ANAC-agents is a Python-based framework that centralizes multiple negotiation agent implementations for the Automated Negotiating Agents Competition (ANAC). Each agent within the repository embodies distinct strategies for utility modeling, proposal generation, concession tactics, and acceptance criteria, facilitating comparative studies and rapid prototyping. Users can define negotiation domains with custom issues and preference profiles, then simulate bilateral negotiations or tournament-style competitions across agents. The toolkit includes configuration scripts, evaluation metrics, and logging utilities to analyze negotiation dynamics. Researchers and developers can extend existing agents, test novel algorithms, or integrate external learning modules, accelerating innovation in automated bargaining and strategic decision-making under incomplete information.

Multi Agent Simulation offers a flexible API to define Agent classes with custom sensors, actuators, and decision logic. Users configure environments with obstacles, resources, and communication protocols, then run step-based or real-time simulation loops. Built-in logging, event scheduling, and Matplotlib integration help track agent states and visualize results. The modular design allows easy extension with new behaviors, environments, and performance optimizations, making it ideal for academic research, educational purposes, and prototyping multi-agent scenarios.