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langsmith-observability

Trace, evaluate, and monitor LLM operations with the LangSmith framework.

6|3|Updated Jan 29, 2026
Search Tags:#testing#debugging#monitoring#ai development#observability#tracing#language models
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill langsmith-observability-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-observability
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/langsmith
Command: npx skills add https://github.com/jonnabio/ace-framework --skill langsmith-observability-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The LangSmith skill solves the problem of manual and inefficient debugging and evaluation in Large Language Model (LLM) applications by providing automated tools for monitoring, testing, and tracing.

Core Features & Use Cases

  • Tracing: Automated capturing and reporting of inputs, outputs, and latency for LLM calls.
  • Evaluation: Systematic testing and evaluation of model outputs against datasets.
  • Monitoring: Tracking metrics and costs for LLM operations.
  • Integrations: Compatibility with OpenAI, Anthropic, and LangChain platforms.

Quick Start

Install LangSmith with pip install langsmith. Configure your environment with the LANGSMITH_API_KEY and start tracing your LLM calls using the @traceable decorator.

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 monitor latency in my AI applications?▼

Tracing LLM calls captures inputs, outputs, and latency automatically. You can monitor model execution details by configuring the langsmith environment with your API key and using the traceable decorator on your functions.

What is the best way to debug and evaluate large language model outputs against datasets?▼

Evaluating large language model outputs involves systematic testing against datasets. This skill automates evaluation by logging model execution details, allowing developers to test and analyze NLP application outputs efficiently.

Does langsmith observability work with OpenAI, Anthropic, and LangChain platforms?▼

Yes, langsmith observability supports integrations with OpenAI, Anthropic, and LangChain platforms. This compatibility allows developers to trace and monitor operations across multiple language model frameworks.

How do I set up LLM monitoring and track metrics for my NLP application?▼

LLM monitoring tracks metrics and costs for operations. Set it up by installing the langsmith package via pip, configuring your environment with the required API key, and applying the traceable decorator to log metrics.

Why do I need observability and tracing for large language model development?▼

Observability solves manual and inefficient debugging in LLM applications. It provides automated tools for monitoring, testing, and tracing, streamlining the analysis of model inputs, outputs, and operational latency.

What are the limitations of using langsmith for tracing AI models?▼

Tracing AI models with langsmith requires the langsmith package version 0.2.0 or higher. Users must configure the LANGSMITH_API_KEY environment variable to capture and report execution details successfully.

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