LangChain

Information

Introduction

LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). It provides a standard interface for chains, many integrations with other tools, and end-to-end chains for common applications. LangChain allows developers to build applications that are:

What is it for?

LangChain provides several modules that address different aspects of building LLM applications:

Usage

Python

LangChain’s original and most feature-complete implementation is in Python.

Installation:

pip install langchain
# or for a more modular installation
pip install langchain-core langchain-community

Basic Example:

from langchain_openai import OpenAI
from langchain_core.prompts import PromptTemplate

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate.from_template("What is a good name for a company that makes {product}?")
chain = prompt | llm

print(chain.invoke({"product": "colorful socks"}))

Node.js / TypeScript

LangChain.js brings the power of the LangChain framework to the JavaScript/TypeScript ecosystem, enabling LLM applications in the browser or on the server with Node.js.

Installation:

npm install langchain @langchain/core @langchain/community

Basic Example:

import {OpenAI} from "@langchain/openai";
import {PromptTemplate} from "@langchain/core/prompts";

const model = new OpenAI({temperature: 0.9});
const template = "What is a good name for a company that makes {product}?";
const prompt = PromptTemplate.fromTemplate(template);

const chain = prompt.pipe(model);

const res = await chain.invoke({product: "colorful socks"});
console.log(res);

Java

In the Java ecosystem, the most prominent implementation inspired by LangChain is LangChain4j.

For Spring Boot projects, another important option is Spring AI. A practical rule of thumb is:

Installation (Maven):


<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-open-ai</artifactId>
    <version>0.35.0</version>
</dependency>

Basic Example:

import dev.langchain4j.model.openai.OpenAiChatModel;

public class Main {
    public static void main(String[] args) {
        OpenAiChatModel model = OpenAiChatModel.withApiKey("your-api-key");
        String response = model.generate("What is a good name for a company that makes colorful socks?");
        System.out.println(response);
    }
}

Spring Boot and Java notes

LangChain vs LangChain4j vs Spring AI

In practice, Java teams often choose between LangChain4j and Spring AI rather than using Python LangChain directly.

When to choose LangChain4j

LangChain4j is a good choice if you need:

When to choose Spring AI instead

Spring AI is often the better fit if your application is already heavily based on Spring Boot and you want:

Spring Boot integration tips

Useful tools often combined with LangChain4j or Spring AI:

Similar Software

Several other frameworks and libraries offer similar capabilities for building LLM-powered applications:

Configuration

Configuration typically involves setting environment variables for API keys (e.g., OPENAI_API_KEY) or using provider-specific configuration objects in code.

See also