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.
Documentation
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
helm repo add kubeshark https://helm.kubeshark.com
helm install kubeshark kubeshark/kubeshark
kubectl port-forward svc/kubeshark-front 8899:80Open `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:
brew install kubeshark
claude mcp add kubeshark -- kubeshark mcpNetwork 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:
| Skill | Description |
|---|---|
| **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:
/plugin marketplace add kubeshark/kubeshark
/plugin install kubesharkOr 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 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
| Feature | Description |
|---|---|
| **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
| Method | Command |
|---|---|
| Helm | `helm repo add kubeshark https://helm.kubeshark.com && helm install kubeshark kubeshark/kubeshark` |
| Homebrew | `brew install kubeshark && kubeshark tap` |
| Binary | Download |
Contributing
We welcome contributions. See CONTRIBUTING.md.
License
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.
Related MCP tools
An AI Gateway, registry, and proxy that sits in front of any MCP, A2A, or REST/gRPC APIs, exposing a unified endpoint with centralized discovery, guardrails and management. Optimizes Agent & Tool calling, and supports plugins.
The missing open-source Kubernetes UI with a built-in MCP server for AI agents. See what's broken, why, and what changed. Issues, Topology, event timeline, Helm, GitOps, live service traffic, and cluster audits - all in one Go binary.
GOWA - WhatsApp REST API with support for UI, Multi Account, Webhooks, and MCP, and Chatwoot. Built with Golang for efficient memory use.
ToolHive makes deploying MCP servers easy, secure and fun Go-based implementation. Trusted by 1300+ developers. Trusted by 1300+ developers.
Open-source AI agent firewall for MCP security and agent egress. Scans mediated HTTP, MCP, A2A, and WebSocket traffic for exfiltration, SSRF, and prompt injection, and emits mediator-signed action receipts: verifiable audit evidence from outside the agent.
mcp-language-server gives MCP enabled clients access semantic tools like get definition, references, rename, and diagnostics.
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP