MGym is a specialized framework for crafting and managing multi-agent reinforcement learning (MARL) environments in Python. It enables users to define complex scenarios with multiple agents, each having customizable observation and action spaces, reward functions, and interaction rules. MGym supports both synchronous and asynchronous execution modes, providing parallel and turn-based agent simulations. Built with a familiar Gym-like API, MGym seamlessly integrates with popular RL libraries such as Stable Baselines, RLlib, and PyTorch. It includes utility modules for environment benchmarking, result visualization, and performance analytics, facilitating systematic evaluation of MARL algorithms. Its modular architecture allows rapid prototyping of cooperative, competitive, or mixed-agent tasks, empowering researchers and developers to accelerate MARL experimentation and research.

Duet GPT is a Python-based open source framework for orchestrating multi-agent conversations between two GPT models. You define distinct agent roles, customized with system prompts, and the framework manages turn-taking, message passing, and conversation history automatically. This cooperative structure accelerates complex task resolution, enabling comparative reasoning, critique cycles, and iterative refinement through back-and-forth exchanges. Its seamless OpenAI API integration, simple configuration, and built-in logging make it ideal for research, prototyping, and production workflows in coding assistance, decision support, and creative ideation. Developers can extend the core classes to integrate new LLM services, adjust the iterator logic, and export transcripts in JSON or Markdown formats for post-analysis.

GenAI Job Agents is a Python-based open-source framework designed to streamline the creation and management of AI-powered job agents. Developers can define customized job types and agent behaviors using simple configuration files or Python classes. The system integrates seamlessly with OpenAI for LLM-powered reasoning and LangChain for chaining calls. Jobs can be queued, executed in parallel, and monitored through built-in logging and error-handling mechanisms. Agents can handle dynamic inputs, retry failures automatically, and produce structured results for downstream processing. With modular architecture, extensible plugins, and clear APIs, GenAI Job Agents empowers teams to automate repetitive tasks, orchestrate complex workflows, and scale AI-driven operations in production environments.