| title | IncidentCommander | |||||
|---|---|---|---|---|---|---|
| emoji | 🚨 | |||||
| colorFrom | blue | |||||
| colorTo | red | |||||
| sdk | docker | |||||
| pinned | false | |||||
| app_port | 7860 | |||||
| base_path | /ui | |||||
| tags |
|
IncidentCommander is a production-grade OpenEnv environment designed to evaluate AI agents in the role of a Site Reliability Engineer (SRE). Agents must triage complex production incidents across an 8-service microservices architecture by analyzing live incident reports and providing prioritized remediation plans.
- Interactive Dashboard: https://vaibhav0714-incidentcommander.hf.space/
- Hugging Face Space: https://huggingface.co/spaces/vaibhav0714/IncidentCommander
- GitHub Repository: https://github.com/alphacoder-hash/meta-hackathon-incident-commander
In this environment, "episodes" consist of high-fidelity incident scenarios. The agent receives a comprehensive Incident Report containing:
- Service Statuses: Latency, error rates, and health metrics for the 8-service graph.
- Active Alerts: Real-time signals including critical outages and misleading "red herring" monitors.
- Log Snippets: Recent logs from affected services (and red herrings) to aid root-cause identification.
The agent succeeds by providing a free-text analysis that correctly identifies the root cause, dismisses false signals, and outlines a prioritized recovery plan.
| Difficulty | Task ID | Description |
|---|---|---|
| 🟢 Easy | single_service_crash |
Identify a single failing service from clear error logs. |
| 🟡 Medium | cascading_failure |
Identify a root cause while navigating misleading "red herring" signals. |
| 🟠 Medium-Hard | bad_deployment |
Diagnose a crash-looping deployment and recommend a rollback. |
| 🔴 Hard | silent_degradation |
Diagnose a slow memory leak under chaos injection conditions. |
The environment simulates an 8-service dependency graph:
api_gateway ──→ auth ──→ database, payment ──→ cache, etc.
Failures propagate topographically, requiring agents to reason about causal chains to find the true source of the "Outage".
git clone https://github.com/alphacoder-hash/meta-hackathon-incident-commander.git
cd incident-commander
pip install -r requirements.txt
# Run the server
uvicorn server.app:app --host 0.0.0.0 --port 7860
# Open http://localhost:7860/ui for the interactive dashboarddocker build -t incident-commander .
docker run -p 7860:7860 \
-e HF_TOKEN=<your-token> \
-e MODEL_NAME=Qwen/Qwen2.5-72B-Instruct \
-e API_BASE_URL=https://router.huggingface.co/v1 \
incident-commanderexport HF_TOKEN=hf_xxx
export MODEL_NAME=Qwen/Qwen2.5-72B-Instruct
export API_BASE_URL=https://router.huggingface.co/v1
# Local environment
python inference.py
# Against a deployed HF Space
python inference.py --env-url https://vaibhav0714-incidentcommander.hf.spaceimport httpx
# Reset the environment
resp = httpx.post("https://vaibhav0714-incidentcommander.hf.space/reset", json={"task_id": "cascading_failure"})
obs = resp.json()["observation"]
print(obs["incident_report"])
# Submit analysis
action = {"response": "The database is overloaded. RECOMMENDATION: Scale up database."}
resp = httpx.post("https://vaibhav0714-incidentcommander.hf.space/step", json=action)
print(f"Score: {resp.json()['reward']}")incident-commander/
├── openenv.yaml # OpenEnv manifest
├── models.py # Core Pydantic types (Action/Observation)
├── Dockerfile # Multi-stage build for HF Spaces
├── server/
│ ├── app.py # FastAPI server with Gradio UI mount
│ ├── environment.py # OpenEnv environment implementation
│ ├── graders.py # Tiered keyword-based grading logic
│ └── gradio_ui.py # Premium SRE Dashboard
└── requirements.txt # Python dependencies
MIT