> ## Documentation Index
> Fetch the complete documentation index at: https://www.traceloop.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Usage with Threads (Python)

> How to use OpenLLMetry with `ThreadPoolExecutor` and other thread-based libraries.

Since many LLM operations tend to be I/O bound, it is often useful to use threads to perform multiple operations at once.
Usually, you'll use the `ThreadPoolExecutor` class from the `concurrent.futures` module in the Python standard library, like this:

```python theme={null}
indexes = [pinecone.Index(f"index{i}") for i in range(3)]
executor = ThreadPoolExecutor(max_workers=3)
for i in range(3):
    executor.submit(indexes[i].query, [1.0, 2.0, 3.0], top_k=10)
```

Unfortunately, this won't work as you expect and may cause you to see "broken" traces or missing spans.
The reason relies in how OpenTelemetry (which is what we use under the hood in OpenLLMetry, hence the name)
uses [Python's context](https://docs.python.org/3/library/contextvars.html) to propagate the trace.
You'll need to explictly propagate the context to the threads:

```python theme={null}
indexes = [pinecone.Index(f"index{i}") for i in range(3)]
executor = ThreadPoolExecutor(max_workers=3)
for i in range(3):
    ctx = contextvars.copy_context()
    executor.submit(
        ctx.run,
        functools.partial(index.query, [1.0, 2.0, 3.0], top_k=10),
    )
```

Also check out the [full example](https://github.com/traceloop/openllmetry/blob/main/packages/sample-app/sample_app/thread_pool_example.py).