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.

LLMChat.me is an online service that aggregates dozens of open-source large language models into a unified chat interface. Users can select from models such as Vicuna, Alpaca, ChatGLM, and MOSS to generate text, code, or creative content. The platform stores conversation history, supports custom system prompts, and allows seamless switching between different model backends. Ideal for experimentation, prototyping, and productivity, LLMChat.me runs entirely in the browser without downloads, offering fast, secure, and free access to leading community-driven AI models.