AI-Agents provides a flexible, modular platform for creating autonomous AI-driven agents. Developers can define agent objectives, chain tasks, and incorporate memory modules to store and retrieve contextual information across sessions. The framework supports integration with leading LLMs via API keys, enabling agents to generate, evaluate, and revise outputs. Customizable tool and plugin support allows agents to interact with external services like web scraping, database queries, and reporting tools. Through clear abstractions for planning, execution, and feedback loops, AI-Agents accelerates prototyping and deployment of intelligent automation workflows.

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.