solaris-wm

solaris-wm / solaris-engine

Public

Scalable Minecraft multiplayer data collection engine

solaris-wm.github.iosolaris-engine
88
2
100% credibility
Found Feb 26, 2026 at 40 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
JavaScript
AI Summary

A framework for recording synchronized videos and actions of AI bots collaboratively playing Minecraft.

How It Works

1
🔍 Discover Solaris Engine

You find this tool on GitHub that lets AI bots play Minecraft together to create training videos and action records.

2
📦 Get everything ready

Download the project and prepare your computer to run the Minecraft worlds and bots.

3
⚙️ Plan your collection

Choose how many games, types of play like building or chasing, and worlds to record.

4
🚀 Launch the bots

Start the bots – watch Alpha and Bravo team up in Minecraft for collaborative adventures like mining or building towers!

5
🎥 Capture and align

The cameras record their views while tracking every move, then everything gets perfectly synced.

✅ Your dataset is ready

You now have videos of bots playing together with exact action details, perfect for training AI.

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Star Growth

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AI-Generated Review

What is solaris-engine?

Solaris-engine is a scalable Minecraft multiplayer data collection engine built in JavaScript with Node.js and Mineflayer bots. It automates collaborative gameplay episodes—like building houses, chasing, mining ores, or PvP—across horizontally scalable Minecraft servers using Docker Compose for parallel instances. Developers get annotated videos from official client renders plus precise JSON ground truth (position, yaw, pitch, actions) for every bot, ready for dataset prep via postprocessing scripts.

Why is it gaining traction?

It stands out for turning Minecraft into a scalable data factory: generate thousands of episodes across GPU-accelerated camera instances without manual setup, with built-in orchestration for flat/normal worlds and eval splits. The Docker-first approach handles port allocation, GPU pinning, and VNC/noVNC previews seamlessly, unlike ad-hoc bot scripts. Post-run tools align videos, filter water episodes, and split train/test sets automatically.

Who should use this?

AI researchers training diffusion models with transformers or vision models on game footage, needing reproducible multiplayer interactions. Minecraft server admins exploring scalable hosting solutions for bot-driven benchmarks. Data engineers at solaris engineering teams building OSS datasets from scripted gameplay.

Verdict

Worth forking for Minecraft AI datasets—solid Docker orchestration and docs make scaling feasible despite 14 stars and 1.0% credibility score. Still early; add tests and examples for broader adoption.

(198 words)

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