# EdgeMind: Verifiable Onboard AI Planning Runtime

EdgeMind provides a modular, contract-based AI planning runtime designed for embedded robotics and space habitats. It supports offline plan generation with safety contracts, a lightweight data-contract layer for cross-vendor interoperability, and an extensible simulation/testbed environment.

What you get in this repository (production-ready base):
- Python-based core with a simple DSL-like planning model (Goals, Actions, Plans)
- Safety contracts and a basic runtime policy engine placeholder
- Data-contract layer scaffolding (Objects, Morphisms, Functors) with canonical mapping
- Lightweight planner capable of solving small, constrained planning tasks on edge hardware
- Tests, packaging metadata, and a small demo CLI
- Documentation and governance files to guide future contributions

How to run locally
- Install dependencies and run tests via test.sh (see root script)
- Package and build with python3 -m build
- Run the CLI demo to observe planning behavior

This repository is designed to be extended in sprint fashion; it starts with a solid core, test coverage, and a path to full production-grade production code.

Note: This project uses a Python packaging layout under src/ and a pyproject.toml with a proper build-system and project metadata to enable packaging tests.