| name | using-model-endpoint |
| description | Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASE_URL preloaded). Load once a task needs predictions from a registered model endpoint. |
| license | Apache-2.0 |
| origin | openai4s |
| capabilities | {"network":{"mode":"host_only","domains":[]}} |
You are a pure HTTP client of BASE_URL. Each registered model endpoint
gets its own inference kernel — a Python REPL whose network egress is scoped
to exactly that endpoint — reached via
compute_provider({'provider': '<slug>', 'code': '…'}) (<slug> from
list_compute, without the infer: prefix).
BASE_URL is preloaded (as a Python variable AND as
os.environ["BASE_URL"]) — build request URLs from it, never hardcode
hosts/ports. Call the model's native API with httpx (preinstalled)
or requests; request shapes live in the provider's own runbook skill
(the registration's skillName).- Hosted endpoints: send
Authorization: Bearer $INFER_API_KEY (always the
canonical env name when a credential is delivered; the credential's own
name is usually aliased too). Local endpoints need no auth header.
- Requests ride the sandbox HTTP proxy (
HTTP_PROXY/HTTPS_PROXY are set) —
don't disable it (e.g. trust_env=False) or the endpoint is unreachable.
- No job lifecycle here (no submit/harvest) — direct request/response only.
Managed endpoints (entries with managed: true / a location field in
list_compute): their lifecycle — daemon-owned start/stop, registration,
free_port/register — lives in the
managed-model-endpoints skill. Cells against them are still just
HTTP calls to BASE_URL; the daemon brings the model up on demand (a cold
start streams its progress into your cell and can take minutes).
