Langfuse Observability

About

The Langfuse Observability skill integrates Claude Code with the Langfuse platform to streamline LLM monitoring and debugging. It enables developers to retrieve detailed execution traces, analyze latency and token usage, and manage prompt versions directly within their workflow. By providing tools for cost calculation and failed generation debugging, this skill helps developers optimize their AI applications, ensure prompt quality, and maintain high-performance LLM infrastructure.

Key Features

• 2 GitHub stars
• Query detailed LLM generation traces including inputs, outputs, and latency
• Manage and fetch prompt versions using production or staging labels
• Monitor token usage and calculate costs across multiple model providers
• Analyze metrics summaries to identify error patterns and success rates
• Convert failed production traces into Promptfoo test cases for evaluation

Use Cases

• Monitoring production token spend and model performance for cost optimization
• Debugging failed LLM generations by inspecting full trace history and metadata
• Retrieving version-controlled prompts from Langfuse to ensure consistency across environments