langfuse-score-analytics

To analyze Langfuse scores and track AI performance trends, you can use a Python script to retrieve score data via the Langfuse API, generating summary statistics and trend lines over specified time periods.

The best way to detect score regressions in Langfuse is to compare score performance between different time periods or dimensions, such as comparing user satisfaction metrics before and after a specific model release.

Yes, you can compare Langfuse scores across different environments and trace names by utilizing the dimension comparison feature to analyze score distributions and performance variations across your specified deployment parameters.

Yes, you need Langfuse API keys to authenticate and retrieve score data, as the Python script requires direct access to the Langfuse API to pull statistics and perform trend analysis.

To get summary statistics for AI quality metrics tracked in Langfuse, execute the Python analysis script to aggregate score data, providing distributions and performance summaries for your specified evaluation metrics.

When analyzing Langfuse score distributions with Python, limitations include dependency on API access for data retrieval and the need for predefined time periods or dimensions to accurately execute regression detection.