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.

Powerful Auto Researcher is a Python-based AI agent framework designed to automate and accelerate research workflows. Users define topics or initial questions, and the agent iteratively generates follow-up questions, submits them to OpenAI models, and aggregates the responses. It supports customizable prompt templates, workflow chaining, and automated export to Markdown, JSON, or PDF. Integrated logging and result management enable reproducibility. This tool is ideal for academic literature reviews, competitive intelligence gathering, market research, and technical deep dives, reducing manual overhead and ensuring systematic coverage of research questions.

MARO (Multi-Agent Resource Optimization) is a Python-based framework designed to support the development and evaluation of multi-agent reinforcement learning agents in supply chain, logistics, and resource management scenarios. It includes environment templates for inventory management, truck scheduling, cross-docking, container rental, and more. MARO offers a unified agent API, built-in trackers for experiment logging, parallel simulation capabilities for large-scale training, and visualization tools for performance analysis. The platform is modular, extensible and integrates with popular RL libraries, enabling reproducible research and rapid prototyping of AI-driven optimization solutions.

Multi-Agent AI Researcher provides a modular, extensible framework where users can configure and deploy multiple AI agents to collaboratively tackle complex scientific inquiries. It includes a hypothesis generation agent that proposes research directions based on literature analysis, an experiment simulation agent that models and tests hypotheses, a data analysis agent that processes simulation outputs, and a drafting agent that compiles findings into structured research documents. With plugin support, users can incorporate custom models and data sources. The orchestrator manages agent interactions, logging each step for traceability. Ideal for automating repetitive tasks and accelerating R&D workflows, it ensures reproducibility and scalability across diverse research domains.

Reinforcement Learning Agents for PettingZoo Games is a Python-based code library delivering off-the-shelf DQN, PPO, and A2C algorithms for multi-agent reinforcement learning on PettingZoo environments. It features standardized training and evaluation scripts, configurable hyperparameters, integrated TensorBoard logging, and support for both competitive and cooperative games. Researchers and developers can clone the repo, adjust environment and algorithm parameters, run training sessions, and visualize metrics to benchmark and iterate quickly on their multi-agent RL experiments.