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langsmith-observability

Trace, evaluate, and monitor LLM applications across LangChain, OpenAI, Anthropic, and LlamaIndex workflows.

Updated Feb 15, 2026
Search Tags:#debugging#monitoring#evaluation#tracing#datasets#langsmith#llmops
One-click install
npx skills add https://github.com/ricable/mcai --skill langsmith-observability-ricable
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/ricable/mcai/tree/main/.agents/skills/langsmith-observability
Command: npx skills add https://github.com/ricable/mcai --skill langsmith-observability-ricable

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LangSmith-observability helps you see what your LLM application is doing, so you can debug failures, compare model outputs, and understand latency, quality, and cost without guessing.

Core Features & Use Cases

  • Tracing: Capture the full path of chains, tools, and model calls so you can inspect where an issue starts.
  • Evaluation: Run datasets, custom evaluators, and comparison tests to catch regressions before deployment.
  • Monitoring: Track production runs, feedback, and performance trends to improve reliability over time.
  • Use Case: A team shipping a RAG chatbot can trace every request, score responses against a test set, and monitor live user feedback to spot degradations quickly.

Quick Start

Install LangSmith, set your API key and tracing environment variables, then wrap your LLM calls or evaluation pipeline so runs begin appearing in your LangSmith project.

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 LLM chains and agents in production?▼

Trace and debug LLM chains by capturing the full execution path of model calls, tools, and agents. This instrumentation lets you inspect exactly where latency, cost, and quality issues originate within your AI pipelines.

What's the best way to evaluate LLM app quality and catch regressions before deployment?▼

Evaluate LLM app quality by running regression test suites against managed datasets and custom evaluators. This dataset-driven evaluation catches performance degradations and prompt regressions before they reach production.

Does LangSmith observability work with OpenAI, Anthropic, and LlamaIndex workflows?▼

LangSmith observability supports tracing and monitoring across OpenAI, Anthropic, and LlamaIndex workflows. It integrates with these platforms to capture traceable instrumentation for comprehensive LLM operations.

How do I monitor RAG chatbot performance and user feedback?▼

Monitor RAG chatbot performance by tracking production runs, user feedback, and performance trends. This observability helps teams spot degradations quickly and improve reliability over time.

Do I need a LangSmith API key to start tracing my AI pipelines?▼

You need a LangSmith API key and specific tracing environment variables to start tracing. Configuring these enables traceable instrumentation so runs appear in your LangSmith project for analysis.

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