What is LangChain? Why Do We Need It? Complete Introduction

Introduction

Artificial Intelligence is changing the way modern software applications are built. Large Language Models (LLMs) can understand natural language, generate text, summarize information, write code, answer questions, and perform many other language-related tasks.

Because of this, developers are using models from providers such as OpenAI, Google, Anthropic, and other AI platforms to build chatbots, AI assistants, document analysis systems, customer support applications, coding assistants, and automation tools.

However, building a real-world AI application is usually more complicated than simply sending a prompt to an LLM and displaying the response.

This is where LangChain becomes useful.

In this article, we will understand what LangChain is, why we need it, how it works, its major components, prompts, chains, tools, agents, embeddings, vector stores, RAG, databases, advantages, limitations, and real-world use cases.

What is LangChain?

LangChain is a framework for developing applications powered by Large Language Models.

In simple words, LangChain provides building blocks that help developers connect an LLM with different parts of an application.

These parts can include prompts, external data, databases, APIs, tools, document retrievers, vector stores, and application workflows.

A very simple LLM application may look like this:

User
  ↓
LLM
  ↓
AI Response

This approach is perfectly fine for a simple application. But real-world AI applications often require additional processing.

Why Do We Need LangChain?

If we can directly call an LLM API, why would we need a framework such as LangChain?

This is one of the most important questions to understand.

Imagine that you are building an AI assistant for a company. A user asks:

What is our company's leave policy?

The company’s leave policy may not be available in the model’s general knowledge. It may instead exist inside company PDFs, internal documentation, databases, or another private knowledge source.

The application may need to:

You can build all of this yourself. But as the application becomes more complex, you may need to manage many different components and workflows.

LLM vs LangChain

One of the first concepts beginners should understand is the difference between an LLM and LangChain.

Can We Build AI Applications Without LangChain?

Yes.

LangChain is not mandatory for building AI applications. You can directly call an LLM provider’s API from your backend or application.

User Input
    ↓
Your Backend
    ↓
LLM API
    ↓
Response
    ↓
User

For a simple application, this approach can be enough.

As the application grows, however, you may need to manage:

How Does LangChain Work?

At a high level, LangChain helps connect different components of an AI application.

For a document-based application, the workflow may look more like this:

Core Components of LangChain

To understand LangChain properly, you should understand the major building blocks and the role each one can play inside an AI application.

1. Models

Models are the AI systems that process input and generate output. In a LangChain application, models form one of the central components of the workflow.

Depending on the application, you may work with different types of models, including language models and models used for generating embeddings.

The model is responsible for the actual AI generation or processing, while the surrounding application determines what information should be provided to it and what should happen with the result.

2. Prompt Templates

A prompt is the instruction or input provided to an AI model. In real applications, prompts are often dynamic.

Instead of manually creating every prompt, you can create reusable prompt templates.

You are a programming teacher.

Explain the following topic in simple language:

{topic}

Include:
- Definition
- Example
- Advantages
- Real-world use case

Here, {topic} is a variable that can be replaced at runtime.

For example, the same template could be used for Python, JavaScript, PHP, or any other programming topic.

3. Chains and LCEL

Real-world AI applications often require multiple operations instead of a single model call.

For example, an application may need to:

These operations can be composed into workflows. LangChain Expression Language, commonly referred to as LCEL, provides a way to compose components into runnable pipelines.

4. What Are Tools in LangChain?

An LLM can generate text and reason about information, but it does not automatically have access to every external system.

Suppose a user asks:

What is the current weather in Delhi?

The application needs access to a weather service to retrieve current information.

A weather API can be exposed to the AI application as a tool.

5. What Are Agents?

Once an AI application has access to multiple tools, another question appears:

A simplified agent workflow can look like this:

User Request
      ↓
    Agent
      ↓
Select Tool
      ↓
Tool Execution
      ↓
Tool Result
      ↓
    LLM
      ↓
Final Answer

6. Documents and External Data

Many AI applications need to work with information that exists outside the model itself.

Common sources include:

7. What Are Text Splitters?

Imagine a company has a 200-page document containing policies and internal information.

Sending the entire document to an LLM every time a user asks a question would generally be inefficient.

The document can instead be divided into smaller sections called chunks.

Large Document
      ↓
   Chunk 1
   Chunk 2
   Chunk 3
   Chunk 4
   Chunk 5
      ↓
   Retrieval

Chunking is an important part of document retrieval and RAG systems.

8. What Are Embeddings?

An embedding converts information such as text into a numerical vector representation.

This allows applications to compare the semantic similarity between different pieces of information.

"What is our refund policy?"

          ↓

    Embedding Model

          ↓

[0.12, -0.45, 0.87, 0.21, ...]

Embeddings are commonly used for semantic search, document retrieval, recommendations, and RAG systems.

9. What Are Vector Stores?

After converting documents into embeddings, the vectors need to be stored somewhere so that they can later be searched for relevant information.

Vector stores and vector databases are designed for this type of workload.

What is RAG?

A basic RAG workflow looks like this:

For example, suppose a company has thousands of internal documents.

A user asks:

What is the company's work-from-home policy?

The application can search the company’s knowledge base, retrieve the relevant sections, and provide those sections to the LLM as context.

The model can then generate an answer based on the retrieved information.

10. LangChain and Databases

AI applications often need to work with structured business information stored in databases.

For example, a user might ask:

Show me the sales for January 2026.

The application may need to retrieve the required information from a business database.

Real-World LangChain Example

Imagine that we are building a Company AI Assistant.

The assistant needs to:

Now imagine the user asks:

Calculate our sales for last month
and send me the report.

The application could use different tools to complete the request.

                  User
                    ↓
                  Agent
                    ↓
          ┌─────────┼─────────┐
          ↓         ↓         ↓
      Database  Calculator   Email
          ↓         ↓         ↓
          └─────────┼─────────┘
                    ↓
              Final Response

This is the type of multi-step application where frameworks such as LangChain can become useful.

LangChain vs Direct LLM API

Advantages of LangChain

Limitations of LangChain

When Should You Use LangChain?

LangChain can be useful when your AI application is more complex than a simple prompt-and-response system.

On the other hand, if your application is simply:

User Input → LLM → Response

a direct API integration may be simpler and completely sufficient.

Is LangChain Only for Python?

No. LangChain can be used with both Python and JavaScript/TypeScript applications.

The programming language is less important than understanding the underlying AI application concepts.

LangChain Learning Roadmap

If you want to learn LangChain from beginner to advanced level, following a structured order makes the learning process easier.

Frequently Asked Questions

Conclusion

LangChain can be understood as a framework for building applications powered by Large Language Models.

An LLM provides powerful language generation and reasoning capabilities, but real-world AI applications often require much more than the model itself.

Applications may need to retrieve information from documents, access databases, call external APIs, use tools, manage application state, and combine multiple AI operations into a single workflow.

LangChain provides useful building blocks for connecting these different components and creating more capable AI applications.

In this article, we covered the fundamentals of LangChain, including what it is, why we need it, how it works, its core components, prompt templates, LCEL, tools, agents, documents, text splitting, embeddings, vector stores, RAG, databases, advantages, limitations, and practical use cases.

Now that the fundamentals are clear, the next step is to start building practical LangChain applications and understand how these concepts work with real code.