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A minimal example demonstrating how to implement basic tracing using the Langfuse SDK. It illustrates the hierarchy of traces, spans, and generations (LLM calls), providing code examples in TypeScript and Python for easy setup and integration testing.

A comprehensive guide for troubleshooting and resolving Langfuse-related incidents and outages. It provides step-by-step procedures for initial triage, severity assessment, implementing fallback modes, and resolving common issues like missing traces or rate limits, ensuring continuous observability monitoring for LLM applications.

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.

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.

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.

LangSmith centralizes tracing, evaluation, and monitoring for LLM applications, letting teams debug prompts, run regression tests, collect latency/cost metrics, and integrate with OpenAI, Anthropic, LangChain, or LlamaIndex to keep production systems observable.

This guide demonstrates the basic workflow for building an AI agent using a webhook trigger. The agent receives structured data (payload), utilizes an LLM to extract key information and generate a concise summary (summarization step), and finally executes an action by posting the summary to Slack or sending an email. Ideal for beginners learning about AI agent architecture, testing data flow, and implementing simple automated system notifications.