Super-Agent-Party allows developers to define a Party object where individual AI agents perform distinct roles such as planning, researching, drafting, and reviewing. Each agent can be configured with custom prompts, tools, and model parameters. The framework manages message routing and shared context, enabling agents to collaborate in real time on subtasks. It supports plugin integration for third-party services, flexible agent orchestration strategies, and error handling routines. With an intuitive API, users can dynamically add or remove agents, chain workflows, and visualize agent interactions. Built on Node.js and compatible with major cloud providers, Super-Agent-Party streamlines the development of scalable, maintainable AI multi-agent systems for automation, content generation, data analysis, and more.

OpenAI Swarm is a versatile orchestration library enabling parallel execution and consensus-driven decision-making across multiple AI agents. It broadcasts tasks to independent model instances, aggregates their outputs, and applies configurable voting or ranking schemes to select the highest-scoring result. Developers can fine-tune agent counts, voting thresholds, and model combinations to enhance reliability, mitigate individual bias, and refine solution quality. Swarm supports chaining responses, iterative feedback loops, and detailed reasoning logs for auditability, elevating performance on summarization, classification, code generation, and complex reasoning tasks through collective intelligence.

multiagent_envs delivers a modular set of Python-based environments tailored for multi-agent reinforcement learning research and development. It includes scenarios like cooperative navigation, predator-prey, social dilemmas, and competitive arenas. Each environment lets you define the number of agents, observation features, reward functions, and collision dynamics. The framework integrates seamlessly with popular RL libraries such as Stable Baselines and RLlib, allowing vectorized training loops, parallel execution, and easy logging. Users can extend existing scenarios or create new ones by following a simple API, accelerating experimentation with algorithms like MADDPG, QMIX, and PPO in a consistent, reproducible setup.