UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

[ICLR'25] OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?

Timeseries Anomaly detection and Root Cause Analysis on data in SQL data warehouses and databases

Papers about Root Cause Analysis in MicroService Systems. Reference to Paper Notes: https://dreamhomes.top/

Official implementation of RiskLoc, a method for localizing multi-dimensional root causes in time-series data.

Root Cause Analysis for Kubernetes

[WWW'25][ASE'24] RCAEval: A Benchmark for Root Cause Analysis.

AERCA: Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery (ICLR 2025 Oral)

This repository contains a reading list of papers on multivariate time series anomaly detection. This repository is still being continuously improved.

[FSE'24 - 🏆 Best Artifact Award] BARO: Robust Root Cause Analysis for Time Series Data.

Awesome resources for failure diagnosis research.

Web UI for OpenRCA

Multi-Agent Debugger: An AI-powered debugging system using CrewAI to orchestrate specialized agents that analyze logs, trace code, and uncover root causes across your stack — powered by LLM providers.

Code for "LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis" paper

RootCLAM: On Root Cause Localization and Anomaly Mitigation through Causal Inference (CIKM 2023)

🧠 A field guide to mnemonics for engineers: outages, RCAs, infra design, burnout, and conflict. Structured entries with when-to-use, pitfalls, combinations, and examples. Pipelines and playbooks for on-call situations.

A toolkit to apply AIOps to distributed systems

This repository provides all necessary code and data to reproduce the experiments from the ICSE - 2025 accepted paper: "Improved Detection and Diagnosis of Faults in Deep Neural Networks using Hierarchical and Explainable Classification."

Root Cause Analysis in the Industrial Domain using Knowledge Graphs: A Case Study on Power Transformers