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