Introduction to LangChain: Chains, Memory, and LLM Integration

🚀 Why LangChain Is Becoming a Core Part of the AI Stack

If you're working with large language models (LLMs), you've probably realized something: raw prompts don't scale. You need structured workflows — memory, tools, context chaining, and decision-making logic — to build anything beyond a toy demo.

That's exactly what LangChain solves.

LangChain gives you a framework to build, manage, and scale LLM-based applications — whether you're building a chatbot, internal agent, smart Q&A system, or a full AI-powered product backend.

This chapter introduces the core building blocks of LangChain: Chains, Memory, and LLM Integration — so you can move from experimentation to real-world systems.

🧠 What is LangChain?

LangChain is an open-source Python/JavaScript framework that helps developers build applications powered by LLMs. It's designed to go beyond single-shot prompts by making it easy to:

• Chain together prompts and model responses
• Add memory to LLMs (so they remember past context)
• Call external tools like search, APIs, or calculators
• Route logic based on user input or task goals

Instead of writing messy glue code every time you want to build something new, LangChain gives you pre-built components and orchestration layers that make development faster and cleaner.

🔧 Core Building Blocks of LangChain

Let's break down the three foundational elements that power everything in LangChain:

1. 🧩 Chains

A Chain is a sequence of steps that take input → process it → return output.

It could be as simple as:

User input → Inserted into prompt → Sent to LLM → Display result

Or more advanced:

User query → Search documents → Retrieve relevant chunks → Feed into LLM → Return answer

LangChain lets you create both simple and composable chains. You can plug in prompts, models, retrievers, and tools like LEGO blocks.

Popular types of chains include:

• LLMChain — Basic one-shot LLM call
• SequentialChain — A step-by-step series of chained prompts
• RouterChain — Routes input to different subchains based on type or intent
• ConversationalRetrievalChain — A full RAG (Retrieval-Augmented Generation) pipeline with memory

2. 🧠 Memory

By default, LLMs are stateless — they forget everything after each message. LangChain adds memory layers so your apps can:

• Track conversation history
• Reference past actions or context
• Enable multi-turn reasoning

Popular memory types include:

• ConversationBufferMemory — Stores raw text history
• SummaryMemory — Keeps a compressed summary of prior chats
• EntityMemory — Tracks and retrieves facts about named entities (like people or companies)

You can attach memory to most chains with just a few lines of code — and suddenly your app feels smarter and more natural.

3. 🤖 LLM Integration

LangChain supports all major LLM providers out of the box:

• OpenAI (GPT-4, GPT-3.5)
• Google Gemini / PaLM 2
• Anthropic Claude
• Cohere, HuggingFace, Ollama (local models)

All LLMs are treated through a common interface. This means you can swap providers or run A/B tests without rewriting your code.

This abstraction layer is especially useful when you're building apps that might migrate from cloud LLMs to local, private deployments for cost or security reasons.

🛠️ What You Can Build with LangChain

Here are just a few real-world applications built with LangChain:

• LLM chatbots with memory and knowledge retrieval
• AI copilots that can search docs, summarize, and write reports
• Internal tools that combine database calls + GPT reasoning
• Multi-agent workflows where LLMs delegate tasks across tools
• Voice and text assistants that talk, plan, and execute on user queries

LangChain provides the backbone to go from idea to prototype to deployed system.

🧪 Example: A Basic LLMChain

Here's a simple LangChain snippet using OpenAI to generate a reply from a prompt:

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

llm = OpenAI(model_name="gpt-3.5-turbo")

prompt = PromptTemplate(
    input_variables=["topic"],
    template="Write a tweet about {topic} in a witty tone."
)

chain = LLMChain(llm=llm, prompt=prompt)

response = chain.run("LangChain and AI workflows")
print(response)

That's it — a basic chain. In Chapter 2, we'll build on this to create full Q&A apps.

📦 Key Concepts to Remember

• LangChain ≠ just an LLM wrapper — it's a system-building framework
• Chains help you control logic and flow
• Memory adds context and state to your apps
• LLMs are interchangeable and easy to plug in

Once you understand these core pieces, everything else — tools, agents, RAG — becomes much easier to layer in.

🔜 Coming Up Next

Now that you've grasped the building blocks, it's time to build your first functional app.

In Chapter 2, we'll walk through:

👉 Create Your First LLM App — Q&A with LangChain and Gemini

You'll learn how to wire up user input, plug in a Gemini LLM, and build a basic Q&A system using LangChain.

Let's move from theory to real output.