PhantomCrowd: Multi-Agent Audience Simulator Using Claude Code

PhantomCrowd is a multi-agent audience simulator built using Claude Code that predicts how real audiences will react to marketing content before it's posted. The tool was inspired by MiroFish, a multi-agent prediction engine with 48K stars on GitHub, and adapted for marketing use cases.

Key Features and Functionality

The system works with any OpenAI-compatible API, including Claude models. For optimal performance:

• Use Haiku for persona reactions (fast, cheap — handles 500 personas)
• Use Sonnet for persona generation, knowledge graph analysis, and marketing reports
• Also works with Ollama (free, local), OpenAI, Groq, and Together AI — just change the base URL and model name in .env

Here's how it works:

• You paste content (ad copy, social post, product launch)
• It generates 10–500 personas with unique demographics, personalities, and social media habits
• Each persona reacts independently — writes comments, decides to like/share/ignore/dislike
• In Campaign mode: personas interact with each other on a simulated social network (up to 100 LLM agents + 2,000 rule-based agents)
• You get a dashboard with sentiment distribution, viral score, and improvement suggestions

The developer notes that results are surprisingly realistic, with different personas reacting authentically based on their profiles. For example, a 19-year-old K-pop fan reacts very differently from a 45-year-old marketing executive. When these personas interact, you get emergent behavior that can't be predicted from individual responses alone.

The project is MIT licensed, includes Docker support, and can simulate audience reactions in 12 languages.