Skill UI

This guide outlines essential security practices for deploying production-grade LLM applications using LangChain. It covers secure secrets management, defending against prompt injection, implementing sandboxed tool execution, validating model outputs for sensitive data, and setting up comprehensive audit logging, ensuring robust and secure AI deployment.

This comprehensive guide details how to safely upgrade LangChain versions (e.g., from 0.1.x to 0.3.x+). It covers critical breaking changes across both TypeScript and Python implementations, including updating import paths, converting legacy LLMChain to the modern LCEL pipe syntax, updating agent creation, and migrating memory management. Essential for maintaining and modernizing existing LLM applications.

This comprehensive skill implements advanced event-driven patterns for LangChain applications. It provides custom callback handlers to observe every stage of chain execution (e.g., tool start/end, LLM token generation). Furthermore, it supports real-time data transmission via Server-Sent Events (SSE) and WebSockets, and integrates webhooks for centralized event logging and comprehensive tracing.

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.

Learn how to monitor, analyze, and optimize the expenditure of Large Language Models (LLMs) using Langfuse's advanced analytics suite. This guide covers techniques like automatic token tracking, programmatic cost reporting via the Metrics API, and implementing smart model routing to automatically select the most cost-effective model based on task complexity and input length. Essential for any developer managing AI budgets and ensuring operational efficiency.

This guide details setting up enterprise-grade access control for Langfuse. Learn how to configure role-based permissions (Owner, Admin, Member), implement scoped API keys, integrate SSO (SAML/OIDC), and set up comprehensive audit logging. It is essential for managing multi-team isolation and ensuring compliance in production environments.

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 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 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.

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.