A Fast And Reliable Enviroment.

Where Speed Meets Intelligence. AI isn't about having one perfect model. It's about having options, speed, and control. Project-X Agent brings all three to your terminal. Race your models. Get the best answer in the least time. That's the future of development.

Built for developers who live in the terminal.

Features

๐Ÿ’ฌ Chat Mode

Natural conversation with full context. Ask questions, brainstorm, debug. The agent remembers everything.

โŒจ๏ธ Code Mode

Write, debug, refactor in any language. Python, JavaScript, TypeScript, Rust, Go, and more.

๐ŸŽจ Vibe Mode

Describe what you want. Watch it materialize into working code, fully structured and ready to run.

๐Ÿ Race Mode

Configure multiple models. They race. The fastest wins automatically. Get the best answer in the least time.

๐Ÿ“ File & Shell

Read, create, edit files. Run shell commands. Standard library first, no unnecessary packages.

โš™๏ธ Fully Configurable

Set up with .projectxrc or environment variables. Control models, endpoints, and behavior.

Lazy Engineer Philosophy

Standard library first. Packages only when absolutely necessary. Every line of code has to earn its place.

// Before Project-X
โŒ import mkdirp from 'mkdirp'
โŒ import { v4 } from 'uuid'
โŒ import _ from 'lodash'

// With Project-X
โœ… fs.mkdirSync(path, { recursive: true })
โœ… crypto.randomUUID()
โœ… [...new Set(array)]

The Rules

1. Is it in the stdlib?

Before reaching for a package, check if your language's standard library does it. If yes, use it.

2. Can you write it in 5 lines?

Simple logic doesn't need a dependency. Inline it instead of adding a package.

3. Does the project already have it?

Reuse existing code before adding something new. Search your codebase first.

4. Real Impact

One "small" package adds bloat, dependencies, and risk. Keep it minimal.

How It Works

Step 1: Chat

Start a conversation. Ask questions. The agent understands context and remembers everything you've discussed.

Step 2: Configure

Set up your models in .projectxrc or via environment variables. Choose which AI services to use.

Step 3: Race

When you ask something, models respond in parallel. The fastest response is selected automatically.

Step 4: Get Results

No waiting for slow APIs. No compromise on quality. Just fast, reliable answers.

Installation

$ curl -fsSL https://projectx.vaibhavrun.space/install.sh | bash

Setup

Create .projectxrc in your home directory:

ApiKey: your_api_key_here
EndPoint: https://api.app.com/v1 (custom endpoint)
models: gpt-4, claude-3, gemini-pro (custom models)