# HolmesGPT — AI-Powered Root Cause Analysis for Kubernetes

> An open-source SRE agent (CNCF Sandbox) that uses LLMs to investigate Kubernetes alerts, correlate signals, and suggest root causes automatically.

## Install

Save as a script file and run:

# HolmesGPT — AI-Powered Root Cause Analysis for Kubernetes

## Quick Use
```bash
pip install holmesgpt
holmes investigate "Pod CrashLoopBackOff in namespace production"
# Or integrate with your alerting pipeline
holmes --alertmanager-url http://alertmanager:9093
```

## Introduction
HolmesGPT is a CNCF Sandbox project that brings AI-powered root cause analysis to Kubernetes operations. When an alert fires, HolmesGPT automatically investigates by gathering logs, events, and metrics, then uses an LLM to correlate signals and produce a human-readable diagnosis.

## What HolmesGPT Does
- Receives alerts from Prometheus Alertmanager, PagerDuty, or manual queries
- Automatically gathers relevant Kubernetes logs, events, and resource states
- Uses LLMs (OpenAI, Azure OpenAI, or local models) to analyze gathered evidence
- Produces structured root cause reports with remediation suggestions
- Integrates with Slack, Jira, and other tools for alert enrichment

## Architecture Overview
HolmesGPT runs as a Python service that connects to your Kubernetes cluster and monitoring stack. When triggered by an alert, it acts as an AI agent that autonomously runs kubectl commands, queries Prometheus, and reads logs to gather evidence. This evidence is fed to an LLM with a specialized prompt for SRE analysis, producing a diagnosis. The tool uses a runbook system to encode domain-specific investigation patterns.

## Self-Hosting & Configuration
- Install via pip or deploy as a Kubernetes deployment/CronJob
- Configure LLM provider via environment variables (OpenAI API key or local model endpoint)
- Connect to Alertmanager for automated alert investigation
- Add custom runbooks to guide investigation for your specific services
- Set up Slack or Jira integrations for posting investigation results

## Key Features
- CNCF Sandbox project with active community governance
- Autonomous investigation gathers evidence without manual intervention
- Custom runbook system encodes team-specific debugging knowledge
- Multi-LLM support including OpenAI, Azure, and self-hosted models
- ChatOps integration posts findings directly to Slack channels

## Comparison with Similar Tools
- **K8sGPT** — identifies cluster issues; HolmesGPT investigates alert-triggered incidents with deeper analysis
- **Robusta** — Kubernetes monitoring platform; HolmesGPT is a focused RCA agent
- **kubectl-ai** — AI-assisted kubectl; HolmesGPT automates full investigation workflows
- **Coroot** — observability with RCA; HolmesGPT is LLM-native and runbook-driven
- **Shoreline** — commercial incident automation; HolmesGPT is open-source and LLM-powered

## FAQ
**Q: Which LLM providers are supported?**
A: OpenAI, Azure OpenAI, AWS Bedrock, and any OpenAI-compatible local model server.

**Q: Does it modify my cluster?**
A: No. HolmesGPT is read-only by default. It gathers evidence but does not take remediation actions unless explicitly configured.

**Q: What is the CNCF Sandbox status?**
A: HolmesGPT was accepted into the CNCF Sandbox, indicating recognition by the cloud-native community as a promising early-stage project.

**Q: Can I use it without Kubernetes?**
A: The primary focus is Kubernetes, but HolmesGPT can investigate any system where you provide tooling for evidence gathering.

## Sources
- https://github.com/HolmesGPT/holmesgpt
- https://docs.holmesgpt.com

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Source: https://tokrepo.com/en/workflows/asset-70f42a52
Author: Script Depot