By default, OpenLLMetry logs prompts, completions, and embeddings to span attributes. This gives you a clear visibility into how your LLM application is working, and can make it easy to debug and evaluate the quality of the outputs. However, you may want to disable this logging for privacy reasons, as they may contain highly sensitive data from your users. You may also simply want to reduce the size of your traces.

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Disabling logging globally

To disable logging, set the TRACELOOP_TRACE_CONTENT environment variable to false. On Typescript/Javascript you can also pass the traceContent option. 
TRACELOOP_TRACE_CONTENT=false
OpenLLMetry SDK, as well as all individual instrumentations will respect this setting.

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Enabling logging selectively in specific workflows / tasks

You can decide to selectively enable prompt logging for specific workflows, tasks, agents, or tools, using the annotations API. If you don’t specify a traceContent option, the global setting will be used. 
return await traceloop.withWorkflow(
    { name: "workflow_name", traceContent: false },
    async () => {
        ...
    }
  );

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Enabling logging selectively for specific users

You can decide to selectively enable or disable prompt logging for specific users or workflows.

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Using the Traceloop Platform

We have an API to enable content tracing for specific users, as defined by association entities. See the Traceloop API documentation for more information.

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Without the Traceloop Platform

Set a key called override_enable_content_tracing in the OpenTelemetry context to True right before making the LLM call you want to trace with prompts. This will create a new context that will instruct instrumentations to log prompts and completions as span attributes.
Python
from opentelemetry.context import attach, set_value

attach(set_value("override_enable_content_tracing", True))
Make sure to also disable it afterwards:
Python
from opentelemetry.context import attach, set_value

attach(set_value("override_enable_content_tracing", False))