Relentless

Relentless

The AI Agent Orchestrator That Never Stops

🆕 Smart Auto Mode: Save 50-75% on AI costs by routing tasks to the right model

Quick Start · Auto Mode · Why Relentless? · Supported Agents · Documentation

What's in a Name?

According to Matt Pocock, if you're trying to sneak Ralph into the enterprise, just pretend it's an acronym:
Relentless Agentic Looping Programming Helper

So naturally, RELENTLESS is the recursive acronym:
RALPH Endlessly Loops, Executing, Navigating Tasks, Learning, Evolving, Solving, Scaling

It's recursive. We loop. That's kind of our thing.

The Problem

You have an AI coding agent. It's brilliant — for about 15 minutes. Then it loses context, forgets what it was doing, or hits a rate limit. You babysit it, re-prompt it, copy-paste context back in. Rinse and repeat.

What if it just... kept going?

The Solution

Relentless runs your AI agent in a loop until all tasks are complete. Each iteration spawns a fresh agent with clean context. Memory persists through git commits and progress files. When one agent hits a rate limit, another takes over.

That's it. Go grab a coffee. Or lunch. Or sleep. Relentless will keep working.

From Ralph to Relentless

The Origin

Geoffrey Huntley created the Ralph Wiggum Pattern — a brilliantly simple idea: run an AI coding agent in a loop until all tasks are done. Watch his original video to understand the concept.

The pattern works. But we found it could work *better*.

The Evolution

Ralph loops. Relentless loops smarter.

The Philosophy
*"Garbage in, garbage out — if your specs are vague, your agent will spin its wheels. And at $0.50 per iteration, those wheels get expensive fast."*

Relentless ensures you know exactly what "done" means before the first line of code is written.

Quick Start

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