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langsmith-observability

Trace, evaluate, and monitor LLM applications using the LangSmith SDK.

Updated Aug 23, 2026
Search Tags:#monitoring#llm#evaluation#observability#tracing#llmops
One-click install
npx skills add https://github.com/choice5346/BiSHE --skill langsmith-observability-choice5346
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/choice5346/BiSHE/tree/main/.github/skills/langsmith
Command: npx skills add https://github.com/choice5346/BiSHE --skill langsmith-observability-choice5346

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langsmith, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides comprehensive tools for understanding, debugging, and improving Large Language Model (LLM) applications by offering detailed tracing, evaluation, and monitoring capabilities.

Core Features & Use Cases

  • LLM Tracing: Capture and visualize the execution flow of LLM calls, chains, and agents.
  • Evaluation: Systematically test model performance against datasets using built-in or custom evaluators.
  • Monitoring: Track key metrics like latency, cost, and errors in production LLM systems.
  • Use Case: Debugging a complex LLM agent that is not producing the desired output by examining its trace to identify where the reasoning or tool usage went wrong.

Quick Start

Use the langsmith skill to trace your LLM application by setting the LANGSMITH_TRACING environment variable to true.

Frequently Asked Questions about langsmith-observability

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

FAQPage Schema
How do I trace LLM application execution to debug reasoning errors?▼

LLM tracing captures and visualizes the execution flow of LLM calls, chains, and agents. You can debug applications by examining the trace to identify where reasoning or tool usage went wrong.

What is the best way to evaluate LLM model outputs against datasets?▼

Evaluating LLM model outputs involves systematically testing performance against datasets using built-in or custom evaluators. This approach helps build systematic testing pipelines for AI applications.

Can I monitor production LLM systems for latency and cost metrics?▼

Monitoring production LLM systems tracks key metrics like latency, cost, and errors. This provides comprehensive tools for understanding, debugging, and improving LLM applications in production.

Do I need the LangSmith SDK to enable LLM observability and tracing?▼

Yes, the LangSmith SDK is required for LLM observability tracing and evaluation functionalities. You enable tracing by setting the LANGSMITH_TRACING environment variable to true.

Why does my LLM agent produce undesired outputs during complex chains?▼

LLM agents producing undesired outputs often have reasoning or tool usage errors within complex chains. LLM observability allows you to examine the execution trace to identify the exact failure point.

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