GitHub – mariovallereyes/research_on_steroids: A simple, free research stack for OpenClaw agents. Layered tool hierarchy with community signal, web scraping, and JS rendering.

**Research on Steroids, A Smarter Way to Let Your AI Actually Research**

If you’ve ever asked an AI agent to “research” something, you’ve probably seen the pattern. It grabs a few articles, summarizes them, and calls it a day. That’s fine for surface-level answers. But you and I both know real research is messier than that.

The GitHub project research_on_steroids takes a very different approach. It’s a **free, structured research stack for OpenClaw agents**, built around a simple idea, depth comes from layers.

Instead of treating every source the same, it uses a **9-step research hierarchy**. And the order matters.

It starts with community signal. Not press releases. Not polished summaries. But what people are actually saying right now on Reddit, X, YouTube, Hacker News, even Polymarket. That’s often where the real story lives. I’ve lost count of how many times a Reddit thread from three days ago told me more than ten news articles combined.

From there, the stack moves through targeted web searches, proper page fetching, JavaScript rendering via a free Cloudflare setup, PDF analysis for research papers, video transcript extraction, and if needed, custom scrapers with Playwright or BeautifulSoup. It even allows full browser control as a last resort.

What I appreciate most is the philosophy behind it. A static blog post and a JS-heavy web app are not the same thing. A press article and a 40-page whitepaper are not the same thing. Treating them the same guarantees shallow results.

This project also includes clear operational files like TOOLS.md and AGENTS.md, so your agent doesn’t just have tools, it has a protocol. Step by step. No guessing.

We’re moving into a world where AI agents will increasingly act on our behalf. Giving them real research depth instead of quick summaries feels like a smart direction. And honestly, it’s refreshing to see a stack that respects how messy, layered, and human real information actually is.