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llm-aiops-guide

Papers on LLMs for IT operations and AIOps research

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llm-aiops-guide

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Use llm-aiops-guide to identify relevant LLM-AIOps research areas, key papers, and intelligent operations patterns for [AIOps problem or use case] to assist with literature tracking or research planning.

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SKILL.md

name: llm-aiops-guidedescription: "Papers on LLMs for IT operations and AIOps research"metadata:   openclaw:     emoji: "🖥️"    category: "domains"    subcategory: "cs"    keywords: ["AIOps", "LLM operations", "IT automation", "log analysis", "incident management", "DevOps AI"]    source: "https://github.com/Jun-jie-Huang/awesome-LLM-AIOps"

LLM for AIOps Guide

Overview

A curated collection of research on applying LLMs to IT Operations (AIOps) — log analysis, anomaly detection, incident management, root cause analysis, and automated remediation. Tracks how foundation models are transforming traditional rule-based operations tooling into intelligent, adaptive systems. Relevant for CS researchers at the intersection of systems, NLP, and operations.

Research Areas

LLM for AIOps
├── Log Analysis
│   ├── Log parsing (template extraction)
│   ├── Anomaly detection (from log sequences)
│   ├── Log summarization
│   └── Root cause from logs
├── Incident Management
│   ├── Incident triage and routing
│   ├── Severity classification
│   ├── Similar incident retrieval
│   └── Resolution recommendation
├── Root Cause Analysis
│   ├── Topology-aware diagnosis
│   ├── Multi-signal correlation
│   └── Causal inference
├── Monitoring & Alerting
│   ├── Metric anomaly detection
│   ├── Alert correlation
│   ├── Noise reduction
│   └── Capacity planning
└── Automated Remediation
    ├── Runbook generation
    ├── Script generation
    ├── Self-healing systems
    └── Change impact analysis

Key Papers

PaperYearFocus
LogPPT2023Few-shot log parsing with prompt tuning
OpsEval2024Benchmark for evaluating LLMs in AIOps
D-Bot2024LLM-based database diagnosis
RCAgent2024Agent for root cause analysis
LogAgent2024Autonomous log analysis agent

Use Cases

  1. Literature tracking: Follow LLM-AIOps research evolution
  2. System design: Learn intelligent operations patterns
  3. Benchmark comparison: Evaluate AIOps approaches
  4. Research planning: Identify under-explored AIOps problems
  5. Industry applications: Bridge research to production AIOps

References