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

AI 產生的概覽

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。
安裝前請注意
Kubeshark 於核心層擷取並索引整個叢集的流量,並解密 TLS/mTLS,因此敏感酬載與憑證可能暴露給助理,並存入快照或雲端儲存(S3、Azure Blob、GCS)。下載 PCAP 與保留快照會把資料寫到叢集之外,啟用前應檢視存取與保留政策。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Kubeshark,將其新增到工作區。
  2. 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

[Kubeshark]

[Release] [Docker pulls] [Discord] [Slack]

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.

[Kubeshark]


Get Started

bash
helm repo add kubeshark https://helm.kubeshark.comhelm install kubeshark kubeshark/kubesharkkubectl 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:

bash
brew install kubesharkclaude mcp add kubeshark -- kubeshark mcp

MCP setup guide →


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.

[MCP Demo]

MCP setup guide →

AI Skills

Open-source, reusable skills that teach AI agents domain-specific workflows on top of Kubeshark's MCP tools:

SkillDescription
Network RCARetrospective root cause analysis — snapshots, dissection, PCAP extraction, trend comparison
KFLKFL (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 →


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.

[Service Map]

Learn more →

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.

[Traffic Retention]

Snapshots guide → · Cloud storage →


Features

FeatureDescription
Traffic SnapshotsPoint-in-time snapshots with cloud storage (S3, Azure Blob, GCS), PCAP export for Wireshark
Traffic IndexingReal-time and delayed L7 indexing with request/response matching and full payloads
Protocol SupportHTTP, gRPC, GraphQL, Redis, Kafka, DNS, and more
TLS DecryptioneBPF-based decryption without key management, included in snapshots
AI IntegrationMCP server + open-source AI skills for network RCA and traffic filtering
KFL Query LanguageCEL-based query language with Kubernetes, API, and network semantics
100% On-PremisesAir-gapped support, no external dependencies

Install

MethodCommand
Helmhelm repo add kubeshark https://helm.kubeshark.com && helm install kubeshark kubeshark/kubeshark
Homebrewbrew install kubeshark && kubeshark tap
BinaryDownload

Installation guide →


Contributing

We welcome contributions. See CONTRIBUTING.md.

License

Apache-2.0

來源:README.md,提交 35e484c

工具

0
SourceWeft 曾在隔離沙箱中以無憑證方式啟動該軟體包一次,以列出其工具,但未能完成:該軟體包類型無法在沙箱中執行。

版本歷史

3
  1. v53.6.0最新Oct 21, 2026
  2. v53.5.0Oct 7, 2026
  3. v53.4.0Sep 16, 2026

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