Debug. Understand. Ship faster.

An open-source AI-powered developer assistant that detects bugs, explains code in plain English,
and gives actionable improvement suggestions - instantly, no account needed.

Live Demo  ·  API Docs  ·  Contributing Guide  ·  Good First Issues

GSSoC 2026 Contributors -- Welcome! Read CONTRIBUTING.md for setup, then grab a good first issue to get started.

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What is QyverixAI?

QyverixAI is a code analysis workspace. Paste any code and get three things back instantly:

	What you get
Explain	Language detection, plain-English summary, complexity estimate, function and class inventory
Debug	40+ pattern checks across 5 languages with exact line numbers, code snippets, and fix suggestions
Improve	Documentation gaps, error handling, testing, type safety - plus a 0–100 quality score and letter grade A–F

No account required. No API key needed. Works fully offline. Fully open source.

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Preview

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Features

Feature	Detail
40+ Bug Patterns	ZeroDivisionError, bare except, hardcoded secrets, eval(), memory leaks, XSS, NullPointerException, and more
5 Languages	Python, JavaScript, TypeScript, Java, C++
Full Analysis Endpoint	One call - explain + debug + improve combined, with timing metrics
Quality Score	0–100 score with letter grade A–F and prioritised suggestions
File Upload	Drag-drop or upload .py .js .ts .java .cpp
Dark / Light Mode	Persisted across sessions
Query History	Last 50 analyses saved locally
Saved Favorites	Bookmark and reload any analysis
Share Links	Generate a short-lived URL for any analysis and send it to teammates
Download Results	Export full report as .txt
LLM-Ready	Plug in OpenAI, Groq, Ollama, or any OpenAI-compatible provider via env vars
Rate Limiting	30 requests/minute per IP - configurable
Swagger Docs	Interactive API docs at /docs
Gzip Compression	Automatic response compression

Languages and patterns

Language	Patterns detected
Python	ZeroDivisionError, bare except, eval/exec, mutable defaults, hardcoded secrets, wildcard imports, global variables, missing type hints, string concat in loops, assert in production, comparison to None
JavaScript	var usage, loose equality ==, console.log, callback hell, innerHTML XSS, unhandled promises
TypeScript	any type, non-null assertion !, unhandled promises, missing env var validation
Java	NullPointerException risk, raw generics, broad catch, String == comparison, System.exit
C++	Memory leaks, unsafe gets/scanf, using namespace std, signed/unsigned mismatch

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Quick Start

Prerequisites

• Python 3.11 or 3.12
• pip
• A modern browser (Chrome, Firefox, Edge, Safari)

1 - Clone

git clone https://github.com/imDarshanGK/AI-dev-assistant.git
cd AI-dev-assistant

2 - Run the backend

cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload

Environment Setup

Copy .env.example to .env

cp .env.example .env

Update the environment variable values if needed before running the app.

Important variables:

• JWT_SECRET
• DATABASE_URL
• RATE_LIMIT_PER_MINUTE
• LLM_API_KEY (optional)

The app can still run without external AI providers when LLM_ENABLED=false.

Endpoint	URL
API root	http://localhost:8000/
Interactive docs	http://localhost:8000/docs
Health check	http://localhost:8000/health
Signup	http://localhost:8000/auth/signup
Login	http://localhost:8000/auth/login
Current user	http://localhost:8000/auth/me

3 - Open the frontend

# No build step required - open directly in your browser
open frontend/index.html

Set the API URL field to http://localhost:8000, click Ping to confirm the green Connected status, then paste any code and click Analyze Code.

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API Reference

All endpoints accept POST with Content-Type: application/json.

Request body

{ "code": "your code here", "language": "python" }

language is optional — the engine auto-detects it from the code.

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POST /explanation/

Returns a plain-English breakdown of the code.

{
  "language": "Python",
  "summary": "A short Python snippet (5 lines) that performs a focused task.",
  "key_points": [
    "Written in Python — 5 non-blank lines of code.",
    "Defines 1 function: calculate.",
    "Contains conditional logic — branching control flow."
  ],
  "complexity": "Beginner",
  "line_count": 6,
  "function_count": 1,
  "class_count": 0
}

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POST /debugging/

Returns detected issues with line numbers, code snippets, and fix suggestions.

{
  "issues": [
    {
      "type": "ZeroDivisionError",
      "line": 2,
      "description": "Potential division by zero — divisor may be 0 at runtime.",
      "suggestion": "Guard the divisor: if b == 0: return None",
      "severity": "error",
      "code_snippet": "result = a / b"
    }
  ],
  "summary": "Found 1 issue: 1 error, 0 warnings, 0 info.",
  "clean": false,
  "error_count": 1,
  "warning_count": 0,
  "info_count": 0
}

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POST /suggestions/

Returns improvement suggestion cards with a quality score.

{
  "suggestions": [
    {
      "category": "Documentation",
      "description": "Less than 10% of lines are comments. Add docstrings.",
      "example": "\"\"\"Calculate the area of a circle given radius r.\"\"\"",
      "priority": "medium"
    }
  ],
  "overall_score": 72,
  "grade": "B",
  "next_step": "Good work. Address the medium-priority items next."
}

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POST /analyze/

All three analyses in one response with timing.

{
  "provider": "rule-based",
  "model": "qyverix-engine-v3",
  "explanation": { "...": "..." },
  "debugging":   { "...": "..." },
  "suggestions": { "...": "..." },
  "analysis_time_ms": 1.84
}

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POST /share/ and GET /share/{id}

Create a share link for a saved analysis, then load it back by ID for seven days after creation.

POST /share/ accepts { "code": "...", "result": { ... } } and returns { "id": "short_id" }.

GET /share/{id} returns the saved { code, result, created_at } payload or 404 if the share is missing or expired.

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Project Structure

AI-dev-assistant/
├── assets/                           # Logo and brand assets
│   ├── logo-dark.svg
│   ├── logo-light.svg
│   └── icon.svg
├── backend/
│   ├── app/
│   │   ├── main.py                   # FastAPI app, middleware, rate limiting
│   │   ├── schemas.py                # Pydantic v2 request/response models
│   │   ├── routers/
│   │   │   ├── analyze.py            # POST /analyze/
│   │   │   ├── debugging.py          # POST /debugging/
│   │   │   ├── explanation.py        # POST /explanation/
│   │   │   ├── suggestions.py        # POST /suggestions/
│   │   │   └── auth.py               # /auth/signup, /auth/login, /auth/me
│   │   └── services/
│   │       ├── code_assistant.py     # Rule-based engine — 40+ patterns, 5 languages
│   │       └── ai_provider.py        # Optional LLM abstraction layer
│   ├── requirements.txt
│   └── tests/
│       └── test_endpoints.py         # 52 tests across all endpoints and languages
├── frontend/
│   └── index.html                    # Complete UI — no build step, self-contained
├── .github/
│   └── workflows/
│       └── ci.yml                    # CI on Python 3.11 + 3.12, lint with Ruff
├── .env.example
├── Dockerfile
├── render.yaml
├── CONTRIBUTING.md
└── README.md

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Running Tests

cd backend
pytest -v

52 tests covering all endpoints, all 5 languages, 10+ individual bug patterns, suggestions scoring, full analysis, and edge cases including empty code, unicode, and single-line input.

Tests run automatically on every push and pull request via GitHub Actions across Python 3.11 and 3.12.

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Deployment

Render - recommended, free tier

1. Fork this repository
2. Go to render.com → New Web Service
3. Connect your fork - render.yaml configures everything automatically
4. Add environment variable: PYTHON_VERSION = 3.12.0
5. Click Deploy - your app goes live at https://your-service.onrender.com

Note: The free tier sleeps after 15 minutes of inactivity. The first request after sleep takes 30–60 seconds to wake up. This is expected.

Docker

docker build -t qyverixai .
docker run -p 8000:8000 qyverixai

Docker Compose

Run the complete local development environment:

docker compose up --build

Available services:

• Frontend → http://localhost:3000
• Backend API → http://localhost:8000
• PostgreSQL → localhost:5432

Stop containers:

docker compose down

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Optional LLM Integration

QyverixAI works fully offline with its built-in rule-based engine. To enable richer AI-powered analysis, add these environment variables:

LLM_ENABLED=true
LLM_API_KEY=your-key-here
LLM_BASE_URL=https://api.openai.com/v1
LLM_MODEL=gpt-4o-mini
LLM_TIMEOUT_SECONDS=30

Compatible with OpenAI, Groq (free tier), Together AI, Ollama (local, free), and any OpenAI-compatible endpoint.

Never commit API keys. Use environment variables or your host's secrets manager.

Provider Reliability

The backend includes built-in resilience for LLM requests:

• Exponential Backoff: Automatic retries on timeouts and connection failures.
• Rate Limit Handling: Pauses and retries on HTTP 429 Rate Limit responses.
• Graceful Fallback: Preserves offline/rule-based features seamlessly if the LLM provider becomes fully unavailable.

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Configuration Reference

Variable	Default	Description
RATE_LIMIT_PER_MINUTE	30	Max requests per IP per minute
LLM_ENABLED	false	Enable LLM provider
LLM_API_KEY	—	API key for your LLM provider
LLM_BASE_URL	https://api.openai.com/v1	LLM base URL
LLM_MODEL	gpt-4o-mini	Model name
LLM_TIMEOUT_SECONDS	30	Request timeout in seconds

Copy .env.example to .env and fill in values as needed.

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Tech Stack

Layer	Technology
Backend	FastAPI 0.115+, Pydantic v2, Python 3.12
Frontend	HTML5, CSS3, Vanilla JS - no build step, zero dependencies
Testing	Pytest, FastAPI TestClient
Linting	Ruff
Deployment	Docker, Render
CI	GitHub Actions - Python 3.11 + 3.12 matrix

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Contributing

QyverixAI is a GSSoC 2026 open source project. Contributors of all levels are welcome.

# 1. Fork the repo on GitHub
# 2. Clone your fork
git clone https://github.com/YOUR_USERNAME/AI-dev-assistant.git

# 3. Create a branch
git checkout -b feat/your-feature-name

# 4. Install and test
cd backend && pip install -r requirements.txt
pytest -v   # all 22 tests must pass

# 5. Push and open a pull request

Read the full workflow, code standards, and pattern guide in CONTRIBUTING.md.

Good first issues for GSSoC contributors

Task	Label
Add a new bug detection pattern for any language	easy
Add test cases for edge cases	easy
Improve explanation key points for a specific language	easy
Add ARIA labels and keyboard navigation to frontend	medium
Add support for a new file type in file upload	medium
Build AST-based deep analysis for Python	hard
Add VS Code extension	hard

Browse all open issues: github.com/imDarshanGK/AI-dev-assistant/issues

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Roadmap

• Rule-based code explanation engine
• Bug detection — 40+ patterns across 5 languages
• Improvement suggestions with quality score and letter grade A–F
• Full-analysis combined endpoint with timing metrics
• Rate limiting per IP — configurable
• Gzip compression middleware
• Dark / light theme, file upload, drag-and-drop, history, favorites, download
• LLM provider abstraction layer — OpenAI, Groq, Ollama compatible
• CI matrix — Python 3.11 + 3.12
• AST-based deep analysis for Python
• Per-user history with database backend (SQLite → PostgreSQL)
• VS Code extension
• AI-powered explanations — LLM integration GA
• Multi-file analysis support
• Diff view — before/after code improvements

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License

MIT © Darshan G K

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Star this repo  ·  Report a bug  ·  Request a feature

Built for the open source community  ·  GSSoC 2026

Contributors Appreciation

We sincerely thank all contributors who have helped improve this project through code contributions, bug fixes, documentation enhancements, feature suggestions, testing, and community support.

Your efforts make this project better for everyone and help grow the open-source community.