5 GitHub Repos for Building Cool AI Stuff

Good for everyday AI applications!

Generated with flux-dev. Prompt: “Generate me a water color image where there is a beige background and a handwritten red text that says: “5 Repos”.

I always keep an eye out for interesting GitHub repositories that I can share at work or use for my side hustle. GitHub repositories can be a source of incredible wisdom and spark creativity.

Let’s get started.

1.) browser-use (37.3K stars)

browser-use makes websites accessible for AI agents by extracting all interactive elements, so agents can focus on getting things done.

It is fairly straightforward to use.

from langchain_openai import ChatOpenAI
from browser_use import Agent
from dotenv import load_dotenv
load_dotenv()

import asyncio

llm = ChatOpenAI(model="gpt-4o")

async def main():
    agent = Agent(
        task="Compare the price of gpt-4o and DeepSeek-V3",
        llm=llm,
    )
    result = await agent.run()
    print(result)

asyncio.run(main())

Take a look at the example below, where an AI agent is doing grocery shopping.

2.) MoneyPrinterTurbo (25K Stars)

This repository allows you to generate short videos with one click using LLMs.

Simply provide a topic or keyword for a video, and it will automatically generate the video copy, video materials, video subtitles, and video background music before synthesizing a high-definition short video.

Web UI of MonePrinter

It also has a web UI, and since it is open-source, you can obviously self-host it. It gained a significant following over the past year.

Stars

Oh, by the way, there is also an English README.

3.) pandas-ai (17.6K stars)

With PandasAI, you can chat with your database or your data lake (SQL, CSV, Parquet). PandasAI makes data analysis conversational using LLMs and RAG.

Take a look at how easy it is to set it up:

import pandasai as pai

# Sample DataFrame
df = pai.DataFrame({
    "country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],
    "revenue": [5000, 3200, 2900, 4100, 2300, 2100, 2500, 2600, 4500, 7000]
})

# By default, unless you choose a different LLM, it will use BambooLLM.
# You can get your free API key signing up at https://app.pandabi.ai (you can also configure it in your .env file)
pai.api_key.set("your-pai-api-key")

df.chat('Which are the top 5 countries by sales?')

You can also ask more complex questions:

df.chat(
    "What is the total sales for the top 3 countries by sales?"
)

# Output: The total sales for the top 3 countries by sales is 16500.

Or you can also ask Pandas-AI to create a chart:

df.chat(
    "Plot the histogram of countries showing for each one the gd. Use different colors for each bar",
)

Output of the query above

I feel like this is a useful library, especially for content creators, to get more in-depth insights into their audience.

4.) repomix (12.6K stars)

Repomix packs your entire repository into a single, AI-friendly file. Some of the features are:

• AI-Optimized: Formats your codebase in a way that’s easy for AI to understand and process.

• Token Counting: Provides token counts for each file and the entire repository, useful for LLM context limits.

• Simple to Use: You need just one command to pack your entire repository.

You can use it like this:

repomix path/to/directory

Some example prompts you could use it for.

Code review and refactoring suggestions:

This file contains my entire codebase. Please review the overall structure and suggest any improvements or refactoring opportunities, focusing on maintainability and scalability.

To generate project documentation:

Based on the codebase in this file, please generate a detailed README.md that includes an overview of the project, its main features, setup instructions, and usage examples.

For generating test cases:

Analyze the code in this file and suggest a comprehensive set of unit tests for the main functions and classes. Include edge cases and potential error scenarios.

Evaluate code quality and adherence to best practices:

Review the codebase for adherence to coding best practices and industry standards. Identify areas where the code could be improved in terms of readability, maintainability, and efficiency. Suggest specific changes to align the code with best practices.

Get a high-level understanding of the library

This file contains the entire codebase of library. Please provide a comprehensive overview of the library, including its main purpose, key features, and overall architecture.

5.) llm-scraper (4.6K stars)

A TypeScript library that allows you to extract structured data from any webpage using LLMs.

Example:

import { chromium } from 'playwright'
import { z } from 'zod'
import { openai } from '@ai-sdk/openai'
import LLMScraper from 'llm-scraper'

// Launch a browser instance
const browser = await chromium.launch()

// Initialize LLM provider
const llm = openai.chat('gpt-4o')

// Create a new LLMScraper
const scraper = new LLMScraper(llm)

// Open new page
const page = await browser.newPage()
await page.goto('https://news.ycombinator.com')

// Define schema to extract contents into
const schema = z.object({
  top: z
    .array(
      z.object({
        title: z.string(),
        points: z.number(),
        by: z.string(),
        commentsURL: z.string(),
      })
    )
    .length(5)
    .describe('Top 5 stories on Hacker News'),
})

// Run the scraper
const { data } = await scraper.run(page, schema, {
  format: 'html',
})

// Show the result from LLM
console.log(data.top)

await page.close()
await browser.close()

Then the output looks like this:

Output of llm-scraper

Keep building 👨‍💻!

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