The Agent Supervisor Example repository demonstrates how to orchestrate several autonomous AI agents in a coordinated workflow. Built in Python, it defines a Supervisor class to dispatch tasks, monitor agent status, handle failures, and aggregate responses. You can extend base agent classes, plug in different model APIs, and configure scheduling policies. It logs activities for auditing, supports parallel execution, and offers a modular design for easy customization and integration into larger AI systems.

multiagent-golang provides a structured approach to building multi-agent systems in Go. It introduces an Agent abstraction where each agent can be equipped with various sensors to perceive its environment and actuators to take actions. Agents run concurrently using Go routines and communicate through dedicated messaging channels. The framework also includes an environment simulation layer to handle events, manage the agent lifecycle, and track state changes. Developers can easily extend or customize agent behaviors, configure simulation parameters, and integrate additional modules for logging or analytics. It streamlines the creation of scalable, concurrent simulations for research and prototyping.

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.

Pipe Pilot is an open-source tool that lets developers build, visualize, and manage AI-driven pipelines in Python. It offers a declarative API or YAML configuration to chain tasks such as text generation, classification, data enrichment, and REST API calls. Users can implement conditional branches, loops, retries, and error handlers to create resilient workflows. Pipe Pilot maintains execution context, logs each step, and supports parallel or sequential execution modes. It integrates with major LLM providers, custom functions, and external services, making it ideal for automating reports, chatbots, intelligent data processing, and complex multi-stage AI applications.

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.