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DoanNgocCuong
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langsmith-observability

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

Updated Aug 23, 2026
Search Tags:#debugging#monitoring#evaluation#observability#langsmith#llmops
One-click install
npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill langsmith-observability-doanngoccuong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026/tree/main/.claude/skills/langsmith
Command: npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill langsmith-observability-doanngoccuong

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive platform for observing and managing Large Language Model (LLM) applications, enabling developers to debug, evaluate, and monitor their AI systems effectively.

Core Features & Use Cases

  • Tracing: Capture detailed logs of LLM calls, chains, and agents for debugging.
  • Evaluation: Systematically test and score model outputs against datasets using built-in or custom metrics.
  • Monitoring: Track performance, errors, and costs of LLM applications in production.
  • Use Case: When an LLM-powered chatbot starts giving incorrect answers, use LangSmith to trace the conversation flow, identify the problematic prompt or tool call, and then create an evaluation dataset to test potential fixes.

Quick Start

Install the LangSmith SDK and set your API key and tracing environment variables.

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 incorrect LLM responses in my application?▼

To debug incorrect LLM responses, you need LLM observability to trace conversation flows, capture detailed logs of LLM calls and chains, and isolate the problematic prompt or tool call causing the issue.

What is LLM observability and when do I need it for my AI application?▼

LLM observability is the process of tracing, evaluating, and monitoring AI applications. You need it to capture logs for debugging, systematically score model outputs, and track production performance, errors, and costs.

How do I evaluate LLM outputs against custom datasets?▼

You can evaluate LLM outputs by systematically testing and scoring model responses against datasets using built-in or custom metrics, enabling you to verify potential fixes and build reliable AI testing pipelines.

Does LangSmith work with my existing LLM frameworks for monitoring production systems?▼

Yes, LangSmith integrates with your LLM frameworks via the LangSmith SDK to monitor production systems, allowing you to track application performance, catch errors, and analyze operational costs.

What do I need to set up LLM tracing and evaluation pipelines?▼

To set up LLM tracing and evaluation pipelines, you need to install the LangSmith SDK, configure your API key, and set the required tracing environment variables to enable observability for your AI applications.

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