Qyvaria

Qyvaria OS • Qyvaria AI Software • qyvaria.py Python Kernel • Multi-Agent AI Engineering Runtime

Qyvaria is an open-source AI engineering project created by Jan Havlasek. This repository is the public home for Qyvaria OS, Qyvaria AI Software, and the qyvaria.py single-file Python kernel bundle.

Qyvaria is a long-running software and research effort focused on prompt generation, AI-engine orchestration, multi-agent simulation, research tooling, voice/runtime experiments, local AI workflows, and public technical documentation.

Qyvaria OS, Qyvaria AI Software, and the qyvaria.py kernel corpus are released under the Apache License 2.0, unless a specific file, package, bundled dependency, or third-party component states otherwise.

Important links

GitHub repository: John6Havlasek/Qyvaria
Hugging Face repository: John6Havlasek/Qyvaria
License: Apache License 2.0

What Qyvaria is

Qyvaria is best understood as a Python-based AI software/kernel bundle rather than a conventional Hugging Face Transformer checkpoint.

The project focuses on:

AI prompt generation and prompt-structure engineering
Multi-agent architecture and orchestration concepts
AI runtime/kernel-style organization
Simulation and evaluation tooling
Local-first AI engineering workflows
Voice, tool, and interface experiments
Research documentation and reproducible proof artifacts

The central artifact is qyvaria.py, a single-file Python bundle intended to carry a large AI-engineering corpus with runtime metadata, embedded files, source integrity information, and CLI-accessible functionality.

What this repository contains

This repository may include:

qyvaria.py — the Qyvaria single-file Python kernel bundle
Documentation about the Qyvaria software architecture
Technical evidence/proof material
Prompt generation examples
Runtime and orchestration notes
Release notes, roadmaps, or public verification files

Because Qyvaria is a software/runtime project, this repository may not behave like a standard model repository with .safetensors, config.json, tokenizer files, or an automatic Inference Provider widget.

Quick start

Clone the repository:

git clone https://huggingface.co/John6Havlasek/Qyvaria
cd Qyvaria

Inspect the bundle:

python qyvaria.py --stats

Example Qyvaria prompt-generation command pattern:

python qyvaria.py --subject "futuristic tram in Prague" --style cinematic --engine sdxl -n 4

Expanded command pattern:

python qyvaria.py --subject "isometric eco-city block" --style pixel --engine generic -n 4 --seed 42

Qyvaria command format

A typical Qyvaria command follows this structure:

python qyvaria.py --subject "{subject}" --style {style} --engine {engine} -n {count} --seed {seed}

Supported engine-style targets may include:

sdxl
midjourney
flux
generic

Common Qyvaria prompt fields include:

subject
style
environment
action
mood
palette
materials
camera
composition
lighting
scale
post-processing
negatives
engine
persona
seed

Intended use

Qyvaria is intended for:

AI prompt generation
AI workflow design
Local AI engineering experiments
Multi-agent software research
Runtime/orchestration experiments
Documentation of AI software architecture
Technical proof-of-work and public reproducibility material

Qyvaria is especially useful for users who want to study or build around a single-file Python AI-engineering bundle and its associated software concepts.

Not intended use

Qyvaria should not be described as any of the following unless independently verified by tests and public benchmarks:

A production operating system kernel
A general artificial intelligence
A certified safety-critical AI system
A drop-in replacement for a standard Hugging Face Transformer model
A fully benchmarked leaderboard model without published evaluation evidence

This repository should make a clear distinction between:

What the source code contains
What has been executed and verified
What is planned or experimental
What is a long-term research direction

Technical verification goals

Public proof for Qyvaria should focus on reproducible evidence such as:

Source hash verification
Reproducible command outputs
Static compile checks
Unit tests
Runtime smoke tests
Agent simulation benchmarks
Performance/overhead comparisons
Security and dependency scans
Release artifacts with checksums

Recommended public proof files:

checksums.txt
verification_report.md
benchmark_report.md
static_audit_report.md
runtime_smoke_test.md
reproducibility_commands.md

Benchmarking and leaderboard proof

GitHub stars, forks, Hugging Face likes, and downloads are useful adoption signals, but they are not technical proof by themselves.

For stronger proof, Qyvaria should publish:

Benchmark methodology
Test environment
Hardware and software versions
Input datasets or generated test cases
Exact commands used
Raw outputs
Failure cases
Comparison baselines
Independent reproduction instructions

Suggested benchmark areas:

Area	What to measure
Bundle integrity	Number of embedded files verified by checksum
Static correctness	Number of Python files that compile
Runtime startup	Time to load, inspect, or run commands
Prompt generation	Determinism under fixed seed
Multi-agent simulation	Memory use, latency, coordination overhead
Safety tooling	Policy blocks, sandbox results, provenance logs
Documentation quality	Reproducible instructions and public artifacts

Limitations

Qyvaria is an evolving AI software project. Users should expect parts of the repository to be experimental.

Known categories to verify before production use:

Dependency availability
CLI entry-point behavior
Syntax compatibility across Python versions
Import layout and packaging
Test coverage
Security hardening
Benchmark reproducibility
License compatibility of bundled or third-party components

Responsible release statement

Qyvaria should be evaluated with honest engineering standards. Strong claims should be attached to reproducible evidence. Experimental architecture claims should be labeled as experimental until they are independently tested.

The goal of this repository is to make Qyvaria public, searchable, understandable, reproducible, and open to technical review.

Citation

If you reference Qyvaria, cite the public repository:

@software{qyvaria,
  title = {Qyvaria OS and Qyvaria AI Software},
  author = {Jan Havlasek},
  url = {https://github.com/John6Havlasek/Qyvaria},
  license = {Apache-2.0},
  year = {2025}
}

License

Released under the Apache License 2.0, unless a specific file, package, bundled dependency, or third-party component states otherwise.

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support