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An open API service indexing awesome lists of open source software.

https://github.com/kdcllc/agentic-ai-github-stars

Discover and leverage the power of AI to analyze and enhance GitHub repositories. This Python-based project offers seamless tools and insights tailored for developers and organizations looking to optimize their projects' visibility and performance. With a focus on simplicity and automation, this repository empowers users to track, analyze, and grow
https://github.com/kdcllc/agentic-ai-github-stars

ai-tools automation developer-tools github-analytics open-source python repository-optimization

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Discover and leverage the power of AI to analyze and enhance GitHub repositories. This Python-based project offers seamless tools and insights tailored for developers and organizations looking to optimize their projects' visibility and performance. With a focus on simplicity and automation, this repository empowers users to track, analyze, and grow

Awesome Lists containing this project

README 

          
# GitHub Stars Analyzer



AI Agent that fetches your GitHub starred repositories, reads their README files, and creates summaries of technologies used and primary goals.



[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/kdcllc/agentic-ai-github-stars/blob/main/LICENSE)



![I stand with Israel](./images/IStandWithIsrael.png)



## Hire Me



Please send [email](mailto:kingdavidconsulting@gmail.com) if you consider hiring me.



[![buymeacoffee](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/vyve0og)



## Give a Star! :star:



If you like or are using this project to learn or start your solution, please give it a star. Thanks!



## Features



- Fetch all starred repositories for a GitHub user

- Download and parse README.md files from each repository

- Generate AI-powered summaries of each repository including:

  - List of technologies/frameworks used

  - Primary goal or purpose of the repository

- Semantic search through your starred repositories

- Web UI for easy interaction (Streamlit)

- Output results to a JSON file for further processing



## Requirements



- Python 3.7+

- GitHub Personal Access Token (for API rate limits)

- One of the following AI providers for generating summaries:

  - OpenAI API Key

  - Azure OpenAI API Key and Endpoint

  - Ollama (running locally or on a server)

  

## Embedding Providers



The application supports multiple embedding providers that can work in a Docker container without GPU:



1. **Sentence Transformer (CPU)** - Default option, runs entirely locally on CPU

2. **OpenAI** - Uses OpenAI's embedding API

3. **Azure OpenAI** - Uses Azure OpenAI's embedding API  

4. **Ollama** - Uses local Ollama instance for embeddings



### Testing Embedding Providers



You can test different embedding providers using the `test_embeddings.py` script:



```powershell

# Test with the default Sentence Transformer provider (CPU-only)

python test_embeddings.py --provider sentence_transformer



# Test with OpenAI

python test_embeddings.py --provider openai --api_key "your_api_key"



# Test with Azure OpenAI

python test_embeddings.py --provider azure --endpoint "https://your-endpoint.openai.azure.com/" --deployment "your_deployment" --api_key "your_api_key"



# Test with Ollama

python test_embeddings.py --provider ollama --host "http://localhost:11434" --model "llama3"

```



The embedding manager can be configured in the web UI or through environment variables when running in Docker.



## Installation



1. Clone this repository

2. Install dependencies using UV (recommended):



```powershell

# Install all dependencies from pyproject.toml

uv sync

```



## Using the Web UI



The application provides a user-friendly web interface built with Streamlit. To start the UI:



```powershell

# Or directly with streamlit

uv run streamlit run streamlit_app.py

```



The web UI provides:

- Interactive form for fetching and analyzing GitHub stars

- Real-time progress tracking during processing

- Semantic search with detailed repository information

- Export functionality with statistics

- Support for all AI provider options



## Docker Deployment



For running in Docker without GPU, see [DOCKER.md](DOCKER.md) for detailed instructions.



Quick start:

```bash

docker compose up -d

```



This will start the application with the default CPU-based embedding provider.



# Or activate the virtual environment and install

uv venv

.\.venv\Scripts\activate

uv pip install -e .

```



## Usage



### Database Migration and Testing



```bash

# Migrate data from JSON to SQLite database

uv run migrate.py --input starred_repos_summary.json --output github_stars.db



# Test database functionality

uv run test_db.py --db github_stars.db



# test Ollama 

uv run python test_ollama.py --model "qwen2.5-coder:latest"

```



### With different AI providers



```bash

# Using OpenAI

uv run main.py --username kdcllc --ai-provider openai --openai-key "your-key" --max-pages 1



# Using Azure OpenAI

uv run main.py --username kdcllc --ai-provider azure --azure-key "your-key" --azure-endpoint "your-endpoint" --azure-deployment "your-deployment" --max-pages 1



# Using Ollama (local)

uv run main.py --username kdcllc --ai-provider ollama --model "qwen2.5-coder:latest" --max-pages 1

```



#### OpenAI (default)



```powershell

# Using command line arguments

uv run main.py  --github-token  --openai-key 



# Using environment variables

$env:GITHUB_TOKEN="your_github_token"

$env:OPENAI_API_KEY="your_openai_api_key"

uv run main.py 



# Specify a different model

uv run main.py  --ai-provider openai --model gpt-4

```



#### Azure OpenAI



```powershell

# Using command line arguments

uv run main.py  --ai-provider azure --azure-key  --azure-endpoint  --azure-deployment 



# Using environment variables

$env:GITHUB_TOKEN="your_github_token"

$env:AZURE_OPENAI_API_KEY="your_azure_api_key"

$env:AZURE_OPENAI_ENDPOINT="your_azure_endpoint"

$env:AZURE_OPENAI_DEPLOYMENT="your_deployment_name"

uv run main.py  --ai-provider azure

```



#### Ollama (Local LLM)



```powershell

# Using local Ollama instance (default URL: http://localhost:11434)

uv run main.py  --ai-provider ollama --model llama3



# Using remote Ollama instance

uv run main.py  --ai-provider ollama --ollama-url http://your-ollama-server:11434 --model mistral

```



### Searching and Querying



```bash

# Search repositories by semantic similarity

uv run main.py --search "docker containerization" --db github_stars.db --username username

```



## Example Output



```json

{

  "repositories": [

    {

      "name": "repo-name",

      "full_name": "owner/repo-name",

      "url": "https://github.com/owner/repo-name",

      "description": "Repository description",

      "stars": 1234,

      "language": "Python",

      "summary": "This repository provides a framework for creating machine learning models with a focus on natural language processing.",

      "technologies": ["Python", "PyTorch", "Transformers", "NLTK"],

      "primary_goal": "To simplify the process of building and deploying NLP models."

    },

    ...

  ]

}

```



## License



MIT