AI Software Development Planner

An advanced web application that bridges the critical gap between high-level project planning and AI agent automation. This tool researches current software engineering methodologies, analyzes user requirements through algorithmic matching, and generates professionally structured development plans optimized for hyper-specialized AI agent orchestration.

Overview

The AI Software Development Planner revolutionizes software project planning by combining comprehensive research capabilities with intelligent algorithmic analysis. It dynamically researches web-based methodologies, frameworks, artifacts, and visualization techniques, then uses sophisticated algorithms to identify the optimal development approach for each unique project. The system automatically adapts these methodologies into production-ready plans structured for maximum parallelism and efficiency, creating atomic, independent tasks with precise output specifications that can be executed by specialized AI agents.

Key Features

Advanced Research & Analysis Engine

• Conducts real-time research of current software engineering methodologies and frameworks
• Analyzes emerging development patterns, tools, and architectural approaches
• Evaluates visualization techniques and artifact structures from industry standards
• Maintains current knowledge of best practices across multiple technology domains

Algorithmic Project Matching

• Employs sophisticated algorithms to analyze user requirements against research database
• Identifies optimal methodology-framework combinations for specific project characteristics
• Performs multi-dimensional analysis considering complexity, scope, technology fit, and team structure
• Ensures precise alignment between project needs and recommended approaches

Professional Plan Adaptation

• Transforms research-based methodologies into AI-agent-executable formats
• Adapts industry-standard templates into structured development workflows
• Generates professionally formatted documentation following established software engineering standards
• Creates hyper-specialized task specifications tailored to individual AI agent capabilities

Intelligent Task Decomposition

• Breaks complex projects into atomic, parallelizable units optimized for AI execution
• Creates precise output specifications that eliminate agent execution ambiguity
• Establishes clear dependency chains for maximum concurrent processing
• Generates specialized agent directives with dynamic system prompt templates

Bridging High-Level to Automation

• Closes the critical gap between strategic project planning and tactical AI agent execution
• Translates business requirements into machine-executable development workflows
• Maintains strategic coherence while enabling tactical automation
• Provides seamless transition from conceptual planning to automated implementation

Multiple Access Methods

• Web Interface: Interactive form-based planning with visual results
• API Endpoints: Programmatic access for AI agent integration
• Console Function: Direct JavaScript access for debugging and testing

Structured Output Formats

• Complete development plans in JSON format
• Supervisor-specific endpoints for leaner responses
• Task dependency graphs with Mermaid.js visualization
• Downloadable PDF reports for documentation

Technology Stack

• Frontend: React 19 with TypeScript for modern, type-safe component architecture
• Build Tool: Vite 6 for optimized development experience and production builds
• AI Research Engine: Google Gemini 2.5 Flash with advanced structured output generation
• Research Integration: Web-based methodology and framework analysis capabilities
• Algorithmic Matching: Proprietary algorithms for optimal project-methodology alignment
• Professional Templates: Industry-standard software engineering documentation formats
• Styling: CSS-in-JS with glassmorphism design for modern user experience
• Visualization: Mermaid.js for architecture diagrams and dependency graph generation

Installation

1. Clone the repository:

git clone <repository-url>
cd ai-software-development-planner-enhanced-endpoint

2. Install dependencies:

npm install

3. Configure environment variables: Create a .env.local file with your Google AI API key:

API_KEY=your_google_ai_api_key_here

4. Start the development server:

npm run dev

5. Build for production:

npm run build

Usage

Web Interface

1. Navigate to the application in your browser
2. Enter a description of your software idea in the input form
3. Click "Generate Plan" to create a comprehensive development plan
4. Review the generated plan with architecture, tech stack, and execution details
5. Download the plan as a PDF for documentation

API Endpoints

Full Plan Endpoint

Get a complete development plan in JSON format:

GET /?prompt=<your_software_idea>&format=json

Example:

https://your-app-url/?prompt=A+social+media+app+for+cats&format=json

Supervisor-Only Endpoint

Get a leaner response containing only agent directives and execution plan:

GET /?prompt=<your_software_idea>&format=json&for=supervisor

Response structure:

{
  "agentSupervisorDirectives": {
    "overview": "Instructions for spawning specialized agents",
    "directives": [...]
  },
  "supervisorExecutionPlan": {
    "overview": "Machine-readable execution plan",
    "jsonRepresentation": [...],
    "textualRepresentation": "..."
  }
}

Console Function (Legacy)

For debugging or manual use:

await window.getSoftwarePlan("A social media app for cats")

Output Structure

System Architecture

• Research-driven overview of optimal application structure for the specific project type
• Algorithmically selected architecture patterns based on requirement analysis
• Visual architecture diagrams in Mermaid.js format following industry standards
• Component relationships and data flow optimized for the chosen methodology

Technology Stack

• Algorithm-selected development methodology recommendations based on project characteristics
• Research-backed technology selections with detailed justifications
• Framework and tool combinations optimized for the specific use case
• Category-based technology organization following professional standards

Agent Specifications

• Hyper-specialized agent types derived from methodology research
• Role descriptions adapted from industry-standard software engineering practices
• Agent coordination requirements tailored to the selected development approach
• Capability definitions optimized for AI agent execution

Agent Supervisor Directives

• Research-informed system prompt templates for each agent specialization
• Dynamic placeholder support for precise task assignment based on methodology requirements
• Professional-grade agent coordination instructions following established patterns
• Adaptation of industry templates for AI agent consumption

Execution Plan

• Methodologically-aligned task breakdown with atomic, independent units
• Research-based task dependency graphs optimized for parallel execution
• Professionally structured output specifications eliminating execution ambiguity
• Agent specialization assignments based on algorithmic capability matching

Supervisor Execution Plan

• Machine-readable JSON task representation following industry data standards
• Algorithm-generated flow diagrams for optimal dependency parsing
• Parallel execution optimization based on methodology research
• Professional workflow adaptation for AI agent orchestration

Agent Integration

This tool is specifically designed to bridge the gap between high-level strategic planning and low-level AI agent automation. The generated plans provide:

• Research-Based Foundations: Each plan is built upon comprehensive analysis of current industry methodologies and frameworks
• Algorithmic Optimization: Task structures are optimized through sophisticated matching algorithms that consider project-specific requirements
• Professional Standards Compliance: All outputs follow established software engineering documentation standards and best practices
• Hyper-Specialized Adaptation: Every task is tailored for execution by AI agents with specific, narrow specializations
• Methodology Alignment: Task breakdowns respect the selected development methodology while optimizing for AI execution
• Industry Template Integration: Professional document structures are adapted for machine consumption while maintaining human readability
• Strategic-to-Tactical Translation: High-level business requirements are systematically converted into precise, executable AI agent instructions

Key Integration Benefits

• Atomic Task Design: Each task is independent, parallelizable, and optimized for AI agent capabilities
• Precise Specifications: Every task includes research-backed output requirements eliminating execution ambiguity
• Dynamic Agent Templates: System prompts are generated using methodology-specific patterns and professional standards
• Intelligent Dependency Management: Task ordering is optimized based on algorithmic analysis of the selected development approach
• Quality Assurance Integration: Validation criteria are embedded based on industry-standard quality gates and checkpoints

Configuration

Environment Variables

• API_KEY: Google AI API key for Gemini model access

Build Configuration

The application uses Vite for build optimization with:

• TypeScript support for type safety
• React 19 for modern component architecture
• Optimized bundle splitting for performance

Development

Project Structure

src/
├── components/           # React components
│   ├── icons/           # Icon components
│   ├── Carousel.tsx     # Plan section navigation
│   ├── DetailedLoader.tsx # Loading state management
│   ├── Header.tsx       # Application header
│   ├── LoadingSpinner.tsx # Simple loading indicator
│   ├── PlanViewer.tsx   # Plan display and visualization
│   ├── ProgrammaticAccessInfo.tsx # API documentation
│   └── UserInputForm.tsx # Input form component
├── hooks/               # Custom React hooks
│   └── usePdfGenerator.ts # PDF generation functionality
├── services/            # External service integrations
│   └── geminiService.ts # Google AI service client
├── App.tsx             # Main application component
├── index.tsx           # Application entry point
└── types.ts            # TypeScript type definitions

Adding Features

1. Update type definitions in types.ts
2. Modify the Gemini service schema in geminiService.ts
3. Update UI components as needed
4. Test both web interface and API endpoints

License

This project is open source and available under the MIT License. See the LICENSE file for more details.

Contributing

We welcome contributions from the community! Please feel free to submit issues, feature requests, and pull requests to help improve this tool for everyone.

Support

For technical support or feature requests, please contact the development team.