langsmith-observability

Name: langsmith-observability
Availability: InStock
Author: gagan114662

Trace, evaluate, and monitor LLM applications with LangSmith observability.

Updated Jan 30, 2026

Search Tags:#debugging #monitoring #llm #evaluation #observability #tracing #langsmith

Ready to run

npx skills add https://github.com/gagan114662/content_books --skill langsmith-observability-gagan114662

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/gagan114662/content_books/tree/main/AI-research-SKILLs/17-observability/langsmith
Command: npx skills add https://github.com/gagan114662/content_books --skill langsmith-observability-gagan114662

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of understanding, debugging, and improving Large Language Model (LLM) applications by providing a comprehensive platform for observability.

Core Features & Use Cases

Tracing: Capture detailed logs of LLM calls, chains, and agents to understand execution flow, latency, and token usage.
Evaluation: Systematically test and score LLM outputs against datasets using built-in or custom evaluators.
Monitoring: Track performance, errors, and costs of LLM applications in production.
Use Case: Debugging a complex RAG (Retrieval Augmented Generation) pipeline by tracing the flow from user query to document retrieval, LLM generation, and final response, identifying bottlenecks or incorrect information retrieval.

Quick Start

Install the LangSmith SDK and set your API key to begin tracing your LLM application.

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 a complex RAG pipeline?▼

Tracing a complex RAG pipeline involves capturing detailed logs of LLM calls, chains, and agents to understand execution flow, latency, and token usage. This identifies bottlenecks from document retrieval to final response generation.

What is LLM observability and when do I need it?▼

LLM observability is the systematic tracing, evaluation, and monitoring of AI applications. You need it to understand execution flow, debug complex chains, and track production performance, errors, and costs.

How do I evaluate LLM outputs against custom datasets?▼

Evaluating LLM outputs against datasets involves systematically testing and scoring results using built-in or custom evaluators. This validates model responses to ensure accuracy and reliability across various test cases.

Can I monitor LLM application costs and errors in production?▼

Monitoring LLM application costs and errors in production tracks performance metrics, failure rates, and token usage expenses. This provides visibility into runtime behavior to maintain application reliability and budget control.

Does LangSmith observability work with LangChain and LlamaIndex?▼

LangSmith observability integrates with major LLM providers and frameworks like LangChain and LlamaIndex. This compatibility allows seamless tracing and evaluation across different AI application architectures.

Why is my LLM chain failing to retrieve correct documents?▼

When an LLM chain fails to retrieve correct documents, tracing the execution flow identifies where the pipeline breaks. Logging each step from user query to document retrieval isolates incorrect information fetching and generation bottlenecks.