Multi-Agent-Seller provides a modular environment for simulating e-commerce negotiations using AI agents. It includes pre-built buyer and seller agents with customizable negotiation strategies, such as dynamic pricing, time-based concessions, and utility-based decision-making. Users can define custom protocols, message formats, and market conditions. The framework handles session management, offer tracking, and result logging with built-in visualization tools for analyzing agent interactions. It integrates easily with machine learning libraries for strategy development, enabling experimentation with reinforcement learning or rule-based agents. Its extensible architecture allows adding new agent types, negotiation rules, and visualization plugins. Multi-Agent-Seller is ideal for testing multi-agent algorithms, studying negotiation behaviors, and teaching concepts in AI and e-commerce domains.

Label Studio is a robust open-source data labeling tool designed to handle various data types such as text, images, audio, and video. It enables data scientists and machine learning engineers to create high-quality training data. The platform offers interactive labeling, model evaluation, and the integration of popular ML models for pre-labeling tasks. Label Studio supports multi-user collaboration and provides both community and enterprise versions to suit different needs.