README.md

metadata

title: SRE Incident Response OpenEnv
emoji: 🚨
colorFrom: red
colorTo: blue
sdk: docker
pinned: false
license: mit
tags:
  - openenv

SRE Incident Response - OpenEnv Environment

This repository provides an OpenEnv-compatible environment where an agent acts as an on-call Site Reliability Engineer (SRE). The environment exposes a simple HTTP API (FastAPI) that supports episodic rollouts via /reset and /step, plus grading via /grader.

What’s in this repo

• Environment server: app/main.py (FastAPI, OpenEnv-style endpoints)
• Task logic: app/tasks/ (three incident scenarios)
• Inference runner: inference.py
  • Uses an OpenAI model if OPENAI_API_KEY is set
  • Otherwise falls back to a deterministic, no-network policy

Run locally (Docker)

Build and run:

docker build -t sre-incident-env .
docker run --rm -p 7860:7860 sre-incident-env

Then check:

curl http://localhost:7860/health
curl http://localhost:7860/tasks

Run locally (Python)

Install:

pip install -r requirements.txt

Start the server:

python -m uvicorn app.main:app --host 0.0.0.0 --port 7860 --workers 1

Inference / evaluation

Run inference against a running environment:

python inference.py --base-url http://localhost:7860

Notes:

• Exit codes: by default inference.py exits 0 if it completes (even if tasks fail), to avoid “runner failed” false negatives. Use --strict-exit if you want non-zero on failed tasks.
• Auto-start: if --base-url is http://localhost:7860 and the server isn’t running, inference.py will try to start the local server automatically.
• Hackathon env vars: the script supports common evaluator variables:
  • API_BASE_URL (LiteLLM proxy base)
  • API_KEY (proxy key; also works with OPENAI_API_KEY)
  • MODEL_NAME (model identifier)
  • ENV_BASE_URL (environment server base URL)

Hugging Face Spaces

This repo is set up for Docker Spaces (see Dockerfile). The server binds to port 7860, and /health is used as a health check.

Meta x PyTorch Hackathon submission notes

If the hackathon evaluator runs inference.py directly, this repo is designed to:

• Install cleanly from requirements.txt
• Bring up the environment server (Docker or local)
• Run inference.py without crashing or returning a non-zero code just because a baseline policy didn’t “pass”