LangChain Hello World

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", "f...

Dicklesworthstone/pi_agent_rust·0installs1.8Kstars

SKILL.md

Full skill instructions

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.

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/Dicklesworthstone/pi_agent_rust --skill langchain-hello-world

View on GitHub

tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/langchain-pack/skills/langchain-hello-world/SKILL.md

deepgram-hello-world
Create a minimal working Deepgram transcription example. Use when starting a new Deepgram integration, testing your setup, or learning basic Deepgram API patterns. Trigger with phrases like "deepgram hello world", "deepgram example", "deepgram quick start", "simple transcription", "transcribe aud...
0 installs
interactive-shell
Cheat sheet + workflow for launching interactive coding-agent CLIs (Claude Code, Gemini CLI, Codex CLI, Cursor CLI, and pi itself) via the interactive_shell overlay or headless dispatch. Use for TUI agents and long-running processes that need supervision, fire-and-forget delegation, or headless b...
0 installs
documenso-prod-checklist
Execute Documenso production deployment checklist and rollback procedures. Use when deploying Documenso integrations to production, preparing for launch, or implementing go-live procedures. Trigger with phrases like "documenso production", "deploy documenso", "documenso go-live", "documenso launc...
0 installs
interactive-portfolio
Expert in building portfolios that actually land jobs and clients - not just showing work, but creating memorable experiences. Covers developer portfolios, designer portfolios, creative portfolios, and portfolios that convert visitors into opportunities. Use when: portfolio, personal website, sho...
0 installs