# Helixdevelopment Helixagent Langchain Hello World

> LangChain Hello World

- Skill: `tomevault-io/helixdevelopment-helixagent-langchain-hello-world` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/helixdevelopment-helixagent-langchain-hello-world`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/helixdevelopment-helixagent-langchain-hello-world/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/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
```python
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

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

### Step 3: Create Your First Chain
```python
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)
```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)
```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
```typescript
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](https://python.langchain.com/docs/concepts/lcel/)
- [Prompt Templates](https://python.langchain.com/docs/concepts/prompt_templates/)
- [Output Parsers](https://python.langchain.com/docs/concepts/output_parsers/)

## Next Steps
Proceed to `langchain-local-dev-loop` for development workflow setup.

---
> Source: [HelixDevelopment/HelixAgent](https://github.com/HelixDevelopment/HelixAgent) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-06-16 -->