LangChain: The Backbone of Modern Generative AI Apps

Generative AI (GenAI) continues to redefine how applications interact with users — from chat assistants and summarizers to autonomous agents. But turning a Large Language Model (LLM) into a reliable application requires more than just calling an API.

Enter LangChain, the framework that makes building generative AI workflows practical, modular, and scalable.

What Is LangChain?

LangChain is an open-source framework that simplifies building applications powered by large language models (LLMs). It provides reusable building blocks — like agents, memory, prompt templates, retrievers, and chains — to compose complex AI workflows.

Core Features That Matter

Prompt Templates & Chains

Prompt templates let you parameterize your prompts and reuse them across applications:

from langchain.prompts import PromptTemplate

template = PromptTemplate(
    input_variables=["topic"],
    template="Write a detailed summary about {topic}",
)

print(template.format(topic='Quantum Computing'))

Memory for Context

Memory components enable context persistence across user interactions:

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()

Integrations with Vector Stores

Retrieve relevant knowledge from external documents using vector databases.

from langchain.vectorstores import Chroma

Building a Simple Chatbot

from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

llm = OpenAI(temperature=0.7)
memory = ConversationBufferMemory()

bot = ConversationChain(llm=llm, memory=memory)
print(bot.run("Explain how LangChain works"))

Summary

LangChain elevates raw generative AI into real applications — standardizing how prompts, contexts, tools, and workflows interact.