Artifacts
Artifacts are structured outputs attached to a run — reports, measurement summaries, annotated images, and links to external files. They appear in the platform UI under the run's Artifacts tab and are persisted for traceability and downstream use.
Artifacts are distinct from logs: logs capture the execution narrative, artifacts capture the scientific results.
Producing artifacts
Use the artifact functions from any phase or step. The most common type is a markdown artifact, which can embed tables, images, and rich text directly in the run view.
Measurement report — summarize results as a markdown table:
from unitelabs.sdk import get_logger
from unitelabs.sdk.automate import phase
from prefect.artifacts import create_markdown_artifact
@phase()
async def report(plate_id: str, hits: list[str], hit_rate: float):
logger = get_logger()
rows = "\n".join(f"| {well} |" for well in hits) or "| — |"
await create_markdown_artifact(
key="plate-read-report",
markdown=f"""## Plate Read Report
**Plate:** `{plate_id}`
**Hit rate:** {hit_rate:.1f}%
| Hit well |
|----------|
{rows}
""",
description=f"{len(hits)} hits on {plate_id}",
)
logger.info(f"Report artifact published — {len(hits)} hits")
Annotated image — embed a base64-encoded image inline:
import base64
from unitelabs.sdk.automate import phase
from prefect.artifacts import create_markdown_artifact
@phase()
async def detection(camera_service_name: str) -> dict:
image = await capture_snapshot(camera_service_name)
result = detect_plate_in_roi(image)
_, buf = cv2.imencode(".png", image)
img_b64 = base64.b64encode(buf).decode()
await create_markdown_artifact(
key="detection-result",
markdown=f"""## Plate Detection
**Result:** {"✅ PLATE PRESENT" if result["plate_present"] else "❌ EMPTY"}
""",
description="Camera-based plate detection",
)
return result
External file link — reference a file in object storage or a local path:
from unitelabs.sdk.automate import phase
from prefect.artifacts import create_link_artifact
@phase()
async def imaging(target: Plate):
image_path = await microscope.capture(target)
await create_link_artifact(
key="plate-image",
link=str(image_path),
description="Captured plate image",
)
Accessing artifacts
Platform UI: open a run and navigate to the Artifacts tab. Each artifact shows its name, type, and the phase that produced it.
API:
curl https://api.unitelabs.io/v1/runs/{run_id}/artifacts \
-H "Authorization: Bearer $API_TOKEN"
Download a specific artifact:
curl https://api.unitelabs.io/v1/runs/{run_id}/artifacts/plate_image \
-H "Authorization: Bearer $API_TOKEN" \
-o plate_image.tiff
Artifact traceability
Every artifact is linked to:
- The run that produced it
- The workflow version that was executing
- The phase and step that created it
- The inputs the run was started with
This chain of custody makes it possible to reproduce any result or trace a measurement back to the exact protocol and reagent batch that generated it.