Langfuse x Label Studio: Debug and evaluate AI agents with observability traces

Overview

Langfuse is an open-source LLM engineering platform for tracing, evaluation, prompt management, datasets, and metrics. It helps teams observe application behavior, debug model outputs, run experiments, and improve the quality of production AI systems. By integrating Langfuse with Label Studio, teams can add human-in-the-loop review to LLM evaluation workflows, using structured annotation to review traces, score outputs, and build better datasets for benchmarking and iteration.

Benefits

• Improved observability: Trace prompts, responses, and tool calls to understand failures and quality issues in production.
• Human-in-the-loop evaluation: Use Label Studio to review and annotate Braintrust traces and model outputs for quality and correctness
• Better benchmark creation: Turn experiments, logs, and feedback into structured datasets for testing and regression analysis.
• Faster iteration: Combine Braintrust evals with annotation feedback to refine prompts, models, and agent workflows.
• Higher reliability: Build more robust AI products with continuous monitoring, scoring, and human review..