StarNet GitHub: Install The Repo, Then Pick A Brain With Real Benchmark Scores

By Julian Goldie · 2026-10-07 · GoldieBench Blog

The StarNet GitHub repo is github.com/androoAGI/starnet, and it holds the free, open-source code for StarNet, a pixel-art space station where your AI agents live and work.

StarNet does not ship with its own model.

You connect a brain, and that one choice decides how smart your agents are and how much they cost you.

I run GoldieBench, the leaderboard where 40 models have now produced 1,609 one-shot demos across 50 tasks.

So in this post I will show you what is in the repo and how to run it, then use real scores from the board to show you which brain to plug in.

What is in the StarNet GitHub repo?

StarNet describes itself as a local-first, gamified desktop harness for building and running real AI agent teams.

Every agent gets its own workspace, transcript, memory and permissions, and the station shows them working in real time.

These are the facts I checked in the repo on 7 October 2026.

The repo had over 1,100 stars and more than 200 forks when I checked, and it was only created in June 2026.

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How to run StarNet from GitHub

These are the README commands, plus the one step my Mac needed.

git clone https://github.com/androoAGI/starnet.git
cd starnet
npm ci
node sidecar/index.js

Then open localhost:8787 in your browser and connect a brain on the first-run screen.

The README says the sidecar runs on Node core modules alone, but mine would not start properly until I ran npm ci.

I run mine with PORT=8788 node sidecar/index.js, because another app on my Mac already uses 8787.

To update, stop it, run git pull and npm ci, and start it again.

If you would rather skip the terminal, the Windows and Mac installers are on the StarNet releases page.

What GoldieBench can and cannot tell you about StarNet

I want to be straight about this before we look at numbers.

GoldieBench gives every model the same one-shot prompt to build a game, a page, a simulation or a visual, then scores the rendered result from 0 to 10.

StarNet asks a model to do something different, which is to act as an agent through many tool calls over a long session.

So a GoldieBench score is a measure of raw building ability, not a direct score for how well a model runs inside StarNet.

Nobody has benched StarNet itself on the board, and I am not going to pretend otherwise.

What the board does give you is a fair, same-prompt comparison of the brains StarNet lets you connect.

The StarNet brains, matched to real GoldieBench scores

StarNet's provider list is long, so I have matched the main sign-in options to the models they unlock on the board.

Which Claude model you get through the Claude CLI depends on your Claude Code plan and settings, so check that before you compare.

Grok 4.6 or Grok 4.7 for StarNet?

In my StarNet video I picked Grok 4.6 over Grok 4.7.

That was a personal choice, because I prefer how Grok 4.6 talks.

The board tells a different story on building ability.

On the same 20 skill-infused game briefs, Grok 4.7 averaged 7.15 and Grok 4.6 averaged 5.95.

The Grok 4.7 vs Grok 4.6 head-to-head went to Grok 4.7 by 14 wins to 5, with one tie.

xAI lists both at $2 per million input tokens and $6 per million output on the standard API tier, but a Grok sign-in in StarNet runs on your subscription instead.

My advice is simple.

If your agents mostly chat, plan and research, pick the Grok that you like talking to.

If your agents build things, pick Grok 4.7, because the scores back it.

The best StarNet brain for each crew role

StarNet lets you recruit roles like strategist, chief of staff, researcher and marketer, and each agent can have its own model.

That means you do not need one brain for the whole station.

Free and local brains for StarNet

StarNet can run with no key and no bill through Ollama.

The README suggests ollama pull qwen3:8b, then picking OLLAMA as the provider.

It also says to choose a model that supports tool calls, and it warns that an 8B model uses about 10 GB of graphics memory while it works.

Qwen3 8B is not on GoldieBench, so I cannot give you a score for that exact model.

Here is what the local models board says about the free options I have benched.

StarNet does have a custom slot for any OpenAI-compatible endpoint, so a local MLX server might reach Qwable that way, but I have not tested it inside StarNet.

The gap is the real story.

The best free local builder scores 7.14, the next one down scores 4.83, and the cloud leaders sit above 8.

The README itself says local models are slower and rougher on long tasks.

If you want my full ranking of local brains for agents, read the best local model for Hermes Agent.

What each StarNet brain costs you

A sign-in brain and an API-key brain are billed in completely different ways.

When you sign in with Grok, Claude Code, Kimi or ChatGPT, StarNet runs on the subscription you already pay for, so there is no per-token bill inside StarNet.

When you paste an API key, every agent turn is billed per token, and a busy crew can burn through tokens quickly.

These are the list prices recorded on the model pages.

Grok 4.7 is the stand-out on value, because it scores 1.2 points higher than Grok 4.6 at exactly the same price.

StarNet also helps you keep the bill under control.

The README says spend, budgets and run history are saved on disk, and v0.13.1 made spending limits hold during retries as well as between turns.

Set a limit before you leave a crew running, whichever brain you pick.

What brain my own StarNet install runs on

My station runs on a hosted brain, not a local model.

The agents sign in with my Grok account and use Grok 4.6.

I do not run local models with StarNet, because I want the agents to be quick and sharp when I am showing them off.

That is the trade-off you are making.

A hosted brain gives you speed and quality, while a local brain gives you privacy and a zero bill.

A short tour of what the brain powers

This is the quick version, and my full walkthrough is the StarNet AI agent guide.

You create an agent with a name, an appearance, a personality such as warm, dry, composed or unhinged, and an approval level of ask for approval or full power.

You pick the model and a reasoning level of low, medium, high or max, and higher levels take longer.

The station view zooms and reframes, and everything you run lives in four docks.

Agents level up the more you use them, a bit like a Tamagotchi for an AI agent.

Each agent has Brief, Growth, History and Memories tabs, plus web and local-file permissions you can switch on or off.

The app catalogue adds Gmail, Notion, GitHub, flight search, physical letters, email and web search through Firecrawl.

The skill market and skill library add things like ad copy testing, calendar scheduling, a decision framework, making a plan, humanising content and handling a hard conversation.

The crew screen recruits roles, where Deploy rewrites an existing agent and Summon adds a new one beside it.

Tasks, quests, the work list, builds and the Systems menu all sit in the same station.

I noticed a slight delay when sending messages, so give each reply a moment.

My honest verdict on StarNet and its brains

I think StarNet is mostly fun to play with right now, and I do not see a massive number of practical use cases yet.

It is still the smoothest office-style agent harness I have tested, and it felt like a much more fun version of Paperclip.

If you already pay for Grok, Claude Code, Kimi or ChatGPT, plug that in first, because it costs you nothing extra.

If you are choosing from scratch, the board says Claude Opus 5 and GPT-5.6 Sol are the strongest brains, and Kimi K3 is the best value for long runs.

And if you live in Hermes, StarNet can import a Hermes or OpenClaw agent's persona, instructions, memory and model, though your API keys stay behind.

Also On Our Network

🌐 the step-by-step StarNet GitHub install for operators

🌐 seven ways a business can use the StarNet repo

🌐 my feature-by-feature StarNet verdict

🌐 running StarNet as a tab inside the Agent OS

🌐 how I wired Grok 4.7 into the Agent OS

Clone it, run npm ci, and connect the brain the board backs for the job, and you will get the most out of the StarNet GitHub repo.

FAQ

Related reading

→ The Best Local Model For Hermes Agent — Decided By 45 Real Builds

→ The Best LLMs For Hermes Agent, Ranked By Real Work

→ The Best Free AI Model For Hermes Agent (All Three $0 Lanes)

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