Python

Run the following command in your terminal:

pip install traceloop-sdk

In your LLM app, initialize the Traceloop tracer like this:

from traceloop.sdk import Traceloop

Traceloop.init()

If you’re running this locally, you may want to disable batch sending, so you can see the traces immediately:

Traceloop.init(disable_batch=True)

If you have complex workflows or chains, you can annotate them to get a better understanding of what’s going on. You’ll see the complete trace of your workflow on Traceloop or any other dashboard you’re using.

We have a set of decorators to make this easier. Assume you have a function that renders a prompt and calls an LLM, simply add @workflow.

from traceloop.sdk.decorators import workflow

@workflow(name="suggest_answers")
def suggest_answers(question: str):
  ...

# Works seamlessly with async functions too
@workflow(name="summarize")
async def summarize(long_text: str):
  ...

For more information, see the dedicated section in the docs.

Lastly, you’ll need to configure where to export your traces. The 2 environment variables controlling this are TRACELOOP_API_KEY and TRACELOOP_BASE_URL.

For Traceloop, read on. For other options, see Exporting.

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Using Traceloop Cloud

Go to Traceloop, and create a new account. Then, click on Environments on the left-hand navigation bar. Or go to directly to https://app.traceloop.com/settings/api-keys. Click Generate API Key to generate an API key for the developement environment and click Copy API Key to copy it over.

Set the copied Traceloop’s API key as an environment variable in your app named TRACELOOP_API_KEY.

Done! You’ll get instant visibility into everything that’s happening with your LLM. If you’re calling a vector DB, or any other external service or database, you’ll also see it in the Traceloop dashboard.