Aegix provides a robust SDK for orchestrating AI Agents capable of handling complex workflows through multi-step reasoning. With support for various LLM providers, it lets developers integrate custom tools—from database connectors to web scrapers—and maintain conversation state with memory modules such as vector stores. Aegix’s flexible agent loop architecture allows the specification of planning, execution, and review phases, enabling agents to refine outputs iteratively. Whether building document question-answering bots, code assistants, or automated support agents, Aegix simplifies development with clear abstractions, configuration-driven pipelines, and easy extension points. It’s designed to scale from prototypes to production, ensuring reliable performance and maintainable codebases for AI-driven applications.

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.

Tongui Agent provides a modular architecture for creating AI agents that can maintain conversation state, leverage external tools, and coordinate multiple sub-agents. Developers configure LLM backends, define custom actions, and attach memory modules to store context. The framework includes an SDK, CLI, and middleware hooks for observability, making it easy to integrate into web or Node.js applications. Supported LLMs include OpenAI, Azure OpenAI, and open-source models.