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.

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.