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Search LLM Tracing , found 8 results

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Langfuse LLM Tracing Workflow

langfuse-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

Guides setup of Langfuse tracing for LLM pipelines, covering automatic OpenAI wrapping, manual spans, streaming responses, and parent-child trace linking for end-to-end observability.

Langfuse Auth Setup

langfuse-install-auth

jeremylongshore/claude-code-plugins-plus-skills

Guides through installing the Langfuse SDK and configuring public/secret keys plus host settings so you can authenticate tracing for new LLM observability projects in Node.js or Python.

Langfuse Architecture Guide

langfuse-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

Production-grade Langfuse architecture patterns for building scalable LLM observability, covering singleton SDK setup, trace context propagation, queue-based ingestion, multi-environment configuration, and service mesh tracing so teams can design reliable tracing pipelines.

Langfuse Security Basics

langfuse-security-basics

jeremylongshore/claude-code-plugins-plus-skills

Guides teams through Langfuse security best practices, covering API key handling, PII scrubbing before tracing, access control on self-hosted deployments, and retention cleanup to keep LLM observability data private.

LangSmith LLM Observability

langsmith-observability

Orchestra-Research/AI-Research-SKILLs

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.

Phoenix AI Observability

phoenix-observability

Orchestra-Research/AI-Research-SKILLs

Phoenix is an open-source observability platform for tracing, evaluating, and monitoring LLM applications; use it to debug prompts, run dataset evaluations, build experiment pipelines, and watch production inference in real time.

Arize Instrumentation Assistant

arize-instrumentation

github/awesome-copilot

Guides adding Arize AX tracing to apps via an agent-assisted two-phase flow, emphasizing read-only analysis before manual instrumentation, favoring auto-integration, and adding manual chain/tool spans for LLM tool calls.

Phoenix LLM Tracing

phoenix-tracing

github/awesome-copilot

Guides hooking Phoenix AI services with OpenInference tracing, covering setup, spans, instrumentation, and production deployment for LLM workflows using Python or TypeScript.

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