GitHub Repo to LLM Context Generator

An intelligent context generator for Large Language Models. This tool analyzes a public or private GitHub repository, extracts key information, and formats it into a single, comprehensive text file. It is available as a command-line tool and as a web UI.

You can use this when:

• You need to provide context to a Large Language Model without a "chat history" feature.
• Your conversation with an LLM is going in circles and you need to restart with full context.
• You have questions about a repository's implementation, dependencies, or structure.

The Problem

When working with Large Language Models on a software project, providing the full context of a repository is challenging:

1. Limited Context Windows: LLMs can't read an entire codebase at once.
2. Tedious Manual Work: Manually copying and pasting dozens of files is slow and error-prone.
3. Irrelevant "Noise": A raw dump includes useless "noise" for an LLM, such as node_modules, __pycache__, and build artifacts, which wastes precious context space.
4. Lack of Structure: A simple concatenation of files makes it hard for the LLM to distinguish where one file ends and another begins.

This tool solves these problems by creating a clean, intelligent, and structured snapshot of your repository's current state.

✨ Key Features

• Dual Interface: Use the friendly web UI or the powerful command-line tool (repo-context) for easy integration into scripts.
• Pip Installable: Easily install the tool with a single command: pip install repo-context-generator.
• Private Repository Support: Securely analyze private repositories using a GitHub Personal Access Token (PAT).
• Automated Dependency Analysis: Detects tech stacks (Python, JavaScript, etc.) and lists dependencies from common package files.
• Intelligent File Tagging: Adds heuristic tags like [⭐ Likely Project Entry Point] to guide the LLM's focus.
• Intelligent Filtering: Automatically respects the repository's .gitignore file and excludes common non-essential files.
• Rich Metadata Header: The context starts with a header including the repo URL, timestamp, and latest commit details.

⚙️ Installation

Install the package directly from PyPI:

pip install repo-context-generator

🚀 Usage

You can use the tool via the Web UI or the Command-Line.

🖥️ Command-Line Interface

Once installed, you can use the repo-context command in your terminal.

1. Generate context and print to console:

repo-context "https://github.com/user/repo"

2. Save context to a file:

repo-context "https://github.com/user/repo/tree/main" -o my_project_context.md

3. Analyze a private repository: You can provide a token via the --token flag or the GITHUB_TOKEN environment variable.

# Using a flag
repo-context "https://github.com/private-user/private-repo" --token YOUR_GITHUB_PAT

# Or by setting an environment variable
export GITHUB_TOKEN="YOUR_GITHUB_PAT"
repo-context "https://github.com/private-user/private-repo"

4. See all available options:

repo-context --help

🌐 Web UI

Try the app live here: https://repo-context.streamlit.app/

1. Navigate to the deployed Streamlit application.
2. Paste the full URL of a public or private GitHub repository.
3. For private repositories, expand the "🔑 Private Repository Access" section and paste your Personal Access Token (PAT).
4. Configure any optional settings, like file extensions or line limits.
5. Click the "🚀 Generate Intelligent Context" button.
6. Use the "📥 Download Context.md" button to save the output.

---

📝 Example Output Structure

The generated output is a single, enriched Markdown file designed to be easily parsed by an LLM.

# LLM CONTEXT SNAPSHOT
- **Repository Source:** https://github.com/user/my-python-app
- **Branch:** main
- **Snapshot Timestamp:** 2025-07-19 20:55:00 UTC
- **Last Commit Hash:** a1b2c3d4e5f6g7h8i9j0
- **Last Commit Message:** "feat: Implement user authentication"
- **Detected Technology Stack:** Python

---

1. Project Dependencies Analysis

Dependencies (requirements.txt):

streamlit==1.33.0
gitpython==3.1.43
pyyaml==6.0.1

2. Repository File Structure

📂 my-python-app
├── 📄 .gitignore
├── 📄 app.py
├── 📄 README.md
└── 📄 requirements.txt

---

3. File Contents

--- FILE: app.py [⭐ Likely Project Entry Point] (Lines: 152 | Chars: 4891 | Funcs/Classes: 6) ---

import streamlit as st
# ... rest of the file content

---

💻 Running for Development

To contribute to this project or run the web UI locally, follow these steps.

Prerequisites:

• Python 3.8+
• Git command-line tool

Setup:

1. Clone the repository:

   git clone https://github.com/cbarkinozer/repo-context.git
   cd repo-context

2. Create and activate a virtual environment:

   # For macOS/Linux
   python3 -m venv venv
   source venv/bin/activate
   
   # For Windows
   python -m venv venv
   .\venv\Scripts\activate

3. Install the project in editable mode: This command reads the pyproject.toml file and installs all necessary dependencies, including Streamlit.

   pip install -e .

4. Run the Streamlit app:

   streamlit run app.py

The application should now be running at http://localhost:8501.

🛠️ Technologies Used

• Framework: Streamlit
• Language: Python 3
• CLI: argparse
• Git Operations: GitPython
• File Parsing: gitignore-parser, tomli, PyYAML

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.