README.md

metadata

title: SRE Gym
emoji: 🚨
colorFrom: red
colorTo: yellow
sdk: docker
app_port: 8000
pinned: false
license: apache-2.0

sre-gym — Fault-injecting SRE training env for OpenEnv

Most SRE agent skills are runbooks and good intentions. sre-gym is the other half: a fault-injecting environment with deterministic grading where an agent diagnoses a real production-style incident, chooses a safe remediation, verifies recovery, and declares resolved. Every run is scored the same way twice.

• Spec-compliant OpenEnv environment (typed Pydantic action / observation / state, reset / step / state, openenv validate green).
• 3 curriculum scenarios — easy, medium, hard — with decoy services and causal dependencies.
• 11 bounded actions. Honest state transitions. No hidden oracles.
• 21 tests passing.
• Ships a Claude Code skill + verified-runbook loop — successful solves write markdown runbooks that the next run reads back.

30-second demo

./demo/run_demo.sh

Starts the env, solves each scenario cold, writes a runbook for each, re-solves to prove the loop. Full transcript takes ~10 seconds.

Curriculum

Difficulty	Scenario	Story	Decoy	Correct path
easy	worker_deploy_cascade	Bad worker deploy → DB crash-loop → login 502s	—	rollback worker → restart db → verify → resolve
medium	db_config_rollout	DB config push shrank connection pool from 80→12	recent worker deploy	rollback db → restart db → verify → resolve
hard	gateway_auth_rollout	Gateway auth-middleware rollout rejects valid logins	recent worker deploy	rollback gateway → verify → resolve (no restart)

Rolling back the wrong service returns a negative reward and failure_type="wrong_remediation_target". Restarting before the cause is removed re-inherits the bad state. declare_resolved is rejected until the scenario's resolution check passes against the actual world model.

Install

# 1. Create a venv and install
python3 -m venv .venv && source .venv/bin/activate
pip install -e '.[dev]'

# 2. Start the env
uvicorn server.app:app --host 127.0.0.1 --port 8000

# 3. Run the baseline inference against it
export HF_TOKEN="…"; export ENV_BASE_URL=http://127.0.0.1:8000
python inference.py

Install the Claude Code skill

ln -s "$PWD/skill" "$HOME/.claude/skills/sre-gym"

Then, in Claude Code, ask: "Solve the db_config_rollout scenario in sre-gym." The skill will drive the env via skill/tools/sre_gym_client.py, load any existing runbook from skill/verified-runbooks/, and append a fresh runbook on any clean solve (score > 0.85).

Architecture

┌────────────────────┐      HTTP / WS       ┌──────────────────────┐
│  Claude Code       │ ──────────────────▶ │  OpenEnv server       │
│  (with sre-gym     │ ◀────────────────── │  (FastAPI, uvicorn)   │
│   skill loaded)    │    obs, reward      │  unified_incident_env │
└────────────────────┘                     └──────────────────────┘
        │                                            ▲
        ▼ on clean solve (score > 0.85)              │
┌────────────────────┐                               │
│ verified-runbooks/ │ ────── loaded at skill load ──┘
│   *.md             │
└────────────────────┘

Scoring

Deterministic, 5 dimensions, sums to a public score in [0.01, 0.99]:

• Recovery (0–0.4): critical-path services healthy
• Containment (0–0.3): root cause removed or offending service isolated
• Verification (0–0.35): database_recovery + end_to_end checks passed
• Impact (0–0.15): user-impact reduced
• Efficiency (0–0.10): budget preserved, no wasteful repeats

Target > 0.85 for "clean solve." That's also the runbook-record threshold.

Repo layout

unified_incident_env/    # env core: models, environment, grader, challenge, tests
server/                  # OpenEnv entrypoint wrapper
skill/                   # Claude Code skill: SKILL.md, tools/, verified-runbooks/
demo/                    # run_demo.sh + pitch.md
inference.py             # OpenAI-client baseline for OpenEnv hackathon submission
openenv.yaml             # OpenEnv manifest
Dockerfile               # HF Space deployment

Verify

pytest unified_incident_env/tests -q          # 21 tests
python -m openenv.cli validate .              # OpenEnv manifest check
docker build -t sre-engineer-llm:v2 .         # HF Space image

Roadmap — v2

Distill the accumulated verified-runbooks/ corpus into a local 3B reviewer via OpenClaw-RL's async GRPO-on-next-state loop. Same reward contract (run_check passes / failure_type absent), same grader, but a compact policy that runs without a frontier API.

License

Apache 2.0