LangChain Introduction

What LangChain is#

LangChain is a framework for building applications that compose multiple LLM calls, tools, and data sources into a working pipeline, instead of gluing them together by hand for every project. A single call to an LLM API is simple — send a prompt, get text back. Real applications usually need more than that: retrieving relevant documents before answering, remembering earlier turns of a conversation, calling external tools (a calculator, a search engine, an internal API), and chaining several model calls together where one step's output feeds the next step's input. LangChain provides reusable building blocks for exactly that plumbing, so you're not reinventing prompt templating, output parsing, retrieval integration, and multi-step orchestration from scratch on every project.

The problem it solves#

Without a framework, a moderately complex LLM application ends up with a lot of repeated, easy-to-get-subtly-wrong boilerplate: manually formatting prompt strings with string concatenation, manually parsing the model's raw text response into structured data, manually re-sending conversation history on every turn, manually wiring up a vector database for retrieval. None of this is conceptually hard in isolation, but it adds up, and it's exactly the kind of plumbing that's easy to get subtly wrong (silently truncating conversation history, mismatched prompt template variables) in ways that only show up as flaky behavior in production.

LangChain standardizes these pieces behind consistent interfaces — prompt templates, chat models, output parsers, retrievers, memory, and agents/tools — so they compose predictably and can be swapped out (a different underlying model, a different vector store) without rewriting the application logic around them.

Installing LangChain#

pip install langchain

Most real applications also install the specific integration packages they need — for example, an integration package for whichever model provider or vector database you're using — alongside the core langchain package.

Common mistakes#

• Reaching for LangChain (or any framework) for a single, simple prompt-in/text-out call — that's just a direct API call, and a framework adds indirection without solving a real problem yet.
• Assuming LangChain replaces the need to understand prompting, tokens, and context windows — it orchestrates calls to an underlying LLM; it doesn't change any of the model's own fundamental behavior or limits.
• Treating every LangChain abstraction (chains, agents, memory) as mandatory for every project — the framework is a toolbox; simple applications often only need a prompt template and a single model call, not the full agent machinery.