Practices for Python LLM Application Observability Based on Langfuse

With the rapid adoption of large language model (LLM), we are witnessing an explosive growth in AI applications and agents. However, as probabilistic black-box systems, LLMs pose significant challenges for developers in terms of debugging, evaluation, and optimization. Their internal mechanisms are opaque, and outputs are often unpredictable and difficult to interpret. This talk will focus on the Python ecosystem and demonstrate how to build end-to-end observability for LLM applications using **Langfuse.**