HyperMachine

Agentic hypervisors for autonomous AI systems.

A high-performance hypervisor framework in Rust with first-class AI agent support. Type-1 bare-metal and Type-2 hosted modes.

Features

Category	Capabilities
Performance	Zero-copy memory, JIT compilation, hardware GPU virtualization
Networking	Full TCP/IP stack, TAP/TUN, gRPC/REST APIs, distributed orchestration
GPU	Vulkan/WebGPU, passthrough, virtual GPU, CUDA/OpenCL
AI-First	MCP server, scriptable API, WASM plugins, LLM tool formats
GUI	Desktop app, AI-driven automation, semantic control API
Security	FIPS-approved + post-quantum crypto, capability-based access, audit logging

Benchmarks

Measured with Criterion on an AMD Ryzen 9 9900X (reproduce with cargo bench):

Benchmark	Median	What it measures
Agent spawn — CoW clone	~9 ns (O(1))	fork a sandbox from a warm baseline; constant at 1 / 16 / 64 baseline units (8.9 / 9.3 / 9.1 ns). A full copy is 206 µs → 9.3 ms and grows with size — so 64 agents cost ~one baseline, not 64.
CoW first-write fault	73 ns	the per-page copy paid once, when a clone first dirties a page
Guest memory read / write	27–96 ns / 9–19 ns	64 B – 4 KiB accesses (zero-copy mapping)
MCP tool dispatch	547 ns	one agent tool call (`vm.list`) end-to-end
MCP dispatch ×64 (concurrent)	47 µs (~0.7 µs/call)	concurrent agent tool calls over the dispatch path
Snapshot — vCPU regs / 10 devices	18 ns / 21 ns	serialize control registers + device state
Tool-schema projection (OpenAI)	3.3 µs	render the MCP registry to an LLM tool format
AES-256-GCM (`ring`, AES-NI)	~9–10 GiB/s	crypto throughput — see Performance

The defining number is O(1) agent spawn: a copy-on-write clone is ~9 ns regardless of fleet size, so 100 idle agents cost roughly one baseline's memory rather than 100 — the foundation of the agent runtime's fleet density.

1. Agentic-First Virtualization

HyperMachine is the first hypervisor designed from the ground up for AI agent workloads. Every VM is an MCP-addressable resource: agents discover capabilities via ontology endpoints, invoke typed tools (vm.create, vm.exec, gpu.reserve), and receive structured results — no shell scraping or brittle CLI wrappers. Multi-LLM tool schemas ship built-in for OpenAI, Anthropic, and Google formats.

A built-in agent runtime turns this into a fleet service: agents spawn in O(1) as copy-on-write clones of a warm baseline (100 idle agents cost ~one baseline, not 100), run tool-calling loops over a fast MCP dispatch path, and have their sessions and memory reclaimed automatically. It is exposed over a tenant-scoped, optionally-authenticated REST API (/api/v1/agents).

2. Dual-Mode Architecture (Type-1 + Type-2)

A single codebase runs as both a Type-2 hosted hypervisor (KVM, WHPX, HVF) and a Type-1 bare-metal hypervisor (Intel VMX, AMD SVM) with no code duplication. The same VM definitions, device models, and API surface work in both modes — develop on your laptop, deploy bare-metal in production.

3. GPU Fabric with Topology-Aware Placement

HyperMachine models GPU interconnect topology (NVLink, NVSwitch, PCIe) and makes placement decisions based on real bandwidth and latency. Capacity reservations with SLA tiers (platinum/gold/silver/bronze) prevent noisy-neighbor GPU contention. Fleet-wide GPU health monitoring tracks utilization, temperature, and ECC errors across hosts.

4. Post-Quantum Cryptography

Alongside classical FIPS-approved algorithms (AES-GCM, RSA, ECDSA), HyperMachine ships ML-KEM (Kyber) for key encapsulation, ML-DSA (Dilithium) for digital signatures, and SLH-DSA (SPHINCS+) for hash-based signatures — all NIST-standardized, quantum-resistant, and backed by the audited pure-Rust RustCrypto implementations (not placeholders).

5. Pure Rust, Zero Unsafe in Business Logic

~238,000 lines of Rust across 13 crates with zero todo!(), unimplemented!(), or placeholder stubs. The full stack — from bare-metal boot sequence to REST middleware to GPU scheduling — is implemented in safe Rust. 4,600+ tests and cargo clippy -D warnings clean. Security advisories in the active dependency graph are triaged in deny.toml (one accepted with justification: the rsa crate's Marvin-attack timing advisory, for which no fixed release exists).

6. Semantic GUI Automation

AI agents control the desktop GUI through a typed command API (gui.navigate, gui.dialog.open, gui.form.set_field) rather than pixel-based screen scraping. This is deterministic, resolution-independent, and orders of magnitude faster than vision-based approaches like screen capture automation.

7. Enterprise API Middleware Stack

The REST API ships with 28 composable middleware layers out of the box: rate limiting, circuit breakers, request replay protection, tenant isolation, geo-IP enrichment, W3C trace propagation, HMAC payload signing, schema validation, slow-request detection, maintenance mode, and more — all configurable, all tested.

Architecture

+----------------------------------------------------------+
|                   AI Agent Interface                     |
|            (MCP Server - OpenAI/Claude/Gemini)           |
+----------------------------------------------------------+
|                    Remote API Layer                      |
|                  (gRPC - REST - WebSocket)               |
+-------------+-------------+-------------+----------------+
|     CPU     |     GPU     |   Network   |     Memory     |
|  Emulation  |   Vulkan    |   TAP/TUN   |   Management   |
+-------------+-------------+-------------+----------------+
|                   HyperMachine Core                      |
|       HV2 (KVM/WHPX/HVF) - HV1 (VMX/SVM bare-metal)      |
+----------------------------------------------------------+

Requirements

Rust 1.87+ (stable) for Type-2 crates; nightly for Type-1 (hv1-core, hv1-boot)
Hypervisor backend (Type-2 mode): KVM (Linux), WHPX (Windows), or HVF (macOS)
protoc (Protocol Buffers compiler) for building gRPC components

Quick Start

# Build (excludes nightly-only Type-1 crates)
git clone https://github.com/nervosys/HyperMachine && cd HyperMachine
cargo build --release --workspace --exclude hv1-core --exclude hv1-boot

# Create and run a VM (Type-2 hosted mode)
hm t2 create --name myvm --cpu 4 --memory 8G --gpu
hm t2 start myvm

# Start MCP server for AI agents
hm mcp serve --api-key "your-key"

AI Agent Integration

HyperMachine exposes a Model Context Protocol (MCP) server for AI agents:

# Discover available tools
curl http://localhost:8080/mcp/tools

# LLM-specific tool formats
curl http://localhost:8080/agentic/tools/openai     # GPT-4o, o1, o3
curl http://localhost:8080/agentic/tools/anthropic  # Claude 4, Sonnet
curl http://localhost:8080/agentic/tools/gemini     # Gemini 2.5

# Execute operations
curl -X POST http://localhost:8080/mcp/call \
  -H "Authorization: Bearer your-key" \
  -d '{"tool": "vm.create", "arguments": {"name": "ai-sandbox", "cpu_cores": 4}}'

Agent runtime API

Spawn and operate a fleet of copy-on-write agents over HTTP. Each agent gets an MCP session plus an O(1) sandbox cloned from a warm baseline. Requests are tenant-scoped via X-Tenant-Id (an agent is owned by the tenant that spawned it), with an optional Authorization: Bearer <token>:

# Spawn a tenant-scoped agent (O(1) CoW sandbox from the warm baseline)
curl -X POST http://localhost:8080/api/v1/agents \
  -H "X-Tenant-Id: acme" -H "Content-Type: application/json" \
  -d '{"agent_id": "researcher", "capabilities": "operator"}'

curl http://localhost:8080/api/v1/agents        -H "X-Tenant-Id: acme"  # list
curl http://localhost:8080/api/v1/agents/fleet  -H "X-Tenant-Id: acme"  # fleet memory
curl -X POST http://localhost:8080/api/v1/agents/reap -d '{"max_idle_secs": 600}'
curl -X DELETE http://localhost:8080/api/v1/agents/<session_id> -H "X-Tenant-Id: acme"

Python SDK (planned — not yet shipped in this repository):

from hypermachine import HyperMachine

hm = HyperMachine("http://localhost:8080", api_key="your-key")
vm = hm.create_vm("sandbox", cpu=4, memory="8G", gpu=True)
vm.start()
vm.exec("echo 'Hello from AI agent'")

Runnable examples

These compile and run today (cargo run -p <crate> --example <name>):

Example	Crate	Shows
`agent_mcp_workflow`	`hv2-agent`	An agent driving a full VM lifecycle over the MCP tool surface (provision → boot → `guest.exec` → snapshot → resize → restore → teardown) with an audit log
`llm_tool_schemas`	`hv2-agent`	The MCP tool registry projected into OpenAI / Anthropic / Gemini tool-use formats
`agent_vm_workflow`	`hm-cli`	Tool discovery + VM lifecycle through the typed `ToolExecutor` and agentic ontology
`multi_agent_orchestration`	`hv2-agent`	Multiple role-scoped agents coordinating: exclusive VM claims, role enforcement, inter-agent messaging
`agent_runtime`	`hv2-agent`	End-to-end agent runtime: 100 agents spawned from one warm baseline (O(1), ~100× memory density), kept isolated, calling tools, then reclaimed
`gpu_fabric_reservation`	`hv2-runtime`	Publishing a GPU VM class and reserving capacity with SLA tiers via `CapacityManager`
`agent_script` / `integrated`	`hv2-agent`	Rhai-scripted agent decision-making and agent↔device (serial/MMIO) interaction

cargo run -p hv2-agent --example agent_mcp_workflow

GUI Automation

HyperMachine includes a desktop GUI with semantic automation API for AI agents:

use hm_gui::{AutomationHandle, GuiCommand, DialogType, FormType};

// Create automation handle
let (handle, receiver) = AutomationHandle::new();

// AI agent controls the GUI
handle.open_dialog(DialogType::CreateVm)?;
handle.set_field(FormType::CreateVm, "name", "ai-sandbox")?;
handle.set_field(FormType::CreateVm, "cpus", 4)?;
handle.set_field(FormType::CreateVm, "memory_mb", 8192)?;
handle.execute(GuiCommand::SubmitDialog(DialogType::CreateVm))?;

Available GUI Tools (13 total):

Tool	Description
`gui.navigate`	Navigate views (welcome, vm_details, console, settings)
`gui.dialog.open/close/submit`	Manage dialogs (create_vm, settings, about)
`gui.vm.select`	Select VM by id, name, or partial match
`gui.vm.action`	VM operations (start, stop, pause, delete, console)
`gui.form.set_field`	Set form values programmatically
`gui.get_state`	Query current GUI state

LLM JSON Commands:

{"type":"OpenDialog","params":"create_vm"}
{"type":"SetFormField","params":{"form":"create_vm","field":"name","value":"my-vm"}}
{"type":"SubmitDialog","params":"create_vm"}

This semantic approach is superior to screen-based automation (like Anthropic Computer Use) because it is deterministic, fast, and layout-independent.

Cryptography

Implementations of FIPS-approved classical algorithms plus the NIST post-quantum schemes. Classical AES-GCM/SHA come from the ring backend, RSA from the pure-Rust rsa crate, and the post-quantum schemes from RustCrypto (ml-kem, ml-dsa, slh-dsa). These are validated algorithm implementations, not a FIPS 140-3 validated module.

Type	Algorithms
Symmetric	AES-256-GCM, SHA-256/384/512, HMAC, HKDF
Asymmetric	RSA-2048/3072/4096, ECDSA P-256/P-384¹
Post-Quantum	ML-KEM (Kyber), ML-DSA (Dilithium), SLH-DSA (SPHINCS+)

Deployment

GPU Fabric API

HyperMachine provides a GPU Fabric REST API for topology-aware GPU placement and fleet management:

# Query GPU topology
curl http://localhost:8080/api/v1/gpu-fabric/topology

# List fleet hosts
curl http://localhost:8080/api/v1/gpu-fabric/fleet

# Check capacity for GPU workloads
curl -X POST http://localhost:8080/api/v1/gpu-fabric/capacity/check \
  -H "Content-Type: application/json" \
  -d '{"gpu_count": 4, "min_vram_mb": 40960, "interconnect": "NvLink"}'

# Reserve GPU capacity
curl -X POST http://localhost:8080/api/v1/gpu-fabric/capacity/reserve \
  -H "Authorization: Bearer your-key" \
  -d '{"gpu_count": 8, "sla_tier": "Premium", "duration_secs": 3600}'

Kubernetes / Terraform

# Kubernetes
helm install hypermachine ./deploy/helm/hypermachine \
  --set environment=production \
  --set replicaCount=3

# Terraform (AWS EKS)
cd deploy/terraform && terraform apply -var="environment=production"

Performance

Crypto throughput from crypto_bench on an AMD Ryzen 9 9900X (64 KiB blocks). AES-GCM and SHA run on the validated ring backend (AES-NI hardware acceleration), which is enabled by default:

Operation	Throughput
AES-256-GCM encrypt	~9.0 GiB/s
AES-256-GCM decrypt	~10.1 GiB/s
SHA-256	~2.5 GiB/s
SHA-512	~0.76 GiB/s
HMAC-SHA256	~2.3 GiB/s

cargo bench -p hv2-core --bench crypto_bench

Numbers are hardware- and backend-dependent. AES-GCM is AES-NI accelerated; SHA throughput reflects ring's software implementation on this CPU. Run the command above to reproduce on your own hardware.

Project Structure

crates/
  hm-cli      # CLI + MCP server
  hm-gui      # Desktop GUI with AI automation API
  hv1-arm     # ARM64 EL2 hypervisor backend (127 tests)
  hv1-boot    # Type-1 bare-metal bootloader (nightly)
  hv1-core    # Type-1 bare-metal hypervisor core (nightly)
  hv2-core    # Core engine (CPU, memory, devices, crypto)
  hv2-cpu     # CPU virtualization and instruction decoding
  hv2-gpu     # GPU virtualization (Vulkan/WebGPU, passthrough)
  hv2-net     # Networking (TCP/IP stack, TAP/TUN, virtio-net)
  hv2-agent   # AI agent interface (MCP, WASM plugins)
  hv2-api     # REST/gRPC API server + GPU Fabric endpoints
  hv2-cli     # Standalone hypervisor CLI
  hv2-runtime # Fleet runtime, scheduler, GPU observability
deploy/
  k8s/        # Kubernetes manifests
  helm/       # Helm charts
  terraform/  # Infrastructure as code

Documentation

Getting Started — Installation, prerequisites, first VM
API Quickstart — REST/gRPC API reference and examples
Architecture — System design and internals
Deployment Guide — Production deployment on Kubernetes/Terraform
GPU Virtualization — Vulkan/WebGPU passthrough and virtual GPU
Guest Programming Guide — Writing code for guest VMs

Troubleshooting

Problem	Solution
`KVM not available`	Enable VT-x/AMD-V in BIOS; `modprobe kvm_intel` or `kvm_amd`
`WHPX not found` (Windows)	Enable "Windows Hypervisor Platform" in Windows Features
`protoc not found`	Install protobuf compiler: `apt install protobuf-compiler` or `brew install protobuf`
Build fails on nightly crates	Type-1 crates require nightly; use `--exclude hv1-core --exclude hv1-boot`
Permission denied on `/dev/kvm`	Add user to kvm group: `sudo usermod -aG kvm $USER`

Contributing

See CONTRIBUTING.md for development setup, coding standards, and PR process.

License

This project is dual-licensed:

AGPL-3.0 (GNU Affero General Public License v3) — free and open source with strong copyleft. See LICENSE.
Commercial License — available for use without AGPL obligations. See LICENSE-COMMERCIAL.

For commercial licensing inquiries, contact licensing@nervosys.ai.

ECDSA P-521 keys can be represented but key generation and signing require a backend other than ring, which does not support that curve. ↩

Name		Name	Last commit message	Last commit date
Latest commit History 169 Commits
.github		.github
benchmarks		benchmarks
crates		crates
deploy		deploy
docs		docs
examples		examples
fuzz		fuzz
img		img
tools		tools
.editorconfig		.editorconfig
.gitattributes		.gitattributes
.gitignore		.gitignore
.mailmap		.mailmap
CHANGELOG.md		CHANGELOG.md
CLA.md		CLA.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
Containerfile		Containerfile
GETTING_STARTED.md		GETTING_STARTED.md
LICENSE		LICENSE
LICENSE-COMMERCIAL		LICENSE-COMMERCIAL
README.md		README.md
ROADMAP.md		ROADMAP.md
SECURITY.md		SECURITY.md
clippy.toml		clippy.toml
config.example.toml		config.example.toml
deny.toml		deny.toml
justfile		justfile
rust-toolchain.toml		rust-toolchain.toml
rustfmt.toml		rustfmt.toml

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HyperMachine

Features

Benchmarks

1. Agentic-First Virtualization

2. Dual-Mode Architecture (Type-1 + Type-2)

3. GPU Fabric with Topology-Aware Placement

4. Post-Quantum Cryptography

5. Pure Rust, Zero Unsafe in Business Logic

6. Semantic GUI Automation

7. Enterprise API Middleware Stack

Architecture

Requirements

Quick Start

AI Agent Integration

Agent runtime API

Runnable examples

GUI Automation

Cryptography

Deployment

GPU Fabric API

Kubernetes / Terraform

Performance

Project Structure

Documentation

Troubleshooting

Contributing

License

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HyperMachine

Features

Benchmarks

1. Agentic-First Virtualization

2. Dual-Mode Architecture (Type-1 + Type-2)

3. GPU Fabric with Topology-Aware Placement

4. Post-Quantum Cryptography

5. Pure Rust, Zero Unsafe in Business Logic

6. Semantic GUI Automation

7. Enterprise API Middleware Stack

Architecture

Requirements

Quick Start

AI Agent Integration

Agent runtime API

Runnable examples

GUI Automation

Cryptography

Deployment

GPU Fabric API

Kubernetes / Terraform

Performance

Project Structure

Documentation

Troubleshooting

Contributing

License

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