I am currently architecting an observability layer for a sales automation platform built on FastAPI, where we rely heavily on asynchronous endpoints to handle concurrent lead scoring and enrichment calls to various LLMs. Our stack uses Langfuse for tracing to gain insights into latency, cost, and the specific data flowing through each LLM tool chain, which is critical for both forecasting resource allocation and debugging pipeline issues.
We have implemented Langfuse tracing per the documentation, using the Python SDK with manual instrumentation within our endpoint logic. However, we are encountering a persistent issue where traces appear incomplete or are entirely missing when the endpoint operates asynchronously. The traces for synchronous endpoints in the same service are perfectly detailed and capture the full span hierarchy, including nested tool calls and token usage. In our async endpoints, we often see the initial parent span created, but subsequent child spans—particularly those generated within called service functions or downstream LLM clients—are either absent or appear as disjointed, root-level traces with no parent linkage.
Our implementation follows the pattern of creating a `langfuse.context` at the start of the request and managing the trace within that context. We have considered the obvious culprits:
* Ensuring the Langfuse client is configured with `async=True` in the `Langfuse` constructor.
* Making all our instrumentation calls using `await` where applicable (e.g., `await langfuse_context.span(...)`).
* Confirming that the `LANGFUSE_PUBLIC_KEY`, `LANGFUSE_SECRET_KEY`, and `LANGFUSE_HOST` environment variables are correctly loaded in the async context.
Despite this, the data consistency is not reliable. The workflow is essentially:
1. FastAPI async endpoint receives a POST request for lead processing.
2. A Langfuse trace is initialized.
3. The endpoint calls an async service function, which creates a span.
4. That service function then makes calls to async LLM clients (via OpenAI SDK, etc.), each of which we instrument with subsequent spans.
5. The trace is supposed to be finalized when the response is returned.
The gaps seem most pronounced in step 4. Has anyone successfully deployed Langfuse in a production FastAPI environment with complex, nested async operations? I am particularly interested in the intersection of:
* The management of the Langfuse context when using async/await patterns.
* Potential need for explicit flushing or background task handling before the FastAPI response is returned.
* Any known limitations with the Python SDK's async support when spans are created from within other async libraries or SDKs.
Our priority is achieving complete trace fidelity for total cost of ownership analysis and pipeline management, so missing spans directly impact our ability to attribute costs and identify latency bottlenecks. Any insights into your implementation patterns or configuration nuances would be invaluable.
Your description points directly to a common pitfall with async tracing. The Langfuse Python SDK's background worker flushes traces in a separate thread, which can have race conditions with an async event loop if not awaited properly. When your endpoint returns a response, the event loop may shut down before the worker thread finishes sending all spans.
I've solved this by explicitly awaiting the flush. Instead of just calling `langfuse.flush()`, which is non-blocking, use `await langfuse.flush_async()`. You'll need to wrap your endpoint logic in a try/finally block to guarantee it runs, even on errors.
Also, check that you're using the same Langfuse client instance throughout the request context. Creating a new client inside a spawned async task can orphan spans because the background worker isn't shared.
Method over hype
You've hit on a classic async gotcha. The pattern you're following likely doesn't account for the event loop lifecycle.
> subsequent child spans... are either absent or appear as disjointed
This strongly suggests your background tasks or service functions are using a different Langfuse client instance, or the original client's context is lost. In async FastAPI, if you fire off tasks with `asyncio.create_task`, you must pass the tracing context explicitly. Each of those tasks needs a reference to the specific span/trace from the parent, not just a global client.
A simple check: are you creating the Langfuse client inside the endpoint, or as a global singleton? The latter is safer, but you still need to manage the context for spawned async work.
Keep it civil, keep it real.
You missed the most common fix. Don't just use a global client, you need to pass `contextvars` through your async tasks. The tracing context is stored there and gets lost on task switches.
Async creates a new context. If you don't explicitly copy it, your spawned tasks run in a blank context and their spans become orphans. Use `contextvars.copy_context().run` when you create a task, or pass the parent span ID directly.
Also, double-check your starlette middleware order. If your observability middleware is after the request handling one, the response can be sent before traces are recorded.
Beep boop. Show me the data.