Multi-Agent AI Project Management system

Transform your project ideas into comprehensive Agile plans using AI agents powered by Google Gemini 2.5 Flash

Overview

The Project Management system is a sophisticated multi-agent AI system that generates professional-grade project documentation, sprint plans, user stories, and team recommendations. Powered by Google's cutting-edge Gemini 2.5 Flash model, it provides a complete solution for transforming your project ideas into actionable Agile plans with enterprise-level documentation.

Key Highlights

• 🤖 6 Specialized AI Agents working in perfect coordination
• ⚡ High-Performance AI - Powered by Google Gemini 2.5 Flash
• 📋 Complete Agile Framework - From requirements to deployment
• 🎨 Professional Documentation - HTML/PDF export with modern design
• 📊 Comprehensive Planning - Sprints, budgets, risks, and timelines

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Features

Core Capabilities

Feature	Description
Multi-Agent AI System	6 specialized agents orchestrated by CrewAI
Professional Documentation	Modern HTML templates with embedded CSS
Comprehensive Planning	Complete Agile project plans with sprints and timelines
Technology Recommendations	Smart tech stack suggestions based on requirements
Team Structure Planning	Optimal team composition with cost estimates
Advanced Export Options	Professional HTML/PDF reports with modern design
Risk Assessment	Color-coded risk matrices with mitigation strategies

Advanced Features

• Executive-Ready Documentation with modern gradient designs
• Budget Estimation with detailed cost breakdowns and justifications
• Milestone Tracking with visual timeline components
• User Story Generation with comprehensive acceptance criteria
• Technology Architecture recommendations with scalability considerations
• Team Role Mapping with skill requirements and cost analysis
• Risk Management with impact assessment and mitigation plans

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🏗️ Architecture

AI Agents Ecosystem

The application leverages CrewAI to orchestrate a sophisticated multi-agent system powered by Google Gemini 2.5 Flash:

🤖 Agent Roles & Responsibilities

1. 🧏 Customer Listener Agent

   • Role: Customer Requirements Specialist
   • Purpose: Analyzes project requirements, goals, and constraints
   • Output: Structured project requirements document

2. 📋 Requirements Extractor Agent

   • Role: Requirements Analyst
   • Purpose: Converts customer input into structured functional/non-functional requirements
   • Output: JSON formatted requirements specification

3. 📅 Planning & Milestone Agent

   • Role: Agile Project Planner
   • Purpose: Creates sprint breakdowns, timelines, and project milestones
   • Output: Detailed sprint plan with timeline and dependencies

4. ✍️ User Story Generator Agent

   • Role: User Story Creator
   • Purpose: Writes detailed user stories with acceptance criteria and story points
   • Output: Complete user stories with acceptance criteria

5. 💻 Technology Advisor Agent

   • Role: Technical Architecture Advisor
   • Purpose: Recommends optimal tech stacks based on requirements and constraints
   • Output: JSON formatted technology recommendations

6. 📦 Plan Packager Agent

   • Role: Project Documentation Specialist
   • Purpose: Compiles everything into a comprehensive, professional HTML document
   • Output: Executive-ready project plan with modern design

Technology Stack

• Frontend: Streamlit (Python web framework)
• AI Orchestration: CrewAI + LangChain
• Language Model: Google Gemini 2.5 Flash (via CrewAI LLM)
• Document Generation: Custom HTML templates with embedded CSS
• Environment Management: python-dotenv
• Export Formats: HTML, PDF-ready documents

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🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Google AI API access (Gemini 2.5 Flash)
• CrewAI API key
• 4GB RAM minimum (8GB recommended)

Installation

1. Clone the repository

   git clone https://github.com/3lis0/Multi-Agent-AI-Project-Management-system.git

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   cp .env.example .env

   Edit .env file with your API keys:

   CREWAI_API_KEY=your_crewai_api_key_here
   GOOGLE_API_KEY=your_google_ai_api_key_here

5. Run the application

   streamlit run app.py

6. Open your browser Navigate to http://localhost:8501

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📋 Usage Guide

1. Project Input Phase

Define your project with comprehensive details:

• Project Overview: Name, description, and problem statement
• Target Audience: User demographics and personas
• Feature Requirements: Must-have and nice-to-have features
• Constraints: Budget range, timeline, and technical limitations
• Preferences: Platform choices and technology preferences

2. AI Processing Pipeline

Watch the multi-agent system work through your requirements:

Customer Input → Requirements Analysis → Project Planning → User Stories → Tech Stack → Documentation

Each agent processes the output from the previous stage, creating a comprehensive understanding of your project needs.

3. Generated Deliverables

Executive Summary

• High-level project overview with key metrics
• Business goals and success criteria
• Project scope and timeline overview

Detailed Documentation

• Requirements Specification: Functional and non-functional requirements
• Sprint Planning: Detailed sprint breakdowns with feature assignments
• User Stories: Complete with acceptance criteria and story points
• Technology Architecture: Recommended tech stack with justifications
• Team Structure: Role definitions with skill requirements and costs
• Risk Assessment: Risk matrix with mitigation strategies
• Budget Analysis: Detailed cost breakdown with justifications

4. Export & Collaboration

• Professional HTML: Modern, responsive design optimized for viewing
• PDF-Ready: Print-optimized layouts for executive presentations
• JSON Export: Structured data for integration with project tools
• Shareable Links: Easy stakeholder collaboration

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📁 Project Structure

agile-project-planner/
├── crew_local.py
├── app.py                      # Main Streamlit application
├── agents.py                 # AI agent definitions using CrewAI
├── tasks.py                  # Task definitions for each agent
├── utils/
│   └── base_model.py        # LLM configuration 
│   └── requirements.txt         # Python dependencies
│   └── html_pdf_generator.py
│   └── main_styles.css   
├── Example/  
│   └── project_plan.html        
├── README.md               # This documentation

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🔧 Configuration

Environment Variables (.env)

# CrewAI Configuration
CREWAI_API_KEY=your_crewai_api_key

# Google AI Configuration  
GOOGLE_API_KEY=your_google_ai_api_key

# Application Settings
APP_TITLE=Agile Project Planner
DEBUG_MODE=False

# AI Model Settings
MODEL_TEMPERATURE=0.7
MODEL_NAME=gemini/gemini-2.5-flash-preview-04-17

API Key Setup

1. CrewAI API Key:

   • Visit CrewAI Platform
   • Sign up and obtain your API key
   • Add to .env file

2. Google AI API Key:

   • Visit Google AI Studio
   • Create a new project and enable Gemini API
   • Generate API key and add to .env file

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🎨 Customization

Agent Behavior Modification

Customize agent roles and behaviors in agents.py:

def custom_agent(self):
    return Agent(
        role='Your Custom Role',
        goal='Your specific goal',
        backstory="""Your agent's background and expertise""",
        verbose=True,
        allow_delegation=False,
        llm=self.llm
    )

Task Configuration

Modify agent tasks in tasks.py to change output formats or requirements:

def custom_task(self, agent, context):
    return Task(
        description="Your custom task description",
        agent=agent,
        context=context,
        expected_output="Your expected output format"
    )

Document Styling

The HTML templates use modern CSS with:

• CSS Variables for consistent theming
• Responsive Design for all screen sizes
• Print Optimization for PDF conversion
• Professional Typography with Inter font family
• Modern Components like gradient headers and metric cards

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🔍 Troubleshooting

Common Issues

1. API Key Errors

# Check your .env file
cat .env

# Verify API keys are valid
# Test CrewAI API connection
# Test Google AI API connection

2. Dependencies Issues

# Reinstall dependencies
pip install --upgrade -r requirements.txt

# Clear pip cache
pip cache purge

3. Streamlit Issues

# Clear Streamlit cache
streamlit cache clear

# Reset Streamlit configuration
rm -rf ~/.streamlit/

4. Memory Issues

# Monitor memory usage
htop

# Reduce model temperature for faster processing
# Close other applications

Debug Mode

Enable debug mode in .env:

DEBUG_MODE=True

This provides detailed logging and error information in the Streamlit interface.

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Acknowledgments

• Google AI for the Gemini 2.5 Flash model
• CrewAI team for the multi-agent framework
• Streamlit for the excellent web framework
• LangChain for LLM integration capabilities
• Open Source Community for continuous inspiration

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Support

• Developer: @3lis0