---

name: langchain-hello-world description: | Create a minimal working LangChain example. Use when starting a new LangChain integration, testing your setup, or learning basic LangChain patterns with chains and prompts. Trigger with phrases like "langchain hello world", "langchain example", "langchain quick start", "simple langchain code", "first langchain app". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

LangChain Hello World

Overview

Minimal working example demonstrating core LangChain functionality with chains and prompts.

Prerequisites

• Completed langchain-install-auth setup
• Valid LLM provider API credentials configured
• Python 3.9+ or Node.js 18+ environment ready

Instructions

Step 1: Create Entry File

Create a new file hello_langchain.py for your hello world example.

Step 2: Import and Initialize

Copyfrom langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o-mini")

Step 3: Create Your First Chain

Copyfrom langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}")
])

chain = prompt | llm | StrOutputParser()

response = chain.invoke({"input": "Hello, LangChain!"})
print(response)

Output

• Working Python file with LangChain chain
• Successful LLM response confirming connection
• Console output showing:

CopyHello! I'm your LangChain-powered assistant. How can I help you today?

Error Handling

Examples

Simple Chain (Python)

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

llm = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
chain = prompt | llm | StrOutputParser()

result = chain.invoke({"topic": "programming"})
print(result)

With Memory (Python)

Copyfrom langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage, AIMessage

llm = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    MessagesPlaceholder(variable_name="history"),
    ("user", "{input}")
])

chain = prompt | llm

history = []
response = chain.invoke({"input": "Hi!", "history": history})
print(response.content)

TypeScript Example

Copyimport { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const llm = new ChatOpenAI({ modelName: "gpt-4o-mini" });
const prompt = ChatPromptTemplate.fromTemplate("Tell me about {topic}");
const chain = prompt.pipe(llm).pipe(new StringOutputParser());

const result = await chain.invoke({ topic: "LangChain" });
console.log(result);

Resources

• LangChain LCEL Guide
• Prompt Templates
• Output Parsers

Next Steps

Proceed to langchain-local-dev-loop for development workflow setup.