langchain-hello-world
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".
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
skills listSkill Instructions
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-authsetup - 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.
More by jeremylongshore
View allRabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.
evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".
building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").
Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.