Every multi-step AI workflow has a silent tax. The model finishes a step. You click continue. It finishes another. You click again. The AI is fast. You are the bottleneck.
And the bottleneck isn’t just annoying. It’s structural. Think about what actually happens when you run a research workflow in ChatGPT or Claude. The model finishes step one, maybe it summarizes a topic or pulls key data out of a document. Then it stops and waits. You’re probably in another tab, or making coffee, or on a call. You come back, see it waiting, click continue, and it runs step two. Five seconds of AI work. Two minutes of human lag. Multiply that across 15 steps and a workflow that could finish in under three minutes takes the better part of an hour, not because of the model, but because of your attention.
A tool called Ghost in the Loop shipped on GitHub this week and it cuts that bottleneck out completely.
What it does: Ghost in the Loop automates continuation across ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Copilot, Grok, Manus, and more. Long multi-step conversations keep running without you clicking anything between steps. That 9-platform coverage matters because different workflows live on different tools. Your research pipeline might run on Perplexity. Your drafting flow might run on Claude. Your coding agent might run on Copilot. Ghost in the Loop doesn’t force you to consolidate everything into one platform just to automate it. It meets the workflow where it already lives. It also handles continuation formatting, meaning it doesn’t just click a button blindly. It understands when a step is actually complete before triggering the next one, which matters when some steps produce long outputs like reports, code files, or structured data tables.
The twist: The AI was never the problem. These models can already reason through complex, multi-step tasks. GPT-4o can write a 10-step business analysis. Claude can refactor a codebase across multiple passes. Gemini can run a research chain from scratch. The thing slowing everything down was the human hitting “next” between each step. Ghost in the Loop doesn’t make AI smarter. It just removes you from the queue. That’s a deceptively simple insight that people have been duct-taping workarounds to for months. Browser extensions, AutoHotkey scripts, Zapier hacks. This is the first tool that treats the continuation problem as the actual product, not a side feature bolted onto something else.
How to run it 🔧
- Clone the repo from GitHub (link below). It’s a lightweight local setup with no heavy dependencies and no cloud account required. Should take under five minutes to get running.
- Pick your platform: Claude, ChatGPT, Gemini, or any of the 9 supported models. Match it to wherever your actual workflow already lives. Don’t migrate just to test this, that defeats the point.
- Build your multi-step prompt flow with continuation logic baked into the structure. This is where most people underinvest. Each prompt step should end with a clear signal that tells the tool what “done” looks like for that step, so the continuation trigger fires at the right moment and not mid-sentence or mid-table. A little upfront structure here pays off across every run after.
- Let it run. The tool handles continuation, formatting, and recovery on its own. 🚀 Watch the first full run manually to catch any early formatting drift before you leave it fully unattended. One supervised run gives you a baseline so you know what “working correctly” looks like.
- Note where the flow breaks and log it. The builder is actively collecting edge cases. A specific note about which model, which step number, and what the output looked like before it broke is worth 10x more than a vague “it stopped working.”
Pro tip 💡 The project is in active testing, which means your break cases actually matter. The builder wants: model used, prompt format, where the flow broke, and any recovery suggestions. If you run multi-step pipelines regularly, now is the best moment to stress-test this and shape how it handles failure. A few specific things worth testing: what happens when the model hits a refusal mid-workflow, whether continuation fires correctly after a long-form output like a 2,000-word draft, and how it handles platform rate limits if you’re running back-to-back steps fast. These aren’t edge cases, they’re the exact failure modes that show up in real production pipelines. Getting them logged now means the tool gets better in exactly the ways that matter for actual work, not just clean demos. The builders who engage with early tools at this stage tend to end up with tools that fit their workflows precisely because they helped shape the thing while it was still soft.
Find it here: github.com/MShneur/ghost-in-the-loop 🔍
Ghost in the Loop: keeping long AI prompts moving without human babysitting
by u/Mstep85 in PromptEngineering