opennvr-adapter-sdk

The boilerplate-free way to write an AI Adapter Contract v1 service.

A minimal adapter is around thirty lines of FastAPI. The SDK gives you AdapterService — the ABC every adapter implements with four abstract methods — and AdapterApp, the builder that wraps your service in a FastAPI app with the six mandatory endpoints, auth, correlation-ID propagation, Prometheus metrics, and multipart plus JSON body parsing. ServiceError is the typed failure envelope from §7 of the contract. Every contract Pydantic type is re-exported from the package root, so adapter authors get one import line.

Install

uv add opennvr-adapter-sdk
# with the uvicorn server bundled too:
uv add 'opennvr-adapter-sdk[serve]'

If you're not on uv yet, pip install opennvr-adapter-sdk works the same — the package is a single wheel with FastAPI, Pydantic, and python-multipart as its only runtime dependencies. We recommend uv because adapter projects tend to grow heavy ML deps fast, and uv sync keeps resolve time and the lockfile manageable as they do.

The minimum viable adapter

# my_adapter/main.py
from datetime import datetime, timezone

from opennvr_adapter_sdk import (
    AdapterApp, AdapterService, BodyShape, ErrorCategory,
    HardwareEvaluationResponse, HardwareVerdict, InferResponse,
    ModelInfo, ServiceError,
)

class MyService(AdapterService):
    def __init__(self):
        self._ready = False

    def load(self):
        # Heavy lifting goes here.
        self._ready = True

    def is_ready(self): return self._ready

    def fingerprint(self):
        return "sha256:..."

    def model_info(self):
        return ModelInfo(
            name="my-model", version="1.0",
            framework="numpy", modalities_in=["text"],
            modalities_out=["text"], fingerprint=self.fingerprint(),
        )

    def hardware_evaluation(self):
        return HardwareEvaluationResponse(
            verdict=HardwareVerdict.OK, reasoning="ready",
            checked_at=datetime.now(timezone.utc), details={},
        )

    def infer(self, payload):
        if "text" not in payload:
            raise ServiceError(
                ErrorCategory.TRANSPORT_ERROR, code="malformed_input",
                message="'text' required", transient=False, http_status=400,
            )
        return InferResponse(
            model_name="my-model", model_version="1.0",
            inference_ms=1, result={"echoed": payload["text"]},
        )

app = AdapterApp(
    service=MyService(),
    name="my-adapter", version="1.0.0",
    vendor="me", license="MIT",
    tasks_advertised=["echo"],
    body_shape=BodyShape.TEXT,
).fastapi_app

Run it:

OPENNVR_ADAPTER_TOKEN=dev-token \
  uvicorn my_adapter.main:app --host 0.0.0.0 --port 9001

Verify conformance:

python -m conformance http://localhost:9001 --token dev-token

That's a complete contract-compliant adapter. The SDK handles /health, /capabilities, /hardware/evaluation, /metrics, /infer, /infer/stream (HTTP 501 refusal), auth, correlation_id, multipart + JSON body parsing, Prometheus metrics, lifespan startup. You write only the model wrapper.

Body shapes

For non-TEXT shapes, the SDK puts the binary content at payload[BODY_BYTES_KEY] (bytes) and merges any params JSON into the dict. BODY_BYTES_KEY is re-exported from the SDK root — import it rather than hard-coding the literal so future renames don't silently break adapters. Caller-supplied params that shadow this key are rejected with malformed_input so collisions surface at the wire, not as silently-overwritten values.

Streaming adapters

Add supports_stream=True to AdapterApp(...) and override AdapterService.handle_stream(websocket):

class MyDetector(AdapterService):
    async def handle_stream(self, websocket):
        await websocket.accept()
        # ... §6 protocol ...

app = AdapterApp(
    service=MyDetector(),
    ...
    supports_stream=True,
    stream_max_concurrent=16,
    stream_supports_shared_memory=False,
).fastapi_app

The SDK handles auth on the WebSocket upgrade (§6.5 close code 4001 on auth failure) and delegates to your handler. The §6 protocol itself — handshake → frame_meta + binary → result_message — is the adapter's responsibility (YOLOv8 has the reference implementation under adapters/yolov8/).

Constructor reference

AdapterApp(
    # Service — exactly one required:
    service: AdapterService | None,           # eager construction
    service_factory: Callable[[], AdapterService] | None,  # lazy (lifespan startup)

    # Adapter identity (required):
    name: str, version: str,
    vendor: str, license: str,
    tasks_advertised: Sequence[str],

    # Body shape + size cap:
    body_shape: BodyShape = BodyShape.TEXT,
    max_body_bytes: int = 32 * 1024 * 1024,

    # Capabilities metadata (optional, with defaults):
    permissions: Permissions = Permissions(),
    scheduling: Scheduling = Scheduling(),  # default max_inflight=1
    cost: Cost = Cost(),
    model_card_url: str | None = None,
    supported_contract_versions: Sequence[str] = ("1",),

    # Streaming (default off):
    supports_stream: bool = False,
    stream_max_concurrent: int = 0,
    stream_supports_shared_memory: bool = False,

    # Tuning:
    latency_buckets_seconds: tuple[float, ...] = (...),  # Prometheus buckets
    cors_origins: Sequence[str] = ("*",),
)

Real-world examples

opennvr-adapter-sdk is the production runtime for the eight adapters shipped in this repo: adapters/yolov8/ for object detection (BodyShape.IMAGE with WebSocket streaming); adapters/piper/ for text-to-speech (BodyShape.TEXT with a custom /voices route and inline-audio response); adapters/whisper/ for speech-to-text (BodyShape.AUDIO, multipart decode); adapters/fast_plate_ocr/ for license-plate text recognition on a pre-cropped plate image (BodyShape.IMAGE, designed to chain downstream of YOLOv8); adapters/insightface/ for face detection plus recognition with a REST face DB; adapters/blip/ for scene captioning, used by the OpenNVR camera-agent; adapters/vlm/ for open-vocabulary detection (OWL-ViT v2, detects free-text queries like "red truck"); and adapters/bytetrack/ for stateful multi-object tracking as a post-processor over an upstream detector's results. Their main.py files are non-trivial reference implementations worth reading before authoring your own.

Versioning

SDK ships with the same major version as the contract. SDK v1.x targets contract v1; a future contract v2 ships SDK v2.x. AdapterApp.supported_contract_versions defaults to ["1"] — bump when you implement multi-version support.

Why this isn't a "framework"

AdapterService is an ABC, not a metaclass. AdapterApp is a builder, not a base class. The SDK lives between you and FastAPI — your service code never imports FastAPI directly, but you can still drop down to app.add_route(...) for adapter-specific endpoints (see Piper's /voices route).

The contract is the source of truth. The SDK is a convenience layer on top of it. If the SDK gets in your way, write the service hand-rolled the way the early reference adapters did before the SDK was extracted — the contract is implementable without it, just more boilerplate.