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Kubeshark

by kubeshark

Real-time Kubernetes network traffic visibility and API analysis for HTTP, gRPC, Redis, Kafka, DNS.

Install Kubeshark

Pick your client, then add it in one click or paste the config.

No confirmed install command yet

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Open setup steps on GitHub

Overview

Security & health

What it can reach and how reliably it runs β€” from our own checks, not the maintainer’s claims.

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Adoption & maintenance

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GitHub stars12kStargazers on the source repository.
Last commit1d agoMost recent push to the default branch.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

From the project README

Kubeshark

Release Docker pulls Discord Slack

Network Observability for SREs & AI Agents

Live Demo Β· Docs


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

What you can do:

  • Download Retrospective PCAPs β€” cluster-wide packet captures filtered by nodes, time, workloads, and IPs. Store PCAPs for long-term retention and later investigation.
  • Visualize Network Data β€” explore traffic matching queries with API, Kubernetes, or network semantics through a real-time dashboard.
  • See Encrypted Traffic in Plain Text β€” automatically decrypt TLS/mTLS traffic using eBPF, with no key management or sidecars required.
  • Integrate with AI β€” 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.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:

bash
brew install kubeshark
claude mcp add kubeshark -- kubeshark mcp

MCP setup guide β†’


Network Data for AI Agents

Kubeshark exposes cluster-wide network data via MCP β€” 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 β†’

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 β€” snapshots, dissection, PCAP extraction, trend comparison
KFLKFL (Kubeshark Filter Language) expert β€” writes, debugs, and optimizes traffic filters

Install as a Claude Code plugin:

Code
/plugin marketplace add kubeshark/kubeshark
/plugin install kubeshark

Or clone and use directly β€” skills trigger automatically based on conversation context.

AI Skills docs β†’


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 β€” Kubernetes identity, API context, and network attributes β€” to pinpoint exactly the traffic you need. No code instrumentation required.

KFL query combining API, Kubernetes, and network semantics

KFL reference β†’ Β· Traffic indexing β†’

Workload Dependency Map

A visual map of how workloads communicate, showing dependencies, traffic volume, and protocol usage across the cluster.

Service Map

Learn more β†’

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 β€” 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 β†’ Β· Cloud storage β†’


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 β†’


Contributing

We welcome contributions. See CONTRIBUTING.md.

License

Apache-2.0

Reviews

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Frequently Asked Questions about Kubeshark

kubeshark/kubeshark is an MCP server that exposes cluster-wide Kubernetes network data for AI agents. It supports natural-language investigation of traffic and API behavior across HTTP, gRPC, Redis, Kafka, and DNS, including workflows such as incident response and root cause analysis.

Connect Kubeshark by installing it with Homebrew and registering its MCP server with your assistant: `brew install kubeshark` followed by `claude mcp add kubeshark -- kubeshark mcp`. The server works with Claude Code, Cursor, and other MCP-compatible AI clients.

Kubeshark analyzes HTTP, gRPC, Redis, Kafka, and DNS traffic, with queries using network, API, and Kubernetes semantics. It indexes cluster-wide network traffic at the kernel level using eBPF and provides real-time network visibility and API analysis.

You can ask the Kubeshark MCP server questions about traffic, API calls, errors, and network behavior, such as why a checkout failed at a specific time, which services have error rates above 1%, TCP retransmission rates across node-to-node paths, or a request trace across services.

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