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Basic Information

This repository is an experimental, opinionated .NET framework for creating and hosting event-driven AI agents. It is built on top of Semantic Kernel and Orleans and is intended to help developers design agent-based systems that communicate and react to events. The code currently lives in a src directory and the team plans to publish the library as a NuGet package in the future. The README emphasizes that the project is experimental and not meant for production use yet. The repository includes example scenarios to demonstrate how agents can be composed into teams and orchestrated to perform multi-role workflows in domains such as software development, marketing, and support.

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Features
The project provides an opinionated .NET framework layer leveraging Semantic Kernel and Orleans to support event-driven agents. It includes sample applications that illustrate common patterns: a GitHub developer team, a marketing campaign team with content and design roles, and a support center team that orchestrates domain experts by intent. The codebase is organized under src with example folders under samples. The README documents contribution expectations and legal notices, and the maintainers plan to publish the library as a NuGet package. The project explicitly notes its experimental status and includes governance details like a contributor license requirement and code of conduct.
Use Cases
This repository helps developers prototype and explore agent-oriented architectures by providing an implementable .NET framework and concrete sample scenarios. The samples show how to model multi-role teams, orchestrate intents, and build event-driven workflows for tasks such as requirements engineering, campaign creation, and support routing. By building on established technologies, it demonstrates integration points and design patterns for hosting agents in a distributed environment. The inclusion of examples, contribution guidelines, and licensing information lowers the barrier to experimentation and collaborative development while making clear the project is not production-ready.

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