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".
/plugin marketplace add jeremylongshore/claude-code-plugins-plus-skills/plugin install langchain-pack@claude-code-plugins-plusThis skill is limited to using the following tools:
Minimal working example demonstrating core LangChain functionality with chains and prompts.
langchain-install-auth setupCreate a new file hello_langchain.py for your hello world example.
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
llm = ChatOpenAI(model="gpt-4o-mini")
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)
Hello! I'm your LangChain-powered assistant. How can I help you today?
| 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 |
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)
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)
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);
Proceed to langchain-local-dev-loop for development workflow setup.
This skill should be used when the user asks to "create a hookify rule", "write a hook rule", "configure hookify", "add a hookify rule", or needs guidance on hookify rule syntax and patterns.
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.