LangChain — LLM Application Framework

LangChain is the most widely used framework for building applications powered by Large Language Models (LLMs). It provides abstractions for chains, agents, memory, and retrieval that make production AI systems practical to build.

What is LangChain?

LangChain is an open-source framework that helps you:

Core Concepts

LLMs / Chat Models — The AI model itself. LangChain supports OpenAI, Anthropic, Google, Mistral, Ollama (local), and 50+ others through a unified interface.

Prompts — PromptTemplates with variables. Reusable, testable prompt structures.

Chains — Sequences of operations. Input → LLM → Output → Next LLM → Final Output.

Retrievers — Fetch relevant documents from a knowledge base for RAG.

Agents — LLMs that autonomously decide which tools to use and in what order.

Memory — Persist conversation history across turns.

Tools — Functions the agent can call (search, calculator, API calls).

LangChain vs. Raw API

\\\`python

Without LangChain

import openai

response = openai.chat.completions.create(

model="gpt-4",

messages=[{"role": "user", "content": f"Answer: {question}"}]

)

Need to handle: retries, streaming, output parsing, memory, tools...

With LangChain

from langchain_openai import ChatOpenAI

from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4")

prompt = ChatPromptTemplate.from_messages([

("system", "You are a helpful assistant."),

("user", "{question}")

])

chain = prompt | llm # LCEL pipe syntax

response = chain.invoke({"question": "What is Kubernetes?"})

Handles: retries, streaming, type safety, tracing, testing

\\\`

When to Use LangChain

✅ Building RAG systems (document Q&A)

✅ Multi-step AI workflows

✅ AI agents with tool use

✅ Chatbots with memory

✅ When switching between LLM providers

⚠️ Consider alternatives (raw API) when: