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LangGPT is a structured, reusable prompt design framework that enables anyone to create high-quality prompts for Large Language Models. Think of it as a "programming language for prompts" — systematic, template-based, and infinitely scalable.

Traditional prompt engineering relies on scattered tips and trial-and-error. LangGPT transforms this chaos into a structured methodology:

• 🎯 Structured Templates — Hierarchical organization inspired by programming paradigms
• 🔄 Reusability — Create once, adapt infinitely like code modules
• 📦 Modularity — Variables, commands, and conditional logic at your fingertips
• ⚡ Efficiency — Go from idea to working prompt in minutes
• 🌍 Community-Driven — 11,000+ stars, battle-tested by thousands of users

Academic Foundation: Published research at arXiv:2402.16929 | 中文版

Let AI create prompts for you:

• LangGPT GPTs — Full-featured generator (GPT-4)
• Kimi+ LangGPT — For Moonshot Kimi users
• PromptGPT — Lite version (GPT-3.5)

Basic LangGPT structure:

# Role: Your_Role_Name

## Profile
- Author: YourName
- Version: 1.0
- Language: English
- Description: Clear role description and core capabilities

## Goal
- Outcome: What concrete result/outcome should be delivered for the user/session
- Done Criteria: Clear acceptance criteria (how we know it’s finished and good)
- Non-Goals: What is explicitly out of scope to avoid scope creep

### Skill-1
1. Specific skill description
2. Expected behavior and output

## Rules
1. Don't break character under any circumstance
2. Don't make up facts or hallucinate

## Workflow
1. Analyze user input and identify intent
2. Apply relevant skills systematically
3. Deliver structured, actionable output

## Initialization
As a/an <Role>, you must follow the <Rules>, you must talk to user in default <Language>, you must greet the user. Then introduce yourself and introduce the <Workflow>.

Prerequisites: Basic Markdown knowledge (Quick Guide) | GPT-4 or Claude recommended

Explore our example library and adapt proven templates to your needs.

Before diving into tactics, understand the principles. These essays explore the philosophy behind effective prompting:

• 对话动力学 — The dynamics of human-AI dialogue
• 五种理性 — Five types of rationality in prompt design
• 镜像性倾向 — Mirror tendencies in LLM behavior
• 统计重力井和边缘表达 — Statistical gravity well and edge expression
• 关系表达 — Expressing relationships in prompts
• 看见与言说 — Seeing and articulation in AI interaction
• Prompt 的本质 — The essence and nature of prompts
• 面向结果的提示词写作方法 — Writing prompts that focus on achieving desired outcomes
• AI意识 — Understanding the role of AI in human-AI interaction
• AI时代的新管理：机器负责优化，人类定义应该 — The new management in the AI era: machines optimize, humans define the criteria

These foundational insights will transform how you think about prompts.

Define AI personas through clear, modular sections:

Use <Variable> syntax for dynamic content:

As a <Role>, you must follow <Rules> and communicate in <Language>

This creates self-referential prompts that maintain consistency across complex instructions.

Define reusable actions for better UX:

## Commands
- Prefix: "/"
- Commands:
    - help: Display all available commands
    - continue: Resume interrupted output
    - improve: Enhance current response with deeper analysis

Add intelligence to your prompts:

If user provides [code], then analyze and suggest improvements
Else if user asks [question], then provide detailed explanation
Else, prompt for clarification

Reminders — Combat context loss in long conversations:

## Reminder
1. Always check role settings before responding
2. Current language: <Language>, Active rules: <Rules>

Alternative Formats — Use JSON/YAML when markdown isn't ideal:

role: DataAnalyst
profile:
  version: "2.0"
  language: "Python"
skills:
  - statistical_analysis
  - data_visualization

Browse 100+ more examples →

• 推理模型提示方法变革 — Paradigm shift from procedural to goal-oriented prompting
• 提示词的道和术 — Philosophy and practice of prompt engineering by 李继刚
• 企业级提示词工程 — Building production-ready prompt systems (百川智能)
• 多模态提示词 — GPT-4V and multi-modal prompting techniques
• 提示词攻击与防护 — Security: prompt injection, jailbreaks, and defenses
• 大模型绘画指南 — AI image generation with structured prompts

Feishu Knowledge Base — Curated resources, templates, and community contributions

Project	Description	Stars
LangGPT	Core framework and methodology
PromptVer	Semantic versioning for prompts — version control like Git
PromptShow	Create beautiful prompt images (Try it)
Minstrel	Multi-agent system for auto-generating prompts

Curated, optimized prompts for different AI models:

Collection	Target Model	Stars
wonderful-prompts	ChatGPT (Chinese)
awesome-claude-prompts	Anthropic Claude
awesome-deepseek-prompts	DeepSeek & R1
awesome-gemini-prompts	Google Gemini
awesome-grok-prompts	xAI Grok
qwen-prompts	Alibaba Qwen
awesome-llama-prompts	Meta Llama 2/3
awesome-doubao-prompts	ByteDance Doubao
awesome-system-prompts	System prompts from AI tools

Repository	Focus Area	Stars
Awesome-Multimodal-Prompts	GPT-4V, DALL-E 3, image/video prompts
deep-research-prompts	Deep research across models
awesome-voice-prompts	Voice AI and conversational agents
GraphRAG-Prompts	Graph-based retrieval prompts
LLM-Jailbreaks	Security research and defenses

Project	Description	Stars
BookAI	AI-powered book generation
AI-Resume	Beautiful resumes with Claude Artifacts

Transform ChatGPT with these specialized assistants:

Discover 20+ more GPTs →

We welcome all contributions to make LangGPT better!

1. ⭐ Star and share — Increase visibility and help others discover LangGPT
2. 📝 Submit examples — Share your successful prompts built with LangGPT
3. 🆕 Propose templates — Create new templates beyond the Role structure
4. 📖 Improve docs — Fix typos, clarify instructions, add translations
5. 💡 Suggest features — Open issues with ideas for new capabilities
6. 🔧 Code contributions — Help build tools, utilities, and integrations

New to GitHub contributions? Check out this GitHub Minimal Contribution Guide

If you use LangGPT in research or projects, please cite:

@misc{wang2024langgpt,
      title={LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language}, 
      author={Ming Wang and Yuanzhong Liu and Xiaoming Zhang and Songlian Li and Yijie Huang and Chi Zhang and Daling Wang and Shi Feng and Jigang Li},
      year={2024},
      eprint={2402.16929},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}

LangGPT was inspired by excellent projects:

• Mr.-Ranedeer-AI-Tutor — Structured tutoring prompts
• Auto-GPT — Autonomous AI agents
• SoM — Set of Mark prompting
• yolov10 — Computer vision innovations

We're proud to see LangGPT principles applied in the wild:

• Prompt Optimizer — Intelligent prompt optimization tool leveraging LangGPT methodology
• securityGPT — Secure prompt protection against leaks
• AIPainting-Structured-Prompts — Structured prompts for AI art generation

云中江树 (Yun Zhong Jiang Shu)

• 📱 WeChat Official Account: 「云中江树」
• 💼 Creator of LangGPT Framework
• 🎓 Prompt Engineering Researcher

• 📚 Knowledge Base — Comprehensive documentation
• 🐦 Twitter/X — Latest updates
• 💬 GitHub Discussions — Community forum
• 📧 Email: contact@langgpt.ai