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langsmith-observability

Trace LLM application runs and evaluate model behavior with LangSmith.

Updated May 2, 2026
Search Tags:#monitoring#llm evaluation#tracing#regression testing#langsmith#llm observability#llm ops
One-click install
npx skills add https://github.com/qcmuu/AI-Research-Skills --skill langsmith-observability-qcmuu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/qcmuu/AI-Research-Skills/tree/main/17-observability/langsmith
Command: npx skills add https://github.com/qcmuu/AI-Research-Skills --skill langsmith-observability-qcmuu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

LangSmith helps teams debug and improve LLM systems by providing end-to-end visibility into prompts, tool calls, model outputs, and production performance.

Core Features & Use Cases

  • Tracing: Capture hierarchical runs (chains, retrievers, tools, and LLM calls) with latency and token/cost insights.
  • Datasets & Evaluation: Create datasets and run systematic evaluations using built-in and custom evaluators for regression testing.
  • Monitoring & Feedback: Track errors, metrics, and user feedback in production to continuously improve quality.
  • Integrations: Works well with common LLM frameworks and providers, including OpenAI and LangChain/LlamaIndex.

Quick Start

Install LangSmith, set LANGSMITH_API_KEY and LANGSMITH_TRACING to true, then wrap your model calls with @traceable or use wrap_openai to automatically generate traces in the LangSmith UI.

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 chains and tool calls for latency and cost insights?▼

To trace LLM applications, you wrap model calls with @traceable or wrap_openai after setting LANGSMITH_API_KEY and LANGSMITH_TRACING to true. This captures hierarchical runs like chains, retrievers, and tool calls, providing latency and token/cost insights in the LangSmith UI.

How does dataset-driven regression testing work for LLM evaluation?▼

Dataset-driven regression testing works by creating datasets and running systematic evaluations using built-in or custom evaluators. This computes repeatable quality metrics to validate model behavior and catch regressions across development cycles.

Can I use LangSmith observability with OpenAI and LangChain frameworks?▼

Yes, LangSmith observability works with common LLM frameworks and providers, including OpenAI and LangChain/LlamaIndex. It requires authenticated LangSmith SDK usage with traceable instrumentation to automatically generate traces.

What is LLM observability and when do I need production monitoring for my models?▼

LLM observability provides end-to-end visibility into prompts, tool calls, model outputs, and production performance. You need production monitoring to track errors, metrics, and user feedback, enabling continuous quality improvement and runtime analysis.

Do I need an API key to start debugging LLM systems with tracing?▼

Yes, you need to set the LANGSMITH_API_KEY and LANGSMITH_TRACING to true in your environment. This authenticated LangSmith SDK setup is required to generate traces and debug model behavior effectively.

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