A multi-agent AI system that creates personalized video advertisements by analyzing viewer behavior, recommending content, and generating video prompts.

# 1. Run the start script ./START_HERE.sh # 2. Open your browser to http://localhost:5000# 3. Enter viewing history and click "Generate Ads"

• Python 3.10+ with uv package manager
• AWS Account with Bedrock access (for Claude 3.5 Sonnet)
• AWS CLI configured (aws configure)
• Wavespeed API Key (optional, for video generation)

# Clone the repository git clone <your-repo-url>cd ad-genius-system # Install uv (if not already installed) curl -LsSf https://astral.sh/uv/install.sh | sh # Install dependencies uv sync --extra web # Configure AWS credentials (if not already done) aws configure

ad-genius-system/ ├── agents/ # AI Agent definitions │ ├── viewer_analytics_agent.py │ ├── content_recommender_agent.py │ ├── ad_strategist_agent.py │ ├── prompt_generator_agent.py │ └── video_generator_agent.py │ ├── orchestration/ # Agent coordination │ ├── ad_swarm.py # Swarm mode (autonomous handoffs) │ └── production_graph.py # Graph mode (structured pipeline) │ ├── tools/ # Agent tools & utilities │ ├── catalog_tools.py # Content catalog access │ ├── persona_builder.py # Viewer profiling │ ├── prompt_templates.py # Prompt engineering │ └── nova_reel_tools.py # Video generation (Wavespeed API) │ ├── config/ # Configuration │ └── bedrock_config.py # AWS Bedrock & model config │ ├── templates/ # Frontend HTML │ └── index.html # Main web interface │ ├── generated_videos/ # Output directory for videos ├── examples/ # Example code & usage ├── docs/ # Additional documentation │ ├── main.py # CLI entry point ├── web_app.py # Web application ├── streaming_hook.py # Real-time progress updates └── START_HERE.sh # Quick start script 

• Analyzes viewing history patterns
• Builds psychological viewer persona
• Identifies viewing archetypes (Trend Chaser, Quality Connoisseur, etc.)

• Uses persona to find matching content
• Calculates affinity scores
• Returns top 3 recommendations with rationale

• Designs creative ad concepts
• Defines visual approach, messaging, emotional hooks
• Creates A/B test variants

• Converts ad concepts to video generation prompts
• Adds technical specifications (duration, resolution, style)
• Optimizes for video generation models

• Generates actual video files using Wavespeed AI
• Downloads videos locally
• Provides video URLs and metadata

./START_HERE.sh # Opens on http://localhost:5000

Features:

• Real-time agent progress updates
• 2x2 agent results grid
• Streaming console logs
• Video generation toggle
• Graph/Swarm mode selector

# Swarm mode (autonomous agent collaboration) uv run python3 main.py --mode swarm --user-id user_001 --viewing-history "Title 1""Title 2"# Graph mode (structured pipeline) uv run python3 main.py --mode graph --user-id user_001 --viewing-history "Title 1""Title 2"

To enable video generation:

1. Get Wavespeed API Key:

   • Sign up at https://wavespeed.ai
   • Copy your API key

2. Set Environment Variable:

   export WAVESPEED_API_KEY="your-api-key-here"

3. Enable in Web UI:

   • Toggle "Generate Video" in the web interface
   • Generated videos appear in generated_videos/

Edit config/bedrock_config.py:

AWS_REGION="us-east-1"# Your AWS regionMODEL_ID="anthropic.claude-3-5-sonnet-20241022-v2:0"

The system fetches content from a remote catalog API. Configure in config/bedrock_config.py:

CATALOG_URL="https://your-catalog-api.com/items"

• Autonomous: Agents decide next steps
• Dynamic: Non-deterministic flow
• Streaming: Real-time UI updates
• Use Case: Interactive development, debugging

• Structured: Fixed execution pipeline
• Deterministic: Same input = same flow
• Reliable: Better for production
• Use Case: Batch processing, consistent results

POST /api/generate # Start ad generation job GET /api/job/{job_id} # Get job status GET /api/job/{job_id}/stream # Server-sent events stream GET /generated_videos/{file} # Download generated video 

# Run with mock backend (no AWS required) cp archive/old_tests/demo_mock.py . uv run python3 demo_mock.py

# AWS Credentials (or use aws configure)export AWS_ACCESS_KEY_ID="your-access-key"export AWS_SECRET_ACCESS_KEY="your-secret-key"export AWS_REGION="us-east-1"# Optional: Video generationexport WAVESPEED_API_KEY="your-wavespeed-key"

# Configure AWS CLI aws configure # Or set environment variablesexport AWS_ACCESS_KEY_ID="..."export AWS_SECRET_ACCESS_KEY="..."

# Kill existing process killall -9 python3 # Or change port in web_app.py app.run(port=5001)

# Check API key is setecho$WAVESPEED_API_KEY# Check debug log tail -f video_gen_debug.log

• docs/CLAUDE.md - Claude Code integration guide

This is an internal project. For questions or contributions, contact the team.

Proprietary - All Rights Reserved

• Strands - Multi-agent orchestration framework
• Flask - Web framework
• AWS Bedrock - Claude 3.5 Sonnet access
• Wavespeed AI - Video generation (optional)

For issues or questions:

1. Check the troubleshooting section
2. Review logs in video_gen_debug.log
3. Contact the development team

Version: 1.0.0 Last Updated: 2025-11-29 Status: Production Ready