GitSurfer: Intelligent Codebase Analysis Tool by Lalan KumarGitSurfer: Intelligent Codebase Analysis Tool by Lalan Kumar

GitSurfer: Intelligent Codebase Analysis Tool

Lalan Kumar

Completed work

AI Automation

AI Developer

AI Engineer

Google Gemini

LangChain

Computer Software

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Completed work

Posted Jul 30, 2025

Developed GitSurfer, a codebase analysis tool for GitHub using LLMs and vector databases.

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GitSurfer

GitSurfer is an intelligent, multi-provider codebase analysis and research assistant for GitHub repositories. It leverages advanced LLMs (Gemini, OpenAI, Anthropic, Cohere) and vector databases to dynamically fetch, summarize, embed, and answer questions about any public GitHub repository, providing deep insights and research capabilities for developers and researchers.

Features

Fetches and analyzes GitHub repositories (tree structure, file contents)

Summarizes repository structure using LLMs

Embeds code and documentation into a vector database (ChromaDB)

Supports multiple LLM and embedding providers: Gemini, OpenAI, Anthropic, Cohere

Interactive research assistant: Ask questions about the codebase and get detailed, contextual answers

Extensible modular architecture using LangGraph and LangChain

Rich logging and error handling

Graphs

Project Structure

GitSurfer/
├── app/
│   ├── core/           # Core utilities, LLM/embedding logic
│   ├── graphs/         # Main assistant, fetcher, embedder, researcher graphs
│   ├── retriever/      # Data ingestion and retriever logic
├── config/             # Settings and environment variable loader
├── DATA/               # Persisted vector DBs
├── temp/               # Temporary files (chunks, summaries)
├── logs/               # Log files
├── logger.py           # Logging configuration
├── requirements.txt    # Python dependencies
├── .env                # Environment variables (not committed)

Installation

Clone the repository

git clone <your-fork-or-repo-url>
cd GitSurfer

Install dependencies

pip install -r requirements.txt

Set up environment variables

Copy .env.example to .env and fill in your API keys:

GOOGLE_API_KEY (for Gemini)

OPENAI_API_KEY (for OpenAI)

ANTHROPIC_API_KEY (for Anthropic)

COHERE_API_KEY (for Cohere)

GITHUB_TOKEN (for increased GitHub API limits)

You can also specify model names and other settings in .env.

Usage

The main entry point is the app/graphs/git_assistant.py script. It runs an interactive CLI assistant:

python app/graphs/git_assistant.py

Workflow:

Enter a GitHub repository URL when prompted.

GitSurfer fetches the repo, summarizes its structure, and creates a vector DB.

Ask any question about the codebase (design, functions, usage, etc.).

Interactively continue the research session or exit.

Example:

🔄 Processing repository...
👤 Input required: Enter GitHub repo URL
🤖 Assistant: Repository fetched and analyzed. Ask your question!
👤 You: What does the main.py file do?
🤖 Assistant: [detailed answer]

Configuration

All settings (provider selection, model names, directories) are managed in config/settings.py and via environment variables.

Supports switching between providers for both LLM and embeddings.

Vector DBs are persisted under DATA/.

Prerequisites

Python 3.9+

API keys for at least one supported LLM/embedding provider

(Optional) GitHub Personal Access Token for higher API rate limits

Environment Variables

Variable Description GOOGLE_API_KEY Gemini API key OPENAI_API_KEY OpenAI API key ANTHROPIC_API_KEY Anthropic API key COHERE_API_KEY Cohere API key GITHUB_TOKEN GitHub token for API calls GEMINI_LLM_MODEL Gemini model name (default set) OPENAI_LLM_MODEL OpenAI model name (default set) ... See config/settings.py for all

Testing

Run tests using:

pytest

Credits

Author: Lalan Kumar (kumar8074)

Built with LangChain, LangGraph, and ChromaDB

License

MIT License