Automatically discover what matters in your production traces with Topics

Braintrust's Topics feature addresses the challenge of managing and interpreting the vast amounts of trace data generated by AI applications in production by automatically clustering and classifying traces based on recurring patterns, enabling teams to review high-level topics instead of individual traces. Utilizing AI-powered clustering methods such as UMAP dimensionality reduction, HDBSCAN clustering, and c-TF-IDF keyword extraction, Topics organizes traces into descriptive groups with representative keywords and examples, facilitating quick understanding of emerging issues like failure modes, user behavior shifts, or prompt drifts. It includes built-in facets for commonly sought patterns—such as user tasks, agent issues, and sentiment analysis—while also allowing for custom facets and preprocessors to tailor the analysis to unique data dimensions. Topics integrates seamlessly into existing Braintrust workflows, offering filterable fields and comparison capabilities across projects, and is available in beta for Pro and Enterprise users, with an option for Free plan users to request access.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.