cheshire-cat-ai/core

Cheshire Cat AI is a framework for building AI agent microservices that emphasizes educational value and extensibility through plugin-based architecture.

The project addresses the challenge of understanding how AI agents function by providing a bottom-up design that prioritizes clarity and ease of extension. It integrates large language models with vector search capabilities, function calling, and support for the Model Context Protocol as a client. The framework allows developers to build conversational agents and chatbots that can be deployed via Docker, with a REST API and web UI for interaction and testing.

The tool is explicitly positioned for education and research rather than production deployment. It suits developers and institutions wanting to learn agent mechanics or publish experimental agents on the web. The README identifies the main use case as learning how AI agents work, making it appropriate for those prioritizing understanding over stability. The project is currently in an unstable alpha state with breaking changes expected, so production use is not recommended at this stage.

Development activity shows the maintainers maintain a flexible approach to planning, explicitly stating that roadmaps are not part of their process and requiring issue assignment before pull requests are accepted. The codebase remains under active modification as the framework undergoes heavy development toward stability.