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langsmith-observability

Trace LLM calls and monitor performance, costs, and errors via LangSmith.

Updated Mar 16, 2026
Search Tags:#monitoring#production#llm#evaluation#observability#tracing
One-click install
npx skills add https://github.com/arsity/scholar-tools --skill langsmith-observability-arsity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/17-observability/langsmith
Command: npx skills add https://github.com/arsity/scholar-tools --skill langsmith-observability-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langsmith>=0.2.0, and includes references (resource) components.

What problem does it solve?

LangSmith Observability provides end-to-end visibility into LLM workflows, enabling debugging, evaluation, and monitoring of prompts, chains, and agents.

Core Features & Use Cases

  • Tracing: capture inputs, outputs, latency, and context for every LLM call.
  • Evaluation & Datasets: built-in evaluators and test datasets for regression checks and quality metrics.
  • Production Monitoring: monitor errors, costs, and performance in production deployments.
  • Integration: works with LangChain, OpenAI, and custom pipelines for robust observability.
  • Use cases: diagnose latency spikes, compare model variants, validate evaluation results in pipelines.

Quick Start

Install LangSmith and enable tracing in your environment, then annotate functions with @traceable to start collecting traces.

Frequently Asked Questions about langsmith-observability

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

FAQPage Schema
How do I add observability and tracing to LLM applications in production?▼

LLM observability captures inputs, outputs, latency, and context for every call to debug production deployments. You enable tracing in your environment and annotate functions with @traceable to start collecting traces.

Can I monitor LLM latency and costs across custom AI pipelines?▼

Yes, production monitoring tracks errors, performance, and costs across custom AI pipelines. It integrates with LangChain, OpenAI, and custom data pipelines to diagnose latency spikes and monitor throughput.

How do I evaluate LLM outputs and create test datasets for regression checks?▼

You evaluate LLM outputs using built-in evaluators and test datasets for regression checks and quality metrics. This validates evaluation results directly within your pipelines to compare model variants.

Does LangSmith tracing work with LangChain and OpenAI integrations?▼

Yes, LangSmith tracing works with LangChain, OpenAI, and custom pipelines for robust observability. It uses the LangSmith SDK to integrate tracing into your existing AI workflows.

What is the best way to debug prompts, chains, and agents for latency spikes?▼

End-to-end LLM tracing is the best way to debug prompts, chains, and agents. It provides visibility into latency, throughput, and context to identify and resolve issues across AI workflows.

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