# GPT-Pilot — AI Developer Agent That Writes Full Apps

> GPT-Pilot is an AI coding agent that builds complete applications from a natural language description, working iteratively with the developer through planning, coding, debugging, and testing phases like a collaborative pair programmer.

## Install

Save as a script file and run:

# GPT-Pilot — AI Developer Agent That Writes Full Apps

## Quick Use
```bash
git clone https://github.com/Pythagora-io/gpt-pilot.git
cd gpt-pilot
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env  # Add your OpenAI API key
python main.py
```

## Introduction
GPT-Pilot is an autonomous AI developer that builds full applications from a text description. Unlike code-completion tools, it handles the entire software development lifecycle: gathering requirements, writing architecture specs, implementing code file by file, running the app, debugging errors, and asking the human developer for clarification when stuck.

## What GPT-Pilot Does
- Takes a natural language app description and creates a full project from scratch
- Breaks development into phases: planning, architecture, coding, testing, debugging
- Writes code file-by-file and runs commands to verify everything works
- Asks the developer for input when it encounters ambiguity or errors
- Maintains a development log that allows resuming interrupted projects

## Architecture Overview
GPT-Pilot uses a multi-agent architecture with specialized roles: Product Owner (requirements), Architect (tech stack decisions), Developer (code generation), Code Monkey (small edits), Reviewer (quality checks), and Troubleshooter (debugging). Each agent has tailored prompts and interacts with the LLM in sequence, passing context through a shared project state stored in a PostgreSQL database.

## Self-Hosting & Configuration
- Requires Python 3.9+ and a PostgreSQL database for project state
- Configure LLM provider (OpenAI GPT-4 recommended) via environment variables
- Set workspace directory where generated projects are written
- Adjust token limits and model selection per agent role in config
- Supports resuming projects from any checkpoint in the development log

## Key Features
- End-to-end app generation from description to working code
- Interactive development loop with human-in-the-loop decision points
- Automatic debugging: detects errors, proposes fixes, retries
- Project state persistence allows stopping and resuming development
- Supports multiple LLM backends including GPT-4, Claude, and open-source models

## Comparison with Similar Tools
- **GPT Engineer** — generates code in one shot; GPT-Pilot iterates with debugging and testing
- **AutoGPT** — general autonomous agent; GPT-Pilot is specialized for software development
- **Aider** — interactive code editing in existing repos; GPT-Pilot builds from scratch
- **Devin** — commercial AI developer; GPT-Pilot is open-source and self-hosted
- **Cursor/Copilot** — code completion assistants; GPT-Pilot handles full project lifecycle

## FAQ
**Q: What types of apps can GPT-Pilot build?**
A: Web apps are the primary focus (Node.js, React, Python/Flask), but it can handle CLI tools and simple backends too.

**Q: How much does it cost in API tokens?**
A: A typical small app costs $5-20 in GPT-4 tokens. Complexity and debugging cycles increase cost.

**Q: Can I use it with local LLMs?**
A: Yes, any OpenAI-compatible API works, including Ollama and vLLM endpoints, though quality depends on model capability.

**Q: Does it write tests?**
A: Yes, GPT-Pilot includes a testing phase and can generate unit and integration tests.

## Sources
- https://github.com/Pythagora-io/gpt-pilot
- https://pythagora.ai

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Source: https://tokrepo.com/en/workflows/asset-6cc2b583
Author: Script Depot