LCEL is the LangChain Expression Language — the pipe (|) syntax that connects components like prompts, models, and parsers into a single runnable. It gives you streaming, batching, and async out of the box, and is the modern way to build chains rather than the older Chain classes.

Do I need LangChain to call an LLM?

No. For a single prompt-and-response, calling the provider SDK directly is simpler. LangChain earns its place when you compose multiple steps, swap providers, add retrieval or memory, or want streaming and batching without writing that plumbing yourself.

What is the difference between a chain and an agent?

A chain runs a fixed sequence of steps you defined. An agent uses the model to decide which tool to call next and loops until it reaches an answer, so its control flow is dynamic. Chains are predictable and cheap; agents are flexible but can loop and cost more.

How does memory work in LangChain?

Memory stores previous turns of a conversation and injects them back into the prompt on the next call, since the model itself is stateless. In modern LangChain you typically wrap a chain with message history keyed by a session ID so each user keeps a separate thread.

Is LangChain only for Python?

No. LangChain has a mature Python library and a JavaScript/TypeScript version (LangChain.js) with the same core concepts — runnables, prompt templates, output parsers, and retrievers — so you can build the same patterns in a Node or browser environment.

How to Use LangChain: Beginner's Guide

What LangChain is for

LangChain is a framework for composing language-model calls into larger programs. A bare API call gives you one prompt and one response; real applications need to chain steps together — fill a template, call a model, parse the output, feed it into the next step, remember the conversation, retrieve documents. LangChain provides the building blocks (prompts, models, parsers, retrievers, memory) and a clean way to wire them together so you write application logic instead of plumbing.

The core ideas

The heart of modern LangChain is LCEL, the expression language that uses the pipe operator to connect components. prompt | model | parser reads left to right: a ChatPromptTemplate turns your variables into messages, a chat model turns messages into a response, and an output parser turns that response into a clean string or structured object. The result is a single runnable you can invoke, stream, or batch.

Beyond a basic chain, four concepts unlock most applications. Output parsers coerce model text into the shape you need (a string, JSON, a Pydantic object). Memory stores past turns and replays them so a stateless model can hold a conversation. Retrievers fetch relevant documents so you can build RAG. And agents let the model choose which tool to call next, looping until it reaches an answer — powerful, but harder to control than a fixed chain. The builder below assembles a runnable LCEL chain from your choices so you can see exactly how the pieces snap together in Python.

Tips for getting started

Reach for a plain provider SDK first if you only need one call — LangChain pays off when you compose. Prefer LCEL (prompt | model | parser) over the legacy LLMChain classes you may find in old tutorials; the docs and ecosystem have moved on. Add an output parser early so downstream code receives clean data, not raw text. Keep agents on a tight leash with a step limit and tool whitelist to avoid runaway loops and cost. And pin your LangChain version — the API has evolved quickly, and mixing tutorial snippets from different eras is the most common source of beginner confusion.