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Deployment

Operonx ships a Python FastAPI server (operonx[serve]).

Python: operonx[serve]

pip install "operonx[serve]"

from operonx.serve import build_app
from operonx.core import Operon

app = build_app(engine_factory=lambda: Operon(my_graph))

Run with uvicorn:

uvicorn myapp:app --host 0.0.0.0 --port 8000

Endpoints:

  • POST /run — synchronous run, returns the final state.
  • POST /stream — server-sent events stream of frames.
  • GET /healthz — readiness probe.

For a static-binary edge deployment, the operonx-rs crate ships an equivalent Axum server (operonx-serve binary) that reads the same graph.json and resources.yaml.

Configuration

The server honours the standard Operonx setup:

  • .env for credentials.
  • resources.yaml for model and consumer configs.
  • bootstrap() at startup.

For Kubernetes / containerised deployments, mount resources.yaml and provide credentials through the platform's secret store rather than a file-based .env.

Production checklist

  • Configure a persistent path for the local trace consumer (or skip it and use Langfuse / OTEL).
  • Cap concurrent requests via uvicorn --limit-concurrency.
  • Wire health checks: /healthz returns 200 once the engine is built and the resource hub is loaded.
  • Pin model versions in resources.yaml — never reference latest.
  • Watch the Tracing backend for token-cost and latency drift.

Where to go next