GEO-search-agent: A Multi-Agent System for Simulating AI Searches and Generating GEO Insights

GEO-search-agent is an open-source multi-agent system that simulates AI search queries across LLM platforms, analyzes brand recommendations and citation sources, and provides data-driven insights for Generative Engine Optimization (GEO). It aims to help marketers and technical teams understand their brand's visibility and performance in the AI search era, and adapt to the paradigm shift from SEO to GEO.

As generative AI tools like ChatGPT and Claude become the preferred entry points for information acquisition, traditional SEO (Search Engine Optimization) is undergoing transformation. Users no longer rely on keyword-linked lists; instead, they directly ask AI for comprehensive answers. This has given rise to GEO (Generative Engine Optimization) — which focuses on the mentions, recommendations, and citations of brands in AI-generated responses, with results directly impacting brand exposure and user decisions.

GEO-search-agent adopts a multi-agent architecture, breaking down tasks into four core modules:

1. Query Simulation Agent: Generates user questions in real dialogue scenarios (e.g., product comparisons, technology selection);
2. Cross-Platform Search Execution Layer: Sends queries to LLMs like GPT, Claude, and Gemini in parallel, with unified interface abstraction;
3. Brand Mention Analysis Engine: Identifies brands, analyzes recommendation rankings, tracks citation sources, and evaluates sentiment trends;
4. Insight Generation Module: Produces reports on competitor comparisons, industry pattern analysis, and optimization recommendations.

This system provides value to multiple stakeholders:

• Brand Marketing Teams: Monitor brand mentions on AI platforms, analyze competitor performance, and adjust content strategies;
• SEO/GEO Professionals: Identify content easily cited by AI, optimize structure to increase selection probability;
• Product Managers/Market Researchers: Understand market preferences through AI recommendation patterns to guide product roadmaps and marketing strategies.

The project's technical features include:

• Multi-Agent Collaboration: Processes large volumes of queries in parallel to improve efficiency; each agent focuses on specific tasks and collaborates;
• Cross-Platform Standardization: Unifies API formats and response structures of different LLMs via an adaptation layer to enable cross-platform comparisons;
• Extensible Plugin Architecture: Supports adding new analysis dimensions or LLM platforms, maintaining flexibility and forward-looking capabilities.

GEO-search-agent is an important exploration in the MarTech field. It lowers the barrier to GEO, enabling small and medium-sized teams to conduct AI visibility analysis, and its transparency and auditability meet AI interpretability requirements. Future outlook: Expand to multi-modal content analysis (images, videos) and implement real-time monitoring of AI response changes to quickly adapt to market dynamics.

GEO-search-agent provides a practical technical foundation for the emerging GEO field. Against the backdrop of AI reshaping information acquisition, understanding and optimizing a brand's presence in generative engines will become a core capability in digital marketing. This open-source project is not only a tool but also an active exploration and contribution to the GEO field.