Has anyone else run into this while using the PromptLayer Python SDK with async/await patterns? I've been wrestling with a data pipeline migration that's heavily async, moving from a legacy logging setup to PromptLayer for tracking LLM calls, and I'm seeing logs get silently dropped about 20% of the time. It's the kind of intermittent issue that makes you question your own instrumentation before you suspect the tool.
My scenario: I'm migrating an ETL process that orchestrates hundreds of parallel data enrichment calls using `asyncio.gather`. Each task makes an LLM call via the OpenAI client wrapped with PromptLayer, and I'm using `pl.track` to log metadata like cost, tokens, and my own internal record IDs. The process completes without errors, but when I check the PromptLayer dashboard, a significant chunk of requests are just... missing. No errors in the console, no failed statuses—just gaps in the timeline.
Here's a simplified version of the pattern I'm using:
```python
import promptlayer
import asyncio
from openai import AsyncOpenAI
promptlayer.api_key = "pl_..."
client = AsyncOpenAI()
openai = promptlayer.openai.AsyncOpenAI(client=client)
async def process_item(item_id):
try:
response = await openai.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": f"Process {item_id}"}],
pl_tags=[f"item_{item_id}"]
)
# Additional tracking with metadata specific to my pipeline
pl_request_id = promptlayer.track(
request_id=response.request_id,
metadata={"pipeline_step": "enrichment", "item_id": item_id}
)
return item_id, pl_request_id
except Exception as e:
print(f"Error on {item_id}: {e}")
return item_id, None
async def main():
items = list(range(100))
tasks = [process_item(i) for i in items]
results = await asyncio.gather(*tasks)
# At this point, all 100 tasks finish, but only ~80 appear in PromptLayer
```
I've tried adding explicit `await asyncio.sleep(0)` yields, wrapping tracks in try/except blocks (which catch nothing), and even batching the tracking calls separately. The issue seems to surface more under high concurrency (50+ parallel tasks). It feels like there might be an unawaited coroutine or a thread-safety issue inside the SDK's logging mechanism.
Before I dive deeper with a custom async queue or switch to manual HTTP calls, I'm curious:
* Is this a known limitation with the async client?
* Are there recommended patterns for high-volume, async logging with PromptLayer?
* Has anyone found a workaround that ensures delivery without sacrificing throughput?
This is a critical path for our migration—we need reliable observability before we can decommission the old system. Any shared experiences or debugging tips would be immensely helpful.
MigrateMentor
Good point about the session closing early. I've been bitten by that in FastAPI apps where the background tasks outlive the request lifespan. Adding an explicit `await client.close()` in a shutdown handler helped, but it's a band-aid.
The real culprit in my case was the SDK's fire-and-forget approach combined with a small threadpool for the sync-to-async bridge. If you're hammering the endpoint with hundreds of parallel calls, the internal queue just gets overwhelmed and drops messages on the floor silently 😒
I ended up wrapping the `pl.track` call in a retry with a small local buffer, like a five-slot asyncio.Queue, and had a separate worker task drain it. Ugly, but it got the logs through. Sometimes you gotta treat these SDKs like unreliable UDP packets.
it worked on my machine