rlancemartin

# LangChain JavaScript

This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly.

[Tutorials](https://js.langchain.com/docs/tutorials/): LLM should read this page when looking for LangChain tutorial resources, needing to get started with LLM application development, or searching for structured learning paths in LangChain. (The page contains a comprehensive collection of tutorials organized into three main sections: "Get started" tutorials covering basic components like chat models and prompts, "Orchestration" tutorials for building complex applications with LangGraph, and "LangSmith" tutorials for evaluation and monitoring of LLM applications.)

[How-to guides](https://js.langchain.com/docs/how_to/): LLM should read this page when (needing to find specific implementation guides for LangChain.js, looking for solutions to common LLM application problems, or planning how to implement particular features in a LangChain project) (Comprehensive index of h

rlancemartin

# LangChain JavaScript

This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly.

[](https://js.langchain.com/docs/tutorials/): LLM should read this page when seeking an overview of LangChain's tutorials, looking for guidance on building LLM applications, or wanting to learn about the LangSmith tool. This page provides an introduction to various tutorials for building applications with LangChain components like chat models, vectorstores, and agents. It also highlights tutorials for more complex orchestration with LangGraph and covers the LangSmith tool for tracing, monitoring and evaluating LLM applications.

[](https://js.langchain.com/docs/how_to/): LLM should read this page when building an application with LangChain, troubleshooting issues, or understanding key LangChain components and concepts. This page provides how-to guides covering installation, key features like structured output and tool calling, LangChain components like prompt templates

rlancemartin

# LangChain

## High level

[Why LangChain?](https://python.langchain.com/docs/concepts/why_langchain): LLM should read this page when considering using LangChain, when building complex AI applications, and when needing to evaluate AI applications This page discusses the main reasons to use LangChain: standardized component interfaces, orchestration capabilities, and observability/evaluation through LangSmith
[Architecture](https://python.langchain.com/docs/concepts/architecture): LLM should read this page when needing an overview of the LangChain architecture, exploring the various packages and components, or deciding which parts to use for a specific application. Provides a high-level overview of the different packages that make up the LangChain framework, including langchain-core, langchain, integration packages, langchain-community, langgraph, langserve, and LangSmith.

## Concepts

[Chat Models](https://python.langchain.com/docs/concepts/chat_models): LLM should read this page when building applications 

rlancemartin

[Evaluation Quick Start | 🦜️🛠️ LangSmith](https://docs.smith.langchain.com/evaluation): LLM should read this page when learning how to set up LangSmith evaluations, creating evaluation datasets, or implementing LLM-based evaluators. This page provides a step-by-step quick start guide for LangSmith's evaluation capabilities, covering installation, API key setup, dataset creation, defining evaluation targets, creating evaluators, and running evaluations in both Python and TypeScript.

[Evaluation concepts | 🦜️🛠️ LangSmith](https://docs.smith.langchain.com/evaluation/concepts): LLM should read this page when wanting to understand LangSmith evaluation concepts, implementing evaluation strategies for LLM applications, or choosing appropriate metrics for different AI application types. This page covers LangSmith's evaluation framework including datasets, evaluators (human, heuristic, LLM-as-judge, pairwise), experiments, annotation queues, offline/online evaluation approaches, testing methodologies, and application