arhupy 1.0.0

A prompt engineering toolkit for LLMs

arhupy

A lightweight prompt engineering toolkit for LLMs

arhupy helps you create, fill, chain, save, version, and estimate prompts for LLMs like Claude, GPT, and Gemini. It is small by design and uses only the Python standard library.

Installation

Install from PyPI:

pip install arhupy

Install from source:

git clone https://github.com/Typeshi-dotcom/arhupy.git
cd arhupy
pip install -e .

Quick Start

from arhupy import Prompt

prompt = Prompt("You are a {role}. Speak in {language}.")
print(prompt.fill(role="teacher", language="English"))

Features

• Fill prompt templates with named placeholders
• Chain multiple prompts into one final prompt
• Save and load prompt templates from a local JSON library
• Export and import prompts or prompt chains as shareable JSON files
• Estimate token counts with a simple standard-library helper
• Score prompts and get simple feedback from local heuristics
• Compare prompts and see scoring differences from the CLI
• Improve prompts with Claude through a simple command
• Launch a local web dashboard for scoring and comparison
• Track prompt template versions with notes
• No external dependencies

CLI Usage

Show available commands:

arhupy --help

Score a prompt:

arhupy score "You are a fitness coach"

Compare two prompts:

arhupy diff "You are a coach" "You are a strict fitness coach"

Improve a prompt:

arhupy improve "You are a coach" --api-key YOUR_KEY

Save, list, export, and import prompts:

arhupy save my_prompt "You are a coach"
arhupy list
arhupy export prompts.json
arhupy import prompts.json

Start the local web dashboard:

arhupy web

Examples

Prompt

from arhupy import Prompt

prompt = Prompt("You are a {role}. Speak in {language}.")
filled = prompt.fill(role="coding assistant", language="English")

print(filled)
prompt.preview()
prompt.reset()

PromptChain

from arhupy import Prompt, PromptChain

system = Prompt("System: {instruction}")
user = Prompt("User: {task}")

system.fill(instruction="Be concise and practical.")
user.fill(task="Explain prompt chaining.")

chain = PromptChain([system, user])
print(chain.build())

Library Save And Load

from arhupy import Prompt, load, save

prompt = Prompt("Summarize this in {style}: {text}")
save("summarizer", prompt)

loaded = load("summarizer")
print(loaded.fill(style="plain English", text="Prompt engineering is useful."))

Saved prompts are stored in arhupy_library.json in your current working directory.

Token Estimation

from arhupy import estimate_tokens

tokens = estimate_tokens("A short prompt for an LLM.")
print(tokens)

Prompt Scoring

arhupy score "You are a fitness coach"

Prompt Comparison

arhupy diff "You are a fitness coach" "You are a helpful assistant"

Web Dashboard

arhupy web

This starts a local dashboard at http://localhost:8000.

Saving and Sharing Prompts

arhupy save my_prompt "You are a coach"
arhupy export prompts.json
arhupy import prompts.json
arhupy list

AI Prompt Improvement

arhupy improve "You are a coach" --api-key YOUR_KEY

Use a real Claude API key for live AI improvement. The YOUR_KEY placeholder runs a local demo improvement so the command can be tested safely.

Claude Integration

from arhupy import Prompt, ClaudeClient

client = ClaudeClient(api_key="your-api-key")

prompt = Prompt("You are a {role}. Answer this: {question}")
response = client.ask_with_template(prompt, role="fitness coach", question="What is progressive overload?")
print(response)

Export and Import

Save a prompt to JSON:

from arhupy import Prompt, export_prompt

prompt = Prompt("Write a {tone} email about {topic}.")
prompt.fill(tone="friendly", topic="a project update")

export_prompt(prompt, "email_prompt.json")

Load a prompt from JSON:

from arhupy import import_prompt

prompt = import_prompt("email_prompt.json")
print(prompt)

Save a prompt chain to JSON:

from arhupy import Prompt, PromptChain, export_chain

system = Prompt("System: {instruction}")
user = Prompt("User: {request}")
system.fill(instruction="Be concise.")
user.fill(request="Summarize this report.")

chain = PromptChain([system, user])
export_chain(chain, "summary_chain.json")

Load a prompt chain from JSON:

from arhupy import import_chain

chain = import_chain("summary_chain.json")
print(chain.build())

Contributing

Contributions are welcome. See CONTRIBUTING.md for setup instructions, test commands, and pull request guidance.

License

This project is licensed under the MIT License. See LICENSE for details.