Harikishanth / Incident-Triage-Environment

What is Incident-Triage-Environment?

This Python-based OpenEnv benchmark environment tests AI agents' ability to act as site reliability engineers, diagnosing incidents by filtering raw production failure logs, spotting root causes amid noise, and suggesting remediation plans. It serves up realistic scenarios across easy, medium, and hard tiers—like single-service crashes or cascading outages—with deterministic scoring via regex heuristics. Users get a live Hugging Face Space demo, a simple async Python client for reset/step loops, and Docker for local runs.

Why is it gaining traction?

It stands out with zero-LLM grading for reproducible results, rotating scenarios to block memorization, and baselines showing top models like Llama-3.3-70B hitting 0.83 scores. Developers hook into standard OpenEnv HTTP/WebSocket endpoints or curl the HF Space without setup, plus inference scripts for quick model evals. The focus on real SRE skills—noise filtering, causal reasoning, ordered fixes—fills a gap in agent benchmarks.

Who should use this?

AI researchers benchmarking LLMs for production incident response, SRE teams prototyping autonomous triage agents, or ML engineers evaluating models on deductive tasks like red-herring dismissal in logs. It's ideal for anyone building agentic tools that handle P0 outages without human hand-holding.

Verdict

Promising early benchmark for SRE agent eval—solid docs, HF deployment, and MIT license—but 38 stars and 1.0% credibility score signal it's nascent; test it via the live Space before integrating. Worth watching as OpenEnv matures.

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