langsmith-evaluator

Build LangSmith evaluation pipelines with LLM-as-Judge and custom code.

1|Updated Apr 30, 2026
One-click install
npx skills add https://github.com/wpsadi/stock-agent --skill langsmith-evaluator-wpsadi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-evaluator
Source: https://github.com/wpsadi/stock-agent/tree/main/.agents/skills/langsmith-evaluator
Command: npx skills add https://github.com/wpsadi/stock-agent --skill langsmith-evaluator-wpsadi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langsmith, langchain-openai, python-dotenv, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill Unit offers a comprehensive approach to building evaluation pipelines for LangSmith, simplifying the creation and execution of evaluators using both LLM-as-Judge and custom code, enabling accurate quality checks for agent outputs.

Core Features & Use Cases

  • LLM-as-Judge: Grade agent outputs with AI-driven accuracy.
  • Custom Code: Validate agent outputs using deterministic logic.
  • Offline vs Online Evaluators: Handle datasets and production runs efficiently.
  • Quick Setup: Simple environment variable configuration for immediate use.
  • Integration with LangSmith: Seamless integration with the LangSmith platform for automated evaluations.

Quick Start

Create a custom evaluator by setting the LANGSMITH_API_KEY environment variable and executing the following command to list available evaluators.

Frequently Asked Questions about langsmith-evaluator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate LangSmith agent outputs using LLM-as-Judge?▼

You can evaluate LangSmith agent outputs by building an evaluation pipeline that grades responses with LLM-as-Judge, using AI-driven accuracy to perform quality checks on your agents.

Can I validate LangSmith agents with custom code instead of AI grading?▼

Yes, you can validate LangSmith agent outputs using deterministic custom code, allowing you to apply specific logic rules alongside or instead of LLM-as-Judge for quality assurance.

What is the difference between offline and online evaluators in LangSmith?▼

Offline evaluators handle testing against static datasets, while online evaluators assess agent performance during production runs, ensuring comprehensive quality checks across different scenarios.

How do I set up the environment to run LangSmith evaluation pipelines?▼

To set up the environment, configure the LANGSMITH_API_KEY environment variable and load dependencies like langchain-openai and python-dotenv to enable immediate integration with the LangSmith platform.

Do I need a LangSmith API key to run agent testing evaluators?▼

Yes, a LangSmith API key is required. Setting the LANGSMITH_API_KEY environment variable provides the necessary authentication for seamless integration and execution of automated evaluations.

What is the best way to automate quality assurance for LangSmith agents?▼

The best way to automate quality assurance is to build a comprehensive evaluation pipeline that creates, defines, and runs both LLM-as-Judge and custom code evaluators within the LangSmith platform.