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 # Using OpenTelemetry [opentelemetry]
 
-You can use [OpenTelemetry](https://opentelemetry.io/) to monitor the performance and behavior of your {{es}} requests through the Elasticsearch Python client. The Python client comes with built-in OpenTelemetry instrumentation that emits [distributed tracing spans](docs-content://solutions/observability/apps/traces-2.md) by default. With that, applications using [manual OpenTelemetry instrumentation](https://www.elastic.co/blog/manual-instrumentation-of-python-applications-opentelemetry) or [automatic OpenTelemetry instrumentation](https://www.elastic.co/blog/auto-instrumentation-of-python-applications-opentelemetry) are enriched with additional spans that contain insightful information about the execution of the {{es}} requests.
+You can use [OpenTelemetry](https://opentelemetry.io/) to monitor the performance and behavior of your {{es}} requests through the Elasticsearch Python client. The Python client comes with built-in OpenTelemetry instrumentation that emits [distributed tracing spans](docs-content://solutions/observability/apm/traces-ui.md) by default. With that, applications using [manual OpenTelemetry instrumentation](https://www.elastic.co/blog/manual-instrumentation-of-python-applications-opentelemetry) or [automatic OpenTelemetry instrumentation](https://www.elastic.co/blog/auto-instrumentation-of-python-applications-opentelemetry) are enriched with additional spans that contain insightful information about the execution of the {{es}} requests.
 
 The native instrumentation in the Python client follows the [OpenTelemetry Semantic Conventions for {{es}}](https://opentelemetry.io/docs/specs/semconv/database/elasticsearch/). In particular, the instrumentation in the client covers the logical layer of {{es}} requests. A single span per request is created that is processed by the service through the Python client. The following image shows a trace that records the handling of two different {{es}} requests: an `info` request and a `search` request.