LibreAI / AI Agents / genesis-world
// agents · Genesis-Embodied-AI/genesis-world

genesis-world

A Python simulation platform for robotics and embodied AI — fast physics, rendering and learning

★ 30.0k stars⑂ 2.9k forksApache-2.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

genesis-world is an open-source ai agents project by Genesis-Embodied-AI (Genesis-Embodied-AI/genesis-world on GitHub). It has 30,035 GitHub stars and 2,878 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 8 October 2026.

GitHub stars
30,035
Forks
2,878
License
Apache-2.0
Last updated on GitHub
8 October 2026
Category
AI Agents
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Clarify your goal

    Decide what you want to achieve with genesis-world and check the README on GitHub for requirements before installing.

  2. Get the code

    Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).

  3. Install and configure

    Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.

  4. Run your first task

    Work through the README's quickstart example end to end, then adapt it to your own project or data.

// features

Why people love it

Multiple physics solvers

A catalogue of solvers for rigid bodies, fluids, soft bodies and more, per the README's physics catalogue.

Built-in rendering

Rendering options for visualizing simulations and generating training imagery.

Python simulation interface

A clean Python API for building scenes, adding robots and stepping the simulation.

Optional extras

Installable extras add solvers and renderers (for example pyuipc for the IPC solver, gs-nyx for the Nyx renderer).

GPU and CPU paths

Documented install paths for NVIDIA CUDA, CPU-only and Apple Silicon (Metal/MPS) via uv/PyTorch builds.

Research-ready

Ships with citation info and is used as a platform for embodied-AI learning research.

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// run it

Run it yourself

  1. Clone the repository

    Get the Genesis source for the development/editable route; most users pip-install instead.

    git clone https://github.com/Genesis-Embodied-AI/genesis-world.git
    cd genesis-world
  2. Install Genesis

    Install the released package (requires Python >=3.10, <3.14).

    pip install genesis-world
  3. Build your first scene

    Follow the documentation quickstart to create a scene, add a robot and step the simulation in Python.

    # see https://genesis-world.readthedocs.io for the first-scene example code
// use cases

What you can build

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// faq

Questions, answered

Who maintains genesis-world?

genesis-world is maintained by the Genesis-Embodied-AI organization/account on GitHub, together with open-source contributors, in the public repository Genesis-Embodied-AI/genesis-world.

Is genesis-world free to use?

Yes — the code is open source under the Apache-2.0 license. You can use, modify and redistribute it under that license's terms.

Does genesis-world need a paid API key?

No API key — Genesis is a local simulation platform. You need suitable hardware (an NVIDIA GPU is recommended for larger scenes) and PyTorch.

How does genesis-world compare to MuJoCo / Isaac Sim?

Genesis aims for a unified, Pythonic platform spanning many physics types and renderers, positioning itself as an accessible all-in-one alternative to single-purpose simulators.

How do I get started with genesis-world?

Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.

// keep exploring

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// alternatives

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Get genesis-world

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