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Quick Start

Get OpenAICE running in under 5 minutes with a replay demo.

1. Run Your First Replay

Replay scenarios let you test the full pipeline without any live infrastructure:

python -m openaice.cli.cli replay \
  --scenario examples/telemetry-replay/k8s-inference-queue-pressure

This loads synthetic telemetry data simulating a Kubernetes inference service under queue pressure, runs it through the policy engine, and outputs recommendations.

Expected Output

You should see a recommendation to scale_replicas for the inference-api service with confidence 0.91 and risk level medium.

2. Try the Slurm Scenario

python -m openaice.cli.cli replay \
  --scenario examples/telemetry-replay/slurm-node-health-warning

Expected Output

Recommendations to quarantine_node for the unhealthy GPU node with risk level high.

3. Start the API Server

python -m openaice.cli.cli serve --config configs/sample-k8s.yaml

Then visit http://localhost:8000/docs for the interactive OpenAPI documentation.

API Endpoints

Endpoint Method Description
/health GET Health check with control/policy mode
/state GET Current canonical entity state
/recommendations GET Active recommendations
/audit GET Audit event history
/replay POST Run a replay scenario via API

4. Run a Replay via API

curl -X POST http://localhost:8000/replay \
  -H "Content-Type: application/json" \
  -d '{"scenario_path": "examples/telemetry-replay/k8s-inference-queue-pressure"}'

5. Inspect State

python -m openaice.cli.cli state \
  --scenario examples/telemetry-replay/k8s-inference-queue-pressure \
  --format yaml

This dumps the canonical entity state that the policy engine reasons over.

6. Run Tests

pytest tests/ -v

All 30 tests should pass, covering schemas, state bus, policy engine, guardrails, and end-to-end replay scenarios.

Next Steps