Expert Panel Simulator

Multi-Agent Expert Review System - Create virtual expert panels to review any topic, document, or idea using AI-powered domain experts.

🚀 What is this?

The Expert Panel Simulator creates virtual panels of domain experts who discuss and review your ideas, documents, or concepts. Think of it as having instant access to a roundtable of industry experts who can provide diverse perspectives and actionable feedback.

Perfect for:

📋 Product design reviews
🏗️ Architecture assessments
💼 Business idea validation
📚 Academic research discussions
🎯 Strategy planning sessions
🔍 System design reviews

✨ Key Features

🧠 Multiple AI Providers: Choose between OpenAI GPT-4 or Anthropic Claude
👥 Expert Templates: Pre-built experts across productivity, tech, business, and academic domains
📊 Comprehensive Analytics: Track token usage, costs, and performance metrics
💰 Cost Transparency: Real-time cost tracking with detailed breakdowns
📄 Rich Outputs: Markdown transcripts, JSON analytics, and session metadata
🎨 Customizable: Create your own expert personas and discussion formats
⚡ Easy Setup: Works out of the box with minimal configuration

🏃‍♂️ Quick Start

1. Installation

# Clone or download this directory
git clone <your-repo-url>
cd expert-panel-simulator

# Install dependencies
pip install -r requirements.txt

2. Configuration

# Copy environment template
cp .env.example .env

# Edit .env and add your API keys
# Get OpenAI key from: https://platform.openai.com/api-keys
# Get Anthropic key from: https://console.anthropic.com/

Minimum .env setup:

# Choose one or both providers
ANTHROPIC_API_KEY=your_anthropic_key_here
# OPENAI_API_KEY=your_openai_key_here

# Set primary provider
PRIMARY_PROVIDER=anthropic

3. Run Your First Simulation

# Review a product idea with tech experts
python expert_panel_simulator.py --topic "AI-powered task manager" --domain technology

# Review a document with business experts
python expert_panel_simulator.py --document my_business_plan.md --domain business

# Use a sample configuration
python expert_panel_simulator.py --sample startup_idea_validation --topic "My SaaS idea"

📖 Usage Examples

Basic Usage

# Product review with 5 tech experts
python expert_panel_simulator.py \
  --topic "Mobile app for habit tracking" \
  --domain technology \
  --experts 5

# Document review with productivity experts
python expert_panel_simulator.py \
  --document design_spec.md \
  --domain productivity

# Business strategy with custom rounds
python expert_panel_simulator.py \
  --topic "Expansion strategy" \
  --domain business \
  --rounds 6

Advanced Options

# Force specific provider
python expert_panel_simulator.py \
  --topic "System architecture" \
  --domain technology \
  --provider openai

# Custom output directory
python expert_panel_simulator.py \
  --topic "Research proposal" \
  --domain academic \
  --output my_reviews/

# Use sample configuration
python expert_panel_simulator.py \
  --sample app_architecture_review \
  --document technical_spec.md

🎯 Available Expert Domains

Productivity (`--domain productivity`)

GTD Specialist - Task capture and context-based action
Digital Organization Expert - PARA method and progressive summarization
Focus & Attention Expert - Deep work and attention management
Time-Boxing Coach - Pomodoro and interval-based productivity
Executive Function Specialist - ADHD and executive function support

Technology (`--domain technology`)

UX Designer - User experience and interface design
Software Architect - System architecture and scalability
DevOps Engineer - Infrastructure and operations
Security Specialist - Cybersecurity and privacy
Frontend Engineer - Frontend development and performance

Business (`--domain business`)

Product Strategist - Product planning and market fit
Startup Mentor - Entrepreneurship and lean validation
Growth Specialist - Marketing funnels and user acquisition
Finance Advisor - Business finance and sustainability

Academic (`--domain academic`)

Cognitive Psychology Researcher - Human cognition and behavior
Learning Scientist - Education and knowledge transfer
Data Science Researcher - Machine learning and statistical modeling

📊 Sample Configurations

Use --sample <name> for pre-configured expert panels:

`task_management_review`

Domain: Productivity Experts: GTD, PARA, ADHD Specialist, Deep Work Focus: Task management system design

`app_architecture_review`

Domain: Technology Experts: Software Architect, UX Designer, DevOps, Security Focus: Application architecture and design

`startup_idea_validation`

Domain: Business Experts: Product Manager, Startup Advisor, Growth Expert, Finance Focus: Startup idea and business model validation

💰 Cost & Analytics

The simulator provides detailed analytics after each session:

{
  "session_info": {
    "duration_minutes": 8.5,
    "total_calls": 12,
    "primary_provider": "anthropic"
  },
  "token_usage": {
    "prompt_tokens": 15420,
    "completion_tokens": 8340,
    "total_tokens": 23760
  },
  "costs": {
    "total_cost_usd": 0.1247,
    "average_cost_per_call": 0.0104,
    "estimated_cost_per_1k_tokens": 0.0052
  }
}

Typical Costs (5 experts, 6 rounds):

Anthropic Claude-3.5-Sonnet: $0.10 - $0.25
OpenAI GPT-4o: $0.15 - $0.35
Anthropic Claude-3-Haiku: $0.03 - $0.08

📁 Output Structure

Each simulation creates a timestamped session directory:

outputs/
└── session_20241120_143022/
    ├── transcript.md       # Full discussion transcript
    ├── analytics.json      # Token usage and cost analytics
    └── metadata.json       # Session configuration and summary

Sample Transcript Format

# Expert Panel Discussion Transcript
Session: 20241120_143022
Generated: 2024-11-20T14:32:45

## UX Designer (14:32:45)
As a UX designer, I see significant potential in this concept. The key challenge will be balancing feature richness with interface simplicity...

## Software Architect (14:33:12)
From an architecture perspective, we need to consider scalability early. I'd recommend starting with a microservices approach...

🛠️ Configuration

Environment Variables

Create a .env file with these options:

# API Keys (get from provider websites)
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
PRIMARY_PROVIDER=anthropic

# Model Selection
ANTHROPIC_MODEL=claude-3-5-sonnet-20241022
OPENAI_MODEL=gpt-4o-2024-08-06

# Model Parameters
TEMPERATURE=0.7          # Creativity (0.0-1.0)
MAX_TOKENS=4000         # Response length limit

# Simulation Settings
MAX_ROUNDS=8            # Discussion rounds
DEFAULT_EXPERT_COUNT=5  # Number of experts
DISCUSSION_STYLE=formal # formal, casual, academic

# Output Options
OUTPUT_DIR=outputs      # Where to save results
SAVE_TRANSCRIPTS=true   # Save full transcripts
ENABLE_TOKEN_COUNTING=true
ENABLE_COST_TRACKING=true

Command Line Options

Usage: expert_panel_simulator.py [OPTIONS]

Options:
  -t, --topic TEXT        Topic for expert panel discussion
  -d, --document PATH     Document file to review
  --domain CHOICE         Expert domain (productivity/technology/business/academic)
  -e, --experts INT       Number of experts (3-7 recommended)
  -c, --config PATH       YAML configuration file
  --sample CHOICE         Use sample configuration
  --provider CHOICE       Override primary LLM provider (openai/anthropic)
  -o, --output PATH       Output directory override
  -r, --rounds INT        Number of discussion rounds
  --help                  Show this message and exit

🎨 Customization

Creating Custom Experts

You can create custom expert personas by extending the expert templates:

from config.expert_templates import create_custom_expert

custom_expert = create_custom_expert(
    name="Dr. Jane Smith (AI Ethics Expert)",
    expertise="AI Ethics and Responsible AI Development",
    perspective="Focuses on ethical implications and societal impact",
    background="PhD in Philosophy, 10+ years in AI ethics research"
)

Custom Configuration Files

Create YAML configs for repeated use:

# my_config.yaml
experts:
  - name: "Custom Expert 1"
    expertise: "Domain Expertise"
    perspective: "Unique viewpoint"
    background: "Professional background"

discussion_rounds:
  - "Round 1: Analysis"
  - "Round 2: Recommendations"
  - "Round 3: Implementation"

settings:
  max_rounds: 6
  temperature: 0.8

🔧 Troubleshooting

Common Issues

❌ "No LLM providers available"

Check your API keys in .env file
Verify keys are valid and have credits
Install required packages: pip install openai anthropic

❌ "Module not found" errors

Install dependencies: pip install -r requirements.txt
Check Python version (3.8+ required)

❌ High costs

Use cheaper models: claude-3-haiku or gpt-3.5-turbo
Reduce expert count or rounds
Set lower MAX_TOKENS limit

❌ Rate limiting

The system includes retry logic with exponential backoff
Consider switching providers or reducing concurrency
Anthropic typically has higher rate limits than OpenAI

API Key Setup

OpenAI:

Go to https://platform.openai.com/api-keys
Create new API key
Add to .env: OPENAI_API_KEY=sk-...

Anthropic:

Go to https://console.anthropic.com/
Create API key
Add to .env: ANTHROPIC_API_KEY=sk-ant-...

🤝 Contributing

We welcome contributions! Areas for improvement:

New Expert Domains: Add experts for healthcare, finance, education, etc.
Enhanced Analytics: Better visualization of expert consensus/disagreement
Integration Features: Export to other tools, API endpoints
UI Development: Web interface for easier use

📜 License

MIT License - feel free to use this for personal or commercial projects.

🙏 Acknowledgments

Built on:

AutoGen - Multi-agent conversation framework
OpenAI API - GPT-4 and other models
Anthropic API - Claude models

💡 Pro Tips:

Start with 3-5 experts for focused discussions
Use document review for detailed feedback
Try different domains for diverse perspectives
Check analytics to optimize costs
Save successful configurations for reuse

Questions or issues? Open an issue on GitHub or check our troubleshooting guide!

Name		Name	Last commit message	Last commit date
config		config
utils		utils
.env.example		.env.example
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
QUICKSTART.md		QUICKSTART.md
README.md		README.md
expert_panel_simulator.py		expert_panel_simulator.py
install.sh		install.sh
requirements.txt		requirements.txt
setup.py		setup.py
test_simulator.py		test_simulator.py

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Expert Panel Simulator

🚀 What is this?

✨ Key Features

🏃‍♂️ Quick Start

1. Installation

2. Configuration

3. Run Your First Simulation

📖 Usage Examples

Basic Usage

Advanced Options

🎯 Available Expert Domains

Productivity (--domain productivity)

Technology (--domain technology)

Business (--domain business)

Academic (--domain academic)

📊 Sample Configurations

task_management_review

app_architecture_review

startup_idea_validation