LangChain Minimal Project Structure

Simple chain/agent setup for quick prototyping. Ideal for learning LangChain and building simple LLM applications.

#langchain #python #llm #ai #minimal

Project Directory

myproject/

main.py

Chain definition and execution

prompts.py

Prompt templates

requirements.txt

.env.example

API keys template

.gitignore

README.md

Why This Structure?

The simplest LangChain structure—a single main.py for chain logic and prompts.py for templates. Perfect for learning, quick experiments, and prototypes with one or two chains. No complex abstractions, just direct LLM interaction.

Key Directories

main.py-Chain setup, model config, and execution logic
prompts.py-PromptTemplate and ChatPromptTemplate definitions
.env.example-OPENAI_API_KEY and other provider keys

Getting Started

python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env (add your API keys)
python main.py

When To Use This

Learning LangChain fundamentals
Quick prototypes and experiments
Single-chain applications
Hackathons and demos
Testing new LLM providers or models

When To Upgrade

Adding retrieval/RAG capabilities
Multiple chains or agents needed
Need for conversation memory persistence
Production deployment requirements
Team collaboration on the codebase

Trade-offs

No separation-All logic in few files, grows unwieldy
No memory persistence-Conversation history lost on restart
Limited testability-Add tests/ when complexity grows