What is LangChain? | LangChain Course

Objectives

Understand what LangChain is and why it exists
Learn the core abstractions LangChain provides
Identify when to use LangChain vs raw API calls

Key Concepts

LLM Orchestration — coordinating multiple AI model calls into coherent workflows

Chains — composable sequences of operations that transform inputs to outputs

Agents — autonomous decision-makers that choose which tools to use

Memory — persistence layer that gives conversations context and continuity

Code Example

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

llm = OpenAI(temperature=0.7)
prompt = PromptTemplate(
    input_variables=["topic"],
    template="Explain {topic} in simple terms."
)

chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run("vector databases")
print(result)

Tasks

Quiz

What is the primary purpose of LangChain?

Which abstraction allows LangChain to remember previous interactions?