AIOpsLab
Basic Information
AIOpsLab is an open-source framework for designing, developing and evaluating autonomous AIOps agents. It provides an environment that can deploy microservice cloud stacks on local simulated clusters or remote Kubernetes clusters, inject faults, generate workloads, collect telemetry and orchestrate interactions between agents and testbeds. The repo includes a built-in benchmark suite and a registry of problems and applications focused on AIOps tasks such as detection, localization, analysis and mitigation. It offers interfaces and examples to onboard agents as Python classes, run agents locally or remotely, record session traces and export results for reproducible evaluation.
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App Details
Features
The project centers on an orchestrator, a problems registry and task/action modules for standard AIOps tasks. It includes workload generators (wrk integration), fault injectors organized by injection level, evaluators with qualitative and quantitative metrics and LLM-as-a-judge prompts, and a session manager that records traces and results. Service components provide Helm, kubectl and shell interfaces plus application metadata and Helm-based auto-deploy support. An observer stack includes filebeat, logstash and prometheus components for telemetry. There is a CLI, example clients (GPT and vLLM), configuration via config.yml, and guidance for adding new apps and problems.
Use Cases
AIOpsLab helps researchers and developers create reproducible benchmarks and iterate on autonomous cloud agents by providing an end-to-end testbed for fault injection, workload generation, telemetry collection and automated evaluation. It makes it easy to onboard custom agents via a simple Python class API, run experiments on local kind clusters or remote Kubernetes, and evaluate agent performance using built-in evaluators and stored session traces. The framework supports extending applications and problems, integrates Helm for deployment, and includes tooling and examples for running LLM-based baselines and exporting results for analysis and visualization.
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