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langsmith-observability

Trace LLM calls and collect inputs, outputs, latency, and metadata.

2|Updated Apr 12, 2026
Search Tags:#monitoring#evaluation#observability#tracing#datasets#langsmith#llm-ops
One-click install
npx skills add https://github.com/Clay-HHK/claude-config --skill langsmith-observability-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/AI-research-SKILLs/17-observability/langsmith
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill langsmith-observability-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Lack of centralized visibility into LLM calls makes debugging, regression testing, and production monitoring difficult; this Skill provides structured tracing, evaluation, and monitoring to locate failures, measure quality, and control costs.

Core Features & Use Cases

  • Tracing: Capture inputs, outputs, latency, and hierarchical run traces for chains, tools, and LLM calls to reproduce issues and analyze execution flows.
  • Evaluation & Datasets: Create datasets from traces or CSVs, run automated evaluators (including LLM-based judges), and integrate evaluation into CI to detect regressions.
  • Monitoring & Feedback: Aggregate metrics, collect user feedback, and sample production traffic to detect drift, errors, and cost anomalies; integrates with OpenAI, LangChain, and LangSmith SDK.
  • Use Case: Debug a failing prompt in a multi-step chain by tracing nested runs, creating a dataset from failing traces, and running comparative evaluations across model versions.

Quick Start

Use the langsmith-observability skill to trace a sample LLM call, collect metrics and traces, and evaluate the output against a test dataset.

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 calls and debug a failing prompt in a multi-step chain?▼

Trace LLM calls by capturing inputs, outputs, latency, and hierarchical run traces for chains, tools, and nested runs. This structured tracing helps reproduce issues and analyze execution flows to debug failing prompts within multi-step chains.

How does observability for LLM applications help detect production drift and cost anomalies?▼

LLM observability aggregates metrics, samples production traffic, and collects user feedback to detect drift, errors, and cost anomalies. It provides centralized visibility into production LLM systems to locate failures and measure quality.

Can I create datasets from traces and run automated evaluations to detect regressions?▼

Yes, you can create datasets from traces or CSVs and run automated evaluators, including LLM-based judges. Integrating these evaluations into CI systematically detects regressions across model versions.

Does LangSmith observability work with OpenAI and LangChain integrations?▼

Yes, LangSmith observability integrates with OpenAI, LangChain, and the LangSmith SDK. It requires API key configuration to capture traces, collect metrics, and monitor LLM systems.

What is the best way to monitor LLM systems for errors and latency without centralized visibility?▼

Apply structured observability to monitor LLM systems for errors, cost, and latency. By capturing inputs, outputs, and metadata, it replaces decentralized visibility with aggregated metrics to locate failures and control costs.

Why should I use LLM tracing instead of manual logging for debugging regression testing?▼

LLM tracing captures hierarchical run traces and metadata that manual logging misses. It enables systematic regression testing by recording latency and creating datasets from failing traces for comparative evaluations.

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