agentic-k8s-aiops
Provides autonomous Kubernetes cluster scanning, anomaly detection, and automated remediation via kubectl command execution.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agentic-k8s-aiopsScan the cluster for issues and auto-fix them"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
β Agentic K8s AIOps π€
An autonomous, AI-powered Kubernetes troubleshooting and remediation agent. This tool continuously monitors your Kubernetes cluster, detects anomalies, and leverages local LLMs (via Ollama) to diagnose and automatically fix issuesβall without relying on external cloud APIs.
π Key Features
π Automated Cluster Scanning: Detects CrashLoopBackOff, OOMKilled, ImagePullBackOff, and pending pods.
π€ Local AI Diagnosis: Powered by the
gemma4:cloudmodel via Ollama for secure, on-premise AI processing.π οΈ Autonomous Auto-Remediation: Executes kubectl commands directly to resolve detected issues using a ReAct agent framework.
π» Claude Code Native (MCP): Fully integrated Model Context Protocol (MCP) server for terminal-based interactions.
π¨ Beautiful Web Dashboard: A sleek, dark-themed UI for visualizing cluster health, viewing AI reasoning, and managing self-healing workflows.
Related MCP server: LUMINO MCP Server
ποΈ Architecture
The platform operates using a modular ReAct (Reasoning and Acting) pattern:
Observation: The
k8s_scanner.pymodule continuously polls the cluster for degraded states.Thinking: The
ai_agent.pyevaluates the issue using the local Ollama LLM.Action: Through the MCP server, the agent executes targeted
kubectlcommands to remediate the issue.
π Quick Start
Ensure you have Python 3.11+, Ollama, and kubectl configured.
# 1. Clone the repository & setup
git clone https://github.com/yuankraj/agentic-k8s-aiops.git
cd agentic-k8s-aiops
bash setup.sh
# 2. Start the local Ollama server
ollama serve
# 3. Launch the Web Dashboard
source .venv/bin/activate
python app.py
# 4. Open in your browser
# http://localhost:8000π Comprehensive Usage Guide
This agent can be run in three different modes:
Web UI Mode (via FastAPI dashboard)
Claude Code Native Mode (via FastMCP terminal server)
Pure Terminal Chat Mode (via Python script)
π Check out the USAGE.md file for detailed step-by-step instructions for all execution modes.
π Project Structure
.
βββ app.py # Main web server (FastAPI)
βββ mcp_server.py # FastMCP Server (Claude Code integration)
βββ k8s_scanner.py # Kubernetes cluster scanner for fault detection
βββ ai_agent.py # Ollama ReAct AI agent with auto-fix tools
βββ terminal_chat.py # Pure terminal interaction script
βββ setup.sh # One-click environment configuration
βββ requirements.txt # Python dependencies
βββ static/ # Frontend assets (UI, styling, logic)
βββ index.html
βββ style.css
βββ app.js π€ Contributing
Contributions, issues, and feature requests are welcome! Feel free to open an issue or submit a pull request.
Built with β€οΈ for resilient Kubernetes operations.
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