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langsmith-evaluator

Automate agent output evaluation with LangSmith evaluators in Python and TypeScript.

113|9|Updated Jan 24, 2026
Search Tags:#evaluation#structured-output#langsmith#evaluator#offline-evaluation#online-evaluation#code-evaluator
One-click install
npx skills add https://github.com/langchain-ai/skills-benchmarks --skill langsmith-evaluator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-evaluator
Source: https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/main/langsmith-evaluator
Command: npx skills add https://github.com/langchain-ai/skills-benchmarks --skill langsmith-evaluator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LangSmith-based evaluation pipelines for agent outputs with reproducible, code-driven evaluators that ensure consistent scoring across tasks and datasets.

Core Features & Use Cases

  • Define deterministic run evaluators in Python or TypeScript to validate outputs, trajectories, and schema conformity.
  • Upload offline evaluators attached to datasets or online evaluators attached to projects for real-time quality checks.
  • Align evaluation with LangSmith workflows, enabling structured scoring and debugging insights.

Quick Start

Capture agent outputs, write a small code evaluator in Python or TypeScript, and upload it to LangSmith to validate results against your dataset or project.

Frequently Asked Questions about langsmith-evaluator

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

FAQPage Schema
How do I automate LangSmith evaluation for agent outputs using code?▼

Yes, you can write deterministic run evaluators in Python or TypeScript to validate agent trajectories and schema conformity. This code-driven approach ensures consistent, reproducible scoring across your LangSmith datasets and projects.

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

Offline LangSmith evaluators validate outputs against uploaded datasets for batch testing, while online evaluators attach to active projects for real-time quality checks. Both workflows support code-driven scoring and structured output formats.

Can I evaluate agent trajectories and schema conformity in TypeScript?▼

Yes, you can define deterministic run evaluators in TypeScript to validate agent trajectories and schema conformity. This allows consistent evaluation of TypeScript agent implementations within LangSmith workflows.

How do I structure output formats for scoring LangSmith agent outputs?▼

Structure output formats for scoring by aligning code evaluators with LangSmith workflows to produce structured scoring data. This ensures consistent validation of outputs and trajectories across offline datasets and online projects.

Do I need LangSmith datasets to run offline evaluation on agent outputs?▼

Yes, offline evaluation requires uploading datasets to LangSmith to validate agent outputs against captured data. You attach deterministic code evaluators to these datasets for reproducible, batch-processing quality checks.

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