Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 659 |
| 2026-08-03 | 659 |
| 2026-08-06 | 659 |
| 2026-08-18 | 658 |
| 2026-09-08 | 660 |
| 2026-09-10 | 659 |
| 2026-09-20 | 659 |
Today
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growth rate 0.00%/day
# Bot Aquarium
This project gives a large language model (LLM) control of a Linux machine.
In the example below, we start with the prompt:
> You now have control of an Ubuntu Linux server. Your goal is to run a Minecraft server. Do not respond with any judgement, questions or explanations. You will give commands and I will respond with current terminal output.
>
> Respond with a linux command to give to the server.
The AI first does a _sudo apt-get update_, then installs openjdk-8-jre-headless. Each time it runs a command we return the result of this command back to OpenAI and ask for a summary of what happened, then use this summary as part of the next prompt.
[](https://asciinema.org/a/0CH4ESDjt4H11WABiMlGZNMYU?&speed=2&i=2&autoplay=1)
Inspired by [xkcd.com/350](https://xkcd.com/350/) and [Optimality is the tiger, agents are its teeth](https://www.lesswrong.com/posts/kpPnReyBC54KESiSn/optimality-is-the-tiger-and-agents-are-its-teeth)
# Usage
## Build
docker network create aquarium
docker build -t aquarium .
go build
## Start
Pass your prompt in the form of a goal. For example, `--goal "Your goal is to run a minecraft server."`
Using OpenAI:
OPENAI_API_KEY=$OPENAI_API_KEY ./aquarium --goal "Your goal is to run a Minecraft server."
Using a local model provided by [llama-cpp-python](https://github.com/abetlen/llama-cpp-python):
./aquarium --goal "Your goal is to run a Minecraft server." --url "http://localhost:8000" --context-mode full
**arguments**
./aquarium -h
Usage of ./aquarium:
-context-mode string
How much context from the previous command do we give the AI? This is used by the AI to determine what to run next.
- partial: We send the last 10 lines of the terminal output to the AI. (cheap, accurate)
- full: We send the entire terminal output to the AI. (expensive, very accurate)
(default "partial")
-debug
Enable logging of AI prompts to debug.log
-goal string
Goal to give the AI. This will be injected within the following statement:
> You now have control of an Ubuntu Linux server.
> [YOUR GOAL WILL BE INSERTED HERE]
> Do not respond with any judgement, questions or explanations. You will give commands and I will respond with current terminal output.
>
> Respond with a linux command to give to the server.
(default "Your goal is to run a Minecraft server.")
-limit int
Maximum number of commands the AI should run. (default 30)
-model string
OpenAI model to use. Ignored if --url is provided. See https://platform.openai.com/docs/models (default "gpt-3.5-turbo")
-preserve-container
Persist docker container after program completes.
-url string
URL to locally hosted endpoint. If provided, this supersedes the --model flag.
## Logs
The left side of the screen contains general information about the state of the program. The right side contains the terminal, as seen by the AI.
<br />These are written to aquarium.log and terminal.log.
Calls to the AI are not logged unless you add the `--debug` flag. API requests and responses will be appended to debug.log.
# How it works
## Agent loop
1. Send the OpenAI api the list of commands (and their outcomes) executed so far, asking it what command should run next
1. Execute command in docker VM
1. Read output of previous command- send this to OpenAI and ask gpt-3.5-turbo for a summary of what happened
1. If the output was too long, OpenAI api will return a 400
1. Recursively break down the output into chunks, ask it for a summary of each chunk
1. Ask OpenAI for a summary-of-summaries to get a final answer about what this command did
## more examples
Prompt: `Your goal is to execute a verbose port scan of amazon.com.`
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:28ce9da4b3e69e88, llm:Repository description: "Agents before they were cool". Language: Go. No topics or README provided. Suggests an implementation of agents (likely AI agents) in Go.
matched fp:28ce9da4b3e69e88, llm:Repository description: "Agents before they were cool". Language: Go. No topics or README provided. Suggests an implementation of agents (likely AI agents) in Go.