Set up kubeledger in CrewAI

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System of Record for Kubernetes cost accounting: per-namespace CPU, memory and GPU usage, with the 30% non-allocatable overhead made visible. Connects to AI assistants via MCP (Claude, Gemini, Mistral, Cursor) for plain-language analysis. Formerly Kube-Opex-Analytics.

01Configuration

# kubeledger via CrewAI
# transport: unknown
# launch: the server's install method

CrewAI connects to MCP servers in code. Use its MCP client with the command/URL below; exact API varies by version.

02Steps

Make sure CrewAI is installed and up to date.
Wire the server into CrewAI using its MCP client API.
Provide any required API keys/credentials as environment variables.
Restart CrewAI and confirm kubeledger’s tools appear.

03Other runtimes

Claude Desktop Cursor Windsurf Cline Continue Goose VS Code OpenAI Agents SDK LangGraph

04Provenance

config_sourcegenerated from captured install method

last_checked2026-06-30 01:12Z

sourcesGitHub repo search [p4]

Next step

Ship kubeledger to your agents as a governed Loadout — config, scopes, and approval rules in one place.

Build a Loadout

See also: server page · is it safe? · alternatives