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HyunjunJeon
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langsmith-evaluator

Build and validate LangSmith evaluation pipelines with standardized evaluators and automated execution.

Updated Aug 27, 2026
Search Tags:#llm-as-judge#evaluation#trajectory#datasets#langsmith#evaluator#run-functions
One-click install
npx skills add https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1 --skill langsmith-evaluator-hyunjunjeon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-evaluator
Source: https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1/tree/main/Day-01/.agents/skills/langsmith-evaluator
Command: npx skills add https://github.com/HyunjunJeon/SDS-AX-Advanced-2026-1 --skill langsmith-evaluator-hyunjunjeon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables building and validating LangSmith evaluation pipelines by standardizing evaluators, capturing run outputs and trajectories, and orchestrating end-to-end assessments.

Core Features & Use Cases

Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2) Defining Run Functions - how to capture outputs and trajectories from your agent; (3) Running Evaluations - locally with evaluate() or auto-run via LangSmith. Uses the langsmith CLI tool.

Quick Start

Run a sample evaluation to verify the run function and evaluator integration before committing to a project.

Frequently Asked Questions about langsmith-evaluator

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

FAQPage Schema
How do I build and validate LangSmith evaluation pipelines for agent trajectories?▼

To build LangSmith evaluation pipelines, you standardize evaluators, define run functions to capture agent outputs and trajectories, and orchestrate automated assessments locally with evaluate() or via the langsmith CLI.

What is an LLM-as-Judge evaluator and how does it work with LangSmith datasets?▼

An LLM-as-Judge evaluator grades responses by comparing agent outputs against traces. LangSmith supports this structured evaluator format to run deterministic checks across offline datasets or online projects.

How do I capture run outputs and trajectories from my agent for evaluation?▼

You capture run outputs and trajectories by defining run functions within your LangSmith evaluation pipeline, which standardize how agent outputs are recorded before applying automated evaluators.

Can I run LangSmith evaluations locally before committing to a project?▼

Yes, you can run a sample evaluation locally using the evaluate() function to verify run function and evaluator integration, ensuring reproducible evaluation workflows before committing to a project.

Does this approach support comparing agent outputs against traces in offline datasets?▼

Yes, the approach supports comparing agent outputs against traces by applying structured evaluators to run deterministic checks across both offline datasets and online LangSmith projects.

What are the limitations of automating LangSmith evaluations via the CLI?▼

While automation via the langsmith CLI streamlines assessments, limitations include ensuring secure run capture and maintaining reproducible evaluation workflows, requiring carefully structured evaluator formats and run-oriented interfaces.

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