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langsmith-evaluator

Create structured evaluators and run-function templates for LangSmith agent evaluations.

7|1|Updated Mar 15, 2026
Search Tags:#typescript#python#evaluation#trajectory#langsmith#evaluator#run-function
One-click install
npx skills add https://github.com/Harmeet10000/skills --skill langsmith-evaluator-harmeet10000
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langsmith-evaluator
Source: https://github.com/Harmeet10000/skills/tree/main/skills/ai-ml/langsmith-evaluator
Command: npx skills add https://github.com/Harmeet10000/skills --skill langsmith-evaluator-harmeet10000

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streamline and standardize evaluation of AI agents by providing structured, reproducible evaluators and run functions for LangSmith pipelines.

Core Features & Use Cases

  • Templates and utilities for offline dataset and online project evaluations
  • Guidance for defining run functions, trajectories, and evaluation formats in Python and TypeScript
  • Best practices, debugging workflows, and safety considerations for LangSmith evaluations

Quick Start

Configure your agent, dataset or project, and evaluators to run an end-to-end LangSmith evaluation and review the resulting metrics.

Frequently Asked Questions about langsmith-evaluator

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

FAQPage Schema
How do I evaluate AI agents with LangSmith using Python and TypeScript?▼

LangSmith evaluation uses structured run functions and deterministic evaluators to process agent trajectories and run data, providing standardized output formats for both Python and TypeScript workflows.

What is the best way to structure deterministic code evaluators for LangSmith?▼

The best way to structure deterministic code evaluators is by using standardized templates for run functions and evaluation formats, ensuring safe and testable deployment across offline dataset evaluations and online project monitoring.

Can I capture agent trajectories for offline dataset evaluations in LangSmith?▼

Yes, LangSmith supports capturing agent trajectories for offline dataset evaluations by utilizing structured run-function templates and evaluators to standardize output formats for both Python and TypeScript workflows.

Does LangSmith evaluation support both online project monitoring and offline datasets?▼

Yes, LangSmith evaluation supports both online project monitoring and offline dataset evaluations by applying structured evaluators and run-function templates to process run data and trajectory capture in Python and TypeScript.

Why do I need standardized output formats for LangSmith run functions?▼

Standardized output formats for LangSmith run functions ensure reproducible evaluations, reliable trajectory capture, and safe, testable deployment of deterministic code evaluators across your AI agent pipelines.

What are the limitations of evaluating AI agents with LangSmith run functions?▼

Evaluating AI agents with LangSmith requires strict adherence to standardized output formats and careful configuration of deterministic evaluators to avoid inconsistencies during trajectory capture and deployment in Python and TypeScript workflows.

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