CrowdSimulator allows users to anticipate and analyze audience reactions to their posts before publishing them. By conducting real-time research on any chosen topic, it generates realistic audience personas based on genuine online conversations, simulating various responses such as support, opposition, and consensus.

Key Features

• Real-Time Audience Insights: Leverage AI to perform extensive web searches and gain insights into topic sentiment, demographic data, and current events related to the matter at hand.
• Persona Generation: The tool creates diverse audience profiles, including supporters, skeptics, influencers, and journalists, providing a well-rounded view of potential reactions.
• Simulation Engine: Using the OASIS multi-agent framework, CrowdSimulator runs simulations to display how different personas might interact with your content, including likes, comments, shares, and reactions across various platforms.
• Detailed Reporting: After the simulation, users receive reports featuring sentiment scores, risk assessments, virality predictions, and strategic recommendations.

Workflow Overview

The process is structured into six main stages:

1. Compose: Draft the content, choose your target audience, and specify where it will be published.
2. Research: The AI agent performs multiple web searches to gather insights relevant to the topic.
3. Persona Generation: Creates profiles of potential audience members based on extensive research findings.
4. Review: Users can validate and adjust the generated personas and sources before simulation.
5. Simulation: The AI agents simulate interactions with the content, revealing how different audience types respond.
6. Report: Users receive an analysis that includes predictions and advice on how to navigate the landscape of audience reactions.

Technical Specifications

• Frontend Technologies: Built with Vue 3, Vue Router, Vite, D3.js, and Axios for a responsive user experience.
• Backend Technologies: Utilizes Node.js and TypeScript, along with pi-agent-core, for efficient processing and real-time interaction.
• Simulation: The backend employs Python’s OASIS framework and SQLite for reliable data management and simulations.
• LLM Integration: CrowdSimulator connects with OpenRouter, supporting various models like Claude and Perplexity for versatile AI response generation.

This project is ideal for content creators, marketers, or anyone looking to refine their communication strategy by understanding audience dynamics before sharing their message.