Helixdevelopment Helixagent Langchain Hello World

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

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

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

Step 3: Create Your First Chain

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:

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

Error Handling

Examples

Simple Chain (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)

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

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

• LangChain LCEL Guide
• Prompt Templates
• Output Parsers

Next Steps

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

Source: HelixDevelopment/HelixAgent — distributed by TomeVault.