🚀 LangGPT
June 7, 2026 · View on GitHub
📖 What is LangGPT?
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.
Why LangGPT?
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
🚀 Quick Start
Method 1: Use Automated Tools (Fastest)
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)
Method 2: Master the Template (5 Minutes)
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
Method 3: Start from Examples
Explore our example library and adapt proven templates to your needs.
Method 4: Claude Code Skill (Recommended)
If you use Claude Code, install the LangGPT Skill to get structured prompt writing capabilities:
Install via the official marketplace (recommended):
/plugin marketplace add langgptai/claude_marketplace
/plugin install structured-prompt-writer@langgpt
The LangGPT marketplace also ships more battle-tested skills by Yunzhong Jiangshu — awesome-design-html (115 brand-themed design references), cto, and mind-clone.
Or install manually:
- Download langgpt.skill
- Extract to
~/.claude/skills/directory - Type
/langgptin Claude Code to use
Skill Features:
- 📝 Structured prompt templates (Role, Profile, Skills, Rules, Workflow)
- 📚 Rich example library (FitnessGPT, Poet, Xiaohongshu Master, Name Master, etc.)
- 🔧 Advanced techniques: variables, commands, conditional logic
- 🎯 Model compatibility guide (GPT-4, Claude, GPT-3.5)
🧠 Theoretical Foundations
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
- AI Native 组织 · 思考碎片 — Fragments on organizational restructuring in the AI-native era
- Prompt、Agent，与一个不再沉默的世界 — How agents reshape the prompt from a transaction into a delegation across the human–AI–world triad
These foundational insights will transform how you think about prompts.
💡 Core Concepts
1. Structured Roles
Define AI personas through clear, modular sections:
| Section | Purpose | Example |
|---|---|---|
| Role | Role name/title | "逻辑学家" / "Expert Analyst" / "FitnessGPT" |
| Profile | Identity and capabilities | "Expert Python developer with 10 years experience" |
| Goal | Desired outcome, done criteria, and non-goals for this session/task | “Refactor a prompt into a reusable template; acceptance criteria: pass three structured checks; non-goal: rewriting the business logic.” |
| Skills | Specific abilities | "Debug complex code, optimize performance" |
| Rules | Boundaries and constraints | "Never execute destructive commands" |
| Workflow | Interaction logic | "1. Analyze → 2. Plan → 3. Execute" |
| Initialization | Opening message and setup | "As a |
2. Variables and References
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.
3. Commands
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
4. Conditional Logic
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
5. Advanced Techniques
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
🌟 Featured Examples
| Prompt | Description | Link |
|---|---|---|
| 🎯 FitnessGPT | Personalized diet and workout planner | View |
| 💻 Code Master CAN | Advanced coding assistant with debugging expertise | View |
| ✍️ Xiaohongshu Writer | Viral social media content generator | View |
| 🎨 Chinese Poet | Classical poetry composer in traditional styles | View |
📚 Learning Resources
Essential Guides
| Resource | Description | Date |
|---|---|---|
| Academic Paper | LangGPT: Rethinking Structured Reusable Prompt Design (中文) | Feb 2024 |
| Structured Prompts Guide | Comprehensive tutorial on building high-performance prompts | Jul 2023 |
| Prompt Chains | Multi-prompt collaboration and task decomposition strategies | Aug 2023 |
| Video Tutorial | BiliBili walkthrough (by AIGCLINK) | Sep 2023 |
Advanced Topics
- 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
Community Hub
Feishu Knowledge Base — Curated resources, templates, and community contributions
🎨 LangGPT Ecosystem
Core Framework & Tools
| 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 | |
| claude_marketplace | Official Claude Code skill marketplace — structured prompt, design, CTO, mind-clone |
Model-Specific Prompt Collections
Rather than writing prompts as procedures, write the persona. Writing prompts as procedures gives the model steps and tools. Writing prompts as a persona gives the model a worldview, motivations, a value system, and a preference profile. Below are prompts that Yunzhong Jiangshu wrote while studying some well-known figures.
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 |
Specialized Domains
| 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 |
Applications
| Project | Description | Stars |
|---|---|---|
| BookAI | AI-powered book generation | |
| AI-Resume | Beautiful resumes with Claude Artifacts |
🛠️ Popular GPTs Built with LangGPT
Transform ChatGPT with these specialized assistants:
| GPT | Purpose | Link |
|---|---|---|
| 🎯 LangGPT Expert | Auto-generate structured prompts | Launch |
| ✍️ PromptGPT | Professional prompt engineer | Launch |
| 🧠 SmartGPT-5 | Never lazy, always diligent assistant | Launch |
| 💻 Coding Expert | Comprehensive programming assistant | Launch |
| 📊 Data Table GPT | Transform messy data into clean tables | Launch |
| 🔥 PytorchGPT | PyTorch code specialist | Launch |
| 🎨 LogoGPT | Professional logo designer | Launch |
| 📄 PDF Reader | Deep document analysis and extraction | Launch |
| 🏅 MathGPT | Precise mathematical problem solver | Launch |
| 📝 WriteGPT | Professional writing across industries | Launch |
| 🎙️ 时事热评员 | Current events commentator | Launch |
| 🎀 翻译大小姐 | Elegant Chinese translations | Launch |
🤝 Contributing
We welcome all contributions to make LangGPT better!
How You Can Help
- ⭐ Star and share — Increase visibility and help others discover LangGPT
- 📝 Submit examples — Share your successful prompts built with LangGPT
- 🆕 Propose templates — Create new templates beyond the Role structure
- 📖 Improve docs — Fix typos, clarify instructions, add translations
- 💡 Suggest features — Open issues with ideas for new capabilities
- 🔧 Code contributions — Help build tools, utilities, and integrations
Getting Started
New to GitHub contributions? Check out this GitHub Minimal Contribution Guide
📊 Star History
📄 Citation
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}
}
🙏 Acknowledgments
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
Projects Built with LangGPT
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
📬 Connect With Us
Author
云中江树 (Yun Zhong Jiang Shu)
- 📱 WeChat Official Account: 「云中江树」
- 💼 Creator of LangGPT Framework
- 🎓 Prompt Engineering Researcher
Community
- 📚 Knowledge Base — Comprehensive documentation
- 🐦 Twitter/X — Latest updates
- 💬 GitHub Discussions — Community forum
- 📧 Email: contact@langgpt.ai
Made with ❤️ by the langgptai Community
Empowering everyone to become a prompt expert 🚀