Troubleshooting: Why are my LangSmith traces missing spans for async calls?

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.

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%.

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.

Spot on with the `create_task` pattern. That 95% success rate you saw matches what I get, but I found the missing 5% are almost always the fastest tasks, where the span finishes before the tracer can attach. That skews latency percentiles downward, which is sneaky.

Have you tested with nested async calls? I've seen the problem reappear if one of those parallel `ainvoke` calls itself uses `gather` internally. The context drops again in the second layer, even with `create_task`.

That's a really insightful observation about the fastest tasks dropping traces - I hadn't considered how that would skew latency metrics. You're right, it creates a deceptively optimistic picture.

Nested async calls are a whole other layer of pain. I've seen the same context drop in second-level `gather` calls. What's worked for me is to propagate the context manually at each nesting level - basically treating each nested parallel section as its own isolated tracing block with explicit parent assignment. It's messy, but it gets the job done.

Have you tried setting `LC_ALL` to force synchronous tracing as a temporary debug flag? It can help isolate whether the issue is purely in the context handoff or if there's something else in your chain setup.

> propagate the context manually at each nesting level

Exactly. We went full manual context passing on our pipelines. It's a pain, but you get full traces. The trick is to use `contextvars` directly, not LangChain's wrapper.

That LC_ALL flag only works for the top-level sync calls. In our tests, it doesn't help with nested async code at all. You still lose the spans on the second gather.

We just accept the overhead now. It's cheaper than the dev time spent debugging incomplete traces.

Yep, classic async context loss. Everyone's over-engineering this.

Just turn down your concurrency. If you're gathering 50 LLM calls, you don't need granular spans for all of them - you need to know the batch latency. Tracing overhead will murder your throughput anyway.

All these workarounds are paying an observability tax that's more expensive than the problem.

> turn down your concurrency

I disagree, but on pragmatic grounds. Reducing concurrency avoids the bug but doesn't fix it, and it's often not an option if you're trying to saturate model endpoints or meet latency SLOs. You're right about the overhead, but the tax only gets steep with manual context passing, not the `create_task` pattern.

The real cost isn't the observability overhead itself. It's the engineering time spent deciding *which* spans you can afford to lose and hoping the missing data doesn't skew your performance analysis.

Oh, that's the exact same issue I ran into last week. I'm still pretty new to LangSmith, but I thought my config was wrong.

Everyone's saying to use `asyncio.create_task` before gather. That worked for me too, but only for simple cases. Like user1229 said, if you have any nested async stuff, the spans drop again. Super frustrating for debugging.

How much concurrency are you actually running? If it's not huge, the create_task overhead might be okay. But I'm already worried about cost creep from all these tracing workarounds.

The pattern you've identified is the textbook case of async context loss in LangChain's tracing. The issue is that `asyncio.gather` schedules the coroutines without preserving the context from the point of creation, which is where the tracer attaches the parent run ID.

Your diagnosis on the environment variables is correct; this is a runtime execution problem, not a configuration error. The solution proposed in the thread to wrap each `ainvoke` in `asyncio.create_task` before gathering is the most effective minimal change. However, for a production RAG pipeline, you must be aware of a critical nuance: this only preserves context for that specific level of concurrency.

If any of your chains internally use another `asyncio.gather` for sub-operations, like parallel document retrieval or multi-step tool calls, you'll experience the same span loss at that nested layer. To get complete traces, you need to apply the `create_task` pattern recursively wherever concurrent execution happens, which adds non-trivial complexity.

A more architectural approach is to design your async functions to accept and propagate an optional parent `run_id` explicitly, decoupling the tracing from LangChain's automatic context management. It's more upfront work but eliminates the unpredictability.

That's a solid starting point for debugging. You've covered the basics, so I'd look at the execution flow next.

I ran into the same pattern and found the missing child spans were because the tracing context wasn't propagating into the scheduled coroutines. Your code is waiting for them to finish, but the tracer loses track of which parent they belong to when they're gathered directly.

The `create_task` wrapper before `gather`, as others mentioned, is the key. It seems like a small change, but it forces the context to be captured at the point of creation. That said, it's a workaround for the current SDK, not a permanent fix. Are you seeing this across all your async chains or just this specific one?

> The `create_task` wrapper before `gather`, as others mentioned, is the key.

It is, but it's also incomplete advice. The real nuance is that `create_task` only captures the context at one moment. If your `ainvoke` coroutine itself does any `await` that yields execution before it starts its own work, the context can still be lost internally.

I've seen spans drop when a chain uses `RunnableParallel` under the hood, because the scheduler there creates new tasks without the captured context. So the answer to your final question is often "both" - it appears in one chain but is actually a latent issue in any chain using the same async patterns internally.