Multi-Agent-Blueprint is a comprehensive open-source codebase for building and orchestrating multiple AI-driven agents that collaborate to address complex tasks. At its core, it offers a modular system for defining distinct agent roles—such as researchers, analysts, and executors—each with dedicated memory stores and prompt templates. The framework integrates seamlessly with large language models, external knowledge APIs, and custom tools, enabling dynamic task delegation and iterative feedback loops between agents. It also includes built-in logging and monitoring to track agent interactions and outputs. With customizable workflows and interchangeable components, developers and researchers can rapidly prototype multi-agent pipelines for applications like content generation, data analysis, product development, or automated customer support.

The Mall Recommendation Multi-Agent System is an AI-driven framework built on a multi-agent architecture to enhance retail experiences in shopping malls. It consists of shopper agents that track visitor interactions, preference agents that analyze past and real-time data, and recommendation agents that generate tailored product and promotion suggestions. Agents communicate via a message-passing protocol to update user models, coordinate cross-agent insights, and adjust recommendations dynamically. The system supports integration with CMS and POS for real-time inventory and sales feedback. Its modular design allows developers to customize agent behaviors, integrate new data sources, and deploy on various platforms. Ideal for large retail environments, it improves customer satisfaction and boosts sales through precise, context-aware recommendations.