What is the difference between tracing and evaluation in this skill?

Tracing captures the step-by-step execution of a single run for debugging, while evaluation tests those runs systematically against datasets to measure accuracy or quality.

How does this skill improve Claude's LLM development capabilities?

It allows Claude to automatically wrap model clients and use decorators like @traceable to provide deep visibility into how your AI code executes, making debugging much faster.

Does it support LangChain applications?

Absolutely; LangSmith is natively integrated with LangChain, making it the primary tool for observability and tracing in LangChain-based ecosystems.

Is a separate API key required to use this skill?

Yes, you will need a LangSmith API key from smith.langchain.com and must set the LANGSMITH_TRACING environment variable to true.

Can I use LangSmith for production monitoring?

Yes, LangSmith is designed for both development-time debugging and production-grade monitoring of latency, costs, and error rates with built-in sampling support.

LangSmith LLM Observability

Name: LangSmith LLM Observability
Author: zechenzhangAGI

byzechenzhangAGI

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384

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分析と監視

Integrates comprehensive tracing, evaluation, and monitoring tools to debug and optimize Large Language Model (LLM) applications.

LangSmith LLM Observability provides a robust framework for tracing the execution flow of AI agents and chains, allowing developers to visualize inputs, outputs, and intermediate steps in real-time. It enables systematic evaluation of model performance against custom datasets, tracks critical production metrics like latency and token costs, and facilitates iterative prompt engineering through versioned feedback loops. This skill is essential for developers moving AI prototypes into production who require reliable debugging, regression testing, and high-fidelity quality assurance for their LLM-powered features.

主な機能

01Seamless integration with LangChain, OpenAI, Anthropic, and LlamaIndex

02Production monitoring for latency, token usage, error rates, and cost tracking

03Real-time execution tracing for LLM calls, chains, and complex agentic workflows

04384 GitHub stars

05Dataset management for regression testing and continuous model improvement

06Systematic performance evaluation using built-in string and CoT evaluators

ユースケース

01Monitoring production AI application health and operational costs at scale

02Debugging complex multi-step agentic workflows to identify logic bottlenecks

03Running systematic regression tests on prompt changes to ensure output quality

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zechenzhangagi/ai-research-skills langsmith

For use in Claude.ai and ChatGPT

主な機能

01Seamless integration with LangChain, OpenAI, Anthropic, and LlamaIndex

02Production monitoring for latency, token usage, error rates, and cost tracking

03Real-time execution tracing for LLM calls, chains, and complex agentic workflows

04384 GitHub stars

05Dataset management for regression testing and continuous model improvement

06Systematic performance evaluation using built-in string and CoT evaluators

ユースケース

01Monitoring production AI application health and operational costs at scale

02Debugging complex multi-step agentic workflows to identify logic bottlenecks

03Running systematic regression tests on prompt changes to ensure output quality