Thank you for your interest. Agent Hypervisor is an early-stage proof-of-concept exploring an architectural approach to AI agent security. At this stage, conceptual feedback is the most valuable contribution.
- Is the "reality virtualization" abstraction compelling?
- Are there fundamental flaws in the approach?
- What attack vectors does this not address?
- How does this compare to your security approach?
Open a Discussion.
- Clarifications to existing docs
- Additional examples or analogies
- Better diagrams
- Typo fixes
Open a Pull Request.
- Integration with agent frameworks (LangChain, LangGraph, raw API)
- Additional demo scenarios
- Physics law implementations
Add to the examples/ directory via Pull Request.
- "I would use this for..."
- "This would solve my problem with..."
- "This doesn't address..."
Open an Issue with the use-case label.
- ❌ Production-readiness improvements (it's a PoC)
- ❌ Enterprise feature requests
- ❌ Performance optimizations
Focus now is on validating the architectural approach.
git clone https://github.com/sv-pro/agent-hypervisor.git
cd agent-hypervisor
pip install pyyaml pytest
pytest # Run tests
python3 demo_scenarios.py # Run demo- Python 3.8+, type hints on all public APIs
- Docstrings that explain why, not just what
- Every safety property must be expressible as a deterministic unit test
- Keep it minimal — this is a PoC, not a framework
When contributing code, keep these in mind:
- Deterministic — no LLM calls or probabilistic logic in the evaluation path
- Educational — code should teach the concept to a new reader
- Testable — every Hypervisor decision must be reproducible in a unit test
- Minimal — resist abstractions not needed for the current proof-of-concept
Start a Discussion.