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MatrAIx is a population-scale, persona-driven infrastructure for evaluating AI systems and interactive products with heterogeneous simulated users. Instead of testing against a generic or interchangeable user, MatrAIx instantiates sampled persona records as LLM agents and runs them through reproducible tasks across four environments — Survey, AI Chatbot, Web, and App (native desktop and mobile, including macOS and iOS).

At its foundation is a shared schema of 1,290 categorical dimensions covering background, psychology, capability, and behavior. Personas combine dependency-aware synthetic generation with evidence-aware human grounding; a deterministic, quality-filtered coreset of one million personas is released for research on Hugging Face. Shared telemetry, task-owned verification, and reporting connect individual responses and trajectories to subgroup- and population-level findings.

The name nods to The Matrix: a simulated world useful for exploration, stress testing, and hypothesis generation, not a replacement for evidence from real people.

News & Recognition

• Academic commentary — Can We Simulate the World? — Mayank Kejriwal, AI Scientist
• Research selection — Featured on Hugging Face Papers (Daily Papers, 2026-08-10)
• Media — NZZ am Sonntag · AI Era (via 36Kr) · Numerama · Infobae · AI타임스 · Startup Fortune · Forbes Türkiye · WIRED Czech
• Industry commentary — Discussed by Cisco VP & CTO Gianpaolo Barozzi
• Social — Featured as an X Trending Story

Releases

• [2026-08-04] Technical report on arXiv: MatrAIx: Simulating the World with 8.3 Billion Persona Agents (2608.04205).
• [2026-08-01] Released Persona 1M on Hugging Face (~1M quality-filtered personas).
• [2026-07-31] Open-sourced the Playground and task library: MatrAIx-Persona-8B.
• [2026-07-29] Position note: From Personas to Simulated Users.

Requirements

• Docker — needed for Web and OS-app tasks
• uv and Python 3.12
• Node.js 20+ (Playground / viewer frontends only)
• Model API keys for real persona runs — see agents.md (the install checks below do not need a key)

Windows users: run everything inside WSL2 — open PowerShell, run wsl --install (installs Ubuntu), then clone this repo inside the WSL filesystem (e.g. ~/MatrAIx, not /mnt/c/…, which is much slower) and enable WSL integration in Docker Desktop → Settings → Resources. Every command in this README then works exactly as written. Native PowerShell/cmd is not supported (the task verifiers require bash).

Installation

git clone <repo-url> && cd MatrAIx
uv venv --python 3.12
uv pip install -e .
uv pip install pytest pytest-asyncio httpx
uv pip install -e packages/playground
uv pip install -e packages/harbor-langsmith
uv pip install -e packages/rewardkit

Run jobs with uv run matraix run …. After install, use the smoke tests below to confirm Survey, Chat, Web, and OS-app are ready (no API key). Summarize a finished job with uv run matraix results <job>. Advanced runtime tools stay under uv run harbor ….

Set a model API key before real GUI or CLI runs (smoke checks do not need one):

export ANTHROPIC_API_KEY="sk-ant-..."   # anthropic/claude-* models
# export OPENAI_API_KEY="sk-..."        # openai/gpt-* models

See agents.md for the full key matrix. Playground can also load keys from application/playground/.env.local.

Import Persona 1M (recommended)

The in-repo matraix-persona-dev-sample (~200) is for smoke only. For real cohorts and Playground sampling, import the public 1M coreset:

huggingface-cli download MatrAIx2026/MatrAIx_Persona_1M_Public_Release \
  --repo-type dataset \
  --local-dir persona/datasets/matraix-persona-1m/release

Playground: Dataset → matraix-persona-1m. CLI: --dataset persona/datasets/matraix-persona-1m. Details: Handbook § Persona 1M.

Quick start

Smoke tests

Two quick checks after install — no API key. Together they cover the default path for all four task types (Survey, Chat, Web, OS-app):

The first finishes in seconds and should print Smoke: ok. The second builds a small local image on first run (a few minutes), then writes under jobs/harbor-smoke-local/. Step-by-step: quickstart §3.

GUI task runs

Playground picks tasks, samples personas, and launches the same Matraix Playground jobs as CLI auto mode. Start API + frontend (two terminals):

# Terminal A — API
VENV=.venv bash application/playground/backend/run_dev.sh

# Terminal B — frontend
cd application/playground/frontend && npm ci && npm run dev

Open http://localhost:5173 → Playground → pick a persona cohort → pick Survey / Chat / Web / OS app tasks → Lock pipeline → Run eval. Details: Playground §10.

CLI task develop / runs

Develop — copy a reference task under application/tasks/, edit task.toml / instruction.md / input/ / verifier, then register it for Playground (task-guide.md):

cp -R application/tasks/example-survey_product-feedback \
  application/tasks/<your-task-name>

Run — generate a Matraix Playground job (pins agent + model), then execute it:

uv run python application/scripts/generate_application_job.py \
  --task application/tasks/example-survey_product-feedback \
  --execution-mode auto \
  --persona-ids 0042 \
  --model-name anthropic/claude-sonnet-4-6

# Use the export lines + recipe path the script prints, e.g.:
uv run matraix run -c configs/jobs/application-task-job-recipe/example-survey-product-feedback-auto-n1.yaml

Batch (--sample-size N), filters, and chat / web / os-app examples: docs/quickstart.md.

Docs

MatrAIx Handbook — guides, persona / application / environment docs.

Repository layout

MatrAIx/
├── persona/                 Schema, datasets, synthesis/curation/validation pipelines
│   ├── schema/              1,290-dimension persona schema
│   ├── datasets/            Dev sample pool and persona YAMLs
│   ├── validation/          Grounding / quality validation suites
│   └── scripts/             Persona job & pipeline helpers
├── application/
│   ├── tasks/               Survey · chat · web · os-app task specs
│   ├── task-spec/           Shared task contracts
│   ├── playground/          Visual runner (backend API + frontend)
│   └── scripts/             generate_application_job.py and task tooling
├── environment/
│   ├── runtime/             Matraix Playground runtime
│   ├── agents/              Persona-conditioned agents
│   ├── task-environments/   Docker images / sidecars
│   └── adapters/            External adapters (e.g. SimpleQA)
├── packages/                playground · rewardkit · harbor-langsmith
├── apps/viewer/             Frontend paired with `harbor view`
├── configs/jobs/            Curated & generated Matraix Playground job recipes
├── docs/                    Handbook — persona/ · application/ · environment/
├── examples/                Minimal example tasks
├── src/matraix/             Python package entrypoints
├── scripts/                 Repo-level helpers
├── tests/                   Unit / environment tests
└── jobs/                    Local Matraix Playground run outputs (gitignored)

Large generated datasets stay outside git (see the Hugging Face release above).

Join the Community

1. Join Discord — nickname Full Name - Affiliation. Fill the Google Form (background, interests, paper authorship / acknowledgements).
2. Say hi to us! We like to connect you for the shared interest or experience!
3. Participating MatrAIx research community for collaboration or contribution!

Citation

If you use MatrAIx, the Persona 1M dataset, or results from this repository, please cite:

@article{li2026matraix,
  title         = {MatrAIx: Simulating the World with 8.3 Billion Persona Agents},
  author        = {Li, Xiaomin and Hao, Yuexing and Hou, Jianheng and Huang, Jintao
                   and Wen, Qianfeng and Huang, Shirley and Liu, Yifan and Liu, Xiaoyi
                   and Fan, Yilan and Wang, Yijun and others},
  year          = {2026},
  eprint        = {2608.04205},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2608.04205}
}

Paper: arXiv:2608.04205 · Full authors: GitHub Cite this repository (CITATION.cff) · Dataset: Persona 1M on Hugging Face.

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License

MIT — see LICENSE.