CodeFlux: Live Code Intelligence Agent

A real-time code intelligence system powered by Pathway and LangGraph, providing live codebase awareness with agentic reasoning capabilities.

🌟 Overview

CodeFlux is an intelligent developer assistant that combines Pathway's live data processing with LangGraph's agentic framework to provide real-time code intelligence. The system watches your codebase, maintains a live index, and answers questions about your code with confidence scoring and multi-step reasoning.

Key Features

• 🔥 Live Code Indexing: Real-time monitoring of code changes using Pathway
• 🧠 Agentic Reasoning: Multi-step reasoning powered by LangGraph
• 🎯 Confidence Scoring: Every answer comes with confidence levels and reasoning
• 📊 Change Intelligence: Track what changed, detect breaking changes, and analyze impact
• 💬 Interactive UI: Modern React-based frontend for seamless interaction
• 🔍 Context-Aware Retrieval: Smart context building from live codebase state
• 🤖 Gemini Integration: Powered by Google's Gemini 2.5 Flash model

🏗️ Architecture

The project consists of two main components:

Backend (FastAPI + Pathway + LangGraph)

backend/
├── main.py                     # 🚀 FastAPI entry point
├── pathway_engine/             # 🔥 CORE LIVE ENGINE (Pathway)
│   ├── ingestion/              # Code and GitHub repo watching
│   ├── indexing/               # Live incremental indexing
│   ├── query/                  # Context retrieval and building
│   └── state/                  # Version tracking
├── agent/                      # 🧠 AGENTIC REASONING
│   ├── agent.py                # LangGraph-based agent
│   ├── planner.py              # Multi-step reasoning
│   ├── tools.py                # Agent tools (diff, search, etc.)
│   └── confidence.py           # Confidence assessment
└── llm/                        # 🤖 LLM CLIENT
    └── gemini_client.py        # Gemini API wrapper

Frontend (React + TypeScript + Vite)

frontend/
├── src/
│   ├── components/             # Reusable UI components
│   ├── pages/
│   │   ├── Dashboard.tsx       # Main dashboard
│   │   ├── AskTheAgentPage.tsx # Query interface
│   │   ├── ChangeIntelligence.tsx
│   │   └── ReasoningConfidence.tsx
│   └── App.tsx                 # Main app component
└── package.json

🚀 Getting Started

Prerequisites

• Python 3.8+ (for backend)
• Node.js 18+ and npm (for frontend)
• Google API Key (for Gemini)
• Git (for version control)

Installation

1. Clone the Repository

git clone https://github.com/ThelastC0debenders/Pathway-Hack.git
cd Pathway-Hack

2. Backend Setup

cd backend

# Install Python dependencies (recommended: use virtual environment)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install required packages
pip install fastapi uvicorn pathway-io langgraph langchain-core google-generativeai python-dotenv

# Create .env file
cat > .env << EOL
GOOGLE_API_KEY=your_gemini_api_key_here
PORT=8003
EOL

3. Frontend Setup

cd ../frontend

# Install dependencies
npm install

# Start development server
npm run dev

4. Start the Application

Terminal 1 - Backend:

cd backend
python main.py
# Backend will start on http://localhost:8003

Terminal 2 - Frontend:

cd frontend
npm run dev
# Frontend will start on http://localhost:5173

🔧 Configuration

Environment Variables

Create a .env file in the backend directory:

# Required
GOOGLE_API_KEY=your_gemini_api_key_here

# Optional
PORT=8003

Pathway Engine Configuration

Configure the watched repository/folder in backend/pathway_engine/config.py:

WATCH_FOLDER = "./watched_folder"
GITHUB_REPO_URL = "https://github.com/ThelastC0debenders/Pathway-Hack.git"
GITHUB_BRANCH = "main"

📖 Usage

1. Access the UI: Open your browser to http://localhost:5173

2. Navigate to "Ask the Agent": Use the sidebar to access different features

3. Ask Questions: Type questions about your codebase:

   • "How is file loading handled?"
   • "What are the main components?"
   • "Explain the agent architecture"

4. View Responses: Get detailed answers with:

   • Explanation
   • Relevant code snippets
   • Confidence score and level
   • Reasoning strategy used
   • Source file references

🎯 API Endpoints

POST /v1/agent/ask

Ask questions to the agent.

Request:

{
  "query": "How is the file loading handled?"
}

Response:

{
  "explanation": "Detailed explanation...",
  "code": "Relevant code snippets...",
  "instruction": "How to use...",
  "confidence": 0.85,
  "confidence_level": "HIGH",
  "strategy": "Direct retrieval",
  "sources": [
    {
      "file": "path/to/file.py",
      "lines": "10-25",
      "text": "Code content..."
    }
  ],
  "trace": ["step1", "step2", "..."]
}

🧪 Testing

Backend Testing

cd backend
python test_agent.py

The test runner provides an interactive prompt to test the agent:

❓ Ask a question (or type 'exit'): How does the agent work?

Frontend Testing

cd frontend
npm run build    # Build for production
npm run lint     # Run ESLint

🛠️ Technology Stack

Backend

• FastAPI: Modern Python web framework
• Pathway: Real-time data processing and live indexing
• LangGraph: Agentic workflow framework
• LangChain: LLM integration utilities
• Google Gemini: AI model for code understanding

Frontend

• React 19: UI framework
• TypeScript: Type-safe JavaScript
• Vite: Fast build tool and dev server
• React Router: Client-side routing
• Lucide React: Icon library
• React Markdown: Markdown rendering with syntax highlighting

📝 Project Structure

Pathway-Hack/
├── backend/                    # Python backend
│   ├── agent/                 # Agentic reasoning system
│   ├── llm/                   # LLM client wrapper
│   ├── pathway_engine/        # Pathway-based live indexing
│   ├── main.py                # FastAPI server
│   └── test_agent.py          # Agent testing
├── frontend/                   # React frontend
│   ├── src/
│   │   ├── components/        # UI components
│   │   ├── pages/            # Application pages
│   │   └── App.tsx           # Main app
│   └── package.json
└── README.md                   # This file

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is part of a hackathon submission. Please check with the team for licensing details.

👥 Team

ThelastC0debenders - Pathway Hackathon Participants

🙏 Acknowledgments

• Pathway for the amazing live data processing framework
• LangChain/LangGraph for the agentic framework
• Google for the Gemini API
• FastAPI and React communities

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Built with ❤️ for the Pathway Hackathon