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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.

12,068 stars GoOthers Updated Sep 3, 2026
kubernetesgolangrestgrpcdevopssnifferobservabilitywiresharkcloud-nativedockerincident-responseebpfmcpnetwork-analysisnetwork-engineeringnetwork-observabilitynetwork-securitypcaproot-cause-analysissre

Documentation

Network Observability for SREs & AI Agents

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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.
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 →


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 RCA**Retrospective root cause analysis — snapshots, dissection, PCAP extraction, trend comparison
**KFL**KFL (Kubeshark Filter Language) expert — 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 — 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 Snapshots**Point-in-time snapshots with cloud storage (S3, Azure Blob, GCS), PCAP export for Wireshark
**Traffic Indexing**Real-time and delayed L7 indexing with request/response matching and full payloads
**Protocol Support**HTTP, gRPC, GraphQL, Redis, Kafka, DNS, and more
**TLS Decryption**eBPF-based decryption without key management, included in snapshots
**AI Integration**MCP server + open-source AI skills for network RCA and traffic filtering
**KFL Query Language**CEL-based query language with Kubernetes, API, and network semantics
**100% On-Premises**Air-gapped support, no external dependencies

Install

MethodCommand
Helm`helm repo add kubeshark https://helm.kubeshark.com && helm install kubeshark kubeshark/kubeshark`
Homebrew`brew install kubeshark && kubeshark tap`
BinaryDownload

Installation guide →


Contributing

We welcome contributions. See CONTRIBUTING.md.

License

Apache-2.0

Frequently asked questions

What is kubeshark?

kubeshark is 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.

How do I install kubeshark?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is kubeshark open source?

Yes — it is hosted on GitHub at https://github.com/kubeshark/kubeshark and has 12,068 stars.

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