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

python

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

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

Step 3: Create Your First Chain

python

from 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:

code

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

Error Handling

Error	Cause	Solution
Import Error	SDK not installed	Run `pip install langchain langchain-openai`
Auth Error	Invalid credentials	Check environment variable is set
Timeout	Network issues	Increase timeout or check connectivity
Rate Limit	Too many requests	Wait and retry with exponential backoff
Model Not Found	Invalid model name	Check available models in provider docs

Examples

Simple Chain (Python)

python

from 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)

python

from 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

typescript

import { 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

Next Steps

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