Hey everyone! 👋 I've been diving deep into Langfuse for tracing our LLM calls and it's been fantastic for synchronous operations. However, I've hit a snag that feels like a cloud IAM policy issue but for observability: **incomplete traces when using async FastAPI endpoints.**
It's like setting up a security group that only logs *some* of the traffic—you get a false sense of coverage. Here's my scenario:
I'm instrumenting a FastAPI app that uses `async def` endpoints and makes calls to external LLM APIs. The sync endpoints trace perfectly, but for async ones, the trace is often created but remains "pending," missing the actual LLM spans and the final output.
My hunch is it's related to how the Langfuse SDK manages context or the lifecycle of the trace in an async event loop. Has anyone else battled this?
Here's a simplified version of my problematic endpoint:
```python
from fastapi import FastAPI
from langfuse.callback import CallbackHandler
app = FastAPI()
langfuse_handler = CallbackHandler()
@app.post("/async-endpoint")
async def generate_async(prompt: str):
trace = langfuse_handler.trace(name="async_generation")
# This span often doesn't get logged
with trace.span(name="llm_call"):
# Simulated async LLM call
response = await dummy_async_llm_call(prompt)
trace.update(output=response) # This update sometimes doesn't persist
return response
```
**What I've checked/tried:**
* The Langfuse client is initialized correctly (works fine in sync contexts).
* No immediate errors in the logs.
* Tried `langfuse_handler.flush()` before the return—helps a bit but not reliably.
* Wondering if I need to manually handle background tasks for the Langfuse SDK in async mode?
This feels analogous to forgetting to set the `iam:PassRole` permission correctly in AWS—the action starts but fails silently downstream. Any insights or proven patterns for getting Langfuse to play nicely with FastAPI's async would be hugely appreciated!
security by default