Getting Started with LangChain: Understanding Agents, Models, and Tools

Introduction

When developers first hear about AI agents, they often imagine complex autonomous systems that can think, plan, and act independently. In reality, most modern AI agents are built using a simple but powerful idea:

Agent = Model + Harness

This concept sits at the heart of LangChain.

In this article, we'll learn what LangChain is, why it exists, and how agents use tools to interact with the outside world.

Why LangChain Exists

Every LLM provider exposes a different SDK, API structure, authentication mechanism, and feature set.

Without LangChain, switching from one provider to another often requires rewriting large portions of your application.

LangChain provides a standardized abstraction layer over multiple providers, allowing developers to focus on building applications rather than managing provider-specific code.

Understanding the Agent = Model + Harness Concept

An agent is not just an LLM.

Think of an agent as:

Agent = Model + Harness

The Model is the LLM itself.

The Harness is everything around the model that enables it to perform useful work:

• Tools
• Memory
• State
• Prompt management
• Execution logic

Architecture

Installing LangChain

npm install langchain @langchain/core
npm install @langchain/openai

Creating a Simple Agent

import { createAgent } from 'langchain';

const agent = createAgent({
  model: 'gpt-5.4'
});

The createAgent() function acts as a minimal and highly configurable harness around your model.

How Agent Execution Works

1. User sends a request.
2. Agent forwards the request to the model.
3. Model determines whether tools are required.
4. Agent executes tools if necessary.
5. Results are returned to the model.
6. Final answer is generated.

Key Takeaways