LangSmith Evaluators

Name: LangSmith Evaluators
Availability: InStock
Author: LangChain

Create and run LLM-as-Judge and custom code evaluators in LangSmith.

1.1k|86|Updated Jan 22, 2026

Search Tags:#testing #quality assurance #llm #agent #evaluation #langsmith

Ready to run

npx skills add https://github.com/langchain-ai/langchain-skills --skill langsmith-evaluators

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: LangSmith Evaluators
Source: https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langsmith-evaluator
Command: npx skills add https://github.com/langchain-ai/langchain-skills --skill langsmith-evaluators

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langsmith, langchain-openai, python-dotenv, commander, chalk, cli-table3, openai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of building, defining, and running evaluations for your AI applications, ensuring quality and performance.

Core Features & Use Cases

Create Evaluators: Define LLM-as-Judge or custom code evaluators to assess AI outputs.
Define Run Functions: Capture agent outputs and trajectories for detailed analysis.
Run Evaluations: Execute evaluations locally or automatically via LangSmith.
Use Case: You've built a customer support chatbot and want to ensure its responses are accurate and helpful. Use this Skill to define an LLM-as-Judge evaluator that scores responses against expected answers and a custom code evaluator that checks for adherence to specific response formats.

Quick Start

Use the LangSmith Evaluators skill to upload your Python evaluator script 'my_evaluators.py' to the dataset 'My Dataset'.

Frequently Asked Questions about LangSmith Evaluators

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

FAQPage Schema

How do I evaluate AI agent outputs using LangSmith?▼

You can evaluate AI agent outputs using LangSmith by defining LLM-as-Judge or custom code evaluators, capturing agent execution trajectories, and running evaluations locally or via automated workflows.

What is an LLM-as-Judge evaluator for quality assurance?▼

An LLM-as-Judge evaluator for quality assurance uses a language model to score AI application outputs against expected answers, ensuring response accuracy and adherence to specific formats.

How do I create custom code evaluators for my AI application?▼

To create custom code evaluators for your AI application, write a Python evaluator script defining your logic, then upload it to a LangSmith dataset to systematically check for specific response formats or criteria.

Can I run LangSmith evaluations locally for my chatbot?▼

Yes, you can run LangSmith evaluations locally for your chatbot by defining run functions to capture agent outputs and executing the evaluation pipeline within your local environment before deploying.

Do I need a LangSmith account to run automated evaluation pipelines?▼

You need a LangSmith account and API setup to run automated evaluation pipelines, as the Skill integrates with LangSmith's evaluation framework to systematically execute workflows and ensure application quality.

How do I capture agent execution trajectories for analysis?▼

To capture agent execution trajectories for analysis, define run functions within your evaluation pipeline that record the intermediate steps and outputs of your AI agents during local or automated LangSmith evaluations.