AetherLabsAI/Video2World
Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos - Rethinking Embodied Real2Sim from a Software Engineering Perspective
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+10
STARS · LAST 30 DAYS
1
PER DAY
#1,838
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
Video2World gained 10 stars in the last 30 days, about 1 a day, and now has 113. It is about 1 year old and has averaged roughly 113 stars a year. It ranks #1,838 among Python repositories and #14,477 across all languages on GitHubRepo.
Trending Record
4
DAYS ON TRENDING
#1270
BEST RANK
Oct 9, 2026
FIRST APPEARANCE
Active
STATUS TODAY
Video2World has maintained a continuous presence across global trending indexes, peaking at #1270. Below is the 30-day activity profile:
💡 Overview
Video2World is an open-source project written in Python: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos - Rethinking Embodied Real2Sim from a Software Engineering Perspective.
Engineered for speed, consistency, and developer ease, it solves common hurdles in benchmark, coding-agent, embodied-ai. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install Video2World⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
Video2World coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with Video2World.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install Video2World using your package manager: `pip install Video2World`
Initialize your project workspace or configuration file for Video2World.
Import Video2World into your codebase or invoke it directly from your terminal.
Execute your test suite or run `Video2World --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
AetherLabsAI/Video2World is currently ranked #14,477 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #14,416 | d4ncboz/museai | Python | ★ 114 | Compare ↗ |
| #14,416 | UniWAM/UniWAM | Python | ★ 114 | Compare ↗ |
| #14,416 | veda-sparse/Veda-on-ComfyUI | Python | ★ 114 | Compare ↗ |
| #14,416 | fatihaydost/brand-identity-skill | Python | ★ 114 | Compare ↗ |
| #14,416 | Zihan-Su/Self_Gradient_Forcing_Plus | Python | ★ 114 | Compare ↗ |
| #14,477 | AetherLabsAI/Video2World This Project | Python | ★ 113 | |
| #14,477 | robmckinnon/rugalytics | Ruby | ★ 113 | Compare ↗ |
| #14,477 | cpjolicoeur/bb-ruby | Ruby | ★ 113 | Compare ↗ |
| #14,477 | tuulos/ringo | Erlang | ★ 113 | Compare ↗ |
| #14,477 | balinterdi/i15r | Ruby | ★ 113 | Compare ↗ |
| #14,477 | nitrogen/simple_bridge | Erlang | ★ 113 | Compare ↗ |
Frequently Asked Questions
What does Video2World do? +
Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos - Rethinking Embodied Real2Sim from a Software Engineering Perspective
What language is Video2World written in? +
The primary language is Python. Topics include: benchmark, coding-agent, embodied-ai, foundation-models, real-to-sim.
Is Video2World actively maintained? +
Yes, the last recorded push was on Oct 6, 2026 with 0 open issues being tracked.
How many stars does Video2World have? +
Video2World has 113 stars and 0 forks on GitHub.
How does Video2World rank among GitHub repositories? +
With 113 stars, AetherLabsAI/Video2World is ranked #14,477 globally across all repositories tracked on GitHubRepo and #1,838 among Python projects.
What license is Video2World distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
