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langgraph-testing-evaluation

Generate test scaffolds and evaluate LangGraph and LangChain agents.

1|Updated Dec 22, 2025
Search Tags:#testing#ci/cd#langchain#llm#agent#evaluation#langgraph
One-click install
npx skills add https://github.com/dhar174/langgraph_system_generator --skill langgraph-testing-evaluation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langgraph-testing-evaluation
Source: https://github.com/dhar174/langgraph_system_generator/tree/main/.github/skills/langgraph-testing-evaluation
Command: npx skills add https://github.com/dhar174/langgraph_system_generator --skill langgraph-testing-evaluation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of testing and evaluating LangGraph and LangChain agents, ensuring their quality, reliability, and performance.

Core Features & Use Cases

  • Automated Testing: Generate unit and integration test scaffolds for Python and JavaScript/TypeScript agents.
  • Trajectory Evaluation: Assess multi-step agent behavior using methods like trajectory matching or LLM-as-judge.
  • LangSmith Integration: Run evaluations against datasets stored in LangSmith for robust quality tracking.
  • A/B Testing: Compare different agent versions offline to validate improvements before deployment.
  • Use Case: Before deploying a new version of your customer support chatbot, use this Skill to automatically generate tests, run it against a dataset of common queries, evaluate its response quality and latency, and compare it against the current production version.

Quick Start

Use the langgraph-testing-evaluation skill to generate Python pytest test scaffolding for your agent defined in my_agent:graph and output it to the tests/ directory.

Frequently Asked Questions about langgraph-testing-evaluation

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

FAQPage Schema
How do I test and evaluate LangGraph agents?▼

You can test and evaluate LangGraph agents by generating unit and integration test scaffolds, running trajectory evaluations, and executing LangSmith dataset evaluations to ensure quality and reliability before deployment.

How do I evaluate multi-step LangChain agent behavior?▼

To evaluate multi-step LangChain agent behavior, you can use trajectory evaluation methods such as trajectory matching or LLM-as-judge to assess the sequence of actions and responses during testing.

Can I compare different versions of my LangChain agent offline?▼

Yes, you can perform A/B testing to compare different agent versions offline, allowing you to validate improvements in response quality and latency against a baseline before production deployment.

Does this LangGraph testing tool support JavaScript and TypeScript agents?▼

Yes, this LangGraph testing tool supports both Python and JavaScript/TypeScript agents, enabling you to automatically generate test scaffolds and run evaluations across your full stack.

How do I automate tests for LLM responses in my CI/CD pipeline?▼

You can automate LLM testing in your CI/CD pipeline by generating pytest scaffolding for your agents and utilizing built-in utilities for mocking LLM responses to manage evaluation workflows consistently.

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