Headroom: Never hit Claude’s Usage Limit Again!

Today, I'm going to show you a new, free, powerful way to reduce the amount of tokens you use, whether you're using Claude, for example, or any sort of model like that. Obviously, it requires more tokens, and the more powerful these models become, usually the more tokens they require. So this is a new, free, open-source project. It's called Headroom, and you can see it's already trending on GitHub.

And what we've got here is basically a way to use 60% to 95% less tokens when we are running an AI agent. And you can plug this into any of your agents. They actually show proof right here. So you can see the amount saved in terms of tokens.

90% saved, 73% on a triage issue, 47% saved on a code-based exploration. So whether you're using this for coding or agenda tasks or anything like that, you can save tokens using this process. And you can also plug it into Claude Code, into Codex, into Cursor, into OpenClaude, and probably even as Hermes agent as well. So this is great if you run agents daily and you want to save tokens without changing your code.

If you work across multiple agents and want shared memory, it's also useful. And also if you need reversible compression. I'm going to walk you through exactly how to use it, how to get it set up, and also how to install it. So the way that I would look at this is like, before your AI agent answers a single question, it quietly reads like a mountain of text.

It could be your files, your tools, your search results, your skills, your logs, your error messages, the whole conversation so far, particularly if it's a long-running conversation like with an AI agent. And so the problem is that every single word, every single request uses up tokens. The more powerful these agents get and the more they can do, the more tokens they use up. So agents, AI agents in general, use up more tokens than just using a single chat, like one-to-one with, for example, chatGBT.

And so if it gets slow to crawl and it's costing you tokens, and particularly if you're using an API as well, then also what also happens is that it can forget what you told it five minutes ago as well. And so this free tool, Headroom, crushes that mountain of text and all that stuff it has to read by 60 to 95%. So you get the same answers, but you don't throw anything away. This is how to use it.

Now, most AI agents that people have problems with, it's not because they aren't very good. It's usually because it's just too much text and too much going on, and that slows it down. We saw that with OpenClaw as well. The more updates they brought in and the more stuff they added to it, the slower the updates got and the less useful the tool became because it was getting slow, forgetful, and also a little bit buggy with all that stuff going on.

And so the problem is every time your agent answers something, it first has to read everything that you've handed it. And that's usually huge. Every word it reads is a token. You pay per token.

So more reading equals more tokens used and more costs and more resources. And so the other problem is as well that your AI only has so much room to hold text at once, the context window. And so if you have a small desk and you pile too much on it and the early pages slide off the edge, your agent forgets the start of the job pathway through. And so quite often, it's not even the intelligence that costs a lot of tokens.

It's actually the stuff that you don't need inside your AI agent. And Headroom is like a zip file for everything your AI reads. Now, if you want to install it, what you can actually do is you can plug this in to your AI agent. So we can copy the information from GitHub here and say, install this to use less tokens.

And we'll just plug that into Hermes, as you can see. And now you can see Hermes is setting up. So Headroom basically sits quietly between your AI and all the text that you have. Before anything reaches the model, it squashes it down and compresses it, keeping the meaning, but dropping the bloat.

So you get the same answer, but you get a fraction of the reading. And three things make it special. So number one, it can run on your machine. Number two, it covers everything, so files, search results, logs, conversation history.

Number three, it's actually free, right? It's a free open source project on GitHub available right now. And so the way that I look at this is something I call the Goldy Token Crusher framework. Most people will never touch this because it sounds technical.

That's a mistake because it's just a simple three-step process, right? Number one is you squash everything the agent reads down to a fraction, which means 60% to 95% smaller without losing the meaning. So you get the same job, but less tokens required. Move number two is you keep it so originals are never deleted.

You've got one shared memory. We actually use Obsidian for that in our memory. And then that spans across all your agents, so nothing is lost. Claude, Codex, Gemini, Hermes, OpenCore, et cetera, they all remember the same things.

And then you can compound it, right? So it actually studies the jobs that failed and then writes itself corrections, and each week your AI agent gets smarter. So if you look, for example, at Hermes, the first time I use this, it might not use it that efficiently, but the more it uses Headroom, the better it's going to get at actually understanding how to get the most out of it. Now, if you want to see some example case studies, there's actually some proof on the GitHub.

So for example, they actually did a test where they crushed 10,144 words to 1,260. And so let's say, for example, an agent is digging through a giant log file to find like one error. Previously, it might have to read 10,144 tokens of log. With Headroom, it can compress into 1,260 tokens and find the same error with the same answer, but with an eighth of the reading.

Now, you might say as well, okay, if you shrink what the AI reads, won't it get worse? It's a great question. So on standard tests, the answer's held. So math score, identical.

Truthfulness test, it actually nudged up. So it drops bloat, not meaning the full version is always one tap away if it needs to bury detail. If you're only using one agent, then it's probably not that useful. But if you're using this across multiple agents that run all day, across several tools with one shared memory, that's really what it's for.

And you can see we've installed it right here. So now it can start using this and it's installed that into Hermes agent, which is great. Now, you might have a few limiting beliefs about this stuff as well. So for example, some people say if you shrink what the agent reads, won't it make it less effective?

But on standard tests, the answer stayed the same. One even improved. So it drops bloat, not meaning the original is always retrievable as well. A lot of people say my agent is very forgetful.

It only forgets because the desk is overloaded, right? If you clear the clutter, it holds the job with everything in mind. And then also some people say this is super technical. I don't understand how it's set up.

Literally, as I've shown you today, you just copy and paste the GitHub into Hermes agent and you can get it installed from there. So just to recap, your AI reads a mountain before it answers. That slows it down, costs tokens, and sometimes it can forget context. Headroom is basically a zip file for all of it.

60 to 95% fewer words, the same answers, nothing lost. And it can always unzip if you need the full context as well. On a real log, it actually crushed 10,144 tokens to 1,260 and found the same issue. So it actually solved the problem in the same way.

And the play is just three moves, right? You crush it, you keep it, and you compound it, which means that you use less API costs, you have a faster agent that forgets less, and one shared memory across your whole team, and an AI agent that can learn from its mistakes. And you can see how easy this is to use inside the agent operating system. So if you want to get my full guide on how to use it, how to get it set up, a 30-day plan, et cetera, you can get that inside the AI Profit Boarding.

You can ask questions inside the community, get access to all my best trainings. We have a full setup for the agent operating system for all your AI agents. You can get the zip file to install it, plus the step-by-step prompts. And we update this daily.

You see all of my new tutorials and daily trainings right here. And you also get four weekly coaching calls where you can ask questions, jump on a call, share your screen, meet other cool people doing similar things. Inside the map, you can actually meet people in your local city who are using AI agents like you. And this is all inside the AI Profit Boarding.

Link in the comments description, or just go to theboarding.com to get access. Thanks for watching.