Enterprise DNA
O Open Source Frameworks medium

Promptify

by Community

Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research

P

OSS

Promptify

Added 1 June 2026

#chatgpt #chatgpt-api #chatgpt-python #gpt-3 #gpt-3-prompts #gpt-4 #gpt-4-api #gpt3-library

Overview

Promptify is an open-source Python library for prompt engineering and versioning. It provides tools to generate structured outputs from GPT and other prompt-based models. The project is maintained by a community on Discord focused on prompt engineering and LLM research.

Best for

Best for
Python developers seeking a straightforward way to produce structured outputs from LLM prompts while managing prompt versions.

Use cases

  • Generate structured data (JSON, lists, etc.) from LLM prompts
  • Version and manage prompts for iterative experimentation
  • Build Python scripts that call GPT or similar models with reusable prompt templates

Notes

Promptify is an open-source Python library for prompt engineering and versioning. It provides tools to generate structured outputs from GPT and other prompt-based models. The project is maintained by a community on Discord focused on prompt engineering and LLM research.

4,612 stars on GitHub. Last updated 2026-03-27. Licensed Apache-2.0.

Use cases

  • Generate structured data (JSON, lists, etc.) from LLM prompts
  • Version and manage prompts for iterative experimentation
  • Build Python scripts that call GPT or similar models with reusable prompt templates

Pros

  • Lightweight and focused on structured output extraction
  • Open source with active community support on Discord
  • Simple API for integrating LLM calls into Python projects

Cons

  • Relies on external LLM providers, requiring API keys and incurring usage costs
  • Limited to Python ecosystem, not a cross-language framework
  • Smaller feature set compared to broader orchestration libraries like LangChain

Indexed from awesome-llm and enriched against its public facts.

Pros

  • Lightweight and focused on structured output extraction
  • Open source with active community support on Discord
  • Simple API for integrating LLM calls into Python projects

Cons

  • Relies on external LLM providers, requiring API keys and incurring usage costs
  • Limited to Python ecosystem, not a cross-language framework
  • Smaller feature set compared to broader orchestration libraries like LangChain
Free 27-page guide

Get the free Developer’s Field Guide

A 27-page field guide to the AI coding workflow with Claude. Claude Code, MCP servers, the prompt patterns that work, and what to delegate. Free.

Enter your work email. We send it straight over, plus a few short notes worth knowing. Unsubscribe any time.

No spam. Unsubscribe any time.