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.

Agentle provides a structured framework for developers to build custom AI agents with minimal boilerplate. It supports defining agent workflows as sequences of tasks, seamless integration with external APIs and tools, conversational memory management for context preservation, and built-in logging for auditability. The library also offers plugin hooks to extend functionality, multi-agent coordination for complex pipelines, and a unified interface to run agents locally or deploy via HTTP APIs.

OpenExec Protocol is a comprehensive specification and toolkit enabling seamless interaction among autonomous AI agents. By standardizing communication channels—like task proposals, acceptances, declines, execution reports, and dispute-resolution messages—OpenExec ensures that agents built on diverse architectures can interoperate smoothly. It provides SDKs in Node.js and Python to define agent identities, register skill sets, and manage reputations. The protocol integrates payment rails for cryptographic token settlements, ensuring secure, auditable transactions for completed tasks. With plug-in adapters for major LLM providers (OpenAI, Anthropic, Cohere) and blockchain networks, developers can orchestrate decentralized workflows, automated service markets, and governance processes. OpenExec’s modular design promotes extensibility, enabling custom extensions for verification, arbitration, and logging to suit enterprise or research needs.