The LLM Agents Simulation Framework enables the design, execution, and analysis of simulated environments where autonomous agents interact through large language models. Users can register multiple agent instances, assign customizable prompts and roles, and specify communication channels such as message passing or shared state. The framework orchestrates simulation cycles, collects logs, and calculates metrics like turn-taking frequency, response latency, and success rates. It supports seamless integration with OpenAI, Hugging Face, and local LLMs. Researchers can create complex scenarios—negotiation, resource allocation, or collaborative problem-solving—to observe emergent behaviors. Extensible plugin architecture allows addition of new agent behaviors, environment constraints, or visualization modules, fostering reproducible experiments.

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.

Flocking Multi-Agent offers a modular library for simulating autonomous agents exhibiting swarm intelligence. It encodes core steering behaviors—cohesion, separation and alignment—alongside obstacle avoidance and dynamic target pursuit. Using Python and Pygame for visualization, the framework allows adjustable parameters such as neighbor radius, maximum speed, and turning force. It supports extensibility through custom behavior functions and integration hooks for robotics or game engines. Ideal for experimentation in AI, robotics, game development, and academic research, it demonstrates how simple local rules lead to complex global formations.