LangChain Tutorial 2026: Build AI Applications in Python

SynfraCore·February 2026·10 min read

What is LangChain?

LangChain is a Python framework for building LLM applications. It provides components for connecting to LLMs, loading documents, splitting text, vector stores, chains, agents, and memory. Think of it as the React of AI development.

LCEL — The Modern Way

python

from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm    = ChatAnthropic(model='claude-sonnet-4-6')
prompt = ChatPromptTemplate.from_template('Explain {topic} simply.')
chain  = prompt | llm | StrOutputParser()

result = chain.invoke({'topic': 'Kubernetes pods'})
print(result)

RAG Chain in 10 Lines

python

from langchain_community.vectorstores import Chroma
from langchain_core.runnables import RunnablePassthrough

retriever = vectorstore.as_retriever()
rag_chain = (
    {'context': retriever, 'question': RunnablePassthrough()}
    | ChatPromptTemplate.from_template(
        'Answer based on context:\n{context}\nQuestion: {question}')
    | llm | StrOutputParser()
)
print(rag_chain.invoke('What is our refund policy?'))

Debug with LangSmith

Set LANGCHAIN_TRACING_V2=true — every chain call is traced: inputs, outputs, latency, token usage, errors. Essential for production debugging. See LangChain Academy.

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