LangChain Architecture

LangChain is the most widely used framework for LLM applications. It abstracts the complexity of prompt management, tool integration, and chain composition. In 2026, LangChain has evolved from a monolithic package to a modular ecosystem.

Core Concepts

Chains

A chain is a sequence of calls — LLM, tool, retriever, or other chains. Chains are the building blocks of every LangChain application.

Agents

Agents use LLMs as a reasoning engine to dynamically decide which tools to call in which order. Unlike chains, the flow is not predefined.

Tools

Tools are functions an agent can call: database queries, API calls, calculations, web search. LangChain provides hundreds of predefined tools.

Memory

Memory systems store conversation histories and context across multiple interactions.

LangChain Expression Language (LCEL)

LCEL is LangChain's declarative composition system. It allows defining chains as pipelines:

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_template("Explain {topic} in 3 sentences.")
model = ChatOpenAI(model="gpt-4o")
parser = StrOutputParser()

chain = prompt | model | parser
result = chain.invoke({"topic": "quantum computing"})

LCEL Advantages

When LangChain vs. Direct SDK?

Practical tip: Don't use LangChain for simple LLM calls — the SDK is more efficient. LangChain pays off when you need to compose chains, integrate retrievers, or support multiple LLM providers.