microsoft/AIOpsLab

A holistic framework to enable the design, development, and evaluation of autonomous AIOps agents.

What it solves

AIOpsLab provides a standardized framework for designing, developing, and evaluating autonomous AIOps agents. It addresses the difficulty of creating reproducible and scalable benchmarks for AI agents tasked with managing cloud environments, specifically by automating the deployment of microservices, fault injection, and telemetry collection.

How it works

The framework acts as an orchestrator between an AI agent and a simulated or remote Kubernetes cluster. It can automatically deploy applications using Helm charts, inject specific faults (such as misconfigurations), and generate synthetic workloads using tools like wrk2. The orchestrator manages the interaction loop: it provides the agent with problem descriptions and APIs, records the agent's actions in a session trace, and then uses built-in evaluators to measure the agent's performance based on quantitative and qualitative metrics.

Who it’s for

  • AI Researchers: Those building autonomous agents for system operations and needing a standardized way to benchmark them.
  • SREs and DevOps Engineers: Professionals looking to test AI-driven automation for fault detection, localization, analysis, and mitigation.
  • Software Developers: Users who want to create custom AIOps problems or applications to test the limits of LLM-based operational agents.

Highlights

  • End-to-End Automation: Handles everything from cluster provisioning (via Terraform/Ansible) to workload generation and fault injection.
  • Flexible Agent Onboarding: Allows any agent wrapped in a Python class with an async get_action method to be integrated.
  • Built-in Benchmark Suite: Includes a registry of predefined problems focusing on detection, localization, analysis, and mitigation tasks.
  • Multi-Cluster Support: Compatible with local simulated clusters (kind), remote Kubernetes clusters, and Azure VMs.

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