# LingBot-World

Infinite Worlds with Versatile Interactions

Repository: https://github.com/Robbyant/lingbot-world-v2
Canonical: https://ross.abutalabs.com/products/lingbot-world
Homepage: https://technology.robbyant.com/lingbot-world-v2
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-07-14T14:04:37+00:00
Link (homepage): https://technology.robbyant.com/lingbot-world-v2
Link (site_page): https://www.robbyant.com/about-robby

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 92, release rhythm 35, longevity 4
- inputs: {"age_days": 56, "days_push": 50, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1560, forks 111 (observed 2026-08-28T04:05:03.698841+00:00)

## What it is
LingBot-World is an open-source world model that generates high-fidelity, interactive simulated environments from video generation, supporting real-time interaction at 16 fps with sub-second latency and minute-level contextual consistency. This original repository is no longer actively maintained in favor of LingBot-World-Infinity (lingbot-world-v2), which adds 720p/60fps real-time generation and multi-user collaborative world steering.

## Use cases
- generate interactive worlds from an image
- real-time playable generative world simulation
- video generation with long-term consistency
- build environments for robot learning
- create game-like worlds with AI
- interactive content creation from a single image

## When to choose
- you need an open-source world model for research in gaming, content creation, or robot learning
- you want real-time interactive world generation with permissive Apache-2.0 licensing
- you need long-horizon consistency in generated environments

## When to avoid
- you want the latest features - use the actively maintained lingbot-world-v2 (LingBot-World-Infinity) repository instead
- you lack GPU hardware for large video generation model inference
- you need a production-ready end-user application rather than model code and weights

## Facets
- artifact type: library
- maturity: maintenance
- function: video-processing, machine-learning, deep-learning, simulation, graphics
- domain: artificial-intelligence, simulation, robotics
- platform: python
- tags: world-model, video-generation, image-to-video, interactive-worlds, generative-ai, aigc, real-time-inference, embodied-ai, game-development, video, gpu, linux

## Member repositories
- Robbyant/lingbot-world-v2 (main) score 54
- Robbyant/lingbot-world (mirror) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:03.698841+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:11.140711+00:00, confidence not recorded.
  - readme: https://github.com/Robbyant/lingbot-world-v2 (fetched 2026-08-28T04:05:03.698841+00:00, sha e127eb9883bb)
  - homepage: https://technology.robbyant.com/lingbot-world-v2 (fetched 2026-08-29T09:09:23.298753+00:00, sha 52df41dc7ed1)
  - site_page: https://www.robbyant.com/about-robby (fetched 2026-08-29T09:09:23.308401+00:00, sha 4a2374d8a183)
- Data as of 2026-08-30T08:39:29.467469+00:00.