Skill UI

This comprehensive guide details the installation and configuration of the Langfuse SDK for LLM observability. It covers setup instructions for both JavaScript/TypeScript and Python environments. Users will learn how to retrieve necessary API keys, configure environment variables, and initialize tracing using OpenTelemetry, enabling full monitoring and debugging of AI application calls.

This guide provides a comprehensive local development setup for LLM applications using Langfuse. It enables fast iteration cycles, real-time trace debugging, and hot reloading, making it ideal for developers building and testing complex AI workflows locally. Instructions cover environment setup, advanced SDK usage, and optional self-hosted Docker deployment.

Provides a deep dive and step-by-step scripts for complex Langfuse migrations. Covers comprehensive data export (traces, scores, prompts, datasets) and import processes. This guide is essential for platform transitions, moving between different Langfuse instances, or migrating from observability tools like LangSmith, supporting zero-downtime dual-write patterns.

This comprehensive guide details the best practices for configuring Langfuse across development, staging, and production environments. It covers essential steps including environment-specific API key management, secret integration (AWS/GCP), and implementing reliable CI/CD pipelines to ensure data isolation and prevent cross-environment leakage, maintaining observability throughout the software lifecycle.

This guide details how to establish comprehensive observability for LLM operations using Langfuse. It covers setting up Prometheus metrics (tracking traces, generation duration, and token usage) and visualizing them in Grafana dashboards. Ideal for monitoring application health, optimizing costs, and tracking performance over time.

Provides comprehensive strategies to optimize Langfuse tracing overhead for high-throughput applications. This guide covers benchmarking, tuning batch processing configurations, implementing non-blocking wrappers, and optimizing payload sizes. Use this when scaling Langfuse for production workloads to minimize latency and maximize stability.

A comprehensive checklist and guide for deploying Langfuse observability to a production environment. It covers best practices for verifying production configuration, implementing robust error handling, ensuring graceful shutdown, and executing pre-deployment validation scripts to guarantee system stability and reliability.

A comprehensive guide on implementing robust strategies for high-volume LLM observability workloads using Langfuse. Learn to manage API rate limits by implementing exponential backoff with jitter, optimizing SDK batching settings, controlling concurrency with queues, and utilizing configurable sampling to prevent data loss during periods of high traffic.

This guide provides production-grade architecture patterns for implementing robust LLM observability using Langfuse. It covers singleton SDK setup, context propagation across asynchronous boundaries, Express middleware for automatic tracing, and cross-service trace correlation using OpenTelemetry. Ideal for designing enterprise-level, scalable tracing infrastructure for LLM applications.

A comprehensive guide covering production-grade patterns for using the Langfuse SDK. Learn how to implement robust LLM observability, including singleton client setup, using the `observe` wrapper for automatic span creation, and utilizing `startActiveObservation` for fine-grained trace control. Essential for maintaining high standards of observability in AI-powered applications.

This guide provides essential security best practices for implementing Langfuse, an LLM observability platform. It covers critical areas such as secure credential management (public vs. secret keys), implementing PII scrubbing techniques before tracing, hardening self-hosted environments, and enforcing the principle of least privilege. Use this guide to ensure your LLM integrations meet strict data privacy standards (e.g., GDPR, HIPAA).

A comprehensive guide providing step-by-step instructions for upgrading Langfuse SDK versions (v3 to v5) and migrating between major API changes. It covers best practices for both TypeScript/JavaScript and Python implementations, focusing on adopting OpenTelemetry standards to ensure seamless LLM observability integration.