How to trace LLM apps in Python (2026)

The text discusses the importance and methodology of tracing in Python applications, particularly those involving large language models (LLMs), to ensure smooth and efficient operation. It emphasizes the need for a comprehensive trace that captures various steps such as retrieval, preprocessing, model calls, and more, enabling teams to identify and resolve issues efficiently. The use of OpenTelemetry is highlighted as a foundational tool that provides a standardized approach for creating and transporting spans, but with additional LLM-specific interpretation required for effective debugging and evaluation. Braintrust is presented as a robust solution for Python-native instrumentation, allowing teams to trace LLM applications by automatically capturing inputs, outputs, latency, and costs, while also integrating with existing OpenTelemetry setups. It supports both auto and manual instrumentation, offering flexibility in tracing provider and framework calls as well as application-specific logic. Additionally, the text explains how traces can be transformed into evaluation datasets to enhance future release checks, using real production cases to ensure continued application reliability.

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