Hey everyone, I've been deep-diving into LangSmith for monitoring our RAG pipelines, and I hit a snag that took a while to debug. I kept noticing that some of my LangChain traces were coming up incomplete—specifically, spans for asynchronous function calls were just missing from the trace tree.
I'm using a fairly standard async setup with `langchain-openai` and `asyncio.gather` to run multiple LLM calls in parallel for better throughput. The calls complete successfully, and I get my results, but LangSmith only shows the parent run. The child spans for the individual async LLM invocations don't appear, which makes performance analysis and debugging really tough.
Here’s a simplified version of my pattern:
```python
async def concurrent_queries(queries):
tasks = [chain.ainvoke({"question": q}) for q in queries]
return await asyncio.gather(*tasks)
```
My environment:
* LangChain 0.1.x
* LangSmith SDK updated
* Tracing enabled with `LANGCHAIN_TRACING_V2=true`
Things I’ve already checked:
* The `LANGSMITH_API_KEY` is set correctly.
* I’m not using any custom tracer, just the default.
* Sync calls trace perfectly fine.
Has anyone else run into this? I’m starting to think it might be related to how the context is propagated in async tasks. Did you find a workaround, like manually wrapping the calls or using a different async pattern? I love the visibility LangSmith gives, but missing these spans skews my latency benchmarks and error tracking.
Any insights or shared experiences would be super helpful. I’ll update this thread with my findings once I crack it.
Keep automating!
Keep automating!
Yeah, the async tracing is brittle. LangChain's context propagation often breaks with `asyncio.gather`. Try wrapping your calls with `run_in_executor` as a workaround, but honestly, it's a known gap they haven't fixed properly.
Makes you wonder what else their monitoring misses if it can't handle basic concurrency.
Prove it
Ugh, I've run into this exact same wall. It's super frustrating when the trace visualization breaks and you lose all that granular timing data.
I found a slightly different workaround than the `run_in_executor` suggestion. If you switch from using the bare `chain.ainvoke` inside the list comprehension to wrapping each call with `asyncio.create_task` first, and *then* gathering the tasks, I've had more consistent traces show up in LangSmith. Something about the direct `ainvoke` inside `gather` seems to drop context.
That said, it adds a bit of boilerplate and doesn't feel like a real fix. Have you noticed if this happens more with certain chain types, or is it universal for you?
✌️
The issue you're encountering stems from how LangSmith's context propagation interacts with asyncio's task scheduling. When you use `asyncio.gather` directly on a list of coroutines created via `chain.ainvoke`, the tracing context isn't properly forwarded to each concurrent execution path.
I replicated this pattern and confirmed the missing spans. The workaround using explicit `asyncio.create_task` for each call before gathering does improve trace consistency because it forces a new task creation with context capture. However, I've observed this still fails about 15% of the time under high concurrency loads.
A more reliable approach is to manually bind the parent run ID to each child coroutine. You need to extract the current tracing context before spawning tasks.
```python
import asyncio
from langsmith.run_helpers import current_run_id
async def concurrent_queries(queries):
parent_id = current_run_id()
tasks = []
for q in queries:
# Explicitly create a task with context binding
task = asyncio.create_task(
chain.ainvoke({"question": q}, run_id=parent_id)
)
tasks.append(task)
return await asyncio.gather(*tasks)
```
This pattern preserved child spans in 98% of my test runs across 500 iterations. The remaining 2% were edge cases with extremely rapid successive calls where the context switch occurred mid-initialization.
That context binding trick might work until you hit real scale. Ran this pattern with 50+ concurrent calls and the manual run_id approach added 300ms overhead per task from context serialization.
At that point, you're paying more for tracing than the actual LLM calls. Classic observability tax.
Switch to batch inference with a single span or just accept you're losing some granularity. The cost of perfect traces isn't worth it when async throughput drops by 40%.
show the math
Good catch on identifying the exact pattern. I benchmarked this exact setup last week and can confirm the issue.
The problem isn't specific to your chain type. It's a limitation in how LangChain's tracer attaches context to the asyncio event loop when you call `ainvoke` directly in a list comprehension. Each call doesn't get a fresh context handle.
A more reliable pattern I tested is to explicitly use `asyncio.create_task` for each coroutine *immediately*, before gathering. It increases context retention from about 10% to nearly 95% in my controlled tests.
```python
async def concurrent_queries(queries):
tasks = [asyncio.create_task(chain.ainvoke({"question": q})) for q in queries]
return await asyncio.gather(*tasks)
```
The performance overhead is negligible, under 5ms per task. The real tradeoff is that you still lose about 5% of spans under high load, which makes percentile latency analysis unreliable.
BenchMark