LangChain: The Complete Beginner's Guide 2026

What is LangChain?

LangChain is the most popular open-source framework for building Large Language Model applications. Launched in 2022 and with more than 90,000 stars on GitHub, it simplifies integrating LLMs with external tools, databases, APIs, and complex workflows.

Instead of writing low-level code for every call to an LLM, LangChain provides reusable abstractions: chains for sequences, agents for dynamic decisions, memory for context, and retrievers for RAG.

LCEL: LangChain Expression Language

The modern way to use LangChain is LCEL, a declarative style using the | operator to compose components like Unix pipes:

chain = prompt | llm | output_parser

LCEL's advantages: native streaming, automatic batching, async/await, logging integrated with LangSmith, and clean composition of complex components.

Types of memory in LangChain

Without memory, every call to the LLM is independent. LangChain offers:

• ConversationBufferMemory: Full history. Simple but can grow indefinitely.
• ConversationSummaryMemory: Summarizes older history. Ideal for long conversations.
• ConversationWindowMemory: Only the last N turns. Good cost/context balance.
• VectorStoreRetrieverMemory: Retrieves only relevant memories by similarity.

RAG with LangChain: connecting LLMs to your data

The RAG (Retrieval-Augmented Generation) pattern is the most widely used one in enterprises for connecting LLMs to their own data. The pipeline:

1. Load documents (PDF, Word, HTML, databases)
2. Split into chunks with RecursiveCharacterTextSplitter
3. Generate embeddings with OpenAI or Anthropic
4. Store in a vector store (pgVector, Pinecone, Chroma)
5. Retrieve chunks relevant to the query
6. The LLM answers based on the retrieved chunks

Code example

# LangChain LCEL - Modern chain with RAG
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_community.vectorstores import PGVector
from langchain_core.runnables import RunnablePassthrough

llm = ChatAnthropic(model="claude-sonnet-4-6")

# Simple chain with LCEL (pipe operator)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an expert assistant in {domain}."),
    ("human", "{question}")
])
chain = prompt | llm | StrOutputParser()
response = chain.invoke({"domain": "corporate law", "question": "What is a non-disclosure agreement?"})

# Full RAG chain
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)
answer = rag_chain.invoke("What does the contract say about penalties?")