AgentFarm provides a comprehensive framework to coordinate diverse AI agents in a unified system. Users can script specialized agent behaviors in Python, assign roles (manager, worker, analyzer), and establish task queues for parallel processing. It integrates seamlessly with major LLM services (OpenAI, Azure OpenAI), enabling dynamic prompt routing and model selection. The built-in dashboard tracks agent status, logs interactions, and visualizes workflow performance. With modular plug-ins for custom APIs, developers can extend functionality, automate error handling, and monitor resource utilization. Ideal for deploying multi-stage pipelines, AgentFarm enhances reliability, scalability, and maintainability in AI-driven automation.

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.