各平台安裝檔安裝,並連線到叢集中的 Kubeshark 部署。叢集端需以 Helm 或二進位檔安裝,通常經由連接埠轉送或 ingress 存取。未宣告任何驗證、環境變數或標頭。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Kubeshark,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
[Release] [Docker pulls] [Discord] [Slack]
Network Observability for SREs & AI Agents
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
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:
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" "Trace request abc123 through all services"
Works with Claude Code, Cursor, and any MCP-compatible AI.
AI Skills
Open-source, reusable skills that teach AI agents domain-specific workflows on top of Kubeshark's MCP tools:
Install as a Claude Code plugin:
Or clone and use directly — skills trigger automatically based on conversation context.
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 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 query combining API, Kubernetes, and network semantics]
KFL reference → · Traffic indexing →
Workload Dependency Map
A visual map of how workloads communicate, showing dependencies, traffic volume, and protocol usage across the cluster.
Traffic Retention & PCAP Export
Capture and retain raw network traffic cluster-wide, including decrypted TLS. Download PCAPs scoped by time range, nodes, workloads, and IPs — ready for Wireshark or any PCAP-compatible tool. Store snapshots in cloud storage (S3, Azure Blob, GCS) for long-term retention and cross-cluster sharing.
Snapshots guide → · Cloud storage →
Features
Install
Contributing
We welcome contributions. See CONTRIBUTING.md.
License
來源:README.md,提交 35e484c
工具
0版本歷史
3- v53.6.0最新Oct 21, 2026
- v53.5.0Oct 7, 2026
- v53.4.0Sep 16, 2026
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