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 <[email protected]>

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

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)

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

Next Steps

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

来源：jeremylongshore/claude-code-plugins-plus-skills · MIT 许可 · 许可原文 · MD 为来源页保存的历史版本