OpenAI_Agent_Swarm

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Basic Information

Hierarchical Autonomous Agent Swarm (HAAS) is a hybrid design-and-demo repository that documents and implements a hierarchical, ethically guided system of autonomous AI agents built on OpenAI's agent APIs. The project presents both a theoretical foundation and practical examples for creating a self-directing, self-correcting, and self-improving swarm governed by a Supreme Oversight Board (SOB) and organized into Executive Agents and specialized Sub-Agents. The repo includes governance concepts, privilege inheritance rules, agent lifecycle controls, conversation structures, and examples of tool creation and usage. It is intended as a high-velocity research and hacking platform for experimenting with agent hierarchies, role-based permissions, automated agent instantiation and termination, and tool integration using Python scripts and environment configuration.

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Features
Defines a multi-tier governance model with a Supreme Oversight Board, Executive Agents, and Sub-Agents to enforce ethics and mission alignment. Specifies agent configuration elements including functions, accessible files, operational instructions, KPIs, and conversational structure. Implements hierarchical privilege inheritance, role-based access controls, and rules for instantiation and termination of descendant agents. Provides checks and balances where higher tiers can override or deprovision lower tiers. Contains practical tooling such as a tool_demo script, tool_creator and tool_user examples, environment variable usage via a .env file, and storage conventions where tools and assistants are saved to tools and assistants directories. Emphasizes use of OpenAI"s latest Agents endpoint and an experimental, cutting-edge development approach.
Use Cases
This repository is useful for developers and researchers who want a structured reference and working examples to build and test hierarchically governed autonomous agent systems. It supplies architectural patterns for ethical oversight, role/level assignment, and controlled agent lifecycles that help prevent runaway behaviors and maintain accountability. The included demos let users create custom tools programmatically and run assistants that load those tools, providing a rapid prototype path from concept to runnable agents. Documentation on conversation structure, supervision, and privilege inheritance aids design of collaborative multi-agent workflows. The project also signals an active community and experimental stance for iterating on agent governance and autonomy.

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