Langgpt Prompt Assistant

A powerful prompt assistant built on the LangGPT framework for generating structured, high-quality prompts. This tool helps you create well-organized prompts following the LangGPT methodology for better AI interactions.

Below is a Single-Page Application (SPA) generated by Gemini, using an untuned prompt generated by this assistant in a single pass.

🌟 Features

• Structured Prompt Generation: Create LangGPT-style prompts with clear role definitions, instructions, and constraints
• Prompt Analysis: Analyze existing prompts for structure, clarity, and effectiveness
• Prompt Optimization: Optimize prompts based on specific goals and constraints
• Predefined Roles: Access to common role templates (Programming Assistant, Writing Assistant, Data Analyst, Research Assistant)
• MCP Integration: Built on the Model Context Protocol for seamless integration with AI applications
• 🌐 Web Frontend: Modern, responsive web interface for easy prompt generation and management

🏗️ Architecture

This project is built using:

• TypeScript for type safety and better development experience
• Model Context Protocol (MCP) for standardized AI tool integration
• LangGPT Framework for structured prompt design methodology
• Express.js for HTTP server capabilities
• Zod for runtime type validation
• Modern Web Technologies for the frontend interface

📦 Installation

1. Clone the repository:

   git clone https://github.com/denven/langgpt-prompt-assistant.git
   cd langgpt-prompt-assistant
   

2. Install dependencies:

   npm install
   

3. Build the project:

   npm run build
   

4. Start the server:

   npm start
   

🚀 Quick Start

Development Mode

npm run dev

Production Mode

npm run build
npm start

The server will start on port 3000 by default. You can change this by setting the PORT environment variable.

🌐 Web Frontend

The LangGPT Prompt Assistant includes a beautiful, modern web interface that makes prompt generation easy and intuitive.

Starting the Frontend

Option 1: Run Both Backend and Frontend Together

# Start both MCP backend and frontend servers
node start-app.js

This will start:

• MCP backend server on http://localhost:3000
• Frontend web interface on http://localhost:8080

Option 2: Run Frontend Only (with Mock Data)

# Start just the frontend server
npm run frontend

Then open http://localhost:8080 in your browser.

Option 3: Simple HTTP Server

# Navigate to frontend directory
cd frontend

# Start a simple HTTP server
python -m http.server 8080
# or
npx http-server -p 8080

Frontend Features

• 🎨 Modern UI Design: Clean, responsive interface with smooth animations
• 📝 Prompt Generator: Create custom LangGPT prompts with detailed configuration
• 🔍 Prompt Analyzer: Analyze existing prompts for structure, clarity, and effectiveness
• ⚡ Prompt Optimizer: Optimize prompts based on specific goals and constraints
• 📋 Template Library: Pre-built templates for common use cases
• 📱 Mobile Responsive: Works perfectly on desktop, tablet, and mobile devices
• 🎯 Real-time Feedback: Toast notifications and loading states for better UX

Using the Frontend

1. Prompt Generator Tab:

   • Configure role settings (type, domain, expertise level)
   • Define specific tasks and requirements
   • Add constraints and additional skills
   • Generate and download your custom prompt

2. Prompt Analyzer Tab:

   • Paste existing prompts for analysis
   • Get detailed feedback on structure, clarity, and completeness
   • Receive specific improvement suggestions

3. Prompt Optimizer Tab:

   • Input prompts to optimize
   • Set optimization goals (clarity, structure, conciseness)
   • Get improved versions with explanations

4. Templates Tab:

   • Choose from pre-built templates
   • One-click template loading
   • Customize templates for your needs

Frontend Development

The frontend is built with vanilla HTML, CSS, and JavaScript for simplicity and performance:

frontend/
├── index.html          # Main HTML file
├── styles.css          # CSS styles and responsive design
├── script.js           # JavaScript functionality
└── README.md           # Frontend documentation

For detailed frontend documentation, see frontend/README.md.

🛠️ Available Tools

1. Generate LangGPT Prompt

Generate structured prompts based on role type, domain, and specific tasks.

Parameters:

• role_type: Type of role (e.g., "assistant", "expert", "tutor")
• domain: Domain or field (e.g., "programming", "writing", "analysis")
• specific_task: Specific task or function
• requirements: Additional requirements (optional)
• constraints: Constraints or limitations (optional)
• style: Communication style (optional)
• expertise_level: Beginner, intermediate, advanced, or expert (optional)
• output_format: Desired output format (optional)
• examples: Whether to include examples (optional)

2. Analyze Prompt

Analyze existing prompts for structure, effectiveness, and improvement opportunities.

Parameters:

• prompt: The prompt to analyze
• analysis_type: Type of analysis (structure, effectiveness, improvement, completeness)
• target_audience: Target audience (optional)
• use_case: Intended use case (optional)

3. Optimize Prompt

Optimize prompts based on specified goals and constraints.

Parameters:

• original_prompt: Original prompt to optimize
• optimization_goals: Goals for optimization
• constraints: Constraints to maintain (optional)
• target_length: Target length in words (optional)
• style_preferences: Style preferences (optional)

4. Get Predefined Roles

Get a list of available predefined LangGPT roles.

Parameters:

• category: Filter by category (programming, writing, analysis, research) (optional)

📝 Available Prompts

1. Quick Role Generator

Generate basic roles quickly with minimal input.

Parameters:

• role_name: Name of the role
• main_task: Main task or responsibility
• expertise_level: Expertise level required (optional)

2. Prompt Analyzer

Analyze and improve existing prompts.

Parameters:

• prompt_text: The prompt to analyze
• analysis_focus: What aspect to focus on
• target_audience: Target audience (optional)

3. Role Customizer

Customize predefined roles for specific needs.

Parameters:

• base_role: Base role to customize
• custom_domain: Specific domain or field
• additional_skills: Additional skills to add (optional)
• specific_constraints: Specific constraints (optional)

📋 LangGPT Framework

This tool is based on the LangGPT framework, which provides a structured approach to prompt design:

Core Components

1. Role Definition: Clear definition of the AI's role and responsibilities
2. Profile: Background and context information
3. Skills: Capabilities and expertise areas
4. Constraints: Limitations and boundaries
5. Instructions: Detailed guidance for behavior
6. Workflow: Step-by-step processes
7. Examples: Sample interactions and responses

Template Structure

# Role: [Role Name]

## Profile
- Description: [Clear description of what this role does]

## Skills
- [Skill 1]
- [Skill 2]
- [Skill 3]

## Constraints
- [Constraint 1]
- [Constraint 2]

## Instructions
[Detailed instructions for the role]

## Workflow
1. [Step 1]
2. [Step 2]
3. [Step 3]

## Response
[Guidance for response format]

🔧 Configuration

Environment Variables

Create a .env file in the root directory:

PORT=3000
NODE_ENV=development

Customization

You can customize the server by modifying:

• src/templates/langgpt-templates.ts: Add new predefined roles and templates
• src/services/prompt-generator.ts: Modify prompt generation logic
• src/server/langgpt-server.ts: Add new tools and prompts

📚 Examples

Example 1: Generate a Programming Assistant

{
  "role_type": "assistant",
  "domain": "programming",
  "specific_task": "help with Python development and debugging",
  "expertise_level": "expert",
  "style": "professional and educational",
  "examples": true
}

Example 2: Analyze a Prompt

{
  "prompt": "You are a helpful assistant. Please help me with my questions.",
  "analysis_type": "effectiveness",
  "target_audience": "general users"
}

Example 3: Optimize a Prompt

{
  "original_prompt": "You are a helpful assistant. Please help me with my questions.",
  "optimization_goals": ["clarity", "structure"],
  "target_length": 100
}

🤝 Contributing

We welcome contributions! Please see our contributing guidelines:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• LangGPT Framework - The foundation for structured prompt design
• Model Context Protocol - For standardized AI tool integration
• The open-source community for inspiration and support

📞 Support

If you have questions or need help:

• Open an issue on GitHub
• Check the LangGPT documentation
• Review the examples in the examples/ directory

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Happy prompt engineering! 🎯