LangChain Tutorial: The Most Popular Framework for Building LLM Applications

LangChain is an open-source framework designed to simplify the development of applications powered by Large Language Models (LLMs). With a mature ecosystem and a large community, LangChain has become the go-to choice for developers building chatbots, RAG systems, AI agents, and various other AI applications. This tutorial covers LangChain comprehensively, from installation to advanced usage patterns.

Why LangChain?

Building LLM applications from scratch requires significant boilerplate code: managing prompts, connecting to various model providers, implementing memory, orchestrating chains of thought, and much more. LangChain simplifies all of this by providing consistent and modular abstractions.

Key advantages of LangChain:

• Consistent Model Abstraction: A single interface for OpenAI, Anthropic, Google, Ollama, and dozens of other providers
• Composable Chains: Build complex pipelines by combining simple components
• Complete Ecosystem: Integrations with vector databases, document loaders, tools, and external services
• LangChain Expression Language (LCEL): Declarative syntax for building chains that natively support streaming and async
• Large Community: Thousands of third-party integrations and comprehensive documentation

Installation and Setup

Basic Installation

pip install langchain langchain-core langchain-community

Provider-Specific Installation

# For OpenAI
pip install langchain-openai

For Anthropic (Claude)
pip install langchain-anthropic

For Google (Gemini)
pip install langchain-google-genai

For local models via Ollama
pip install langchain-ollama

For vector stores
pip install langchain-chroma  # ChromaDB
pip install langchain-pinecone  # Pinecone
pip install faiss-cpu  # FAISS

API Key Configuration

import os

Set API keys as environment variables
os.environ["OPENAIAPIKEY"] = "sk-..."
os.environ["ANTHROPICAPIKEY"] = "sk-ant-..."

Or use .env file with python-dotenv
from dotenv import loaddotenv

loaddotenv()

Core Concepts

1. Chat Models

Chat models are the core component of LangChain. Here's how to use various providers:

from langchainopenai import ChatOpenAI
from langchainanthropic import ChatAnthropic
from langchainollama import ChatOllama


OpenAI GPT-4
llmopenai = ChatOpenAI(model="gpt-4o", temperature=0.7)

Anthropic Claude
llmclaude = ChatAnthropic(model="claude-sonnet-4-20250514", temperature=0.7)


Ollama (local models)
llmollama = ChatOllama(model="llama3.1")

Call the model
response = llmopenai.invoke("Explain what machine learning is in 2 sentences")

print(response.content)

from langchaincore.prompts import ChatPromptTemplate, MessagesPlaceholder

Simple prompt
prompt = ChatPromptTemplate.frommessages([

    ("system", "You are an AI assistant specialized in {domain}."),
    ("human", "{question}")
])

Use the prompt
formatted = prompt.invoke({
    "domain": "data science",
    "question": "What is the difference between supervised and unsupervised learning?"
})
print(formatted)

from langchaincore.outputparsers import JsonOutputParser, StrOutputParser
from langchaincore.pydanticv1 import BaseModel, Field


String output parser (simplest)
parser = StrOutputParser()

JSON output parser with schema
class BookReview(BaseModel):
    title: str = Field(description="Book title")
    rating: int = Field(description="Rating 1-5")
    summary: str = Field(description="Review summary")

jsonparser = JsonOutputParser(pydanticobject=BookReview)


prompt = ChatPromptTemplate.frommessages([
    ("system", "Provide a book review in JSON format.\n{formatinstructions}"),

    ("human", "Review the book: {booktitle}")