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langsmith-observability

Trace, evaluate, and monitor LLM application runs with LangSmith.

Updated May 4, 2026
Search Tags:#monitoring#evaluation#observability#regression testing#langsmith#llm tracing#dataset testing
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill langsmith-observability-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/langsmith
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill langsmith-observability-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LangSmith observability helps you understand why LLM applications fail, slow down, or produce low-quality outputs by capturing traces, feedback, and evaluation results in one workflow.

Core Features & Use Cases

  • Tracing: Record prompts, tool calls, latency, and nested execution flow across chains, agents, and model calls.
  • Evaluation: Run dataset-based tests, custom evaluators, and CI regression checks to measure response quality consistently.
  • Monitoring: Track production runs, user feedback, and performance trends to spot issues before they spread.
  • Use Case: A team debugging a customer-support chatbot can trace every model call, compare outputs against a test dataset, and monitor feedback to catch regressions after each release.

Quick Start

Use the langsmith observability skill to trace my LLM app, evaluate it against a dataset, and summarize the main failure patterns.

Frequently Asked Questions about langsmith-observability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I trace and debug LLM application behavior in production?▼

You can trace LLM application behavior by recording prompts, tool calls, latency, and nested execution flow across chains and agents. This captures execution details to help you debug why language model products fail or slow down.

What is the best way to run regression testing for LLM chains?▼

Running dataset-based regression tests for LLM chains involves comparing outputs against a test dataset using custom evaluators. This systematically measures response quality consistency to catch regressions after each release.

Does LLM tracing work with OpenAI and LangChain model clients?▼

Yes, LLM tracing integrates with OpenAI, LangChain, and other model clients. It logs run tracing and feedback collection natively within these frameworks to capture nested execution flows.

How do I evaluate LLM outputs against a custom test dataset?▼

You evaluate LLM outputs by running dataset-driven tests with custom evaluators. This process logs feedback and measures response quality consistently to identify main failure patterns in language model products.

Why do I need observability for my LLM application?▼

LLM observability helps you understand why applications fail, slow down, or produce low-quality outputs. It captures traces, feedback, and evaluation results in one workflow to debug prompts and production runs effectively.

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